prompt
stringlengths
19
1.03M
completion
stringlengths
4
2.12k
api
stringlengths
8
90
import matplotlib.pyplot as plt import pandas as pd import numpy as np from scipy.stats import pearsonr pd.options.display.max_colwidth = 540 # show more characters when printing tables pd.options.display.max_rows = 50 # copy the following in any new notebook: %matplotlib inline def corr(data, significance=False, decimals=3): '''Generates a correlation matrix with p values and sample size, just like SPSS. Args: data (pandas.DataFrame): Data for calculating correlations. significance (bool): Determines whether to include asterisks in correlations. decimals (int): Used to round values. Returns: pandas.DataFrame: SPSS-like correlation matrix. ''' # (adapted from https://stackoverflow.com/questions/25571882/pandas-columns-correlation-with-statistical-significance) # generate matrices (r, p vals, sample size) rs = data.corr(method=lambda x, y: pearsonr(x,y)[0]).round(decimals) pvals = data.corr(method=lambda x, y: pearsonr(x,y)[1]).round(decimals) ns = data.corr(method=lambda x, y: len(x)).replace(1, np.nan).round(decimals) # sample size = 1 would be misleading if(significance): p = pvals.applymap(lambda x: ''.join(['*' for t in [0.001, 0.01, 0.05] if x <= t])) # matrix with asterisks rs = rs.astype(str) + p # (adapted from https://stackoverflow.com/questions/58282538/merging-pandas-dataframes-alternating-rows-without-soritng-rows) # create new index level enumerating the number of columns s1 = rs.assign(_col = np.arange(len(rs))).set_index('_col', append=True) s2 = pvals.assign(_col = np.arange(len(pvals))).set_index('_col', append=True) s3 = ns.assign(_col = np.arange(len(ns))).set_index('_col', append=True) # merge these matrices corr_matrix = (pd.concat([s1, s2, s3], keys=('Pearson\'s r','p value', 'Sample size')) # new index with each indicator .sort_index(kind='merge', level=2) # sort index, so names in previous line are important .reset_index(level=2, drop=True) # drop _col index .swaplevel(0,1)) # invert index levels return corr_matrix def pie(values, labels=None, title='', slices=None, percent_only=False, explode=True, color='white'): '''Display a pie plot. Args: values (list): list or pandas.Series of unique values. Using value_counts() is highly recommended. labels (list): if None, indices of values will be used. title (str): header for the plot. slices (int): set a number of slices, to avoid clutter. percent_only (bool): if False it will show count and percent. explode (bool): slightly separate the slice with the highest value. color (str): text color. ''' if isinstance(values, list): values =
pd.Series(values)
pandas.Series
import unittest import qteasy as qt import pandas as pd from pandas import Timestamp import numpy as np import math from numpy import int64 import itertools import datetime from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list from qteasy.utilfuncs import maybe_trade_day, is_market_trade_day, prev_trade_day, next_trade_day from qteasy.utilfuncs import next_market_trade_day, unify, mask_to_signal, list_or_slice, labels_to_dict from qteasy.utilfuncs import weekday_name, prev_market_trade_day, is_number_like, list_truncate, input_to_list from qteasy.space import Space, Axis, space_around_centre, ResultPool from qteasy.core import apply_loop from qteasy.built_in import SelectingFinanceIndicator, TimingDMA, TimingMACD, TimingCDL, TimingTRIX from qteasy.tsfuncs import income, indicators, name_change, get_bar from qteasy.tsfuncs import stock_basic, trade_calendar, new_share, get_index from qteasy.tsfuncs import balance, cashflow, top_list, index_indicators, composite from qteasy.tsfuncs import future_basic, future_daily, options_basic, options_daily from qteasy.tsfuncs import fund_basic, fund_net_value, index_basic, stock_company from qteasy.evaluate import eval_alpha, eval_benchmark, eval_beta, eval_fv from qteasy.evaluate import eval_info_ratio, eval_max_drawdown, eval_sharp from qteasy.evaluate import eval_volatility from qteasy.tafuncs import bbands, dema, ema, ht, kama, ma, mama, mavp, mid_point from qteasy.tafuncs import mid_price, sar, sarext, sma, t3, tema, trima, wma, adx, adxr from qteasy.tafuncs import apo, bop, cci, cmo, dx, macd, macdext, aroon, aroonosc from qteasy.tafuncs import macdfix, mfi, minus_di, minus_dm, mom, plus_di, plus_dm from qteasy.tafuncs import ppo, roc, rocp, rocr, rocr100, rsi, stoch, stochf, stochrsi from qteasy.tafuncs import trix, ultosc, willr, ad, adosc, obv, atr, natr, trange from qteasy.tafuncs import avgprice, medprice, typprice, wclprice, ht_dcperiod from qteasy.tafuncs import ht_dcphase, ht_phasor, ht_sine, ht_trendmode, cdl2crows from qteasy.tafuncs import cdl3blackcrows, cdl3inside, cdl3linestrike, cdl3outside from qteasy.tafuncs import cdl3starsinsouth, cdl3whitesoldiers, cdlabandonedbaby from qteasy.tafuncs import cdladvanceblock, cdlbelthold, cdlbreakaway, cdlclosingmarubozu from qteasy.tafuncs import cdlconcealbabyswall, cdlcounterattack, cdldarkcloudcover from qteasy.tafuncs import cdldoji, cdldojistar, cdldragonflydoji, cdlengulfing from qteasy.tafuncs import cdleveningdojistar, cdleveningstar, cdlgapsidesidewhite from qteasy.tafuncs import cdlgravestonedoji, cdlhammer, cdlhangingman, cdlharami from qteasy.tafuncs import cdlharamicross, cdlhighwave, cdlhikkake, cdlhikkakemod from qteasy.tafuncs import cdlhomingpigeon, cdlidentical3crows, cdlinneck from qteasy.tafuncs import cdlinvertedhammer, cdlkicking, cdlkickingbylength from qteasy.tafuncs import cdlladderbottom, cdllongleggeddoji, cdllongline, cdlmarubozu from qteasy.tafuncs import cdlmatchinglow, cdlmathold, cdlmorningdojistar, cdlmorningstar from qteasy.tafuncs import cdlonneck, cdlpiercing, cdlrickshawman, cdlrisefall3methods from qteasy.tafuncs import cdlseparatinglines, cdlshootingstar, cdlshortline, cdlspinningtop from qteasy.tafuncs import cdlstalledpattern, cdlsticksandwich, cdltakuri, cdltasukigap from qteasy.tafuncs import cdlthrusting, cdltristar, cdlunique3river, cdlupsidegap2crows from qteasy.tafuncs import cdlxsidegap3methods, beta, correl, linearreg, linearreg_angle from qteasy.tafuncs import linearreg_intercept, linearreg_slope, stddev, tsf, var, acos from qteasy.tafuncs import asin, atan, ceil, cos, cosh, exp, floor, ln, log10, sin, sinh from qteasy.tafuncs import sqrt, tan, tanh, add, div, max, maxindex, min, minindex, minmax from qteasy.tafuncs import minmaxindex, mult, sub, sum from qteasy.history import get_financial_report_type_raw_data, get_price_type_raw_data from qteasy.history import stack_dataframes, dataframe_to_hp, HistoryPanel from qteasy.database import DataSource from qteasy.strategy import Strategy, SimpleTiming, RollingTiming, SimpleSelecting, FactoralSelecting from qteasy._arg_validators import _parse_string_kwargs, _valid_qt_kwargs from qteasy.blender import _exp_to_token, blender_parser, signal_blend class TestCost(unittest.TestCase): def setUp(self): self.amounts = np.array([10000., 20000., 10000.]) self.op = np.array([0., 1., -0.33333333]) self.amounts_to_sell = np.array([0., 0., -3333.3333]) self.cash_to_spend = np.array([0., 20000., 0.]) self.prices = np.array([10., 20., 10.]) self.r = qt.Cost(0.0) def test_rate_creation(self): """测试对象生成""" print('testing rates objects\n') self.assertIsInstance(self.r, qt.Cost, 'Type should be Rate') self.assertEqual(self.r.buy_fix, 0) self.assertEqual(self.r.sell_fix, 0) def test_rate_operations(self): """测试交易费率对象""" self.assertEqual(self.r['buy_fix'], 0.0, 'Item got is incorrect') self.assertEqual(self.r['sell_fix'], 0.0, 'Item got is wrong') self.assertEqual(self.r['buy_rate'], 0.003, 'Item got is incorrect') self.assertEqual(self.r['sell_rate'], 0.001, 'Item got is incorrect') self.assertEqual(self.r['buy_min'], 5., 'Item got is incorrect') self.assertEqual(self.r['sell_min'], 0.0, 'Item got is incorrect') self.assertEqual(self.r['slipage'], 0.0, 'Item got is incorrect') self.assertEqual(np.allclose(self.r.calculate(self.amounts), [0.003, 0.003, 0.003]), True, 'fee calculation wrong') def test_rate_fee(self): """测试买卖交易费率""" self.r.buy_rate = 0.003 self.r.sell_rate = 0.001 self.r.buy_fix = 0. self.r.sell_fix = 0. self.r.buy_min = 0. self.r.sell_min = 0. self.r.slipage = 0. print('\nSell result with fixed rate = 0.001 and moq = 0:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell)) test_rate_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 0., -3333.3333]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], 33299.999667, msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 33.333332999999996, msg='result incorrect') print('\nSell result with fixed rate = 0.001 and moq = 1:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 1.)) test_rate_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 1) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 0., -3333]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], 33296.67, msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 33.33, msg='result incorrect') print('\nSell result with fixed rate = 0.001 and moq = 100:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 100)) test_rate_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 100) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 0., -3300]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], 32967.0, msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 33, msg='result incorrect') print('\nPurchase result with fixed rate = 0.003 and moq = 0:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 0)) test_rate_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 0) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 997.00897308, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], -20000.0, msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 59.82053838484547, msg='result incorrect') print('\nPurchase result with fixed rate = 0.003 and moq = 1:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 1)) test_rate_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 1) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 997., 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], -19999.82, msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 59.82, msg='result incorrect') print('\nPurchase result with fixed rate = 0.003 and moq = 100:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 100)) test_rate_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 100) self.assertIs(np.allclose(test_rate_fee_result[0], [0., 900., 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_fee_result[1], -18054., msg='result incorrect') self.assertAlmostEqual(test_rate_fee_result[2], 54.0, msg='result incorrect') def test_min_fee(self): """测试最低交易费用""" self.r.buy_rate = 0. self.r.sell_rate = 0. self.r.buy_fix = 0. self.r.sell_fix = 0. self.r.buy_min = 300 self.r.sell_min = 300 self.r.slipage = 0. print('\npurchase result with fixed cost rate with min fee = 300 and moq = 0:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 0)) test_min_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 0) self.assertIs(np.allclose(test_min_fee_result[0], [0., 985, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], -20000.0, msg='result incorrect') self.assertAlmostEqual(test_min_fee_result[2], 300.0, msg='result incorrect') print('\npurchase result with fixed cost rate with min fee = 300 and moq = 10:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 10)) test_min_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 10) self.assertIs(np.allclose(test_min_fee_result[0], [0., 980, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], -19900.0, msg='result incorrect') self.assertAlmostEqual(test_min_fee_result[2], 300.0, msg='result incorrect') print('\npurchase result with fixed cost rate with min fee = 300 and moq = 100:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 100)) test_min_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 100) self.assertIs(np.allclose(test_min_fee_result[0], [0., 900, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], -18300.0, msg='result incorrect') self.assertAlmostEqual(test_min_fee_result[2], 300.0, msg='result incorrect') print('\nselling result with fixed cost rate with min fee = 300 and moq = 0:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell)) test_min_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell) self.assertIs(np.allclose(test_min_fee_result[0], [0, 0, -3333.3333]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], 33033.333) self.assertAlmostEqual(test_min_fee_result[2], 300.0) print('\nselling result with fixed cost rate with min fee = 300 and moq = 1:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 1)) test_min_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 1) self.assertIs(np.allclose(test_min_fee_result[0], [0, 0, -3333]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], 33030) self.assertAlmostEqual(test_min_fee_result[2], 300.0) print('\nselling result with fixed cost rate with min fee = 300 and moq = 100:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 100)) test_min_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 100) self.assertIs(np.allclose(test_min_fee_result[0], [0, 0, -3300]), True, 'result incorrect') self.assertAlmostEqual(test_min_fee_result[1], 32700) self.assertAlmostEqual(test_min_fee_result[2], 300.0) def test_rate_with_min(self): """测试最低交易费用对其他交易费率参数的影响""" self.r.buy_rate = 0.0153 self.r.sell_rate = 0.01 self.r.buy_fix = 0. self.r.sell_fix = 0. self.r.buy_min = 300 self.r.sell_min = 333 self.r.slipage = 0. print('\npurchase result with fixed cost rate with buy_rate = 0.0153, min fee = 300 and moq = 0:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 0)) test_rate_with_min_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 0) self.assertIs(np.allclose(test_rate_with_min_result[0], [0., 984.9305624, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], -20000.0, msg='result incorrect') self.assertAlmostEqual(test_rate_with_min_result[2], 301.3887520929774, msg='result incorrect') print('\npurchase result with fixed cost rate with buy_rate = 0.0153, min fee = 300 and moq = 10:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 10)) test_rate_with_min_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 10) self.assertIs(np.allclose(test_rate_with_min_result[0], [0., 980, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], -19900.0, msg='result incorrect') self.assertAlmostEqual(test_rate_with_min_result[2], 300.0, msg='result incorrect') print('\npurchase result with fixed cost rate with buy_rate = 0.0153, min fee = 300 and moq = 100:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 100)) test_rate_with_min_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 100) self.assertIs(np.allclose(test_rate_with_min_result[0], [0., 900, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], -18300.0, msg='result incorrect') self.assertAlmostEqual(test_rate_with_min_result[2], 300.0, msg='result incorrect') print('\nselling result with fixed cost rate with sell_rate = 0.01, min fee = 333 and moq = 0:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell)) test_rate_with_min_result = self.r.get_selling_result(self.prices, self.amounts_to_sell) self.assertIs(np.allclose(test_rate_with_min_result[0], [0, 0, -3333.3333]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], 32999.99967) self.assertAlmostEqual(test_rate_with_min_result[2], 333.33333) print('\nselling result with fixed cost rate with sell_rate = 0.01, min fee = 333 and moq = 1:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 1)) test_rate_with_min_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 1) self.assertIs(np.allclose(test_rate_with_min_result[0], [0, 0, -3333]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], 32996.7) self.assertAlmostEqual(test_rate_with_min_result[2], 333.3) print('\nselling result with fixed cost rate with sell_rate = 0.01, min fee = 333 and moq = 100:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 100)) test_rate_with_min_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 100) self.assertIs(np.allclose(test_rate_with_min_result[0], [0, 0, -3300]), True, 'result incorrect') self.assertAlmostEqual(test_rate_with_min_result[1], 32667.0) self.assertAlmostEqual(test_rate_with_min_result[2], 333.0) def test_fixed_fee(self): """测试固定交易费用""" self.r.buy_rate = 0. self.r.sell_rate = 0. self.r.buy_fix = 200 self.r.sell_fix = 150 self.r.buy_min = 0 self.r.sell_min = 0 self.r.slipage = 0 print('\nselling result of fixed cost with fixed fee = 150 and moq=0:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 0)) test_fixed_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell) self.assertIs(np.allclose(test_fixed_fee_result[0], [0, 0, -3333.3333]), True, 'result incorrect') self.assertAlmostEqual(test_fixed_fee_result[1], 33183.333, msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 150.0, msg='result incorrect') print('\nselling result of fixed cost with fixed fee = 150 and moq=100:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell, 100)) test_fixed_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell, 100) self.assertIs(np.allclose(test_fixed_fee_result[0], [0, 0, -3300.]), True, f'result incorrect, {test_fixed_fee_result[0]} does not equal to [0,0,-3400]') self.assertAlmostEqual(test_fixed_fee_result[1], 32850., msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 150., msg='result incorrect') print('\npurchase result of fixed cost with fixed fee = 200:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 0)) test_fixed_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 0) self.assertIs(np.allclose(test_fixed_fee_result[0], [0., 990., 0.]), True, 'result incorrect') self.assertAlmostEqual(test_fixed_fee_result[1], -20000.0, msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 200.0, msg='result incorrect') print('\npurchase result of fixed cost with fixed fee = 200:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 100)) test_fixed_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 100) self.assertIs(np.allclose(test_fixed_fee_result[0], [0., 900., 0.]), True, 'result incorrect') self.assertAlmostEqual(test_fixed_fee_result[1], -18200.0, msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 200.0, msg='result incorrect') def test_slipage(self): """测试交易滑点""" self.r.buy_fix = 0 self.r.sell_fix = 0 self.r.buy_min = 0 self.r.sell_min = 0 self.r.buy_rate = 0.003 self.r.sell_rate = 0.001 self.r.slipage = 1E-9 print('\npurchase result of fixed rate = 0.003 and slipage = 1E-10 and moq = 0:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 0)) print('\npurchase result of fixed rate = 0.003 and slipage = 1E-10 and moq = 100:') print(self.r.get_purchase_result(self.prices, self.cash_to_spend, 100)) print('\nselling result with fixed rate = 0.001 and slipage = 1E-10:') print(self.r.get_selling_result(self.prices, self.amounts_to_sell)) test_fixed_fee_result = self.r.get_selling_result(self.prices, self.amounts_to_sell) self.assertIs(np.allclose(test_fixed_fee_result[0], [0, 0, -3333.3333]), True, f'{test_fixed_fee_result[0]} does not equal to [0, 0, -10000]') self.assertAlmostEqual(test_fixed_fee_result[1], 33298.88855591, msg=f'{test_fixed_fee_result[1]} does not equal to 99890.') self.assertAlmostEqual(test_fixed_fee_result[2], 34.44444409, msg=f'{test_fixed_fee_result[2]} does not equal to -36.666663.') test_fixed_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 0) self.assertIs(np.allclose(test_fixed_fee_result[0], [0., 996.98909294, 0.]), True, 'result incorrect') self.assertAlmostEqual(test_fixed_fee_result[1], -20000.0, msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 60.21814121353513, msg='result incorrect') test_fixed_fee_result = self.r.get_purchase_result(self.prices, self.cash_to_spend, 100) self.assertIs(np.allclose(test_fixed_fee_result[0], [0., 900., 0.]), True, 'result incorrect') self.assertAlmostEqual(test_fixed_fee_result[1], -18054.36, msg='result incorrect') self.assertAlmostEqual(test_fixed_fee_result[2], 54.36, msg='result incorrect') class TestSpace(unittest.TestCase): def test_creation(self): """ test if creation of space object is fine """ # first group of inputs, output Space with two discr axis from [0,10] print('testing space objects\n') # pars_list = [[(0, 10), (0, 10)], # [[0, 10], [0, 10]]] # # types_list = ['discr', # ['discr', 'discr']] # # input_pars = itertools.product(pars_list, types_list) # for p in input_pars: # # print(p) # s = qt.Space(*p) # b = s.boes # t = s.types # # print(s, t) # self.assertIsInstance(s, qt.Space) # self.assertEqual(b, [(0, 10), (0, 10)], 'boes incorrect!') # self.assertEqual(t, ['discr', 'discr'], 'types incorrect') # pars_list = [[(0, 10), (0, 10)], [[0, 10], [0, 10]]] types_list = ['foo, bar', ['foo', 'bar']] input_pars = itertools.product(pars_list, types_list) for p in input_pars: # print(p) s = Space(*p) b = s.boes t = s.types # print(s, t) self.assertEqual(b, [(0, 10), (0, 10)], 'boes incorrect!') self.assertEqual(t, ['enum', 'enum'], 'types incorrect') pars_list = [[(0, 10), (0, 10)], [[0, 10], [0, 10]]] types_list = [['discr', 'foobar']] input_pars = itertools.product(pars_list, types_list) for p in input_pars: # print(p) s = Space(*p) b = s.boes t = s.types # print(s, t) self.assertEqual(b, [(0, 10), (0, 10)], 'boes incorrect!') self.assertEqual(t, ['discr', 'enum'], 'types incorrect') pars_list = [(0., 10), (0, 10)] s = Space(pars=pars_list, par_types=None) self.assertEqual(s.types, ['conti', 'discr']) self.assertEqual(s.dim, 2) self.assertEqual(s.size, (10.0, 11)) self.assertEqual(s.shape, (np.inf, 11)) self.assertEqual(s.count, np.inf) self.assertEqual(s.boes, [(0., 10), (0, 10)]) pars_list = [(0., 10), (0, 10)] s = Space(pars=pars_list, par_types='conti, enum') self.assertEqual(s.types, ['conti', 'enum']) self.assertEqual(s.dim, 2) self.assertEqual(s.size, (10.0, 2)) self.assertEqual(s.shape, (np.inf, 2)) self.assertEqual(s.count, np.inf) self.assertEqual(s.boes, [(0., 10), (0, 10)]) pars_list = [(1, 2), (2, 3), (3, 4)] s = Space(pars=pars_list) self.assertEqual(s.types, ['discr', 'discr', 'discr']) self.assertEqual(s.dim, 3) self.assertEqual(s.size, (2, 2, 2)) self.assertEqual(s.shape, (2, 2, 2)) self.assertEqual(s.count, 8) self.assertEqual(s.boes, [(1, 2), (2, 3), (3, 4)]) pars_list = [(1, 2, 3), (2, 3, 4), (3, 4, 5)] s = Space(pars=pars_list) self.assertEqual(s.types, ['enum', 'enum', 'enum']) self.assertEqual(s.dim, 3) self.assertEqual(s.size, (3, 3, 3)) self.assertEqual(s.shape, (3, 3, 3)) self.assertEqual(s.count, 27) self.assertEqual(s.boes, [(1, 2, 3), (2, 3, 4), (3, 4, 5)]) pars_list = [((1, 2, 3), (2, 3, 4), (3, 4, 5))] s = Space(pars=pars_list) self.assertEqual(s.types, ['enum']) self.assertEqual(s.dim, 1) self.assertEqual(s.size, (3,)) self.assertEqual(s.shape, (3,)) self.assertEqual(s.count, 3) pars_list = ((1, 2, 3), (2, 3, 4), (3, 4, 5)) s = Space(pars=pars_list) self.assertEqual(s.types, ['enum', 'enum', 'enum']) self.assertEqual(s.dim, 3) self.assertEqual(s.size, (3, 3, 3)) self.assertEqual(s.shape, (3, 3, 3)) self.assertEqual(s.count, 27) self.assertEqual(s.boes, [(1, 2, 3), (2, 3, 4), (3, 4, 5)]) def test_extract(self): """ :return: """ pars_list = [(0, 10), (0, 10)] types_list = ['discr', 'discr'] s = Space(pars=pars_list, par_types=types_list) extracted_int, count = s.extract(3, 'interval') extracted_int_list = list(extracted_int) print('extracted int\n', extracted_int_list) self.assertEqual(count, 16, 'extraction count wrong!') self.assertEqual(extracted_int_list, [(0, 0), (0, 3), (0, 6), (0, 9), (3, 0), (3, 3), (3, 6), (3, 9), (6, 0), (6, 3), (6, 6), (6, 9), (9, 0), (9, 3), (9, 6), (9, 9)], 'space extraction wrong!') extracted_rand, count = s.extract(10, 'rand') extracted_rand_list = list(extracted_rand) self.assertEqual(count, 10, 'extraction count wrong!') print('extracted rand\n', extracted_rand_list) for point in list(extracted_rand_list): self.assertEqual(len(point), 2) self.assertLessEqual(point[0], 10) self.assertGreaterEqual(point[0], 0) self.assertLessEqual(point[1], 10) self.assertGreaterEqual(point[1], 0) pars_list = [(0., 10), (0, 10)] s = Space(pars=pars_list, par_types=None) extracted_int2, count = s.extract(3, 'interval') self.assertEqual(count, 16, 'extraction count wrong!') extracted_int_list2 = list(extracted_int2) self.assertEqual(extracted_int_list2, [(0, 0), (0, 3), (0, 6), (0, 9), (3, 0), (3, 3), (3, 6), (3, 9), (6, 0), (6, 3), (6, 6), (6, 9), (9, 0), (9, 3), (9, 6), (9, 9)], 'space extraction wrong!') print('extracted int list 2\n', extracted_int_list2) self.assertIsInstance(extracted_int_list2[0][0], float) self.assertIsInstance(extracted_int_list2[0][1], (int, int64)) extracted_rand2, count = s.extract(10, 'rand') self.assertEqual(count, 10, 'extraction count wrong!') extracted_rand_list2 = list(extracted_rand2) print('extracted rand list 2:\n', extracted_rand_list2) for point in extracted_rand_list2: self.assertEqual(len(point), 2) self.assertIsInstance(point[0], float) self.assertLessEqual(point[0], 10) self.assertGreaterEqual(point[0], 0) self.assertIsInstance(point[1], (int, int64)) self.assertLessEqual(point[1], 10) self.assertGreaterEqual(point[1], 0) pars_list = [(0., 10), ('a', 'b')] s = Space(pars=pars_list, par_types='enum, enum') extracted_int3, count = s.extract(1, 'interval') self.assertEqual(count, 4, 'extraction count wrong!') extracted_int_list3 = list(extracted_int3) self.assertEqual(extracted_int_list3, [(0., 'a'), (0., 'b'), (10, 'a'), (10, 'b')], 'space extraction wrong!') print('extracted int list 3\n', extracted_int_list3) self.assertIsInstance(extracted_int_list3[0][0], float) self.assertIsInstance(extracted_int_list3[0][1], str) extracted_rand3, count = s.extract(3, 'rand') self.assertEqual(count, 3, 'extraction count wrong!') extracted_rand_list3 = list(extracted_rand3) print('extracted rand list 3:\n', extracted_rand_list3) for point in extracted_rand_list3: self.assertEqual(len(point), 2) self.assertIsInstance(point[0], (float, int)) self.assertLessEqual(point[0], 10) self.assertGreaterEqual(point[0], 0) self.assertIsInstance(point[1], str) self.assertIn(point[1], ['a', 'b']) pars_list = [((0, 10), (1, 'c'), ('a', 'b'), (1, 14))] s = Space(pars=pars_list, par_types='enum') extracted_int4, count = s.extract(1, 'interval') self.assertEqual(count, 4, 'extraction count wrong!') extracted_int_list4 = list(extracted_int4) it = zip(extracted_int_list4, [(0, 10), (1, 'c'), (0, 'b'), (1, 14)]) for item, item2 in it: print(item, item2) self.assertTrue(all([tuple(ext_item) == item for ext_item, item in it])) print('extracted int list 4\n', extracted_int_list4) self.assertIsInstance(extracted_int_list4[0], tuple) extracted_rand4, count = s.extract(3, 'rand') self.assertEqual(count, 3, 'extraction count wrong!') extracted_rand_list4 = list(extracted_rand4) print('extracted rand list 4:\n', extracted_rand_list4) for point in extracted_rand_list4: self.assertEqual(len(point), 2) self.assertIsInstance(point[0], (int, str)) self.assertIn(point[0], [0, 1, 'a']) self.assertIsInstance(point[1], (int, str)) self.assertIn(point[1], [10, 14, 'b', 'c']) self.assertIn(point, [(0., 10), (1, 'c'), ('a', 'b'), (1, 14)]) pars_list = [((0, 10), (1, 'c'), ('a', 'b'), (1, 14)), (1, 4)] s = Space(pars=pars_list, par_types='enum, discr') extracted_int5, count = s.extract(1, 'interval') self.assertEqual(count, 16, 'extraction count wrong!') extracted_int_list5 = list(extracted_int5) for item, item2 in extracted_int_list5: print(item, item2) self.assertTrue(all([tuple(ext_item) == item for ext_item, item in it])) print('extracted int list 5\n', extracted_int_list5) self.assertIsInstance(extracted_int_list5[0], tuple) extracted_rand5, count = s.extract(5, 'rand') self.assertEqual(count, 5, 'extraction count wrong!') extracted_rand_list5 = list(extracted_rand5) print('extracted rand list 5:\n', extracted_rand_list5) for point in extracted_rand_list5: self.assertEqual(len(point), 2) self.assertIsInstance(point[0], tuple) print(f'type of point[1] is {type(point[1])}') self.assertIsInstance(point[1], (int, np.int64)) self.assertIn(point[0], [(0., 10), (1, 'c'), ('a', 'b'), (1, 14)]) print(f'test incremental extraction') pars_list = [(10., 250), (10., 250), (10., 250), (10., 250), (10., 250), (10., 250)] s = Space(pars_list) ext, count = s.extract(64, 'interval') self.assertEqual(count, 4096) points = list(ext) # 已经取出所有的点,围绕其中10个点生成十个subspaces # 检查是否每个subspace都为Space,是否都在s范围内,使用32生成点集,检查生成数量是否正确 for point in points[1000:1010]: subspace = s.from_point(point, 64) self.assertIsInstance(subspace, Space) self.assertTrue(subspace in s) self.assertEqual(subspace.dim, 6) self.assertEqual(subspace.types, ['conti', 'conti', 'conti', 'conti', 'conti', 'conti']) ext, count = subspace.extract(32) points = list(ext) self.assertGreaterEqual(count, 512) self.assertLessEqual(count, 4096) print(f'\n---------------------------------' f'\nthe space created around point <{point}> is' f'\n{subspace.boes}' f'\nand extracted {count} points, the first 5 are:' f'\n{points[:5]}') def test_axis_extract(self): # test axis object with conti type axis = Axis((0., 5)) self.assertIsInstance(axis, Axis) self.assertEqual(axis.axis_type, 'conti') self.assertEqual(axis.axis_boe, (0., 5.)) self.assertEqual(axis.count, np.inf) self.assertEqual(axis.size, 5.0) self.assertTrue(np.allclose(axis.extract(1, 'int'), [0., 1., 2., 3., 4.])) self.assertTrue(np.allclose(axis.extract(0.5, 'int'), [0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5])) extracted = axis.extract(8, 'rand') self.assertEqual(len(extracted), 8) self.assertTrue(all([(0 <= item <= 5) for item in extracted])) # test axis object with discrete type axis = Axis((1, 5)) self.assertIsInstance(axis, Axis) self.assertEqual(axis.axis_type, 'discr') self.assertEqual(axis.axis_boe, (1, 5)) self.assertEqual(axis.count, 5) self.assertEqual(axis.size, 5) self.assertTrue(np.allclose(axis.extract(1, 'int'), [1, 2, 3, 4, 5])) self.assertRaises(ValueError, axis.extract, 0.5, 'int') extracted = axis.extract(8, 'rand') self.assertEqual(len(extracted), 8) self.assertTrue(all([(item in [1, 2, 3, 4, 5]) for item in extracted])) # test axis object with enumerate type axis = Axis((1, 5, 7, 10, 'A', 'F')) self.assertIsInstance(axis, Axis) self.assertEqual(axis.axis_type, 'enum') self.assertEqual(axis.axis_boe, (1, 5, 7, 10, 'A', 'F')) self.assertEqual(axis.count, 6) self.assertEqual(axis.size, 6) self.assertEqual(axis.extract(1, 'int'), [1, 5, 7, 10, 'A', 'F']) self.assertRaises(ValueError, axis.extract, 0.5, 'int') extracted = axis.extract(8, 'rand') self.assertEqual(len(extracted), 8) self.assertTrue(all([(item in [1, 5, 7, 10, 'A', 'F']) for item in extracted])) def test_from_point(self): """测试从一个点生成一个space""" # 生成一个space,指定space中的一个点以及distance,生成一个sub-space pars_list = [(0., 10), (0, 10)] s = Space(pars=pars_list, par_types=None) self.assertEqual(s.types, ['conti', 'discr']) self.assertEqual(s.dim, 2) self.assertEqual(s.size, (10., 11)) self.assertEqual(s.shape, (np.inf, 11)) self.assertEqual(s.count, np.inf) self.assertEqual(s.boes, [(0., 10), (0, 10)]) print('create subspace from a point in space') p = (3, 3) distance = 2 subspace = s.from_point(p, distance) self.assertIsInstance(subspace, Space) self.assertEqual(subspace.types, ['conti', 'discr']) self.assertEqual(subspace.dim, 2) self.assertEqual(subspace.size, (4.0, 5)) self.assertEqual(subspace.shape, (np.inf, 5)) self.assertEqual(subspace.count, np.inf) self.assertEqual(subspace.boes, [(1, 5), (1, 5)]) print('create subspace from a 6 dimensional discrete space') s = Space(pars=[(10, 250), (10, 250), (10, 250), (10, 250), (10, 250), (10, 250)]) p = (15, 200, 150, 150, 150, 150) d = 10 subspace = s.from_point(p, d) self.assertIsInstance(subspace, Space) self.assertEqual(subspace.types, ['discr', 'discr', 'discr', 'discr', 'discr', 'discr']) self.assertEqual(subspace.dim, 6) self.assertEqual(subspace.volume, 65345616) self.assertEqual(subspace.size, (16, 21, 21, 21, 21, 21)) self.assertEqual(subspace.shape, (16, 21, 21, 21, 21, 21)) self.assertEqual(subspace.count, 65345616) self.assertEqual(subspace.boes, [(10, 25), (190, 210), (140, 160), (140, 160), (140, 160), (140, 160)]) print('create subspace from a 6 dimensional continuous space') s = Space(pars=[(10., 250), (10., 250), (10., 250), (10., 250), (10., 250), (10., 250)]) p = (15, 200, 150, 150, 150, 150) d = 10 subspace = s.from_point(p, d) self.assertIsInstance(subspace, Space) self.assertEqual(subspace.types, ['conti', 'conti', 'conti', 'conti', 'conti', 'conti']) self.assertEqual(subspace.dim, 6) self.assertEqual(subspace.volume, 48000000) self.assertEqual(subspace.size, (15.0, 20.0, 20.0, 20.0, 20.0, 20.0)) self.assertEqual(subspace.shape, (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf)) self.assertEqual(subspace.count, np.inf) self.assertEqual(subspace.boes, [(10, 25), (190, 210), (140, 160), (140, 160), (140, 160), (140, 160)]) print('create subspace with different distances on each dimension') s = Space(pars=[(10., 250), (10., 250), (10., 250), (10., 250), (10., 250), (10., 250)]) p = (15, 200, 150, 150, 150, 150) d = [10, 5, 5, 10, 10, 5] subspace = s.from_point(p, d) self.assertIsInstance(subspace, Space) self.assertEqual(subspace.types, ['conti', 'conti', 'conti', 'conti', 'conti', 'conti']) self.assertEqual(subspace.dim, 6) self.assertEqual(subspace.volume, 6000000) self.assertEqual(subspace.size, (15.0, 10.0, 10.0, 20.0, 20.0, 10.0)) self.assertEqual(subspace.shape, (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf)) self.assertEqual(subspace.count, np.inf) self.assertEqual(subspace.boes, [(10, 25), (195, 205), (145, 155), (140, 160), (140, 160), (145, 155)]) class TestCashPlan(unittest.TestCase): def setUp(self): self.cp1 = qt.CashPlan(['2012-01-01', '2010-01-01'], [10000, 20000], 0.1) self.cp1.info() self.cp2 = qt.CashPlan(['20100501'], 10000) self.cp2.info() self.cp3 = qt.CashPlan(pd.date_range(start='2019-01-01', freq='Y', periods=12), [i * 1000 + 10000 for i in range(12)], 0.035) self.cp3.info() def test_creation(self): self.assertIsInstance(self.cp1, qt.CashPlan, 'CashPlan object creation wrong') self.assertIsInstance(self.cp2, qt.CashPlan, 'CashPlan object creation wrong') self.assertIsInstance(self.cp3, qt.CashPlan, 'CashPlan object creation wrong') # test __repr__() print(self.cp1) print(self.cp2) print(self.cp3) # test __str__() self.cp1.info() self.cp2.info() self.cp3.info() # test assersion errors self.assertRaises(AssertionError, qt.CashPlan, '2016-01-01', [10000, 10000]) self.assertRaises(KeyError, qt.CashPlan, '2020-20-20', 10000) def test_properties(self): self.assertEqual(self.cp1.amounts, [20000, 10000], 'property wrong') self.assertEqual(self.cp1.first_day, Timestamp('2010-01-01')) self.assertEqual(self.cp1.last_day, Timestamp('2012-01-01')) self.assertEqual(self.cp1.investment_count, 2) self.assertEqual(self.cp1.period, 730) self.assertEqual(self.cp1.dates, [Timestamp('2010-01-01'), Timestamp('2012-01-01')]) self.assertEqual(self.cp1.ir, 0.1) self.assertAlmostEqual(self.cp1.closing_value, 34200) self.assertAlmostEqual(self.cp2.closing_value, 10000) self.assertAlmostEqual(self.cp3.closing_value, 220385.3483685) self.assertIsInstance(self.cp1.plan, pd.DataFrame) self.assertIsInstance(self.cp2.plan, pd.DataFrame) self.assertIsInstance(self.cp3.plan, pd.DataFrame) def test_operation(self): cp_self_add = self.cp1 + self.cp1 cp_add = self.cp1 + self.cp2 cp_add_int = self.cp1 + 10000 cp_mul_int = self.cp1 * 2 cp_mul_float = self.cp2 * 1.5 cp_mul_time = 3 * self.cp2 cp_mul_time2 = 2 * self.cp1 cp_mul_time3 = 2 * self.cp3 cp_mul_float2 = 2. * self.cp3 self.assertIsInstance(cp_self_add, qt.CashPlan) self.assertEqual(cp_self_add.amounts, [40000, 20000]) self.assertEqual(cp_add.amounts, [20000, 10000, 10000]) self.assertEqual(cp_add_int.amounts, [30000, 20000]) self.assertEqual(cp_mul_int.amounts, [40000, 20000]) self.assertEqual(cp_mul_float.amounts, [15000]) self.assertEqual(cp_mul_float.dates, [Timestamp('2010-05-01')]) self.assertEqual(cp_mul_time.amounts, [10000, 10000, 10000]) self.assertEqual(cp_mul_time.dates, [Timestamp('2010-05-01'), Timestamp('2011-05-01'), Timestamp('2012-04-30')]) self.assertEqual(cp_mul_time2.amounts, [20000, 10000, 20000, 10000]) self.assertEqual(cp_mul_time2.dates, [Timestamp('2010-01-01'), Timestamp('2012-01-01'), Timestamp('2014-01-01'), Timestamp('2016-01-01')]) self.assertEqual(cp_mul_time3.dates, [Timestamp('2019-12-31'), Timestamp('2020-12-31'), Timestamp('2021-12-31'), Timestamp('2022-12-31'), Timestamp('2023-12-31'), Timestamp('2024-12-31'), Timestamp('2025-12-31'), Timestamp('2026-12-31'), Timestamp('2027-12-31'), Timestamp('2028-12-31'), Timestamp('2029-12-31'), Timestamp('2030-12-31'), Timestamp('2031-12-29'), Timestamp('2032-12-29'), Timestamp('2033-12-29'), Timestamp('2034-12-29'), Timestamp('2035-12-29'), Timestamp('2036-12-29'), Timestamp('2037-12-29'), Timestamp('2038-12-29'), Timestamp('2039-12-29'), Timestamp('2040-12-29'), Timestamp('2041-12-29'), Timestamp('2042-12-29')]) self.assertEqual(cp_mul_float2.dates, [Timestamp('2019-12-31'), Timestamp('2020-12-31'), Timestamp('2021-12-31'), Timestamp('2022-12-31'), Timestamp('2023-12-31'), Timestamp('2024-12-31'), Timestamp('2025-12-31'), Timestamp('2026-12-31'), Timestamp('2027-12-31'), Timestamp('2028-12-31'), Timestamp('2029-12-31'), Timestamp('2030-12-31')]) self.assertEqual(cp_mul_float2.amounts, [20000.0, 22000.0, 24000.0, 26000.0, 28000.0, 30000.0, 32000.0, 34000.0, 36000.0, 38000.0, 40000.0, 42000.0]) class TestPool(unittest.TestCase): def setUp(self): self.p = ResultPool(5) self.items = ['first', 'second', (1, 2, 3), 'this', 24] self.perfs = [1, 2, 3, 4, 5] self.additional_result1 = ('abc', 12) self.additional_result2 = ([1, 2], -1) self.additional_result3 = (12, 5) def test_create(self): self.assertIsInstance(self.p, ResultPool) def test_operation(self): self.p.in_pool(self.additional_result1[0], self.additional_result1[1]) self.p.cut() self.assertEqual(self.p.item_count, 1) self.assertEqual(self.p.items, ['abc']) for item, perf in zip(self.items, self.perfs): self.p.in_pool(item, perf) self.assertEqual(self.p.item_count, 6) self.assertEqual(self.p.items, ['abc', 'first', 'second', (1, 2, 3), 'this', 24]) self.p.cut() self.assertEqual(self.p.items, ['second', (1, 2, 3), 'this', 24, 'abc']) self.assertEqual(self.p.perfs, [2, 3, 4, 5, 12]) self.p.in_pool(self.additional_result2[0], self.additional_result2[1]) self.p.in_pool(self.additional_result3[0], self.additional_result3[1]) self.assertEqual(self.p.item_count, 7) self.p.cut(keep_largest=False) self.assertEqual(self.p.items, [[1, 2], 'second', (1, 2, 3), 'this', 24]) self.assertEqual(self.p.perfs, [-1, 2, 3, 4, 5]) class TestCoreSubFuncs(unittest.TestCase): """Test all functions in core.py""" def setUp(self): pass def test_input_to_list(self): print('Testing input_to_list() function') input_str = 'first' self.assertEqual(qt.utilfuncs.input_to_list(input_str, 3), ['first', 'first', 'first']) self.assertEqual(qt.utilfuncs.input_to_list(input_str, 4), ['first', 'first', 'first', 'first']) self.assertEqual(qt.utilfuncs.input_to_list(input_str, 2, None), ['first', 'first']) input_list = ['first', 'second'] self.assertEqual(qt.utilfuncs.input_to_list(input_list, 3), ['first', 'second', None]) self.assertEqual(qt.utilfuncs.input_to_list(input_list, 4, 'padder'), ['first', 'second', 'padder', 'padder']) self.assertEqual(qt.utilfuncs.input_to_list(input_list, 1), ['first', 'second']) self.assertEqual(qt.utilfuncs.input_to_list(input_list, -5), ['first', 'second']) def test_point_in_space(self): sp = Space([(0., 10.), (0., 10.), (0., 10.)]) p1 = (5.5, 3.2, 7) p2 = (-1, 3, 10) self.assertTrue(p1 in sp) print(f'point {p1} is in space {sp}') self.assertFalse(p2 in sp) print(f'point {p2} is not in space {sp}') sp = Space([(0., 10.), (0., 10.), range(40, 3, -2)], 'conti, conti, enum') p1 = (5.5, 3.2, 8) self.assertTrue(p1 in sp) print(f'point {p1} is in space {sp}') def test_space_in_space(self): print('test if a space is in another space') sp = Space([(0., 10.), (0., 10.), (0., 10.)]) sp2 = Space([(0., 10.), (0., 10.), (0., 10.)]) self.assertTrue(sp2 in sp) self.assertTrue(sp in sp2) print(f'space {sp2} is in space {sp}\n' f'and space {sp} is in space {sp2}\n' f'they are equal to each other\n') sp2 = Space([(0, 5.), (2, 7.), (3., 9.)]) self.assertTrue(sp2 in sp) self.assertFalse(sp in sp2) print(f'space {sp2} is in space {sp}\n' f'and space {sp} is not in space {sp2}\n' f'{sp2} is a sub space of {sp}\n') sp2 = Space([(0, 5), (2, 7), (3., 9)]) self.assertFalse(sp2 in sp) self.assertFalse(sp in sp2) print(f'space {sp2} is not in space {sp}\n' f'and space {sp} is not in space {sp2}\n' f'they have different types of axes\n') sp = Space([(0., 10.), (0., 10.), range(40, 3, -2)]) self.assertFalse(sp in sp2) self.assertFalse(sp2 in sp) print(f'space {sp2} is not in space {sp}\n' f'and space {sp} is not in space {sp2}\n' f'they have different types of axes\n') def test_space_around_centre(self): sp = Space([(0., 10.), (0., 10.), (0., 10.)]) p1 = (5.5, 3.2, 7) ssp = space_around_centre(space=sp, centre=p1, radius=1.2) print(ssp.boes) print('\ntest multiple diameters:') self.assertEqual(ssp.boes, [(4.3, 6.7), (2.0, 4.4), (5.8, 8.2)]) ssp = space_around_centre(space=sp, centre=p1, radius=[1, 2, 1]) print(ssp.boes) self.assertEqual(ssp.boes, [(4.5, 6.5), (1.2000000000000002, 5.2), (6.0, 8.0)]) print('\ntest points on edge:') p2 = (5.5, 3.2, 10) ssp = space_around_centre(space=sp, centre=p1, radius=3.9) print(ssp.boes) self.assertEqual(ssp.boes, [(1.6, 9.4), (0.0, 7.1), (3.1, 10.0)]) print('\ntest enum spaces') sp = Space([(0, 100), range(40, 3, -2)], 'discr, enum') p1 = [34, 12] ssp = space_around_centre(space=sp, centre=p1, radius=5, ignore_enums=False) self.assertEqual(ssp.boes, [(29, 39), (22, 20, 18, 16, 14, 12, 10, 8, 6, 4)]) print(ssp.boes) print('\ntest enum space and ignore enum axis') ssp = space_around_centre(space=sp, centre=p1, radius=5) self.assertEqual(ssp.boes, [(29, 39), (40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4)]) print(sp.boes) def test_get_stock_pool(self): print(f'start test building stock pool function\n') share_basics = stock_basic(fields='ts_code,symbol,name,area,industry,market,list_date,exchange') print(f'\nselect all stocks by area') stock_pool = qt.get_stock_pool(area='上海') print(f'{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stock areas are "上海"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['area'].eq('上海').all()) print(f'\nselect all stocks by multiple areas') stock_pool = qt.get_stock_pool(area='贵州,北京,天津') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stock areas are in list of ["贵州", "北京", "天津"]\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['area'].isin(['贵州', '北京', '天津']).all()) print(f'\nselect all stocks by area and industry') stock_pool = qt.get_stock_pool(area='四川', industry='银行, 金融') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stock areas are "四川", and industry in ["银行", "金融"]\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['industry'].isin(['银行', '金融']).all()) self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['area'].isin(['四川']).all()) print(f'\nselect all stocks by industry') stock_pool = qt.get_stock_pool(industry='银行, 金融') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stocks industry in ["银行", "金融"]\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['industry'].isin(['银行', '金融']).all()) print(f'\nselect all stocks by market') stock_pool = qt.get_stock_pool(market='主板') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stock market is "主板"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['market'].isin(['主板']).all()) print(f'\nselect all stocks by market and list date') stock_pool = qt.get_stock_pool(date='2000-01-01', market='主板') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all stock market is "主板", and list date after "2000-01-01"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['market'].isin(['主板']).all()) self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['list_date'].le('2000-01-01').all()) print(f'\nselect all stocks by list date') stock_pool = qt.get_stock_pool(date='1997-01-01') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all list date after "1997-01-01"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['list_date'].le('1997-01-01').all()) print(f'\nselect all stocks by exchange') stock_pool = qt.get_stock_pool(exchange='SSE') print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all exchanges are "SSE"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['exchange'].eq('SSE').all()) print(f'\nselect all stocks by industry, area and list date') industry_list = ['银行', '全国地产', '互联网', '环境保护', '区域地产', '酒店餐饮', '运输设备', '综合类', '建筑工程', '玻璃', '家用电器', '文教休闲', '其他商业', '元器件', 'IT设备', '其他建材', '汽车服务', '火力发电', '医药商业', '汽车配件', '广告包装', '轻工机械', '新型电力', '多元金融', '饲料'] area_list = ['深圳', '北京', '吉林', '江苏', '辽宁', '广东', '安徽', '四川', '浙江', '湖南', '河北', '新疆', '山东', '河南', '山西', '江西', '青海', '湖北', '内蒙', '海南', '重庆', '陕西', '福建', '广西', '上海'] stock_pool = qt.get_stock_pool(date='19980101', industry=industry_list, area=area_list) print(f'\n{len(stock_pool)} shares selected, first 5 are: {stock_pool[0:5]}\n' f'check if all exchanges are "SSE"\n' f'{share_basics[np.isin(share_basics.ts_code, stock_pool)].head()}') self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['list_date'].le('1998-01-01').all()) self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['industry'].isin(industry_list).all()) self.assertTrue(share_basics[np.isin(share_basics.ts_code, stock_pool)]['area'].isin(area_list).all()) self.assertRaises(KeyError, qt.get_stock_pool, industry=25) self.assertRaises(KeyError, qt.get_stock_pool, share_name='000300.SH') self.assertRaises(KeyError, qt.get_stock_pool, markets='SSE') class TestEvaluations(unittest.TestCase): """Test all evaluation functions in core.py""" # 以下手动计算结果在Excel文件中 def setUp(self): """用np.random生成测试用数据,使用cumsum()模拟股票走势""" self.test_data1 = pd.DataFrame([5.34892759, 5.65768696, 5.79227076, 5.56266871, 5.88189632, 6.24795001, 5.92755558, 6.38748165, 6.31331899, 5.86001665, 5.61048472, 5.30696736, 5.40406792, 5.03180571, 5.37886353, 5.78608307, 6.26540339, 6.59348026, 6.90943801, 6.70911677, 6.33015954, 6.06697417, 5.9752499, 6.45786408, 6.95273763, 6.7691991, 6.70355481, 6.28048969, 6.61344541, 6.24620003, 6.47409983, 6.4522311, 6.8773094, 6.99727832, 6.59262674, 6.59014938, 6.63758237, 6.38331869, 6.09902105, 6.35390109, 6.51993567, 6.87244592, 6.83963485, 7.08797815, 6.88003144, 6.83657323, 6.97819483, 7.01600276, 7.12554256, 7.58941523, 7.61014457, 7.21224091, 7.48174399, 7.66490854, 7.51371968, 7.11586198, 6.97147399, 6.67453301, 6.2042138, 6.33967015, 6.22187938, 5.98426993, 6.37096079, 6.55897161, 6.26422645, 6.69363762, 7.12668015, 6.83232926, 7.30524081, 7.4262041, 7.54031383, 7.17545919, 7.20659257, 7.44886016, 7.37094393, 6.88011022, 7.08142491, 6.74992833, 6.5967097, 6.21336693, 6.35565105, 6.82347596, 6.44773408, 6.84538053, 6.47966466, 6.09699528, 5.63927014, 6.01081024, 6.20585303, 6.60528206, 7.01594726, 7.03684251, 6.76574977, 7.08740846, 6.65336462, 7.07126686, 6.80058956, 6.79241977, 6.47843472, 6.39245474], columns=['value']) self.test_data2 = pd.DataFrame([5.09276527, 4.83828592, 4.6000911, 4.63170487, 4.63566451, 4.50546921, 4.96390044, 4.64557907, 4.25787855, 3.76585551, 3.38826334, 3.76243422, 4.06365426, 3.87084726, 3.91400935, 4.13438822, 4.27064542, 4.56776104, 5.03800296, 5.31070529, 5.39902276, 5.21186286, 5.05683114, 4.68842046, 5.11895168, 5.27151571, 5.72294993, 6.09961056, 6.26569635, 6.48806151, 6.16058885, 6.2582459, 6.38934791, 6.57831057, 6.19508831, 5.70155153, 5.20435735, 5.36538825, 5.40450056, 5.2227697, 5.37828693, 5.53058991, 6.02996797, 5.76802181, 5.66166713, 6.07988994, 5.61794367, 5.63218151, 6.10728013, 6.0324168, 6.27164431, 6.27551239, 6.52329665, 7.00470007, 7.34163113, 7.33699083, 7.67661334, 8.09395749, 7.68086668, 7.58341161, 7.46219819, 7.58671899, 7.19348298, 7.40088323, 7.47562005, 7.93342043, 8.2286081, 8.3521632, 8.43590025, 8.34977395, 8.57563095, 8.81586328, 9.08738649, 9.01542031, 8.8653815, 9.21763111, 9.04233017, 8.59533999, 8.47590075, 8.70857222, 8.78890756, 8.92697606, 9.35743773, 9.68280866, 10.15622021, 10.55908549, 10.6337894, 10.55197128, 10.65435176, 10.54611045, 10.19432562, 10.48320884, 10.36176768, 10.03186854, 10.23656092, 10.0062843, 10.13669686, 10.30758958, 9.87904176, 10.05126375], columns=['value']) self.test_data3 = pd.DataFrame([5.02851874, 5.20700348, 5.02410709, 5.49836387, 5.06834371, 5.10956737, 5.15314979, 5.02256472, 5.09746382, 5.23909247, 4.93410336, 4.96316186, 5.40026682, 5.7353255, 5.53438319, 5.79092139, 5.67528173, 5.89840855, 5.75379463, 6.10855386, 5.77322365, 5.84538021, 5.6103973, 5.7518655, 5.49729695, 5.13610628, 5.30524121, 5.68093462, 5.73251319, 6.04420783, 6.26929843, 6.59610234, 6.09872345, 6.25475121, 6.72927396, 6.91395783, 7.00693283, 7.36217783, 7.71516676, 7.67580263, 7.62477511, 7.73600568, 7.53457914, 7.46170277, 7.83658014, 8.11481319, 8.03705544, 7.64948845, 7.52043731, 7.67247943, 7.46511982, 7.43541798, 7.58856517, 7.9392717, 8.25406287, 7.77031632, 8.03223447, 7.86799055, 7.57630999, 7.33230519, 7.22378732, 6.85972264, 7.17548456, 7.5387846, 7.2392632, 6.8455644, 6.59557185, 6.6496796, 6.73685623, 7.18598015, 7.13619128, 6.88060157, 7.1399681, 7.30308077, 6.94942434, 7.0247815, 7.37567798, 7.50080197, 7.59719284, 7.14520561, 7.29913484, 7.79551341, 8.15497781, 8.40456095, 8.86516528, 8.53042688, 8.94268762, 8.52048006, 8.80036284, 8.91602364, 9.19953385, 8.70828953, 8.24613093, 8.18770453, 7.79548389, 7.68627967, 7.23205036, 6.98302636, 7.06515819, 6.95068113], columns=['value']) self.test_data4 = pd.DataFrame([4.97926539, 5.44016005, 5.45122915, 5.74485615, 5.45600553, 5.44858945, 5.2435413, 5.47315161, 5.58464303, 5.36179749, 5.38236326, 5.29614981, 5.76523508, 5.75102892, 6.15316618, 6.03852528, 6.01442228, 5.70510182, 5.22748133, 5.46762379, 5.78926267, 5.8221362, 5.61236849, 5.30615725, 5.24200611, 5.41042642, 5.59940342, 5.28306781, 4.99451932, 5.08799266, 5.38865647, 5.58229139, 5.33492845, 5.48206276, 5.09721379, 5.39190493, 5.29965087, 5.0374415, 5.50798022, 5.43107577, 5.22759507, 4.991809, 5.43153084, 5.39966868, 5.59916352, 5.66412137, 6.00611838, 5.63564902, 5.66723484, 5.29863863, 4.91115153, 5.3749929, 5.75082334, 6.08308148, 6.58091182, 6.77848803, 7.19588758, 7.64862286, 7.99818347, 7.91824794, 8.30341071, 8.45984973, 7.98700002, 8.18924931, 8.60755649, 8.66233396, 8.91018407, 9.0782739, 9.33515448, 8.95870245, 8.98426422, 8.50340317, 8.64916085, 8.93592407, 8.63145745, 8.65322862, 8.39543204, 8.37969997, 8.23394504, 8.04062872, 7.91259763, 7.57252171, 7.72670114, 7.74486117, 8.06908188, 7.99166889, 7.92155906, 8.39956136, 8.80181323, 8.47464091, 8.06557064, 7.87145573, 8.0237959, 8.39481998, 8.68525692, 8.81185461, 8.98632237, 9.0989835, 8.89787405, 8.86508591], columns=['value']) self.test_data5 = pd.DataFrame([4.50258923, 4.35142568, 4.07459514, 3.87791297, 3.73715985, 3.98455684, 4.07587908, 4.00042472, 4.28276612, 4.01362051, 4.13713565, 4.49312372, 4.48633159, 4.4641207, 4.13444605, 3.79107217, 4.22941629, 4.56548511, 4.92472163, 5.27723158, 5.67409193, 6.00176917, 5.88889928, 5.55256103, 5.39308314, 5.2610492, 5.30738908, 5.22222408, 4.90332238, 4.57499908, 4.96097146, 4.81531011, 4.39115442, 4.63200662, 5.04588813, 4.67866025, 5.01705123, 4.83562258, 4.60381702, 4.66187576, 4.41292828, 4.86604507, 4.42280124, 4.07517294, 4.16317319, 4.10316596, 4.42913598, 4.06609666, 3.96725913, 4.15965746, 4.12379564, 4.04054068, 3.84342851, 3.45902867, 3.17649855, 3.09773586, 3.5502119, 3.66396995, 3.66306483, 3.29131401, 2.79558533, 2.88319542, 3.03671098, 3.44645857, 3.88167161, 3.57961874, 3.60180276, 3.96702102, 4.05429995, 4.40056979, 4.05653231, 3.59600456, 3.60792477, 4.09989922, 3.73503663, 4.01892626, 3.94597242, 3.81466605, 3.71417992, 3.93767156, 4.42806557, 4.06988106, 4.03713636, 4.34408673, 4.79810156, 5.18115011, 4.89798406, 5.3960077, 5.72504875, 5.61894017, 5.1958197, 4.85275896, 5.17550207, 4.71548987, 4.62408567, 4.55488535, 4.36532649, 4.26031979, 4.25225607, 4.58627048], columns=['value']) self.test_data6 = pd.DataFrame([5.08639513, 5.05761083, 4.76160923, 4.62166504, 4.62923183, 4.25070173, 4.13447513, 3.90890013, 3.76687608, 3.43342482, 3.67648224, 3.6274775, 3.9385404, 4.39771627, 4.03199346, 3.93265288, 3.50059789, 3.3851961, 3.29743973, 3.2544872, 2.93692949, 2.70893003, 2.55461976, 2.20922332, 2.29054475, 2.2144714, 2.03726827, 2.39007617, 2.29866155, 2.40607111, 2.40440444, 2.79374649, 2.66541922, 2.27018079, 2.08505127, 2.55478864, 2.22415625, 2.58517923, 2.58802256, 2.94870959, 2.69301739, 2.19991535, 2.69473146, 2.64704637, 2.62753542, 2.14240825, 2.38565154, 1.94592117, 2.32243877, 2.69337246, 2.51283854, 2.62484451, 2.15559054, 2.35410875, 2.31219177, 1.96018265, 2.34711266, 2.58083322, 2.40290041, 2.20439791, 2.31472425, 2.16228248, 2.16439749, 2.20080737, 1.73293206, 1.9264407, 2.25089861, 2.69269101, 2.59296687, 2.1420998, 1.67819153, 1.98419023, 2.14479494, 1.89055376, 1.96720648, 1.9916694, 2.37227761, 2.14446036, 2.34573903, 1.86162546, 2.1410721, 2.39204939, 2.52529064, 2.47079939, 2.9299031, 3.09452923, 2.93276708, 3.21731309, 3.06248964, 2.90413406, 2.67844632, 2.45621213, 2.41463398, 2.7373913, 3.14917045, 3.4033949, 3.82283446, 4.02285451, 3.7619638, 4.10346795], columns=['value']) self.test_data7 = pd.DataFrame([4.75233583, 4.47668283, 4.55894263, 4.61765848, 4.622892, 4.58941116, 4.32535872, 3.88112797, 3.47237806, 3.50898953, 3.82530406, 3.6718017, 3.78918195, 4.1800752, 4.01818557, 4.40822582, 4.65474654, 4.89287256, 4.40879274, 4.65505126, 4.36876403, 4.58418934, 4.75687172, 4.3689799, 4.16126498, 4.0203982, 3.77148242, 3.38198096, 3.07261764, 2.9014741, 2.5049543, 2.756105, 2.28779058, 2.16986991, 1.8415962, 1.83319008, 2.20898291, 2.00128981, 1.75747025, 1.26676663, 1.40316876, 1.11126484, 1.60376367, 1.22523829, 1.58816681, 1.49705679, 1.80244138, 1.55128293, 1.35339409, 1.50985759, 1.0808451, 1.05892796, 1.43414812, 1.43039101, 1.73631655, 1.43940867, 1.82864425, 1.71088265, 2.12015154, 2.45417128, 2.84777618, 2.7925612, 2.90975121, 3.25920745, 3.13801182, 3.52733677, 3.65468491, 3.69395211, 3.49862035, 3.24786017, 3.64463138, 4.00331929, 3.62509565, 3.78013949, 3.4174012, 3.76312271, 3.62054004, 3.67206716, 3.60596058, 3.38636199, 3.42580676, 3.32921095, 3.02976759, 3.28258676, 3.45760838, 3.24917528, 2.94618304, 2.86980011, 2.63191259, 2.39566759, 2.53159917, 2.96273967, 3.25626185, 2.97425402, 3.16412191, 3.58280763, 3.23257727, 3.62353556, 3.12806399, 2.92532313], columns=['value']) # 建立一个长度为 500 个数据点的测试数据, 用于测试数据点多于250个的情况下的评价过程 self.long_data = pd.DataFrame([9.879, 9.916, 10.109, 10.214, 10.361, 10.768, 10.594, 10.288, 10.082, 9.994, 10.125, 10.126, 10.384, 10.734, 10.4, 10.87, 11.338, 11.061, 11.415, 11.724, 12.077, 12.196, 12.064, 12.423, 12.19, 11.729, 11.677, 11.448, 11.485, 10.989, 11.242, 11.239, 11.113, 11.075, 11.471, 11.745, 11.754, 11.782, 12.079, 11.97, 12.178, 11.95, 12.438, 12.612, 12.804, 12.952, 12.612, 12.867, 12.832, 12.832, 13.015, 13.315, 13.249, 12.904, 12.776, 12.64, 12.543, 12.287, 12.225, 11.844, 11.985, 11.945, 11.542, 11.871, 12.245, 12.228, 12.362, 11.899, 11.962, 12.374, 12.816, 12.649, 12.252, 12.579, 12.3, 11.988, 12.177, 12.312, 12.744, 12.599, 12.524, 12.82, 12.67, 12.876, 12.986, 13.271, 13.606, 13.82, 14.161, 13.833, 13.831, 14.137, 13.705, 13.414, 13.037, 12.759, 12.642, 12.948, 13.297, 13.483, 13.836, 14.179, 13.709, 13.655, 13.198, 13.508, 13.953, 14.387, 14.043, 13.987, 13.561, 13.391, 12.923, 12.555, 12.503, 12.292, 11.877, 12.34, 12.141, 11.687, 11.992, 12.458, 12.131, 11.75, 11.739, 11.263, 11.762, 11.976, 11.578, 11.854, 12.136, 12.422, 12.311, 12.56, 12.879, 12.861, 12.973, 13.235, 13.53, 13.531, 13.137, 13.166, 13.31, 13.103, 13.007, 12.643, 12.69, 12.216, 12.385, 12.046, 12.321, 11.9, 11.772, 11.816, 11.871, 11.59, 11.518, 11.94, 11.803, 11.924, 12.183, 12.136, 12.361, 12.406, 11.932, 11.684, 11.292, 11.388, 11.874, 12.184, 12.002, 12.16, 11.741, 11.26, 11.123, 11.534, 11.777, 11.407, 11.275, 11.679, 11.62, 11.218, 11.235, 11.352, 11.366, 11.061, 10.661, 10.582, 10.899, 11.352, 11.792, 11.475, 11.263, 11.538, 11.183, 10.936, 11.399, 11.171, 11.214, 10.89, 10.728, 11.191, 11.646, 11.62, 11.195, 11.178, 11.18, 10.956, 11.205, 10.87, 11.098, 10.639, 10.487, 10.507, 10.92, 10.558, 10.119, 9.882, 9.573, 9.515, 9.845, 9.852, 9.495, 9.726, 10.116, 10.452, 10.77, 11.225, 10.92, 10.824, 11.096, 11.542, 11.06, 10.568, 10.585, 10.884, 10.401, 10.068, 9.964, 10.285, 10.239, 10.036, 10.417, 10.132, 9.839, 9.556, 9.084, 9.239, 9.304, 9.067, 8.587, 8.471, 8.007, 8.321, 8.55, 9.008, 9.138, 9.088, 9.434, 9.156, 9.65, 9.431, 9.654, 10.079, 10.411, 10.865, 10.51, 10.205, 10.519, 10.367, 10.855, 10.642, 10.298, 10.622, 10.173, 9.792, 9.995, 9.904, 9.771, 9.597, 9.506, 9.212, 9.688, 10.032, 9.723, 9.839, 9.918, 10.332, 10.236, 9.989, 10.192, 10.685, 10.908, 11.275, 11.72, 12.158, 12.045, 12.244, 12.333, 12.246, 12.552, 12.958, 13.11, 13.53, 13.123, 13.138, 13.57, 13.389, 13.511, 13.759, 13.698, 13.744, 13.467, 13.795, 13.665, 13.377, 13.423, 13.772, 13.295, 13.073, 12.718, 12.388, 12.399, 12.185, 11.941, 11.818, 11.465, 11.811, 12.163, 11.86, 11.935, 11.809, 12.145, 12.624, 12.768, 12.321, 12.277, 11.889, 12.11, 12.606, 12.943, 12.945, 13.112, 13.199, 13.664, 14.051, 14.189, 14.339, 14.611, 14.656, 15.112, 15.086, 15.263, 15.021, 15.346, 15.572, 15.607, 15.983, 16.151, 16.215, 16.096, 16.089, 16.32, 16.59, 16.657, 16.752, 16.583, 16.743, 16.373, 16.662, 16.243, 16.163, 16.491, 16.958, 16.977, 17.225, 17.637, 17.344, 17.684, 17.892, 18.036, 18.182, 17.803, 17.588, 17.101, 17.538, 17.124, 16.787, 17.167, 17.138, 16.955, 17.148, 17.135, 17.635, 17.718, 17.675, 17.622, 17.358, 17.754, 17.729, 17.576, 17.772, 18.239, 18.441, 18.729, 18.319, 18.608, 18.493, 18.069, 18.122, 18.314, 18.423, 18.709, 18.548, 18.384, 18.391, 17.988, 17.986, 17.653, 17.249, 17.298, 17.06, 17.36, 17.108, 17.348, 17.596, 17.46, 17.635, 17.275, 17.291, 16.933, 17.337, 17.231, 17.146, 17.148, 16.751, 16.891, 17.038, 16.735, 16.64, 16.231, 15.957, 15.977, 16.077, 16.054, 15.797, 15.67, 15.911, 16.077, 16.17, 15.722, 15.258, 14.877, 15.138, 15., 14.811, 14.698, 14.407, 14.583, 14.704, 15.153, 15.436, 15.634, 15.453, 15.877, 15.696, 15.563, 15.927, 16.255, 16.696, 16.266, 16.698, 16.365, 16.493, 16.973, 16.71, 16.327, 16.605, 16.486, 16.846, 16.935, 17.21, 17.389, 17.546, 17.773, 17.641, 17.485, 17.794, 17.354, 16.904, 16.675, 16.43, 16.898, 16.819, 16.921, 17.201, 17.617, 17.368, 17.864, 17.484], columns=['value']) self.long_bench = pd.DataFrame([9.7, 10.179, 10.321, 9.855, 9.936, 10.096, 10.331, 10.662, 10.59, 11.031, 11.154, 10.945, 10.625, 10.233, 10.284, 10.252, 10.221, 10.352, 10.444, 10.773, 10.904, 11.104, 10.797, 10.55, 10.943, 11.352, 11.641, 11.983, 11.696, 12.138, 12.365, 12.379, 11.969, 12.454, 12.947, 13.119, 13.013, 12.763, 12.632, 13.034, 12.681, 12.561, 12.938, 12.867, 13.202, 13.132, 13.539, 13.91, 13.456, 13.692, 13.771, 13.904, 14.069, 13.728, 13.97, 14.228, 13.84, 14.041, 13.963, 13.689, 13.543, 13.858, 14.118, 13.987, 13.611, 14.028, 14.229, 14.41, 14.74, 15.03, 14.915, 15.207, 15.354, 15.665, 15.877, 15.682, 15.625, 15.175, 15.105, 14.893, 14.86, 15.097, 15.178, 15.293, 15.238, 15., 15.283, 14.994, 14.907, 14.664, 14.888, 15.297, 15.313, 15.368, 14.956, 14.802, 14.506, 14.257, 14.619, 15.019, 15.049, 14.625, 14.894, 14.978, 15.434, 15.578, 16.038, 16.107, 16.277, 16.365, 16.204, 16.465, 16.401, 16.895, 17.057, 16.621, 16.225, 16.075, 15.863, 16.292, 16.551, 16.724, 16.817, 16.81, 17.192, 16.86, 16.745, 16.707, 16.552, 16.133, 16.301, 16.08, 15.81, 15.75, 15.909, 16.127, 16.457, 16.204, 16.329, 16.748, 16.624, 17.011, 16.548, 16.831, 16.653, 16.791, 16.57, 16.778, 16.928, 16.932, 17.22, 16.876, 17.301, 17.422, 17.689, 17.316, 17.547, 17.534, 17.409, 17.669, 17.416, 17.859, 17.477, 17.307, 17.245, 17.352, 17.851, 17.412, 17.144, 17.138, 17.085, 16.926, 16.674, 16.854, 17.064, 16.95, 16.609, 16.957, 16.498, 16.552, 16.175, 15.858, 15.697, 15.781, 15.583, 15.36, 15.558, 16.046, 15.968, 15.905, 16.358, 16.783, 17.048, 16.762, 17.224, 17.363, 17.246, 16.79, 16.608, 16.423, 15.991, 15.527, 15.147, 14.759, 14.792, 15.206, 15.148, 15.046, 15.429, 14.999, 15.407, 15.124, 14.72, 14.713, 15.022, 15.092, 14.982, 15.001, 14.734, 14.713, 14.841, 14.562, 15.005, 15.483, 15.472, 15.277, 15.503, 15.116, 15.12, 15.442, 15.476, 15.789, 15.36, 15.764, 16.218, 16.493, 16.642, 17.088, 16.816, 16.645, 16.336, 16.511, 16.2, 15.994, 15.86, 15.929, 16.316, 16.416, 16.746, 17.173, 17.531, 17.627, 17.407, 17.49, 17.768, 17.509, 17.795, 18.147, 18.63, 18.945, 19.021, 19.518, 19.6, 19.744, 19.63, 19.32, 18.933, 19.297, 19.598, 19.446, 19.236, 19.198, 19.144, 19.159, 19.065, 19.032, 18.586, 18.272, 18.119, 18.3, 17.894, 17.744, 17.5, 17.083, 17.092, 16.864, 16.453, 16.31, 16.681, 16.342, 16.447, 16.715, 17.068, 17.067, 16.822, 16.673, 16.675, 16.592, 16.686, 16.397, 15.902, 15.597, 15.357, 15.162, 15.348, 15.603, 15.283, 15.257, 15.082, 14.621, 14.366, 14.039, 13.957, 14.141, 13.854, 14.243, 14.414, 14.033, 13.93, 14.104, 14.461, 14.249, 14.053, 14.165, 14.035, 14.408, 14.501, 14.019, 14.265, 14.67, 14.797, 14.42, 14.681, 15.16, 14.715, 14.292, 14.411, 14.656, 15.094, 15.366, 15.055, 15.198, 14.762, 14.294, 13.854, 13.811, 13.549, 13.927, 13.897, 13.421, 13.037, 13.32, 13.721, 13.511, 13.999, 13.529, 13.418, 13.881, 14.326, 14.362, 13.987, 14.015, 13.599, 13.343, 13.307, 13.689, 13.851, 13.404, 13.577, 13.395, 13.619, 13.195, 12.904, 12.553, 12.294, 12.649, 12.425, 11.967, 12.062, 11.71, 11.645, 12.058, 12.136, 11.749, 11.953, 12.401, 12.044, 11.901, 11.631, 11.396, 11.036, 11.244, 10.864, 11.207, 11.135, 11.39, 11.723, 12.084, 11.8, 11.471, 11.33, 11.504, 11.295, 11.3, 10.901, 10.494, 10.825, 11.054, 10.866, 10.713, 10.875, 10.846, 10.947, 11.422, 11.158, 10.94, 10.521, 10.36, 10.411, 10.792, 10.472, 10.305, 10.525, 10.853, 10.556, 10.72, 10.54, 10.583, 10.299, 10.061, 10.004, 9.903, 9.796, 9.472, 9.246, 9.54, 9.456, 9.177, 9.484, 9.557, 9.493, 9.968, 9.536, 9.39, 8.922, 8.423, 8.518, 8.686, 8.771, 9.098, 9.281, 8.858, 9.027, 8.553, 8.784, 8.996, 9.379, 9.846, 9.855, 9.502, 9.608, 9.761, 9.409, 9.4, 9.332, 9.34, 9.284, 8.844, 8.722, 8.376, 8.775, 8.293, 8.144, 8.63, 8.831, 8.957, 9.18, 9.601, 9.695, 10.018, 9.841, 9.743, 9.292, 8.85, 9.316, 9.288, 9.519, 9.738, 9.289, 9.785, 9.804, 10.06, 10.188, 10.095, 9.739, 9.881, 9.7, 9.991, 10.391, 10.002], columns=['value']) def test_performance_stats(self): """test the function performance_statistics() """ pass def test_fv(self): print(f'test with test data and empty DataFrame') self.assertAlmostEqual(eval_fv(self.test_data1), 6.39245474) self.assertAlmostEqual(eval_fv(self.test_data2), 10.05126375) self.assertAlmostEqual(eval_fv(self.test_data3), 6.95068113) self.assertAlmostEqual(eval_fv(self.test_data4), 8.86508591) self.assertAlmostEqual(eval_fv(self.test_data5), 4.58627048) self.assertAlmostEqual(eval_fv(self.test_data6), 4.10346795) self.assertAlmostEqual(eval_fv(self.test_data7), 2.92532313) self.assertAlmostEqual(eval_fv(pd.DataFrame()), -np.inf) print(f'Error testing') self.assertRaises(AssertionError, eval_fv, 15) self.assertRaises(KeyError, eval_fv, pd.DataFrame([1, 2, 3], columns=['non_value'])) def test_max_drawdown(self): print(f'test with test data and empty DataFrame') self.assertAlmostEqual(eval_max_drawdown(self.test_data1)[0], 0.264274308) self.assertEqual(eval_max_drawdown(self.test_data1)[1], 53) self.assertEqual(eval_max_drawdown(self.test_data1)[2], 86) self.assertTrue(np.isnan(eval_max_drawdown(self.test_data1)[3])) self.assertAlmostEqual(eval_max_drawdown(self.test_data2)[0], 0.334690849) self.assertEqual(eval_max_drawdown(self.test_data2)[1], 0) self.assertEqual(eval_max_drawdown(self.test_data2)[2], 10) self.assertEqual(eval_max_drawdown(self.test_data2)[3], 19) self.assertAlmostEqual(eval_max_drawdown(self.test_data3)[0], 0.244452899) self.assertEqual(eval_max_drawdown(self.test_data3)[1], 90) self.assertEqual(eval_max_drawdown(self.test_data3)[2], 99) self.assertTrue(np.isnan(eval_max_drawdown(self.test_data3)[3])) self.assertAlmostEqual(eval_max_drawdown(self.test_data4)[0], 0.201849684) self.assertEqual(eval_max_drawdown(self.test_data4)[1], 14) self.assertEqual(eval_max_drawdown(self.test_data4)[2], 50) self.assertEqual(eval_max_drawdown(self.test_data4)[3], 54) self.assertAlmostEqual(eval_max_drawdown(self.test_data5)[0], 0.534206456) self.assertEqual(eval_max_drawdown(self.test_data5)[1], 21) self.assertEqual(eval_max_drawdown(self.test_data5)[2], 60) self.assertTrue(np.isnan(eval_max_drawdown(self.test_data5)[3])) self.assertAlmostEqual(eval_max_drawdown(self.test_data6)[0], 0.670062689) self.assertEqual(eval_max_drawdown(self.test_data6)[1], 0) self.assertEqual(eval_max_drawdown(self.test_data6)[2], 70) self.assertTrue(np.isnan(eval_max_drawdown(self.test_data6)[3])) self.assertAlmostEqual(eval_max_drawdown(self.test_data7)[0], 0.783577449) self.assertEqual(eval_max_drawdown(self.test_data7)[1], 17) self.assertEqual(eval_max_drawdown(self.test_data7)[2], 51) self.assertTrue(np.isnan(eval_max_drawdown(self.test_data7)[3])) self.assertEqual(eval_max_drawdown(pd.DataFrame()), -np.inf) print(f'Error testing') self.assertRaises(AssertionError, eval_fv, 15) self.assertRaises(KeyError, eval_fv, pd.DataFrame([1, 2, 3], columns=['non_value'])) # test max drawdown == 0: # TODO: investigate: how does divide by zero change? self.assertAlmostEqual(eval_max_drawdown(self.test_data4 - 5)[0], 1.0770474121951792) self.assertEqual(eval_max_drawdown(self.test_data4 - 5)[1], 14) self.assertEqual(eval_max_drawdown(self.test_data4 - 5)[2], 50) def test_info_ratio(self): reference = self.test_data1 self.assertAlmostEqual(eval_info_ratio(self.test_data2, reference, 'value'), 0.075553316) self.assertAlmostEqual(eval_info_ratio(self.test_data3, reference, 'value'), 0.018949457) self.assertAlmostEqual(eval_info_ratio(self.test_data4, reference, 'value'), 0.056328143) self.assertAlmostEqual(eval_info_ratio(self.test_data5, reference, 'value'), -0.004270068) self.assertAlmostEqual(eval_info_ratio(self.test_data6, reference, 'value'), 0.009198027) self.assertAlmostEqual(eval_info_ratio(self.test_data7, reference, 'value'), -0.000890283) def test_volatility(self): self.assertAlmostEqual(eval_volatility(self.test_data1), 0.748646166) self.assertAlmostEqual(eval_volatility(self.test_data2), 0.75527442) self.assertAlmostEqual(eval_volatility(self.test_data3), 0.654188853) self.assertAlmostEqual(eval_volatility(self.test_data4), 0.688375814) self.assertAlmostEqual(eval_volatility(self.test_data5), 1.089989522) self.assertAlmostEqual(eval_volatility(self.test_data6), 1.775419308) self.assertAlmostEqual(eval_volatility(self.test_data7), 1.962758406) self.assertAlmostEqual(eval_volatility(self.test_data1, logarithm=False), 0.750993311) self.assertAlmostEqual(eval_volatility(self.test_data2, logarithm=False), 0.75571473) self.assertAlmostEqual(eval_volatility(self.test_data3, logarithm=False), 0.655331424) self.assertAlmostEqual(eval_volatility(self.test_data4, logarithm=False), 0.692683021) self.assertAlmostEqual(eval_volatility(self.test_data5, logarithm=False), 1.09602969) self.assertAlmostEqual(eval_volatility(self.test_data6, logarithm=False), 1.774789504) self.assertAlmostEqual(eval_volatility(self.test_data7, logarithm=False), 2.003329156) self.assertEqual(eval_volatility(pd.DataFrame()), -np.inf) self.assertRaises(AssertionError, eval_volatility, [1, 2, 3]) # 测试长数据的Volatility计算 expected_volatility = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.39955371, 0.39974258, 0.40309866, 0.40486593, 0.4055514, 0.40710639, 0.40708157, 0.40609006, 0.4073625, 0.40835305, 0.41155304, 0.41218193, 0.41207489, 0.41300276, 0.41308415, 0.41292392, 0.41207645, 0.41238397, 0.41229291, 0.41164056, 0.41316317, 0.41348842, 0.41462249, 0.41474574, 0.41652625, 0.41649176, 0.41701556, 0.4166593, 0.41684221, 0.41491689, 0.41435209, 0.41549087, 0.41849338, 0.41998049, 0.41959106, 0.41907311, 0.41916103, 0.42120773, 0.42052391, 0.42111225, 0.42124589, 0.42356445, 0.42214672, 0.42324022, 0.42476639, 0.42621689, 0.42549439, 0.42533678, 0.42539414, 0.42545038, 0.42593637, 0.42652095, 0.42665489, 0.42699563, 0.42798159, 0.42784512, 0.42898006, 0.42868781, 0.42874188, 0.42789631, 0.4277768, 0.42776827, 0.42685216, 0.42660989, 0.42563155, 0.42618281, 0.42606281, 0.42505222, 0.42653242, 0.42555378, 0.42500842, 0.42561939, 0.42442059, 0.42395414, 0.42384356, 0.42319135, 0.42397497, 0.42488579, 0.42449729, 0.42508766, 0.42509878, 0.42456616, 0.42535577, 0.42681884, 0.42688552, 0.42779918, 0.42706058, 0.42792887, 0.42762114, 0.42894045, 0.42977398, 0.42919859, 0.42829041, 0.42780946, 0.42825318, 0.42858952, 0.42858315, 0.42805601, 0.42764751, 0.42744107, 0.42775518, 0.42707283, 0.4258592, 0.42615335, 0.42526286, 0.4248906, 0.42368986, 0.4232565, 0.42265079, 0.42263954, 0.42153046, 0.42132051, 0.41995353, 0.41916605, 0.41914271, 0.41876945, 0.41740175, 0.41583884, 0.41614026, 0.41457908, 0.41472411, 0.41310876, 0.41261041, 0.41212369, 0.41211677, 0.4100645, 0.40852504, 0.40860297, 0.40745338, 0.40698661, 0.40644546, 0.40591375, 0.40640744, 0.40620663, 0.40656649, 0.40727154, 0.40797605, 0.40807137, 0.40808913, 0.40809676, 0.40711767, 0.40724628, 0.40713077, 0.40772698, 0.40765157, 0.40658297, 0.4065991, 0.405011, 0.40537645, 0.40432626, 0.40390177, 0.40237701, 0.40291623, 0.40301797, 0.40324145, 0.40312864, 0.40328316, 0.40190955, 0.40246506, 0.40237663, 0.40198407, 0.401969, 0.40185623, 0.40198313, 0.40005643, 0.39940743, 0.39850438, 0.39845398, 0.39695093, 0.39697295, 0.39663201, 0.39675444, 0.39538699, 0.39331959, 0.39326074, 0.39193287, 0.39157266, 0.39021327, 0.39062591, 0.38917591, 0.38976991, 0.38864187, 0.38872158, 0.38868096, 0.38868377, 0.38842057, 0.38654784, 0.38649517, 0.38600464, 0.38408115, 0.38323049, 0.38260215, 0.38207663, 0.38142669, 0.38003262, 0.37969367, 0.37768092, 0.37732108, 0.37741991, 0.37617779, 0.37698504, 0.37606784, 0.37499276, 0.37533731, 0.37350437, 0.37375172, 0.37385382, 0.37384003, 0.37338938, 0.37212288, 0.37273075, 0.370559, 0.37038506, 0.37062153, 0.36964661, 0.36818564, 0.3656634, 0.36539259, 0.36428672, 0.36502487, 0.3647148, 0.36551435, 0.36409919, 0.36348181, 0.36254383, 0.36166601, 0.36142665, 0.35954942, 0.35846915, 0.35886759, 0.35813867, 0.35642888, 0.35375231, 0.35061783, 0.35078463, 0.34995508, 0.34688918, 0.34548257, 0.34633158, 0.34622833, 0.34652111, 0.34622774, 0.34540951, 0.34418809, 0.34276593, 0.34160916, 0.33811193, 0.33822709, 0.3391685, 0.33883381]) test_volatility = eval_volatility(self.long_data) test_volatility_roll = self.long_data['volatility'].values self.assertAlmostEqual(test_volatility, np.nanmean(expected_volatility)) self.assertTrue(np.allclose(expected_volatility, test_volatility_roll, equal_nan=True)) def test_sharp(self): self.assertAlmostEqual(eval_sharp(self.test_data1, 5, 0), 0.06135557) self.assertAlmostEqual(eval_sharp(self.test_data2, 5, 0), 0.167858667) self.assertAlmostEqual(eval_sharp(self.test_data3, 5, 0), 0.09950547) self.assertAlmostEqual(eval_sharp(self.test_data4, 5, 0), 0.154928241) self.assertAlmostEqual(eval_sharp(self.test_data5, 5, 0.002), 0.007868673) self.assertAlmostEqual(eval_sharp(self.test_data6, 5, 0.002), 0.018306537) self.assertAlmostEqual(eval_sharp(self.test_data7, 5, 0.002), 0.006259971) # 测试长数据的sharp率计算 expected_sharp = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, -0.02346815, -0.02618783, -0.03763912, -0.03296276, -0.03085698, -0.02851101, -0.02375842, -0.02016746, -0.01107885, -0.01426613, -0.00787204, -0.01135784, -0.01164232, -0.01003481, -0.00022512, -0.00046792, -0.01209378, -0.01278892, -0.01298135, -0.01938214, -0.01671044, -0.02120509, -0.0244281, -0.02416067, -0.02763238, -0.027579, -0.02372774, -0.02215294, -0.02467094, -0.02091266, -0.02590194, -0.03049876, -0.02077131, -0.01483653, -0.02488144, -0.02671638, -0.02561547, -0.01957986, -0.02479803, -0.02703162, -0.02658087, -0.01641755, -0.01946472, -0.01647757, -0.01280889, -0.00893643, -0.00643275, -0.00698457, -0.00549962, -0.00654677, -0.00494757, -0.0035633, -0.00109037, 0.00750654, 0.00451208, 0.00625502, 0.01221367, 0.01326454, 0.01535037, 0.02269538, 0.02028715, 0.02127712, 0.02333264, 0.02273159, 0.01670643, 0.01376513, 0.01265342, 0.02211647, 0.01612449, 0.00856706, -0.00077147, -0.00268848, 0.00210993, -0.00443934, -0.00411912, -0.0018756, -0.00867461, -0.00581601, -0.00660835, -0.00861137, -0.00678614, -0.01188408, -0.00589617, -0.00244323, -0.00201891, -0.01042846, -0.01471016, -0.02167034, -0.02258554, -0.01306809, -0.00909086, -0.01233746, -0.00595166, -0.00184208, 0.00750497, 0.01481886, 0.01761972, 0.01562886, 0.01446414, 0.01285826, 0.01357719, 0.00967613, 0.01636272, 0.01458437, 0.02280183, 0.02151903, 0.01700276, 0.01597368, 0.02114336, 0.02233297, 0.02585631, 0.02768459, 0.03519235, 0.04204535, 0.04328161, 0.04672855, 0.05046191, 0.04619848, 0.04525853, 0.05381529, 0.04598861, 0.03947394, 0.04665006, 0.05586077, 0.05617728, 0.06495018, 0.06205172, 0.05665466, 0.06500615, 0.0632062, 0.06084328, 0.05851466, 0.05659229, 0.05159347, 0.0432977, 0.0474047, 0.04231723, 0.03613176, 0.03618391, 0.03591012, 0.03885674, 0.0402686, 0.03846423, 0.04534014, 0.04721458, 0.05130912, 0.05026281, 0.05394312, 0.05529349, 0.05949243, 0.05463304, 0.06195165, 0.06767606, 0.06880985, 0.07048996, 0.07078815, 0.07420767, 0.06773439, 0.0658441, 0.06470875, 0.06302349, 0.06456876, 0.06411282, 0.06216669, 0.067094, 0.07055075, 0.07254976, 0.07119253, 0.06173308, 0.05393352, 0.05681246, 0.05250643, 0.06099845, 0.0655544, 0.06977334, 0.06636514, 0.06177949, 0.06869908, 0.06719767, 0.06178738, 0.05915714, 0.06882277, 0.06756821, 0.06507994, 0.06489791, 0.06553941, 0.073123, 0.07576757, 0.06805446, 0.06063571, 0.05033801, 0.05206971, 0.05540306, 0.05249118, 0.05755587, 0.0586174, 0.05051288, 0.0564852, 0.05757284, 0.06358355, 0.06130082, 0.04925482, 0.03834472, 0.04163981, 0.04648316, 0.04457858, 0.04324626, 0.04328791, 0.04156207, 0.04818652, 0.04972634, 0.06024123, 0.06489556, 0.06255485, 0.06069815, 0.06466389, 0.07081163, 0.07895358, 0.0881782, 0.09374151, 0.08336506, 0.08764795, 0.09080174, 0.08808926, 0.08641158, 0.07811943, 0.06885318, 0.06479503, 0.06851185, 0.07382819, 0.07047903, 0.06658251, 0.07638379, 0.08667974, 0.08867918, 0.08245323, 0.08961866, 0.09905298, 0.0961908, 0.08562706, 0.0839014, 0.0849072, 0.08338395, 0.08783487, 0.09463609, 0.10332336, 0.11806497, 0.11220297, 0.11589097, 0.11678405]) test_sharp = eval_sharp(self.long_data, 5, 0.00035) self.assertAlmostEqual(np.nanmean(expected_sharp), test_sharp) self.assertTrue(np.allclose(self.long_data['sharp'].values, expected_sharp, equal_nan=True)) def test_beta(self): reference = self.test_data1 self.assertAlmostEqual(eval_beta(self.test_data2, reference, 'value'), -0.017148939) self.assertAlmostEqual(eval_beta(self.test_data3, reference, 'value'), -0.042204233) self.assertAlmostEqual(eval_beta(self.test_data4, reference, 'value'), -0.15652986) self.assertAlmostEqual(eval_beta(self.test_data5, reference, 'value'), -0.049195532) self.assertAlmostEqual(eval_beta(self.test_data6, reference, 'value'), -0.026995082) self.assertAlmostEqual(eval_beta(self.test_data7, reference, 'value'), -0.01147809) self.assertRaises(TypeError, eval_beta, [1, 2, 3], reference, 'value') self.assertRaises(TypeError, eval_beta, self.test_data3, [1, 2, 3], 'value') self.assertRaises(KeyError, eval_beta, self.test_data3, reference, 'not_found_value') # 测试长数据的beta计算 expected_beta = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, -0.04988841, -0.05127618, -0.04692104, -0.04272652, -0.04080598, -0.0493347, -0.0460858, -0.0416761, -0.03691527, -0.03724924, -0.03678865, -0.03987324, -0.03488321, -0.02567672, -0.02690303, -0.03010128, -0.02437967, -0.02571932, -0.02455681, -0.02839811, -0.03358653, -0.03396697, -0.03466321, -0.03050966, -0.0247583, -0.01629325, -0.01880895, -0.01480403, -0.01348783, -0.00544294, -0.00648176, -0.00467036, -0.01135331, -0.0156841, -0.02340763, -0.02615705, -0.02730771, -0.02906174, -0.02860664, -0.02412914, -0.02066416, -0.01744816, -0.02185133, -0.02145285, -0.02681765, -0.02827694, -0.02394581, -0.02744096, -0.02778825, -0.02703065, -0.03160023, -0.03615371, -0.03681072, -0.04265126, -0.04344738, -0.04232421, -0.04705272, -0.04533344, -0.04605934, -0.05272737, -0.05156463, -0.05134196, -0.04730733, -0.04425352, -0.03869831, -0.04159571, -0.04223998, -0.04346747, -0.04229844, -0.04740093, -0.04992507, -0.04621232, -0.04477644, -0.0486915, -0.04598224, -0.04943463, -0.05006391, -0.05362256, -0.04994067, -0.05464769, -0.05443275, -0.05513493, -0.05173594, -0.04500994, -0.04662891, -0.03903505, -0.0419592, -0.04307773, -0.03925718, -0.03711574, -0.03992631, -0.0433058, -0.04533641, -0.0461183, -0.05600344, -0.05758377, -0.05959874, -0.05605942, -0.06002859, -0.06253002, -0.06747014, -0.06427915, -0.05931947, -0.05769974, -0.04791515, -0.05175088, -0.05748039, -0.05385232, -0.05072975, -0.05052637, -0.05125567, -0.05005785, -0.05325104, -0.04977727, -0.04947867, -0.05148544, -0.05739156, -0.05742069, -0.06047279, -0.0558414, -0.06086126, -0.06265151, -0.06411129, -0.06828052, -0.06781762, -0.07083409, -0.07211207, -0.06799162, -0.06913295, -0.06775162, -0.0696265, -0.06678248, -0.06867502, -0.06581961, -0.07055823, -0.06448184, -0.06097973, -0.05795587, -0.0618383, -0.06130145, -0.06050652, -0.05936661, -0.05749424, -0.0499, -0.05050495, -0.04962687, -0.05033439, -0.05070116, -0.05422009, -0.05369759, -0.05548943, -0.05907353, -0.05933035, -0.05927918, -0.06227663, -0.06011455, -0.05650432, -0.05828134, -0.05620949, -0.05715323, -0.05482478, -0.05387113, -0.05095559, -0.05377999, -0.05334267, -0.05220438, -0.04001521, -0.03892434, -0.03660782, -0.04282708, -0.04324623, -0.04127048, -0.04227559, -0.04275226, -0.04347049, -0.04125853, -0.03806295, -0.0330632, -0.03155531, -0.03277152, -0.03304518, -0.03878731, -0.03830672, -0.03727434, -0.0370571, -0.04509224, -0.04207632, -0.04116198, -0.04545179, -0.04584584, -0.05287341, -0.05417433, -0.05175836, -0.05005509, -0.04268674, -0.03442321, -0.03457309, -0.03613426, -0.03524391, -0.03629479, -0.04361312, -0.02626705, -0.02406115, -0.03046384, -0.03181044, -0.03375164, -0.03661673, -0.04520779, -0.04926951, -0.05726738, -0.0584486, -0.06220608, -0.06800563, -0.06797431, -0.07562211, -0.07481996, -0.07731229, -0.08413381, -0.09031826, -0.09691925, -0.11018071, -0.11952675, -0.10826026, -0.11173895, -0.10756359, -0.10775916, -0.11664559, -0.10505051, -0.10606547, -0.09855355, -0.10004159, -0.10857084, -0.12209301, -0.11605758, -0.11105113, -0.1155195, -0.11569505, -0.10513348, -0.09611072, -0.10719791, -0.10843965, -0.11025856, -0.10247839, -0.10554044, -0.10927647, -0.10645088, -0.09982498, -0.10542734, -0.09631372, -0.08229695]) test_beta_mean = eval_beta(self.long_data, self.long_bench, 'value') test_beta_roll = self.long_data['beta'].values self.assertAlmostEqual(test_beta_mean, np.nanmean(expected_beta)) self.assertTrue(np.allclose(test_beta_roll, expected_beta, equal_nan=True)) def test_alpha(self): reference = self.test_data1 self.assertAlmostEqual(eval_alpha(self.test_data2, 5, reference, 'value', 0.5), 11.63072977) self.assertAlmostEqual(eval_alpha(self.test_data3, 5, reference, 'value', 0.5), 1.886590071) self.assertAlmostEqual(eval_alpha(self.test_data4, 5, reference, 'value', 0.5), 6.827021872) self.assertAlmostEqual(eval_alpha(self.test_data5, 5, reference, 'value', 0.92), -1.192265168) self.assertAlmostEqual(eval_alpha(self.test_data6, 5, reference, 'value', 0.92), -1.437142359) self.assertAlmostEqual(eval_alpha(self.test_data7, 5, reference, 'value', 0.92), -1.781311545) # 测试长数据的alpha计算 expected_alpha = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, -0.09418119, -0.11188463, -0.17938358, -0.15588172, -0.1462678, -0.13089586, -0.10780125, -0.09102891, -0.03987585, -0.06075686, -0.02459503, -0.04104284, -0.0444565, -0.04074585, 0.02191275, 0.02255955, -0.05583375, -0.05875539, -0.06055551, -0.09648245, -0.07913737, -0.10627829, -0.12320965, -0.12368335, -0.1506743, -0.15768033, -0.13638829, -0.13065298, -0.14537834, -0.127428, -0.15504529, -0.18184636, -0.12652146, -0.09190138, -0.14847221, -0.15840648, -0.1525789, -0.11859418, -0.14700954, -0.16295761, -0.16051645, -0.10364859, -0.11961134, -0.10258267, -0.08090148, -0.05727746, -0.0429945, -0.04672356, -0.03581408, -0.0439215, -0.03429495, -0.0260362, -0.01075022, 0.04931808, 0.02779388, 0.03984083, 0.08311951, 0.08995566, 0.10522428, 0.16159058, 0.14238174, 0.14759783, 0.16257712, 0.158908, 0.11302115, 0.0909566, 0.08272888, 0.15261884, 0.10546376, 0.04990313, -0.01284111, -0.02720704, 0.00454725, -0.03965491, -0.03818265, -0.02186992, -0.06574751, -0.04846454, -0.05204211, -0.06316498, -0.05095099, -0.08502656, -0.04681162, -0.02362027, -0.02205091, -0.07706374, -0.10371841, -0.14434688, -0.14797935, -0.09055402, -0.06739549, -0.08824959, -0.04855888, -0.02291244, 0.04027138, 0.09370505, 0.11472939, 0.10243593, 0.0921445, 0.07662648, 0.07946651, 0.05450718, 0.10497677, 0.09068334, 0.15462924, 0.14231034, 0.10544952, 0.09980256, 0.14035223, 0.14942974, 0.17624102, 0.19035477, 0.2500807, 0.30724652, 0.31768915, 0.35007521, 0.38412975, 0.34356521, 0.33614463, 0.41206165, 0.33999177, 0.28045963, 0.34076789, 0.42220356, 0.42314636, 0.50790423, 0.47713348, 0.42520169, 0.50488411, 0.48705211, 0.46252601, 0.44325578, 0.42640573, 0.37986783, 0.30652822, 0.34503393, 0.2999069, 0.24928617, 0.24730218, 0.24326897, 0.26657905, 0.27861168, 0.26392824, 0.32552649, 0.34177792, 0.37837011, 0.37025267, 0.4030612, 0.41339361, 0.45076809, 0.40383354, 0.47093422, 0.52505036, 0.53614256, 0.5500943, 0.55319293, 0.59021451, 0.52358459, 0.50605947, 0.49359168, 0.47895956, 0.49320243, 0.4908336, 0.47310767, 0.51821564, 0.55105932, 0.57291504, 0.5599809, 0.46868842, 0.39620087, 0.42086934, 0.38317217, 0.45934108, 0.50048866, 0.53941991, 0.50676751, 0.46500915, 0.52993663, 0.51668366, 0.46405428, 0.44100603, 0.52726147, 0.51565458, 0.49186248, 0.49001081, 0.49367648, 0.56422294, 0.58882785, 0.51334664, 0.44386256, 0.35056709, 0.36490029, 0.39205071, 0.3677061, 0.41134736, 0.42315067, 0.35356394, 0.40324562, 0.41340007, 0.46503322, 0.44355762, 0.34854314, 0.26412842, 0.28633753, 0.32335224, 0.30761141, 0.29709569, 0.29570487, 0.28000063, 0.32802547, 0.33967726, 0.42511212, 0.46252357, 0.44244974, 0.42152907, 0.45436727, 0.50482359, 0.57339198, 0.6573356, 0.70912003, 0.60328917, 0.6395092, 0.67015805, 0.64241557, 0.62779142, 0.55028063, 0.46448736, 0.43709245, 0.46777983, 0.51789439, 0.48594916, 0.4456216, 0.52008189, 0.60548684, 0.62792473, 0.56645031, 0.62766439, 0.71829315, 0.69481356, 0.59550329, 0.58133754, 0.59014148, 0.58026655, 0.61719273, 0.67373203, 0.75573056, 0.89501633, 0.8347253, 0.87964685, 0.89015835]) test_alpha_mean = eval_alpha(self.long_data, 100, self.long_bench, 'value') test_alpha_roll = self.long_data['alpha'].values self.assertAlmostEqual(test_alpha_mean, np.nanmean(expected_alpha)) self.assertTrue(np.allclose(test_alpha_roll, expected_alpha, equal_nan=True)) def test_calmar(self): """test evaluate function eval_calmar()""" pass def test_benchmark(self): reference = self.test_data1 tr, yr = eval_benchmark(self.test_data2, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) tr, yr = eval_benchmark(self.test_data3, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) tr, yr = eval_benchmark(self.test_data4, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) tr, yr = eval_benchmark(self.test_data5, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) tr, yr = eval_benchmark(self.test_data6, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) tr, yr = eval_benchmark(self.test_data7, reference, 'value') self.assertAlmostEqual(tr, 0.19509091) self.assertAlmostEqual(yr, 0.929154957) def test_evaluate(self): pass class TestLoop(unittest.TestCase): """通过一个假设但精心设计的例子来测试loop_step以及loop方法的正确性""" def setUp(self): # 精心设计的模拟股票名称、交易日期、以及股票价格 self.shares = ['share1', 'share2', 'share3', 'share4', 'share5', 'share6', 'share7'] self.dates = ['2016/07/01', '2016/07/04', '2016/07/05', '2016/07/06', '2016/07/07', '2016/07/08', '2016/07/11', '2016/07/12', '2016/07/13', '2016/07/14', '2016/07/15', '2016/07/18', '2016/07/19', '2016/07/20', '2016/07/21', '2016/07/22', '2016/07/25', '2016/07/26', '2016/07/27', '2016/07/28', '2016/07/29', '2016/08/01', '2016/08/02', '2016/08/03', '2016/08/04', '2016/08/05', '2016/08/08', '2016/08/09', '2016/08/10', '2016/08/11', '2016/08/12', '2016/08/15', '2016/08/16', '2016/08/17', '2016/08/18', '2016/08/19', '2016/08/22', '2016/08/23', '2016/08/24', '2016/08/25', '2016/08/26', '2016/08/29', '2016/08/30', '2016/08/31', '2016/09/01', '2016/09/02', '2016/09/05', '2016/09/06', '2016/09/07', '2016/09/08', '2016/09/09', '2016/09/12', '2016/09/13', '2016/09/14', '2016/09/15', '2016/09/16', '2016/09/19', '2016/09/20', '2016/09/21', '2016/09/22', '2016/09/23', '2016/09/26', '2016/09/27', '2016/09/28', '2016/09/29', '2016/09/30', '2016/10/10', '2016/10/11', '2016/10/12', '2016/10/13', '2016/10/14', '2016/10/17', '2016/10/18', '2016/10/19', '2016/10/20', '2016/10/21', '2016/10/23', '2016/10/24', '2016/10/25', '2016/10/26', '2016/10/27', '2016/10/29', '2016/10/30', '2016/10/31', '2016/11/01', '2016/11/02', '2016/11/05', '2016/11/06', '2016/11/07', '2016/11/08', '2016/11/09', '2016/11/12', '2016/11/13', '2016/11/14', '2016/11/15', '2016/11/16', '2016/11/19', '2016/11/20', '2016/11/21', '2016/11/22'] self.dates = [pd.Timestamp(date_text) for date_text in self.dates] self.prices = np.array([[5.35, 5.09, 5.03, 4.98, 4.50, 5.09, 4.75], [5.66, 4.84, 5.21, 5.44, 4.35, 5.06, 4.48], [5.79, 4.60, 5.02, 5.45, 4.07, 4.76, 4.56], [5.56, 4.63, 5.50, 5.74, 3.88, 4.62, 4.62], [5.88, 4.64, 5.07, 5.46, 3.74, 4.63, 4.62], [6.25, 4.51, 5.11, 5.45, 3.98, 4.25, 4.59], [5.93, 4.96, 5.15, 5.24, 4.08, 4.13, 4.33], [6.39, 4.65, 5.02, 5.47, 4.00, 3.91, 3.88], [6.31, 4.26, 5.10, 5.58, 4.28, 3.77, 3.47], [5.86, 3.77, 5.24, 5.36, 4.01, 3.43, 3.51], [5.61, 3.39, 4.93, 5.38, 4.14, 3.68, 3.83], [5.31, 3.76, 4.96, 5.30, 4.49, 3.63, 3.67], [5.40, 4.06, 5.40, 5.77, 4.49, 3.94, 3.79], [5.03, 3.87, 5.74, 5.75, 4.46, 4.40, 4.18], [5.38, 3.91, 5.53, 6.15, 4.13, 4.03, 4.02], [5.79, 4.13, 5.79, 6.04, 3.79, 3.93, 4.41], [6.27, 4.27, 5.68, 6.01, 4.23, 3.50, 4.65], [6.59, 4.57, 5.90, 5.71, 4.57, 3.39, 4.89], [6.91, 5.04, 5.75, 5.23, 4.92, 3.30, 4.41], [6.71, 5.31, 6.11, 5.47, 5.28, 3.25, 4.66], [6.33, 5.40, 5.77, 5.79, 5.67, 2.94, 4.37], [6.07, 5.21, 5.85, 5.82, 6.00, 2.71, 4.58], [5.98, 5.06, 5.61, 5.61, 5.89, 2.55, 4.76], [6.46, 4.69, 5.75, 5.31, 5.55, 2.21, 4.37], [6.95, 5.12, 5.50, 5.24, 5.39, 2.29, 4.16], [6.77, 5.27, 5.14, 5.41, 5.26, 2.21, 4.02], [6.70, 5.72, 5.31, 5.60, 5.31, 2.04, 3.77], [6.28, 6.10, 5.68, 5.28, 5.22, 2.39, 3.38], [6.61, 6.27, 5.73, 4.99, 4.90, 2.30, 3.07], [6.25, 6.49, 6.04, 5.09, 4.57, 2.41, 2.90], [6.47, 6.16, 6.27, 5.39, 4.96, 2.40, 2.50], [6.45, 6.26, 6.60, 5.58, 4.82, 2.79, 2.76], [6.88, 6.39, 6.10, 5.33, 4.39, 2.67, 2.29], [7.00, 6.58, 6.25, 5.48, 4.63, 2.27, 2.17], [6.59, 6.20, 6.73, 5.10, 5.05, 2.09, 1.84], [6.59, 5.70, 6.91, 5.39, 4.68, 2.55, 1.83], [6.64, 5.20, 7.01, 5.30, 5.02, 2.22, 2.21], [6.38, 5.37, 7.36, 5.04, 4.84, 2.59, 2.00], [6.10, 5.40, 7.72, 5.51, 4.60, 2.59, 1.76], [6.35, 5.22, 7.68, 5.43, 4.66, 2.95, 1.27], [6.52, 5.38, 7.62, 5.23, 4.41, 2.69, 1.40], [6.87, 5.53, 7.74, 4.99, 4.87, 2.20, 1.11], [6.84, 6.03, 7.53, 5.43, 4.42, 2.69, 1.60], [7.09, 5.77, 7.46, 5.40, 4.08, 2.65, 1.23], [6.88, 5.66, 7.84, 5.60, 4.16, 2.63, 1.59], [6.84, 6.08, 8.11, 5.66, 4.10, 2.14, 1.50], [6.98, 5.62, 8.04, 6.01, 4.43, 2.39, 1.80], [7.02, 5.63, 7.65, 5.64, 4.07, 1.95, 1.55], [7.13, 6.11, 7.52, 5.67, 3.97, 2.32, 1.35], [7.59, 6.03, 7.67, 5.30, 4.16, 2.69, 1.51], [7.61, 6.27, 7.47, 4.91, 4.12, 2.51, 1.08], [7.21, 6.28, 7.44, 5.37, 4.04, 2.62, 1.06], [7.48, 6.52, 7.59, 5.75, 3.84, 2.16, 1.43], [7.66, 7.00, 7.94, 6.08, 3.46, 2.35, 1.43], [7.51, 7.34, 8.25, 6.58, 3.18, 2.31, 1.74], [7.12, 7.34, 7.77, 6.78, 3.10, 1.96, 1.44], [6.97, 7.68, 8.03, 7.20, 3.55, 2.35, 1.83], [6.67, 8.09, 7.87, 7.65, 3.66, 2.58, 1.71], [6.20, 7.68, 7.58, 8.00, 3.66, 2.40, 2.12], [6.34, 7.58, 7.33, 7.92, 3.29, 2.20, 2.45], [6.22, 7.46, 7.22, 8.30, 2.80, 2.31, 2.85], [5.98, 7.59, 6.86, 8.46, 2.88, 2.16, 2.79], [6.37, 7.19, 7.18, 7.99, 3.04, 2.16, 2.91], [6.56, 7.40, 7.54, 8.19, 3.45, 2.20, 3.26], [6.26, 7.48, 7.24, 8.61, 3.88, 1.73, 3.14], [6.69, 7.93, 6.85, 8.66, 3.58, 1.93, 3.53], [7.13, 8.23, 6.60, 8.91, 3.60, 2.25, 3.65], [6.83, 8.35, 6.65, 9.08, 3.97, 2.69, 3.69], [7.31, 8.44, 6.74, 9.34, 4.05, 2.59, 3.50], [7.43, 8.35, 7.19, 8.96, 4.40, 2.14, 3.25], [7.54, 8.58, 7.14, 8.98, 4.06, 1.68, 3.64], [7.18, 8.82, 6.88, 8.50, 3.60, 1.98, 4.00], [7.21, 9.09, 7.14, 8.65, 3.61, 2.14, 3.63], [7.45, 9.02, 7.30, 8.94, 4.10, 1.89, 3.78], [7.37, 8.87, 6.95, 8.63, 3.74, 1.97, 3.42], [6.88, 9.22, 7.02, 8.65, 4.02, 1.99, 3.76], [7.08, 9.04, 7.38, 8.40, 3.95, 2.37, 3.62], [6.75, 8.60, 7.50, 8.38, 3.81, 2.14, 3.67], [6.60, 8.48, 7.60, 8.23, 3.71, 2.35, 3.61], [6.21, 8.71, 7.15, 8.04, 3.94, 1.86, 3.39], [6.36, 8.79, 7.30, 7.91, 4.43, 2.14, 3.43], [6.82, 8.93, 7.80, 7.57, 4.07, 2.39, 3.33], [6.45, 9.36, 8.15, 7.73, 4.04, 2.53, 3.03], [6.85, 9.68, 8.40, 7.74, 4.34, 2.47, 3.28], [6.48, 10.16, 8.87, 8.07, 4.80, 2.93, 3.46], [6.10, 10.56, 8.53, 7.99, 5.18, 3.09, 3.25], [5.64, 10.63, 8.94, 7.92, 4.90, 2.93, 2.95], [6.01, 10.55, 8.52, 8.40, 5.40, 3.22, 2.87], [6.21, 10.65, 8.80, 8.80, 5.73, 3.06, 2.63], [6.61, 10.55, 8.92, 8.47, 5.62, 2.90, 2.40], [7.02, 10.19, 9.20, 8.07, 5.20, 2.68, 2.53], [7.04, 10.48, 8.71, 7.87, 4.85, 2.46, 2.96], [6.77, 10.36, 8.25, 8.02, 5.18, 2.41, 3.26], [7.09, 10.03, 8.19, 8.39, 4.72, 2.74, 2.97], [6.65, 10.24, 7.80, 8.69, 4.62, 3.15, 3.16], [7.07, 10.01, 7.69, 8.81, 4.55, 3.40, 3.58], [6.80, 10.14, 7.23, 8.99, 4.37, 3.82, 3.23], [6.79, 10.31, 6.98, 9.10, 4.26, 4.02, 3.62], [6.48, 9.88, 7.07, 8.90, 4.25, 3.76, 3.13], [6.39, 10.05, 6.95, 8.87, 4.59, 4.10, 2.93]]) # 精心设计的模拟PT持股仓位目标信号: self.pt_signals = np.array([[0.000, 0.000, 0.000, 0.000, 0.250, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.250, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.250, 0.100, 0.150], [0.200, 0.200, 0.000, 0.000, 0.250, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.250, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.200, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.100, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.050, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.050, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.050, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.050, 0.000, 0.062, 0.100, 0.150], [0.133, 0.200, 0.050, 0.000, 0.062, 0.100, 0.000], [0.133, 0.200, 0.050, 0.000, 0.262, 0.100, 0.000], [0.133, 0.200, 0.050, 0.000, 0.262, 0.100, 0.000], [0.133, 0.200, 0.050, 0.000, 0.262, 0.100, 0.000], [0.066, 0.200, 0.050, 0.150, 0.262, 0.100, 0.000], [0.066, 0.200, 0.050, 0.150, 0.262, 0.100, 0.000], [0.066, 0.200, 0.050, 0.150, 0.262, 0.100, 0.000], [0.066, 0.200, 0.050, 0.150, 0.262, 0.100, 0.000], [0.066, 0.200, 0.050, 0.150, 0.262, 0.100, 0.000], [0.066, 0.200, 0.250, 0.150, 0.000, 0.300, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.386, 0.136, 0.170, 0.102, 0.000, 0.204, 0.000], [0.460, 0.119, 0.149, 0.089, 0.000, 0.179, 0.000], [0.460, 0.119, 0.149, 0.089, 0.000, 0.179, 0.000], [0.460, 0.119, 0.149, 0.089, 0.000, 0.179, 0.000], [0.446, 0.116, 0.145, 0.087, 0.000, 0.087, 0.116], [0.446, 0.116, 0.145, 0.087, 0.000, 0.087, 0.116], [0.446, 0.116, 0.145, 0.087, 0.000, 0.087, 0.116], [0.446, 0.116, 0.145, 0.087, 0.000, 0.087, 0.116], [0.446, 0.116, 0.145, 0.087, 0.000, 0.087, 0.116], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.400, 0.208, 0.130, 0.078, 0.000, 0.078, 0.104], [0.370, 0.193, 0.120, 0.072, 0.072, 0.072, 0.096], [0.000, 0.222, 0.138, 0.222, 0.083, 0.222, 0.111], [0.000, 0.222, 0.138, 0.222, 0.083, 0.222, 0.111], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.121, 0.195, 0.121, 0.195, 0.073, 0.195, 0.097], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.200, 0.320, 0.200, 0.000, 0.120, 0.000, 0.160], [0.047, 0.380, 0.238, 0.000, 0.142, 0.000, 0.190], [0.047, 0.380, 0.238, 0.000, 0.142, 0.000, 0.190], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.043, 0.434, 0.217, 0.000, 0.130, 0.000, 0.173], [0.045, 0.454, 0.227, 0.000, 0.000, 0.000, 0.272], [0.045, 0.454, 0.227, 0.000, 0.000, 0.000, 0.272], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.050, 0.000, 0.250, 0.000, 0.000, 0.000, 0.300], [0.000, 0.000, 0.400, 0.000, 0.000, 0.000, 0.300], [0.000, 0.000, 0.400, 0.000, 0.000, 0.000, 0.300], [0.000, 0.000, 0.400, 0.000, 0.000, 0.000, 0.300]]) # 精心设计的模拟PS比例交易信号,与模拟PT信号高度相似 self.ps_signals = np.array([[0.000, 0.000, 0.000, 0.000, 0.250, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.100, 0.150], [0.200, 0.200, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.100, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, -0.750, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [-0.333, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, -0.500, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, -1.000], [0.000, 0.000, 0.000, 0.000, 0.200, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [-0.500, 0.000, 0.000, 0.150, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.200, 0.000, -1.000, 0.200, 0.000], [0.500, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.200, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, -0.500, 0.200], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.200, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.150, 0.000, 0.000], [-1.000, 0.000, 0.000, 0.250, 0.000, 0.250, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.250, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, -1.000, 0.000, -1.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [-0.800, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.100, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, -1.000, 0.000, 0.100], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, -1.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [-1.000, 0.000, 0.150, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000], [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000]]) # 精心设计的模拟VS股票交易信号,与模拟PS信号类似 self.vs_signals = np.array([[000, 000, 000, 000, 500, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 300, 300], [400, 400, 000, 000, 000, 000, 000], [000, 000, 250, 000, 000, 000, 000], [000, 000, 000, 000, -400, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [-200, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, -200, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, -300], [000, 000, 000, 000, 500, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [-200, 000, 000, 300, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 400, 000, -300, 600, 000], [500, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [600, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, -400, 600], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 500, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 300, 000, 000], [-500, 000, 000, 500, 000, 200, 000], [000, 000, 000, 000, 000, 000, 000], [500, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, -700, 000, -600, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [-400, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 300, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, -600, 000, 300], [000, 000, 000, 000, 000, 000, 000], [000, -300, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [-200, 000, 700, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000], [000, 000, 000, 000, 000, 000, 000]]) # 精心设计的模拟多价格交易信号,模拟50个交易日对三只股票的操作 self.multi_shares = ['000010', '000030', '000039'] self.multi_dates = ['2016/07/01', '2016/07/04', '2016/07/05', '2016/07/06', '2016/07/07', '2016/07/08', '2016/07/11', '2016/07/12', '2016/07/13', '2016/07/14', '2016/07/15', '2016/07/18', '2016/07/19', '2016/07/20', '2016/07/21', '2016/07/22', '2016/07/25', '2016/07/26', '2016/07/27', '2016/07/28', '2016/07/29', '2016/08/01', '2016/08/02', '2016/08/03', '2016/08/04', '2016/08/05', '2016/08/08', '2016/08/09', '2016/08/10', '2016/08/11', '2016/08/12', '2016/08/15', '2016/08/16', '2016/08/17', '2016/08/18', '2016/08/19', '2016/08/22', '2016/08/23', '2016/08/24', '2016/08/25', '2016/08/26', '2016/08/29', '2016/08/30', '2016/08/31', '2016/09/01', '2016/09/02', '2016/09/05', '2016/09/06', '2016/09/07', '2016/09/08'] self.multi_dates = [pd.Timestamp(date_text) for date_text in self.multi_dates] # 操作的交易价格包括开盘价、最高价和收盘价 self.multi_prices_open = np.array([[10.02, 9.88, 7.26], [10.00, 9.88, 7.00], [9.98, 9.89, 6.88], [9.97, 9.75, 6.91], [9.99, 9.74, np.nan], [10.01, 9.80, 6.81], [10.04, 9.62, 6.63], [10.06, 9.65, 6.45], [10.06, 9.58, 6.16], [10.11, 9.67, 6.24], [10.11, 9.81, 5.96], [10.07, 9.80, 5.97], [10.06, 10.00, 5.96], [10.09, 9.95, 6.20], [10.03, 10.10, 6.35], [10.02, 10.06, 6.11], [10.06, 10.14, 6.37], [10.08, 9.90, 5.58], [9.99, 10.20, 5.65], [10.00, 10.29, 5.65], [10.03, 9.86, 5.19], [10.02, 9.48, 5.42], [10.06, 10.01, 6.30], [10.03, 10.24, 6.15], [9.97, 10.26, 6.05], [9.94, 10.24, 5.89], [9.83, 10.12, 5.22], [9.78, 10.65, 5.20], [9.77, 10.64, 5.07], [9.91, 10.56, 6.04], [9.92, 10.42, 6.12], [9.97, 10.43, 5.85], [9.91, 10.29, 5.67], [9.90, 10.30, 6.02], [9.88, 10.44, 6.04], [9.91, 10.60, 7.07], [9.63, 10.67, 7.64], [9.64, 10.46, 7.99], [9.57, 10.39, 7.59], [9.55, 10.90, 8.73], [9.58, 11.01, 8.72], [9.61, 11.01, 8.97], [9.62, np.nan, 8.58], [9.55, np.nan, 8.71], [9.57, 10.82, 8.77], [9.61, 11.02, 8.40], [9.63, 10.96, 7.95], [9.64, 11.55, 7.76], [9.61, 11.74, 8.25], [9.56, 11.80, 7.51]]) self.multi_prices_high = np.array([[10.07, 9.91, 7.41], [10.00, 10.04, 7.31], [10.00, 9.93, 7.14], [10.00, 10.04, 7.00], [10.03, 9.84, np.nan], [10.03, 9.88, 6.82], [10.04, 9.99, 6.96], [10.09, 9.70, 6.85], [10.10, 9.67, 6.50], [10.14, 9.71, 6.34], [10.11, 9.85, 6.04], [10.10, 9.90, 6.02], [10.09, 10.00, 6.12], [10.09, 10.20, 6.38], [10.10, 10.11, 6.43], [10.05, 10.18, 6.46], [10.07, 10.21, 6.43], [10.09, 10.26, 6.27], [10.10, 10.38, 5.77], [10.00, 10.47, 6.01], [10.04, 10.42, 5.67], [10.04, 10.07, 5.67], [10.06, 10.24, 6.35], [10.09, 10.27, 6.32], [10.05, 10.38, 6.43], [9.97, 10.43, 6.36], [9.96, 10.39, 5.79], [9.86, 10.65, 5.47], [9.77, 10.84, 5.65], [9.92, 10.65, 6.04], [9.94, 10.73, 6.14], [9.97, 10.63, 6.23], [9.97, 10.51, 5.83], [9.92, 10.35, 6.25], [9.92, 10.46, 6.27], [9.92, 10.63, 7.12], [9.93, 10.74, 7.82], [9.64, 10.76, 8.14], [9.58, 10.54, 8.27], [9.60, 11.02, 8.92], [9.58, 11.12, 8.76], [9.62, 11.17, 9.15], [9.62, np.nan, 8.90], [9.64, np.nan, 9.01], [9.59, 10.92, 9.16], [9.62, 11.15, 9.00], [9.63, 11.11, 8.27], [9.70, 11.55, 7.99], [9.66, 11.95, 8.33], [9.64, 11.93, 8.25]]) self.multi_prices_close = np.array([[10.04, 9.68, 6.64], [10.00, 9.87, 7.26], [10.00, 9.86, 7.03], [9.99, 9.87, 6.87], [9.97, 9.79, np.nan], [9.99, 9.82, 6.64], [10.03, 9.80, 6.85], [10.03, 9.66, 6.70], [10.06, 9.62, 6.39], [10.06, 9.58, 6.22], [10.11, 9.69, 5.92], [10.09, 9.78, 5.91], [10.07, 9.75, 6.11], [10.06, 9.96, 5.91], [10.09, 9.90, 6.23], [10.03, 10.04, 6.28], [10.03, 10.06, 6.28], [10.06, 10.08, 6.27], [10.08, 10.24, 5.70], [10.00, 10.24, 5.56], [9.99, 10.24, 5.67], [10.03, 9.86, 5.16], [10.03, 10.13, 5.69], [10.06, 10.12, 6.32], [10.03, 10.10, 6.14], [9.97, 10.25, 6.25], [9.94, 10.24, 5.79], [9.83, 10.22, 5.26], [9.77, 10.75, 5.05], [9.84, 10.64, 5.45], [9.91, 10.56, 6.06], [9.93, 10.60, 6.21], [9.96, 10.42, 5.69], [9.91, 10.25, 5.46], [9.91, 10.24, 6.02], [9.88, 10.49, 6.69], [9.91, 10.57, 7.43], [9.64, 10.63, 7.72], [9.56, 10.48, 8.16], [9.57, 10.37, 7.83], [9.55, 10.96, 8.70], [9.57, 11.02, 8.71], [9.61, np.nan, 8.88], [9.61, np.nan, 8.54], [9.55, 10.88, 8.87], [9.57, 10.87, 8.87], [9.63, 11.01, 8.18], [9.64, 11.01, 7.80], [9.65, 11.58, 7.97], [9.62, 11.80, 8.25]]) # 交易信号包括三组,分别作用与开盘价、最高价和收盘价 # 此时的关键是股票交割期的处理,交割期不为0时,以交易日为单位交割 self.multi_signals = [] # multisignal的第一组信号为开盘价信号 self.multi_signals.append( pd.DataFrame(np.array([[0.000, 0.000, 0.000], [0.000, -0.500, 0.000], [0.000, -0.500, 0.000], [0.000, 0.000, 0.000], [0.150, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.300, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.300], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.350, 0.250], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.100, 0.000, 0.350], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.200, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.050, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000]]), columns=self.multi_shares, index=self.multi_dates ) ) # 第二组信号为最高价信号 self.multi_signals.append( pd.DataFrame(np.array([[0.000, 0.150, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, -0.200, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.200], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000]]), columns=self.multi_shares, index=self.multi_dates ) ) # 第三组信号为收盘价信号 self.multi_signals.append( pd.DataFrame(np.array([[0.000, 0.200, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [-0.500, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, -0.800], [0.000, 0.000, 0.000], [0.000, -1.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [-0.750, 0.000, 0.000], [0.000, 0.000, -0.850], [0.000, 0.000, 0.000], [0.000, -0.700, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [0.000, 0.000, -1.000], [0.000, 0.000, 0.000], [0.000, 0.000, 0.000], [-1.000, 0.000, 0.000], [0.000, -1.000, 0.000], [0.000, 0.000, 0.000]]), columns=self.multi_shares, index=self.multi_dates ) ) # 交易回测所需的价格也有三组,分别是开盘价、最高价和收盘价 self.multi_histories = [] # multisignal的第一组信号为开盘价信号 self.multi_histories.append( pd.DataFrame(self.multi_prices_open, columns=self.multi_shares, index=self.multi_dates ) ) # 第二组信号为最高价信号 self.multi_histories.append( pd.DataFrame(self.multi_prices_high, columns=self.multi_shares, index=self.multi_dates ) ) # 第三组信号为收盘价信号 self.multi_histories.append( pd.DataFrame(self.multi_prices_close, columns=self.multi_shares, index=self.multi_dates ) ) # 设置回测参数 self.cash = qt.CashPlan(['2016/07/01', '2016/08/12', '2016/09/23'], [10000, 10000, 10000]) self.rate = qt.Cost(buy_fix=0, sell_fix=0, buy_rate=0, sell_rate=0, buy_min=0, sell_min=0, slipage=0) self.rate2 = qt.Cost(buy_fix=0, sell_fix=0, buy_rate=0, sell_rate=0, buy_min=10, sell_min=5, slipage=0) self.pt_signal_hp = dataframe_to_hp( pd.DataFrame(self.pt_signals, index=self.dates, columns=self.shares), htypes='close' ) self.ps_signal_hp = dataframe_to_hp( pd.DataFrame(self.ps_signals, index=self.dates, columns=self.shares), htypes='close' ) self.vs_signal_hp = dataframe_to_hp( pd.DataFrame(self.vs_signals, index=self.dates, columns=self.shares), htypes='close' ) self.multi_signal_hp = stack_dataframes( self.multi_signals, stack_along='htypes', htypes='open, high, close' ) self.history_list = dataframe_to_hp( pd.DataFrame(self.prices, index=self.dates, columns=self.shares), htypes='close' ) self.multi_history_list = stack_dataframes( self.multi_histories, stack_along='htypes', htypes='open, high, close' ) # 模拟PT信号回测结果 # PT信号,先卖后买,交割期为0 self.pt_res_sb00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 10000.0000], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 9916.6667], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 6035.8333, 0.0000, 9761.1111], [348.0151, 417.9188, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 2165.9050, 0.0000, 9674.8209], [348.0151, 417.9188, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 2165.9050, 0.0000, 9712.5872], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9910.7240], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9919.3782], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9793.0692], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9513.8217], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9123.5935], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9000.5995], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9053.4865], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9248.7142], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9161.1372], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9197.3369], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9504.6981], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9875.2461], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10241.5400], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10449.2398], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10628.3269], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10500.7893], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 0.0000, 5233.1396, 0.0000, 10449.2776], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10338.2857], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10194.3474], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10471.0008], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10411.2629], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10670.0618], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10652.4799], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10526.1488], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10458.6614], [101.4983, 417.9188, 821.7315, 288.6672, 0.0000, 2576.1284, 0.0000, 4487.0722, 0.0000, 20609.0270], [1216.3282, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21979.4972], [1216.3282, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21880.9628], [1216.3282, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21630.0454], [1216.3282, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 20968.0007], [1216.3282, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21729.9339], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 21107.6400], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 21561.1745], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 21553.0916], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 22316.9366], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 22084.2862], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 21777.3543], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 22756.8225], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 22843.4697], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 0.0000, 2172.0393, 0.0000, 22762.1766], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 22257.0973], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 23136.5259], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 21813.7852], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 22395.3204], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 23717.6858], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 1607.1030, 1448.0262, 0.0000, 0.0000, 22715.4263], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 22498.3254], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 23341.1733], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24162.3941], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24847.1508], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 23515.9755], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24555.8997], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24390.6372], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24073.3309], [1216.3282, 417.9188, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 2455.7405, 0.0000, 24394.6500], [2076.3314, 903.0334, 511.8829, 288.6672, 0.0000, 669.7975, 1448.0262, 3487.5655, 0.0000, 34904.8150], [0.0000, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 4608.8037, 0.0000, 34198.4475], [0.0000, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 4608.8037, 0.0000, 33753.0190], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 34953.8178], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 33230.2498], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 35026.7819], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 36976.2649], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 38673.8147], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 38717.3429], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 36659.0854], [644.7274, 903.0334, 511.8829, 897.4061, 0.0000, 3514.8404, 1448.0262, 379.3918, 0.0000, 35877.9607], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 36874.4840], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 37010.2695], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 38062.3510], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 36471.1357], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 37534.9927], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 37520.2569], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 36747.7952], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 36387.9409], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 35925.9715], [644.7274, 1337.8498, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 2853.5665, 0.0000, 36950.7028], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 37383.2463], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 37761.2724], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 39548.2653], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 41435.1291], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 41651.6261], [644.7274, 1657.3981, 1071.9327, 0.0000, 1229.1495, 0.0000, 1448.0262, 0.0000, 0.0000, 41131.9920], [644.7274, 1657.3981, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 0.0000, 0.0000, 41286.4702], [644.7274, 1657.3981, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 0.0000, 0.0000, 40978.7259], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 40334.5453], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 41387.9172], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 42492.6707], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 42953.7188], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 42005.1092], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 42017.9106], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 43750.2824], [644.7274, 0.0000, 1071.9327, 0.0000, 0.0000, 0.0000, 3760.7116, 17485.5497, 0.0000, 41766.8679], [0.0000, 0.0000, 2461.8404, 0.0000, 0.0000, 0.0000, 3760.7116, 12161.6930, 0.0000, 42959.1150], [0.0000, 0.0000, 2461.8404, 0.0000, 0.0000, 0.0000, 3760.7116, 12161.6930, 0.0000, 41337.9320], [0.0000, 0.0000, 2461.8404, 0.0000, 0.0000, 0.0000, 3760.7116, 12161.6930, 0.0000, 40290.3688]]) # PT信号,先买后卖,交割期为0 self.pt_res_bs00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 10000.0000], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 9916.6667], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 6035.8333, 0.0000, 9761.1111], [348.0151, 417.9188, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 2165.9050, 0.0000, 9674.8209], [348.0151, 417.9188, 0.0000, 0.0000, 555.5556, 0.0000, 321.0892, 2165.9050, 0.0000, 9712.5872], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9910.7240], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9919.3782], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9793.0692], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9513.8217], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9123.5935], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9000.5995], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9053.4865], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9248.7142], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9161.1372], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9197.3369], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9504.6981], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 9875.2461], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10241.5400], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10449.2398], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10628.3269], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 321.0892, 3762.5512, 0.0000, 10500.7893], [348.0151, 417.9188, 0.0000, 0.0000, 154.3882, 0.0000, 0.0000, 5233.1396, 0.0000, 10449.2776], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10338.2857], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10194.3474], [348.0151, 417.9188, 0.0000, 0.0000, 459.8694, 0.0000, 0.0000, 3433.8551, 0.0000, 10471.0008], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10411.2629], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10670.0618], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10652.4799], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10526.1488], [101.4983, 417.9188, 0.0000, 288.6672, 459.8694, 0.0000, 0.0000, 3541.0848, 0.0000, 10458.6614], [101.4983, 417.9188, 821.7315, 288.6672, 0.0000, 2576.1284, 0.0000, 4487.0722, 0.0000, 20609.0270], [797.1684, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 2703.5808, 0.0000, 21979.4972], [1190.1307, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21700.7241], [1190.1307, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21446.6630], [1190.1307, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 20795.3593], [1190.1307, 417.9188, 821.7315, 288.6672, 0.0000, 1607.1030, 0.0000, 0.0000, 0.0000, 21557.2924], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 20933.6887], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 21392.5581], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 21390.2918], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 22147.7562], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 21910.9053], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 21594.2980], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 22575.4380], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 22655.8312], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 0.0000, 2201.6110, 0.0000, 22578.4365], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 1467.7407, 0.0000, 0.0000, 22073.2661], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 1467.7407, 0.0000, 0.0000, 22955.2367], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 1467.7407, 0.0000, 0.0000, 21628.1647], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 1467.7407, 0.0000, 0.0000, 22203.4237], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 1607.1030, 1467.7407, 0.0000, 0.0000, 23516.2598], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 22505.8428], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 22199.1042], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 23027.9302], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 23848.5806], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 24540.8871], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 23205.6838], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 24267.6685], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 24115.3796], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 23814.3667], [1190.1307, 417.9188, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 2278.3728, 0.0000, 24133.6611], [2061.6837, 896.6628, 507.6643, 288.6672, 0.0000, 699.3848, 1467.7407, 3285.8830, 0.0000, 34658.5742], [0.0000, 896.6628, 507.6643, 466.6033, 0.0000, 1523.7106, 1467.7407, 12328.8684, 0.0000, 33950.7917], [0.0000, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 4380.3797, 0.0000, 33711.4045], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 34922.0959], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 33237.1081], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 35031.8071], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 36976.3376], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 38658.5245], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 38712.2854], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 36655.3125], [644.1423, 896.6628, 507.6643, 936.6623, 0.0000, 3464.7832, 1467.7407, 154.8061, 0.0000, 35904.3692], [644.1423, 902.2617, 514.8253, 0.0000, 15.5990, 0.0000, 1467.7407, 14821.9004, 0.0000, 36873.9080], [644.1423, 902.2617, 514.8253, 0.0000, 1220.8683, 0.0000, 1467.7407, 10470.8781, 0.0000, 36727.7895], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 37719.9840], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 36138.1277], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 37204.0760], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 37173.1201], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 36398.2298], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 36034.2178], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 35583.6399], [644.1423, 1338.1812, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 2753.1120, 0.0000, 36599.2645], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 37013.3408], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 37367.7449], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 39143.8273], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 41007.3074], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 41225.4657], [644.1423, 1646.4805, 1033.4242, 0.0000, 1220.8683, 0.0000, 1467.7407, 0.0000, 0.0000, 40685.9525], [644.1423, 1646.4805, 1033.4242, 0.0000, 0.0000, 0.0000, 1467.7407, 6592.6891, 0.0000, 40851.5435], [644.1423, 1646.4805, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 0.0000, 0.0000, 41082.1210], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 40385.0135], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 41455.1513], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 42670.6769], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 43213.7233], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 42205.2480], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 42273.9386], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 44100.0777], [644.1423, 0.0000, 1033.4242, 0.0000, 0.0000, 0.0000, 3974.4666, 17370.3689, 0.0000, 42059.7208], [0.0000, 0.0000, 2483.9522, 0.0000, 0.0000, 0.0000, 3974.4666, 11619.4102, 0.0000, 43344.9653], [0.0000, 0.0000, 2483.9522, 0.0000, 0.0000, 0.0000, 3974.4666, 11619.4102, 0.0000, 41621.0324], [0.0000, 0.0000, 2483.9522, 0.0000, 0.0000, 0.0000, 3974.4666, 11619.4102, 0.0000, 40528.0648]]) # PT信号,先卖后买,交割期为2天(股票)0天(现金)以便利用先卖的现金继续买入 self.pt_res_sb20 = np.array( [[0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 9916.667], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 321.089, 6035.833, 0.000, 9761.111], [348.015, 417.919, 0.000, 0.000, 555.556, 0.000, 321.089, 2165.905, 0.000, 9674.821], [348.015, 417.919, 0.000, 0.000, 555.556, 0.000, 321.089, 2165.905, 0.000, 9712.587], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9910.724], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9919.378], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9793.069], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9513.822], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9123.593], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9000.600], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9053.487], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9248.714], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9161.137], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9197.337], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9504.698], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9875.246], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10241.540], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10449.240], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10628.327], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10500.789], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 0.000, 5233.140, 0.000, 10449.278], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10338.286], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10194.347], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10471.001], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10411.263], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10670.062], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10652.480], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10526.149], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10458.661], [101.498, 417.919, 821.732, 288.667, 0.000, 2576.128, 0.000, 4487.072, 0.000, 20609.027], [797.168, 417.919, 821.732, 288.667, 0.000, 2576.128, 0.000, 0.000, 0.000, 21979.497], [1156.912, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21584.441], [1156.912, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21309.576], [1156.912, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 20664.323], [1156.912, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21445.597], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 20806.458], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 21288.441], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 21294.365], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 22058.784], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 21805.540], [1156.912, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 2223.240, 0.000, 21456.333], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22459.720], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22611.602], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22470.912], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21932.634], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22425.864], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21460.103], [1481.947, 417.919, 504.579, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22376.968], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 23604.295], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 22704.826], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 22286.293], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 23204.755], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 24089.017], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 24768.185], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 23265.196], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 24350.540], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 24112.706], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 23709.076], [1481.947, 417.919, 504.579, 288.667, 0.000, 763.410, 1577.904, 0.000, 0.000, 24093.545], [2060.275, 896.050, 504.579, 288.667, 0.000, 763.410, 1577.904, 2835.944, 0.000, 34634.888], [578.327, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 732.036, 0.000, 33912.261], [0.000, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 4415.981, 0.000, 33711.951], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 34951.433], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 33224.596], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 35065.209], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 37018.699], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 38706.035], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 38724.569], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 36647.268], [644.683, 896.050, 504.579, 889.896, 0.000, 3485.427, 1577.904, 186.858, 0.000, 35928.930], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 36967.229], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 37056.598], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 38129.862], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 36489.333], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 37599.602], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 37566.823], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 36799.280], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 36431.196], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 35940.942], [644.683, 1341.215, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 2367.759, 0.000, 36973.050], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 37393.292], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 37711.276], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 39515.991], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 41404.440], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 41573.523], [644.683, 1606.361, 1074.629, 0.000, 1232.241, 0.000, 1577.904, 0.000, 0.000, 41011.613], [644.683, 1606.361, 1074.629, 0.000, 0.000, 0.000, 3896.406, 0.000, 0.000, 41160.181], [644.683, 1606.361, 1074.629, 0.000, 0.000, 0.000, 3896.406, 0.000, 0.000, 40815.512], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 40145.531], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 41217.281], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 42379.061], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 42879.589], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 41891.452], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 41929.003], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 43718.052], [644.683, 0.000, 1074.629, 0.000, 0.000, 0.000, 3896.406, 16947.110, 0.000, 41685.916], [0.000, 0.000, 2460.195, 0.000, 0.000, 0.000, 3896.406, 11653.255, 0.000, 42930.410], [0.000, 0.000, 2460.195, 0.000, 0.000, 0.000, 3896.406, 11653.255, 0.000, 41242.589], [0.000, 0.000, 2460.195, 0.000, 0.000, 0.000, 3896.406, 11653.255, 0.000, 40168.084]]) # PT信号,先买后卖,交割期为2天(股票)1天(现金) self.pt_res_bs21 = np.array([ [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 9916.667], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 321.089, 6035.833, 0.000, 9761.111], [348.015, 417.919, 0.000, 0.000, 555.556, 0.000, 321.089, 2165.905, 0.000, 9674.821], [348.015, 417.919, 0.000, 0.000, 555.556, 0.000, 321.089, 2165.905, 0.000, 9712.587], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9910.724], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9919.378], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9793.069], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9513.822], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9123.593], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9000.600], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9053.487], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9248.714], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9161.137], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9197.337], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9504.698], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 9875.246], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10241.540], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10449.240], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10628.327], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 321.089, 3762.551, 0.000, 10500.789], [348.015, 417.919, 0.000, 0.000, 154.388, 0.000, 0.000, 5233.140, 0.000, 10449.278], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10338.286], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10194.347], [348.015, 417.919, 0.000, 0.000, 459.869, 0.000, 0.000, 3433.855, 0.000, 10471.001], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10411.263], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10670.062], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10652.480], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10526.149], [101.498, 417.919, 0.000, 288.667, 459.869, 0.000, 0.000, 3541.085, 0.000, 10458.661], [101.498, 417.919, 821.732, 288.667, 0.000, 2576.128, 0.000, 4487.072, 0.000, 20609.027], [797.168, 417.919, 821.732, 288.667, 0.000, 2576.128, 0.000, 0.000, 0.000, 21979.497], [797.168, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 2475.037, 0.000, 21584.441], [1150.745, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21266.406], [1150.745, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 20623.683], [1150.745, 417.919, 821.732, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21404.957], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 20765.509], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 21248.748], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 21256.041], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 22018.958], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 21764.725], [1150.745, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 2230.202, 0.000, 21413.241], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22417.021], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22567.685], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22427.699], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21889.359], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22381.938], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 21416.358], [1476.798, 417.919, 503.586, 288.667, 0.000, 1649.148, 0.000, 0.000, 0.000, 22332.786], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 0.000, 2386.698, 0.000, 23557.595], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 23336.992], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 22907.742], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 24059.201], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 24941.902], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 25817.514], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 24127.939], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 25459.688], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 25147.370], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 2209.906, 0.000, 0.000, 25005.842], [1476.798, 417.919, 503.586, 288.667, 0.000, 761.900, 1086.639, 2752.004, 0.000, 25598.700], [2138.154, 929.921, 503.586, 288.667, 0.000, 761.900, 1086.639, 4818.835, 0.000, 35944.098], [661.356, 929.921, 503.586, 553.843, 0.000, 1954.237, 1086.639, 8831.252, 0.000, 35237.243], [0.000, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 9460.955, 0.000, 35154.442], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 36166.632], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 34293.883], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 35976.901], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 37848.552], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 39512.574], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 39538.024], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 37652.984], [667.098, 929.921, 503.586, 553.843, 0.000, 3613.095, 1086.639, 5084.792, 0.000, 36687.909], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 37749.277], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 37865.518], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 38481.190], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 37425.087], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 38051.341], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 38065.478], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 37429.495], [667.098, 1108.871, 745.260, 0.000, 512.148, 0.000, 1086.639, 11861.593, 0.000, 37154.479], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 36692.717], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 37327.055], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 37937.630], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 38298.645], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 39689.369], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 40992.397], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 41092.265], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 1086.639, 7576.628, 0.000, 40733.622], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 3726.579, 0.000, 0.000, 40708.515], [667.098, 1600.830, 745.260, 0.000, 512.148, 0.000, 3726.579, 0.000, 0.000, 40485.321], [667.098, 0.000, 745.260, 0.000, 512.148, 0.000, 3726.579, 16888.760, 0.000, 39768.059], [667.098, 0.000, 745.260, 0.000, 512.148, 0.000, 3726.579, 16888.760, 0.000, 40519.595], [667.098, 0.000, 745.260, 0.000, 512.148, 0.000, 3726.579, 16888.760, 0.000, 41590.937], [667.098, 0.000, 1283.484, 0.000, 512.148, 0.000, 3726.579, 12448.413, 0.000, 42354.983], [667.098, 0.000, 1283.484, 0.000, 512.148, 0.000, 3726.579, 12448.413, 0.000, 41175.149], [667.098, 0.000, 1283.484, 0.000, 512.148, 0.000, 3726.579, 12448.413, 0.000, 41037.902], [667.098, 0.000, 1283.484, 0.000, 512.148, 0.000, 3726.579, 12448.413, 0.000, 42706.213], [667.098, 0.000, 1283.484, 0.000, 512.148, 0.000, 3726.579, 12448.413, 0.000, 40539.205], [0.000, 0.000, 2384.452, 0.000, 512.148, 0.000, 3726.579, 9293.252, 0.000, 41608.692], [0.000, 0.000, 2384.452, 0.000, 512.148, 0.000, 3726.579, 9293.252, 0.000, 39992.148], [0.000, 0.000, 2384.452, 0.000, 512.148, 0.000, 3726.579, 9293.252, 0.000, 39134.828]]) # 模拟PS信号回测结果 # PS信号,先卖后买,交割期为0 self.ps_res_sb00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 10000.0000], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 9916.6667], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 205.0654, 321.0892, 5059.7222, 0.0000, 9761.1111], [346.9824, 416.6787, 0.0000, 0.0000, 555.5556, 205.0654, 321.0892, 1201.2775, 0.0000, 9646.1118], [346.9824, 416.6787, 191.0372, 0.0000, 555.5556, 205.0654, 321.0892, 232.7189, 0.0000, 9685.5858], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9813.2184], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9803.1288], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9608.0198], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9311.5727], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8883.6246], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8751.3900], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8794.1811], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9136.5704], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9209.3588], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9093.8294], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9387.5537], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9585.9589], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 9928.7771], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10060.3806], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10281.0021], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10095.5613], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 0.0000, 4506.3926, 0.0000, 10029.9571], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9875.6133], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9614.9463], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9824.1722], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9732.5743], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9968.3391], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 10056.1579], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9921.4925], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9894.1621], [115.7186, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 6179.7742, 0.0000, 20067.9370], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21133.5080], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20988.8485], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20596.7429], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 19910.7730], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20776.7070], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20051.7969], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20725.3884], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20828.8795], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21647.1811], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21310.1687], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20852.0993], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21912.3952], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21937.8282], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21962.4576], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 21389.4018], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22027.4535], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 20939.9992], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 21250.0636], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22282.7812], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 21407.0658], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 21160.2373], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 21826.7682], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22744.9403], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 23466.1185], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22017.8821], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 23191.4662], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 23099.0822], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22684.7671], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 1339.2073, 0.0000, 0.0000, 22842.1346], [1073.8232, 416.6787, 735.6442, 269.8496, 1785.2055, 938.6967, 1339.2073, 5001.4246, 0.0000, 33323.8359], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 32820.2901], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 32891.2308], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 34776.5296], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 33909.0325], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 34560.1906], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 36080.4552], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 38618.4454], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 38497.9230], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 37110.0991], [0.0000, 416.6787, 735.6442, 944.9611, 1785.2055, 3582.8836, 1339.2073, 0.0000, 0.0000, 35455.2467], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35646.1860], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35472.3020], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 36636.4694], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35191.7035], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 36344.2242], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 36221.6005], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35943.5708], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35708.2608], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 35589.0286], [0.0000, 416.6787, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 15126.2788, 0.0000, 36661.0285], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 36310.5909], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 36466.7637], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 37784.4918], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 39587.6766], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 40064.0191], [0.0000, 823.2923, 735.6442, 0.0000, 1785.2055, 0.0000, 1339.2073, 11495.2197, 0.0000, 39521.6439], [0.0000, 823.2923, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 17142.1018, 0.0000, 39932.2761], [0.0000, 823.2923, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 17142.1018, 0.0000, 39565.2475], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 38943.1632], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 39504.1184], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 40317.8004], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 40798.5768], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 39962.5711], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 40194.4793], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 41260.4003], [0.0000, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 2730.5758, 25827.8351, 0.0000, 39966.3024], [0.0000, 0.0000, 1613.4518, 0.0000, 0.0000, 0.0000, 2730.5758, 19700.7377, 0.0000, 40847.3160], [0.0000, 0.0000, 1613.4518, 0.0000, 0.0000, 0.0000, 2730.5758, 19700.7377, 0.0000, 39654.5445], [0.0000, 0.0000, 1613.4518, 0.0000, 0.0000, 0.0000, 2730.5758, 19700.7377, 0.0000, 38914.8151]]) # PS信号,先买后卖,交割期为0 self.ps_res_bs00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 10000.0000], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 0.0000, 0.0000, 7500.0000, 0.0000, 9916.6667], [0.0000, 0.0000, 0.0000, 0.0000, 555.5556, 205.0654, 321.0892, 5059.7222, 0.0000, 9761.1111], [346.9824, 416.6787, 0.0000, 0.0000, 555.5556, 205.0654, 321.0892, 1201.2775, 0.0000, 9646.1118], [346.9824, 416.6787, 191.0372, 0.0000, 555.5556, 205.0654, 321.0892, 232.7189, 0.0000, 9685.5858], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9813.2184], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9803.1288], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9608.0198], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9311.5727], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8883.6246], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8751.3900], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 8794.1811], [346.9824, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 1891.0523, 0.0000, 9136.5704], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9209.3588], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9093.8294], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9387.5537], [231.4373, 416.6787, 191.0372, 0.0000, 138.8889, 205.0654, 321.0892, 2472.2444, 0.0000, 9585.9589], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 9928.7771], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10060.3806], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10281.0021], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 321.0892, 3035.8041, 0.0000, 10095.5613], [231.4373, 416.6787, 95.5186, 0.0000, 138.8889, 205.0654, 0.0000, 4506.3926, 0.0000, 10029.9571], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9875.6133], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9614.9463], [231.4373, 416.6787, 95.5186, 0.0000, 474.2238, 205.0654, 0.0000, 2531.2699, 0.0000, 9824.1722], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9732.5743], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9968.3391], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 10056.1579], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9921.4925], [115.7186, 416.6787, 95.5186, 269.8496, 474.2238, 205.0654, 0.0000, 1854.7990, 0.0000, 9894.1621], [115.7186, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 6179.7742, 0.0000, 20067.9370], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21133.5080], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20988.8485], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20596.7429], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 19910.7730], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20776.7070], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20051.7969], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20725.3884], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20828.8795], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21647.1811], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21310.1687], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 20852.0993], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21912.3952], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21937.8282], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 1877.3934, 0.0000, 0.0000, 0.0000, 21962.4576], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 2008.8110, 0.0000, 21389.4018], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 2008.8110, 0.0000, 21625.6913], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 2008.8110, 0.0000, 20873.0389], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 2008.8110, 0.0000, 21450.9447], [1073.8232, 416.6787, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 2008.8110, 0.0000, 22269.3892], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 21969.5329], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 21752.6924], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 22000.6088], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 23072.5655], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 23487.5201], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 22441.0460], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 23201.2700], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 23400.9485], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 22306.2008], [1073.8232, 737.0632, 735.6442, 269.8496, 0.0000, 938.6967, 0.0000, 0.0000, 0.0000, 21989.5913], [1073.8232, 737.0632, 735.6442, 269.8496, 1708.7766, 938.6967, 0.0000, 5215.4255, 0.0000, 31897.1636], [0.0000, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 6421.4626, 0.0000, 31509.5059], [0.0000, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 6421.4626, 0.0000, 31451.7888], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 32773.4592], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 32287.0318], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 32698.1938], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 34031.5183], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 35537.8336], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 36212.6487], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 36007.5294], [978.8815, 737.0632, 735.6442, 578.0898, 1708.7766, 2145.9711, 0.0000, 0.0000, 0.0000, 34691.3797], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 33904.8810], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 34341.6098], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 35479.9505], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 34418.4455], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 34726.7182], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 34935.0407], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 34136.7505], [978.8815, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 9162.7865, 0.0000, 33804.1575], [195.7763, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 14025.8697, 0.0000, 33653.8970], [195.7763, 737.0632, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 14025.8697, 0.0000, 34689.8757], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 34635.7841], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 35253.2755], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 36388.1051], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 37987.4204], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 38762.2103], [195.7763, 1124.9219, 735.6442, 0.0000, 1708.7766, 0.0000, 0.0000, 10562.2913, 0.0000, 38574.0544], [195.7763, 1124.9219, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 15879.4935, 0.0000, 39101.9156], [195.7763, 1124.9219, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 15879.4935, 0.0000, 39132.5587], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 38873.2941], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39336.6594], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39565.9568], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39583.4317], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39206.8350], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39092.6551], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 39666.1834], [195.7763, 0.0000, 735.6442, 0.0000, 0.0000, 0.0000, 1362.4361, 27747.4200, 0.0000, 38798.0749], [0.0000, 0.0000, 1576.8381, 0.0000, 0.0000, 0.0000, 1362.4361, 23205.2077, 0.0000, 39143.5561], [0.0000, 0.0000, 1576.8381, 0.0000, 0.0000, 0.0000, 1362.4361, 23205.2077, 0.0000, 38617.8779], [0.0000, 0.0000, 1576.8381, 0.0000, 0.0000, 0.0000, 1362.4361, 23205.2077, 0.0000, 38156.1701]]) # PS信号,先卖后买,交割期为2天(股票)1天(现金) self.ps_res_sb20 = np.array( [[0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 9916.667], [0.000, 0.000, 0.000, 0.000, 555.556, 205.065, 321.089, 5059.722, 0.000, 9761.111], [346.982, 416.679, 0.000, 0.000, 555.556, 205.065, 321.089, 1201.278, 0.000, 9646.112], [346.982, 416.679, 191.037, 0.000, 555.556, 205.065, 321.089, 232.719, 0.000, 9685.586], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 9813.218], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 9803.129], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 9608.020], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 9311.573], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 8883.625], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 8751.390], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 8794.181], [346.982, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 1891.052, 0.000, 9136.570], [231.437, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 2472.244, 0.000, 9209.359], [231.437, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 2472.244, 0.000, 9093.829], [231.437, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 2472.244, 0.000, 9387.554], [231.437, 416.679, 191.037, 0.000, 138.889, 205.065, 321.089, 2472.244, 0.000, 9585.959], [231.437, 416.679, 95.519, 0.000, 138.889, 205.065, 321.089, 3035.804, 0.000, 9928.777], [231.437, 416.679, 95.519, 0.000, 138.889, 205.065, 321.089, 3035.804, 0.000, 10060.381], [231.437, 416.679, 95.519, 0.000, 138.889, 205.065, 321.089, 3035.804, 0.000, 10281.002], [231.437, 416.679, 95.519, 0.000, 138.889, 205.065, 321.089, 3035.804, 0.000, 10095.561], [231.437, 416.679, 95.519, 0.000, 138.889, 205.065, 0.000, 4506.393, 0.000, 10029.957], [231.437, 416.679, 95.519, 0.000, 474.224, 205.065, 0.000, 2531.270, 0.000, 9875.613], [231.437, 416.679, 95.519, 0.000, 474.224, 205.065, 0.000, 2531.270, 0.000, 9614.946], [231.437, 416.679, 95.519, 0.000, 474.224, 205.065, 0.000, 2531.270, 0.000, 9824.172], [115.719, 416.679, 95.519, 269.850, 474.224, 205.065, 0.000, 1854.799, 0.000, 9732.574], [115.719, 416.679, 95.519, 269.850, 474.224, 205.065, 0.000, 1854.799, 0.000, 9968.339], [115.719, 416.679, 95.519, 269.850, 474.224, 205.065, 0.000, 1854.799, 0.000, 10056.158], [115.719, 416.679, 95.519, 269.850, 474.224, 205.065, 0.000, 1854.799, 0.000, 9921.492], [115.719, 416.679, 95.519, 269.850, 474.224, 205.065, 0.000, 1854.799, 0.000, 9894.162], [115.719, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 6179.774, 0.000, 20067.937], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21133.508], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20988.848], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20596.743], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 19910.773], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20776.707], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20051.797], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20725.388], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20828.880], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21647.181], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21310.169], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 20852.099], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21912.395], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21937.828], [1073.823, 416.679, 735.644, 269.850, 0.000, 1877.393, 0.000, 0.000, 0.000, 21962.458], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 21389.402], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22027.453], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 20939.999], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 21250.064], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22282.781], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 21407.066], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 21160.237], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 21826.768], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22744.940], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 23466.118], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22017.882], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 23191.466], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 23099.082], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22684.767], [1073.823, 416.679, 735.644, 269.850, 0.000, 938.697, 1339.207, 0.000, 0.000, 22842.135], [1073.823, 416.679, 735.644, 269.850, 1785.205, 938.697, 1339.207, 5001.425, 0.000, 33323.836], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 32820.290], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 32891.231], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 34776.530], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 33909.032], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 34560.191], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 36080.455], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 38618.445], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 38497.923], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 37110.099], [0.000, 416.679, 735.644, 944.961, 1785.205, 3582.884, 1339.207, 0.000, 0.000, 35455.247], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35646.186], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35472.302], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 36636.469], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35191.704], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 36344.224], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 36221.601], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35943.571], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35708.261], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 35589.029], [0.000, 416.679, 735.644, 0.000, 1785.205, 0.000, 1339.207, 15126.279, 0.000, 36661.029], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 36310.591], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 36466.764], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 37784.492], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 39587.677], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 40064.019], [0.000, 823.292, 735.644, 0.000, 1785.205, 0.000, 1339.207, 11495.220, 0.000, 39521.644], [0.000, 823.292, 735.644, 0.000, 0.000, 0.000, 2730.576, 17142.102, 0.000, 39932.276], [0.000, 823.292, 735.644, 0.000, 0.000, 0.000, 2730.576, 17142.102, 0.000, 39565.248], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 38943.163], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 39504.118], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 40317.800], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 40798.577], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 39962.571], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 40194.479], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 41260.400], [0.000, 0.000, 735.644, 0.000, 0.000, 0.000, 2730.576, 25827.835, 0.000, 39966.302], [0.000, 0.000, 1613.452, 0.000, 0.000, 0.000, 2730.576, 19700.738, 0.000, 40847.316], [0.000, 0.000, 1613.452, 0.000, 0.000, 0.000, 2730.576, 19700.738, 0.000, 39654.544], [0.000, 0.000, 1613.452, 0.000, 0.000, 0.000, 2730.576, 19700.738, 0.000, 38914.815]]) # PS信号,先买后卖,交割期为2天(股票)1天(现金) self.ps_res_bs21 = np.array( [[0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 555.556, 0.000, 0.000, 7500.000, 0.000, 9916.667], [0.000, 0.000, 0.000, 0.000, 555.556, 208.333, 326.206, 5020.833, 0.000, 9761.111], [351.119, 421.646, 0.000, 0.000, 555.556, 208.333, 326.206, 1116.389, 0.000, 9645.961], [351.119, 421.646, 190.256, 0.000, 555.556, 208.333, 326.206, 151.793, 0.000, 9686.841], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 9813.932], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 9803.000], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 9605.334], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 9304.001], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 8870.741], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 8738.282], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 8780.664], [351.119, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 1810.126, 0.000, 9126.199], [234.196, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 2398.247, 0.000, 9199.746], [234.196, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 2398.247, 0.000, 9083.518], [234.196, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 2398.247, 0.000, 9380.932], [234.196, 421.646, 190.256, 0.000, 138.889, 208.333, 326.206, 2398.247, 0.000, 9581.266], [234.196, 421.646, 95.128, 0.000, 138.889, 208.333, 326.206, 2959.501, 0.000, 9927.154], [234.196, 421.646, 95.128, 0.000, 138.889, 208.333, 326.206, 2959.501, 0.000, 10059.283], [234.196, 421.646, 95.128, 0.000, 138.889, 208.333, 326.206, 2959.501, 0.000, 10281.669], [234.196, 421.646, 95.128, 0.000, 138.889, 208.333, 326.206, 2959.501, 0.000, 10093.263], [234.196, 421.646, 95.128, 0.000, 138.889, 208.333, 0.000, 4453.525, 0.000, 10026.289], [234.196, 421.646, 95.128, 0.000, 479.340, 208.333, 0.000, 2448.268, 0.000, 9870.523], [234.196, 421.646, 95.128, 0.000, 479.340, 208.333, 0.000, 2448.268, 0.000, 9606.437], [234.196, 421.646, 95.128, 0.000, 479.340, 208.333, 0.000, 2448.268, 0.000, 9818.691], [117.098, 421.646, 95.128, 272.237, 479.340, 208.333, 0.000, 1768.219, 0.000, 9726.556], [117.098, 421.646, 95.128, 272.237, 479.340, 208.333, 0.000, 1768.219, 0.000, 9964.547], [117.098, 421.646, 95.128, 272.237, 479.340, 208.333, 0.000, 1768.219, 0.000, 10053.449], [117.098, 421.646, 95.128, 272.237, 479.340, 208.333, 0.000, 1768.219, 0.000, 9917.440], [117.098, 421.646, 95.128, 272.237, 479.340, 208.333, 0.000, 1768.219, 0.000, 9889.495], [117.098, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 6189.948, 0.000, 20064.523], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 21124.484], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20827.077], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20396.124], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 19856.445], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20714.156], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 19971.485], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20733.948], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20938.903], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 21660.772], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 21265.298], [708.171, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 2377.527, 0.000, 20684.378], [1055.763, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 0.000, 0.000, 21754.770], [1055.763, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 0.000, 0.000, 21775.215], [1055.763, 421.646, 729.561, 272.237, 0.000, 1865.791, 0.000, 0.000, 0.000, 21801.488], [1055.763, 421.646, 729.561, 272.237, 0.000, 932.896, 0.000, 1996.397, 0.000, 21235.427], [1055.763, 421.646, 729.561, 272.237, 0.000, 932.896, 0.000, 1996.397, 0.000, 21466.714], [1055.763, 421.646, 729.561, 272.237, 0.000, 932.896, 0.000, 1996.397, 0.000, 20717.431], [1055.763, 421.646, 729.561, 272.237, 0.000, 932.896, 0.000, 1996.397, 0.000, 21294.450], [1055.763, 421.646, 729.561, 272.237, 0.000, 932.896, 0.000, 1996.397, 0.000, 22100.247], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 21802.552], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 21593.608], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 21840.028], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 22907.725], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 23325.945], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 22291.942], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 23053.050], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 23260.084], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 22176.244], [1055.763, 740.051, 729.561, 272.237, 0.000, 932.896, 0.000, 0.000, 0.000, 21859.297], [1055.763, 740.051, 729.561, 272.237, 1706.748, 932.896, 0.000, 5221.105, 0.000, 31769.617], [0.000, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 6313.462, 0.000, 31389.961], [0.000, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 6313.462, 0.000, 31327.498], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 32647.140], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 32170.095], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 32577.742], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 33905.444], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 35414.492], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 36082.120], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 35872.293], [962.418, 740.051, 729.561, 580.813, 1706.748, 2141.485, 0.000, 0.000, 0.000, 34558.132], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 33778.138], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 34213.578], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 35345.791], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 34288.014], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 34604.406], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 34806.850], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 34012.232], [962.418, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 9177.053, 0.000, 33681.345], [192.484, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 13958.345, 0.000, 33540.463], [192.484, 740.051, 729.561, 0.000, 1706.748, 0.000, 0.000, 13958.345, 0.000, 34574.280], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 34516.781], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 35134.412], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 36266.530], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 37864.376], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 38642.633], [192.484, 1127.221, 729.561, 0.000, 1706.748, 0.000, 0.000, 10500.917, 0.000, 38454.227], [192.484, 1127.221, 729.561, 0.000, 0.000, 0.000, 1339.869, 15871.934, 0.000, 38982.227], [192.484, 1127.221, 729.561, 0.000, 0.000, 0.000, 1339.869, 15871.934, 0.000, 39016.154], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 38759.803], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 39217.182], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 39439.690], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 39454.081], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 39083.341], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 38968.694], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 39532.030], [192.484, 0.000, 729.561, 0.000, 0.000, 0.000, 1339.869, 27764.114, 0.000, 38675.507], [0.000, 0.000, 1560.697, 0.000, 0.000, 0.000, 1339.869, 23269.751, 0.000, 39013.741], [0.000, 0.000, 1560.697, 0.000, 0.000, 0.000, 1339.869, 23269.751, 0.000, 38497.668], [0.000, 0.000, 1560.697, 0.000, 0.000, 0.000, 1339.869, 23269.751, 0.000, 38042.410]]) # 模拟VS信号回测结果 # VS信号,先卖后买,交割期为0 self.vs_res_sb00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 0.0000, 0.0000, 7750.0000, 0.0000, 10000.0000], [0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 0.0000, 0.0000, 7750.0000, 0.0000, 9925.0000], [0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 300.0000, 300.0000, 4954.0000, 0.0000, 9785.0000], [400.0000, 400.0000, 0.0000, 0.0000, 500.0000, 300.0000, 300.0000, 878.0000, 0.0000, 9666.0000], [400.0000, 400.0000, 173.1755, 0.0000, 500.0000, 300.0000, 300.0000, 0.0000, 0.0000, 9731.0000], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 9830.9270], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 9785.8540], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 9614.3412], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 9303.1953], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 8834.4398], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 8712.7554], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 8717.9507], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592.0000, 0.0000, 9079.1479], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598.0000, 0.0000, 9166.0276], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598.0000, 0.0000, 9023.6607], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598.0000, 0.0000, 9291.6864], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598.0000, 0.0000, 9411.6371], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9706.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9822.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9986.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9805.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 0.0000, 4993.7357, 0.0000, 9704.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9567.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9209.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9407.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9329.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9545.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9652.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9414.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9367.7357], [0.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 9319.7357, 0.0000, 19556.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 20094.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19849.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19802.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19487.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19749.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19392.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19671.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19756.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 20111.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19867.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19775.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20314.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20310.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20253.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20044.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20495.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 19798.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20103.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20864.7357], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 20425.7357], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 20137.8405], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 20711.3567], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21470.3891], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21902.9538], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 20962.9538], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21833.5184], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21941.8169], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21278.5184], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0.0000, 0.0000, 21224.4700], [1100.0000, 710.4842, 400.0000, 300.0000, 600.0000, 500.0000, 600.0000, 9160.0000, 0.0000, 31225.2119], [600.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 7488.0000, 0.0000, 30894.5748], [600.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 7488.0000, 0.0000, 30764.3811], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 31815.5828], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 31615.4215], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 32486.1394], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 33591.2847], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 34056.5428], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 34756.4863], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 34445.5428], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208.0000, 0.0000, 34433.9541], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 33870.4703], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 34014.3010], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 34680.5671], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 33890.9945], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 34004.6640], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 34127.7768], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 33421.1638], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346.0000, 0.0000, 33120.9057], [700.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 13830.0000, 0.0000, 32613.3171], [700.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 13830.0000, 0.0000, 33168.1558], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 33504.6236], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 33652.1318], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 34680.4867], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 35557.5191], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 35669.7128], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151.0000, 0.0000, 35211.4466], [700.0000, 1010.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13530.0000, 0.0000, 35550.6079], [700.0000, 1010.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13530.0000, 0.0000, 35711.6563], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 35682.6079], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 35880.8336], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 36249.8740], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 36071.6159], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 35846.1562], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 35773.3578], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 36274.9465], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695.0000, 0.0000, 35739.3094], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167.0000, 0.0000, 36135.0917], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167.0000, 0.0000, 35286.5835], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167.0000, 0.0000, 35081.3658]]) # VS信号,先买后卖,交割期为0 self.vs_res_bs00 = np.array( [[0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 0.0000, 0.0000, 7750, 0.0000, 10000], [0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 0.0000, 0.0000, 7750, 0.0000, 9925], [0.0000, 0.0000, 0.0000, 0.0000, 500.0000, 300.0000, 300.0000, 4954, 0.0000, 9785], [400.0000, 400.0000, 0.0000, 0.0000, 500.0000, 300.0000, 300.0000, 878, 0.0000, 9666], [400.0000, 400.0000, 173.1755, 0.0000, 500.0000, 300.0000, 300.0000, 0, 0.0000, 9731], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 9830.927022], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 9785.854043], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 9614.341223], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 9303.195266], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 8834.439842], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 8712.755424], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 8717.95069], [400.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 1592, 0.0000, 9079.147929], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598, 0.0000, 9166.027613], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598, 0.0000, 9023.66075], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598, 0.0000, 9291.686391], [200.0000, 400.0000, 173.1755, 0.0000, 100.0000, 300.0000, 300.0000, 2598, 0.0000, 9411.637081], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9706.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9822.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9986.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 300.0000, 3619.7357, 0.0000, 9805.7357], [200.0000, 400.0000, 0.0000, 0.0000, 100.0000, 300.0000, 0.0000, 4993.7357, 0.0000, 9704.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9567.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9209.7357], [200.0000, 400.0000, 0.0000, 0.0000, 600.0000, 300.0000, 0.0000, 2048.7357, 0.0000, 9407.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9329.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9545.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9652.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9414.7357], [0.0000, 400.0000, 0.0000, 300.0000, 600.0000, 300.0000, 0.0000, 1779.7357, 0.0000, 9367.7357], [0.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 9319.7357, 0.0000, 19556.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 20094.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19849.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19802.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19487.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19749.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19392.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19671.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19756.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 20111.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19867.7357], [500.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 6094.7357, 0.0000, 19775.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20314.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20310.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 900.0000, 0.0000, 1990.7357, 0.0000, 20253.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20044.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20495.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 19798.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20103.7357], [1100.0000, 400.0000, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 1946.7357, 0.0000, 20864.7357], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 20425.7357], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 20137.84054], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 20711.35674], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21470.38914], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21902.95375], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 20962.95375], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21833.51837], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21941.81688], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21278.51837], [1100.0000, 710.4842, 400.0000, 300.0000, 300.0000, 500.0000, 600.0000, 0, 0.0000, 21224.46995], [1100.0000, 710.4842, 400.0000, 300.0000, 600.0000, 500.0000, 600.0000, 9160, 0.0000, 31225.21185], [600.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 7488, 0.0000, 30894.57479], [600.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 7488, 0.0000, 30764.38113], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 31815.5828], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 31615.42154], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 32486.13941], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 33591.28466], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 34056.54276], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 34756.48633], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 34445.54276], [1100.0000, 710.4842, 400.0000, 800.0000, 600.0000, 700.0000, 600.0000, 4208, 0.0000, 34433.95412], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 33870.47032], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 34014.30104], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 34680.56715], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 33890.99452], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 34004.66398], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 34127.77683], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 33421.1638], [1100.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11346, 0.0000, 33120.9057], [700.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 13830, 0.0000, 32613.31706], [700.0000, 710.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 13830, 0.0000, 33168.15579], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 33504.62357], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 33652.13176], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 34680.4867], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 35557.51909], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 35669.71276], [700.0000, 1010.4842, 400.0000, 100.0000, 600.0000, 100.0000, 600.0000, 11151, 0.0000, 35211.44665], [700.0000, 1010.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13530, 0.0000, 35550.60792], [700.0000, 1010.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13530, 0.0000, 35711.65633], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 35682.60792], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 35880.83362], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 36249.87403], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 36071.61593], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 35846.15615], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 35773.35783], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 36274.94647], [700.0000, 710.4842, 400.0000, 100.0000, 0.0000, 100.0000, 900.0000, 16695, 0.0000, 35739.30941], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167, 0.0000, 36135.09172], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167, 0.0000, 35286.58353], [500.0000, 710.4842, 1100.0000, 100.0000, 0.0000, 100.0000, 900.0000, 13167, 0.0000, 35081.36584]]) # VS信号,先卖后买,交割期为2天(股票)1天(现金) self.vs_res_sb20 = np.array( [[0.000, 0.000, 0.000, 0.000, 500.000, 0.000, 0.000, 7750.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 500.000, 0.000, 0.000, 7750.000, 0.000, 9925.000], [0.000, 0.000, 0.000, 0.000, 500.000, 300.000, 300.000, 4954.000, 0.000, 9785.000], [400.000, 400.000, 0.000, 0.000, 500.000, 300.000, 300.000, 878.000, 0.000, 9666.000], [400.000, 400.000, 173.176, 0.000, 500.000, 300.000, 300.000, 0.000, 0.000, 9731.000], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9830.927], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9785.854], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9614.341], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9303.195], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8834.440], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8712.755], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8717.951], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9079.148], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9166.028], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9023.661], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9291.686], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9411.637], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9706.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9822.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9986.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9805.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 0.000, 4993.736, 0.000, 9704.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9567.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9209.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9407.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9329.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9545.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9652.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9414.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9367.736], [0.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 9319.736, 0.000, 19556.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 20094.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19849.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19802.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19487.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19749.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19392.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19671.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19756.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 20111.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19867.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19775.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20314.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20310.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20253.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20044.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20495.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 19798.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20103.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20864.736], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20425.736], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20137.841], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20711.357], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21470.389], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21902.954], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20962.954], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21833.518], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21941.817], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21278.518], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21224.470], [1100.000, 710.484, 400.000, 300.000, 600.000, 500.000, 600.000, 9160.000, 0.000, 31225.212], [600.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 7488.000, 0.000, 30894.575], [600.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 7488.000, 0.000, 30764.381], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 31815.583], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 31615.422], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 32486.139], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 33591.285], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34056.543], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34756.486], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34445.543], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34433.954], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33870.470], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34014.301], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34680.567], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33890.995], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34004.664], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34127.777], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33421.164], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33120.906], [700.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 13830.000, 0.000, 32613.317], [700.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 13830.000, 0.000, 33168.156], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 33504.624], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 33652.132], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 34680.487], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35557.519], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35669.713], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35211.447], [700.000, 1010.484, 400.000, 100.000, 0.000, 100.000, 900.000, 13530.000, 0.000, 35550.608], [700.000, 1010.484, 400.000, 100.000, 0.000, 100.000, 900.000, 13530.000, 0.000, 35711.656], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35682.608], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35880.834], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36249.874], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36071.616], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35846.156], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35773.358], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36274.946], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35739.309], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 36135.092], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 35286.584], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 35081.366]]) # VS信号,先买后卖,交割期为2天(股票)1天(现金) self.vs_res_bs21 = np.array( [[0.000, 0.000, 0.000, 0.000, 500.000, 0.000, 0.000, 7750.000, 0.000, 10000.000], [0.000, 0.000, 0.000, 0.000, 500.000, 0.000, 0.000, 7750.000, 0.000, 9925.000], [0.000, 0.000, 0.000, 0.000, 500.000, 300.000, 300.000, 4954.000, 0.000, 9785.000], [400.000, 400.000, 0.000, 0.000, 500.000, 300.000, 300.000, 878.000, 0.000, 9666.000], [400.000, 400.000, 173.176, 0.000, 500.000, 300.000, 300.000, 0.000, 0.000, 9731.000], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9830.927], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9785.854], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9614.341], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9303.195], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8834.440], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8712.755], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 8717.951], [400.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 1592.000, 0.000, 9079.148], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9166.028], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9023.661], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9291.686], [200.000, 400.000, 173.176, 0.000, 100.000, 300.000, 300.000, 2598.000, 0.000, 9411.637], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9706.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9822.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9986.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 300.000, 3619.736, 0.000, 9805.736], [200.000, 400.000, 0.000, 0.000, 100.000, 300.000, 0.000, 4993.736, 0.000, 9704.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9567.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9209.736], [200.000, 400.000, 0.000, 0.000, 600.000, 300.000, 0.000, 2048.736, 0.000, 9407.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9329.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9545.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9652.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9414.736], [0.000, 400.000, 0.000, 300.000, 600.000, 300.000, 0.000, 1779.736, 0.000, 9367.736], [0.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 9319.736, 0.000, 19556.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 20094.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19849.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19802.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19487.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19749.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19392.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19671.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19756.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 20111.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19867.736], [500.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 6094.736, 0.000, 19775.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20314.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20310.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 900.000, 0.000, 1990.736, 0.000, 20253.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20044.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20495.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 19798.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20103.736], [1100.000, 400.000, 400.000, 300.000, 300.000, 500.000, 600.000, 1946.736, 0.000, 20864.736], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20425.736], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20137.841], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20711.357], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21470.389], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21902.954], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 20962.954], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21833.518], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21941.817], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21278.518], [1100.000, 710.484, 400.000, 300.000, 300.000, 500.000, 600.000, 0.000, 0.000, 21224.470], [1100.000, 710.484, 400.000, 300.000, 600.000, 500.000, 600.000, 9160.000, 0.000, 31225.212], [600.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 7488.000, 0.000, 30894.575], [600.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 7488.000, 0.000, 30764.381], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 31815.583], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 31615.422], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 32486.139], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 33591.285], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34056.543], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34756.486], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34445.543], [1100.000, 710.484, 400.000, 800.000, 600.000, 700.000, 600.000, 4208.000, 0.000, 34433.954], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33870.470], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34014.301], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34680.567], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33890.995], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34004.664], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 34127.777], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33421.164], [1100.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11346.000, 0.000, 33120.906], [700.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 13830.000, 0.000, 32613.317], [700.000, 710.484, 400.000, 100.000, 600.000, 100.000, 600.000, 13830.000, 0.000, 33168.156], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 33504.624], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 33652.132], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 34680.487], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35557.519], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35669.713], [700.000, 1010.484, 400.000, 100.000, 600.000, 100.000, 600.000, 11151.000, 0.000, 35211.447], [700.000, 1010.484, 400.000, 100.000, 0.000, 100.000, 900.000, 13530.000, 0.000, 35550.608], [700.000, 1010.484, 400.000, 100.000, 0.000, 100.000, 900.000, 13530.000, 0.000, 35711.656], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35682.608], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35880.834], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36249.874], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36071.616], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35846.156], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35773.358], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 36274.946], [700.000, 710.484, 400.000, 100.000, 0.000, 100.000, 900.000, 16695.000, 0.000, 35739.309], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 36135.092], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 35286.584], [500.000, 710.484, 1100.000, 100.000, 0.000, 100.000, 900.000, 13167.000, 0.000, 35081.366]]) # Multi信号处理结果,先卖后买,使用卖出的现金买进,交割期为2天(股票)0天(现金) self.multi_res = np.array( [[0.0000, 357.2545, 0.0000, 6506.9627, 0.0000, 9965.1867], [0.0000, 357.2545, 0.0000, 6506.9627, 0.0000, 10033.0650], [0.0000, 178.6273, 0.0000, 8273.5864, 0.0000, 10034.8513], [0.0000, 178.6273, 0.0000, 8273.5864, 0.0000, 10036.6376], [150.3516, 178.6273, 0.0000, 6771.5740, 0.0000, 10019.3404], [150.3516, 178.6273, 0.0000, 6771.5740, 0.0000, 10027.7062], [150.3516, 178.6273, 0.0000, 6771.5740, 0.0000, 10030.1477], [150.3516, 178.6273, 0.0000, 6771.5740, 0.0000, 10005.1399], [150.3516, 178.6273, 0.0000, 6771.5740, 0.0000, 10002.5054], [150.3516, 489.4532, 0.0000, 3765.8877, 0.0000, 9967.3860], [75.1758, 391.5625, 0.0000, 5490.1377, 0.0000, 10044.4059], [75.1758, 391.5625, 0.0000, 5490.1377, 0.0000, 10078.1430], [75.1758, 391.5625, 846.3525, 392.3025, 0.0000, 10138.2709], [75.1758, 391.5625, 846.3525, 392.3025, 0.0000, 10050.4768], [75.1758, 391.5625, 846.3525, 392.3025, 0.0000, 10300.0711], [75.1758, 391.5625, 846.3525, 392.3025, 0.0000, 10392.6970], [75.1758, 391.5625, 169.2705, 4644.3773, 0.0000, 10400.5282], [75.1758, 391.5625, 169.2705, 4644.3773, 0.0000, 10408.9220], [75.1758, 0.0000, 169.2705, 8653.9776, 0.0000, 10376.5914], [75.1758, 0.0000, 169.2705, 8653.9776, 0.0000, 10346.8794], [75.1758, 0.0000, 169.2705, 8653.9776, 0.0000, 10364.7474], [75.1758, 381.1856, 645.5014, 2459.1665, 0.0000, 10302.4570], [18.7939, 381.1856, 645.5014, 3024.6764, 0.0000, 10747.4929], [18.7939, 381.1856, 96.8252, 6492.3097, 0.0000, 11150.9107], [18.7939, 381.1856, 96.8252, 6492.3097, 0.0000, 11125.2946], [18.7939, 114.3557, 96.8252, 9227.3166, 0.0000, 11191.9956], [18.7939, 114.3557, 96.8252, 9227.3166, 0.0000, 11145.7486], [18.7939, 114.3557, 96.8252, 9227.3166, 0.0000, 11090.0768], [132.5972, 114.3557, 864.3802, 4223.9548, 0.0000, 11113.8733], [132.5972, 114.3557, 864.3802, 4223.9548, 0.0000, 11456.3281], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 21983.7333], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 22120.6165], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 21654.5327], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 21429.6550], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 21912.5643], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 22516.3100], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 23169.0777], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 23390.8080], [132.5972, 114.3557, 864.3802, 14223.9548, 0.0000, 23743.3742], [132.5972, 559.9112, 864.3802, 9367.3999, 0.0000, 23210.7311], [132.5972, 559.9112, 864.3802, 9367.3999, 0.0000, 24290.4375], [132.5972, 559.9112, 864.3802, 9367.3999, 0.0000, 24335.3279], [132.5972, 559.9112, 864.3802, 9367.3999, 0.0000, 18317.3553], [132.5972, 559.9112, 864.3802, 9367.3999, 0.0000, 18023.4660], [259.4270, 559.9112, 0.0000, 15820.6915, 0.0000, 24390.0527], [259.4270, 559.9112, 0.0000, 15820.6915, 0.0000, 24389.6421], [259.4270, 559.9112, 0.0000, 15820.6915, 0.0000, 24483.5953], [0.0000, 559.9112, 0.0000, 18321.5674, 0.0000, 24486.1895], [0.0000, 0.0000, 0.0000, 24805.3389, 0.0000, 24805.3389], [0.0000, 0.0000, 0.0000, 24805.3389, 0.0000, 24805.3389]]) def test_loop_step_pt_sb00(self): """ test loop step PT-signal, sell first""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.pt_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7500) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 555.5555556, 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_sb00[2][7], own_amounts=self.pt_res_sb00[2][0:7], available_cash=self.pt_res_sb00[2][7], available_amounts=self.pt_res_sb00[2][0:7], op=self.pt_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_sb00[2][7] + c_g + c_s amounts = self.pt_res_sb00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[3][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_sb00[30][7], own_amounts=self.pt_res_sb00[30][0:7], available_cash=self.pt_res_sb00[30][7], available_amounts=self.pt_res_sb00[30][0:7], op=self.pt_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_sb00[30][7] + c_g + c_s amounts = self.pt_res_sb00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[31][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_sb00[59][7] + 10000, own_amounts=self.pt_res_sb00[59][0:7], available_cash=self.pt_res_sb00[59][7] + 10000, available_amounts=self.pt_res_sb00[59][0:7], op=self.pt_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_sb00[59][7] + c_g + c_s + 10000 amounts = self.pt_res_sb00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[60][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.pt_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[61][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_sb00[95][7], own_amounts=self.pt_res_sb00[95][0:7], available_cash=self.pt_res_sb00[95][7], available_amounts=self.pt_res_sb00[95][0:7], op=self.pt_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_sb00[96][7] + c_g + c_s amounts = self.pt_res_sb00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[96][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.pt_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_sb00[97][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_sb00[97][0:7])) def test_loop_step_pt_bs00(self): """ test loop step PT-signal, buy first""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.pt_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7500) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 555.5555556, 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_bs00[2][7], own_amounts=self.pt_res_bs00[2][0:7], available_cash=self.pt_res_bs00[2][7], available_amounts=self.pt_res_bs00[2][0:7], op=self.pt_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_bs00[2][7] + c_g + c_s amounts = self.pt_res_bs00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[3][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_bs00[30][7], own_amounts=self.pt_res_bs00[30][0:7], available_cash=self.pt_res_bs00[30][7], available_amounts=self.pt_res_bs00[30][0:7], op=self.pt_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_bs00[30][7] + c_g + c_s amounts = self.pt_res_bs00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[31][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_bs00[59][7] + 10000, own_amounts=self.pt_res_bs00[59][0:7], available_cash=self.pt_res_bs00[59][7] + 10000, available_amounts=self.pt_res_bs00[59][0:7], op=self.pt_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_bs00[59][7] + c_g + c_s + 10000 amounts = self.pt_res_bs00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[60][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.pt_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[61][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=self.pt_res_bs00[95][7], own_amounts=self.pt_res_bs00[95][0:7], available_cash=self.pt_res_bs00[95][7], available_amounts=self.pt_res_bs00[95][0:7], op=self.pt_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.pt_res_bs00[96][7] + c_g + c_s amounts = self.pt_res_bs00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[96][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=0, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.pt_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.pt_res_bs00[97][7], 2) self.assertTrue(np.allclose(amounts, self.pt_res_bs00[97][0:7])) def test_loop_step_ps_sb00(self): """ test loop step PS-signal, sell first""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.ps_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7500) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 555.5555556, 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_sb00[2][7], own_amounts=self.ps_res_sb00[2][0:7], available_cash=self.ps_res_sb00[2][7], available_amounts=self.ps_res_sb00[2][0:7], op=self.ps_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_sb00[2][7] + c_g + c_s amounts = self.ps_res_sb00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[3][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_sb00[30][7], own_amounts=self.ps_res_sb00[30][0:7], available_cash=self.ps_res_sb00[30][7], available_amounts=self.ps_res_sb00[30][0:7], op=self.ps_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_sb00[30][7] + c_g + c_s amounts = self.ps_res_sb00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[31][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_sb00[59][7] + 10000, own_amounts=self.ps_res_sb00[59][0:7], available_cash=self.ps_res_sb00[59][7] + 10000, available_amounts=self.ps_res_sb00[59][0:7], op=self.ps_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_sb00[59][7] + c_g + c_s + 10000 amounts = self.ps_res_sb00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[60][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.ps_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[61][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_sb00[95][7], own_amounts=self.ps_res_sb00[95][0:7], available_cash=self.ps_res_sb00[95][7], available_amounts=self.ps_res_sb00[95][0:7], op=self.ps_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_sb00[96][7] + c_g + c_s amounts = self.ps_res_sb00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[96][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.ps_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_sb00[97][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_sb00[97][0:7])) def test_loop_step_ps_bs00(self): """ test loop step PS-signal, buy first""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.ps_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7500) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 555.5555556, 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_bs00[2][7], own_amounts=self.ps_res_sb00[2][0:7], available_cash=self.ps_res_bs00[2][7], available_amounts=self.ps_res_bs00[2][0:7], op=self.ps_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_bs00[2][7] + c_g + c_s amounts = self.ps_res_bs00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[3][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_bs00[30][7], own_amounts=self.ps_res_sb00[30][0:7], available_cash=self.ps_res_bs00[30][7], available_amounts=self.ps_res_bs00[30][0:7], op=self.ps_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_bs00[30][7] + c_g + c_s amounts = self.ps_res_bs00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[31][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_bs00[59][7] + 10000, own_amounts=self.ps_res_bs00[59][0:7], available_cash=self.ps_res_bs00[59][7] + 10000, available_amounts=self.ps_res_bs00[59][0:7], op=self.ps_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_bs00[59][7] + c_g + c_s + 10000 amounts = self.ps_res_bs00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[60][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.ps_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[61][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=self.ps_res_bs00[95][7], own_amounts=self.ps_res_bs00[95][0:7], available_cash=self.ps_res_bs00[95][7], available_amounts=self.ps_res_bs00[95][0:7], op=self.ps_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.ps_res_bs00[96][7] + c_g + c_s amounts = self.ps_res_bs00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[96][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=1, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.ps_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.ps_res_bs00[97][7], 2) self.assertTrue(np.allclose(amounts, self.ps_res_bs00[97][0:7])) def test_loop_step_vs_sb00(self): """test loop step of Volume Signal type of signals""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.vs_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7750) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 500., 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_sb00[2][7], own_amounts=self.vs_res_sb00[2][0:7], available_cash=self.vs_res_sb00[2][7], available_amounts=self.vs_res_sb00[2][0:7], op=self.vs_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_sb00[2][7] + c_g + c_s amounts = self.vs_res_sb00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[3][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_sb00[30][7], own_amounts=self.vs_res_sb00[30][0:7], available_cash=self.vs_res_sb00[30][7], available_amounts=self.vs_res_sb00[30][0:7], op=self.vs_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_sb00[30][7] + c_g + c_s amounts = self.vs_res_sb00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[31][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_sb00[59][7] + 10000, own_amounts=self.vs_res_sb00[59][0:7], available_cash=self.vs_res_sb00[59][7] + 10000, available_amounts=self.vs_res_sb00[59][0:7], op=self.vs_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_sb00[59][7] + c_g + c_s + 10000 amounts = self.vs_res_sb00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[60][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.vs_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[61][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_sb00[95][7], own_amounts=self.vs_res_sb00[95][0:7], available_cash=self.vs_res_sb00[95][7], available_amounts=self.vs_res_sb00[95][0:7], op=self.vs_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_sb00[96][7] + c_g + c_s amounts = self.vs_res_sb00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[96][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.vs_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=True, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_sb00[97][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_sb00[97][0:7])) def test_loop_step_vs_bs00(self): """test loop step of Volume Signal type of signals""" c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=10000, own_amounts=np.zeros(7, dtype='float'), available_cash=10000, available_amounts=np.zeros(7, dtype='float'), op=self.vs_signals[0], prices=self.prices[0], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 1 result in complete looping: \n' f'cash_change: +{c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = 10000 + c_g + c_s amounts = np.zeros(7, dtype='float') + a_p + a_s self.assertAlmostEqual(cash, 7750) self.assertTrue(np.allclose(amounts, np.array([0, 0, 0, 0, 500., 0, 0]))) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_bs00[2][7], own_amounts=self.vs_res_bs00[2][0:7], available_cash=self.vs_res_bs00[2][7], available_amounts=self.vs_res_bs00[2][0:7], op=self.vs_signals[3], prices=self.prices[3], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 4 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_bs00[2][7] + c_g + c_s amounts = self.vs_res_bs00[2][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[3][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[3][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_bs00[30][7], own_amounts=self.vs_res_bs00[30][0:7], available_cash=self.vs_res_bs00[30][7], available_amounts=self.vs_res_bs00[30][0:7], op=self.vs_signals[31], prices=self.prices[31], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 32 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_bs00[30][7] + c_g + c_s amounts = self.vs_res_bs00[30][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[31][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[31][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_bs00[59][7] + 10000, own_amounts=self.vs_res_bs00[59][0:7], available_cash=self.vs_res_bs00[59][7] + 10000, available_amounts=self.vs_res_bs00[59][0:7], op=self.vs_signals[60], prices=self.prices[60], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 61 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_bs00[59][7] + c_g + c_s + 10000 amounts = self.vs_res_bs00[59][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[60][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[60][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.vs_signals[61], prices=self.prices[61], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 62 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[61][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[61][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=self.vs_res_bs00[95][7], own_amounts=self.vs_res_bs00[95][0:7], available_cash=self.vs_res_bs00[95][7], available_amounts=self.vs_res_bs00[95][0:7], op=self.vs_signals[96], prices=self.prices[96], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 97 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = self.vs_res_bs00[96][7] + c_g + c_s amounts = self.vs_res_bs00[96][0:7] + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[96][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[96][0:7])) c_g, c_s, a_p, a_s, fee = qt.core._loop_step(signal_type=2, own_cash=cash, own_amounts=amounts, available_cash=cash, available_amounts=amounts, op=self.vs_signals[97], prices=self.prices[97], rate=self.rate, pt_buy_threshold=0.1, pt_sell_threshold=0.1, maximize_cash_usage=False, allow_sell_short=False, moq_buy=0, moq_sell=0, print_log=True) print(f'day 98 result in complete looping: \n' f'cash_change: + {c_g:.2f} / {c_s:.2f}\n' f'amount_changed: \npurchased: {np.round(a_p, 2)}\nsold:{np.round(a_s, 2)}\n' f'----------------------------------\n') cash = cash + c_g + c_s amounts = amounts + a_p + a_s self.assertAlmostEqual(cash, self.vs_res_bs00[97][7], 2) self.assertTrue(np.allclose(amounts, self.vs_res_bs00[97][0:7])) def test_loop_pt(self): """ Test looping of PT proportion target signals, with stock delivery delay = 0 days cash delivery delay = 0 day buy-sell sequence = sell first """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 0 days \n' 'cash delivery delay = 0 day \n' 'buy-sell sequence = sell first') res = apply_loop(op_type=0, op_list=self.pt_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) # print(f'in test_loop:\nresult of loop test is \n{res}') self.assertTrue(np.allclose(res, self.pt_res_bs00, 2)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop(op_type=0, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) # print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_pt_with_delay(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 1 day use_sell_cash = False """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize_cash = False (buy and sell at the same time)') res = apply_loop( op_type=0, op_list=self.pt_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, cash_delivery_period=1, stock_delivery_period=2, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.pt_res_bs21[i])) print() self.assertTrue(np.allclose(res, self.pt_res_bs21, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_pt_with_delay_use_cash(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 0 day use sell cash = True (sell stock first to use cash when possible (not possible when cash delivery period != 0)) """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize cash usage = True \n' 'but not applicable because cash delivery period == 1') res = apply_loop( op_type=0, op_list=self.pt_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, cash_delivery_period=0, stock_delivery_period=2, inflation_rate=0, max_cash_usage=True, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.pt_res_sb20[i])) print() self.assertTrue(np.allclose(res, self.pt_res_sb20, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, cash_delivery_period=1, stock_delivery_period=2, inflation_rate=0, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_ps(self): """ Test looping of PS Proportion Signal type of signals """ res = apply_loop(op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') self.assertTrue(np.allclose(res, self.ps_res_bs00, 5)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop(op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_ps_with_delay(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 1 day use_sell_cash = False """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize_cash = False (buy and sell at the same time)') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, cash_delivery_period=1, stock_delivery_period=2, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.ps_res_bs21[i])) print() self.assertTrue(np.allclose(res, self.ps_res_bs21, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_ps_with_delay_use_cash(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 0 day use sell cash = True (sell stock first to use cash when possible (not possible when cash delivery period != 0)) """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize cash usage = True \n' 'but not applicable because cash delivery period == 1') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, cash_delivery_period=0, stock_delivery_period=2, inflation_rate=0, max_cash_usage=True, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.ps_res_sb20[i])) print() self.assertTrue(np.allclose(res, self.ps_res_sb20, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.ps_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, cash_delivery_period=1, stock_delivery_period=2, inflation_rate=0, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_vs(self): """ Test looping of VS Volume Signal type of signals """ res = apply_loop(op_type=2, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') self.assertTrue(np.allclose(res, self.vs_res_bs00, 5)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop(op_type=2, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_vs_with_delay(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 1 day use_sell_cash = False """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize_cash = False (buy and sell at the same time)') res = apply_loop( op_type=2, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, inflation_rate=0, cash_delivery_period=1, stock_delivery_period=2, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.vs_res_bs21[i])) print() self.assertTrue(np.allclose(res, self.vs_res_bs21, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.vs_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.vs_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_vs_with_delay_use_cash(self): """ Test looping of PT proportion target signals, with: stock delivery delay = 2 days cash delivery delay = 0 day use sell cash = True (sell stock first to use cash when possible (not possible when cash delivery period != 0)) """ print('Test looping of PT proportion target signals, with:\n' 'stock delivery delay = 2 days \n' 'cash delivery delay = 1 day \n' 'maximize cash usage = True \n' 'but not applicable because cash delivery period == 1') res = apply_loop( op_type=2, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, cash_delivery_period=0, stock_delivery_period=2, inflation_rate=0, max_cash_usage=True, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.vs_res_sb20[i])) print() self.assertTrue(np.allclose(res, self.vs_res_sb20, 3)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.vs_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.vs_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop( op_type=1, op_list=self.vs_signal_hp, history_list=self.history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, cash_delivery_period=1, stock_delivery_period=2, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') def test_loop_multiple_signal(self): """ Test looping of PS Proportion Signal type of signals """ res = apply_loop(op_type=1, op_list=self.multi_signal_hp, history_list=self.multi_history_list, cash_plan=self.cash, cost_rate=self.rate, moq_buy=0, moq_sell=0, cash_delivery_period=0, stock_delivery_period=2, max_cash_usage=True, inflation_rate=0, print_log=False) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}\n' f'result comparison line by line:') for i in range(len(res)): print(np.around(res.values[i])) print(np.around(self.multi_res[i])) print() self.assertTrue(np.allclose(res, self.multi_res, 5)) print(f'test assertion errors in apply_loop: detect moqs that are not compatible') self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 0, 1, 0, False) self.assertRaises(AssertionError, apply_loop, 0, self.ps_signal_hp, self.history_list, self.cash, self.rate, 1, 5, 0, False) print(f'test loop results with moq equal to 100') res = apply_loop(op_type=1, op_list=self.multi_signal_hp, history_list=self.multi_history_list, cash_plan=self.cash, cost_rate=self.rate2, moq_buy=100, moq_sell=1, cash_delivery_period=0, stock_delivery_period=2, max_cash_usage=False, inflation_rate=0, print_log=True) self.assertIsInstance(res, pd.DataFrame) print(f'in test_loop:\nresult of loop test is \n{res}') class TestStrategy(unittest.TestCase): """ test all properties and methods of strategy base class""" def setUp(self) -> None: pass class TestLSStrategy(RollingTiming): """用于test测试的简单多空蒙板生成策略。基于RollingTiming滚动择时方法生成 该策略有两个参数,N与Price N用于计算OHLC价格平均值的N日简单移动平均,判断,当移动平均值大于等于Price时,状态为看多,否则为看空 """ def __init__(self): super().__init__(stg_name='test_LS', stg_text='test long/short strategy', par_count=2, par_types='discr, conti', par_bounds_or_enums=([1, 5], [2, 10]), data_types='close, open, high, low', data_freq='d', window_length=5) pass def _realize(self, hist_data: np.ndarray, params: tuple): n, price = params h = hist_data.T avg = (h[0] + h[1] + h[2] + h[3]) / 4 ma = sma(avg, n) if ma[-1] < price: return 0 else: return 1 class TestSelStrategy(SimpleSelecting): """用于Test测试的简单选股策略,基于Selecting策略生成 策略没有参数,选股周期为5D 在每个选股周期内,从股票池的三只股票中选出今日变化率 = (今收-昨收)/平均股价(OHLC平均股价)最高的两支,放入中选池,否则落选。 选股比例为平均分配 """ def __init__(self): super().__init__(stg_name='test_SEL', stg_text='test portfolio selection strategy', par_count=0, par_types='', par_bounds_or_enums=(), data_types='high, low, close', data_freq='d', sample_freq='10d', window_length=5) pass def _realize(self, hist_data: np.ndarray, params: tuple): avg = np.nanmean(hist_data, axis=(1, 2)) dif = (hist_data[:, :, 2] - np.roll(hist_data[:, :, 2], 1, 1)) dif_no_nan = np.array([arr[~np.isnan(arr)][-1] for arr in dif]) difper = dif_no_nan / avg large2 = difper.argsort()[1:] chosen = np.zeros_like(avg) chosen[large2] = 0.5 return chosen class TestSelStrategyDiffTime(SimpleSelecting): """用于Test测试的简单选股策略,基于Selecting策略生成 策略没有参数,选股周期为5D 在每个选股周期内,从股票池的三只股票中选出今日变化率 = (今收-昨收)/平均股价(OHLC平均股价)最高的两支,放入中选池,否则落选。 选股比例为平均分配 """ # TODO: This strategy is not working, find out why and improve def __init__(self): super().__init__(stg_name='test_SEL', stg_text='test portfolio selection strategy', par_count=0, par_types='', par_bounds_or_enums=(), data_types='close, low, open', data_freq='d', sample_freq='w', window_length=2) pass def _realize(self, hist_data: np.ndarray, params: tuple): avg = hist_data.mean(axis=1).squeeze() difper = (hist_data[:, :, 0] - np.roll(hist_data[:, :, 0], 1))[:, -1] / avg large2 = difper.argsort()[0:2] chosen = np.zeros_like(avg) chosen[large2] = 0.5 return chosen class TestSigStrategy(SimpleTiming): """用于Test测试的简单信号生成策略,基于SimpleTiming策略生成 策略有三个参数,第一个参数为ratio,另外两个参数为price1以及price2 ratio是k线形状比例的阈值,定义为abs((C-O)/(H-L))。当这个比值小于ratio阈值时,判断该K线为十字交叉(其实还有丁字等多种情形,但这里做了 简化处理。 信号生成的规则如下: 1,当某个K线出现十字交叉,且昨收与今收之差大于price1时,买入信号 2,当某个K线出现十字交叉,且昨收与今收之差小于price2时,卖出信号 """ def __init__(self): super().__init__(stg_name='test_SIG', stg_text='test signal creation strategy', par_count=3, par_types='conti, conti, conti', par_bounds_or_enums=([2, 10], [0, 3], [0, 3]), data_types='close, open, high, low', window_length=2) pass def _realize(self, hist_data: np.ndarray, params: tuple): r, price1, price2 = params h = hist_data.T ratio = np.abs((h[0] - h[1]) / (h[3] - h[2])) diff = h[0] - np.roll(h[0], 1) sig = np.where((ratio < r) & (diff > price1), 1, np.where((ratio < r) & (diff < price2), -1, 0)) return sig class MyStg(qt.RollingTiming): """自定义双均线择时策略策略""" def __init__(self): """这个均线择时策略只有三个参数: - SMA 慢速均线,所选择的股票 - FMA 快速均线 - M 边界值 策略的其他说明 """ """ 必须初始化的关键策略参数清单: """ super().__init__( pars=(20, 100, 0.01), par_count=3, par_types=['discr', 'discr', 'conti'], par_bounds_or_enums=[(10, 250), (10, 250), (0.0, 0.5)], stg_name='CUSTOM ROLLING TIMING STRATEGY', stg_text='Customized Rolling Timing Strategy for Testing', data_types='close', window_length=100, ) print(f'=====================\n====================\n' f'custom strategy initialized, \npars: {self.pars}\npar_count:{self.par_count}\npar_types:' f'{self.par_types}\n' f'{self.info()}') # 策略的具体实现代码写在策略的_realize()函数中 # 这个函数固定接受两个参数: hist_price代表特定组合的历史数据, params代表具体的策略参数 def _realize(self, hist_price, params): """策略的具体实现代码: s:短均线计算日期;l:长均线计算日期;m:均线边界宽度;hesitate:均线跨越类型""" f, s, m = params # 临时处理措施,在策略实现层对传入的数据切片,后续应该在策略实现层以外事先对数据切片,保证传入的数据符合data_types参数即可 h = hist_price.T # 计算长短均线的当前值 s_ma = qt.sma(h[0], s)[-1] f_ma = qt.sma(h[0], f)[-1] # 计算慢均线的停止边界,当快均线在停止边界范围内时,平仓,不发出买卖信号 s_ma_u = s_ma * (1 + m) s_ma_l = s_ma * (1 - m) # 根据观望模式在不同的点位产生Long/short/empty标记 if f_ma > s_ma_u: # 当快均线在慢均线停止范围以上时,持有多头头寸 return 1 elif s_ma_l < f_ma < s_ma_u: # 当均线在停止边界以内时,平仓 return 0 else: # f_ma < s_ma_l 当快均线在慢均线停止范围以下时,持有空头头寸 return -1 class TestOperator(unittest.TestCase): """全面测试Operator对象的所有功能。包括: 1, Strategy 参数的设置 2, 历史数据的获取与分配提取 3, 策略优化参数的批量设置和优化空间的获取 4, 策略输出值的正确性验证 5, 策略结果的混合结果确认 """ def setUp(self): """prepare data for Operator test""" print('start testing HistoryPanel object\n') # build up test data: a 4-type, 3-share, 50-day matrix of prices that contains nan values in some days # for some share_pool # for share1: data_rows = 50 share1_close = [10.04, 10, 10, 9.99, 9.97, 9.99, 10.03, 10.03, 10.06, 10.06, 10.11, 10.09, 10.07, 10.06, 10.09, 10.03, 10.03, 10.06, 10.08, 10, 9.99, 10.03, 10.03, 10.06, 10.03, 9.97, 9.94, 9.83, 9.77, 9.84, 9.91, 9.93, 9.96, 9.91, 9.91, 9.88, 9.91, 9.64, 9.56, 9.57, 9.55, 9.57, 9.61, 9.61, 9.55, 9.57, 9.63, 9.64, 9.65, 9.62] share1_open = [10.02, 10, 9.98, 9.97, 9.99, 10.01, 10.04, 10.06, 10.06, 10.11, 10.11, 10.07, 10.06, 10.09, 10.03, 10.02, 10.06, 10.08, 9.99, 10, 10.03, 10.02, 10.06, 10.03, 9.97, 9.94, 9.83, 9.78, 9.77, 9.91, 9.92, 9.97, 9.91, 9.9, 9.88, 9.91, 9.63, 9.64, 9.57, 9.55, 9.58, 9.61, 9.62, 9.55, 9.57, 9.61, 9.63, 9.64, 9.61, 9.56] share1_high = [10.07, 10, 10, 10, 10.03, 10.03, 10.04, 10.09, 10.1, 10.14, 10.11, 10.1, 10.09, 10.09, 10.1, 10.05, 10.07, 10.09, 10.1, 10, 10.04, 10.04, 10.06, 10.09, 10.05, 9.97, 9.96, 9.86, 9.77, 9.92, 9.94, 9.97, 9.97, 9.92, 9.92, 9.92, 9.93, 9.64, 9.58, 9.6, 9.58, 9.62, 9.62, 9.64, 9.59, 9.62, 9.63, 9.7, 9.66, 9.64] share1_low = [9.99, 10, 9.97, 9.97, 9.97, 9.98, 9.99, 10.03, 10.03, 10.04, 10.11, 10.07, 10.05, 10.03, 10.03, 10.01, 9.99, 10.03, 9.95, 10, 9.95, 10, 10.01, 9.99, 9.96, 9.89, 9.83, 9.77, 9.77, 9.8, 9.9, 9.91, 9.89, 9.89, 9.87, 9.85, 9.6, 9.64, 9.53, 9.55, 9.54, 9.55, 9.58, 9.54, 9.53, 9.53, 9.63, 9.64, 9.59, 9.56] # for share2: share2_close = [9.68, 9.87, 9.86, 9.87, 9.79, 9.82, 9.8, 9.66, 9.62, 9.58, 9.69, 9.78, 9.75, 9.96, 9.9, 10.04, 10.06, 10.08, 10.24, 10.24, 10.24, 9.86, 10.13, 10.12, 10.1, 10.25, 10.24, 10.22, 10.75, 10.64, 10.56, 10.6, 10.42, 10.25, 10.24, 10.49, 10.57, 10.63, 10.48, 10.37, 10.96, 11.02, np.nan, np.nan, 10.88, 10.87, 11.01, 11.01, 11.58, 11.8] share2_open = [9.88, 9.88, 9.89, 9.75, 9.74, 9.8, 9.62, 9.65, 9.58, 9.67, 9.81, 9.8, 10, 9.95, 10.1, 10.06, 10.14, 9.9, 10.2, 10.29, 9.86, 9.48, 10.01, 10.24, 10.26, 10.24, 10.12, 10.65, 10.64, 10.56, 10.42, 10.43, 10.29, 10.3, 10.44, 10.6, 10.67, 10.46, 10.39, 10.9, 11.01, 11.01, np.nan, np.nan, 10.82, 11.02, 10.96, 11.55, 11.74, 11.8] share2_high = [9.91, 10.04, 9.93, 10.04, 9.84, 9.88, 9.99, 9.7, 9.67, 9.71, 9.85, 9.9, 10, 10.2, 10.11, 10.18, 10.21, 10.26, 10.38, 10.47, 10.42, 10.07, 10.24, 10.27, 10.38, 10.43, 10.39, 10.65, 10.84, 10.65, 10.73, 10.63, 10.51, 10.35, 10.46, 10.63, 10.74, 10.76, 10.54, 11.02, 11.12, 11.17, np.nan, np.nan, 10.92, 11.15, 11.11, 11.55, 11.95, 11.93] share2_low = [9.63, 9.84, 9.81, 9.74, 9.67, 9.72, 9.57, 9.54, 9.51, 9.47, 9.68, 9.63, 9.75, 9.65, 9.9, 9.93, 10.03, 9.8, 10.14, 10.09, 9.78, 9.21, 9.11, 9.68, 10.05, 10.12, 9.89, 9.89, 10.59, 10.43, 10.34, 10.32, 10.21, 10.2, 10.18, 10.36, 10.51, 10.41, 10.32, 10.37, 10.87, 10.95, np.nan, np.nan, 10.65, 10.71, 10.75, 10.91, 11.31, 11.58] # for share3: share3_close = [6.64, 7.26, 7.03, 6.87, np.nan, 6.64, 6.85, 6.7, 6.39, 6.22, 5.92, 5.91, 6.11, 5.91, 6.23, 6.28, 6.28, 6.27, np.nan, 5.56, 5.67, 5.16, 5.69, 6.32, 6.14, 6.25, 5.79, 5.26, 5.05, 5.45, 6.06, 6.21, 5.69, 5.46, 6.02, 6.69, 7.43, 7.72, 8.16, 7.83, 8.7, 8.71, 8.88, 8.54, 8.87, 8.87, 8.18, 7.8, 7.97, 8.25] share3_open = [7.26, 7, 6.88, 6.91, np.nan, 6.81, 6.63, 6.45, 6.16, 6.24, 5.96, 5.97, 5.96, 6.2, 6.35, 6.11, 6.37, 5.58, np.nan, 5.65, 5.19, 5.42, 6.3, 6.15, 6.05, 5.89, 5.22, 5.2, 5.07, 6.04, 6.12, 5.85, 5.67, 6.02, 6.04, 7.07, 7.64, 7.99, 7.59, 8.73, 8.72, 8.97, 8.58, 8.71, 8.77, 8.4, 7.95, 7.76, 8.25, 7.51] share3_high = [7.41, 7.31, 7.14, 7, np.nan, 6.82, 6.96, 6.85, 6.5, 6.34, 6.04, 6.02, 6.12, 6.38, 6.43, 6.46, 6.43, 6.27, np.nan, 6.01, 5.67, 5.67, 6.35, 6.32, 6.43, 6.36, 5.79, 5.47, 5.65, 6.04, 6.14, 6.23, 5.83, 6.25, 6.27, 7.12, 7.82, 8.14, 8.27, 8.92, 8.76, 9.15, 8.9, 9.01, 9.16, 9, 8.27, 7.99, 8.33, 8.25] share3_low = [6.53, 6.87, 6.83, 6.7, np.nan, 6.63, 6.57, 6.41, 6.15, 6.07, 5.89, 5.82, 5.73, 5.81, 6.1, 6.06, 6.16, 5.57, np.nan, 5.51, 5.19, 5.12, 5.69, 6.01, 5.97, 5.86, 5.18, 5.19, 4.96, 5.45, 5.84, 5.85, 5.28, 5.42, 6.02, 6.69, 7.28, 7.64, 7.25, 7.83, 8.41, 8.66, 8.53, 8.54, 8.73, 8.27, 7.95, 7.67, 7.8, 7.51] # for sel_finance test shares_eps = np.array([[np.nan, np.nan, np.nan], [0.1, np.nan, np.nan], [np.nan, 0.2, np.nan], [np.nan, np.nan, 0.3], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 0.2], [0.1, np.nan, np.nan], [np.nan, 0.3, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [0.3, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, 0.3, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 0.3], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, 0, 0.2], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [0.1, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 0.2], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [0.15, np.nan, np.nan], [np.nan, 0.1, np.nan], [np.nan, np.nan, np.nan], [0.1, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 0.3], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [0.2, np.nan, np.nan], [np.nan, 0.5, np.nan], [0.4, np.nan, 0.3], [np.nan, np.nan, np.nan], [np.nan, 0.3, np.nan], [0.9, np.nan, np.nan], [np.nan, np.nan, 0.1]]) self.date_indices = ['2016-07-01', '2016-07-04', '2016-07-05', '2016-07-06', '2016-07-07', '2016-07-08', '2016-07-11', '2016-07-12', '2016-07-13', '2016-07-14', '2016-07-15', '2016-07-18', '2016-07-19', '2016-07-20', '2016-07-21', '2016-07-22', '2016-07-25', '2016-07-26', '2016-07-27', '2016-07-28', '2016-07-29', '2016-08-01', '2016-08-02', '2016-08-03', '2016-08-04', '2016-08-05', '2016-08-08', '2016-08-09', '2016-08-10', '2016-08-11', '2016-08-12', '2016-08-15', '2016-08-16', '2016-08-17', '2016-08-18', '2016-08-19', '2016-08-22', '2016-08-23', '2016-08-24', '2016-08-25', '2016-08-26', '2016-08-29', '2016-08-30', '2016-08-31', '2016-09-01', '2016-09-02', '2016-09-05', '2016-09-06', '2016-09-07', '2016-09-08'] self.shares = ['000010', '000030', '000039'] self.types = ['close', 'open', 'high', 'low'] self.sel_finance_tyeps = ['eps'] self.test_data_3D = np.zeros((3, data_rows, 4)) self.test_data_2D = np.zeros((data_rows, 3)) self.test_data_2D2 = np.zeros((data_rows, 4)) self.test_data_sel_finance = np.empty((3, data_rows, 1)) # Build up 3D data self.test_data_3D[0, :, 0] = share1_close self.test_data_3D[0, :, 1] = share1_open self.test_data_3D[0, :, 2] = share1_high self.test_data_3D[0, :, 3] = share1_low self.test_data_3D[1, :, 0] = share2_close self.test_data_3D[1, :, 1] = share2_open self.test_data_3D[1, :, 2] = share2_high self.test_data_3D[1, :, 3] = share2_low self.test_data_3D[2, :, 0] = share3_close self.test_data_3D[2, :, 1] = share3_open self.test_data_3D[2, :, 2] = share3_high self.test_data_3D[2, :, 3] = share3_low self.test_data_sel_finance[:, :, 0] = shares_eps.T self.hp1 = qt.HistoryPanel(values=self.test_data_3D, levels=self.shares, columns=self.types, rows=self.date_indices) print(f'in test Operator, history panel is created for timing test') self.hp1.info() self.hp2 = qt.HistoryPanel(values=self.test_data_sel_finance, levels=self.shares, columns=self.sel_finance_tyeps, rows=self.date_indices) print(f'in test_Operator, history panel is created for selection finance test:') self.hp2.info() self.op = qt.Operator(strategies='dma', signal_type='PS') self.op2 = qt.Operator(strategies='dma, macd, trix') def test_init(self): """ test initialization of Operator class""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.signal_type, 'pt') self.assertIsInstance(op.strategies, list) self.assertEqual(len(op.strategies), 0) op = qt.Operator('dma') self.assertIsInstance(op, qt.Operator) self.assertIsInstance(op.strategies, list) self.assertIsInstance(op.strategies[0], TimingDMA) op = qt.Operator('dma, macd') self.assertIsInstance(op, qt.Operator) op = qt.Operator(['dma', 'macd']) self.assertIsInstance(op, qt.Operator) def test_repr(self): """ test basic representation of Opeartor class""" op = qt.Operator() self.assertEqual(op.__repr__(), 'Operator()') op = qt.Operator('macd, dma, trix, random, avg_low') self.assertEqual(op.__repr__(), 'Operator(macd, dma, trix, random, avg_low)') self.assertEqual(op['dma'].__repr__(), 'Q-TIMING(DMA)') self.assertEqual(op['macd'].__repr__(), 'R-TIMING(MACD)') self.assertEqual(op['trix'].__repr__(), 'R-TIMING(TRIX)') self.assertEqual(op['random'].__repr__(), 'SELECT(RANDOM)') self.assertEqual(op['avg_low'].__repr__(), 'FACTOR(AVG LOW)') def test_info(self): """Test information output of Operator""" print(f'test printing information of operator object') self.op.info() def test_get_strategy_by_id(self): """ test get_strategy_by_id()""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') self.assertEqual(op.strategy_ids, ['macd', 'dma', 'trix']) self.assertIs(op.get_strategy_by_id('macd'), op.strategies[0]) self.assertIs(op.get_strategy_by_id(1), op.strategies[1]) self.assertIs(op.get_strategy_by_id('trix'), op.strategies[2]) def test_get_items(self): """ test method __getitem__(), it should be the same as geting strategies by id""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') self.assertEqual(op.strategy_ids, ['macd', 'dma', 'trix']) self.assertIs(op['macd'], op.strategies[0]) self.assertIs(op['trix'], op.strategies[2]) self.assertIs(op[1], op.strategies[1]) self.assertIs(op[3], op.strategies[2]) def test_get_strategies_by_price_type(self): """ test get_strategies_by_price_type""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') op.set_parameter('macd', price_type='open') op.set_parameter('dma', price_type='close') op.set_parameter('trix', price_type='open') stg_close = op.get_strategies_by_price_type('close') stg_open = op.get_strategies_by_price_type('open') stg_high = op.get_strategies_by_price_type('high') self.assertIsInstance(stg_close, list) self.assertIsInstance(stg_open, list) self.assertIsInstance(stg_high, list) self.assertEqual(stg_close, [op.strategies[1]]) self.assertEqual(stg_open, [op.strategies[0], op.strategies[2]]) self.assertEqual(stg_high, []) stg_wrong = op.get_strategies_by_price_type(123) self.assertIsInstance(stg_wrong, list) self.assertEqual(stg_wrong, []) def test_get_strategy_count_by_price_type(self): """ test get_strategy_count_by_price_type""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') op.set_parameter('macd', price_type='open') op.set_parameter('dma', price_type='close') op.set_parameter('trix', price_type='open') stg_close = op.get_strategy_count_by_price_type('close') stg_open = op.get_strategy_count_by_price_type('open') stg_high = op.get_strategy_count_by_price_type('high') self.assertIsInstance(stg_close, int) self.assertIsInstance(stg_open, int) self.assertIsInstance(stg_high, int) self.assertEqual(stg_close, 1) self.assertEqual(stg_open, 2) self.assertEqual(stg_high, 0) stg_wrong = op.get_strategy_count_by_price_type(123) self.assertIsInstance(stg_wrong, int) self.assertEqual(stg_wrong, 0) def test_get_strategy_names_by_price_type(self): """ test get_strategy_names_by_price_type""" op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') op.set_parameter('macd', price_type='open') op.set_parameter('dma', price_type='close') op.set_parameter('trix', price_type='open') stg_close = op.get_strategy_names_by_price_type('close') stg_open = op.get_strategy_names_by_price_type('open') stg_high = op.get_strategy_names_by_price_type('high') self.assertIsInstance(stg_close, list) self.assertIsInstance(stg_open, list) self.assertIsInstance(stg_high, list) self.assertEqual(stg_close, ['DMA']) self.assertEqual(stg_open, ['MACD', 'TRIX']) self.assertEqual(stg_high, []) stg_wrong = op.get_strategy_names_by_price_type(123) self.assertIsInstance(stg_wrong, list) self.assertEqual(stg_wrong, []) def test_get_strategy_id_by_price_type(self): """ test get_strategy_IDs_by_price_type""" print('-----Test get strategy IDs by price type------\n') op = qt.Operator() self.assertIsInstance(op, qt.Operator) self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op = qt.Operator('macd, dma, trix') op.set_parameter('macd', price_type='open') op.set_parameter('dma', price_type='close') op.set_parameter('trix', price_type='open') stg_close = op.get_strategy_id_by_price_type('close') stg_open = op.get_strategy_id_by_price_type('open') stg_high = op.get_strategy_id_by_price_type('high') self.assertIsInstance(stg_close, list) self.assertIsInstance(stg_open, list) self.assertIsInstance(stg_high, list) self.assertEqual(stg_close, ['dma']) self.assertEqual(stg_open, ['macd', 'trix']) self.assertEqual(stg_high, []) op.add_strategies('dma, macd') op.set_parameter('dma_1', price_type='open') op.set_parameter('macd', price_type='open') op.set_parameter('macd_1', price_type='high') op.set_parameter('trix', price_type='close') print(f'Operator strategy id:\n' f'{op.strategies} on memory pos:\n' f'{[id(stg) for stg in op.strategies]}') stg_close = op.get_strategy_id_by_price_type('close') stg_open = op.get_strategy_id_by_price_type('open') stg_high = op.get_strategy_id_by_price_type('high') stg_all = op.get_strategy_id_by_price_type() print(f'All IDs of strategies:\n' f'{stg_all}\n' f'All price types of strategies:\n' f'{[stg.price_type for stg in op.strategies]}') self.assertEqual(stg_close, ['dma', 'trix']) self.assertEqual(stg_open, ['macd', 'dma_1']) self.assertEqual(stg_high, ['macd_1']) stg_wrong = op.get_strategy_id_by_price_type(123) self.assertIsInstance(stg_wrong, list) self.assertEqual(stg_wrong, []) def test_property_strategies(self): """ test property strategies""" print(f'created a new simple Operator with only one strategy: DMA') op = qt.Operator('dma') strategies = op.strategies self.assertIsInstance(strategies, list) op.info() print(f'created the second simple Operator with three strategies') self.assertIsInstance(strategies[0], TimingDMA) op = qt.Operator('dma, macd, cdl') strategies = op.strategies op.info() self.assertIsInstance(strategies, list) self.assertIsInstance(strategies[0], TimingDMA) self.assertIsInstance(strategies[1], TimingMACD) self.assertIsInstance(strategies[2], TimingCDL) def test_property_strategy_count(self): """ test Property strategy_count, and the method get_strategy_count_by_price_type()""" self.assertEqual(self.op.strategy_count, 1) self.assertEqual(self.op2.strategy_count, 3) self.assertEqual(self.op.get_strategy_count_by_price_type(), 1) self.assertEqual(self.op2.get_strategy_count_by_price_type(), 3) self.assertEqual(self.op.get_strategy_count_by_price_type('close'), 1) self.assertEqual(self.op.get_strategy_count_by_price_type('high'), 0) self.assertEqual(self.op2.get_strategy_count_by_price_type('close'), 3) self.assertEqual(self.op2.get_strategy_count_by_price_type('open'), 0) def test_property_strategy_names(self): """ test property strategy_ids""" op = qt.Operator('dma') self.assertIsInstance(op.strategy_ids, list) names = op.strategy_ids[0] print(f'names are {names}') self.assertEqual(names, 'dma') op = qt.Operator('dma, macd, trix, cdl') self.assertIsInstance(op.strategy_ids, list) self.assertEqual(op.strategy_ids[0], 'dma') self.assertEqual(op.strategy_ids[1], 'macd') self.assertEqual(op.strategy_ids[2], 'trix') self.assertEqual(op.strategy_ids[3], 'cdl') op = qt.Operator('dma, macd, trix, dma, dma') self.assertIsInstance(op.strategy_ids, list) self.assertEqual(op.strategy_ids[0], 'dma') self.assertEqual(op.strategy_ids[1], 'macd') self.assertEqual(op.strategy_ids[2], 'trix') self.assertEqual(op.strategy_ids[3], 'dma_1') self.assertEqual(op.strategy_ids[4], 'dma_2') def test_property_strategy_blenders(self): """ test property strategy blenders including property setter, and test the method get_blender()""" print(f'------- Test property strategy blenders ---------') op = qt.Operator() self.assertIsInstance(op.strategy_blenders, dict) self.assertIsInstance(op.signal_type, str) self.assertEqual(op.strategy_blenders, {}) self.assertEqual(op.signal_type, 'pt') # test adding blender to empty operator op.strategy_blenders = '1 + 2' op.signal_type = 'proportion signal' self.assertEqual(op.strategy_blenders, {}) self.assertEqual(op.signal_type, 'ps') op.add_strategy('dma') op.strategy_blenders = '1+2' self.assertEqual(op.strategy_blenders, {'close': ['+', '2', '1']}) op.clear_strategies() self.assertEqual(op.strategy_blenders, {}) op.add_strategies('dma, trix, macd, dma') op.set_parameter('dma', price_type='open') op.set_parameter('trix', price_type='high') op.set_blender('open', '1+2') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, ['+', '2', '1']) self.assertEqual(blender_close, None) self.assertEqual(blender_high, None) op.set_blender('open', '1+2+3') op.set_blender('abc', '1+2+3') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') blender_abc = op.get_blender('abc') self.assertEqual(op.strategy_blenders, {'open': ['+', '3', '+', '2', '1']}) self.assertEqual(blender_open, ['+', '3', '+', '2', '1']) self.assertEqual(blender_close, None) self.assertEqual(blender_high, None) self.assertEqual(blender_abc, None) op.set_blender('open', 123) blender_open = op.get_blender('open') self.assertEqual(blender_open, []) op.set_blender(None, '1+1') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(op.bt_price_types, ['close', 'high', 'open']) self.assertEqual(op.get_blender(), {'close': ['+', '1', '1'], 'open': ['+', '1', '1'], 'high': ['+', '1', '1']}) self.assertEqual(blender_open, ['+', '1', '1']) self.assertEqual(blender_close, ['+', '1', '1']) self.assertEqual(blender_high, ['+', '1', '1']) op.set_blender(None, ['1+1', '3+4']) blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, ['+', '4', '3']) self.assertEqual(blender_close, ['+', '1', '1']) self.assertEqual(blender_high, ['+', '4', '3']) self.assertEqual(op.view_blender('open'), '3+4') self.assertEqual(op.view_blender('close'), '1+1') self.assertEqual(op.view_blender('high'), '3+4') op.strategy_blenders = (['1+2', '2*3', '1+4']) blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, ['+', '4', '1']) self.assertEqual(blender_close, ['+', '2', '1']) self.assertEqual(blender_high, ['*', '3', '2']) self.assertEqual(op.view_blender('open'), '1+4') self.assertEqual(op.view_blender('close'), '1+2') self.assertEqual(op.view_blender('high'), '2*3') # test error inputs: # wrong type of price_type self.assertRaises(TypeError, op.set_blender, 1, '1+3') # price_type not found, no change is made op.set_blender('volume', '1+3') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, ['+', '4', '1']) self.assertEqual(blender_close, ['+', '2', '1']) self.assertEqual(blender_high, ['*', '3', '2']) # price_type not valid, no change is made op.set_blender('closee', '1+2') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, ['+', '4', '1']) self.assertEqual(blender_close, ['+', '2', '1']) self.assertEqual(blender_high, ['*', '3', '2']) # wrong type of blender, set to empty list op.set_blender('open', 55) blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, []) self.assertEqual(blender_close, ['+', '2', '1']) self.assertEqual(blender_high, ['*', '3', '2']) # wrong type of blender, set to empty list op.set_blender('close', ['1+2']) blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, []) self.assertEqual(blender_close, []) self.assertEqual(blender_high, ['*', '3', '2']) # can't parse blender, set to empty list op.set_blender('high', 'a+bc') blender_open = op.get_blender('open') blender_close = op.get_blender('close') blender_high = op.get_blender('high') self.assertEqual(blender_open, []) self.assertEqual(blender_close, []) self.assertEqual(blender_high, []) def test_property_singal_type(self): """ test property signal_type""" op = qt.Operator() self.assertIsInstance(op.signal_type, str) self.assertEqual(op.signal_type, 'pt') op = qt.Operator(signal_type='ps') self.assertIsInstance(op.signal_type, str) self.assertEqual(op.signal_type, 'ps') op = qt.Operator(signal_type='PS') self.assertEqual(op.signal_type, 'ps') op = qt.Operator(signal_type='proportion signal') self.assertEqual(op.signal_type, 'ps') print(f'"pt" will be the default type if wrong value is given') op = qt.Operator(signal_type='wrong value') self.assertEqual(op.signal_type, 'pt') print(f'test signal_type.setter') op.signal_type = 'ps' self.assertEqual(op.signal_type, 'ps') print(f'test error raising') self.assertRaises(TypeError, setattr, op, 'signal_type', 123) self.assertRaises(ValueError, setattr, op, 'signal_type', 'wrong value') def test_property_op_data_types(self): """ test property op_data_types""" op = qt.Operator() self.assertIsInstance(op.op_data_types, list) self.assertEqual(op.op_data_types, []) op = qt.Operator('macd, dma, trix') dt = op.op_data_types self.assertEqual(dt[0], 'close') op = qt.Operator('macd, cdl') dt = op.op_data_types self.assertEqual(dt[0], 'close') self.assertEqual(dt[1], 'high') self.assertEqual(dt[2], 'low') self.assertEqual(dt[3], 'open') self.assertEqual(dt, ['close', 'high', 'low', 'open']) op.add_strategy('dma') dt = op.op_data_types self.assertEqual(dt[0], 'close') self.assertEqual(dt[1], 'high') self.assertEqual(dt[2], 'low') self.assertEqual(dt[3], 'open') self.assertEqual(dt, ['close', 'high', 'low', 'open']) def test_property_op_data_type_count(self): """ test property op_data_type_count""" op = qt.Operator() self.assertIsInstance(op.op_data_type_count, int) self.assertEqual(op.op_data_type_count, 0) op = qt.Operator('macd, dma, trix') dtn = op.op_data_type_count self.assertEqual(dtn, 1) op = qt.Operator('macd, cdl') dtn = op.op_data_type_count self.assertEqual(dtn, 4) op.add_strategy('dma') dtn = op.op_data_type_count self.assertEqual(dtn, 4) def test_property_op_data_freq(self): """ test property op_data_freq""" op = qt.Operator() self.assertIsInstance(op.op_data_freq, str) self.assertEqual(len(op.op_data_freq), 0) self.assertEqual(op.op_data_freq, '') op = qt.Operator('macd, dma, trix') dtf = op.op_data_freq self.assertIsInstance(dtf, str) self.assertEqual(dtf[0], 'd') op.set_parameter('macd', data_freq='m') dtf = op.op_data_freq self.assertIsInstance(dtf, list) self.assertEqual(len(dtf), 2) self.assertEqual(dtf[0], 'd') self.assertEqual(dtf[1], 'm') def test_property_bt_price_types(self): """ test property bt_price_types""" print('------test property bt_price_tyeps-------') op = qt.Operator() self.assertIsInstance(op.bt_price_types, list) self.assertEqual(len(op.bt_price_types), 0) self.assertEqual(op.bt_price_types, []) op = qt.Operator('macd, dma, trix') btp = op.bt_price_types self.assertIsInstance(btp, list) self.assertEqual(btp[0], 'close') op.set_parameter('macd', price_type='open') btp = op.bt_price_types btpc = op.bt_price_type_count print(f'price_types are \n{btp}') self.assertIsInstance(btp, list) self.assertEqual(len(btp), 2) self.assertEqual(btp[0], 'close') self.assertEqual(btp[1], 'open') self.assertEqual(btpc, 2) op.add_strategies(['dma', 'macd']) op.set_parameter('dma_1', price_type='high') btp = op.bt_price_types btpc = op.bt_price_type_count self.assertEqual(btp[0], 'close') self.assertEqual(btp[1], 'high') self.assertEqual(btp[2], 'open') self.assertEqual(btpc, 3) op.remove_strategy('dma_1') btp = op.bt_price_types btpc = op.bt_price_type_count self.assertEqual(btp[0], 'close') self.assertEqual(btp[1], 'open') self.assertEqual(btpc, 2) op.remove_strategy('macd_1') btp = op.bt_price_types btpc = op.bt_price_type_count self.assertEqual(btp[0], 'close') self.assertEqual(btp[1], 'open') self.assertEqual(btpc, 2) def test_property_op_data_type_list(self): """ test property op_data_type_list""" op = qt.Operator() self.assertIsInstance(op.op_data_type_list, list) self.assertEqual(len(op.op_data_type_list), 0) self.assertEqual(op.op_data_type_list, []) op = qt.Operator('macd, dma, trix, cdl') ohd = op.op_data_type_list print(f'ohd is {ohd}') self.assertIsInstance(ohd, list) self.assertEqual(ohd[0], ['close']) op.set_parameter('macd', data_types='open, close') ohd = op.op_data_type_list print(f'ohd is {ohd}') self.assertIsInstance(ohd, list) self.assertEqual(len(ohd), 4) self.assertEqual(ohd[0], ['open', 'close']) self.assertEqual(ohd[1], ['close']) self.assertEqual(ohd[2], ['close']) self.assertEqual(ohd[3], ['open', 'high', 'low', 'close']) def test_property_op_history_data(self): """ Test this important function to get operation history data that shall be used in signal generation these data are stored in list of nd-arrays, each ndarray represents the data that is needed for each and every strategy """ print(f'------- Test getting operation history data ---------') op = qt.Operator() self.assertIsInstance(op.strategy_blenders, dict) self.assertIsInstance(op.signal_type, str) self.assertEqual(op.strategy_blenders, {}) self.assertEqual(op.op_history_data, {}) self.assertEqual(op.signal_type, 'pt') def test_property_opt_space_par(self): """ test property opt_space_par""" print(f'-----test property opt_space_par--------:\n') op = qt.Operator() self.assertIsInstance(op.opt_space_par, tuple) self.assertIsInstance(op.opt_space_par[0], list) self.assertIsInstance(op.opt_space_par[1], list) self.assertEqual(len(op.opt_space_par), 2) self.assertEqual(op.opt_space_par, ([], [])) op = qt.Operator('macd, dma, trix, cdl') osp = op.opt_space_par print(f'before setting opt_tags opt_space_par is empty:\n' f'osp is {osp}\n') self.assertIsInstance(osp, tuple) self.assertEqual(osp[0], []) self.assertEqual(osp[1], []) op.set_parameter('macd', opt_tag=1) op.set_parameter('dma', opt_tag=1) osp = op.opt_space_par print(f'after setting opt_tags opt_space_par is not empty:\n' f'osp is {osp}\n') self.assertIsInstance(osp, tuple) self.assertEqual(len(osp), 2) self.assertIsInstance(osp[0], list) self.assertIsInstance(osp[1], list) self.assertEqual(len(osp[0]), 6) self.assertEqual(len(osp[1]), 6) self.assertEqual(osp[0], [(10, 250), (10, 250), (10, 250), (10, 250), (10, 250), (10, 250)]) self.assertEqual(osp[1], ['discr', 'discr', 'discr', 'discr', 'discr', 'discr']) def test_property_opt_types(self): """ test property opt_tags""" print(f'-----test property opt_tags--------:\n') op = qt.Operator() self.assertIsInstance(op.opt_tags, list) self.assertEqual(len(op.opt_tags), 0) self.assertEqual(op.opt_tags, []) op = qt.Operator('macd, dma, trix, cdl') otp = op.opt_tags print(f'before setting opt_tags opt_space_par is empty:\n' f'otp is {otp}\n') self.assertIsInstance(otp, list) self.assertEqual(otp, [0, 0, 0, 0]) op.set_parameter('macd', opt_tag=1) op.set_parameter('dma', opt_tag=1) otp = op.opt_tags print(f'after setting opt_tags opt_space_par is not empty:\n' f'otp is {otp}\n') self.assertIsInstance(otp, list) self.assertEqual(len(otp), 4) self.assertEqual(otp, [1, 1, 0, 0]) def test_property_max_window_length(self): """ test property max_window_length""" print(f'-----test property max window length--------:\n') op = qt.Operator() self.assertIsInstance(op.max_window_length, int) self.assertEqual(op.max_window_length, 0) op = qt.Operator('macd, dma, trix, cdl') mwl = op.max_window_length print(f'before setting window_length the value is 270:\n' f'mwl is {mwl}\n') self.assertIsInstance(mwl, int) self.assertEqual(mwl, 270) op.set_parameter('macd', window_length=300) op.set_parameter('dma', window_length=350) mwl = op.max_window_length print(f'after setting window_length the value is new set value:\n' f'mwl is {mwl}\n') self.assertIsInstance(mwl, int) self.assertEqual(mwl, 350) def test_property_bt_price_type_count(self): """ test property bt_price_type_count""" print(f'-----test property bt_price_type_count--------:\n') op = qt.Operator() self.assertIsInstance(op.bt_price_type_count, int) self.assertEqual(op.bt_price_type_count, 0) op = qt.Operator('macd, dma, trix, cdl') otp = op.bt_price_type_count print(f'before setting price_type the price count is 1:\n' f'otp is {otp}\n') self.assertIsInstance(otp, int) self.assertEqual(otp, 1) op.set_parameter('macd', price_type='open') op.set_parameter('dma', price_type='open') otp = op.bt_price_type_count print(f'after setting price_type the price type count is 2:\n' f'otp is {otp}\n') self.assertIsInstance(otp, int) self.assertEqual(otp, 2) def test_property_set(self): """ test all property setters: setting following properties: - strategy_blenders - signal_type other properties can not be set""" print(f'------- Test setting properties ---------') op = qt.Operator() self.assertIsInstance(op.strategy_blenders, dict) self.assertIsInstance(op.signal_type, str) self.assertEqual(op.strategy_blenders, {}) self.assertEqual(op.signal_type, 'pt') op.strategy_blenders = '1 + 2' op.signal_type = 'proportion signal' self.assertEqual(op.strategy_blenders, {}) self.assertEqual(op.signal_type, 'ps') op = qt.Operator('macd, dma, trix, cdl') # TODO: 修改set_parameter(),使下面的用法成立 # a_to_sell.set_parameter('dma, cdl', price_type='open') op.set_parameter('dma', price_type='open') op.set_parameter('cdl', price_type='open') sb = op.strategy_blenders st = op.signal_type self.assertIsInstance(sb, dict) print(f'before setting: strategy_blenders={sb}') self.assertEqual(sb, {}) op.strategy_blenders = '1+2 * 3' sb = op.strategy_blenders print(f'after setting strategy_blender={sb}') self.assertEqual(sb, {'close': ['+', '*', '3', '2', '1'], 'open': ['+', '*', '3', '2', '1']}) op.strategy_blenders = ['1+2', '3-4'] sb = op.strategy_blenders print(f'after setting strategy_blender={sb}') self.assertEqual(sb, {'close': ['+', '2', '1'], 'open': ['-', '4', '3']}) def test_operator_ready(self): """test the method ready of Operator""" op = qt.Operator() print(f'operator is ready? "{op.ready}"') def test_operator_add_strategy(self): """test adding strategies to Operator""" op = qt.Operator('dma, all, urgent') self.assertIsInstance(op, qt.Operator) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[1], qt.SelectingAll) self.assertIsInstance(op.strategies[2], qt.RiconUrgent) self.assertIsInstance(op[0], qt.TimingDMA) self.assertIsInstance(op[1], qt.SelectingAll) self.assertIsInstance(op[2], qt.RiconUrgent) self.assertIsInstance(op['dma'], qt.TimingDMA) self.assertIsInstance(op['all'], qt.SelectingAll) self.assertIsInstance(op['urgent'], qt.RiconUrgent) self.assertEqual(op.strategy_count, 3) print(f'test adding strategies into existing op') print('test adding strategy by string') op.add_strategy('macd') self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[3], qt.TimingMACD) self.assertEqual(op.strategy_count, 4) op.add_strategy('random') self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[4], qt.SelectingRandom) self.assertEqual(op.strategy_count, 5) test_ls = TestLSStrategy() op.add_strategy(test_ls) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[5], TestLSStrategy) self.assertEqual(op.strategy_count, 6) print(f'Test different instance of objects are added to operator') op.add_strategy('dma') self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[6], qt.TimingDMA) self.assertIsNot(op.strategies[0], op.strategies[6]) def test_operator_add_strategies(self): """ etst adding multiple strategies to Operator""" op = qt.Operator('dma, all, urgent') self.assertEqual(op.strategy_count, 3) print('test adding multiple strategies -- adding strategy by list of strings') op.add_strategies(['dma', 'macd']) self.assertEqual(op.strategy_count, 5) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[3], qt.TimingDMA) self.assertIsInstance(op.strategies[4], qt.TimingMACD) print('test adding multiple strategies -- adding strategy by comma separated strings') op.add_strategies('dma, macd') self.assertEqual(op.strategy_count, 7) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[5], qt.TimingDMA) self.assertIsInstance(op.strategies[6], qt.TimingMACD) print('test adding multiple strategies -- adding strategy by list of strategies') op.add_strategies([qt.TimingDMA(), qt.TimingMACD()]) self.assertEqual(op.strategy_count, 9) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[7], qt.TimingDMA) self.assertIsInstance(op.strategies[8], qt.TimingMACD) print('test adding multiple strategies -- adding strategy by list of strategy and str') op.add_strategies(['DMA', qt.TimingMACD()]) self.assertEqual(op.strategy_count, 11) self.assertIsInstance(op.strategies[0], qt.TimingDMA) self.assertIsInstance(op.strategies[9], qt.TimingDMA) self.assertIsInstance(op.strategies[10], qt.TimingMACD) self.assertIsNot(op.strategies[0], op.strategies[9]) self.assertIs(type(op.strategies[0]), type(op.strategies[9])) print('test adding fault data') self.assertRaises(AssertionError, op.add_strategies, 123) self.assertRaises(AssertionError, op.add_strategies, None) def test_opeartor_remove_strategy(self): """ test method remove strategy""" op = qt.Operator('dma, all, urgent') op.add_strategies(['dma', 'macd']) op.add_strategies(['DMA', TestLSStrategy()]) self.assertEqual(op.strategy_count, 7) print('test removing strategies from Operator') op.remove_strategy('dma') self.assertEqual(op.strategy_count, 6) self.assertEqual(op.strategy_ids, ['all', 'urgent', 'dma_1', 'macd', 'dma_2', 'custom']) self.assertEqual(op.strategies[0], op['all']) self.assertEqual(op.strategies[1], op['urgent']) self.assertEqual(op.strategies[2], op['dma_1']) self.assertEqual(op.strategies[3], op['macd']) self.assertEqual(op.strategies[4], op['dma_2']) self.assertEqual(op.strategies[5], op['custom']) op.remove_strategy('dma_1') self.assertEqual(op.strategy_count, 5) self.assertEqual(op.strategy_ids, ['all', 'urgent', 'macd', 'dma_2', 'custom']) self.assertEqual(op.strategies[0], op['all']) self.assertEqual(op.strategies[1], op['urgent']) self.assertEqual(op.strategies[2], op['macd']) self.assertEqual(op.strategies[3], op['dma_2']) self.assertEqual(op.strategies[4], op['custom']) def test_opeartor_clear_strategies(self): """ test operator clear strategies""" op = qt.Operator('dma, all, urgent') op.add_strategies(['dma', 'macd']) op.add_strategies(['DMA', TestLSStrategy()]) self.assertEqual(op.strategy_count, 7) print('test removing strategies from Operator') op.clear_strategies() self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) op.add_strategy('dma', pars=(12, 123, 25)) self.assertEqual(op.strategy_count, 1) self.assertEqual(op.strategy_ids, ['dma']) self.assertEqual(type(op.strategies[0]), TimingDMA) self.assertEqual(op.strategies[0].pars, (12, 123, 25)) op.clear_strategies() self.assertEqual(op.strategy_count, 0) self.assertEqual(op.strategy_ids, []) def test_operator_prepare_data(self): """test processes that related to prepare data""" test_ls = TestLSStrategy() test_sel = TestSelStrategy() test_sig = TestSigStrategy() self.op = qt.Operator(strategies=[test_ls, test_sel, test_sig]) too_early_cash = qt.CashPlan(dates='2016-01-01', amounts=10000) early_cash = qt.CashPlan(dates='2016-07-01', amounts=10000) on_spot_cash = qt.CashPlan(dates='2016-07-08', amounts=10000) no_trade_cash = qt.CashPlan(dates='2016-07-08, 2016-07-30, 2016-08-11, 2016-09-03', amounts=[10000, 10000, 10000, 10000]) # 在所有策略的参数都设置好之前调用prepare_data会发生assertion Error self.assertRaises(AssertionError, self.op.prepare_data, hist_data=self.hp1, cash_plan=qt.CashPlan(dates='2016-07-08', amounts=10000)) late_cash = qt.CashPlan(dates='2016-12-31', amounts=10000) multi_cash = qt.CashPlan(dates='2016-07-08, 2016-08-08', amounts=[10000, 10000]) self.op.set_parameter(stg_id='custom', pars={'000300': (5, 10.), '000400': (5, 10.), '000500': (5, 6.)}) self.assertEqual(self.op.strategies[0].pars, {'000300': (5, 10.), '000400': (5, 10.), '000500': (5, 6.)}) self.op.set_parameter(stg_id='custom_1', pars=()) self.assertEqual(self.op.strategies[1].pars, ()), self.op.set_parameter(stg_id='custom_2', pars=(0.2, 0.02, -0.02)) self.assertEqual(self.op.strategies[2].pars, (0.2, 0.02, -0.02)), self.op.prepare_data(hist_data=self.hp1, cash_plan=on_spot_cash) self.assertIsInstance(self.op._op_history_data, dict) self.assertEqual(len(self.op._op_history_data), 3) # test if automatic strategy blenders are set self.assertEqual(self.op.strategy_blenders, {'close': ['+', '2', '+', '1', '0']}) tim_hist_data = self.op._op_history_data['custom'] sel_hist_data = self.op._op_history_data['custom_1'] ric_hist_data = self.op._op_history_data['custom_2'] print(f'in test_prepare_data in TestOperator:') print('selecting history data:\n', sel_hist_data) print('originally passed data in correct sequence:\n', self.test_data_3D[:, 3:, [2, 3, 0]]) print('difference is \n', sel_hist_data - self.test_data_3D[:, :, [2, 3, 0]]) self.assertTrue(np.allclose(sel_hist_data, self.test_data_3D[:, :, [2, 3, 0]], equal_nan=True)) self.assertTrue(np.allclose(tim_hist_data, self.test_data_3D, equal_nan=True)) self.assertTrue(np.allclose(ric_hist_data, self.test_data_3D[:, 3:, :], equal_nan=True)) # raises Value Error if empty history panel is given empty_hp = qt.HistoryPanel() correct_hp = qt.HistoryPanel(values=np.random.randint(10, size=(3, 50, 4)), columns=self.types, levels=self.shares, rows=self.date_indices) too_many_shares = qt.HistoryPanel(values=np.random.randint(10, size=(5, 50, 4))) too_many_types = qt.HistoryPanel(values=np.random.randint(10, size=(3, 50, 5))) # raises Error when history panel is empty self.assertRaises(ValueError, self.op.prepare_data, empty_hp, on_spot_cash) # raises Error when first investment date is too early self.assertRaises(AssertionError, self.op.prepare_data, correct_hp, early_cash) # raises Error when last investment date is too late self.assertRaises(AssertionError, self.op.prepare_data, correct_hp, late_cash) # raises Error when some of the investment dates are on no-trade-days self.assertRaises(ValueError, self.op.prepare_data, correct_hp, no_trade_cash) # raises Error when number of shares in history data does not fit self.assertRaises(AssertionError, self.op.prepare_data, too_many_shares, on_spot_cash) # raises Error when too early cash investment date self.assertRaises(AssertionError, self.op.prepare_data, correct_hp, too_early_cash) # raises Error when number of d_types in history data does not fit self.assertRaises(AssertionError, self.op.prepare_data, too_many_types, on_spot_cash) # test the effect of data type sequence in strategy definition def test_operator_generate(self): """ Test signal generation process of operator objects :return: """ # 使用test模块的自定义策略生成三种交易策略 test_ls = TestLSStrategy() test_sel = TestSelStrategy() test_sel2 = TestSelStrategyDiffTime() test_sig = TestSigStrategy() print('--Test PT type signal generation--') # 测试PT类型的信号生成: # 创建一个Operator对象,信号类型为PT(比例目标信号) # 这个Operator对象包含两个策略,分别为LS-Strategy以及Sel-Strategy,代表择时和选股策略 # 两个策略分别生成PT信号后混合成一个信号输出 self.op = qt.Operator(strategies=[test_ls, test_sel]) self.op.set_parameter(stg_id='custom', pars={'000010': (5, 10.), '000030': (5, 10.), '000039': (5, 6.)}) self.op.set_parameter(stg_id=1, pars=()) # self.a_to_sell.set_blender(blender='0+1+2') self.op.prepare_data(hist_data=self.hp1, cash_plan=qt.CashPlan(dates='2016-07-08', amounts=10000)) print('--test operator information in normal mode--') self.op.info() self.assertEqual(self.op.strategy_blenders, {'close': ['+', '1', '0']}) self.op.set_blender(None, '0*1') self.assertEqual(self.op.strategy_blenders, {'close': ['*', '1', '0']}) print('--test operation signal created in Proportional Target (PT) Mode--') op_list = self.op.create_signal(hist_data=self.hp1) self.assertTrue(isinstance(op_list, HistoryPanel)) backtest_price_types = op_list.htypes self.assertEqual(backtest_price_types[0], 'close') self.assertEqual(op_list.shape, (3, 45, 1)) reduced_op_list = op_list.values.squeeze().T print(f'op_list created, it is a 3 share/45 days/1 htype array, to make comparison happen, \n' f'it will be squeezed to a 2-d array to compare on share-wise:\n' f'{reduced_op_list}') target_op_values = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0]]) self.assertTrue(np.allclose(target_op_values, reduced_op_list, equal_nan=True)) print('--Test two separate signal generation for different price types--') # 测试两组PT类型的信号生成: # 在Operator对象中增加两个SigStrategy策略,策略类型相同但是策略的参数不同,回测价格类型为"OPEN" # Opeartor应该生成两组交易信号,分别用于"close"和"open"两中不同的价格类型 # 这里需要重新生成两个新的交易策略对象,否则在op的strategies列表中产生重复的对象引用,从而引起错误 test_ls = TestLSStrategy() test_sel = TestSelStrategy() self.op.add_strategies([test_ls, test_sel]) self.op.set_parameter(stg_id='custom_2', price_type='open') self.op.set_parameter(stg_id='custom_3', price_type='open') self.assertEqual(self.op['custom'].price_type, 'close') self.assertEqual(self.op['custom_2'].price_type, 'open') self.op.set_parameter(stg_id='custom_2', pars={'000010': (5, 10.), '000030': (5, 10.), '000039': (5, 6.)}) self.op.set_parameter(stg_id='custom_3', pars=()) self.op.set_blender(blender='0 or 1', price_type='open') self.op.prepare_data(hist_data=self.hp1, cash_plan=qt.CashPlan(dates='2016-07-08', amounts=10000)) print('--test how operator information is printed out--') self.op.info() self.assertEqual(self.op.strategy_blenders, {'close': ['*', '1', '0'], 'open': ['or', '1', '0']}) print('--test opeartion signal created in Proportional Target (PT) Mode--') op_list = self.op.create_signal(hist_data=self.hp1) self.assertTrue(isinstance(op_list, HistoryPanel)) signal_close = op_list['close'].squeeze().T signal_open = op_list['open'].squeeze().T self.assertEqual(signal_close.shape, (45, 3)) self.assertEqual(signal_open.shape, (45, 3)) target_op_close = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.0, 0.0], [0.5, 0.0, 0.0], [0.5, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0], [0.0, 0.5, 0.0]]) target_op_open = np.array([[0.5, 0.5, 1.0], [0.5, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 0.5, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 1.0, 0.0], [1.0, 1.0, 0.0], [1.0, 0.5, 0.0], [1.0, 0.5, 0.0], [1.0, 1.0, 0.0], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.0, 1.0, 0.5], [0.5, 1.0, 0.0], [0.5, 1.0, 0.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 1.0, 1.0]]) signal_pairs = [[list(sig1), list(sig2), sig1 == sig2] for sig1, sig2 in zip(list(target_op_close), list(signal_close))] print(f'signals side by side:\n' f'{signal_pairs}') self.assertTrue(np.allclose(target_op_close, signal_close, equal_nan=True)) signal_pairs = [[list(sig1), list(sig2), sig1 == sig2] for sig1, sig2 in zip(list(target_op_open), list(signal_open))] print(f'signals side by side:\n' f'{signal_pairs}') self.assertTrue(np.allclose(target_op_open, signal_open, equal_nan=True)) print('--Test two separate signal generation for different price types--') # 更多测试集合 def test_stg_parameter_setting(self): """ test setting parameters of strategies test the method set_parameters :return: """ op = qt.Operator(strategies='dma, all, urgent') print(op.strategies, '\n', [qt.TimingDMA, qt.SelectingAll, qt.RiconUrgent]) print(f'info of Timing strategy in new op: \n{op.strategies[0].info()}') # TODO: allow set_parameters to a list of strategies or str-listed strategies # TODO: allow set_parameters to all strategies of specific bt price type print(f'Set up strategy parameters by strategy id') op.set_parameter('dma', pars=(5, 10, 5), opt_tag=1, par_boes=((5, 10), (5, 15), (10, 15)), window_length=10, data_types=['close', 'open', 'high']) op.set_parameter('all', window_length=20) op.set_parameter('all', price_type='high') print(f'Can also set up strategy parameters by strategy index') op.set_parameter(2, price_type='open') op.set_parameter(2, opt_tag=1, pars=(9, -0.09), window_length=10) self.assertEqual(op.strategies[0].pars, (5, 10, 5)) self.assertEqual(op.strategies[0].par_boes, ((5, 10), (5, 15), (10, 15))) self.assertEqual(op.strategies[2].pars, (9, -0.09)) self.assertEqual(op.op_data_freq, 'd') self.assertEqual(op.op_data_types, ['close', 'high', 'open']) self.assertEqual(op.opt_space_par, ([(5, 10), (5, 15), (10, 15), (1, 40), (-0.5, 0.5)], ['discr', 'discr', 'discr', 'discr', 'conti'])) self.assertEqual(op.max_window_length, 20) print(f'KeyError will be raised if wrong strategy id is given') self.assertRaises(KeyError, op.set_parameter, stg_id='t-1', pars=(1, 2)) self.assertRaises(KeyError, op.set_parameter, stg_id='wrong_input', pars=(1, 2)) print(f'ValueError will be raised if parameter can be set') self.assertRaises(ValueError, op.set_parameter, stg_id=0, pars=('wrong input', 'wrong input')) # test blenders of different price types # test setting blenders to different price types # TODO: to allow operands like "and", "or", "not", "xor" # a_to_sell.set_blender('close', '0 and 1 or 2') # self.assertEqual(a_to_sell.get_blender('close'), 'str-1.2') self.assertEqual(op.bt_price_types, ['close', 'high', 'open']) op.set_blender('open', '0 & 1 | 2') self.assertEqual(op.get_blender('open'), ['|', '2', '&', '1', '0']) op.set_blender('high', '(0|1) & 2') self.assertEqual(op.get_blender('high'), ['&', '2', '|', '1', '0']) op.set_blender('close', '0 & 1 | 2') self.assertEqual(op.get_blender(), {'close': ['|', '2', '&', '1', '0'], 'high': ['&', '2', '|', '1', '0'], 'open': ['|', '2', '&', '1', '0']}) self.assertEqual(op.opt_space_par, ([(5, 10), (5, 15), (10, 15), (1, 40), (-0.5, 0.5)], ['discr', 'discr', 'discr', 'discr', 'conti'])) self.assertEqual(op.opt_tags, [1, 0, 1]) def test_signal_blend(self): self.assertEqual(blender_parser('0 & 1'), ['&', '1', '0']) self.assertEqual(blender_parser('0 or 1'), ['or', '1', '0']) self.assertEqual(blender_parser('0 & 1 | 2'), ['|', '2', '&', '1', '0']) blender = blender_parser('0 & 1 | 2') self.assertEqual(signal_blend([1, 1, 1], blender), 1) self.assertEqual(signal_blend([1, 0, 1], blender), 1) self.assertEqual(signal_blend([1, 1, 0], blender), 1) self.assertEqual(signal_blend([0, 1, 1], blender), 1) self.assertEqual(signal_blend([0, 0, 1], blender), 1) self.assertEqual(signal_blend([1, 0, 0], blender), 0) self.assertEqual(signal_blend([0, 1, 0], blender), 0) self.assertEqual(signal_blend([0, 0, 0], blender), 0) # parse: '0 & ( 1 | 2 )' self.assertEqual(blender_parser('0 & ( 1 | 2 )'), ['&', '|', '2', '1', '0']) blender = blender_parser('0 & ( 1 | 2 )') self.assertEqual(signal_blend([1, 1, 1], blender), 1) self.assertEqual(signal_blend([1, 0, 1], blender), 1) self.assertEqual(signal_blend([1, 1, 0], blender), 1) self.assertEqual(signal_blend([0, 1, 1], blender), 0) self.assertEqual(signal_blend([0, 0, 1], blender), 0) self.assertEqual(signal_blend([1, 0, 0], blender), 0) self.assertEqual(signal_blend([0, 1, 0], blender), 0) self.assertEqual(signal_blend([0, 0, 0], blender), 0) # parse: '(1-2)/3 + 0' self.assertEqual(blender_parser('(1-2)/3 + 0'), ['+', '0', '/', '3', '-', '2', '1']) blender = blender_parser('(1-2)/3 + 0') self.assertEqual(signal_blend([5, 9, 1, 4], blender), 7) # pars: '(0*1/2*(3+4))+5*(6+7)-8' self.assertEqual(blender_parser('(0*1/2*(3+4))+5*(6+7)-8'), ['-', '8', '+', '*', '+', '7', '6', '5', '*', '+', '4', '3', '/', '2', '*', '1', '0']) blender = blender_parser('(0*1/2*(3+4))+5*(6+7)-8') self.assertEqual(signal_blend([1, 1, 1, 1, 1, 1, 1, 1, 1], blender), 3) self.assertEqual(signal_blend([2, 1, 4, 3, 5, 5, 2, 2, 10], blender), 14) # parse: '0/max(2,1,3 + 5)+4' self.assertEqual(blender_parser('0/max(2,1,3 + 5)+4'), ['+', '4', '/', 'max(3)', '+', '5', '3', '1', '2', '0']) blender = blender_parser('0/max(2,1,3 + 5)+4') self.assertEqual(signal_blend([8.0, 4, 3, 5.0, 0.125, 5], blender), 0.925) self.assertEqual(signal_blend([2, 1, 4, 3, 5, 5, 2, 2, 10], blender), 5.25) print('speed test') import time st = time.time() blender = blender_parser('0+max(1,2,(3+4)*5, max(6, (7+8)*9), 10-11) * (12+13)') res = [] for i in range(10000): res = signal_blend([1, 1, 2, 3, 4, 5, 3, 4, 5, 6, 7, 8, 2, 3], blender) et = time.time() print(f'total time for RPN processing: {et - st}, got result: {res}') blender = blender_parser("0 + 1 * 2") self.assertEqual(signal_blend([1, 2, 3], blender), 7) blender = blender_parser("(0 + 1) * 2") self.assertEqual(signal_blend([1, 2, 3], blender), 9) blender = blender_parser("(0+1) * 2") self.assertEqual(signal_blend([1, 2, 3], blender), 9) blender = blender_parser("(0 + 1) * 2") self.assertEqual(signal_blend([1, 2, 3], blender), 9) # TODO: 目前对于-(1+2)这样的表达式还无法处理 # self.a_to_sell.set_blender('selecting', "-(0 + 1) * 2") # self.assertEqual(self.a_to_sell.signal_blend([1, 2, 3]), -9) blender = blender_parser("(0-1)/2 + 3") print(f'RPN of notation: "(0-1)/2 + 3" is:\n' f'{" ".join(blender[::-1])}') self.assertAlmostEqual(signal_blend([1, 2, 3, 0.0], blender), -0.33333333) blender = blender_parser("0 + 1 / 2") print(f'RPN of notation: "0 + 1 / 2" is:\n' f'{" ".join(blender[::-1])}') self.assertAlmostEqual(signal_blend([1, math.pi, 4], blender), 1.78539816) blender = blender_parser("(0 + 1) / 2") print(f'RPN of notation: "(0 + 1) / 2" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(signal_blend([1, 2, 3], blender), 1) blender = blender_parser("(0 + 1 * 2) / 3") print(f'RPN of notation: "(0 + 1 * 2) / 3" is:\n' f'{" ".join(blender[::-1])}') self.assertAlmostEqual(signal_blend([3, math.e, 10, 10], blender), 3.0182818284590454) blender = blender_parser("0 / 1 * 2") print(f'RPN of notation: "0 / 1 * 2" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(signal_blend([1, 3, 6], blender), 2) blender = blender_parser("(0 - 1 + 2) * 4") print(f'RPN of notation: "(0 - 1 + 2) * 4" is:\n' f'{" ".join(blender[::-1])}') self.assertAlmostEqual(signal_blend([1, 1, -1, np.nan, math.pi], blender), -3.141592653589793) blender = blender_parser("0 * 1") print(f'RPN of notation: "0 * 1" is:\n' f'{" ".join(blender[::-1])}') self.assertAlmostEqual(signal_blend([math.pi, math.e], blender), 8.539734222673566) blender = blender_parser('abs(3-sqrt(2) / cos(1))') print(f'RPN of notation: "abs(3-sqrt(2) / cos(1))" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(blender, ['abs(1)', '-', '/', 'cos(1)', '1', 'sqrt(1)', '2', '3']) blender = blender_parser('0/max(2,1,3 + 5)+4') print(f'RPN of notation: "0/max(2,1,3 + 5)+4" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(blender, ['+', '4', '/', 'max(3)', '+', '5', '3', '1', '2', '0']) blender = blender_parser('1 + sum(1,2,3+3, sum(1, 2) + 3) *5') print(f'RPN of notation: "1 + sum(1,2,3+3, sum(1, 2) + 3) *5" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(blender, ['+', '*', '5', 'sum(4)', '+', '3', 'sum(2)', '2', '1', '+', '3', '3', '2', '1', '1']) blender = blender_parser('1+sum(1,2,(3+5)*4, sum(3, (4+5)*6), 7-8) * (2+3)') print(f'RPN of notation: "1+sum(1,2,(3+5)*4, sum(3, (4+5)*6), 7-8) * (2+3)" is:\n' f'{" ".join(blender[::-1])}') self.assertEqual(blender, ['+', '*', '+', '3', '2', 'sum(5)', '-', '8', '7', 'sum(2)', '*', '6', '+', '5', '4', '3', '*', '4', '+', '5', '3', '2', '1', '1']) # TODO: ndarray type of signals to be tested: def test_set_opt_par(self): """ test setting opt pars in batch""" print(f'--------- Testing setting Opt Pars: set_opt_par -------') op = qt.Operator('dma, random, crossline') op.set_parameter('dma', pars=(5, 10, 5), opt_tag=1, par_boes=((5, 10), (5, 15), (10, 15)), window_length=10, data_types=['close', 'open', 'high']) self.assertEqual(op.strategies[0].pars, (5, 10, 5)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (35, 120, 10, 'buy')) self.assertEqual(op.opt_tags, [1, 0, 0]) op.set_opt_par((5, 12, 9)) self.assertEqual(op.strategies[0].pars, (5, 12, 9)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (35, 120, 10, 'buy')) op.set_parameter('crossline', pars=(5, 10, 5, 'sell'), opt_tag=1, par_boes=((5, 10), (5, 15), (10, 15), ('buy', 'sell', 'none')), window_length=10, data_types=['close', 'open', 'high']) self.assertEqual(op.opt_tags, [1, 0, 1]) op.set_opt_par((5, 12, 9, 8, 26, 9, 'buy')) self.assertEqual(op.strategies[0].pars, (5, 12, 9)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (8, 26, 9, 'buy')) op.set_opt_par((9, 200, 155, 8, 26, 9, 'buy', 5, 12, 9)) self.assertEqual(op.strategies[0].pars, (9, 200, 155)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (8, 26, 9, 'buy')) # test set_opt_par when opt_tag is set to be 2 (enumerate type of parameters) op.set_parameter('crossline', pars=(5, 10, 5, 'sell'), opt_tag=2, par_boes=((5, 10), (5, 15), (10, 15), ('buy', 'sell', 'none')), window_length=10, data_types=['close', 'open', 'high']) self.assertEqual(op.opt_tags, [1, 0, 2]) self.assertEqual(op.strategies[0].pars, (9, 200, 155)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (5, 10, 5, 'sell')) op.set_opt_par((5, 12, 9, (8, 26, 9, 'buy'))) self.assertEqual(op.strategies[0].pars, (5, 12, 9)) self.assertEqual(op.strategies[1].pars, (0.5,)) self.assertEqual(op.strategies[2].pars, (8, 26, 9, 'buy')) # Test Errors # Not enough values for parameter op.set_parameter('crossline', opt_tag=1) self.assertRaises(ValueError, op.set_opt_par, (5, 12, 9, 8)) # wrong type of input self.assertRaises(AssertionError, op.set_opt_par, [5, 12, 9, 7, 15, 12, 'sell']) def test_stg_attribute_get_and_set(self): self.stg = qt.TimingCrossline() self.stg_type = 'R-TIMING' self.stg_name = "CROSSLINE" self.stg_text = 'Moving average crossline strategy, determine long/short position according to the cross ' \ 'point' \ ' of long and short term moving average prices ' self.pars = (35, 120, 10, 'buy') self.par_boes = [(10, 250), (10, 250), (1, 100), ('buy', 'sell', 'none')] self.par_count = 4 self.par_types = ['discr', 'discr', 'conti', 'enum'] self.opt_tag = 0 self.data_types = ['close'] self.data_freq = 'd' self.sample_freq = 'd' self.window_length = 270 self.assertEqual(self.stg.stg_type, self.stg_type) self.assertEqual(self.stg.stg_name, self.stg_name) self.assertEqual(self.stg.stg_text, self.stg_text) self.assertEqual(self.stg.pars, self.pars) self.assertEqual(self.stg.par_types, self.par_types) self.assertEqual(self.stg.par_boes, self.par_boes) self.assertEqual(self.stg.par_count, self.par_count) self.assertEqual(self.stg.opt_tag, self.opt_tag) self.assertEqual(self.stg.data_freq, self.data_freq) self.assertEqual(self.stg.sample_freq, self.sample_freq) self.assertEqual(self.stg.data_types, self.data_types) self.assertEqual(self.stg.window_length, self.window_length) self.stg.stg_name = 'NEW NAME' self.stg.stg_text = 'NEW TEXT' self.assertEqual(self.stg.stg_name, 'NEW NAME') self.assertEqual(self.stg.stg_text, 'NEW TEXT') self.stg.pars = (1, 2, 3, 4) self.assertEqual(self.stg.pars, (1, 2, 3, 4)) self.stg.par_count = 3 self.assertEqual(self.stg.par_count, 3) self.stg.par_boes = [(1, 10), (1, 10), (1, 10), (1, 10)] self.assertEqual(self.stg.par_boes, [(1, 10), (1, 10), (1, 10), (1, 10)]) self.stg.par_types = ['conti', 'conti', 'discr', 'enum'] self.assertEqual(self.stg.par_types, ['conti', 'conti', 'discr', 'enum']) self.stg.par_types = 'conti, conti, discr, conti' self.assertEqual(self.stg.par_types, ['conti', 'conti', 'discr', 'conti']) self.stg.data_types = 'close, open' self.assertEqual(self.stg.data_types, ['close', 'open']) self.stg.data_types = ['close', 'high', 'low'] self.assertEqual(self.stg.data_types, ['close', 'high', 'low']) self.stg.data_freq = 'w' self.assertEqual(self.stg.data_freq, 'w') self.stg.window_length = 300 self.assertEqual(self.stg.window_length, 300) def test_rolling_timing(self): stg = TestLSStrategy() stg_pars = {'000100': (5, 10), '000200': (5, 10), '000300': (5, 6)} stg.set_pars(stg_pars) history_data = self.hp1.values output = stg.generate(hist_data=history_data) self.assertIsInstance(output, np.ndarray) self.assertEqual(output.shape, (45, 3)) lsmask = np.array([[0., 0., 1.], [0., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 0., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 0.], [1., 1., 0.], [1., 1., 0.], [1., 0., 0.], [1., 0., 0.], [1., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 0.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.], [0., 1., 1.]]) # TODO: Issue to be solved: the np.nan value are converted to 0 in the lsmask,这样做可能会有意想不到的后果 # TODO: 需要解决nan值的问题 self.assertEqual(output.shape, lsmask.shape) self.assertTrue(np.allclose(output, lsmask, equal_nan=True)) def test_sel_timing(self): stg = TestSelStrategy() stg_pars = () stg.set_pars(stg_pars) history_data = self.hp1['high, low, close', :, :] seg_pos, seg_length, seg_count = stg._seg_periods(dates=self.hp1.hdates, freq=stg.sample_freq) self.assertEqual(list(seg_pos), [0, 5, 11, 19, 26, 33, 41, 47, 49]) self.assertEqual(list(seg_length), [5, 6, 8, 7, 7, 8, 6, 2]) self.assertEqual(seg_count, 8) output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) self.assertIsInstance(output, np.ndarray) self.assertEqual(output.shape, (45, 3)) selmask = np.array([[0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask)) def test_simple_timing(self): stg = TestSigStrategy() stg_pars = (0.2, 0.02, -0.02) stg.set_pars(stg_pars) history_data = self.hp1['close, open, high, low', :, 3:50] output = stg.generate(hist_data=history_data, shares=self.shares, dates=self.date_indices) self.assertIsInstance(output, np.ndarray) self.assertEqual(output.shape, (45, 3)) sigmatrix = np.array([[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [-1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) side_by_side_array = np.array([[i, out_line, sig_line] for i, out_line, sig_line in zip(range(len(output)), output, sigmatrix)]) print(f'output and signal matrix lined up side by side is \n' f'{side_by_side_array}') self.assertEqual(sigmatrix.shape, output.shape) self.assertTrue(np.allclose(output, sigmatrix)) def test_sel_finance(self): """Test selecting_finance strategy, test all built-in strategy parameters""" stg = SelectingFinanceIndicator() stg_pars = (False, 'even', 'greater', 0, 0, 0.67) stg.set_pars(stg_pars) stg.window_length = 5 stg.data_freq = 'd' stg.sample_freq = '10d' stg.sort_ascending = False stg.condition = 'greater' stg.lbound = 0 stg.ubound = 0 stg._poq = 0.67 history_data = self.hp2.values print(f'Start to test financial selection parameter {stg_pars}') seg_pos, seg_length, seg_count = stg._seg_periods(dates=self.hp1.hdates, freq=stg.sample_freq) self.assertEqual(list(seg_pos), [0, 5, 11, 19, 26, 33, 41, 47, 49]) self.assertEqual(list(seg_length), [5, 6, 8, 7, 7, 8, 6, 2]) self.assertEqual(seg_count, 8) output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) self.assertIsInstance(output, np.ndarray) self.assertEqual(output.shape, (45, 3)) selmask = np.array([[0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask)) # test single factor, get mininum factor stg_pars = (True, 'even', 'less', 1, 1, 0.67) stg.sort_ascending = True stg.condition = 'less' stg.lbound = 1 stg.ubound = 1 stg.set_pars(stg_pars) print(f'Start to test financial selection parameter {stg_pars}') output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) selmask = np.array([[0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5], [0.5, 0.0, 0.5]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask)) # test single factor, get max factor in linear weight stg_pars = (False, 'linear', 'greater', 0, 0, 0.67) stg.sort_ascending = False stg.weighting = 'linear' stg.condition = 'greater' stg.lbound = 0 stg.ubound = 0 stg.set_pars(stg_pars) print(f'Start to test financial selection parameter {stg_pars}') output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) selmask = np.array([[0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.66667, 0.33333], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.00000, 0.33333, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.00000, 0.66667], [0.33333, 0.66667, 0.00000], [0.33333, 0.66667, 0.00000], [0.33333, 0.66667, 0.00000]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask)) # test single factor, get max factor in linear weight stg_pars = (False, 'proportion', 'greater', 0, 0, 0.67) stg.sort_ascending = False stg.weighting = 'proportion' stg.condition = 'greater' stg.lbound = 0 stg.ubound = 0 stg.set_pars(stg_pars) print(f'Start to test financial selection parameter {stg_pars}') output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) selmask = np.array([[0.00000, 0.08333, 0.91667], [0.00000, 0.08333, 0.91667], [0.00000, 0.08333, 0.91667], [0.00000, 0.08333, 0.91667], [0.00000, 0.08333, 0.91667], [0.00000, 0.08333, 0.91667], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.91667, 0.08333], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.00000, 0.50000, 0.50000], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.00000, 0.00000, 1.00000], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.00000, 0.91667], [0.08333, 0.91667, 0.00000], [0.08333, 0.91667, 0.00000], [0.08333, 0.91667, 0.00000]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask, 0.001)) # test single factor, get max factor in linear weight, threshold 0.2 stg_pars = (False, 'even', 'greater', 0.2, 0.2, 0.67) stg.sort_ascending = False stg.weighting = 'even' stg.condition = 'greater' stg.lbound = 0.2 stg.ubound = 0.2 stg.set_pars(stg_pars) print(f'Start to test financial selection parameter {stg_pars}') output = stg.generate(hist_data=history_data, shares=self.hp1.shares, dates=self.hp1.hdates) selmask = np.array([[0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0], [0.5, 0.5, 0.0]]) self.assertEqual(output.shape, selmask.shape) self.assertTrue(np.allclose(output, selmask, 0.001)) def test_tokenizer(self): self.assertListEqual(_exp_to_token('1+1'), ['1', '+', '1']) print(_exp_to_token('1+1')) self.assertListEqual(_exp_to_token('1 & 1'), ['1', '&', '1']) print(_exp_to_token('1&1')) self.assertListEqual(_exp_to_token('1 and 1'), ['1', 'and', '1']) print(_exp_to_token('1 and 1')) self.assertListEqual(_exp_to_token('1 or 1'), ['1', 'or', '1']) print(_exp_to_token('1 or 1')) self.assertListEqual(_exp_to_token('(1 - 1 + -1) * pi'), ['(', '1', '-', '1', '+', '-1', ')', '*', 'pi']) print(_exp_to_token('(1 - 1 + -1) * pi')) self.assertListEqual(_exp_to_token('abs(5-sqrt(2) / cos(pi))'), ['abs(', '5', '-', 'sqrt(', '2', ')', '/', 'cos(', 'pi', ')', ')']) print(_exp_to_token('abs(5-sqrt(2) / cos(pi))')) self.assertListEqual(_exp_to_token('sin(pi) + 2.14'), ['sin(', 'pi', ')', '+', '2.14']) print(_exp_to_token('sin(pi) + 2.14')) self.assertListEqual(_exp_to_token('(1-2)/3.0 + 0.0000'), ['(', '1', '-', '2', ')', '/', '3.0', '+', '0.0000']) print(_exp_to_token('(1-2)/3.0 + 0.0000')) self.assertListEqual(_exp_to_token('-(1. + .2) * max(1, 3, 5)'), ['-', '(', '1.', '+', '.2', ')', '*', 'max(', '1', ',', '3', ',', '5', ')']) print(_exp_to_token('-(1. + .2) * max(1, 3, 5)')) self.assertListEqual(_exp_to_token('(x + e * 10) / 10'), ['(', 'x', '+', 'e', '*', '10', ')', '/', '10']) print(_exp_to_token('(x + e * 10) / 10')) self.assertListEqual(_exp_to_token('8.2/((-.1+abs3(3,4,5))*0.12)'), ['8.2', '/', '(', '(', '-.1', '+', 'abs3(', '3', ',', '4', ',', '5', ')', ')', '*', '0.12', ')']) print(_exp_to_token('8.2/((-.1+abs3(3,4,5))*0.12)')) self.assertListEqual(_exp_to_token('8.2/abs3(3,4,25.34 + 5)*0.12'), ['8.2', '/', 'abs3(', '3', ',', '4', ',', '25.34', '+', '5', ')', '*', '0.12']) print(_exp_to_token('8.2/abs3(3,4,25.34 + 5)*0.12')) class TestLog(unittest.TestCase): def test_init(self): pass class TestConfig(unittest.TestCase): """测试Config对象以及QT_CONFIG变量的设置和获取值""" def test_init(self): pass def test_invest(self): pass def test_pars_string_to_type(self): _parse_string_kwargs('000300', 'asset_pool', _valid_qt_kwargs()) class TestHistoryPanel(unittest.TestCase): def setUp(self): print('start testing HistoryPanel object\n') self.data = np.random.randint(10, size=(5, 10, 4)) self.index = pd.date_range(start='20200101', freq='d', periods=10) self.index2 = ['2016-07-01', '2016-07-04', '2016-07-05', '2016-07-06', '2016-07-07', '2016-07-08', '2016-07-11', '2016-07-12', '2016-07-13', '2016-07-14'] self.index3 = '2016-07-01, 2016-07-04, 2016-07-05, 2016-07-06, 2016-07-07, ' \ '2016-07-08, 2016-07-11, 2016-07-12, 2016-07-13, 2016-07-14' self.shares = '000100,000101,000102,000103,000104' self.htypes = 'close,open,high,low' self.data2 = np.random.randint(10, size=(10, 5)) self.data3 = np.random.randint(10, size=(10, 4)) self.data4 = np.random.randint(10, size=(10)) self.hp = qt.HistoryPanel(values=self.data, levels=self.shares, columns=self.htypes, rows=self.index) self.hp2 = qt.HistoryPanel(values=self.data2, levels=self.shares, columns='close', rows=self.index) self.hp3 = qt.HistoryPanel(values=self.data3, levels='000100', columns=self.htypes, rows=self.index2) self.hp4 = qt.HistoryPanel(values=self.data4, levels='000100', columns='close', rows=self.index3) self.hp5 = qt.HistoryPanel(values=self.data) self.hp6 = qt.HistoryPanel(values=self.data, levels=self.shares, rows=self.index3) def test_properties(self): """ test all properties of HistoryPanel """ self.assertFalse(self.hp.is_empty) self.assertEqual(self.hp.row_count, 10) self.assertEqual(self.hp.column_count, 4) self.assertEqual(self.hp.level_count, 5) self.assertEqual(self.hp.shape, (5, 10, 4)) self.assertSequenceEqual(self.hp.htypes, ['close', 'open', 'high', 'low']) self.assertSequenceEqual(self.hp.shares, ['000100', '000101', '000102', '000103', '000104']) self.assertSequenceEqual(list(self.hp.hdates), list(self.index)) self.assertDictEqual(self.hp.columns, {'close': 0, 'open': 1, 'high': 2, 'low': 3}) self.assertDictEqual(self.hp.levels, {'000100': 0, '000101': 1, '000102': 2, '000103': 3, '000104': 4}) row_dict = {Timestamp('2020-01-01 00:00:00', freq='D'): 0, Timestamp('2020-01-02 00:00:00', freq='D'): 1, Timestamp('2020-01-03 00:00:00', freq='D'): 2, Timestamp('2020-01-04 00:00:00', freq='D'): 3, Timestamp('2020-01-05 00:00:00', freq='D'): 4, Timestamp('2020-01-06 00:00:00', freq='D'): 5, Timestamp('2020-01-07 00:00:00', freq='D'): 6, Timestamp('2020-01-08 00:00:00', freq='D'): 7, Timestamp('2020-01-09 00:00:00', freq='D'): 8, Timestamp('2020-01-10 00:00:00', freq='D'): 9} self.assertDictEqual(self.hp.rows, row_dict) def test_len(self): """ test the function len(HistoryPanel) :return: """ self.assertEqual(len(self.hp), 10) def test_empty_history_panel(self): """测试空HP或者特殊HP如维度标签为纯数字的HP""" test_hp = qt.HistoryPanel(self.data) self.assertFalse(test_hp.is_empty) self.assertIsInstance(test_hp, qt.HistoryPanel) self.assertEqual(test_hp.shape[0], 5) self.assertEqual(test_hp.shape[1], 10) self.assertEqual(test_hp.shape[2], 4) self.assertEqual(test_hp.level_count, 5) self.assertEqual(test_hp.row_count, 10) self.assertEqual(test_hp.column_count, 4) self.assertEqual(test_hp.shares, list(range(5))) self.assertEqual(test_hp.hdates, list(pd.date_range(start='20200730', periods=10, freq='d'))) self.assertEqual(test_hp.htypes, list(range(4))) self.assertTrue(np.allclose(test_hp.values, self.data)) print(f'shares: {test_hp.shares}\nhtypes: {test_hp.htypes}') print(test_hp) # HistoryPanel should be empty if no value is given empty_hp = qt.HistoryPanel() self.assertTrue(empty_hp.is_empty) self.assertIsInstance(empty_hp, qt.HistoryPanel) self.assertEqual(empty_hp.shape[0], 0) self.assertEqual(empty_hp.shape[1], 0) self.assertEqual(empty_hp.shape[2], 0) self.assertEqual(empty_hp.level_count, 0) self.assertEqual(empty_hp.row_count, 0) self.assertEqual(empty_hp.column_count, 0) # HistoryPanel should also be empty if empty value (np.array([])) is given empty_hp = qt.HistoryPanel(np.empty((5, 0, 4)), levels=self.shares, columns=self.htypes) self.assertTrue(empty_hp.is_empty) self.assertIsInstance(empty_hp, qt.HistoryPanel) self.assertEqual(empty_hp.shape[0], 0) self.assertEqual(empty_hp.shape[1], 0) self.assertEqual(empty_hp.shape[2], 0) self.assertEqual(empty_hp.level_count, 0) self.assertEqual(empty_hp.row_count, 0) self.assertEqual(empty_hp.column_count, 0) def test_create_history_panel(self): """ test the creation of a HistoryPanel object by passing all data explicitly """ self.assertIsInstance(self.hp, qt.HistoryPanel) self.assertEqual(self.hp.shape[0], 5) self.assertEqual(self.hp.shape[1], 10) self.assertEqual(self.hp.shape[2], 4) self.assertEqual(self.hp.level_count, 5) self.assertEqual(self.hp.row_count, 10) self.assertEqual(self.hp.column_count, 4) self.assertEqual(list(self.hp.levels.keys()), self.shares.split(',')) self.assertEqual(list(self.hp.columns.keys()), self.htypes.split(',')) self.assertEqual(list(self.hp.rows.keys())[0], pd.Timestamp('20200101')) self.assertIsInstance(self.hp2, qt.HistoryPanel) self.assertEqual(self.hp2.shape[0], 5) self.assertEqual(self.hp2.shape[1], 10) self.assertEqual(self.hp2.shape[2], 1) self.assertEqual(self.hp2.level_count, 5) self.assertEqual(self.hp2.row_count, 10) self.assertEqual(self.hp2.column_count, 1) self.assertEqual(list(self.hp2.levels.keys()), self.shares.split(',')) self.assertEqual(list(self.hp2.columns.keys()), ['close']) self.assertEqual(list(self.hp2.rows.keys())[0], pd.Timestamp('20200101')) self.assertIsInstance(self.hp3, qt.HistoryPanel) self.assertEqual(self.hp3.shape[0], 1) self.assertEqual(self.hp3.shape[1], 10) self.assertEqual(self.hp3.shape[2], 4) self.assertEqual(self.hp3.level_count, 1) self.assertEqual(self.hp3.row_count, 10) self.assertEqual(self.hp3.column_count, 4) self.assertEqual(list(self.hp3.levels.keys()), ['000100']) self.assertEqual(list(self.hp3.columns.keys()), self.htypes.split(',')) self.assertEqual(list(self.hp3.rows.keys())[0], pd.Timestamp('2016-07-01')) self.assertIsInstance(self.hp4, qt.HistoryPanel) self.assertEqual(self.hp4.shape[0], 1) self.assertEqual(self.hp4.shape[1], 10) self.assertEqual(self.hp4.shape[2], 1) self.assertEqual(self.hp4.level_count, 1) self.assertEqual(self.hp4.row_count, 10) self.assertEqual(self.hp4.column_count, 1) self.assertEqual(list(self.hp4.levels.keys()), ['000100']) self.assertEqual(list(self.hp4.columns.keys()), ['close']) self.assertEqual(list(self.hp4.rows.keys())[0], pd.Timestamp('2016-07-01')) self.hp5.info() self.assertIsInstance(self.hp5, qt.HistoryPanel) self.assertTrue(np.allclose(self.hp5.values, self.data)) self.assertEqual(self.hp5.shape[0], 5) self.assertEqual(self.hp5.shape[1], 10) self.assertEqual(self.hp5.shape[2], 4) self.assertEqual(self.hp5.level_count, 5) self.assertEqual(self.hp5.row_count, 10) self.assertEqual(self.hp5.column_count, 4) self.assertEqual(list(self.hp5.levels.keys()), [0, 1, 2, 3, 4]) self.assertEqual(list(self.hp5.columns.keys()), [0, 1, 2, 3]) self.assertEqual(list(self.hp5.rows.keys())[0], pd.Timestamp('2020-07-30')) self.hp6.info() self.assertIsInstance(self.hp6, qt.HistoryPanel) self.assertTrue(np.allclose(self.hp6.values, self.data)) self.assertEqual(self.hp6.shape[0], 5) self.assertEqual(self.hp6.shape[1], 10) self.assertEqual(self.hp6.shape[2], 4) self.assertEqual(self.hp6.level_count, 5) self.assertEqual(self.hp6.row_count, 10) self.assertEqual(self.hp6.column_count, 4) self.assertEqual(list(self.hp6.levels.keys()), ['000100', '000101', '000102', '000103', '000104']) self.assertEqual(list(self.hp6.columns.keys()), [0, 1, 2, 3]) self.assertEqual(list(self.hp6.rows.keys())[0], pd.Timestamp('2016-07-01')) print('test creating HistoryPanel with very limited data') print('test creating HistoryPanel with 2D data') temp_data = np.random.randint(10, size=(7, 3)).astype('float') temp_hp = qt.HistoryPanel(temp_data) # Error testing during HistoryPanel creating # shape does not match self.assertRaises(AssertionError, qt.HistoryPanel, self.data, levels=self.shares, columns='close', rows=self.index) # valus is not np.ndarray self.assertRaises(TypeError, qt.HistoryPanel, list(self.data)) # dimension/shape does not match self.assertRaises(AssertionError, qt.HistoryPanel, self.data2, levels='000100', columns=self.htypes, rows=self.index) # value dimension over 3 self.assertRaises(AssertionError, qt.HistoryPanel, np.random.randint(10, size=(5, 10, 4, 2))) # lebel value not valid self.assertRaises(ValueError, qt.HistoryPanel, self.data2, levels=self.shares, columns='close', rows='a,b,c,d,e,f,g,h,i,j') def test_history_panel_slicing(self): """测试HistoryPanel的各种切片方法 包括通过标签名称切片,通过数字切片,通过逗号分隔的标签名称切片,通过冒号分隔的标签名称切片等切片方式""" self.assertTrue(np.allclose(self.hp['close'], self.data[:, :, 0:1])) self.assertTrue(np.allclose(self.hp['close,open'], self.data[:, :, 0:2])) self.assertTrue(np.allclose(self.hp[['close', 'open']], self.data[:, :, 0:2])) self.assertTrue(np.allclose(self.hp['close:high'], self.data[:, :, 0:3])) self.assertTrue(np.allclose(self.hp['close,high'], self.data[:, :, [0, 2]])) self.assertTrue(np.allclose(self.hp[:, '000100'], self.data[0:1, :, ])) self.assertTrue(np.allclose(self.hp[:, '000100,000101'], self.data[0:2, :])) self.assertTrue(np.allclose(self.hp[:, ['000100', '000101']], self.data[0:2, :])) self.assertTrue(np.allclose(self.hp[:, '000100:000102'], self.data[0:3, :])) self.assertTrue(np.allclose(self.hp[:, '000100,000102'], self.data[[0, 2], :])) self.assertTrue(np.allclose(self.hp['close,open', '000100,000102'], self.data[[0, 2], :, 0:2])) print('start testing HistoryPanel') data = np.random.randint(10, size=(10, 5)) # index = pd.date_range(start='20200101', freq='d', periods=10) shares = '000100,000101,000102,000103,000104' dtypes = 'close' df = pd.DataFrame(data) print('=========================\nTesting HistoryPanel creation from DataFrame') hp = qt.dataframe_to_hp(df=df, shares=shares, htypes=dtypes) hp.info() hp = qt.dataframe_to_hp(df=df, shares='000100', htypes='close, open, high, low, middle', column_type='htypes') hp.info() print('=========================\nTesting HistoryPanel creation from initialization') data = np.random.randint(10, size=(5, 10, 4)).astype('float') index = pd.date_range(start='20200101', freq='d', periods=10) dtypes = 'close, open, high,low' data[0, [5, 6, 9], [0, 1, 3]] = np.nan data[1:4, [4, 7, 6, 2], [1, 1, 3, 0]] = np.nan data[4:5, [2, 9, 1, 2], [0, 3, 2, 1]] = np.nan hp = qt.HistoryPanel(data, levels=shares, columns=dtypes, rows=index) hp.info() print('==========================\n输出close类型的所有历史数据\n') self.assertTrue(np.allclose(hp['close', :, :], data[:, :, 0:1], equal_nan=True)) print(f'==========================\n输出close和open类型的所有历史数据\n') self.assertTrue(np.allclose(hp[[0, 1], :, :], data[:, :, 0:2], equal_nan=True)) print(f'==========================\n输出第一只股票的所有类型历史数据\n') self.assertTrue(np.allclose(hp[:, [0], :], data[0:1, :, :], equal_nan=True)) print('==========================\n输出第0、1、2个htype对应的所有股票全部历史数据\n') self.assertTrue(np.allclose(hp[[0, 1, 2]], data[:, :, 0:3], equal_nan=True)) print('==========================\n输出close、high两个类型的所有历史数据\n') self.assertTrue(np.allclose(hp[['close', 'high']], data[:, :, [0, 2]], equal_nan=True)) print('==========================\n输出0、1两个htype的所有历史数据\n') self.assertTrue(np.allclose(hp[[0, 1]], data[:, :, 0:2], equal_nan=True)) print('==========================\n输出close、high两个类型的所有历史数据\n') self.assertTrue(np.allclose(hp['close,high'], data[:, :, [0, 2]], equal_nan=True)) print('==========================\n输出close起到high止的三个类型的所有历史数据\n') self.assertTrue(np.allclose(hp['close:high'], data[:, :, 0:3], equal_nan=True)) print('==========================\n输出0、1、3三个股票的全部历史数据\n') self.assertTrue(np.allclose(hp[:, [0, 1, 3]], data[[0, 1, 3], :, :], equal_nan=True)) print('==========================\n输出000100、000102两只股票的所有历史数据\n') self.assertTrue(np.allclose(hp[:, ['000100', '000102']], data[[0, 2], :, :], equal_nan=True)) print('==========================\n输出0、1、2三个股票的历史数据\n', hp[:, 0: 3]) self.assertTrue(np.allclose(hp[:, 0: 3], data[0:3, :, :], equal_nan=True)) print('==========================\n输出000100、000102两只股票的所有历史数据\n') self.assertTrue(np.allclose(hp[:, '000100, 000102'], data[[0, 2], :, :], equal_nan=True)) print('==========================\n输出所有股票的0-7日历史数据\n') self.assertTrue(np.allclose(hp[:, :, 0:8], data[:, 0:8, :], equal_nan=True)) print('==========================\n输出000100股票的0-7日历史数据\n') self.assertTrue(np.allclose(hp[:, '000100', 0:8], data[0, 0:8, :], equal_nan=True)) print('==========================\nstart testing multy axis slicing of HistoryPanel object') print('==========================\n输出000100、000120两只股票的close、open两组历史数据\n', hp['close,open', ['000100', '000102']]) print('==========================\n输出000100、000120两只股票的close到open三组历史数据\n', hp['close,open', '000100, 000102']) print(f'historyPanel: hp:\n{hp}') print(f'data is:\n{data}') hp.htypes = 'open,high,low,close' hp.info() hp.shares = ['000300', '600227', '600222', '000123', '000129'] hp.info() def test_segment(self): """测试历史数据片段的获取""" test_hp = qt.HistoryPanel(self.data, levels=self.shares, columns=self.htypes, rows=self.index2) self.assertFalse(test_hp.is_empty) self.assertIsInstance(test_hp, qt.HistoryPanel) self.assertEqual(test_hp.shape[0], 5) self.assertEqual(test_hp.shape[1], 10) self.assertEqual(test_hp.shape[2], 4) print(f'Test segment with None parameters') seg1 = test_hp.segment() seg2 = test_hp.segment('20150202') seg3 = test_hp.segment(end_date='20201010') self.assertIsInstance(seg1, qt.HistoryPanel) self.assertIsInstance(seg2, qt.HistoryPanel) self.assertIsInstance(seg3, qt.HistoryPanel) # check values self.assertTrue(np.allclose( seg1.values, test_hp.values )) self.assertTrue(np.allclose( seg2.values, test_hp.values )) self.assertTrue(np.allclose( seg3.values, test_hp.values )) # check that htypes and shares should be same self.assertEqual(seg1.htypes, test_hp.htypes) self.assertEqual(seg1.shares, test_hp.shares) self.assertEqual(seg2.htypes, test_hp.htypes) self.assertEqual(seg2.shares, test_hp.shares) self.assertEqual(seg3.htypes, test_hp.htypes) self.assertEqual(seg3.shares, test_hp.shares) # check that hdates are the same self.assertEqual(seg1.hdates, test_hp.hdates) self.assertEqual(seg2.hdates, test_hp.hdates) self.assertEqual(seg3.hdates, test_hp.hdates) print(f'Test segment with proper dates') seg1 = test_hp.segment() seg2 = test_hp.segment('20160704') seg3 = test_hp.segment(start_date='2016-07-05', end_date='20160708') self.assertIsInstance(seg1, qt.HistoryPanel) self.assertIsInstance(seg2, qt.HistoryPanel) self.assertIsInstance(seg3, qt.HistoryPanel) # check values self.assertTrue(np.allclose( seg1.values, test_hp[:, :, :] )) self.assertTrue(np.allclose( seg2.values, test_hp[:, :, 1:10] )) self.assertTrue(np.allclose( seg3.values, test_hp[:, :, 2:6] )) # check that htypes and shares should be same self.assertEqual(seg1.htypes, test_hp.htypes) self.assertEqual(seg1.shares, test_hp.shares) self.assertEqual(seg2.htypes, test_hp.htypes) self.assertEqual(seg2.shares, test_hp.shares) self.assertEqual(seg3.htypes, test_hp.htypes) self.assertEqual(seg3.shares, test_hp.shares) # check that hdates are the same self.assertEqual(seg1.hdates, test_hp.hdates) self.assertEqual(seg2.hdates, test_hp.hdates[1:10]) self.assertEqual(seg3.hdates, test_hp.hdates[2:6]) print(f'Test segment with non-existing but in range dates') seg1 = test_hp.segment() seg2 = test_hp.segment('20160703') seg3 = test_hp.segment(start_date='2016-07-03', end_date='20160710') self.assertIsInstance(seg1, qt.HistoryPanel) self.assertIsInstance(seg2, qt.HistoryPanel) self.assertIsInstance(seg3, qt.HistoryPanel) # check values self.assertTrue(np.allclose( seg1.values, test_hp[:, :, :] )) self.assertTrue(np.allclose( seg2.values, test_hp[:, :, 1:10] )) self.assertTrue(np.allclose( seg3.values, test_hp[:, :, 1:6] )) # check that htypes and shares should be same self.assertEqual(seg1.htypes, test_hp.htypes) self.assertEqual(seg1.shares, test_hp.shares) self.assertEqual(seg2.htypes, test_hp.htypes) self.assertEqual(seg2.shares, test_hp.shares) self.assertEqual(seg3.htypes, test_hp.htypes) self.assertEqual(seg3.shares, test_hp.shares) # check that hdates are the same self.assertEqual(seg1.hdates, test_hp.hdates) self.assertEqual(seg2.hdates, test_hp.hdates[1:10]) self.assertEqual(seg3.hdates, test_hp.hdates[1:6]) print(f'Test segment with out-of-range dates') seg1 = test_hp.segment(start_date='2016-05-03', end_date='20160910') self.assertIsInstance(seg1, qt.HistoryPanel) # check values self.assertTrue(np.allclose( seg1.values, test_hp[:, :, :] )) # check that htypes and shares should be same self.assertEqual(seg1.htypes, test_hp.htypes) self.assertEqual(seg1.shares, test_hp.shares) # check that hdates are the same self.assertEqual(seg1.hdates, test_hp.hdates) def test_slice(self): """测试历史数据切片的获取""" test_hp = qt.HistoryPanel(self.data, levels=self.shares, columns=self.htypes, rows=self.index2) self.assertFalse(test_hp.is_empty) self.assertIsInstance(test_hp, qt.HistoryPanel) self.assertEqual(test_hp.shape[0], 5) self.assertEqual(test_hp.shape[1], 10) self.assertEqual(test_hp.shape[2], 4) print(f'Test slice with shares') share = '000101' slc = test_hp.slice(shares=share) self.assertIsInstance(slc, qt.HistoryPanel) self.assertEqual(slc.shares, ['000101']) self.assertEqual(slc.htypes, test_hp.htypes) self.assertEqual(slc.hdates, test_hp.hdates) self.assertTrue(np.allclose(slc.values, test_hp[:, '000101'])) share = '000101, 000103' slc = test_hp.slice(shares=share) self.assertIsInstance(slc, qt.HistoryPanel) self.assertEqual(slc.shares, ['000101', '000103']) self.assertEqual(slc.htypes, test_hp.htypes) self.assertEqual(slc.hdates, test_hp.hdates) self.assertTrue(np.allclose(slc.values, test_hp[:, '000101, 000103'])) print(f'Test slice with htypes') htype = 'open' slc = test_hp.slice(htypes=htype) self.assertIsInstance(slc, qt.HistoryPanel) self.assertEqual(slc.shares, test_hp.shares) self.assertEqual(slc.htypes, ['open']) self.assertEqual(slc.hdates, test_hp.hdates) self.assertTrue(np.allclose(slc.values, test_hp['open'])) htype = 'open, close' slc = test_hp.slice(htypes=htype) self.assertIsInstance(slc, qt.HistoryPanel) self.assertEqual(slc.shares, test_hp.shares) self.assertEqual(slc.htypes, ['open', 'close']) self.assertEqual(slc.hdates, test_hp.hdates) self.assertTrue(np.allclose(slc.values, test_hp['open, close'])) # test that slicing of "open, close" does NOT equal to "close, open" self.assertFalse(np.allclose(slc.values, test_hp['close, open'])) print(f'Test slicing with both htypes and shares') share = '000103, 000101' htype = 'high, low, close' slc = test_hp.slice(shares=share, htypes=htype) self.assertIsInstance(slc, qt.HistoryPanel) self.assertEqual(slc.shares, ['000103', '000101']) self.assertEqual(slc.htypes, ['high', 'low', 'close']) self.assertEqual(slc.hdates, test_hp.hdates) self.assertTrue(np.allclose(slc.values, test_hp['high, low, close', '000103, 000101'])) print(f'Test Error cases') # duplicated input htype = 'open, close, open' self.assertRaises(AssertionError, test_hp.slice, htypes=htype) def test_relabel(self): new_shares_list = ['000001', '000002', '000003', '000004', '000005'] new_shares_str = '000001, 000002, 000003, 000004, 000005' new_htypes_list = ['close', 'volume', 'value', 'exchange'] new_htypes_str = 'close, volume, value, exchange' temp_hp = self.hp.copy() temp_hp.re_label(shares=new_shares_list) print(temp_hp.info()) print(temp_hp.htypes) self.assertTrue(np.allclose(self.hp.values, temp_hp.values)) self.assertEqual(self.hp.htypes, temp_hp.htypes) self.assertEqual(self.hp.hdates, temp_hp.hdates) self.assertEqual(temp_hp.shares, new_shares_list) temp_hp = self.hp.copy() temp_hp.re_label(shares=new_shares_str) self.assertTrue(np.allclose(self.hp.values, temp_hp.values)) self.assertEqual(self.hp.htypes, temp_hp.htypes) self.assertEqual(self.hp.hdates, temp_hp.hdates) self.assertEqual(temp_hp.shares, new_shares_list) temp_hp = self.hp.copy() temp_hp.re_label(htypes=new_htypes_list) self.assertTrue(np.allclose(self.hp.values, temp_hp.values)) self.assertEqual(self.hp.shares, temp_hp.shares) self.assertEqual(self.hp.hdates, temp_hp.hdates) self.assertEqual(temp_hp.htypes, new_htypes_list) temp_hp = self.hp.copy() temp_hp.re_label(htypes=new_htypes_str) self.assertTrue(np.allclose(self.hp.values, temp_hp.values)) self.assertEqual(self.hp.shares, temp_hp.shares) self.assertEqual(self.hp.hdates, temp_hp.hdates) self.assertEqual(temp_hp.htypes, new_htypes_list) print(f'test errors raising') temp_hp = self.hp.copy() self.assertRaises(AssertionError, temp_hp.re_label, htypes=new_shares_str) self.assertRaises(TypeError, temp_hp.re_label, htypes=123) self.assertRaises(AssertionError, temp_hp.re_label, htypes='wrong input!') def test_csv_to_hp(self): pass def test_hdf_to_hp(self): pass def test_hp_join(self): # TODO: 这里需要加强,需要用具体的例子确认hp_join的结果正确 # TODO: 尤其是不同的shares、htypes、hdates,以及它们在顺 # TODO: 序不同的情况下是否能正确地组合 print(f'join two simple HistoryPanels with same shares') temp_hp = self.hp.join(self.hp2, same_shares=True) self.assertIsInstance(temp_hp, qt.HistoryPanel) def test_df_to_hp(self): print(f'test converting DataFrame to HistoryPanel') data = np.random.randint(10, size=(10, 5)) df1 = pd.DataFrame(data) df2 = pd.DataFrame(data, columns=str_to_list(self.shares)) df3 = pd.DataFrame(data[:, 0:4]) df4 = pd.DataFrame(data[:, 0:4], columns=str_to_list(self.htypes)) hp = qt.dataframe_to_hp(df1, htypes='close') self.assertIsInstance(hp, qt.HistoryPanel) self.assertEqual(hp.shares, [0, 1, 2, 3, 4]) self.assertEqual(hp.htypes, ['close']) self.assertEqual(hp.hdates, [pd.Timestamp('1970-01-01 00:00:00'), pd.Timestamp('1970-01-01 00:00:00.000000001'), pd.Timestamp('1970-01-01 00:00:00.000000002'), pd.Timestamp('1970-01-01 00:00:00.000000003'), pd.Timestamp('1970-01-01 00:00:00.000000004'), pd.Timestamp('1970-01-01 00:00:00.000000005'), pd.Timestamp('1970-01-01 00:00:00.000000006'), pd.Timestamp('1970-01-01 00:00:00.000000007'), pd.Timestamp('1970-01-01 00:00:00.000000008'), pd.Timestamp('1970-01-01 00:00:00.000000009')]) hp = qt.dataframe_to_hp(df2, shares=self.shares, htypes='close') self.assertIsInstance(hp, qt.HistoryPanel) self.assertEqual(hp.shares, str_to_list(self.shares)) self.assertEqual(hp.htypes, ['close']) hp = qt.dataframe_to_hp(df3, shares='000100', column_type='htypes') self.assertIsInstance(hp, qt.HistoryPanel) self.assertEqual(hp.shares, ['000100']) self.assertEqual(hp.htypes, [0, 1, 2, 3]) hp = qt.dataframe_to_hp(df4, shares='000100', htypes=self.htypes, column_type='htypes') self.assertIsInstance(hp, qt.HistoryPanel) self.assertEqual(hp.shares, ['000100']) self.assertEqual(hp.htypes, str_to_list(self.htypes)) hp.info() self.assertRaises(KeyError, qt.dataframe_to_hp, df1) def test_to_dataframe(self): """ 测试HistoryPanel对象的to_dataframe方法 """ print(f'START TEST == test_to_dataframe') print(f'test converting test hp to dataframe with share == "000102":') df_test = self.hp.to_dataframe(share='000102') self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates), list(df_test.index)) self.assertEqual(list(self.hp.htypes), list(df_test.columns)) values = df_test.values self.assertTrue(np.allclose(self.hp[:, '000102'], values)) print(f'test DataFrame conversion with share == "000100"') df_test = self.hp.to_dataframe(share='000100') self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates), list(df_test.index)) self.assertEqual(list(self.hp.htypes), list(df_test.columns)) values = df_test.values self.assertTrue(np.allclose(self.hp[:, '000100'], values)) print(f'test DataFrame conversion error: type incorrect') self.assertRaises(AssertionError, self.hp.to_dataframe, share=3.0) print(f'test DataFrame error raising with share not found error') self.assertRaises(KeyError, self.hp.to_dataframe, share='000300') print(f'test DataFrame conversion with htype == "close"') df_test = self.hp.to_dataframe(htype='close') self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates), list(df_test.index)) self.assertEqual(list(self.hp.shares), list(df_test.columns)) values = df_test.values self.assertTrue(np.allclose(self.hp['close'].T, values)) print(f'test DataFrame conversion with htype == "high"') df_test = self.hp.to_dataframe(htype='high') self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates), list(df_test.index)) self.assertEqual(list(self.hp.shares), list(df_test.columns)) values = df_test.values self.assertTrue(np.allclose(self.hp['high'].T, values)) print(f'test DataFrame conversion with htype == "high" and dropna') v = self.hp.values.astype('float') v[:, 3, :] = np.nan v[:, 4, :] = np.inf test_hp = qt.HistoryPanel(v, levels=self.shares, columns=self.htypes, rows=self.index) df_test = test_hp.to_dataframe(htype='high', dropna=True) self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates[:3]) + list(self.hp.hdates[4:]), list(df_test.index)) self.assertEqual(list(self.hp.shares), list(df_test.columns)) values = df_test.values target_values = test_hp['high'].T target_values = target_values[np.where(~np.isnan(target_values))].reshape(9, 5) self.assertTrue(np.allclose(target_values, values)) print(f'test DataFrame conversion with htype == "high", dropna and treat infs as na') v = self.hp.values.astype('float') v[:, 3, :] = np.nan v[:, 4, :] = np.inf test_hp = qt.HistoryPanel(v, levels=self.shares, columns=self.htypes, rows=self.index) df_test = test_hp.to_dataframe(htype='high', dropna=True, inf_as_na=True) self.assertIsInstance(df_test, pd.DataFrame) self.assertEqual(list(self.hp.hdates[:3]) + list(self.hp.hdates[5:]), list(df_test.index)) self.assertEqual(list(self.hp.shares), list(df_test.columns)) values = df_test.values target_values = test_hp['high'].T target_values = target_values[np.where(~np.isnan(target_values) & ~np.isinf(target_values))].reshape(8, 5) self.assertTrue(np.allclose(target_values, values)) print(f'test DataFrame conversion error: type incorrect') self.assertRaises(AssertionError, self.hp.to_dataframe, htype=pd.DataFrame()) print(f'test DataFrame error raising with share not found error') self.assertRaises(KeyError, self.hp.to_dataframe, htype='non_type') print(f'Raises ValueError when both or none parameter is given') self.assertRaises(KeyError, self.hp.to_dataframe) self.assertRaises(KeyError, self.hp.to_dataframe, share='000100', htype='close') def test_to_df_dict(self): """测试HistoryPanel公有方法to_df_dict""" print('test convert history panel slice by share') df_dict = self.hp.to_df_dict('share') self.assertEqual(self.hp.shares, list(df_dict.keys())) df_dict = self.hp.to_df_dict() self.assertEqual(self.hp.shares, list(df_dict.keys())) print('test convert historypanel slice by htype ') df_dict = self.hp.to_df_dict('htype') self.assertEqual(self.hp.htypes, list(df_dict.keys())) print('test raise assertion error') self.assertRaises(AssertionError, self.hp.to_df_dict, by='random text') self.assertRaises(AssertionError, self.hp.to_df_dict, by=3) print('test empty hp') df_dict = qt.HistoryPanel().to_df_dict('share') self.assertEqual(df_dict, {}) def test_stack_dataframes(self): print('test stack dataframes in a list') df1 = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [2, 3, 4, 5], 'c': [3, 4, 5, 6]}) df1.index = ['20200101', '20200102', '20200103', '20200104'] df2 = pd.DataFrame({'b': [4, 3, 2, 1], 'd': [1, 1, 1, 1], 'c': [6, 5, 4, 3]}) df2.index = ['20200101', '20200102', '20200104', '20200105'] df3 = pd.DataFrame({'a': [6, 6, 6, 6], 'd': [4, 4, 4, 4], 'b': [2, 4, 6, 8]}) df3.index = ['20200101', '20200102', '20200103', '20200106'] values1 = np.array([[[1., 2., 3., np.nan], [2., 3., 4., np.nan], [3., 4., 5., np.nan], [4., 5., 6., np.nan], [np.nan, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan]], [[np.nan, 4., 6., 1.], [np.nan, 3., 5., 1.], [np.nan, np.nan, np.nan, np.nan], [np.nan, 2., 4., 1.], [np.nan, 1., 3., 1.], [np.nan, np.nan, np.nan, np.nan]], [[6., 2., np.nan, 4.], [6., 4., np.nan, 4.], [6., 6., np.nan, 4.], [np.nan, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan], [6., 8., np.nan, 4.]]]) values2 = np.array([[[1., np.nan, 6.], [2., np.nan, 6.], [3., np.nan, 6.], [4., np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 6.]], [[2., 4., 2.], [3., 3., 4.], [4., np.nan, 6.], [5., 2., np.nan], [np.nan, 1., np.nan], [np.nan, np.nan, 8.]], [[3., 6., np.nan], [4., 5., np.nan], [5., np.nan, np.nan], [6., 4., np.nan], [np.nan, 3., np.nan], [np.nan, np.nan, np.nan]], [[np.nan, 1., 4.], [np.nan, 1., 4.], [np.nan, np.nan, 4.], [np.nan, 1., np.nan], [np.nan, 1., np.nan], [np.nan, np.nan, 4.]]]) print(df1.rename(index=pd.to_datetime)) print(df2.rename(index=pd.to_datetime)) print(df3.rename(index=pd.to_datetime)) hp1 = stack_dataframes([df1, df2, df3], stack_along='shares', shares=['000100', '000200', '000300']) hp2 = stack_dataframes([df1, df2, df3], stack_along='shares', shares='000100, 000300, 000200') print('hp1 is:\n', hp1) print('hp2 is:\n', hp2) self.assertEqual(hp1.htypes, ['a', 'b', 'c', 'd']) self.assertEqual(hp1.shares, ['000100', '000200', '000300']) self.assertTrue(np.allclose(hp1.values, values1, equal_nan=True)) self.assertEqual(hp2.htypes, ['a', 'b', 'c', 'd']) self.assertEqual(hp2.shares, ['000100', '000300', '000200']) self.assertTrue(np.allclose(hp2.values, values1, equal_nan=True)) hp3 = stack_dataframes([df1, df2, df3], stack_along='htypes', htypes=['close', 'high', 'low']) hp4 = stack_dataframes([df1, df2, df3], stack_along='htypes', htypes='open, close, high') print('hp3 is:\n', hp3.values) print('hp4 is:\n', hp4.values) self.assertEqual(hp3.htypes, ['close', 'high', 'low']) self.assertEqual(hp3.shares, ['a', 'b', 'c', 'd']) self.assertTrue(np.allclose(hp3.values, values2, equal_nan=True)) self.assertEqual(hp4.htypes, ['open', 'close', 'high']) self.assertEqual(hp4.shares, ['a', 'b', 'c', 'd']) self.assertTrue(np.allclose(hp4.values, values2, equal_nan=True)) print('test stack dataframes in a dict') df1 = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [2, 3, 4, 5], 'c': [3, 4, 5, 6]}) df1.index = ['20200101', '20200102', '20200103', '20200104'] df2 = pd.DataFrame({'b': [4, 3, 2, 1], 'd': [1, 1, 1, 1], 'c': [6, 5, 4, 3]}) df2.index = ['20200101', '20200102', '20200104', '20200105'] df3 = pd.DataFrame({'a': [6, 6, 6, 6], 'd': [4, 4, 4, 4], 'b': [2, 4, 6, 8]}) df3.index = ['20200101', '20200102', '20200103', '20200106'] values1 = np.array([[[1., 2., 3., np.nan], [2., 3., 4., np.nan], [3., 4., 5., np.nan], [4., 5., 6., np.nan], [np.nan, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan]], [[np.nan, 4., 6., 1.], [np.nan, 3., 5., 1.], [np.nan, np.nan, np.nan, np.nan], [np.nan, 2., 4., 1.], [np.nan, 1., 3., 1.], [np.nan, np.nan, np.nan, np.nan]], [[6., 2., np.nan, 4.], [6., 4., np.nan, 4.], [6., 6., np.nan, 4.], [np.nan, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan], [6., 8., np.nan, 4.]]]) values2 = np.array([[[1., np.nan, 6.], [2., np.nan, 6.], [3., np.nan, 6.], [4., np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, 6.]], [[2., 4., 2.], [3., 3., 4.], [4., np.nan, 6.], [5., 2., np.nan], [np.nan, 1., np.nan], [np.nan, np.nan, 8.]], [[3., 6., np.nan], [4., 5., np.nan], [5., np.nan, np.nan], [6., 4., np.nan], [np.nan, 3., np.nan], [np.nan, np.nan, np.nan]], [[np.nan, 1., 4.], [np.nan, 1., 4.], [np.nan, np.nan, 4.], [np.nan, 1., np.nan], [np.nan, 1., np.nan], [np.nan, np.nan, 4.]]]) print(df1.rename(index=pd.to_datetime)) print(df2.rename(index=pd.to_datetime)) print(df3.rename(index=pd.to_datetime)) hp1 = stack_dataframes(dfs={'000001.SZ': df1, '000002.SZ': df2, '000003.SZ': df3}, stack_along='shares') hp2 = stack_dataframes(dfs={'000001.SZ': df1, '000002.SZ': df2, '000003.SZ': df3}, stack_along='shares', shares='000100, 000300, 000200') print('hp1 is:\n', hp1) print('hp2 is:\n', hp2) self.assertEqual(hp1.htypes, ['a', 'b', 'c', 'd']) self.assertEqual(hp1.shares, ['000001.SZ', '000002.SZ', '000003.SZ']) self.assertTrue(np.allclose(hp1.values, values1, equal_nan=True)) self.assertEqual(hp2.htypes, ['a', 'b', 'c', 'd']) self.assertEqual(hp2.shares, ['000100', '000300', '000200']) self.assertTrue(np.allclose(hp2.values, values1, equal_nan=True)) hp3 = stack_dataframes(dfs={'close': df1, 'high': df2, 'low': df3}, stack_along='htypes') hp4 = stack_dataframes(dfs={'close': df1, 'low': df2, 'high': df3}, stack_along='htypes', htypes='open, close, high') print('hp3 is:\n', hp3.values) print('hp4 is:\n', hp4.values) self.assertEqual(hp3.htypes, ['close', 'high', 'low']) self.assertEqual(hp3.shares, ['a', 'b', 'c', 'd']) self.assertTrue(np.allclose(hp3.values, values2, equal_nan=True)) self.assertEqual(hp4.htypes, ['open', 'close', 'high']) self.assertEqual(hp4.shares, ['a', 'b', 'c', 'd']) self.assertTrue(np.allclose(hp4.values, values2, equal_nan=True)) def test_to_csv(self): pass def test_to_hdf(self): pass def test_fill_na(self): """测试填充无效值""" print(self.hp) new_values = self.hp.values.astype(float) new_values[[0, 1, 3, 2], [1, 3, 0, 2], [1, 3, 2, 2]] = np.nan print(new_values) temp_hp = qt.HistoryPanel(values=new_values, levels=self.hp.levels, rows=self.hp.rows, columns=self.hp.columns) self.assertTrue(np.allclose(temp_hp.values[[0, 1, 3, 2], [1, 3, 0, 2], [1, 3, 2, 2]], np.nan, equal_nan=True)) temp_hp.fillna(2.3) filled_values = new_values.copy() filled_values[[0, 1, 3, 2], [1, 3, 0, 2], [1, 3, 2, 2]] = 2.3 self.assertTrue(np.allclose(temp_hp.values, filled_values, equal_nan=True)) def test_fill_inf(self): """测试填充无限值""" def test_get_history_panel(self): # TODO: implement this test case # test get only one line of data pass def test_get_price_type_raw_data(self): shares = '000039.SZ, 600748.SH, 000040.SZ' start = '20200101' end = '20200131' htypes = 'open, high, low, close' target_price_000039 = [[9.45, 9.49, 9.12, 9.17], [9.46, 9.56, 9.4, 9.5], [9.7, 9.76, 9.5, 9.51], [9.7, 9.75, 9.7, 9.72], [9.73, 9.77, 9.7, 9.73], [9.83, 9.85, 9.71, 9.72], [9.85, 9.85, 9.75, 9.79], [9.96, 9.96, 9.83, 9.86], [9.87, 9.94, 9.77, 9.93], [9.82, 9.9, 9.76, 9.87], [9.8, 9.85, 9.77, 9.82], [9.84, 9.86, 9.71, 9.72], [9.83, 9.93, 9.81, 9.86], [9.7, 9.87, 9.7, 9.82], [9.83, 9.86, 9.69, 9.79], [9.8, 9.94, 9.8, 9.86]] target_price_600748 = [[5.68, 5.68, 5.32, 5.37], [5.62, 5.68, 5.46, 5.65], [5.72, 5.72, 5.61, 5.62], [5.76, 5.77, 5.6, 5.73], [5.78, 5.84, 5.73, 5.75], [5.89, 5.91, 5.76, 5.77], [6.03, 6.04, 5.87, 5.89], [5.94, 6.07, 5.94, 6.02], [5.96, 5.98, 5.88, 5.97], [6.04, 6.06, 5.95, 5.96], [5.98, 6.04, 5.96, 6.03], [6.1, 6.11, 5.89, 5.94], [6.02, 6.12, 6., 6.1], [5.96, 6.05, 5.88, 6.01], [6.03, 6.03, 5.95, 5.99], [6.02, 6.12, 5.99, 5.99]] target_price_000040 = [[3.63, 3.83, 3.63, 3.65], [3.99, 4.07, 3.97, 4.03], [4.1, 4.11, 3.93, 3.95], [4.12, 4.13, 4.06, 4.11], [4.13, 4.19, 4.07, 4.13], [4.27, 4.28, 4.11, 4.12], [4.37, 4.38, 4.25, 4.29], [4.34, 4.5, 4.32, 4.41], [4.28, 4.35, 4.2, 4.34], [4.41, 4.43, 4.29, 4.31], [4.42, 4.45, 4.36, 4.41], [4.51, 4.56, 4.33, 4.35], [4.35, 4.55, 4.31, 4.55], [4.3, 4.41, 4.22, 4.36], [4.27, 4.44, 4.23, 4.34], [4.23, 4.27, 4.18, 4.25]] print(f'test get price type raw data with single thread') df_list = get_price_type_raw_data(start=start, end=end, shares=shares, htypes=htypes, freq='d') self.assertIsInstance(df_list, dict) self.assertEqual(len(df_list), 3) self.assertTrue(np.allclose(df_list['000039.SZ'].values, np.array(target_price_000039))) self.assertTrue(np.allclose(df_list['600748.SH'].values, np.array(target_price_600748))) self.assertTrue(np.allclose(df_list['000040.SZ'].values, np.array(target_price_000040))) print(f'in get financial report type raw data, got DataFrames: \n"000039.SZ":\n' f'{df_list["000039.SZ"]}\n"600748.SH":\n' f'{df_list["600748.SH"]}\n"000040.SZ":\n{df_list["000040.SZ"]}') print(f'test get price type raw data with with multi threads') df_list = get_price_type_raw_data(start=start, end=end, shares=shares, htypes=htypes, freq='d', parallel=10) self.assertIsInstance(df_list, dict) self.assertEqual(len(df_list), 3) self.assertTrue(np.allclose(df_list['000039.SZ'].values, np.array(target_price_000039))) self.assertTrue(np.allclose(df_list['600748.SH'].values, np.array(target_price_600748))) self.assertTrue(np.allclose(df_list['000040.SZ'].values, np.array(target_price_000040))) print(f'in get financial report type raw data, got DataFrames: \n"000039.SZ":\n' f'{df_list["000039.SZ"]}\n"600748.SH":\n' f'{df_list["600748.SH"]}\n"000040.SZ":\n{df_list["000040.SZ"]}') def test_get_financial_report_type_raw_data(self): shares = '000039.SZ, 600748.SH, 000040.SZ' start = '20160101' end = '20201231' htypes = 'eps,basic_eps,diluted_eps,total_revenue,revenue,total_share,' \ 'cap_rese,undistr_porfit,surplus_rese,net_profit' target_eps_000039 = [[1.41], [0.1398], [-0.0841], [-0.1929], [0.37], [0.1357], [0.1618], [0.1191], [1.11], [0.759], [0.3061], [0.1409], [0.81], [0.4187], [0.2554], [0.1624], [0.14], [-0.0898], [-0.1444], [0.1291]] target_eps_600748 = [[0.41], [0.22], [0.22], [0.09], [0.42], [0.23], [0.22], [0.09], [0.36], [0.16], [0.15], [0.07], [0.47], [0.19], [0.12], [0.07], [0.32], [0.22], [0.14], [0.07]] target_eps_000040 = [[-0.6866], [-0.134], [-0.189], [-0.036], [-0.6435], [0.05], [0.062], [0.0125], [0.8282], [1.05], [0.985], [0.811], [0.41], [0.242], [0.113], [0.027], [0.19], [0.17], [0.17], [0.064]] target_basic_eps_000039 = [[1.3980000e-01, 1.3980000e-01, 6.3591954e+10, 6.3591954e+10], [-8.4100000e-02, -8.4100000e-02, 3.9431807e+10, 3.9431807e+10], [-1.9290000e-01, -1.9290000e-01, 1.5852177e+10, 1.5852177e+10], [3.7000000e-01, 3.7000000e-01, 8.5815341e+10, 8.5815341e+10], [1.3570000e-01, 1.3430000e-01, 6.1660271e+10, 6.1660271e+10], [1.6180000e-01, 1.6040000e-01, 4.2717729e+10, 4.2717729e+10], [1.1910000e-01, 1.1900000e-01, 1.9099547e+10, 1.9099547e+10], [1.1100000e+00, 1.1000000e+00, 9.3497622e+10, 9.3497622e+10], [7.5900000e-01, 7.5610000e-01, 6.6906147e+10, 6.6906147e+10], [3.0610000e-01, 3.0380000e-01, 4.3560398e+10, 4.3560398e+10], [1.4090000e-01, 1.4050000e-01, 1.9253639e+10, 1.9253639e+10], [8.1000000e-01, 8.1000000e-01, 7.6299930e+10, 7.6299930e+10], [4.1870000e-01, 4.1710000e-01, 5.3962706e+10, 5.3962706e+10], [2.5540000e-01, 2.5440000e-01, 3.3387152e+10, 3.3387152e+10], [1.6240000e-01, 1.6200000e-01, 1.4675987e+10, 1.4675987e+10], [1.4000000e-01, 1.4000000e-01, 5.1111652e+10, 5.1111652e+10], [-8.9800000e-02, -8.9800000e-02, 3.4982614e+10, 3.4982614e+10], [-1.4440000e-01, -1.4440000e-01, 2.3542843e+10, 2.3542843e+10], [1.2910000e-01, 1.2860000e-01, 1.0412416e+10, 1.0412416e+10], [7.2000000e-01, 7.1000000e-01, 5.8685804e+10, 5.8685804e+10]] target_basic_eps_600748 = [[2.20000000e-01, 2.20000000e-01, 5.29423397e+09, 5.29423397e+09], [2.20000000e-01, 2.20000000e-01, 4.49275653e+09, 4.49275653e+09], [9.00000000e-02, 9.00000000e-02, 1.59067065e+09, 1.59067065e+09], [4.20000000e-01, 4.20000000e-01, 8.86555586e+09, 8.86555586e+09], [2.30000000e-01, 2.30000000e-01, 5.44850143e+09, 5.44850143e+09], [2.20000000e-01, 2.20000000e-01, 4.34978927e+09, 4.34978927e+09], [9.00000000e-02, 9.00000000e-02, 1.73793793e+09, 1.73793793e+09], [3.60000000e-01, 3.60000000e-01, 8.66375241e+09, 8.66375241e+09], [1.60000000e-01, 1.60000000e-01, 4.72875116e+09, 4.72875116e+09], [1.50000000e-01, 1.50000000e-01, 3.76879016e+09, 3.76879016e+09], [7.00000000e-02, 7.00000000e-02, 1.31785454e+09, 1.31785454e+09], [4.70000000e-01, 4.70000000e-01, 7.23391685e+09, 7.23391685e+09], [1.90000000e-01, 1.90000000e-01, 3.76072215e+09, 3.76072215e+09], [1.20000000e-01, 1.20000000e-01, 2.35845364e+09, 2.35845364e+09], [7.00000000e-02, 7.00000000e-02, 1.03831865e+09, 1.03831865e+09], [3.20000000e-01, 3.20000000e-01, 6.48880919e+09, 6.48880919e+09], [2.20000000e-01, 2.20000000e-01, 3.72209142e+09, 3.72209142e+09], [1.40000000e-01, 1.40000000e-01, 2.22563924e+09, 2.22563924e+09], [7.00000000e-02, 7.00000000e-02, 8.96647052e+08, 8.96647052e+08], [4.80000000e-01, 4.80000000e-01, 6.61917508e+09, 6.61917508e+09]] target_basic_eps_000040 = [[-1.34000000e-01, -1.34000000e-01, 2.50438755e+09, 2.50438755e+09], [-1.89000000e-01, -1.89000000e-01, 1.32692347e+09, 1.32692347e+09], [-3.60000000e-02, -3.60000000e-02, 5.59073338e+08, 5.59073338e+08], [-6.43700000e-01, -6.43700000e-01, 6.80576162e+09, 6.80576162e+09], [5.00000000e-02, 5.00000000e-02, 6.38891620e+09, 6.38891620e+09], [6.20000000e-02, 6.20000000e-02, 5.23267082e+09, 5.23267082e+09], [1.25000000e-02, 1.25000000e-02, 2.22420874e+09, 2.22420874e+09], [8.30000000e-01, 8.30000000e-01, 8.67628947e+09, 8.67628947e+09], [1.05000000e+00, 1.05000000e+00, 5.29431716e+09, 5.29431716e+09], [9.85000000e-01, 9.85000000e-01, 3.56822382e+09, 3.56822382e+09], [8.11000000e-01, 8.11000000e-01, 1.06613439e+09, 1.06613439e+09], [4.10000000e-01, 4.10000000e-01, 8.13102532e+09, 8.13102532e+09], [2.42000000e-01, 2.42000000e-01, 5.17971521e+09, 5.17971521e+09], [1.13000000e-01, 1.13000000e-01, 3.21704120e+09, 3.21704120e+09], [2.70000000e-02, 2.70000000e-02, 8.41966738e+08, 8.24272235e+08], [1.90000000e-01, 1.90000000e-01, 3.77350171e+09, 3.77350171e+09], [1.70000000e-01, 1.70000000e-01, 2.38643892e+09, 2.38643892e+09], [1.70000000e-01, 1.70000000e-01, 1.29127117e+09, 1.29127117e+09], [6.40000000e-02, 6.40000000e-02, 6.03256858e+08, 6.03256858e+08], [1.30000000e-01, 1.30000000e-01, 1.66572918e+09, 1.66572918e+09]] target_total_share_000039 = [[3.5950140e+09, 4.8005360e+09, 2.1573660e+10, 3.5823430e+09], [3.5860750e+09, 4.8402300e+09, 2.0750827e+10, 3.5823430e+09], [3.5860750e+09, 4.9053550e+09, 2.0791307e+10, 3.5823430e+09], [3.5845040e+09, 4.8813110e+09, 2.1482857e+10, 3.5823430e+09], [3.5831490e+09, 4.9764250e+09, 2.0926816e+10, 3.2825850e+09], [3.5825310e+09, 4.8501270e+09, 2.1020418e+10, 3.2825850e+09], [2.9851110e+09, 5.4241420e+09, 2.2438350e+10, 3.2825850e+09], [2.9849890e+09, 4.1284000e+09, 2.2082769e+10, 3.2825850e+09], [2.9849610e+09, 4.0838010e+09, 2.1045994e+10, 3.2815350e+09], [2.9849560e+09, 4.2491510e+09, 1.9694345e+10, 3.2815350e+09], [2.9846970e+09, 4.2351600e+09, 2.0016361e+10, 3.2815350e+09], [2.9828890e+09, 4.2096630e+09, 1.9734494e+10, 3.2815350e+09], [2.9813960e+09, 3.4564240e+09, 1.8562738e+10, 3.2793790e+09], [2.9803530e+09, 3.0759650e+09, 1.8076208e+10, 3.2793790e+09], [2.9792680e+09, 3.1376690e+09, 1.7994776e+10, 3.2793790e+09], [2.9785770e+09, 3.1265850e+09, 1.7495053e+10, 3.2793790e+09], [2.9783640e+09, 3.1343850e+09, 1.6740840e+10, 3.2035780e+09], [2.9783590e+09, 3.1273880e+09, 1.6578389e+10, 3.2035780e+09], [2.9782780e+09, 3.1169280e+09, 1.8047639e+10, 3.2035780e+09], [2.9778200e+09, 3.1818630e+09, 1.7663145e+10, 3.2035780e+09]] target_total_share_600748 = [[1.84456289e+09, 2.60058426e+09, 5.72443733e+09, 4.58026529e+08], [1.84456289e+09, 2.60058426e+09, 5.72096899e+09, 4.58026529e+08], [1.84456289e+09, 2.60058426e+09, 5.65738237e+09, 4.58026529e+08], [1.84456289e+09, 2.60058426e+09, 5.50257806e+09, 4.58026529e+08], [1.84456289e+09, 2.59868164e+09, 5.16741523e+09, 4.44998882e+08], [1.84456289e+09, 2.59684471e+09, 5.14677280e+09, 4.44998882e+08], [1.84456289e+09, 2.59684471e+09, 4.94955591e+09, 4.44998882e+08], [1.84456289e+09, 2.59684471e+09, 4.79001451e+09, 4.44998882e+08], [1.84456289e+09, 3.11401684e+09, 4.46326988e+09, 4.01064256e+08], [1.84456289e+09, 3.11596723e+09, 4.45419136e+09, 4.01064256e+08], [1.84456289e+09, 3.11596723e+09, 4.39652948e+09, 4.01064256e+08], [1.84456289e+09, 3.18007783e+09, 4.26608403e+09, 4.01064256e+08], [1.84456289e+09, 3.10935622e+09, 3.78417688e+09, 3.65651701e+08], [1.84456289e+09, 3.10935622e+09, 3.65806574e+09, 3.65651701e+08], [1.84456289e+09, 3.10935622e+09, 3.62063090e+09, 3.65651701e+08], [1.84456289e+09, 3.10935622e+09, 3.50063915e+09, 3.65651701e+08], [1.41889453e+09, 3.55940850e+09, 3.22272993e+09, 3.62124939e+08], [1.41889453e+09, 3.56129650e+09, 3.11477476e+09, 3.62124939e+08], [1.41889453e+09, 3.59632888e+09, 3.06836903e+09, 3.62124939e+08], [1.08337087e+09, 3.37400726e+07, 3.00918704e+09, 3.62124939e+08]] target_total_share_000040 = [[1.48687387e+09, 1.06757900e+10, 8.31900755e+08, 2.16091994e+08], [1.48687387e+09, 1.06757900e+10, 7.50177302e+08, 2.16091994e+08], [1.48687387e+09, 1.06757899e+10, 9.90255974e+08, 2.16123282e+08], [1.48687387e+09, 1.06757899e+10, 1.03109866e+09, 2.16091994e+08], [1.48687387e+09, 1.06757910e+10, 2.07704745e+09, 2.16123282e+08], [1.48687387e+09, 1.06757910e+10, 2.09608665e+09, 2.16123282e+08], [1.48687387e+09, 1.06803833e+10, 2.13354083e+09, 2.16123282e+08], [1.48687387e+09, 1.06804090e+10, 2.11489364e+09, 2.16123282e+08], [1.33717327e+09, 8.87361727e+09, 2.42939924e+09, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 2.34220254e+09, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 2.16390368e+09, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 1.07961915e+09, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 8.58866066e+08, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 6.87024393e+08, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 5.71554565e+08, 1.88489589e+08], [1.33717327e+09, 8.87361727e+09, 5.54241222e+08, 1.88489589e+08], [1.33717327e+09, 8.87361726e+09, 5.10059576e+08, 1.88489589e+08], [1.33717327e+09, 8.87361726e+09, 4.59351639e+08, 1.88489589e+08], [4.69593364e+08, 2.78355875e+08, 4.13430814e+08, 1.88489589e+08], [4.69593364e+08, 2.74235459e+08, 3.83557678e+08, 1.88489589e+08]] target_net_profit_000039 = [[np.nan], [2.422180e+08], [np.nan], [2.510113e+09], [np.nan], [1.102220e+09], [np.nan], [4.068455e+09], [np.nan], [1.315957e+09], [np.nan], [3.158415e+09], [np.nan], [1.066509e+09], [np.nan], [7.349830e+08], [np.nan], [-5.411600e+08], [np.nan], [2.271961e+09]] target_net_profit_600748 = [[np.nan], [4.54341757e+08], [np.nan], [9.14476670e+08], [np.nan], [5.25360283e+08], [np.nan], [9.24502415e+08], [np.nan], [4.66560302e+08], [np.nan], [9.15265285e+08], [np.nan], [2.14639674e+08], [np.nan], [7.45093049e+08], [np.nan], [2.10967312e+08], [np.nan], [6.04572711e+08]] target_net_profit_000040 = [[np.nan], [-2.82458846e+08], [np.nan], [-9.57130872e+08], [np.nan], [9.22114527e+07], [np.nan], [1.12643819e+09], [np.nan], [1.31715269e+09], [np.nan], [5.39940093e+08], [np.nan], [1.51440838e+08], [np.nan], [1.75339071e+08], [np.nan], [8.04740415e+07], [np.nan], [6.20445815e+07]] print('test get financial data, in multi thread mode') df_list = get_financial_report_type_raw_data(start=start, end=end, shares=shares, htypes=htypes, parallel=4) self.assertIsInstance(df_list, tuple) self.assertEqual(len(df_list), 4) self.assertEqual(len(df_list[0]), 3) self.assertEqual(len(df_list[1]), 3) self.assertEqual(len(df_list[2]), 3) self.assertEqual(len(df_list[3]), 3) # 检查确认所有数据类型正确 self.assertTrue(all(isinstance(item, pd.DataFrame) for subdict in df_list for item in subdict.values())) # 检查是否有空数据 print(all(item.empty for subdict in df_list for item in subdict.values())) # 检查获取的每组数据正确,且所有数据的顺序一致, 如果取到空数据,则忽略 if df_list[0]['000039.SZ'].empty: print(f'income data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[0]['000039.SZ'].values, target_basic_eps_000039)) if df_list[0]['600748.SH'].empty: print(f'income data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[0]['600748.SH'].values, target_basic_eps_600748)) if df_list[0]['000040.SZ'].empty: print(f'income data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[0]['000040.SZ'].values, target_basic_eps_000040)) if df_list[1]['000039.SZ'].empty: print(f'indicator data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[1]['000039.SZ'].values, target_eps_000039)) if df_list[1]['600748.SH'].empty: print(f'indicator data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[1]['600748.SH'].values, target_eps_600748)) if df_list[1]['000040.SZ'].empty: print(f'indicator data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[1]['000040.SZ'].values, target_eps_000040)) if df_list[2]['000039.SZ'].empty: print(f'balance data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[2]['000039.SZ'].values, target_total_share_000039)) if df_list[2]['600748.SH'].empty: print(f'balance data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[2]['600748.SH'].values, target_total_share_600748)) if df_list[2]['000040.SZ'].empty: print(f'balance data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[2]['000040.SZ'].values, target_total_share_000040)) if df_list[3]['000039.SZ'].empty: print(f'cash flow data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[3]['000039.SZ'].values, target_net_profit_000039, equal_nan=True)) if df_list[3]['600748.SH'].empty: print(f'cash flow data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[3]['600748.SH'].values, target_net_profit_600748, equal_nan=True)) if df_list[3]['000040.SZ'].empty: print(f'cash flow data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[3]['000040.SZ'].values, target_net_profit_000040, equal_nan=True)) print('test get financial data, in single thread mode') df_list = get_financial_report_type_raw_data(start=start, end=end, shares=shares, htypes=htypes, parallel=0) self.assertIsInstance(df_list, tuple) self.assertEqual(len(df_list), 4) self.assertEqual(len(df_list[0]), 3) self.assertEqual(len(df_list[1]), 3) self.assertEqual(len(df_list[2]), 3) self.assertEqual(len(df_list[3]), 3) # 检查确认所有数据类型正确 self.assertTrue(all(isinstance(item, pd.DataFrame) for subdict in df_list for item in subdict.values())) # 检查是否有空数据,因为网络问题,有可能会取到空数据 self.assertFalse(all(item.empty for subdict in df_list for item in subdict.values())) # 检查获取的每组数据正确,且所有数据的顺序一致, 如果取到空数据,则忽略 if df_list[0]['000039.SZ'].empty: print(f'income data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[0]['000039.SZ'].values, target_basic_eps_000039)) if df_list[0]['600748.SH'].empty: print(f'income data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[0]['600748.SH'].values, target_basic_eps_600748)) if df_list[0]['000040.SZ'].empty: print(f'income data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[0]['000040.SZ'].values, target_basic_eps_000040)) if df_list[1]['000039.SZ'].empty: print(f'indicator data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[1]['000039.SZ'].values, target_eps_000039)) if df_list[1]['600748.SH'].empty: print(f'indicator data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[1]['600748.SH'].values, target_eps_600748)) if df_list[1]['000040.SZ'].empty: print(f'indicator data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[1]['000040.SZ'].values, target_eps_000040)) if df_list[2]['000039.SZ'].empty: print(f'balance data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[2]['000039.SZ'].values, target_total_share_000039)) if df_list[2]['600748.SH'].empty: print(f'balance data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[2]['600748.SH'].values, target_total_share_600748)) if df_list[2]['000040.SZ'].empty: print(f'balance data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[2]['000040.SZ'].values, target_total_share_000040)) if df_list[3]['000039.SZ'].empty: print(f'cash flow data for "000039.SZ" is empty') else: self.assertTrue(np.allclose(df_list[3]['000039.SZ'].values, target_net_profit_000039, equal_nan=True)) if df_list[3]['600748.SH'].empty: print(f'cash flow data for "600748.SH" is empty') else: self.assertTrue(np.allclose(df_list[3]['600748.SH'].values, target_net_profit_600748, equal_nan=True)) if df_list[3]['000040.SZ'].empty: print(f'cash flow data for "000040.SZ" is empty') else: self.assertTrue(np.allclose(df_list[3]['000040.SZ'].values, target_net_profit_000040, equal_nan=True)) def test_get_composite_type_raw_data(self): pass class TestUtilityFuncs(unittest.TestCase): def setUp(self): pass def test_time_string_format(self): print('Testing qt.time_string_format() function:') t = 3.14 self.assertEqual(time_str_format(t), '3s 140.0ms') self.assertEqual(time_str_format(t, estimation=True), '3s ') self.assertEqual(time_str_format(t, short_form=True), '3"140') self.assertEqual(time_str_format(t, estimation=True, short_form=True), '3"') t = 300.14 self.assertEqual(time_str_format(t), '5min 140.0ms') self.assertEqual(time_str_format(t, estimation=True), '5min ') self.assertEqual(time_str_format(t, short_form=True), "5'140") self.assertEqual(time_str_format(t, estimation=True, short_form=True), "5'") t = 7435.0014 self.assertEqual(time_str_format(t), '2hrs 3min 55s 1.4ms') self.assertEqual(time_str_format(t, estimation=True), '2hrs ') self.assertEqual(time_str_format(t, short_form=True), "2H3'55\"001") self.assertEqual(time_str_format(t, estimation=True, short_form=True), "2H") t = 88425.0509 self.assertEqual(time_str_format(t), '1days 33min 45s 50.9ms') self.assertEqual(time_str_format(t, estimation=True), '1days ') self.assertEqual(time_str_format(t, short_form=True), "1D33'45\"051") self.assertEqual(time_str_format(t, estimation=True, short_form=True), "1D") def test_str_to_list(self): self.assertEqual(str_to_list('a,b,c,d,e'), ['a', 'b', 'c', 'd', 'e']) self.assertEqual(str_to_list('a, b, c '), ['a', 'b', 'c']) self.assertEqual(str_to_list('a, b: c', sep_char=':'), ['a,b', 'c']) self.assertEqual(str_to_list('abc'), ['abc']) self.assertEqual(str_to_list(''), []) self.assertRaises(AssertionError, str_to_list, 123) def test_list_or_slice(self): str_dict = {'close': 0, 'open': 1, 'high': 2, 'low': 3} self.assertEqual(list_or_slice(slice(1, 2, 1), str_dict), slice(1, 2, 1)) self.assertEqual(list_or_slice('open', str_dict), [1]) self.assertEqual(list(list_or_slice('close, high, low', str_dict)), [0, 2, 3]) self.assertEqual(list(list_or_slice('close:high', str_dict)), [0, 1, 2]) self.assertEqual(list(list_or_slice(['open'], str_dict)), [1]) self.assertEqual(list(list_or_slice(['open', 'high'], str_dict)), [1, 2]) self.assertEqual(list(list_or_slice(0, str_dict)), [0]) self.assertEqual(list(list_or_slice([0, 2], str_dict)), [0, 2]) self.assertEqual(list(list_or_slice([True, False, True, False], str_dict)), [0, 2]) def test_labels_to_dict(self): target_list = [0, 1, 10, 100] target_dict = {'close': 0, 'open': 1, 'high': 2, 'low': 3} target_dict2 = {'close': 0, 'open': 2, 'high': 1, 'low': 3} self.assertEqual(labels_to_dict('close, open, high, low', target_list), target_dict) self.assertEqual(labels_to_dict(['close', 'open', 'high', 'low'], target_list), target_dict) self.assertEqual(labels_to_dict('close, high, open, low', target_list), target_dict2) self.assertEqual(labels_to_dict(['close', 'high', 'open', 'low'], target_list), target_dict2) def test_input_to_list(self): """ test util function input_to_list()""" self.assertEqual(input_to_list(5, 3), [5, 5, 5]) self.assertEqual(input_to_list(5, 3, 0), [5, 5, 5]) self.assertEqual(input_to_list([5], 3, 0), [5, 0, 0]) self.assertEqual(input_to_list([5, 4], 3, 0), [5, 4, 0]) def test_regulate_date_format(self): self.assertEqual(regulate_date_format('2019/11/06'), '20191106') self.assertEqual(regulate_date_format('2019-11-06'), '20191106') self.assertEqual(regulate_date_format('20191106'), '20191106') self.assertEqual(regulate_date_format('191106'), '20061119') self.assertEqual(regulate_date_format('830522'), '19830522') self.assertEqual(regulate_date_format(datetime.datetime(2010, 3, 15)), '20100315') self.assertEqual(regulate_date_format(pd.Timestamp('2010.03.15')), '20100315') self.assertRaises(ValueError, regulate_date_format, 'abc') self.assertRaises(ValueError, regulate_date_format, '2019/13/43') def test_list_to_str_format(self): self.assertEqual(list_to_str_format(['close', 'open', 'high', 'low']), 'close,open,high,low') self.assertEqual(list_to_str_format(['letters', ' ', '123 4', 123, ' kk l']), 'letters,,1234,kkl') self.assertEqual(list_to_str_format('a string input'), 'a,string,input') self.assertEqual(list_to_str_format('already,a,good,string'), 'already,a,good,string') self.assertRaises(AssertionError, list_to_str_format, 123) def test_is_trade_day(self): """test if the funcion maybe_trade_day() and is_market_trade_day() works properly """ date_trade = '20210401' date_holiday = '20210102' date_weekend = '20210424' date_seems_trade_day = '20210217' date_too_early = '19890601' date_too_late = '20230105' date_christmas = '20201225' self.assertTrue(maybe_trade_day(date_trade)) self.assertFalse(maybe_trade_day(date_holiday)) self.assertFalse(maybe_trade_day(date_weekend)) self.assertTrue(maybe_trade_day(date_seems_trade_day)) self.assertTrue(maybe_trade_day(date_too_early)) self.assertTrue(maybe_trade_day(date_too_late)) self.assertTrue(maybe_trade_day(date_christmas)) self.assertTrue(is_market_trade_day(date_trade)) self.assertFalse(is_market_trade_day(date_holiday)) self.assertFalse(is_market_trade_day(date_weekend)) self.assertFalse(is_market_trade_day(date_seems_trade_day)) self.assertFalse(is_market_trade_day(date_too_early)) self.assertFalse(is_market_trade_day(date_too_late)) self.assertTrue(is_market_trade_day(date_christmas)) self.assertFalse(is_market_trade_day(date_christmas, exchange='XHKG')) date_trade = pd.to_datetime('20210401') date_holiday = pd.to_datetime('20210102') date_weekend = pd.to_datetime('20210424') self.assertTrue(maybe_trade_day(date_trade)) self.assertFalse(maybe_trade_day(date_holiday)) self.assertFalse(maybe_trade_day(date_weekend)) def test_weekday_name(self): """ test util func weekday_name()""" self.assertEqual(weekday_name(0), 'Monday') self.assertEqual(weekday_name(1), 'Tuesday') self.assertEqual(weekday_name(2), 'Wednesday') self.assertEqual(weekday_name(3), 'Thursday') self.assertEqual(weekday_name(4), 'Friday') self.assertEqual(weekday_name(5), 'Saturday') self.assertEqual(weekday_name(6), 'Sunday') def test_list_truncate(self): """ test util func list_truncate()""" l = [1,2,3,4,5] ls = list_truncate(l, 2) self.assertEqual(ls[0], [1, 2]) self.assertEqual(ls[1], [3, 4]) self.assertEqual(ls[2], [5]) self.assertRaises(AssertionError, list_truncate, l, 0) self.assertRaises(AssertionError, list_truncate, 12, 0) self.assertRaises(AssertionError, list_truncate, 0, l) def test_maybe_trade_day(self): """ test util function maybe_trade_day()""" self.assertTrue(maybe_trade_day('20220104')) self.assertTrue(maybe_trade_day('2021-12-31')) self.assertTrue(maybe_trade_day(
pd.to_datetime('2020/03/06')
pandas.to_datetime
# -*- coding: utf-8 -*- # pylint: disable=E1101 # flake8: noqa from datetime import datetime import csv import os import sys import re import nose import platform from multiprocessing.pool import ThreadPool from numpy import nan import numpy as np from pandas.io.common import DtypeWarning from pandas import DataFrame, Series, Index, MultiIndex, DatetimeIndex from pandas.compat import( StringIO, BytesIO, PY3, range, long, lrange, lmap, u ) from pandas.io.common import URLError import pandas.io.parsers as parsers from pandas.io.parsers import (read_csv, read_table, read_fwf, TextFileReader, TextParser) import pandas.util.testing as tm import pandas as pd from pandas.compat import parse_date import pandas.lib as lib from pandas import compat from pandas.lib import Timestamp from pandas.tseries.index import date_range import pandas.tseries.tools as tools from numpy.testing.decorators import slow import pandas.parser class ParserTests(object): """ Want to be able to test either C+Cython or Python+Cython parsers """ data1 = """index,A,B,C,D foo,2,3,4,5 bar,7,8,9,10 baz,12,13,14,15 qux,12,13,14,15 foo2,12,13,14,15 bar2,12,13,14,15 """ def read_csv(self, *args, **kwargs): raise NotImplementedError def read_table(self, *args, **kwargs): raise NotImplementedError def setUp(self): import warnings warnings.filterwarnings(action='ignore', category=FutureWarning) self.dirpath = tm.get_data_path() self.csv1 = os.path.join(self.dirpath, 'test1.csv') self.csv2 = os.path.join(self.dirpath, 'test2.csv') self.xls1 = os.path.join(self.dirpath, 'test.xls') def construct_dataframe(self, num_rows): df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde')) df['foo'] = 'foo' df['bar'] = 'bar' df['baz'] = 'baz' df['date'] = pd.date_range('20000101 09:00:00', periods=num_rows, freq='s') df['int'] = np.arange(num_rows, dtype='int64') return df def generate_multithread_dataframe(self, path, num_rows, num_tasks): def reader(arg): start, nrows = arg if not start: return pd.read_csv(path, index_col=0, header=0, nrows=nrows, parse_dates=['date']) return pd.read_csv(path, index_col=0, header=None, skiprows=int(start) + 1, nrows=nrows, parse_dates=[9]) tasks = [ (num_rows * i / num_tasks, num_rows / num_tasks) for i in range(num_tasks) ] pool = ThreadPool(processes=num_tasks) results = pool.map(reader, tasks) header = results[0].columns for r in results[1:]: r.columns = header final_dataframe = pd.concat(results) return final_dataframe def test_converters_type_must_be_dict(self): with tm.assertRaisesRegexp(TypeError, 'Type converters.+'): self.read_csv(StringIO(self.data1), converters=0) def test_empty_decimal_marker(self): data = """A|B|C 1|2,334|5 10|13|10. """ self.assertRaises(ValueError, read_csv, StringIO(data), decimal='') def test_empty_thousands_marker(self): data = """A|B|C 1|2,334|5 10|13|10. """ self.assertRaises(ValueError, read_csv, StringIO(data), thousands='') def test_multi_character_decimal_marker(self): data = """A|B|C 1|2,334|5 10|13|10. """ self.assertRaises(ValueError, read_csv, StringIO(data), thousands=',,') def test_empty_string(self): data = """\ One,Two,Three a,1,one b,2,two ,3,three d,4,nan e,5,five nan,6, g,7,seven """ df = self.read_csv(StringIO(data)) xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], 'Two': [1, 2, 3, 4, 5, 6, 7], 'Three': ['one', 'two', 'three', np.nan, 'five', np.nan, 'seven']}) tm.assert_frame_equal(xp.reindex(columns=df.columns), df) df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}, keep_default_na=False) xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], 'Two': [1, 2, 3, 4, 5, 6, 7], 'Three': ['one', 'two', 'three', 'nan', 'five', '', 'seven']}) tm.assert_frame_equal(xp.reindex(columns=df.columns), df) df = self.read_csv( StringIO(data), na_values=['a'], keep_default_na=False) xp = DataFrame({'One': [np.nan, 'b', '', 'd', 'e', 'nan', 'g'], 'Two': [1, 2, 3, 4, 5, 6, 7], 'Three': ['one', 'two', 'three', 'nan', 'five', '', 'seven']}) tm.assert_frame_equal(xp.reindex(columns=df.columns), df) df = self.read_csv(StringIO(data), na_values={'One': [], 'Three': []}) xp = DataFrame({'One': ['a', 'b', np.nan, 'd', 'e', np.nan, 'g'], 'Two': [1, 2, 3, 4, 5, 6, 7], 'Three': ['one', 'two', 'three', np.nan, 'five', np.nan, 'seven']}) tm.assert_frame_equal(xp.reindex(columns=df.columns), df) # GH4318, passing na_values=None and keep_default_na=False yields # 'None' as a na_value data = """\ One,Two,Three a,1,None b,2,two ,3,None d,4,nan e,5,five nan,6, g,7,seven """ df = self.read_csv( StringIO(data), keep_default_na=False) xp = DataFrame({'One': ['a', 'b', '', 'd', 'e', 'nan', 'g'], 'Two': [1, 2, 3, 4, 5, 6, 7], 'Three': ['None', 'two', 'None', 'nan', 'five', '', 'seven']}) tm.assert_frame_equal(xp.reindex(columns=df.columns), df) def test_read_csv(self): if not compat.PY3: if compat.is_platform_windows(): prefix = u("file:///") else: prefix = u("file://") fname = prefix + compat.text_type(self.csv1) # it works! read_csv(fname, index_col=0, parse_dates=True) def test_dialect(self): data = """\ label1,label2,label3 index1,"a,c,e index2,b,d,f """ dia = csv.excel() dia.quoting = csv.QUOTE_NONE df = self.read_csv(StringIO(data), dialect=dia) data = '''\ label1,label2,label3 index1,a,c,e index2,b,d,f ''' exp = self.read_csv(StringIO(data)) exp.replace('a', '"a', inplace=True) tm.assert_frame_equal(df, exp) def test_dialect_str(self): data = """\ fruit:vegetable apple:brocolli pear:tomato """ exp = DataFrame({ 'fruit': ['apple', 'pear'], 'vegetable': ['brocolli', 'tomato'] }) dia = csv.register_dialect('mydialect', delimiter=':') # noqa df = self.read_csv(StringIO(data), dialect='mydialect') tm.assert_frame_equal(df, exp) csv.unregister_dialect('mydialect') def test_1000_sep(self): data = """A|B|C 1|2,334|5 10|13|10. """ expected = DataFrame({ 'A': [1, 10], 'B': [2334, 13], 'C': [5, 10.] }) df = self.read_csv(StringIO(data), sep='|', thousands=',') tm.assert_frame_equal(df, expected) df = self.read_table(StringIO(data), sep='|', thousands=',') tm.assert_frame_equal(df, expected) def test_1000_sep_with_decimal(self): data = """A|B|C 1|2,334.01|5 10|13|10. """ expected = DataFrame({ 'A': [1, 10], 'B': [2334.01, 13], 'C': [5, 10.] }) tm.assert_equal(expected.A.dtype, 'int64') tm.assert_equal(expected.B.dtype, 'float') tm.assert_equal(expected.C.dtype, 'float') df = self.read_csv(StringIO(data), sep='|', thousands=',', decimal='.') tm.assert_frame_equal(df, expected) df = self.read_table(StringIO(data), sep='|', thousands=',', decimal='.')
tm.assert_frame_equal(df, expected)
pandas.util.testing.assert_frame_equal
import os import glob import json import logging import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from core.utils import Directories from core.viz import plot_class_dist class DataHandling(object): def __init__(self): pass def drop_unique_cols(self, train, test, add_cols: list): ''' Drops unique columns as a part of data-preprocessing; as they won't make any difference in model predictions. Unique columns are automatically decided based on the logic below. :param train: train set :param test: test set :param add_cols: any additional columns which need to be dropped. Needs to passed a an entry in list object :return: ''' df = pd.concat([train, test]) unique_cols = [col for col in df.columns if len(df[col].unique()) == 1] df = df.drop(unique_cols, axis=1) logging.info('**Dropped {} columns with unique values: {}'.format(len(unique_cols), ' '.join(unique_cols))) print('**Dropped {} columns with unique values: {}'.format(len(unique_cols), ' '.join(unique_cols))) df = df.drop(add_cols, axis=1) logging.info('**Dropped {} additional columns: {}'.format(len(add_cols), ', '.join(add_cols))) print('**Dropped {} additional columns: {}'.format(len(add_cols), ', '.join(add_cols))) train, test = df.iloc[:len(train)], df.iloc[len(train):] return train, test def rename_class_labels(self, df, dataset: str): ''' Preprocessing of class labels; some labels are binned under a common cluster of labels. for CICDDoS: train & test sets had different naming conventions for nameing hte labels :param df: whole dataset combined: pandas DataFrame() :param dataset: name of the dataset :return: clean dataframe ''' if dataset == 'CICDDoS': print('Class labels renamed for CICDDoS') df.Label = df.Label.replace('DrDoS_DNS', 'DNS') df.Label = df.Label.replace('DrDoS_LDAP', 'LDAP') df.Label = df.Label.replace('DrDoS_MSSQL', 'MSSQL') df.Label = df.Label.replace('DrDoS_NTP', 'NTP') df.Label = df.Label.replace('DrDoS_NetBIOS', 'NetBIOS') df.Label = df.Label.replace('DrDoS_SNMP', 'SNMP') df.Label = df.Label.replace('DrDoS_SSDP', 'SSDP') df.Label = df.Label.replace('DrDoS_UDP', 'UDP') df.Label = df.Label.replace('UDP-lag', 'UDPLag') elif dataset == 'CICIDS': df.Label = df.Label.replace('DoS Hulk', 'DoS') df.Label = df.Label.replace('DoS GoldenEye', 'DoS') df.Label = df.Label.replace('DoS slowloris', 'DoS') df.Label = df.Label.replace('DoS Slowhttptest', 'DoS') df.Label = df.Label.replace('Web Attack � Brute Force', 'Web Attack') df.Label = df.Label.replace('Web Attack � XSS', 'Web Attack') df.Label = df.Label.replace('Web Attack � Sql Injection', 'Web Attack') df.Label = df.Label.replace('Heartbleed', 'Others') df.Label = df.Label.replace('Infiltration', 'Others') df.Label = df.Label.replace('FTP-Patator', 'Patator') df.Label = df.Label.replace('SSH-Patator', 'Patator') return df class FeatureEngineering(Directories): def __init__(self, config): super().__init__(config) self.dhObj = DataHandling() def _sample_cicddos(self, path: str, tr_samples: int, ts_samples: int): ''' In CICDDoS undersampling was required as count of benign samples are limited to ~110K. Equal number of samples for all other labels are picked from both sets. :param path: directory of the raw files. :param tr_samples: count of samples from training set. :param ts_samples: count of samples from test set. :return: None; saves the samples as individual <label_name>.csv files. ''' logging.info('+++++ Sampling CICDDoS2019 dataset +++++') print('+++++ Sampling CICDDoS2019 dataset +++++') def helper_function(data, label: str, n_samples: int): # Clean data for CICDDoS2019 by removing negative entries. data.replace([np.inf, -np.inf], np.nan, inplace=True) data.dropna(inplace=True) data = data[data[' Fwd Header Length'] >= 0] data = data[data[' Bwd Header Length'] >= 0] data = data[data[' min_seg_size_forward'] >= 0] if len(data) < n_samples: # consider all examples of this label return data else: if label == 'Benign': # consider all benign samples as they are less return data else: data = data.sample(n=n_samples).reset_index(drop=True) return data samples_path = os.path.join(path, 'samples') if not os.path.exists(samples_path): # Create sample directory the first time os.makedirs(samples_path) os.makedirs(os.path.join(samples_path, 'train')) os.makedirs(os.path.join(samples_path, 'test')) logging.info('***** CICDDoS samples directory created for the first time *****') # Consider class labels present in both datasets class_labels = ['Benign', 'LDAP', 'MSSQL', 'NetBIOS', 'Syn', 'UDPLag','UDP'] for label in class_labels: print('Sampling on label: {}...'.format(label)) filename = str(label) + '.csv' train = pd.read_csv(os.path.join(path, 'training', filename)) test = pd.read_csv(os.path.join(path, 'testing', filename)) train = helper_function(train, label, tr_samples) # data cleaning & sampling test = helper_function(test, label, ts_samples) train.to_csv(os.path.join(samples_path, 'train', filename), index=False) test.to_csv(os.path.join(samples_path, 'test', filename), index=False) logging.info(f'***** {label} sampled in both directories\tTotal length: {len(train)} | {len(test)} *****') print(f'***** {label} sampled in both directories\tTotal length: {len(train)} | {len(test)} *****') def _nsl_kdd(self, path, add_cols): cols = pd.read_csv(os.path.join(path, 'Field Names.csv'), header=None) cols = cols.append([['label', 'Symbolic'], ['difficulty', 'Symbolic']]) cols.columns = ['Name', 'Type'] train = pd.read_csv(os.path.join(path, 'KDDTrain+.csv'), header=None) train.columns = cols['Name'].T test = pd.read_csv(os.path.join(path, 'KDDTest+.csv'), header=None) test.columns = cols['Name'].T with open(os.path.join(path, 'label_map.json'), 'r') as file: label_map = json.load(file) train['Label'] = train.label.map(label_map) test['Label'] = test.label.map(label_map) train, test = self.dhObj.drop_unique_cols(train, test, add_cols) return train, test def _cicddos(self, path: str, sample_data_flag: bool, add_cols: list): ''' Reads CICDDoS dataset and performs basic preprocessing steps. :param path: raw dataset directory path :param sample_data_flag: if True samples from the raw dataset in defined ratios :param add_cols: additional columns to be dropped [dataset specific] :return: train and test sets. ''' def helper_function(pth): df = pd.DataFrame() # holds records from all individual files as [train, test] for fPath in glob.glob(pth): subset = pd.read_csv(fPath) df = df.append(subset) column_names = [c.replace(' ', '') for c in df.columns] df.columns = column_names df = self.dhObj.rename_class_labels(df, 'CICDDoS') return df if sample_data_flag: self._sample_cicddos(path, tr_samples=60000, ts_samples=60000) tr_path = os.path.join(path, 'samples', 'train', '*csv') ts_path = os.path.join(path, 'samples', 'test', '*csv') train = helper_function(tr_path) test = helper_function(ts_path) train, test = self.dhObj.drop_unique_cols(train, test, add_cols) return train, test def _cicids(self, path: str, add_cols: list): ''' Reads CICIDS dataset and performs basic preprocessing steps. :param path: raw dataset directory path :param add_cols: additional columns to be dropped [dataset specific] :return: train and test sets. ''' def helper_function(path): df = pd.DataFrame() for root, dirs, files in os.walk(path): for file in files: if file.endswith(".csv"): print(f'Reading {file}...') subset = pd.read_csv(os.path.join(root, file)) df = pd.concat([df, subset], ignore_index=True) column_names = [c.replace(' ', '') for c in df.columns] df.columns = column_names df = self.dhObj.rename_class_labels(df, 'CICIDS') return df df = helper_function(path) Y = df.pop('Label').to_frame() X = df x_train, x_test, y_train, y_test = train_test_split(X, Y, stratify=Y, test_size=0.2) train =
pd.concat([x_train, y_train], axis=1)
pandas.concat
import numpy as np import pandas as pd from pandas.testing import assert_series_equal import pytest from ber_public.deap import dim from ber_public.deap import fab from ber_public.deap import vent def test_calculate_fabric_heat_loss(): """Output is equivalent to DEAP 4.2.0 example A""" floor_area = pd.Series([63]) roof_area = pd.Series([63]) wall_area = pd.Series([85.7]) window_area = pd.Series([29.6]) door_area = pd.Series([1.85]) floor_uvalue = pd.Series([0.14]) roof_uvalue = pd.Series([0.11]) wall_uvalue = pd.Series([0.13]) window_uvalue = pd.Series([0.87]) door_uvalue = pd.Series([1.5]) thermal_bridging_factor = pd.Series([0.05]) expected_output = pd.Series([68], dtype="int64") output = fab.calculate_fabric_heat_loss( roof_area=roof_area, roof_uvalue=roof_uvalue, wall_area=wall_area, wall_uvalue=wall_uvalue, floor_area=floor_area, floor_uvalue=floor_uvalue, window_area=window_area, window_uvalue=window_uvalue, door_area=door_area, door_uvalue=door_uvalue, thermal_bridging_factor=thermal_bridging_factor, ) rounded_output = output.round().astype("int64") assert_series_equal(rounded_output, expected_output) def test_calculate_heat_loss_parameter(): """Output is equivalent to DEAP 4.2.0 example A""" fabric_heat_loss =
pd.Series([0.5])
pandas.Series
import numpy as np import pandas as pd import re import glob from flask import Flask, request, render_template, url_for from flask_cors import CORS from werkzeug.utils import secure_filename import os import logging logging.basicConfig(level=logging.INFO) import tensorflow as tf import silence_tensorflow.auto # pylint: disable=unused-import #physical_devices = tf.config.experimental.list_physical_devices('GPU') #tf.config.experimental.set_memory_growth(physical_devices[0], True) from tensorflow.keras.applications import VGG19 from tensorflow.keras.layers import Input from tensorflow.keras.preprocessing import sequence from models.arch import build_model from models.layers import ContextVector, PhraseLevelFeatures, AttentionMaps from utils.load_pickles import tok, labelencoder from utils.helper_functions import image_feature_extractor, process_sentence, predict_answers max_answers = 1000 max_seq_len = 22 vocab_size = len(tok.word_index) + 1 dim_d = 512 dim_k = 256 l_rate = 1e-4 d_rate = 0.5 reg_value = 0.01 MODEL_PATH = 'pickles/complete_model.h5' IMAGE_PATH = 'static' custom_objects = { 'PhraseLevelFeatures': PhraseLevelFeatures, 'AttentionMaps': AttentionMaps, 'ContextVector': ContextVector } # load the model model = tf.keras.models.load_model(MODEL_PATH, custom_objects=custom_objects) vgg_model = VGG19(weights="imagenet", include_top=False) # Create Flask application app = Flask(__name__, static_url_path='/static') CORS(app) @app.route('/') def home(): return render_template('index.html') @app.route('/', methods=['POST']) def predict(): try: # delete images uploaded in previous session files = glob.glob(IMAGE_PATH+'/*') for f in files: os.remove(f) #0 --- Get the image file and question f = request.files['image_file'] fname = secure_filename(f.filename) f.save(IMAGE_PATH +'/'+ fname) question = request.form["question"] #1 --- Extract image features img_feat = image_feature_extractor(IMAGE_PATH +'/'+ fname, vgg_model) #2 --- Clean the question questions_processed =
pd.Series(question)
pandas.Series
"""Problems module for mathematical optimization and simulation problem type definitions.""" import itertools from multimethod import multimethod import numpy as np import pandas as pd import tqdm import typing import mesmo.config import mesmo.data_interface import mesmo.der_models import mesmo.electric_grid_models import mesmo.solutions import mesmo.thermal_grid_models import mesmo.utils logger = mesmo.config.get_logger(__name__) class Results( mesmo.electric_grid_models.ElectricGridOperationResults, mesmo.thermal_grid_models.ThermalGridOperationResults, mesmo.der_models.DERModelSetOperationResults, mesmo.electric_grid_models.ElectricGridDLMPResults, mesmo.thermal_grid_models.ThermalGridDLMPResults, ): """Results object, which serves as data object to hold structured results variables from solved problems.""" price_data: mesmo.data_interface.PriceData class ResultsDict(typing.Dict[str, Results]): """Results dictionary, which serves as collection object for labelled results objects.""" class ProblemBase(mesmo.utils.ObjectBase): """Problem base object, which serves as abstract base class for problem objects.""" def solve(self): raise NotImplementedError def get_results(self) -> Results: raise NotImplementedError class ProblemDict(typing.Dict[str, ProblemBase]): """Problem dictionary, which serves as collection object for labelled problem objects.""" def solve(self): """Solve all problems within this `ProblemDict`.""" # Loop over problems with tqdm to show progress bar. mesmo.utils.log_time("solve problems", logger_object=logger) for problem in tqdm.tqdm( self.values(), total=len(self), disable=(mesmo.config.config["logs"]["level"] != "debug"), # Progress bar only shown in debug mode. ): # Solve individual problem. problem.solve() mesmo.utils.log_time("solve problems", logger_object=logger) def get_results(self) -> ResultsDict: """Get results for all problems within this `ProblemDict`.""" # Instantiate results dict. results_dict = ResultsDict({label: Results() for label in self.keys()}) # Loop over problems with tqdm to show progress bar. mesmo.utils.log_time("get results", logger_object=logger) for label, problem in tqdm.tqdm( self.items(), total=len(self), disable=(mesmo.config.config["logs"]["level"] != "debug"), # Progress bar only shown in debug mode. ): # Get individual results. results_dict[label] = problem.get_results() mesmo.utils.log_time("get results", logger_object=logger) return results_dict class NominalOperationProblem(ProblemBase): """Nominal operation problem object, consisting of the corresponding electric / thermal grid models, reference power flow solutions and DER model set for the given scenario. - The nominal operation problem (alias: simulation problem, power flow problem) represents the simulation problem of the DERs and grids considering the nominal operation schedule for all DERs. - The problem formulation is able to consider combined as well as individual operation of thermal and electric grids. """ scenario_name: str timesteps: pd.Index price_data: mesmo.data_interface.PriceData electric_grid_model: mesmo.electric_grid_models.ElectricGridModel = None thermal_grid_model: mesmo.thermal_grid_models.ThermalGridModel = None der_model_set: mesmo.der_models.DERModelSet results: Results @multimethod def __init__( self, scenario_name: str, electric_grid_model: mesmo.electric_grid_models.ElectricGridModel = None, thermal_grid_model: mesmo.thermal_grid_models.ThermalGridModel = None, der_model_set: mesmo.der_models.DERModelSet = None, ): # Obtain data. scenario_data = mesmo.data_interface.ScenarioData(scenario_name) self.price_data = mesmo.data_interface.PriceData(scenario_name) # Store timesteps. self.timesteps = scenario_data.timesteps # Obtain electric grid model, power flow solution and linear model, if defined. if pd.notnull(scenario_data.scenario.at["electric_grid_name"]): if electric_grid_model is not None: self.electric_grid_model = electric_grid_model else: mesmo.utils.log_time("electric grid model instantiation") self.electric_grid_model = mesmo.electric_grid_models.ElectricGridModel(scenario_name) mesmo.utils.log_time("electric grid model instantiation") # Obtain thermal grid model, power flow solution and linear model, if defined. if pd.notnull(scenario_data.scenario.at["thermal_grid_name"]): if thermal_grid_model is not None: self.thermal_grid_model = thermal_grid_model else: mesmo.utils.log_time("thermal grid model instantiation") self.thermal_grid_model = mesmo.thermal_grid_models.ThermalGridModel(scenario_name) mesmo.utils.log_time("thermal grid model instantiation") # Obtain DER model set. if der_model_set is not None: self.der_model_set = der_model_set else: mesmo.utils.log_time("DER model instantiation") self.der_model_set = mesmo.der_models.DERModelSet(scenario_name) mesmo.utils.log_time("DER model instantiation") def solve(self): # Instantiate results variables. if self.electric_grid_model is not None: der_power_vector = pd.DataFrame(columns=self.electric_grid_model.ders, index=self.timesteps, dtype=complex) node_voltage_vector = pd.DataFrame( columns=self.electric_grid_model.nodes, index=self.timesteps, dtype=complex ) branch_power_vector_1 = pd.DataFrame( columns=self.electric_grid_model.branches, index=self.timesteps, dtype=complex ) branch_power_vector_2 = pd.DataFrame( columns=self.electric_grid_model.branches, index=self.timesteps, dtype=complex ) loss = pd.DataFrame(columns=["total"], index=self.timesteps, dtype=complex) if self.thermal_grid_model is not None: der_thermal_power_vector = pd.DataFrame( columns=self.thermal_grid_model.ders, index=self.timesteps, dtype=float ) node_head_vector = pd.DataFrame(columns=self.thermal_grid_model.nodes, index=self.timesteps, dtype=float) branch_flow_vector = pd.DataFrame( columns=self.thermal_grid_model.branches, index=self.timesteps, dtype=float ) pump_power = pd.DataFrame(columns=["total"], index=self.timesteps, dtype=float) # Obtain nominal DER power vector. if self.electric_grid_model is not None: for der in self.electric_grid_model.ders: # TODO: Use ders instead of der_names for der_models index. der_name = der[1] der_power_vector.loc[:, der] = self.der_model_set.der_models[ der_name ].active_power_nominal_timeseries + ( 1.0j * self.der_model_set.der_models[der_name].reactive_power_nominal_timeseries ) if self.thermal_grid_model is not None: for der in self.electric_grid_model.ders: der_name = der[1] der_thermal_power_vector.loc[:, der] = self.der_model_set.der_models[ der_name ].thermal_power_nominal_timeseries # Solve power flow. mesmo.utils.log_time("power flow solution") if self.electric_grid_model is not None: power_flow_solutions = mesmo.utils.starmap( mesmo.electric_grid_models.PowerFlowSolutionFixedPoint, zip(itertools.repeat(self.electric_grid_model), der_power_vector.values), ) power_flow_solutions = dict(zip(self.timesteps, power_flow_solutions)) if self.thermal_grid_model is not None: thermal_power_flow_solutions = mesmo.utils.starmap( mesmo.thermal_grid_models.ThermalPowerFlowSolution, [(self.thermal_grid_model, row) for row in der_thermal_power_vector.values], ) thermal_power_flow_solutions = dict(zip(self.timesteps, thermal_power_flow_solutions)) mesmo.utils.log_time("power flow solution") # Obtain results. if self.electric_grid_model is not None: for timestep in self.timesteps: power_flow_solution = power_flow_solutions[timestep] # TODO: Flatten power flow solution arrays. node_voltage_vector.loc[timestep, :] = power_flow_solution.node_voltage_vector branch_power_vector_1.loc[timestep, :] = power_flow_solution.branch_power_vector_1 branch_power_vector_2.loc[timestep, :] = power_flow_solution.branch_power_vector_2 loss.loc[timestep, :] = power_flow_solution.loss der_active_power_vector = der_power_vector.apply(np.real) der_reactive_power_vector = der_power_vector.apply(np.imag) node_voltage_magnitude_vector = np.abs(node_voltage_vector) node_voltage_angle_vector = np.angle(node_voltage_vector) branch_power_magnitude_vector_1 = np.abs(branch_power_vector_1) branch_active_power_vector_1 = np.real(branch_power_vector_1) branch_reactive_power_vector_1 = np.imag(branch_power_vector_1) branch_power_magnitude_vector_2 = np.abs(branch_power_vector_2) branch_active_power_vector_2 = np.real(branch_power_vector_2) branch_reactive_power_vector_2 = np.imag(branch_power_vector_2) loss_active = loss.apply(np.real) loss_reactive = loss.apply(np.imag) if self.thermal_grid_model is not None: for timestep in self.timesteps: thermal_power_flow_solution = thermal_power_flow_solutions[timestep] node_head_vector.loc[timestep, :] = thermal_power_flow_solution.node_head_vector branch_flow_vector.loc[timestep, :] = thermal_power_flow_solution.branch_flow_vector pump_power.loc[timestep, :] = thermal_power_flow_solution.pump_power # Obtain per-unit values. if self.electric_grid_model is not None: der_active_power_vector_per_unit = der_active_power_vector * mesmo.utils.get_inverse_with_zeros( np.real(self.electric_grid_model.der_power_vector_reference) ) der_reactive_power_vector_per_unit = der_reactive_power_vector * mesmo.utils.get_inverse_with_zeros( np.imag(self.electric_grid_model.der_power_vector_reference) ) node_voltage_magnitude_vector_per_unit = node_voltage_magnitude_vector * mesmo.utils.get_inverse_with_zeros( np.abs(self.electric_grid_model.node_voltage_vector_reference) ) branch_power_magnitude_vector_1_per_unit = ( branch_power_magnitude_vector_1 * mesmo.utils.get_inverse_with_zeros(self.electric_grid_model.branch_power_vector_magnitude_reference) ) branch_active_power_vector_1_per_unit = branch_active_power_vector_1 * mesmo.utils.get_inverse_with_zeros( self.electric_grid_model.branch_power_vector_magnitude_reference ) branch_reactive_power_vector_1_per_unit = ( branch_reactive_power_vector_1 * mesmo.utils.get_inverse_with_zeros(self.electric_grid_model.branch_power_vector_magnitude_reference) ) branch_power_magnitude_vector_2_per_unit = ( branch_power_magnitude_vector_2 * mesmo.utils.get_inverse_with_zeros(self.electric_grid_model.branch_power_vector_magnitude_reference) ) branch_active_power_vector_2_per_unit = branch_active_power_vector_2 * mesmo.utils.get_inverse_with_zeros( self.electric_grid_model.branch_power_vector_magnitude_reference ) branch_reactive_power_vector_2_per_unit = ( branch_reactive_power_vector_2 * mesmo.utils.get_inverse_with_zeros(self.electric_grid_model.branch_power_vector_magnitude_reference) ) if self.thermal_grid_model is not None: der_thermal_power_vector_per_unit = der_thermal_power_vector * mesmo.utils.get_inverse_with_zeros( self.thermal_grid_model.der_thermal_power_vector_reference ) node_head_vector_per_unit = node_head_vector * mesmo.utils.get_inverse_with_zeros( self.thermal_grid_model.node_head_vector_reference ) branch_flow_vector_per_unit = branch_flow_vector * mesmo.utils.get_inverse_with_zeros( self.thermal_grid_model.branch_flow_vector_reference ) # Store results. self.results = Results(price_data=self.price_data, der_model_set=self.der_model_set) if self.electric_grid_model is not None: self.results.update( Results( electric_grid_model=self.electric_grid_model, der_active_power_vector=der_active_power_vector, der_active_power_vector_per_unit=der_active_power_vector_per_unit, der_reactive_power_vector=der_reactive_power_vector, der_reactive_power_vector_per_unit=der_reactive_power_vector_per_unit, node_voltage_magnitude_vector=node_voltage_magnitude_vector, node_voltage_magnitude_vector_per_unit=node_voltage_magnitude_vector_per_unit, node_voltage_angle_vector=node_voltage_angle_vector, branch_power_magnitude_vector_1=branch_power_magnitude_vector_1, branch_power_magnitude_vector_1_per_unit=branch_power_magnitude_vector_1_per_unit, branch_active_power_vector_1=branch_active_power_vector_1, branch_active_power_vector_1_per_unit=branch_active_power_vector_1_per_unit, branch_reactive_power_vector_1=branch_reactive_power_vector_1, branch_reactive_power_vector_1_per_unit=branch_reactive_power_vector_1_per_unit, branch_power_magnitude_vector_2=branch_power_magnitude_vector_2, branch_power_magnitude_vector_2_per_unit=branch_power_magnitude_vector_2_per_unit, branch_active_power_vector_2=branch_active_power_vector_2, branch_active_power_vector_2_per_unit=branch_active_power_vector_2_per_unit, branch_reactive_power_vector_2=branch_reactive_power_vector_2, branch_reactive_power_vector_2_per_unit=branch_reactive_power_vector_2_per_unit, loss_active=loss_active, loss_reactive=loss_reactive, ) ) if self.thermal_grid_model is not None: self.results.update( Results( thermal_grid_model=self.thermal_grid_model, der_thermal_power_vector=der_thermal_power_vector, der_thermal_power_vector_per_unit=der_thermal_power_vector_per_unit, node_head_vector=node_head_vector, node_head_vector_per_unit=node_head_vector_per_unit, branch_flow_vector=branch_flow_vector, branch_flow_vector_per_unit=branch_flow_vector_per_unit, pump_power=pump_power, ) ) def get_results(self): return self.results class OptimalOperationProblem(ProblemBase): """Optimal operation problem object, consisting of an optimization problem as well as the corresponding electric / thermal grid models, reference power flow solutions, linear grid models and DER model set for the given scenario. - The optimal operation problem (alias: optimal dispatch problem, optimal power flow problem) formulates the optimization problem for minimizing the objective functions of DERs and grid operators subject to the model constraints of all DERs and grids. - The problem formulation is able to consider combined as well as individual operation of thermal and electric grids. Keyword Arguments: solve_method (str): Solve method for the optimization problem. If `None` or 'default', it will use the default method of solving a single-shot optimization using the global approximation method. If 'trust_region', it will solve iteratively via trust-region method using the local approximation method. Choices: 'default', 'trust_region', `None`. Default: `None`. """ solve_method: str scenario_name: str scenario_data: mesmo.data_interface.ScenarioData timesteps: pd.Index price_data: mesmo.data_interface.PriceData electric_grid_model: mesmo.electric_grid_models.ElectricGridModel = None power_flow_solution_reference: mesmo.electric_grid_models.PowerFlowSolutionBase = None linear_electric_grid_model_set: mesmo.electric_grid_models.LinearElectricGridModelSet = None thermal_grid_model: mesmo.thermal_grid_models.ThermalGridModel = None thermal_power_flow_solution_reference: mesmo.thermal_grid_models.ThermalPowerFlowSolution = None linear_thermal_grid_model_set: mesmo.thermal_grid_models.LinearThermalGridModelSet = None der_model_set: mesmo.der_models.DERModelSet optimization_problem: mesmo.solutions.OptimizationProblem results: Results @multimethod def __init__( self, scenario_name: str, electric_grid_model: mesmo.electric_grid_models.ElectricGridModel = None, thermal_grid_model: mesmo.thermal_grid_models.ThermalGridModel = None, der_model_set: mesmo.der_models.DERModelSet = None, solve_method: str = None, ): # Obtain solve method. if solve_method in [None, "default"]: self.solve_method = "default" elif solve_method == "trust_region": self.solve_method = "trust_region" else: raise ValueError(f"Unknown solve method for optimal operation problem: {solve_method}") # Obtain and store data. self.scenario_name = scenario_name self.scenario_data = mesmo.data_interface.ScenarioData(scenario_name) self.timesteps = self.scenario_data.timesteps self.price_data = mesmo.data_interface.PriceData(scenario_name) # Obtain electric grid model, power flow solution and linear model, if defined. if pd.notnull(self.scenario_data.scenario.at["electric_grid_name"]): mesmo.utils.log_time("electric grid model instantiation") if electric_grid_model is not None: self.electric_grid_model = electric_grid_model else: self.electric_grid_model = mesmo.electric_grid_models.ElectricGridModel(scenario_name) self.power_flow_solution_reference = mesmo.electric_grid_models.PowerFlowSolutionFixedPoint( self.electric_grid_model ) self.linear_electric_grid_model_set = mesmo.electric_grid_models.LinearElectricGridModelSet( self.electric_grid_model, self.power_flow_solution_reference, linear_electric_grid_model_method=mesmo.electric_grid_models.LinearElectricGridModelGlobal, ) mesmo.utils.log_time("electric grid model instantiation") # Obtain thermal grid model, power flow solution and linear model, if defined. if
pd.notnull(self.scenario_data.scenario.at["thermal_grid_name"])
pandas.notnull
import pytest import inspect try: import pandas as pd import test_aide.pandas as ph has_pandas = True except ModuleNotFoundError: has_pandas = False @pytest.mark.skipif(not has_pandas, reason="pandas not installed") def test_arguments(): """Test arguments for arguments of test_aide.pandas._check_dfs_passed.""" expected_arguments = ["df_1", "df_2"] arg_spec = inspect.getfullargspec(ph._check_dfs_passed) arguments = arg_spec.args assert len(expected_arguments) == len( arguments ), f"Incorrect number of arguments -\n Expected: {len(expected_arguments)}\n Actual: {len(arguments)}" assert ( expected_arguments == arguments ), f"Incorrect arguments -\n Expected: {expected_arguments}\n Actual: {arguments}" default_values = arg_spec.defaults assert ( default_values is None ), f"Unexpected default values -\n Expected: None\n Actual: {default_values}" @pytest.mark.skipif(not has_pandas, reason="pandas not installed") def test_exceptions_raised(): """Test that the expected exceptions are raised by test_aide.pandas._check_dfs_passed.""" with pytest.raises( TypeError, match=r"expecting first positional arg to be a pd.DataFrame.*" ): ph._check_dfs_passed(1, pd.DataFrame()) with pytest.raises( TypeError, match=r"expecting second positional arg to be a pd.DataFrame.*" ): ph._check_dfs_passed(pd.DataFrame(), 1) with pytest.raises( ValueError, match=r"expecting first positional arg and second positional arg to have equal number of rows but got\n 1\n 0", ): ph._check_dfs_passed(pd.DataFrame({"a": 1}, index=[0]),
pd.DataFrame()
pandas.DataFrame
import sys,os #os.chdir("/Users/utkarshvirendranigam/Desktop/Homework/Project") # required_packages=["PyQt5","re", "scipy","itertools","random","matplotlib","pandas","numpy","sklearn","pydotplus","collections","warnings","seaborn"] #print(os.getcwd()) # for my_package in required_packages: # try: # command_string="conda install "+ my_package+ " --yes" # os.system(command_string) # except: # count=1 from PyQt5.QtWidgets import (QMainWindow, QApplication, QWidget, QPushButton, QAction, QComboBox, QLabel, QGridLayout, QCheckBox, QGroupBox, QVBoxLayout, QHBoxLayout, QLineEdit, QPlainTextEdit) from PyQt5.QtGui import QIcon from PyQt5.QtCore import pyqtSlot, QRect from PyQt5.QtCore import pyqtSignal from PyQt5.QtCore import Qt # from scipy import interp from itertools import cycle, combinations import random from PyQt5.QtWidgets import QDialog, QVBoxLayout, QSizePolicy, QFormLayout, QRadioButton, QScrollArea, QMessageBox from PyQt5.QtGui import QPixmap from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar from matplotlib.figure import Figure import pandas as pd import numpy as np import pickle from numpy.polynomial.polynomial import polyfit from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split, cross_val_score from sklearn.tree import DecisionTreeClassifier, export_graphviz from sklearn.compose import make_column_transformer from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_curve, auc, log_loss, brier_score_loss from sklearn.calibration import calibration_curve from sklearn.linear_model import LogisticRegression from sklearn.ensemble import GradientBoostingClassifier from sklearn import feature_selection from sklearn import metrics from sklearn.preprocessing import label_binarize from sklearn.model_selection import cross_val_predict # Libraries to display decision tree from pydotplus import graph_from_dot_data import collections from sklearn.tree import export_graphviz import webbrowser import warnings warnings.filterwarnings("ignore") import matplotlib.pyplot as plt from Preprocessing import PreProcessing import random import seaborn as sns #%%----------------------------------------------------------------------- import os os.environ["PATH"] += os.pathsep + 'C:\\Program Files (x86)\\graphviz-2.38\\release\\bin' #%%----------------------------------------------------------------------- #::-------------------------------- # Deafault font size for all the windows #::-------------------------------- font_size_window = 'font-size:18px' class DecisionTree(QMainWindow): #::-------------------------------------------------------------------------------- # Implementation of Random Forest Classifier using the happiness dataset # the methods in this class are # _init_ : initialize the class # initUi : creates the canvas and all the elements in the canvas # update : populates the elements of the canvas base on the parametes # chosen by the user #::--------------------------------------------------------------------------------- send_fig = pyqtSignal(str) def __init__(self): super(DecisionTree, self).__init__() self.Title = "Decision Tree Classifier" self.initUi() def initUi(self): #::----------------------------------------------------------------- # Create the canvas and all the element to create a dashboard with # all the necessary elements to present the results from the algorithm # The canvas is divided using a grid loyout to facilitate the drawing # of the elements #::----------------------------------------------------------------- self.setWindowTitle(self.Title) self.setStyleSheet(font_size_window) self.main_widget = QWidget(self) self.layout = QGridLayout(self.main_widget) self.groupBox1 = QGroupBox('Decision Tree Features') self.groupBox1Layout= QGridLayout() self.groupBox1.setLayout(self.groupBox1Layout) self.feature0 = QCheckBox(features_list[0],self) self.feature1 = QCheckBox(features_list[1],self) self.feature2 = QCheckBox(features_list[2], self) self.feature3 = QCheckBox(features_list[3], self) self.feature4 = QCheckBox(features_list[4],self) self.feature5 = QCheckBox(features_list[5],self) self.feature6 = QCheckBox(features_list[6], self) self.feature7 = QCheckBox(features_list[7], self) self.feature8 = QCheckBox(features_list[8], self) self.feature9 = QCheckBox(features_list[9], self) self.feature10 = QCheckBox(features_list[10], self) self.feature11 = QCheckBox(features_list[11], self) self.feature12 = QCheckBox(features_list[12], self) self.feature13 = QCheckBox(features_list[13], self) self.feature14 = QCheckBox(features_list[14], self) self.feature15 = QCheckBox(features_list[15], self) self.feature16 = QCheckBox(features_list[16], self) self.feature17 = QCheckBox(features_list[17], self) self.feature18 = QCheckBox(features_list[18], self) self.feature19 = QCheckBox(features_list[19], self) self.feature20 = QCheckBox(features_list[20], self) self.feature21 = QCheckBox(features_list[21], self) self.feature22 = QCheckBox(features_list[22], self) self.feature23 = QCheckBox(features_list[23], self) self.feature24 = QCheckBox(features_list[24], self) self.feature0.setChecked(True) self.feature1.setChecked(True) self.feature2.setChecked(True) self.feature3.setChecked(True) self.feature4.setChecked(True) self.feature5.setChecked(True) self.feature6.setChecked(True) self.feature7.setChecked(True) self.feature8.setChecked(True) self.feature9.setChecked(True) self.feature10.setChecked(True) self.feature11.setChecked(True) self.feature12.setChecked(True) self.feature13.setChecked(True) self.feature14.setChecked(True) self.feature15.setChecked(True) self.feature16.setChecked(True) self.feature17.setChecked(True) self.feature18.setChecked(True) self.feature19.setChecked(True) self.feature20.setChecked(True) self.feature21.setChecked(True) self.feature22.setChecked(True) self.feature23.setChecked(True) self.feature24.setChecked(True) self.lblPercentTest = QLabel('Percentage for Test :') self.lblPercentTest.adjustSize() self.txtPercentTest = QLineEdit(self) self.txtPercentTest.setText("30") self.lblMaxDepth = QLabel('Maximun Depth :') self.txtMaxDepth = QLineEdit(self) self.txtMaxDepth.setText("3") self.btnExecute = QPushButton("Run Model") self.btnExecute.setGeometry(QRect(60, 500, 75, 23)) self.btnExecute.clicked.connect(self.update) self.btnDTFigure = QPushButton("View Tree") self.btnDTFigure.setGeometry(QRect(60, 500, 75, 23)) self.btnDTFigure.clicked.connect(self.view_tree) # We create a checkbox for each feature self.groupBox1Layout.addWidget(self.feature0, 0, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature1, 0, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature2, 1, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature3, 1, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature4, 2, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature5, 2, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature6, 3, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature7, 3, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature8, 4, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature9, 4, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature10, 5, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature11, 5, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature12, 6, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature13, 6, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature14, 7, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature15, 7, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature16, 8, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature17, 8, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature18, 9, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature19, 9, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature20, 10, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature21, 10, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature22, 11, 0, 1, 1) self.groupBox1Layout.addWidget(self.feature23, 11, 1, 1, 1) self.groupBox1Layout.addWidget(self.feature24, 12, 0, 1, 1) self.groupBox1Layout.addWidget(self.lblPercentTest, 19, 0, 1, 1) self.groupBox1Layout.addWidget(self.txtPercentTest, 19, 1, 1, 1) self.groupBox1Layout.addWidget(self.lblMaxDepth, 20, 0, 1, 1) self.groupBox1Layout.addWidget(self.txtMaxDepth, 20, 1, 1, 1) self.groupBox1Layout.addWidget(self.btnExecute, 21, 0, 1, 1) self.groupBox1Layout.addWidget(self.btnDTFigure, 21, 1, 1, 1) self.groupBox2 = QGroupBox('Measurements:') self.groupBox2Layout = QVBoxLayout() self.groupBox2.setLayout(self.groupBox2Layout) # self.groupBox2.setMinimumSize(400, 100) self.current_model_summary = QWidget(self) self.current_model_summary.layout = QFormLayout(self.current_model_summary) self.txtCurrentAccuracy = QLineEdit() self.txtCurrentPrecision = QLineEdit() self.txtCurrentRecall = QLineEdit() self.txtCurrentF1score = QLineEdit() self.current_model_summary.layout.addRow('Accuracy:', self.txtCurrentAccuracy) self.current_model_summary.layout.addRow('Precision:', self.txtCurrentPrecision) self.current_model_summary.layout.addRow('Recall:', self.txtCurrentRecall) self.current_model_summary.layout.addRow('F1 Score:', self.txtCurrentF1score) self.groupBox2Layout.addWidget(self.current_model_summary) self.groupBox3 = QGroupBox('Other Models Accuracy:') self.groupBox3Layout = QVBoxLayout() self.groupBox3.setLayout(self.groupBox3Layout) self.other_models = QWidget(self) self.other_models.layout = QFormLayout(self.other_models) self.txtAccuracy_lr = QLineEdit() self.txtAccuracy_gb = QLineEdit() self.txtAccuracy_rf = QLineEdit() self.other_models.layout.addRow('Logistic:', self.txtAccuracy_lr) self.other_models.layout.addRow('Random Forest:', self.txtAccuracy_rf) self.other_models.layout.addRow('Gradient Boosting:', self.txtAccuracy_gb) self.groupBox3Layout.addWidget(self.other_models) #::------------------------------------- # Graphic 1 : Confusion Matrix #::------------------------------------- self.fig = Figure() self.ax1 = self.fig.add_subplot(111) self.axes=[self.ax1] self.canvas = FigureCanvas(self.fig) self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.canvas.updateGeometry() self.groupBoxG1 = QGroupBox('Confusion Matrix') self.groupBoxG1Layout= QVBoxLayout() self.groupBoxG1.setLayout(self.groupBoxG1Layout) self.groupBoxG1Layout.addWidget(self.canvas) #::--------------------------------------------- # Graphic 2 : ROC Curve #::--------------------------------------------- self.fig2 = Figure() self.ax2 = self.fig2.add_subplot(111) self.axes2 = [self.ax2] self.canvas2 = FigureCanvas(self.fig2) self.canvas2.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.canvas2.updateGeometry() self.groupBoxG2 = QGroupBox('ROC Curve') self.groupBoxG2Layout = QVBoxLayout() self.groupBoxG2.setLayout(self.groupBoxG2Layout) self.groupBoxG2Layout.addWidget(self.canvas2) #::------------------------------------------- # Graphic 3 : Importance of Features #::------------------------------------------- self.fig3 = Figure() self.ax3 = self.fig3.add_subplot(111) self.axes3 = [self.ax3] self.canvas3 = FigureCanvas(self.fig3) self.canvas3.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.canvas3.updateGeometry() self.groupBoxG3 = QGroupBox('Importance of Features') self.groupBoxG3Layout = QVBoxLayout() self.groupBoxG3.setLayout(self.groupBoxG3Layout) self.groupBoxG3Layout.addWidget(self.canvas3) #::-------------------------------------------- # Graphic 4 : ROC Curve by class #::-------------------------------------------- self.fig4 = Figure() self.ax4 = self.fig4.add_subplot(111) self.axes4 = [self.ax4] self.canvas4 = FigureCanvas(self.fig4) self.canvas4.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) self.canvas4.updateGeometry() self.groupBoxG4 = QGroupBox('ROC Curve by Class') self.groupBoxG4Layout = QVBoxLayout() self.groupBoxG4.setLayout(self.groupBoxG4Layout) self.groupBoxG4Layout.addWidget(self.canvas4) #::------------------------------------------------- # End of graphs #::------------------------------------------------- self.layout.addWidget(self.groupBox1, 0, 0, 3, 2) self.layout.addWidget(self.groupBoxG1, 0, 2, 1, 1) self.layout.addWidget(self.groupBoxG3, 0, 3, 1, 1) self.layout.addWidget(self.groupBoxG2, 1, 2, 1, 1) self.layout.addWidget(self.groupBoxG4, 1, 3, 1, 1) self.layout.addWidget(self.groupBox2, 2, 2, 1, 1) self.layout.addWidget(self.groupBox3, 2, 3, 1, 1) self.setCentralWidget(self.main_widget) self.resize(1800, 1200) self.show() def update(self): ''' Random Forest Classifier We pupulate the dashboard using the parametres chosen by the user The parameters are processed to execute in the skit-learn Random Forest algorithm then the results are presented in graphics and reports in the canvas :return:None ''' # processing the parameters self.list_corr_features = pd.DataFrame([]) if self.feature0.isChecked(): if len(self.list_corr_features)==0: self.list_corr_features = df[features_list[0]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[0]]],axis=1) if self.feature1.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[1]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[1]]],axis=1) if self.feature2.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[2]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[2]]],axis=1) if self.feature3.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[3]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[3]]],axis=1) if self.feature4.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[4]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[4]]],axis=1) if self.feature5.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[5]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[5]]],axis=1) if self.feature6.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[6]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[6]]],axis=1) if self.feature7.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[7]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[7]]],axis=1) if self.feature8.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[8]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[8]]],axis=1) if self.feature9.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[9]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[9]]],axis=1) if self.feature10.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[10]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[10]]], axis=1) if self.feature11.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[11]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[11]]], axis=1) if self.feature12.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[12]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[12]]], axis=1) if self.feature13.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[13]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[13]]], axis=1) if self.feature14.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[14]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[14]]], axis=1) if self.feature15.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[15]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[15]]], axis=1) if self.feature16.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[16]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[16]]], axis=1) if self.feature17.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[17]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[17]]], axis=1) if self.feature18.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[18]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[18]]], axis=1) if self.feature19.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[19]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[19]]], axis=1) if self.feature20.isChecked(): if len(self.list_corr_features)==0: self.list_corr_features = df[features_list[20]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[20]]],axis=1) if self.feature21.isChecked(): if len(self.list_corr_features) == 20: self.list_corr_features = df[features_list[1]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[21]]],axis=1) if self.feature22.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[22]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[22]]],axis=1) if self.feature23.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[23]] else: self.list_corr_features = pd.concat([self.list_corr_features, df[features_list[23]]],axis=1) if self.feature24.isChecked(): if len(self.list_corr_features) == 0: self.list_corr_features = df[features_list[24]] else: self.list_corr_features =
pd.concat([self.list_corr_features, df[features_list[24]]],axis=1)
pandas.concat
import bt import pandas as pd import yfinance as yf import matplotlib.pyplot as plt # Disable SettingWithCopyWarning pd.options.mode.chained_assignment = None ###### Fetching Data ###### # The tickers we're interested in tickers = [ 'SPY', 'VIRT', 'QQQ', 'TLT', 'GLD', 'UVXY', '^VIX' ] # Get maximum historical data for a single ticker def get_yf_hist(ticker): data = yf.Ticker(ticker).history(period='max')['Close'] data.rename(ticker, inplace=True) return data # For each ticker, get the historical data and add it to a list def get_data_for_tickers(tickers): data = [] for ticker in tickers: data.append(get_yf_hist(ticker)) df = pd.concat(data, axis=1) return df data = get_data_for_tickers(tickers) print(data) ###### Benchmark ###### # The name of our strategy name = 'long_spy' # Defining the actual strategy benchmark_strat = bt.Strategy( name, [ bt.algos.RunOnce(), bt.algos.SelectAll(), bt.algos.WeighEqually(), bt.algos.Rebalance() ] ) # Make sure we're only running on the SPY data by selecting it out, # and dropping the rows for which we have no data spy_data = data[['SPY']] spy_data.dropna(inplace=True) # Generate the backtest using the defined strategy and data and run it benchmark_test = bt.Backtest(benchmark_strat, spy_data) res = bt.run(benchmark_test) # Print the summary and plot our equity progression res.plot() res.display() plt.show() ###### Strategy 1 ###### name = 'spy_virt_7030' strategy_1 = bt.Strategy( name, [ bt.algos.RunDaily(), bt.algos.SelectAll(), bt.algos.WeighSpecified(SPY=0.7, VIRT=0.3), bt.algos.Rebalance() ] ) spy_virt_data = data[['SPY', 'VIRT']] spy_virt_data.dropna(inplace=True) backtest_1 = bt.Backtest(strategy_1, spy_virt_data) res = bt.run(backtest_1, benchmark_test) res.plot() res.display() plt.show() ###### Strategy 2 ###### name = 'eq_wt_monthly' strategy_2 = bt.Strategy( name, [ bt.algos.RunMonthly(run_on_end_of_period=True), bt.algos.SelectAll(), bt.algos.WeighEqually(), bt.algos.Rebalance() ] ) eq_wt_data = data[['SPY', 'QQQ', 'TLT', 'GLD']] eq_wt_data.dropna(inplace=True) backtest_2 = bt.Backtest(strategy_2, eq_wt_data) res = bt.run(backtest_2, benchmark_test) res.plot() res.display() plt.show() ###### Strategy 3 ###### # We're going to isolate our data here first, and then drop nulls, # since we need this series for the weights with the proper dates spy_hl_data = data[['SPY']] spy_hl_data.dropna(inplace=True) # Generate returns and isolate the return series for simplicity spy_ret = (spy_hl_data['SPY']/spy_hl_data['SPY'].shift(1)) - 1 # Rename for validation later spy_ret.rename('SPY_returns', inplace=True) # Create our weights series for SPY by copying the SPY price series target_weights = spy_hl_data['SPY'].copy() # Let's clear it and set all of them to None (you'll see why) target_weights[:] = None # We're going to start our strategy on day 1 with 100% SPY, so let's set the first weight to 1.0 (100%) target_weights.iloc[0] = 1.0 # Now we need to fill in the dates where we know we want to make a change: target_weights[spy_ret < 0.02] = 1.0 target_weights[spy_ret >= 0.02] = 0.5 # Weights need to be a DataFrame, not a series target_weights = pd.DataFrame(target_weights) # Now we want to fill each value forward to keep its previous allocation until we get an update # That is, since we initially set every day's weight to have no value, # and we only filled in day 1 at 100%, and filled in the days when we drop or gain, # we need to maintain the previous day's allocation until we get a change. # So, we use ffill to forward-fill our weights, which will fill in our nulls in order using # the most recent value seen that's not None/null. target_weights.ffill(inplace=True) # Let's make sure our prices, returns, and weights look ok validation = pd.concat([spy_hl_data, spy_ret, target_weights], axis=1) print(validation.tail(50)) name = 'spy_high_low' strategy_3 = bt.Strategy( name, [ bt.algos.RunDaily(), bt.algos.SelectAll(), bt.algos.WeighTarget(target_weights), bt.algos.Rebalance() ] ) backtest_3 = bt.Backtest(strategy_3, spy_hl_data) res = bt.run(backtest_3, benchmark_test) res.plot() res.plot_weights() # Let's also plot our weights this time res.display() plt.show() # How many days are our target weights at 0.5 divided by the total number of days print(target_weights[target_weights == 0.5].count()/len(target_weights)) ###### Strategy 4 ###### # Load in our VX continuous futures stream from our CSV, # specifying the 'Date' column as the index and converting it to datetime (so we can concat) vx_cont =
pd.read_csv('vx_cont.csv', index_col='Date')
pandas.read_csv
# -*- coding: utf-8 -*- import pdb, importlib, inspect, time, datetime, json # from PyFin.api import advanceDateByCalendar # from data.polymerize import DBPolymerize from data.storage_engine import StorageEngine import time import pandas as pd import numpy as np from datetime import timedelta, datetime from financial import factor_per_share_indicators from data.model import BalanceMRQ, BalanceTTM, BalanceReport from data.model import CashFlowTTM, CashFlowReport from data.model import IndicatorReport from data.model import IncomeReport, IncomeTTM from vision.table.valuation import Valuation from vision.db.signletion_engine import * from data.sqlengine import sqlEngine # pd.set_option('display.max_columns', None) # pd.set_option('display.max_rows', None) # from ultron.cluster.invoke.cache_data import cache_data class CalcEngine(object): def __init__(self, name, url, methods=[{'packet': 'financial.factor_pre_share_indicators', 'class': 'FactorPerShareIndicators'}, ]): self._name = name self._methods = methods self._url = url def get_trade_date(self, trade_date, n, days=365): """ 获取当前时间前n年的时间点,且为交易日,如果非交易日,则往前提取最近的一天。 :param days: :param trade_date: 当前交易日 :param n: :return: """ syn_util = SyncUtil() trade_date_sets = syn_util.get_all_trades('001002', '19900101', trade_date) trade_date_sets = trade_date_sets['TRADEDATE'].values time_array = datetime.strptime(str(trade_date), "%Y%m%d") time_array = time_array - timedelta(days=days) * n date_time = int(datetime.strftime(time_array, "%Y%m%d")) if str(date_time) < min(trade_date_sets): # print('date_time %s is out of trade_date_sets' % date_time) return str(date_time) else: while str(date_time) not in trade_date_sets: date_time = date_time - 1 # print('trade_date pre %s year %s' % (n, date_time)) return str(date_time) def _func_sets(self, method): # 私有函数和保护函数过滤 return list(filter(lambda x: not x.startswith('_') and callable(getattr(method, x)), dir(method))) def loading_data(self, trade_date): """ 获取基础数据 按天获取当天交易日所有股票的基础数据 :param trade_date: 交易日 :return: """ # 转换时间格式 time_array = datetime.strptime(trade_date, "%Y-%m-%d") trade_date = datetime.strftime(time_array, '%Y%m%d') # 读取目前涉及到的因子 engine = sqlEngine() columns = ['COMPCODE', 'PUBLISHDATE', 'ENDDATE', 'symbol', 'company_id', 'trade_date'] # Report data cash_flow_sets = engine.fetch_fundamentals_pit_extend_company_id(CashFlowReport, [CashFlowReport.FINALCASHBALA, # 期末现金及现金等价物余额 ], dates=[trade_date]) for col in columns: if col in list(cash_flow_sets.keys()): cash_flow_sets = cash_flow_sets.drop(col, axis=1) cash_flow_sets = cash_flow_sets.rename(columns={'FINALCASHBALA': 'cash_and_equivalents_at_end', # 期末现金及现金等价物余额 }) income_sets = engine.fetch_fundamentals_pit_extend_company_id(IncomeReport, [IncomeReport.BIZINCO, # 营业收入 IncomeReport.BIZTOTINCO, # 营业总收入 IncomeReport.PERPROFIT, # 营业利润 IncomeReport.DILUTEDEPS, # 稀释每股收益 ], dates=[trade_date]) for col in columns: if col in list(income_sets.keys()): income_sets = income_sets.drop(col, axis=1) income_sets = income_sets.rename(columns={'BIZINCO': 'operating_revenue', # 营业收入 'BIZTOTINCO': 'total_operating_revenue', # 营业总收入 'PERPROFIT': 'operating_profit', # 营业利润 'DILUTEDEPS': 'diluted_eps', # 稀释每股收益 }) balance_sets = engine.fetch_fundamentals_pit_extend_company_id(BalanceReport, [BalanceReport.PARESHARRIGH, # 归属于母公司的所有者权益 BalanceReport.CAPISURP, BalanceReport.RESE, BalanceReport.UNDIPROF, ], dates=[trade_date]) for col in columns: if col in list(balance_sets.keys()): balance_sets = balance_sets.drop(col, axis=1) balance_sets = balance_sets.rename(columns={'PARESHARRIGH': 'total_owner_equities', # 归属于母公司的所有者权益 'CAPISURP': 'capital_reserve_fund', # 资本公积 'RESE': 'surplus_reserve_fund', # 盈余公积 'UNDIPROF': 'retained_profit', # 未分配利润 }) indicator_sets = engine.fetch_fundamentals_pit_extend_company_id(IndicatorReport, [IndicatorReport.FCFE, # 股东自由现金流量 IndicatorReport.FCFF, # 企业自由现金流量 IndicatorReport.EPSBASIC, # 基本每股收益 IndicatorReport.DPS, # 每股股利(税前) ], dates=[trade_date]) for col in columns: if col in list(indicator_sets.keys()): indicator_sets = indicator_sets.drop(col, axis=1) indicator_sets = indicator_sets.rename(columns={'FCFE': 'shareholder_fcfps', # 股东自由现金流量 'FCFF': 'enterprise_fcfps', # 企业自由现金流量 'EPSBASIC': 'basic_eps', # 基本每股收益 'DPS': 'dividend_receivable', # 每股股利(税前) }) # TTM data cash_flow_ttm_sets = engine.fetch_fundamentals_pit_extend_company_id(CashFlowTTM, [CashFlowTTM.CASHNETI, # 现金及现金等价物净增加额 CashFlowTTM.MANANETR, # 经营活动现金流量净额 ], dates=[trade_date]) for col in columns: if col in list(cash_flow_ttm_sets.keys()): cash_flow_ttm_sets = cash_flow_ttm_sets.drop(col, axis=1) cash_flow_ttm_sets = cash_flow_ttm_sets.rename( columns={'CASHNETI': 'cash_equivalent_increase_ttm', # 现金及现金等价物净增加额 'MANANETR': 'net_operate_cash_flow_ttm', # 经营活动现金流量净额 }) income_ttm_sets = engine.fetch_fundamentals_pit_extend_company_id(IncomeTTM, [IncomeTTM.PARENETP, # 归属于母公司所有者的净利润 IncomeTTM.PERPROFIT, # 营业利润 IncomeTTM.BIZINCO, # 营业收入 IncomeTTM.BIZTOTINCO, # 营业总收入 ], dates=[trade_date]) for col in columns: if col in list(income_ttm_sets.keys()): income_ttm_sets = income_ttm_sets.drop(col, axis=1) income_ttm_sets = income_ttm_sets.rename(columns={'PARENETP': 'np_parent_company_owners_ttm', # 归属于母公司所有者的净利润 'PERPROFIT': 'operating_profit_ttm', # 营业利润 'BIZINCO': 'operating_revenue_ttm', # 营业收入 'BIZTOTINCO': 'total_operating_revenue_ttm', # 营业总收入 }) column = ['trade_date'] valuation_data = get_fundamentals(query(Valuation.security_code, Valuation.trade_date, Valuation.capitalization, ).filter(Valuation.trade_date.in_([trade_date]))) for col in column: if col in list(valuation_data.keys()): valuation_data = valuation_data.drop(col, axis=1) valuation_sets =
pd.merge(cash_flow_sets, income_sets, on='security_code')
pandas.merge
import numpy as np import pandas as pd def fourier(s: pd.Series) -> pd.Series: # TODO need to resample.. (fill with avgs?) warn about large gaps? ts = list(range(len(s))) # FIXME assert index is continuous? otherwise it's not properly sampled f = ts[1] - ts[0] vals = list(s) ft = np.abs(np.fft.rfft(vals)) freqs = np.fft.rfftfreq(len(vals), f) mags = abs(ft) periods = 1 / freqs # todo hmm. plotting with periods results in basically 'exponential' axis... not sure what to do return pd.Series(mags, index=freqs) # TODO strip away .iloc[1:]? it's always big def periods(s: pd.Series, *, n: int=3): from scipy.signal import find_peaks # type: ignore f = fourier(s) f.index = 1 / f.index pidx, _ = find_peaks(list(f)) # todo yield instead? for _, p in list(reversed(sorted((f.iloc[p], p) for p in pidx)))[:n]: print(f'{p:5d} {f.index[p]:6.2f} {f.iloc[p]:7.2f}') def deseasonalize(df): from statsmodels.tsa.seasonal import seasonal_decompose # TODO meh, not sure if should be here.. df = df.resample('D').interpolate('linear') dec = seasonal_decompose(df) # (can pass period=) # todo wonder which periods is it guessing.. # TODO wonder how it calculates seasonal? also it's not catching yearly trends? # ddd = dec.seasonal + dec.trend + dec.resid return df - dec.seasonal def test_periods(): ts = np.arange(0, 1500, 1) vals = [ 60 + \ 10 * np.sin(6.29 / 365 * i) + \ 20 * np.sin(6.29 / 7 * i) + \ 5 * np.sin(6.29 / 28 * i) for i in ts] s =
pd.Series(vals, index=ts)
pandas.Series
import numpy as np import pandas as pd from sklearn.ensemble import RandomForestClassifier, BaggingClassifier from sklearn.model_selection import train_test_split, StratifiedKFold from sklearn.model_selection._split import _BaseKFold from sklearn.metrics import roc_curve, classification_report, log_loss, accuracy_score, recall_score, auc, average_precision_score, f1_score def getTrainTimes(t1, testTimes): ''' Given testTimes, find the times of the training observations. There are three conditions that would make a sample to be dropped. Let i be the index of a train sample and j the index of a test sample. Let 0,1 be the start and end of a sample, then: - t_{j,0} <= t_{i,0} <= t_{j,1} --> train starts between test - t_{j,0} <= t_{i,1} <= t_{j,1} --> train ends between test - t_{i,0} <= t_{j,0} <= t_{j,1} <= t_{i,1} --> test is contained in train See Advances in Financial Analytics, snippet 7.1, page 106. @param t1 A pandas Series where the index tells when the observation started and the value when it ended. @param testTimes Times of testing observations. @return A purged t1. ''' trn = t1.copy(deep=True) for i, j in testTimes.iteritems(): # Train stars with index df0 = trn[(i <= trn.index) & (trn.index <= j)].index # Train ends within test df1 = trn[(i <= trn) & (trn <= j)].index # Train envelops test df2 = trn[(trn.index <= i) & (j <= trn)].index # Removes the union of the previous three data frames. trn = trn.drop(df0.union(df1).union(df2)) return trn def getEmbargoTimes(times, pctEmbargo): ''' Drops 2 * pctEmbargo percentage of samples at the beginning and end of times to further prevent leakage. See Advances in Financial Analytics, snippet 7.2, page 108. @param times A data series of times to drop labels from. @param pctEmbargo The percentage of times's size to drop. @return A copy of times but with dropped items at the beginning and end because of pctEmbargo. ''' step = int(times.shape[0] * pctEmbargo) if step == 0: mbrg = pd.Series(times, index=times) else: mbrg =
pd.Series(times[step:], index=times[:-step])
pandas.Series
import os from pprint import pprint import pandas as pd import requests from utils import load_json, save_json class TwitterApi: def __init__(self, timeline_params_path="timeline_params.json"): self.bearer_token = self._auth() self.headers = self._create_headers() self.timeline_params = load_json(timeline_params_path) def build_user_dataset(self, user_name, params=None, data_dir="data"): filepath = os.path.join( data_dir, f"{user_name}.json") tweets = self.load_storred_tweets(filepath) latest_stored_tweet = self.get_latest_stored_tweet(tweets) if latest_stored_tweet: latest_stored_tweet_id = latest_stored_tweet["id"] if params: params["since_id"] = latest_stored_tweet_id else: params = {"since_id": latest_stored_tweet_id} user_id = self.query_user_data_by_name( user_name, params={"user.fields": "id"})["id"] new_tweets = self.get_user_tweets(user_id, params) tweets += new_tweets save_json(tweets, filepath) print(f"{len(new_tweets)} tweets queried and stored.") def get_latest_stored_tweet(self, tweets): if tweets: latest_stored_tweet =
pd.DataFrame(tweets)
pandas.DataFrame
# -*- coding: utf-8 -*- from __future__ import print_function from datetime import datetime, timedelta import functools import itertools import numpy as np import numpy.ma as ma import numpy.ma.mrecords as mrecords from numpy.random import randn import pytest from pandas.compat import ( PY3, PY36, OrderedDict, is_platform_little_endian, lmap, long, lrange, lzip, range, zip) from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike from pandas.core.dtypes.common import is_integer_dtype import pandas as pd from pandas import ( Categorical, DataFrame, Index, MultiIndex, Series, Timedelta, Timestamp, compat, date_range, isna) from pandas.tests.frame.common import TestData import pandas.util.testing as tm MIXED_FLOAT_DTYPES = ['float16', 'float32', 'float64'] MIXED_INT_DTYPES = ['uint8', 'uint16', 'uint32', 'uint64', 'int8', 'int16', 'int32', 'int64'] class TestDataFrameConstructors(TestData): def test_constructor(self): df = DataFrame() assert len(df.index) == 0 df = DataFrame(data={}) assert len(df.index) == 0 def test_constructor_mixed(self): index, data = tm.getMixedTypeDict() # TODO(wesm), incomplete test? indexed_frame = DataFrame(data, index=index) # noqa unindexed_frame = DataFrame(data) # noqa assert self.mixed_frame['foo'].dtype == np.object_ def test_constructor_cast_failure(self): foo = DataFrame({'a': ['a', 'b', 'c']}, dtype=np.float64) assert foo['a'].dtype == object # GH 3010, constructing with odd arrays df = DataFrame(np.ones((4, 2))) # this is ok df['foo'] = np.ones((4, 2)).tolist() # this is not ok pytest.raises(ValueError, df.__setitem__, tuple(['test']), np.ones((4, 2))) # this is ok df['foo2'] = np.ones((4, 2)).tolist() def test_constructor_dtype_copy(self): orig_df = DataFrame({ 'col1': [1.], 'col2': [2.], 'col3': [3.]}) new_df = pd.DataFrame(orig_df, dtype=float, copy=True) new_df['col1'] = 200. assert orig_df['col1'][0] == 1. def test_constructor_dtype_nocast_view(self): df = DataFrame([[1, 2]]) should_be_view = DataFrame(df, dtype=df[0].dtype) should_be_view[0][0] = 99 assert df.values[0, 0] == 99 should_be_view = DataFrame(df.values, dtype=df[0].dtype) should_be_view[0][0] = 97 assert df.values[0, 0] == 97 def test_constructor_dtype_list_data(self): df = DataFrame([[1, '2'], [None, 'a']], dtype=object) assert df.loc[1, 0] is None assert df.loc[0, 1] == '2' def test_constructor_list_frames(self): # see gh-3243 result = DataFrame([DataFrame([])]) assert result.shape == (1, 0) result = DataFrame([DataFrame(dict(A=lrange(5)))]) assert isinstance(result.iloc[0, 0], DataFrame) def test_constructor_mixed_dtypes(self): def _make_mixed_dtypes_df(typ, ad=None): if typ == 'int': dtypes = MIXED_INT_DTYPES arrays = [np.array(np.random.rand(10), dtype=d) for d in dtypes] elif typ == 'float': dtypes = MIXED_FLOAT_DTYPES arrays = [np.array(np.random.randint( 10, size=10), dtype=d) for d in dtypes] zipper = lzip(dtypes, arrays) for d, a in zipper: assert(a.dtype == d) if ad is None: ad = dict() ad.update({d: a for d, a in zipper}) return DataFrame(ad) def _check_mixed_dtypes(df, dtypes=None): if dtypes is None: dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES for d in dtypes: if d in df: assert(df.dtypes[d] == d) # mixed floating and integer coexinst in the same frame df = _make_mixed_dtypes_df('float') _check_mixed_dtypes(df) # add lots of types df = _make_mixed_dtypes_df('float', dict(A=1, B='foo', C='bar')) _check_mixed_dtypes(df) # GH 622 df = _make_mixed_dtypes_df('int') _check_mixed_dtypes(df) def test_constructor_complex_dtypes(self): # GH10952 a = np.random.rand(10).astype(np.complex64) b = np.random.rand(10).astype(np.complex128) df = DataFrame({'a': a, 'b': b}) assert a.dtype == df.a.dtype assert b.dtype == df.b.dtype def test_constructor_dtype_str_na_values(self, string_dtype): # https://github.com/pandas-dev/pandas/issues/21083 df = DataFrame({'A': ['x', None]}, dtype=string_dtype) result = df.isna() expected = DataFrame({"A": [False, True]}) tm.assert_frame_equal(result, expected) assert df.iloc[1, 0] is None df = DataFrame({'A': ['x', np.nan]}, dtype=string_dtype) assert np.isnan(df.iloc[1, 0]) def test_constructor_rec(self): rec = self.frame.to_records(index=False) if PY3: # unicode error under PY2 rec.dtype.names = list(rec.dtype.names)[::-1] index = self.frame.index df = DataFrame(rec) tm.assert_index_equal(df.columns, pd.Index(rec.dtype.names)) df2 = DataFrame(rec, index=index) tm.assert_index_equal(df2.columns, pd.Index(rec.dtype.names)) tm.assert_index_equal(df2.index, index) rng = np.arange(len(rec))[::-1] df3 = DataFrame(rec, index=rng, columns=['C', 'B']) expected = DataFrame(rec, index=rng).reindex(columns=['C', 'B']) tm.assert_frame_equal(df3, expected) def test_constructor_bool(self): df = DataFrame({0: np.ones(10, dtype=bool), 1: np.zeros(10, dtype=bool)}) assert df.values.dtype == np.bool_ def test_constructor_overflow_int64(self): # see gh-14881 values = np.array([2 ** 64 - i for i in range(1, 10)], dtype=np.uint64) result = DataFrame({'a': values}) assert result['a'].dtype == np.uint64 # see gh-2355 data_scores = [(6311132704823138710, 273), (2685045978526272070, 23), (8921811264899370420, 45), (long(17019687244989530680), 270), (long(9930107427299601010), 273)] dtype = [('uid', 'u8'), ('score', 'u8')] data = np.zeros((len(data_scores),), dtype=dtype) data[:] = data_scores df_crawls = DataFrame(data) assert df_crawls['uid'].dtype == np.uint64 @pytest.mark.parametrize("values", [np.array([2**64], dtype=object), np.array([2**65]), [2**64 + 1], np.array([-2**63 - 4], dtype=object), np.array([-2**64 - 1]), [-2**65 - 2]]) def test_constructor_int_overflow(self, values): # see gh-18584 value = values[0] result = DataFrame(values) assert result[0].dtype == object assert result[0][0] == value def test_constructor_ordereddict(self): import random nitems = 100 nums = lrange(nitems) random.shuffle(nums) expected = ['A%d' % i for i in nums] df = DataFrame(OrderedDict(zip(expected, [[0]] * nitems))) assert expected == list(df.columns) def test_constructor_dict(self): frame = DataFrame({'col1': self.ts1, 'col2': self.ts2}) # col2 is padded with NaN assert len(self.ts1) == 30 assert len(self.ts2) == 25 tm.assert_series_equal(self.ts1, frame['col1'], check_names=False) exp = pd.Series(np.concatenate([[np.nan] * 5, self.ts2.values]), index=self.ts1.index, name='col2') tm.assert_series_equal(exp, frame['col2']) frame = DataFrame({'col1': self.ts1, 'col2': self.ts2}, columns=['col2', 'col3', 'col4']) assert len(frame) == len(self.ts2) assert 'col1' not in frame assert isna(frame['col3']).all() # Corner cases assert len(DataFrame({})) == 0 # mix dict and array, wrong size - no spec for which error should raise # first with pytest.raises(ValueError): DataFrame({'A': {'a': 'a', 'b': 'b'}, 'B': ['a', 'b', 'c']}) # Length-one dict micro-optimization frame = DataFrame({'A': {'1': 1, '2': 2}}) tm.assert_index_equal(frame.index, pd.Index(['1', '2'])) # empty dict plus index idx = Index([0, 1, 2]) frame = DataFrame({}, index=idx) assert frame.index is idx # empty with index and columns idx = Index([0, 1, 2]) frame = DataFrame({}, index=idx, columns=idx) assert frame.index is idx assert frame.columns is idx assert len(frame._series) == 3 # with dict of empty list and Series frame = DataFrame({'A': [], 'B': []}, columns=['A', 'B']) tm.assert_index_equal(frame.index, Index([], dtype=np.int64)) # GH 14381 # Dict with None value frame_none = DataFrame(dict(a=None), index=[0]) frame_none_list = DataFrame(dict(a=[None]), index=[0]) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): assert frame_none.get_value(0, 'a') is None with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): assert frame_none_list.get_value(0, 'a') is None tm.assert_frame_equal(frame_none, frame_none_list) # GH10856 # dict with scalar values should raise error, even if columns passed msg = 'If using all scalar values, you must pass an index' with pytest.raises(ValueError, match=msg): DataFrame({'a': 0.7}) with pytest.raises(ValueError, match=msg): DataFrame({'a': 0.7}, columns=['a']) @pytest.mark.parametrize("scalar", [2, np.nan, None, 'D']) def test_constructor_invalid_items_unused(self, scalar): # No error if invalid (scalar) value is in fact not used: result = DataFrame({'a': scalar}, columns=['b']) expected = DataFrame(columns=['b']) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("value", [2, np.nan, None, float('nan')]) def test_constructor_dict_nan_key(self, value): # GH 18455 cols = [1, value, 3] idx = ['a', value] values = [[0, 3], [1, 4], [2, 5]] data = {cols[c]: Series(values[c], index=idx) for c in range(3)} result = DataFrame(data).sort_values(1).sort_values('a', axis=1) expected = DataFrame(np.arange(6, dtype='int64').reshape(2, 3), index=idx, columns=cols) tm.assert_frame_equal(result, expected) result = DataFrame(data, index=idx).sort_values('a', axis=1) tm.assert_frame_equal(result, expected) result = DataFrame(data, index=idx, columns=cols) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("value", [np.nan, None, float('nan')]) def test_constructor_dict_nan_tuple_key(self, value): # GH 18455 cols = Index([(11, 21), (value, 22), (13, value)]) idx = Index([('a', value), (value, 2)]) values = [[0, 3], [1, 4], [2, 5]] data = {cols[c]: Series(values[c], index=idx) for c in range(3)} result = (DataFrame(data) .sort_values((11, 21)) .sort_values(('a', value), axis=1)) expected = DataFrame(np.arange(6, dtype='int64').reshape(2, 3), index=idx, columns=cols) tm.assert_frame_equal(result, expected) result = DataFrame(data, index=idx).sort_values(('a', value), axis=1) tm.assert_frame_equal(result, expected) result = DataFrame(data, index=idx, columns=cols) tm.assert_frame_equal(result, expected) @pytest.mark.skipif(not PY36, reason='Insertion order for Python>=3.6') def test_constructor_dict_order_insertion(self): # GH19018 # initialization ordering: by insertion order if python>= 3.6 d = {'b': self.ts2, 'a': self.ts1} frame = DataFrame(data=d) expected = DataFrame(data=d, columns=list('ba')) tm.assert_frame_equal(frame, expected) @pytest.mark.skipif(PY36, reason='order by value for Python<3.6') def test_constructor_dict_order_by_values(self): # GH19018 # initialization ordering: by value if python<3.6 d = {'b': self.ts2, 'a': self.ts1} frame = DataFrame(data=d) expected = DataFrame(data=d, columns=list('ab')) tm.assert_frame_equal(frame, expected) def test_constructor_multi_index(self): # GH 4078 # construction error with mi and all-nan frame tuples = [(2, 3), (3, 3), (3, 3)] mi = MultiIndex.from_tuples(tuples) df = DataFrame(index=mi, columns=mi) assert pd.isna(df).values.ravel().all() tuples = [(3, 3), (2, 3), (3, 3)] mi = MultiIndex.from_tuples(tuples) df = DataFrame(index=mi, columns=mi) assert pd.isna(df).values.ravel().all() def test_constructor_error_msgs(self): msg = "Empty data passed with indices specified." # passing an empty array with columns specified. with pytest.raises(ValueError, match=msg): DataFrame(np.empty(0), columns=list('abc')) msg = "Mixing dicts with non-Series may lead to ambiguous ordering." # mix dict and array, wrong size with pytest.raises(ValueError, match=msg): DataFrame({'A': {'a': 'a', 'b': 'b'}, 'B': ['a', 'b', 'c']}) # wrong size ndarray, GH 3105 msg = r"Shape of passed values is \(3, 4\), indices imply \(3, 3\)" with pytest.raises(ValueError, match=msg): DataFrame(np.arange(12).reshape((4, 3)), columns=['foo', 'bar', 'baz'], index=pd.date_range('2000-01-01', periods=3)) # higher dim raise exception with pytest.raises(ValueError, match='Must pass 2-d input'): DataFrame(np.zeros((3, 3, 3)), columns=['A', 'B', 'C'], index=[1]) # wrong size axis labels msg = ("Shape of passed values " r"is \(3, 2\), indices " r"imply \(3, 1\)") with pytest.raises(ValueError, match=msg): DataFrame(np.random.rand(2, 3), columns=['A', 'B', 'C'], index=[1]) msg = ("Shape of passed values " r"is \(3, 2\), indices " r"imply \(2, 2\)") with pytest.raises(ValueError, match=msg): DataFrame(np.random.rand(2, 3), columns=['A', 'B'], index=[1, 2]) msg = ("If using all scalar " "values, you must pass " "an index") with pytest.raises(ValueError, match=msg): DataFrame({'a': False, 'b': True}) def test_constructor_with_embedded_frames(self): # embedded data frames df1 = DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]}) df2 = DataFrame([df1, df1 + 10]) df2.dtypes str(df2) result = df2.loc[0, 0] tm.assert_frame_equal(result, df1) result = df2.loc[1, 0] tm.assert_frame_equal(result, df1 + 10) def test_constructor_subclass_dict(self): # Test for passing dict subclass to constructor data = {'col1': tm.TestSubDict((x, 10.0 * x) for x in range(10)), 'col2': tm.TestSubDict((x, 20.0 * x) for x in range(10))} df = DataFrame(data) refdf = DataFrame({col: dict(compat.iteritems(val)) for col, val in compat.iteritems(data)}) tm.assert_frame_equal(refdf, df) data = tm.TestSubDict(compat.iteritems(data)) df = DataFrame(data) tm.assert_frame_equal(refdf, df) # try with defaultdict from collections import defaultdict data = {} self.frame['B'][:10] = np.nan for k, v in compat.iteritems(self.frame): dct = defaultdict(dict) dct.update(v.to_dict()) data[k] = dct frame = DataFrame(data) tm.assert_frame_equal(self.frame.sort_index(), frame) def test_constructor_dict_block(self): expected = np.array([[4., 3., 2., 1.]]) df = DataFrame({'d': [4.], 'c': [3.], 'b': [2.], 'a': [1.]}, columns=['d', 'c', 'b', 'a']) tm.assert_numpy_array_equal(df.values, expected) def test_constructor_dict_cast(self): # cast float tests test_data = { 'A': {'1': 1, '2': 2}, 'B': {'1': '1', '2': '2', '3': '3'}, } frame = DataFrame(test_data, dtype=float) assert len(frame) == 3 assert frame['B'].dtype == np.float64 assert frame['A'].dtype == np.float64 frame = DataFrame(test_data) assert len(frame) == 3 assert frame['B'].dtype == np.object_ assert frame['A'].dtype == np.float64 # can't cast to float test_data = { 'A': dict(zip(range(20), tm.makeStringIndex(20))), 'B': dict(zip(range(15), randn(15))) } frame = DataFrame(test_data, dtype=float) assert len(frame) == 20 assert frame['A'].dtype == np.object_ assert frame['B'].dtype == np.float64 def test_constructor_dict_dont_upcast(self): d = {'Col1': {'Row1': 'A String', 'Row2': np.nan}} df = DataFrame(d) assert isinstance(df['Col1']['Row2'], float) dm = DataFrame([[1, 2], ['a', 'b']], index=[1, 2], columns=[1, 2]) assert isinstance(dm[1][1], int) def test_constructor_dict_of_tuples(self): # GH #1491 data = {'a': (1, 2, 3), 'b': (4, 5, 6)} result = DataFrame(data) expected = DataFrame({k: list(v) for k, v in compat.iteritems(data)}) tm.assert_frame_equal(result, expected, check_dtype=False) def test_constructor_dict_multiindex(self): def check(result, expected): return tm.assert_frame_equal(result, expected, check_dtype=True, check_index_type=True, check_column_type=True, check_names=True) d = {('a', 'a'): {('i', 'i'): 0, ('i', 'j'): 1, ('j', 'i'): 2}, ('b', 'a'): {('i', 'i'): 6, ('i', 'j'): 5, ('j', 'i'): 4}, ('b', 'c'): {('i', 'i'): 7, ('i', 'j'): 8, ('j', 'i'): 9}} _d = sorted(d.items()) df = DataFrame(d) expected = DataFrame( [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])).T expected.index = MultiIndex.from_tuples(expected.index) check(df, expected) d['z'] = {'y': 123., ('i', 'i'): 111, ('i', 'j'): 111, ('j', 'i'): 111} _d.insert(0, ('z', d['z'])) expected = DataFrame( [x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)).T expected.index = Index(expected.index, tupleize_cols=False) df = DataFrame(d) df = df.reindex(columns=expected.columns, index=expected.index) check(df, expected) def test_constructor_dict_datetime64_index(self): # GH 10160 dates_as_str = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15'] def create_data(constructor): return {i: {constructor(s): 2 * i} for i, s in enumerate(dates_as_str)} data_datetime64 = create_data(np.datetime64) data_datetime = create_data(lambda x: datetime.strptime(x, '%Y-%m-%d')) data_Timestamp = create_data(Timestamp) expected = DataFrame([{0: 0, 1: None, 2: None, 3: None}, {0: None, 1: 2, 2: None, 3: None}, {0: None, 1: None, 2: 4, 3: None}, {0: None, 1: None, 2: None, 3: 6}], index=[Timestamp(dt) for dt in dates_as_str]) result_datetime64 = DataFrame(data_datetime64) result_datetime = DataFrame(data_datetime) result_Timestamp = DataFrame(data_Timestamp) tm.assert_frame_equal(result_datetime64, expected) tm.assert_frame_equal(result_datetime, expected) tm.assert_frame_equal(result_Timestamp, expected) def test_constructor_dict_timedelta64_index(self): # GH 10160 td_as_int = [1, 2, 3, 4] def create_data(constructor): return {i: {constructor(s): 2 * i} for i, s in enumerate(td_as_int)} data_timedelta64 = create_data(lambda x: np.timedelta64(x, 'D')) data_timedelta = create_data(lambda x: timedelta(days=x)) data_Timedelta = create_data(lambda x: Timedelta(x, 'D')) expected = DataFrame([{0: 0, 1: None, 2: None, 3: None}, {0: None, 1: 2, 2: None, 3: None}, {0: None, 1: None, 2: 4, 3: None}, {0: None, 1: None, 2: None, 3: 6}], index=[Timedelta(td, 'D') for td in td_as_int]) result_timedelta64 = DataFrame(data_timedelta64) result_timedelta = DataFrame(data_timedelta) result_Timedelta = DataFrame(data_Timedelta) tm.assert_frame_equal(result_timedelta64, expected) tm.assert_frame_equal(result_timedelta, expected) tm.assert_frame_equal(result_Timedelta, expected) def test_constructor_period(self): # PeriodIndex a = pd.PeriodIndex(['2012-01', 'NaT', '2012-04'], freq='M') b = pd.PeriodIndex(['2012-02-01', '2012-03-01', 'NaT'], freq='D') df = pd.DataFrame({'a': a, 'b': b}) assert df['a'].dtype == a.dtype assert df['b'].dtype == b.dtype # list of periods df = pd.DataFrame({'a': a.astype(object).tolist(), 'b': b.astype(object).tolist()}) assert df['a'].dtype == a.dtype assert df['b'].dtype == b.dtype def test_nested_dict_frame_constructor(self): rng = pd.period_range('1/1/2000', periods=5) df = DataFrame(randn(10, 5), columns=rng) data = {} for col in df.columns: for row in df.index: with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): data.setdefault(col, {})[row] = df.get_value(row, col) result = DataFrame(data, columns=rng) tm.assert_frame_equal(result, df) data = {} for col in df.columns: for row in df.index: with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): data.setdefault(row, {})[col] = df.get_value(row, col) result = DataFrame(data, index=rng).T tm.assert_frame_equal(result, df) def _check_basic_constructor(self, empty): # mat: 2d matrix with shape (3, 2) to input. empty - makes sized # objects mat = empty((2, 3), dtype=float) # 2-D input frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert len(frame.index) == 2 assert len(frame.columns) == 3 # 1-D input frame = DataFrame(empty((3,)), columns=['A'], index=[1, 2, 3]) assert len(frame.index) == 3 assert len(frame.columns) == 1 # cast type frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2], dtype=np.int64) assert frame.values.dtype == np.int64 # wrong size axis labels msg = r'Shape of passed values is \(3, 2\), indices imply \(3, 1\)' with pytest.raises(ValueError, match=msg): DataFrame(mat, columns=['A', 'B', 'C'], index=[1]) msg = r'Shape of passed values is \(3, 2\), indices imply \(2, 2\)' with pytest.raises(ValueError, match=msg): DataFrame(mat, columns=['A', 'B'], index=[1, 2]) # higher dim raise exception with pytest.raises(ValueError, match='Must pass 2-d input'): DataFrame(empty((3, 3, 3)), columns=['A', 'B', 'C'], index=[1]) # automatic labeling frame = DataFrame(mat) tm.assert_index_equal(frame.index, pd.Index(lrange(2))) tm.assert_index_equal(frame.columns, pd.Index(lrange(3))) frame = DataFrame(mat, index=[1, 2]) tm.assert_index_equal(frame.columns, pd.Index(lrange(3))) frame = DataFrame(mat, columns=['A', 'B', 'C']) tm.assert_index_equal(frame.index, pd.Index(lrange(2))) # 0-length axis frame = DataFrame(empty((0, 3))) assert len(frame.index) == 0 frame = DataFrame(empty((3, 0))) assert len(frame.columns) == 0 def test_constructor_ndarray(self): self._check_basic_constructor(np.ones) frame = DataFrame(['foo', 'bar'], index=[0, 1], columns=['A']) assert len(frame) == 2 def test_constructor_maskedarray(self): self._check_basic_constructor(ma.masked_all) # Check non-masked values mat = ma.masked_all((2, 3), dtype=float) mat[0, 0] = 1.0 mat[1, 2] = 2.0 frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert 1.0 == frame['A'][1] assert 2.0 == frame['C'][2] # what is this even checking?? mat = ma.masked_all((2, 3), dtype=float) frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert np.all(~np.asarray(frame == frame)) def test_constructor_maskedarray_nonfloat(self): # masked int promoted to float mat = ma.masked_all((2, 3), dtype=int) # 2-D input frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert len(frame.index) == 2 assert len(frame.columns) == 3 assert np.all(~np.asarray(frame == frame)) # cast type frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2], dtype=np.float64) assert frame.values.dtype == np.float64 # Check non-masked values mat2 = ma.copy(mat) mat2[0, 0] = 1 mat2[1, 2] = 2 frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2]) assert 1 == frame['A'][1] assert 2 == frame['C'][2] # masked np.datetime64 stays (use NaT as null) mat = ma.masked_all((2, 3), dtype='M8[ns]') # 2-D input frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert len(frame.index) == 2 assert len(frame.columns) == 3 assert isna(frame).values.all() # cast type frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2], dtype=np.int64) assert frame.values.dtype == np.int64 # Check non-masked values mat2 = ma.copy(mat) mat2[0, 0] = 1 mat2[1, 2] = 2 frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2]) assert 1 == frame['A'].view('i8')[1] assert 2 == frame['C'].view('i8')[2] # masked bool promoted to object mat = ma.masked_all((2, 3), dtype=bool) # 2-D input frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2]) assert len(frame.index) == 2 assert len(frame.columns) == 3 assert np.all(~np.asarray(frame == frame)) # cast type frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2], dtype=object) assert frame.values.dtype == object # Check non-masked values mat2 = ma.copy(mat) mat2[0, 0] = True mat2[1, 2] = False frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2]) assert frame['A'][1] is True assert frame['C'][2] is False def test_constructor_mrecarray(self): # Ensure mrecarray produces frame identical to dict of masked arrays # from GH3479 assert_fr_equal = functools.partial(tm.assert_frame_equal, check_index_type=True, check_column_type=True, check_frame_type=True) arrays = [ ('float', np.array([1.5, 2.0])), ('int', np.array([1, 2])), ('str', np.array(['abc', 'def'])), ] for name, arr in arrays[:]: arrays.append(('masked1_' + name, np.ma.masked_array(arr, mask=[False, True]))) arrays.append(('masked_all', np.ma.masked_all((2,)))) arrays.append(('masked_none', np.ma.masked_array([1.0, 2.5], mask=False))) # call assert_frame_equal for all selections of 3 arrays for comb in itertools.combinations(arrays, 3): names, data = zip(*comb) mrecs = mrecords.fromarrays(data, names=names) # fill the comb comb = {k: (v.filled() if hasattr(v, 'filled') else v) for k, v in comb} expected = DataFrame(comb, columns=names) result = DataFrame(mrecs) assert_fr_equal(result, expected) # specify columns expected = DataFrame(comb, columns=names[::-1]) result = DataFrame(mrecs, columns=names[::-1]) assert_fr_equal(result, expected) # specify index expected = DataFrame(comb, columns=names, index=[1, 2]) result = DataFrame(mrecs, index=[1, 2]) assert_fr_equal(result, expected) def test_constructor_corner_shape(self): df = DataFrame(index=[]) assert df.values.shape == (0, 0) @pytest.mark.parametrize("data, index, columns, dtype, expected", [ (None, lrange(10), ['a', 'b'], object, np.object_), (None, None, ['a', 'b'], 'int64', np.dtype('int64')), (None, lrange(10), ['a', 'b'], int, np.dtype('float64')), ({}, None, ['foo', 'bar'], None, np.object_), ({'b': 1}, lrange(10), list('abc'), int, np.dtype('float64')) ]) def test_constructor_dtype(self, data, index, columns, dtype, expected): df = DataFrame(data, index, columns, dtype) assert df.values.dtype == expected def test_constructor_scalar_inference(self): data = {'int': 1, 'bool': True, 'float': 3., 'complex': 4j, 'object': 'foo'} df = DataFrame(data, index=np.arange(10)) assert df['int'].dtype == np.int64 assert df['bool'].dtype == np.bool_ assert df['float'].dtype == np.float64 assert df['complex'].dtype == np.complex128 assert df['object'].dtype == np.object_ def test_constructor_arrays_and_scalars(self): df = DataFrame({'a': randn(10), 'b': True}) exp = DataFrame({'a': df['a'].values, 'b': [True] * 10}) tm.assert_frame_equal(df, exp) with pytest.raises(ValueError, match='must pass an index'): DataFrame({'a': False, 'b': True}) def test_constructor_DataFrame(self): df = DataFrame(self.frame) tm.assert_frame_equal(df, self.frame) df_casted = DataFrame(self.frame, dtype=np.int64) assert df_casted.values.dtype == np.int64 def test_constructor_more(self): # used to be in test_matrix.py arr = randn(10) dm = DataFrame(arr, columns=['A'], index=np.arange(10)) assert dm.values.ndim == 2 arr = randn(0) dm = DataFrame(arr) assert dm.values.ndim == 2 assert dm.values.ndim == 2 # no data specified dm = DataFrame(columns=['A', 'B'], index=np.arange(10)) assert dm.values.shape == (10, 2) dm = DataFrame(columns=['A', 'B']) assert dm.values.shape == (0, 2) dm = DataFrame(index=np.arange(10)) assert dm.values.shape == (10, 0) # can't cast mat = np.array(['foo', 'bar'], dtype=object).reshape(2, 1) with pytest.raises(ValueError, match='cast'): DataFrame(mat, index=[0, 1], columns=[0], dtype=float) dm = DataFrame(DataFrame(self.frame._series)) tm.assert_frame_equal(dm, self.frame) # int cast dm = DataFrame({'A': np.ones(10, dtype=int), 'B': np.ones(10, dtype=np.float64)}, index=np.arange(10)) assert len(dm.columns) == 2 assert dm.values.dtype == np.float64 def test_constructor_empty_list(self): df = DataFrame([], index=[]) expected = DataFrame(index=[]) tm.assert_frame_equal(df, expected) # GH 9939 df = DataFrame([], columns=['A', 'B']) expected = DataFrame({}, columns=['A', 'B']) tm.assert_frame_equal(df, expected) # Empty generator: list(empty_gen()) == [] def empty_gen(): return yield df = DataFrame(empty_gen(), columns=['A', 'B']) tm.assert_frame_equal(df, expected) def test_constructor_list_of_lists(self): # GH #484 df = DataFrame(data=[[1, 'a'], [2, 'b']], columns=["num", "str"]) assert is_integer_dtype(df['num']) assert df['str'].dtype == np.object_ # GH 4851 # list of 0-dim ndarrays expected = DataFrame({0: np.arange(10)}) data = [np.array(x) for x in range(10)] result = DataFrame(data) tm.assert_frame_equal(result, expected) def test_constructor_sequence_like(self): # GH 3783 # collections.Squence like class DummyContainer(compat.Sequence): def __init__(self, lst): self._lst = lst def __getitem__(self, n): return self._lst.__getitem__(n) def __len__(self, n): return self._lst.__len__() lst_containers = [DummyContainer([1, 'a']), DummyContainer([2, 'b'])] columns = ["num", "str"] result = DataFrame(lst_containers, columns=columns) expected = DataFrame([[1, 'a'], [2, 'b']], columns=columns) tm.assert_frame_equal(result, expected, check_dtype=False) # GH 4297 # support Array import array result = DataFrame({'A': array.array('i', range(10))}) expected = DataFrame({'A': list(range(10))}) tm.assert_frame_equal(result, expected, check_dtype=False) expected = DataFrame([list(range(10)), list(range(10))]) result = DataFrame([array.array('i', range(10)), array.array('i', range(10))]) tm.assert_frame_equal(result, expected, check_dtype=False) def test_constructor_iterable(self): # GH 21987 class Iter(): def __iter__(self): for i in range(10): yield [1, 2, 3] expected = DataFrame([[1, 2, 3]] * 10) result = DataFrame(Iter()) tm.assert_frame_equal(result, expected) def test_constructor_iterator(self): expected = DataFrame([list(range(10)), list(range(10))]) result = DataFrame([range(10), range(10)]) tm.assert_frame_equal(result, expected) def test_constructor_generator(self): # related #2305 gen1 = (i for i in range(10)) gen2 = (i for i in range(10)) expected = DataFrame([list(range(10)), list(range(10))]) result = DataFrame([gen1, gen2]) tm.assert_frame_equal(result, expected) gen = ([i, 'a'] for i in range(10)) result = DataFrame(gen) expected = DataFrame({0: range(10), 1: 'a'}) tm.assert_frame_equal(result, expected, check_dtype=False) def test_constructor_list_of_dicts(self): data = [OrderedDict([['a', 1.5], ['b', 3], ['c', 4], ['d', 6]]), OrderedDict([['a', 1.5], ['b', 3], ['d', 6]]), OrderedDict([['a', 1.5], ['d', 6]]), OrderedDict(), OrderedDict([['a', 1.5], ['b', 3], ['c', 4]]), OrderedDict([['b', 3], ['c', 4], ['d', 6]])] result = DataFrame(data) expected = DataFrame.from_dict(dict(zip(range(len(data)), data)), orient='index') tm.assert_frame_equal(result, expected.reindex(result.index)) result = DataFrame([{}]) expected = DataFrame(index=[0]) tm.assert_frame_equal(result, expected) def test_constructor_ordered_dict_preserve_order(self): # see gh-13304 expected = DataFrame([[2, 1]], columns=['b', 'a']) data = OrderedDict() data['b'] = [2] data['a'] = [1] result = DataFrame(data) tm.assert_frame_equal(result, expected) data = OrderedDict() data['b'] = 2 data['a'] = 1 result = DataFrame([data]) tm.assert_frame_equal(result, expected) def test_constructor_ordered_dict_conflicting_orders(self): # the first dict element sets the ordering for the DataFrame, # even if there are conflicting orders from subsequent ones row_one = OrderedDict() row_one['b'] = 2 row_one['a'] = 1 row_two = OrderedDict() row_two['a'] = 1 row_two['b'] = 2 row_three = {'b': 2, 'a': 1} expected = DataFrame([[2, 1], [2, 1]], columns=['b', 'a']) result = DataFrame([row_one, row_two]) tm.assert_frame_equal(result, expected) expected = DataFrame([[2, 1], [2, 1], [2, 1]], columns=['b', 'a']) result = DataFrame([row_one, row_two, row_three]) tm.assert_frame_equal(result, expected) def test_constructor_list_of_series(self): data = [OrderedDict([['a', 1.5], ['b', 3.0], ['c', 4.0]]), OrderedDict([['a', 1.5], ['b', 3.0], ['c', 6.0]])] sdict = OrderedDict(zip(['x', 'y'], data)) idx = Index(['a', 'b', 'c']) # all named data2 = [Series([1.5, 3, 4], idx, dtype='O', name='x'), Series([1.5, 3, 6], idx, name='y')] result = DataFrame(data2) expected = DataFrame.from_dict(sdict, orient='index') tm.assert_frame_equal(result, expected) # some unnamed data2 = [Series([1.5, 3, 4], idx, dtype='O', name='x'), Series([1.5, 3, 6], idx)] result = DataFrame(data2) sdict = OrderedDict(zip(['x', 'Unnamed 0'], data)) expected = DataFrame.from_dict(sdict, orient='index') tm.assert_frame_equal(result.sort_index(), expected) # none named data = [OrderedDict([['a', 1.5], ['b', 3], ['c', 4], ['d', 6]]), OrderedDict([['a', 1.5], ['b', 3], ['d', 6]]), OrderedDict([['a', 1.5], ['d', 6]]), OrderedDict(), OrderedDict([['a', 1.5], ['b', 3], ['c', 4]]), OrderedDict([['b', 3], ['c', 4], ['d', 6]])] data = [Series(d) for d in data] result = DataFrame(data) sdict = OrderedDict(zip(range(len(data)), data)) expected = DataFrame.from_dict(sdict, orient='index') tm.assert_frame_equal(result, expected.reindex(result.index)) result2 = DataFrame(data, index=np.arange(6)) tm.assert_frame_equal(result, result2) result = DataFrame([Series({})]) expected = DataFrame(index=[0]) tm.assert_frame_equal(result, expected) data = [OrderedDict([['a', 1.5], ['b', 3.0], ['c', 4.0]]), OrderedDict([['a', 1.5], ['b', 3.0], ['c', 6.0]])] sdict = OrderedDict(zip(range(len(data)), data)) idx = Index(['a', 'b', 'c']) data2 = [Series([1.5, 3, 4], idx, dtype='O'), Series([1.5, 3, 6], idx)] result = DataFrame(data2) expected = DataFrame.from_dict(sdict, orient='index') tm.assert_frame_equal(result, expected) def test_constructor_list_of_series_aligned_index(self): series = [pd.Series(i, index=['b', 'a', 'c'], name=str(i)) for i in range(3)] result = pd.DataFrame(series) expected = pd.DataFrame({'b': [0, 1, 2], 'a': [0, 1, 2], 'c': [0, 1, 2]}, columns=['b', 'a', 'c'], index=['0', '1', '2']) tm.assert_frame_equal(result, expected) def test_constructor_list_of_derived_dicts(self): class CustomDict(dict): pass d = {'a': 1.5, 'b': 3} data_custom = [CustomDict(d)] data = [d] result_custom = DataFrame(data_custom) result = DataFrame(data) tm.assert_frame_equal(result, result_custom) def test_constructor_ragged(self): data = {'A': randn(10), 'B': randn(8)} with pytest.raises(ValueError, match='arrays must all be same length'): DataFrame(data) def test_constructor_scalar(self): idx = Index(lrange(3)) df = DataFrame({"a": 0}, index=idx) expected = DataFrame({"a": [0, 0, 0]}, index=idx) tm.assert_frame_equal(df, expected, check_dtype=False) def test_constructor_Series_copy_bug(self): df = DataFrame(self.frame['A'], index=self.frame.index, columns=['A']) df.copy() def test_constructor_mixed_dict_and_Series(self): data = {} data['A'] = {'foo': 1, 'bar': 2, 'baz': 3} data['B'] = Series([4, 3, 2, 1], index=['bar', 'qux', 'baz', 'foo']) result = DataFrame(data) assert result.index.is_monotonic # ordering ambiguous, raise exception with pytest.raises(ValueError, match='ambiguous ordering'): DataFrame({'A': ['a', 'b'], 'B': {'a': 'a', 'b': 'b'}}) # this is OK though result = DataFrame({'A': ['a', 'b'], 'B': Series(['a', 'b'], index=['a', 'b'])}) expected = DataFrame({'A': ['a', 'b'], 'B': ['a', 'b']}, index=['a', 'b']) tm.assert_frame_equal(result, expected) def test_constructor_tuples(self): result = DataFrame({'A': [(1, 2), (3, 4)]}) expected = DataFrame({'A': Series([(1, 2), (3, 4)])}) tm.assert_frame_equal(result, expected) def test_constructor_namedtuples(self): # GH11181 from collections import namedtuple named_tuple = namedtuple("Pandas", list('ab')) tuples = [named_tuple(1, 3), named_tuple(2, 4)] expected = DataFrame({'a': [1, 2], 'b': [3, 4]}) result = DataFrame(tuples) tm.assert_frame_equal(result, expected) # with columns expected = DataFrame({'y': [1, 2], 'z': [3, 4]}) result = DataFrame(tuples, columns=['y', 'z']) tm.assert_frame_equal(result, expected) def test_constructor_orient(self): data_dict = self.mixed_frame.T._series recons = DataFrame.from_dict(data_dict, orient='index') expected = self.mixed_frame.sort_index() tm.assert_frame_equal(recons, expected) # dict of sequence a = {'hi': [32, 3, 3], 'there': [3, 5, 3]} rs = DataFrame.from_dict(a, orient='index') xp = DataFrame.from_dict(a).T.reindex(list(a.keys())) tm.assert_frame_equal(rs, xp) def test_from_dict_columns_parameter(self): # GH 18529 # Test new columns parameter for from_dict that was added to make # from_items(..., orient='index', columns=[...]) easier to replicate result = DataFrame.from_dict(OrderedDict([('A', [1, 2]), ('B', [4, 5])]), orient='index', columns=['one', 'two']) expected = DataFrame([[1, 2], [4, 5]], index=['A', 'B'], columns=['one', 'two']) tm.assert_frame_equal(result, expected) msg = "cannot use columns parameter with orient='columns'" with pytest.raises(ValueError, match=msg): DataFrame.from_dict(dict([('A', [1, 2]), ('B', [4, 5])]), orient='columns', columns=['one', 'two']) with pytest.raises(ValueError, match=msg): DataFrame.from_dict(dict([('A', [1, 2]), ('B', [4, 5])]), columns=['one', 'two']) def test_constructor_Series_named(self): a = Series([1, 2, 3], index=['a', 'b', 'c'], name='x') df = DataFrame(a) assert df.columns[0] == 'x' tm.assert_index_equal(df.index, a.index) # ndarray like arr = np.random.randn(10) s = Series(arr, name='x') df = DataFrame(s) expected = DataFrame(dict(x=s)) tm.assert_frame_equal(df, expected) s = Series(arr, index=range(3, 13)) df = DataFrame(s) expected = DataFrame({0: s}) tm.assert_frame_equal(df, expected) pytest.raises(ValueError, DataFrame, s, columns=[1, 2]) # #2234 a = Series([], name='x') df = DataFrame(a) assert df.columns[0] == 'x' # series with name and w/o s1 = Series(arr, name='x') df = DataFrame([s1, arr]).T expected = DataFrame({'x': s1, 'Unnamed 0': arr}, columns=['x', 'Unnamed 0']) tm.assert_frame_equal(df, expected) # this is a bit non-intuitive here; the series collapse down to arrays df = DataFrame([arr, s1]).T expected = DataFrame({1: s1, 0: arr}, columns=[0, 1]) tm.assert_frame_equal(df, expected) def test_constructor_Series_named_and_columns(self): # GH 9232 validation s0 = Series(range(5), name=0) s1 = Series(range(5), name=1) # matching name and column gives standard frame tm.assert_frame_equal(pd.DataFrame(s0, columns=[0]), s0.to_frame()) tm.assert_frame_equal(pd.DataFrame(s1, columns=[1]), s1.to_frame()) # non-matching produces empty frame assert pd.DataFrame(s0, columns=[1]).empty assert pd.DataFrame(s1, columns=[0]).empty def test_constructor_Series_differently_indexed(self): # name s1 = Series([1, 2, 3], index=['a', 'b', 'c'], name='x') # no name s2 = Series([1, 2, 3], index=['a', 'b', 'c']) other_index = Index(['a', 'b']) df1 = DataFrame(s1, index=other_index) exp1 = DataFrame(s1.reindex(other_index)) assert df1.columns[0] == 'x' tm.assert_frame_equal(df1, exp1) df2 = DataFrame(s2, index=other_index) exp2 = DataFrame(s2.reindex(other_index)) assert df2.columns[0] == 0 tm.assert_index_equal(df2.index, other_index) tm.assert_frame_equal(df2, exp2) def test_constructor_manager_resize(self): index = list(self.frame.index[:5]) columns = list(self.frame.columns[:3]) result = DataFrame(self.frame._data, index=index, columns=columns) tm.assert_index_equal(result.index, Index(index)) tm.assert_index_equal(result.columns, Index(columns)) def test_constructor_from_items(self): items = [(c, self.frame[c]) for c in self.frame.columns] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): recons = DataFrame.from_items(items) tm.assert_frame_equal(recons, self.frame) # pass some columns with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): recons = DataFrame.from_items(items, columns=['C', 'B', 'A']) tm.assert_frame_equal(recons, self.frame.loc[:, ['C', 'B', 'A']]) # orient='index' row_items = [(idx, self.mixed_frame.xs(idx)) for idx in self.mixed_frame.index] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): recons = DataFrame.from_items(row_items, columns=self.mixed_frame.columns, orient='index') tm.assert_frame_equal(recons, self.mixed_frame) assert recons['A'].dtype == np.float64 msg = "Must pass columns with orient='index'" with pytest.raises(TypeError, match=msg): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): DataFrame.from_items(row_items, orient='index') # orient='index', but thar be tuples arr = construct_1d_object_array_from_listlike( [('bar', 'baz')] * len(self.mixed_frame)) self.mixed_frame['foo'] = arr row_items = [(idx, list(self.mixed_frame.xs(idx))) for idx in self.mixed_frame.index] with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): recons = DataFrame.from_items(row_items, columns=self.mixed_frame.columns, orient='index') tm.assert_frame_equal(recons, self.mixed_frame) assert isinstance(recons['foo'][0], tuple) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): rs = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])], orient='index', columns=['one', 'two', 'three']) xp = DataFrame([[1, 2, 3], [4, 5, 6]], index=['A', 'B'], columns=['one', 'two', 'three']) tm.assert_frame_equal(rs, xp) def test_constructor_from_items_scalars(self): # GH 17312 msg = (r'The value in each \(key, value\) ' 'pair must be an array, Series, or dict') with pytest.raises(ValueError, match=msg): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): DataFrame.from_items([('A', 1), ('B', 4)]) msg = (r'The value in each \(key, value\) ' 'pair must be an array, Series, or dict') with pytest.raises(ValueError, match=msg): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): DataFrame.from_items([('A', 1), ('B', 2)], columns=['col1'], orient='index') def test_from_items_deprecation(self): # GH 17320 with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])]) with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])], columns=['col1', 'col2', 'col3'], orient='index') def test_constructor_mix_series_nonseries(self): df = DataFrame({'A': self.frame['A'], 'B': list(self.frame['B'])}, columns=['A', 'B']) tm.assert_frame_equal(df, self.frame.loc[:, ['A', 'B']]) msg = 'does not match index length' with pytest.raises(ValueError, match=msg): DataFrame({'A': self.frame['A'], 'B': list(self.frame['B'])[:-2]}) def test_constructor_miscast_na_int_dtype(self): df = DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64) expected = DataFrame([[np.nan, 1], [1, 0]]) tm.assert_frame_equal(df, expected) def test_constructor_column_duplicates(self): # it works! #2079 df = DataFrame([[8, 5]], columns=['a', 'a']) edf = DataFrame([[8, 5]]) edf.columns = ['a', 'a'] tm.assert_frame_equal(df, edf) idf = DataFrame.from_records([(8, 5)], columns=['a', 'a']) tm.assert_frame_equal(idf, edf) pytest.raises(ValueError, DataFrame.from_dict, OrderedDict([('b', 8), ('a', 5), ('a', 6)])) def test_constructor_empty_with_string_dtype(self): # GH 9428 expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object) df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str) tm.assert_frame_equal(df, expected) df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_) tm.assert_frame_equal(df, expected) df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.unicode_) tm.assert_frame_equal(df, expected) df = DataFrame(index=[0, 1], columns=[0, 1], dtype='U5') tm.assert_frame_equal(df, expected) def test_constructor_single_value(self): # expecting single value upcasting here df = DataFrame(0., index=[1, 2, 3], columns=['a', 'b', 'c']) tm.assert_frame_equal(df, DataFrame(np.zeros(df.shape).astype('float64'), df.index, df.columns)) df = DataFrame(0, index=[1, 2, 3], columns=['a', 'b', 'c']) tm.assert_frame_equal(df, DataFrame(np.zeros(df.shape).astype('int64'), df.index, df.columns)) df = DataFrame('a', index=[1, 2], columns=['a', 'c']) tm.assert_frame_equal(df, DataFrame(np.array([['a', 'a'], ['a', 'a']], dtype=object), index=[1, 2], columns=['a', 'c'])) pytest.raises(ValueError, DataFrame, 'a', [1, 2]) pytest.raises(ValueError, DataFrame, 'a', columns=['a', 'c']) msg = 'incompatible data and dtype' with pytest.raises(TypeError, match=msg): DataFrame('a', [1, 2], ['a', 'c'], float) def test_constructor_with_datetimes(self): intname = np.dtype(np.int_).name floatname = np.dtype(np.float_).name datetime64name = np.dtype('M8[ns]').name objectname = np.dtype(np.object_).name # single item df = DataFrame({'A': 1, 'B': 'foo', 'C': 'bar', 'D': Timestamp("20010101"), 'E': datetime(2001, 1, 2, 0, 0)}, index=np.arange(10)) result = df.get_dtype_counts() expected = Series({'int64': 1, datetime64name: 2, objectname: 2}) result.sort_index() expected.sort_index() tm.assert_series_equal(result, expected) # check with ndarray construction ndim==0 (e.g. we are passing a ndim 0 # ndarray with a dtype specified) df = DataFrame({'a': 1., 'b': 2, 'c': 'foo', floatname: np.array(1., dtype=floatname), intname: np.array(1, dtype=intname)}, index=np.arange(10)) result = df.get_dtype_counts() expected = {objectname: 1} if intname == 'int64': expected['int64'] = 2 else: expected['int64'] = 1 expected[intname] = 1 if floatname == 'float64': expected['float64'] = 2 else: expected['float64'] = 1 expected[floatname] = 1 result = result.sort_index() expected = Series(expected).sort_index() tm.assert_series_equal(result, expected) # check with ndarray construction ndim>0 df = DataFrame({'a': 1., 'b': 2, 'c': 'foo', floatname: np.array([1.] * 10, dtype=floatname), intname: np.array([1] * 10, dtype=intname)}, index=np.arange(10)) result = df.get_dtype_counts() result = result.sort_index() tm.assert_series_equal(result, expected) # GH 2809 ind = date_range(start="2000-01-01", freq="D", periods=10) datetimes = [ts.to_pydatetime() for ts in ind] datetime_s = Series(datetimes) assert datetime_s.dtype == 'M8[ns]' df = DataFrame({'datetime_s': datetime_s}) result = df.get_dtype_counts() expected = Series({datetime64name: 1}) result = result.sort_index() expected = expected.sort_index() tm.assert_series_equal(result, expected) # GH 2810 ind = date_range(start="2000-01-01", freq="D", periods=10) datetimes = [ts.to_pydatetime() for ts in ind] dates = [ts.date() for ts in ind] df = DataFrame({'datetimes': datetimes, 'dates': dates}) result = df.get_dtype_counts() expected = Series({datetime64name: 1, objectname: 1}) result = result.sort_index() expected = expected.sort_index() tm.assert_series_equal(result, expected) # GH 7594 # don't coerce tz-aware import pytz tz = pytz.timezone('US/Eastern') dt = tz.localize(datetime(2012, 1, 1)) df = DataFrame({'End Date': dt}, index=[0]) assert df.iat[0, 0] == dt tm.assert_series_equal(df.dtypes, Series( {'End Date': 'datetime64[ns, US/Eastern]'})) df = DataFrame([{'End Date': dt}]) assert df.iat[0, 0] == dt tm.assert_series_equal(df.dtypes, Series( {'End Date': 'datetime64[ns, US/Eastern]'})) # tz-aware (UTC and other tz's) # GH 8411 dr = date_range('20130101', periods=3) df = DataFrame({'value': dr}) assert df.iat[0, 0].tz is None dr = date_range('20130101', periods=3, tz='UTC') df = DataFrame({'value': dr}) assert str(df.iat[0, 0].tz) == 'UTC' dr = date_range('20130101', periods=3, tz='US/Eastern') df = DataFrame({'value': dr}) assert str(df.iat[0, 0].tz) == 'US/Eastern' # GH 7822 # preserver an index with a tz on dict construction i = date_range('1/1/2011', periods=5, freq='10s', tz='US/Eastern') expected = DataFrame( {'a': i.to_series(keep_tz=True).reset_index(drop=True)}) df = DataFrame() df['a'] = i tm.assert_frame_equal(df, expected) df = DataFrame({'a': i}) tm.assert_frame_equal(df, expected) # multiples i_no_tz = date_range('1/1/2011', periods=5, freq='10s') df = DataFrame({'a': i, 'b': i_no_tz}) expected = DataFrame({'a': i.to_series(keep_tz=True) .reset_index(drop=True), 'b': i_no_tz}) tm.assert_frame_equal(df, expected) def test_constructor_datetimes_with_nulls(self): # gh-15869 for arr in [np.array([None, None, None, None, datetime.now(), None]), np.array([None, None, datetime.now(), None])]: result = DataFrame(arr).get_dtype_counts() expected = Series({'datetime64[ns]': 1}) tm.assert_series_equal(result, expected) def test_constructor_for_list_with_dtypes(self): # TODO(wesm): unused intname = np.dtype(np.int_).name # noqa floatname = np.dtype(np.float_).name # noqa datetime64name = np.dtype('M8[ns]').name objectname = np.dtype(np.object_).name # test list of lists/ndarrays df = DataFrame([np.arange(5) for x in range(5)]) result = df.get_dtype_counts() expected = Series({'int64': 5}) df = DataFrame([np.array(np.arange(5), dtype='int32') for x in range(5)]) result = df.get_dtype_counts() expected = Series({'int32': 5}) # overflow issue? (we always expecte int64 upcasting here) df = DataFrame({'a': [2 ** 31, 2 ** 31 + 1]}) result = df.get_dtype_counts() expected = Series({'int64': 1}) tm.assert_series_equal(result, expected) # GH #2751 (construction with no index specified), make sure we cast to # platform values df = DataFrame([1, 2]) result = df.get_dtype_counts() expected = Series({'int64': 1}) tm.assert_series_equal(result, expected) df = DataFrame([1., 2.]) result = df.get_dtype_counts() expected = Series({'float64': 1}) tm.assert_series_equal(result, expected) df = DataFrame({'a': [1, 2]}) result = df.get_dtype_counts() expected = Series({'int64': 1}) tm.assert_series_equal(result, expected) df = DataFrame({'a': [1., 2.]}) result = df.get_dtype_counts() expected = Series({'float64': 1}) tm.assert_series_equal(result, expected) df = DataFrame({'a': 1}, index=lrange(3)) result = df.get_dtype_counts() expected = Series({'int64': 1}) tm.assert_series_equal(result, expected) df = DataFrame({'a': 1.}, index=lrange(3)) result = df.get_dtype_counts() expected = Series({'float64': 1}) tm.assert_series_equal(result, expected) # with object list df = DataFrame({'a': [1, 2, 4, 7], 'b': [1.2, 2.3, 5.1, 6.3], 'c': list('abcd'), 'd': [datetime(2000, 1, 1) for i in range(4)], 'e': [1., 2, 4., 7]}) result = df.get_dtype_counts() expected = Series( {'int64': 1, 'float64': 2, datetime64name: 1, objectname: 1}) result = result.sort_index() expected = expected.sort_index() tm.assert_series_equal(result, expected) def test_constructor_frame_copy(self): cop = DataFrame(self.frame, copy=True) cop['A'] = 5 assert (cop['A'] == 5).all() assert not (self.frame['A'] == 5).all() def test_constructor_ndarray_copy(self): df = DataFrame(self.frame.values) self.frame.values[5] = 5 assert (df.values[5] == 5).all() df = DataFrame(self.frame.values, copy=True) self.frame.values[6] = 6 assert not (df.values[6] == 6).all() def test_constructor_series_copy(self): series = self.frame._series df = DataFrame({'A': series['A']}) df['A'][:] = 5 assert not (series['A'] == 5).all() def test_constructor_with_nas(self): # GH 5016 # na's in indices def check(df): for i in range(len(df.columns)): df.iloc[:, i] indexer = np.arange(len(df.columns))[isna(df.columns)] # No NaN found -> error if len(indexer) == 0: def f(): df.loc[:, np.nan] pytest.raises(TypeError, f) # single nan should result in Series elif len(indexer) == 1: tm.assert_series_equal(df.iloc[:, indexer[0]], df.loc[:, np.nan]) # multiple nans should result in DataFrame else: tm.assert_frame_equal(df.iloc[:, indexer], df.loc[:, np.nan]) df = DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan]) check(df) df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1.1, 2.2, np.nan]) check(df) df = DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]) check(df) df = DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]) check(df) # GH 21428 (non-unique columns) df = DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1, 2, 2]) check(df) def test_constructor_lists_to_object_dtype(self): # from #1074 d = DataFrame({'a': [np.nan, False]}) assert d['a'].dtype == np.object_ assert not d['a'][1] def test_constructor_categorical(self): # GH8626 # dict creation df = DataFrame({'A': list('abc')}, dtype='category') expected = Series(list('abc'), dtype='category', name='A') tm.assert_series_equal(df['A'], expected) # to_frame s = Series(list('abc'), dtype='category') result = s.to_frame() expected = Series(list('abc'), dtype='category', name=0) tm.assert_series_equal(result[0], expected) result = s.to_frame(name='foo') expected = Series(list('abc'), dtype='category', name='foo') tm.assert_series_equal(result['foo'], expected) # list-like creation df = DataFrame(list('abc'), dtype='category') expected = Series(list('abc'), dtype='category', name=0) tm.assert_series_equal(df[0], expected) # ndim != 1 df = DataFrame([Categorical(list('abc'))]) expected = DataFrame({0: Series(list('abc'), dtype='category')}) tm.assert_frame_equal(df, expected) df = DataFrame([Categorical(list('abc')), Categorical(list('abd'))]) expected = DataFrame({0: Series(list('abc'), dtype='category'), 1: Series(list('abd'), dtype='category')}, columns=[0, 1]) tm.assert_frame_equal(df, expected) # mixed df = DataFrame([Categorical(list('abc')), list('def')]) expected = DataFrame({0: Series(list('abc'), dtype='category'), 1: list('def')}, columns=[0, 1]) tm.assert_frame_equal(df, expected) # invalid (shape) pytest.raises(ValueError, lambda: DataFrame([Categorical(list('abc')), Categorical(list('abdefg'))])) # ndim > 1 pytest.raises(NotImplementedError, lambda: Categorical(np.array([list('abcd')]))) def test_constructor_categorical_series(self): items = [1, 2, 3, 1] exp = Series(items).astype('category') res = Series(items, dtype='category') tm.assert_series_equal(res, exp) items = ["a", "b", "c", "a"] exp = Series(items).astype('category') res = Series(items, dtype='category') tm.assert_series_equal(res, exp) # insert into frame with different index # GH 8076 index = date_range('20000101', periods=3) expected = Series(Categorical(values=[np.nan, np.nan, np.nan], categories=['a', 'b', 'c'])) expected.index = index expected = DataFrame({'x': expected}) df = DataFrame( {'x': Series(['a', 'b', 'c'], dtype='category')}, index=index) tm.assert_frame_equal(df, expected) def test_from_records_to_records(self): # from numpy documentation arr = np.zeros((2,), dtype=('i4,f4,a10')) arr[:] = [(1, 2., 'Hello'), (2, 3., "World")] # TODO(wesm): unused frame = DataFrame.from_records(arr) # noqa index = pd.Index(np.arange(len(arr))[::-1]) indexed_frame = DataFrame.from_records(arr, index=index) tm.assert_index_equal(indexed_frame.index, index) # without names, it should go to last ditch arr2 = np.zeros((2, 3)) tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2)) # wrong length msg = r'Shape of passed values is \(3, 2\), indices imply \(3, 1\)' with pytest.raises(ValueError, match=msg): DataFrame.from_records(arr, index=index[:-1]) indexed_frame = DataFrame.from_records(arr, index='f1') # what to do? records = indexed_frame.to_records() assert len(records.dtype.names) == 3 records = indexed_frame.to_records(index=False) assert len(records.dtype.names) == 2 assert 'index' not in records.dtype.names def test_from_records_nones(self): tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)] df = DataFrame.from_records(tuples, columns=['a', 'b', 'c', 'd']) assert np.isnan(df['c'][0]) def test_from_records_iterator(self): arr = np.array([(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5., 5., 6, 6), (7., 7., 8, 8)], dtype=[('x', np.float64), ('u', np.float32), ('y', np.int64), ('z', np.int32)]) df = DataFrame.from_records(iter(arr), nrows=2) xp = DataFrame({'x': np.array([1.0, 3.0], dtype=np.float64), 'u': np.array([1.0, 3.0], dtype=np.float32), 'y': np.array([2, 4], dtype=np.int64), 'z': np.array([2, 4], dtype=np.int32)}) tm.assert_frame_equal(df.reindex_like(xp), xp) # no dtypes specified here, so just compare with the default arr = [(1.0, 2), (3.0, 4), (5., 6), (7., 8)] df = DataFrame.from_records(iter(arr), columns=['x', 'y'], nrows=2) tm.assert_frame_equal(df, xp.reindex(columns=['x', 'y']), check_dtype=False) def test_from_records_tuples_generator(self): def tuple_generator(length): for i in range(length): letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' yield (i, letters[i % len(letters)], i / length) columns_names = ['Integer', 'String', 'Float'] columns = [[i[j] for i in tuple_generator( 10)] for j in range(len(columns_names))] data = {'Integer': columns[0], 'String': columns[1], 'Float': columns[2]} expected = DataFrame(data, columns=columns_names) generator = tuple_generator(10) result = DataFrame.from_records(generator, columns=columns_names) tm.assert_frame_equal(result, expected) def test_from_records_lists_generator(self): def list_generator(length): for i in range(length): letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' yield [i, letters[i % len(letters)], i / length] columns_names = ['Integer', 'String', 'Float'] columns = [[i[j] for i in list_generator( 10)] for j in range(len(columns_names))] data = {'Integer': columns[0], 'String': columns[1], 'Float': columns[2]} expected = DataFrame(data, columns=columns_names) generator = list_generator(10) result = DataFrame.from_records(generator, columns=columns_names) tm.assert_frame_equal(result, expected) def test_from_records_columns_not_modified(self): tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)] columns = ['a', 'b', 'c'] original_columns = list(columns) df = DataFrame.from_records(tuples, columns=columns, index='a') # noqa assert columns == original_columns def test_from_records_decimal(self): from decimal import Decimal tuples = [(Decimal('1.5'),), (Decimal('2.5'),), (None,)] df = DataFrame.from_records(tuples, columns=['a']) assert df['a'].dtype == object df = DataFrame.from_records(tuples, columns=['a'], coerce_float=True) assert df['a'].dtype == np.float64 assert np.isnan(df['a'].values[-1]) def test_from_records_duplicates(self): result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'a']) expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'a']) tm.assert_frame_equal(result, expected) def test_from_records_set_index_name(self): def create_dict(order_id): return {'order_id': order_id, 'quantity': np.random.randint(1, 10), 'price': np.random.randint(1, 10)} documents = [create_dict(i) for i in range(10)] # demo missing data documents.append({'order_id': 10, 'quantity': 5}) result = DataFrame.from_records(documents, index='order_id') assert result.index.name == 'order_id' # MultiIndex result = DataFrame.from_records(documents, index=['order_id', 'quantity']) assert result.index.names == ('order_id', 'quantity') def test_from_records_misc_brokenness(self): # #2179 data = {1: ['foo'], 2: ['bar']} result = DataFrame.from_records(data, columns=['a', 'b']) exp = DataFrame(data, columns=['a', 'b']) tm.assert_frame_equal(result, exp) # overlap in index/index_names data = {'a': [1, 2, 3], 'b': [4, 5, 6]} result = DataFrame.from_records(data, index=['a', 'b', 'c']) exp = DataFrame(data, index=['a', 'b', 'c']) tm.assert_frame_equal(result, exp) # GH 2623 rows = [] rows.append([datetime(2010, 1, 1), 1]) rows.append([datetime(2010, 1, 2), 'hi']) # test col upconverts to obj df2_obj = DataFrame.from_records(rows, columns=['date', 'test']) results = df2_obj.get_dtype_counts() expected = Series({'datetime64[ns]': 1, 'object': 1}) rows = [] rows.append([datetime(2010, 1, 1), 1]) rows.append([datetime(2010, 1, 2), 1]) df2_obj = DataFrame.from_records(rows, columns=['date', 'test']) results = df2_obj.get_dtype_counts().sort_index() expected = Series({'datetime64[ns]': 1, 'int64': 1}) tm.assert_series_equal(results, expected) def test_from_records_empty(self): # 3562 result = DataFrame.from_records([], columns=['a', 'b', 'c']) expected = DataFrame(columns=['a', 'b', 'c']) tm.assert_frame_equal(result, expected) result = DataFrame.from_records([], columns=['a', 'b', 'b']) expected = DataFrame(columns=['a', 'b', 'b']) tm.assert_frame_equal(result, expected) def test_from_records_empty_with_nonempty_fields_gh3682(self): a = np.array([(1, 2)], dtype=[('id', np.int64), ('value', np.int64)]) df = DataFrame.from_records(a, index='id') tm.assert_index_equal(df.index, Index([1], name='id')) assert df.index.name == 'id' tm.assert_index_equal(df.columns, Index(['value'])) b = np.array([], dtype=[('id', np.int64), ('value', np.int64)]) df = DataFrame.from_records(b, index='id') tm.assert_index_equal(df.index, Index([], name='id')) assert df.index.name == 'id' def test_from_records_with_datetimes(self): # this may fail on certain platforms because of a numpy issue # related GH6140 if not is_platform_little_endian(): pytest.skip("known failure of test on non-little endian") # construction with a null in a recarray # GH 6140 expected = DataFrame({'EXPIRY': [datetime(2005, 3, 1, 0, 0), None]}) arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])] dtypes = [('EXPIRY', '<M8[ns]')] try: recarray = np.core.records.fromarrays(arrdata, dtype=dtypes) except (ValueError): pytest.skip("known failure of numpy rec array creation") result = DataFrame.from_records(recarray) tm.assert_frame_equal(result, expected) # coercion should work too arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])] dtypes = [('EXPIRY', '<M8[m]')] recarray = np.core.records.fromarrays(arrdata, dtype=dtypes) result = DataFrame.from_records(recarray) tm.assert_frame_equal(result, expected) def test_from_records_sequencelike(self): df = DataFrame({'A': np.array(np.random.randn(6), dtype=np.float64), 'A1': np.array(np.random.randn(6), dtype=np.float64), 'B': np.array(np.arange(6), dtype=np.int64), 'C': ['foo'] * 6, 'D': np.array([True, False] * 3, dtype=bool), 'E': np.array(np.random.randn(6), dtype=np.float32), 'E1': np.array(np.random.randn(6), dtype=np.float32), 'F': np.array(np.arange(6), dtype=np.int32)}) # this is actually tricky to create the recordlike arrays and # have the dtypes be intact blocks = df._to_dict_of_blocks() tuples = [] columns = [] dtypes = [] for dtype, b in compat.iteritems(blocks): columns.extend(b.columns) dtypes.extend([(c, np.dtype(dtype).descr[0][1]) for c in b.columns]) for i in range(len(df.index)): tup = [] for _, b in compat.iteritems(blocks): tup.extend(b.iloc[i].values) tuples.append(tuple(tup)) recarray = np.array(tuples, dtype=dtypes).view(np.recarray) recarray2 = df.to_records() lists = [list(x) for x in tuples] # tuples (lose the dtype info) result = (DataFrame.from_records(tuples, columns=columns) .reindex(columns=df.columns)) # created recarray and with to_records recarray (have dtype info) result2 = (DataFrame.from_records(recarray, columns=columns) .reindex(columns=df.columns)) result3 = (DataFrame.from_records(recarray2, columns=columns) .reindex(columns=df.columns)) # list of tupels (no dtype info) result4 = (DataFrame.from_records(lists, columns=columns) .reindex(columns=df.columns)) tm.assert_frame_equal(result, df, check_dtype=False) tm.assert_frame_equal(result2, df) tm.assert_frame_equal(result3, df) tm.assert_frame_equal(result4, df, check_dtype=False) # tuples is in the order of the columns result = DataFrame.from_records(tuples) tm.assert_index_equal(result.columns, pd.Index(lrange(8))) # test exclude parameter & we are casting the results here (as we don't # have dtype info to recover) columns_to_test = [columns.index('C'), columns.index('E1')] exclude = list(set(range(8)) - set(columns_to_test)) result = DataFrame.from_records(tuples, exclude=exclude) result.columns = [columns[i] for i in sorted(columns_to_test)] tm.assert_series_equal(result['C'], df['C']) tm.assert_series_equal(result['E1'], df['E1'].astype('float64')) # empty case result = DataFrame.from_records([], columns=['foo', 'bar', 'baz']) assert len(result) == 0 tm.assert_index_equal(result.columns, pd.Index(['foo', 'bar', 'baz'])) result = DataFrame.from_records([]) assert len(result) == 0 assert len(result.columns) == 0 def test_from_records_dictlike(self): # test the dict methods df = DataFrame({'A': np.array(np.random.randn(6), dtype=np.float64), 'A1': np.array(np.random.randn(6), dtype=np.float64), 'B': np.array(np.arange(6), dtype=np.int64), 'C': ['foo'] * 6, 'D': np.array([True, False] * 3, dtype=bool), 'E': np.array(np.random.randn(6), dtype=np.float32), 'E1': np.array(np.random.randn(6), dtype=np.float32), 'F': np.array(np.arange(6), dtype=np.int32)}) # columns is in a different order here than the actual items iterated # from the dict blocks = df._to_dict_of_blocks() columns = [] for dtype, b in compat.iteritems(blocks): columns.extend(b.columns) asdict = {x: y for x, y in compat.iteritems(df)} asdict2 = {x: y.values for x, y in compat.iteritems(df)} # dict of series & dict of ndarrays (have dtype info) results = [] results.append(DataFrame.from_records( asdict).reindex(columns=df.columns)) results.append(DataFrame.from_records(asdict, columns=columns) .reindex(columns=df.columns)) results.append(DataFrame.from_records(asdict2, columns=columns) .reindex(columns=df.columns)) for r in results: tm.assert_frame_equal(r, df) def test_from_records_with_index_data(self): df = DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C']) data = np.random.randn(10) df1 = DataFrame.from_records(df, index=data) tm.assert_index_equal(df1.index, Index(data)) def test_from_records_bad_index_column(self): df = DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C']) # should pass df1 = DataFrame.from_records(df, index=['C']) tm.assert_index_equal(df1.index, Index(df.C)) df1 = DataFrame.from_records(df, index='C') tm.assert_index_equal(df1.index, Index(df.C)) # should fail pytest.raises(ValueError, DataFrame.from_records, df, index=[2]) pytest.raises(KeyError, DataFrame.from_records, df, index=2) def test_from_records_non_tuple(self): class Record(object): def __init__(self, *args): self.args = args def __getitem__(self, i): return self.args[i] def __iter__(self): return iter(self.args) recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)] tups = lmap(tuple, recs) result = DataFrame.from_records(recs) expected = DataFrame.from_records(tups) tm.assert_frame_equal(result, expected) def test_from_records_len0_with_columns(self): # #2633 result = DataFrame.from_records([], index='foo', columns=['foo', 'bar']) expected = Index(['bar']) assert len(result) == 0 assert result.index.name == 'foo' tm.assert_index_equal(result.columns, expected) def test_to_frame_with_falsey_names(self): # GH 16114 result = Series(name=0).to_frame().dtypes expected = Series({0: np.float64}) tm.assert_series_equal(result, expected) result = DataFrame(Series(name=0)).dtypes tm.assert_series_equal(result, expected) @pytest.mark.parametrize('dtype', [None, 'uint8', 'category']) def test_constructor_range_dtype(self, dtype): # GH 16804 expected = DataFrame({'A': [0, 1, 2, 3, 4]}, dtype=dtype or 'int64') result = DataFrame({'A': range(5)}, dtype=dtype) tm.assert_frame_equal(result, expected) def test_frame_from_list_subclass(self): # GH21226 class List(list): pass expected = DataFrame([[1, 2, 3], [4, 5, 6]]) result = DataFrame(List([List([1, 2, 3]), List([4, 5, 6])])) tm.assert_frame_equal(result, expected) class TestDataFrameConstructorWithDatetimeTZ(TestData): def test_from_dict(self): # 8260 # support datetime64 with tz idx = Index(date_range('20130101', periods=3, tz='US/Eastern'), name='foo') dr = date_range('20130110', periods=3) # construction df = DataFrame({'A': idx, 'B': dr}) assert df['A'].dtype, 'M8[ns, US/Eastern' assert df['A'].name == 'A' tm.assert_series_equal(df['A'], Series(idx, name='A')) tm.assert_series_equal(df['B'], Series(dr, name='B')) def test_from_index(self): # from index idx2 = date_range('20130101', periods=3, tz='US/Eastern', name='foo') df2 = DataFrame(idx2) tm.assert_series_equal(df2['foo'], Series(idx2, name='foo')) df2 = DataFrame(Series(idx2)) tm.assert_series_equal(df2['foo'], Series(idx2, name='foo')) idx2 = date_range('20130101', periods=3, tz='US/Eastern') df2 = DataFrame(idx2) tm.assert_series_equal(df2[0],
Series(idx2, name=0)
pandas.Series
# -*- coding: utf-8 -*- """ Created on Fri Apr 12 12:29:19 2019 @author: sdenaro """ import matplotlib.pyplot as plt import pandas as pd from datetime import datetime as dt from datetime import timedelta import numpy as np import numpy.matlib as matlib import seaborn as sns from sklearn import linear_model #from sklearn.metrics import mean_squared_error, r2_score from scipy import stats def r2(x, y): return stats.pearsonr(x, y)[0] ** 2 #Set Preference Customers reduction percent (number) custom_redux=0 # Yearly firm loads (aMW) # upload BPA firm load column from file df_load=pd.read_excel('../DATA/net_rev_data.xlsx',sheet_name=0,skiprows=[0,1], usecols=[9]) #Save as Preference Firm (PF), Industrial Firm (IF) an Export (ET) PF_load_y=df_load.loc[[13]].values - custom_redux*df_load.loc[[13]].values IP_load_y=df_load.loc[[3]].values - custom_redux* df_load.loc[[3]].values ET_load_y=df_load.loc[[14]] # Hourly hydro generation from FCRPS stochastic simulation #df_hydro=pd.read_csv('../../CAPOW/CAPOW_SD/Stochastic_engine/PNW_hydro/FCRPS/BPA_owned_dams.csv', header=None) df_hydro=pd.read_csv('new_BPA_hydro_daily.csv', usecols=([1])) BPA_hydro=pd.DataFrame(data=df_hydro.loc[0:365*1200-1,:].sum(axis=1)/24, columns=['hydro']) BPA_hydro[BPA_hydro>45000]=45000 #Remove CAISO bad_years BPA_hydro=pd.DataFrame(np.reshape(BPA_hydro.values, (365,1200), order='F')) BPA_hydro.drop([82, 150, 374, 377, 540, 616, 928, 940, 974, 980, 1129, 1191],axis=1, inplace=True) #reshuffle #BPA_hydro[[1, 122, 364, 543]]=BPA_hydro[[16, 126, 368, 547]] BPA_hydro=pd.DataFrame(np.reshape(BPA_hydro.values, (365*1188), order='F')) # Yearly resources other than hydro (aMW) df_resources=
pd.read_excel('../DATA/net_rev_data.xlsx',sheet_name=1,skiprows=[0,1], usecols=[9])
pandas.read_excel
# -*- coding: utf-8 -*- """ Tools for calculating open-water evaporation using the aerodynmaic mass-transfer approach. """ import cmath as cm import numpy as np import math as m import pandas as pd import multiprocessing as mp class Aero(object): """ Manages meterological time series input/output for aerodynamic mass-transfer evaporation calculation and contains methods for batch and single calculations. An :obj:`Aero` object allows the aerodynamic mass-transfer evaporation estimation to be calculated from meterological data that is stored in a :obj:`pandas.DataFrame` with a date or datetime-like index. The :attr:`Aero.df` can be assigned on initialization or later, it can also be reassigned at anytime. The :meth:`Aero.single_calc` static method calculates evaporation for a single measurement set and can be used without creating an :obj:`Aero` object, e.g. in another module. For calculating evaporation for a time series of input meterological data use the :meth:`Aero.run` method which uses multiple processors (if they are available). """ def __init__(self, df=None): if df is not None and not isinstance(df, pd.DataFrame): raise TypeError("Must assign a pandas.DataFrame object") self._df = df def run(self, sensor_height, timestep, variable_names=None, nproc=None): """ Run aerodynamic mass-transfer evaporation routine on time series data that contains necessary input in-place and in parallel. Arguments: sensor_height (float): height of sensor in meters. timestep (float or int): sensor sampling frequency in seconds. Keyword Arguments: variable_names (None or dict): default None. Dictionary with user variable names as keys and variable names needed for :mod:`aeroevap` as values. If None, the needed input variables must be named correctly in the :attr:`Aero.df` dataframe: 'WS', 'P', 'T_air', 'T_skin', and 'RH' for windspeed, air pressure, air temperature, skin temperature, and relative humidity resepctively. nproc (None or int): default None. If none use half of the available cores for parallel calculations. Returns: None Hint: A :obj:`pandas.DataFrame` must be assigned to the :attr:`Aero.df` instance property before calling :meth:`Aero.run`. If the names of the required meterological variables in the dataframe are not named correctly you may pass a dictionary to the ``variable_names`` argument which maps your names to those used by ``AeroEvap``. For example if your surface temperature column is named 'surface_temp' then >>> variable_names = {'surface_temp' : 'T_skin'} """ if not isinstance(self._df, pd.DataFrame): print( 'ERROR: no pandas.DataFrame assigned to Aero.df, please ' 'assign first.' ) return if variable_names is not None: df = self._df.rename(columns=variable_names) else: df = self._df df['date'] = df.index df['SH'] = sensor_height df['dt'] = timestep input_vars = ['date', 'WS', 'P', 'T_air', 'T_skin', 'RH', 'SH', 'dt'] if not set(input_vars).issubset(df.columns): print( 'ERROR: missing on or more needed columns for calculation:\n' '{}'.format(', '.join(input_vars)) ) return numeric_vars = ['WS', 'P', 'T_air', 'T_skin', 'RH', 'SH'] df[numeric_vars] = df[numeric_vars].astype(float) # run each input using n processors inputs = df[input_vars].values.tolist() if not nproc: nproc = mp.cpu_count() // 2 # use half cores pool = mp.Pool(processes=nproc) results = pool.map(_calc,inputs) pool.close() pool.join() results_df =
pd.concat(results)
pandas.concat
import numpy as np import pytest import pandas as pd import pandas._testing as tm @pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"]) def test_compare_axis(align_axis): # GH#30429 s1 = pd.Series(["a", "b", "c"]) s2 = pd.Series(["x", "b", "z"]) result = s1.compare(s2, align_axis=align_axis) if align_axis in (1, "columns"): indices = pd.Index([0, 2]) columns = pd.Index(["self", "other"]) expected = pd.DataFrame( [["a", "x"], ["c", "z"]], index=indices, columns=columns ) tm.assert_frame_equal(result, expected) else: indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]]) expected = pd.Series(["a", "x", "c", "z"], index=indices) tm.assert_series_equal(result, expected) @pytest.mark.parametrize( "keep_shape, keep_equal", [ (True, False), (False, True), (True, True), # False, False case is already covered in test_compare_axis ], ) def test_compare_various_formats(keep_shape, keep_equal): s1 = pd.Series(["a", "b", "c"]) s2 = pd.Series(["x", "b", "z"]) result = s1.compare(s2, keep_shape=keep_shape, keep_equal=keep_equal) if keep_shape: indices =
pd.Index([0, 1, 2])
pandas.Index
import gc as _gc import pandas as _pd import numpy as _np from . import databases as _databases from . import profiles as _profiles class Columns(_databases.Columns): """ Container for the columns names defined in this module. """ SPLIT_SUF = '_SPLIT' REF = 'REF' QRY = 'QRY' REF_SPLIT = '{}{}'.format(REF, SPLIT_SUF) QRY_SPLIT = '{}{}'.format(QRY, SPLIT_SUF) PROF_Q = _databases.Columns.PROF_Q PROF_A = _databases.Columns.PROF_A STR_SEP = '|' def get_IDs_names( species, ): """ Returns dict of KEGG Organism IDs as keys and biological names as values. Parameters ------- species: list of str List of full biological names to convert into KEGG Organism IDs. Returns ------ dict """ kegg_db = _databases.KEGG('Orthology') kegg_db.parse_organism_info( organism=None, reference_species=species, IDs=None, X_ref=None, KOs=None, IDs_only=True, ) return {k.lower(): v for k, v in kegg_db.ID_name.items()} def profilize_organism(*args, **kwargs): """ Returns pandas.DataFrame with Phylogenetic Profile for each ORF name of an organism. Parameters ------- organism: str Full biological name of the organism. reference_species: list of str List of full biological names to build the Phylogenetic Profile. IDs: str, path Filename of the KEGG Organism IDs. Downloaded to a temporary file if <None>. X_ref: str, path Filename of the ORF-KEGG Orthology Group cross-reference. Downloaded to a temporary file if <None>. KOs: str, path Filename of the KEGG Orthology Group-Organism cross-reference. Downloaded to a temporary file if <None>. threads: int Number of threads to utilize when downloading from KEGG. More means faster but can make KEGG block the download temporarily. Default: <2> Returns ------ pandas.DataFrame """ kegg_db = _databases.KEGG('Orthology') kegg_db.parse_organism_info(*args, **kwargs) return kegg_db.organism_info.drop(columns=_databases.Columns.KEGG_ID) def read_sga( filename, version=2, ): """ Returns pandas.DataFrame with Genetic Interaction Network from the Costanzo's SGA experiment either version 1 or 2. Parameters ------- filename: str, path Filename of the SGA. version: int Version number of the Costanzo's SGA experiment. 1 or 2 available. Returns ------- pandas.DataFrame """ if version == 1: sga = _databases.SGA1() elif version == 2: sga = _databases.SGA2() else: raise errors.ParserError("Only versions 1 and 2 of Costanzo's SGA experiment are supported.") sga.parse(filename=filename) return sga.sga def read_profiles( filename, **kwargs ): """ Returns pandas.Series with prwlr.profiles.Profile objects from CSV file. Together with prwlr.core.save_profiles provides a convenient way of saving/reading-in prwlr.profiles.Profile objects to/from a flat text file. Parameters ------- filename: str, path CSV file name. Returns ------ pandas.Series """ ref_qry_df = _pd.read_csv(filename, **kwargs) ref_qry_df[Columns.REF_SPLIT] = ref_qry_df[Columns.REF].str.split(Columns.STR_SEP) ref_qry_df[Columns.QRY_SPLIT] = ref_qry_df[Columns.QRY].str.split(Columns.STR_SEP) return ref_qry_df[[Columns.REF_SPLIT, Columns.QRY_SPLIT]].apply( lambda x: _profiles.Profile( reference=x[Columns.REF_SPLIT], query=x[Columns.QRY_SPLIT], ), axis=1, ) def save_profiles( series, filename, **kwargs ): """ Writes pandas.Series with prwlr.profiles.Profile objects to CSV file. Together with prwlr.core.read_profiles provides a convenient way of saving/reading-in prwlr.profiles.Profile objects to/from a flat text file. Parameters ------- Filename: str, path CSV file name. """ _pd.DataFrame( { Columns.REF: series.apply(lambda x: x.reference).str.join(Columns.STR_SEP), Columns.QRY: series.apply(lambda x: x.query).str.join(Columns.STR_SEP), }, ).to_csv(filename, **kwargs) def read_network( filename, **kwargs ): """ Returns pandas.DataFrame representing a Genetic Interaction Network with prwlr.profiles.Profile objects from CSV file. Together with prwlr.core.save_profiles provides a convenient way of saving/reading-in prwlr.profiles.Profile objects to/from a flat text file. Parameters ------- filename: str, path CSV file name. Returns ------- pandas.DataFrame """ qry_ref_col = '{}_{}'.format(Columns.PROF_Q, Columns.REF) qry_qry_col = '{}_{}'.format(Columns.PROF_Q, Columns.QRY) arr_ref_col = '{}_{}'.format(Columns.PROF_A, Columns.REF) arr_qry_col = '{}_{}'.format(Columns.PROF_A, Columns.QRY) df =
_pd.read_csv(filename, **kwargs)
pandas.read_csv
import matplotlib import pandas as pd matplotlib.use('agg') import matplotlib.pyplot as plt import seaborn as sns import string import glob import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as clr import numpy as np from matplotlib.colors import ListedColormap import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from matplotlib.colors import ListedColormap, LinearSegmentedColormap import pandas as pd import matplotlib.pylab as plt import numpy as np import scipy import seaborn as sns import glob cadd = pd.read_csv("DeepSEA_CADD_GERP/gRNA_all_A.CADD.vcf",sep="\t") deepsea = pd.read_csv("DeepSEA_CADD_GERP/DeepSEA.out.funsig",index_col=0) gerp =pd.read_csv("DeepSEA_CADD_GERP/gRNA_all_GERP.tsv",sep="\t") gerp.index = gerp['chrom']+":"+gerp['start'].astype(str)+"-"+gerp['end'].astype(str) deepsea['name'] = deepsea['chr']+":"+(deepsea['pos']-1).astype(str)+"-"+deepsea['pos'].astype(str) deepsea.index = deepsea['name'] cadd['name'] = "chr"+cadd['#Chrom'].astype(str)+":"+(cadd['Pos']-1).astype(str)+"-"+cadd['Pos'].astype(str) cadd.index = cadd['name'] df = pd.read_csv("Editable_A_scores.tsv",sep="\t",index_col=0) df['CADD'] = cadd['PHRED'] df['DeepSEA'] = deepsea['Functional significance score'] df['GERP'] = gerp['gerp_bp_score'] df['DeepSEA'] = df['DeepSEA'].apply(lambda x:-np.log10(x)) df.index = df.coord df[['CADD','DeepSEA','GERP','HbFBase']].to_csv("Editable_A_scores.combined.scores.csv") df = pd.read_csv("Editable_A_scores.combined.scores.csv",index_col=0) # deepsea violin from decimal import Decimal sns.set_style("whitegrid") plt.figure() top_n = df[df['HbFBase']>=50]['DeepSEA'].tolist() bot_n = df[df['HbFBase']==0]['DeepSEA'].tolist() plot_df =
pd.DataFrame([top_n,bot_n])
pandas.DataFrame
#!/usr/bin/env python from __future__ import print_function import pandas as pd import numpy as np import random import progressbar import argparse import json import os import sys import glob import csv import re import pickle ap = argparse.ArgumentParser() ap.add_argument("-o", "--outdir", required=True, help="path to the output directory") ap.add_argument("-d", "--dataset", required=True, help="path to directory containing images") ap.add_argument("-v", "--validfrac", type=float, default=0.02, help="fraction of data to use for validation [0.02]") ap.add_argument("-l", "--labels", default=[], nargs='+', help="list of allowed class labels []") ap.add_argument("-m", "--merge", action='append', nargs='+', default=[], help="list of classes to merge []") ap.add_argument("-c", "--clip", type=int, default=0, help="clip the total number of images in each class [no clip]") ap.add_argument("-s", "--sample", type=float, default=0.0, help="oversample class if < frac of overlaps [no oversample]") ap.add_argument("-r", "--dateRange", action='append', nargs=2, default=[], help='filter for data between dates [no filter]') ap.add_argument("-nD", "--notDate", action="store_true", help="treat date selection as exclusion mask") ap.add_argument("-p", "--places", default=[], nargs='+', metavar="beachname", help="list of allowed places []") ap.add_argument("-nP", "--notPlace", action="store_true", help="treat place selection as exclusion mask") ap.add_argument("-D", "--debug", action="store_true", help="print debug information") ap.add_argument("-b", "--batch", action="store_false", help="batch mode (do not pause)") args = vars(ap.parse_args()) def parse_daterange(dateRange): startDateStr = dateRange[0] endDateStr = dateRange[1] dateRe = re.compile(r".*(\d{4})-(\d{2})-(\d{2}).*") if dateRe.match(startDateStr) and dateRe.match(endDateStr): return startDateStr, endDateStr else: return None, None startDateLst = [] endDateLst = [] for e in args["dateRange"]: startDateStr, endDateStr = parse_daterange(e) startDateLst.append(startDateStr) endDateLst.append(endDateStr) annFile = os.path.join(args['dataset'], "BOXES.csv") print("[INFO] loading {}".format(annFile)) annTab = pd.read_csv(annFile, header=None, skipinitialspace=True, names=["imageName","x1","y1","x2","y2","label", "nXpix", "nYpix", "date", "location", "qual"]) annTab.drop(columns=["nXpix", "nYpix"], inplace=True) s = annTab[annTab.label == "NEG"].duplicated(subset=["imageName"]) indices = s[s].index annTab.drop(indices, inplace=True) print("[INFO] dropped {:d} duplicate empty (NEG) images".format(len(indices))) allowedLabels = args["labels"] if len(allowedLabels) > 0: print("[INFO] restricting labels to {}".format(allowedLabels)) annTab = annTab[annTab["label"].isin(allowedLabels)] annTab["date"] = pd.to_datetime(annTab["date"]) maskLst = [] for startDateStr, endDateStr in zip(startDateLst, endDateLst): if startDateStr is not None and endDateStr is not None: print("[INFO] filtering between dates {} and {}".format(startDateStr, endDateStr)) mask = (annTab["date"] >= startDateStr) & (annTab["date"] <= endDateStr) maskLst.append(mask) for mask in maskLst[1:]: maskLst[0] = maskLst[0] | mask if len(maskLst) > 0: if args["notDate"]: print("[INFO] treating date mask as exclusion") maskLst[0] = ~maskLst[0] print("[INFO] applying date mask accepting {:d} / {:d} boxes" .format(maskLst[0].sum(), len(maskLst[0]))) annTab = annTab[maskLst[0]] if len(args["places"]) > 0: mask = annTab["location"].isin(args["places"]) if args["notPlace"]: print("[INFO] treating place mask as exclusion") mask = ~mask print("[INFO] applying place mask accepting {:d} / {:d} boxes" .format(mask.sum(), len(mask))) annTab = annTab[mask] if len(annTab) == 0: exit("[ERR] table has no entries - check selection criteria. Exiting ...") else: print("[INFO] processing data from {}".format(annTab["location"].unique())) annTab.drop(columns=["date", "location"], inplace=True) uniqueLabels = annTab["label"].unique().tolist() uniqueLabels.sort() labLookup = dict(zip(uniqueLabels, uniqueLabels)) for mergeLst in args["merge"]: if len(mergeLst) < 2: exit("[ERR] list of keys to be merged is too short: " "{}".format(mergeLst)) print("[INFO] merging labels ['{}']<-{}".format(mergeLst[0], mergeLst[1:])) for k in mergeLst[1:]: labLookup[k] = mergeLst[0] for k, v in labLookup.items(): annTab.loc[annTab.label == k, "label"] = v uniqueLabels = annTab["label"].unique().tolist() uniqueLabels.sort() nClasses = len(uniqueLabels) print("[INFO] labels in dataset: {}".format(uniqueLabels)) labGroups = annTab.groupby("label") boxCntSer = labGroups.count()["imageName"] nTargetGlobal = boxCntSer.max() if args["clip"] > 0: nTargetGlobal = args["clip"] print("[INFO] aiming for {:d} samples in each class".format(nTargetGlobal)) posCntLst = [] olapCntLst = [] for label in uniqueLabels: imgLst = annTab[annTab.label == label]["imageName"].unique().tolist() posCntLst.append(len(imgLst)) tmpTab = annTab[annTab["imageName"].isin(imgLst)] imgLst = tmpTab[tmpTab.label != label]["imageName"].unique().tolist() olapCntLst.append(len(imgLst)) imgCntSer = pd.Series(posCntLst, uniqueLabels) olapCntSer = pd.Series(olapCntLst, uniqueLabels) olapFracSer = olapCntSer / imgCntSer olapFracSer.sort_values(ascending=False, inplace=True) overlapTab = np.zeros((nClasses, nClasses), dtype=np.int) for i in range(nClasses): labX = uniqueLabels[i] imgXlst = set(labGroups["imageName"].get_group(labX)) for j in range(i+1, nClasses): labY = uniqueLabels[j] imgYlst = set(labGroups["imageName"].get_group(labY)) overlapTab[j, i] = len(imgXlst & imgYlst) print("[INFO] image overlap matrix:\n") for i in range(nClasses): print(uniqueLabels[i], end=" ") for j in range(0, i+1): print("{:6d}".format(overlapTab[i, j]), end=" ") print(" "*(nClasses - j -1), end="") print(" {:9d}".format(boxCntSer[uniqueLabels[i]]), end="") print(" {:9d}".format(imgCntSer[uniqueLabels[i]])) print(" " + " ".join(uniqueLabels), end="") print(" #BOXES #IMAGES") print("\nPercentage of images with overlaps in class:") print(" ", end=" ") for i in range(nClasses): print(" {:3.0f}".format(olapFracSer[uniqueLabels[i]]*100), end="") print("\n") if args["batch"]: input("\nPress <Return> to continue ...") splitSer = pd.Series(np.zeros(len(uniqueLabels), dtype="i8"), uniqueLabels, name="splitCount") splitSer.index.name = "label" validGrpLst = [] testGrpLst = [] trainGrpLst = [] print("[INFO] splitting off validation set {:2.2f}%".format( args["validfrac"]*100)) for label, olapFracCnt in olapFracSer.items(): nTarget = int(min(nTargetGlobal, boxCntSer[label]) * args["validfrac"] - splitSer[label]) if nTarget <=0: continue imgGroups = annTab[annTab["label"] == label].groupby("imageName") imgCntTab = imgGroups.count()["label"].to_frame("nCurrentObjects") imgCntTab.reset_index(inplace=True) imgCntTab = imgCntTab.sample(frac=1).reset_index(drop=True) cumSumArr = imgCntTab["nCurrentObjects"].cumsum().to_numpy() indx = np.argwhere(cumSumArr >= nTarget) imgSel = imgCntTab["imageName"][:indx[0, 0]+1] selAnnTab = annTab[annTab["imageName"].isin(imgSel)] indices = annTab[annTab["imageName"].isin(imgSel)].index annTab.drop(indices, inplace=True) selGrpLst = [df for _, df in selAnnTab.groupby("imageName")] random.shuffle(selGrpLst) validGrpLst += selGrpLst selTab = pd.concat(selGrpLst, ignore_index=True) labSelGroups = selTab.groupby("label") splitCntSer = labSelGroups.count()["imageName"] splitCntSer.name = "splitCount" splitSer = splitSer.add(splitCntSer, fill_value=0) random.shuffle(validGrpLst) print("[INFO] validation split (number of boxes):") print(splitSer) masterImgGroups = annTab.groupby("imageName") masterImgCountTab= masterImgGroups.count()["label"].to_frame("nObjects") masterImgCountTab.reset_index(inplace=True) masterImgCountTab["splitCount"] = 0 splitSer[:] = 0 for label, olapFracCnt in olapFracSer.items(): print("[INFO] processing the '{}' class with {:02.2f}% overlap". format(label, olapFracCnt*100)) nTarget = max(0, nTargetGlobal - splitSer[label]) print("[INFO] > previously selected boxes {:d}". format(int(splitSer[label]))) print("[INFO] > target boxes to select is {:d}".format(int(nTarget))) if nTarget == 0: print("[INFO] > target number of boxes already reached") continue imgPosGroups = annTab[annTab["label"] == label].groupby("imageName") imgPosCntTab = imgPosGroups.count()["label"].to_frame("nCurrentObjects") imgPosCntTab.reset_index(inplace=True) imgLst = imgPosGroups.groups.keys() annLocTab = annTab[annTab["imageName"].isin(imgLst)] imgNegGroups = annLocTab[annLocTab["label"] != label].groupby("imageName") imgNegCntTab = imgNegGroups.count()["label"].to_frame("nOtherObjects") imgNegCntTab.reset_index(inplace=True) imgCntTab = imgPosCntTab.merge(imgNegCntTab, how="left", left_on="imageName", right_on="imageName") imgCntTab.fillna(0, inplace=True) if False: print("Percentage free = {:02.2f} ({:d})".format( np.sum(imgCntTab["nOtherObjects"] == 0) *100 / len(imgCntTab), len(imgCntTab))) print(imgCntTab.head()) imgCntTab = imgCntTab.merge(masterImgCountTab, how="left", left_on="imageName", right_on="imageName") imgCntTab = imgCntTab[imgCntTab["splitCount"] == 0] if len(imgCntTab) == 0: print("[INFO] > all images in this class have already been split") continue imgCntTab = imgCntTab.sample(frac=1).reset_index(drop=True) cumSumArr = imgCntTab["nCurrentObjects"].cumsum().to_numpy() indx = np.argwhere(cumSumArr >= nTarget) if len(indx) == 0: imgSel = imgCntTab["imageName"] else: imgSel = imgCntTab["imageName"][:indx[0, 0]+1] selAnnTab = annLocTab[annLocTab["imageName"].isin(imgSel)] selGrpLst = [df for _, df in selAnnTab.groupby("imageName")] random.shuffle(selGrpLst) tmp = pd.concat(selGrpLst).reset_index(drop=True) curBoxCnt = len(tmp[tmp.label == label]) print("[INFO] > currently available boxes: {:d}".format(curBoxCnt)) if args["sample"] > 0 and args["sample"] < 1 and curBoxCnt < nTarget: if olapFracCnt <= args["sample"]: imgCntTab = imgCntTab[imgCntTab["nOtherObjects"] == 0] cumSumArr = imgCntTab["nCurrentObjects"].cumsum().to_numpy() print("[INFO] > replicating using {:d} images ". format(len(imgCntTab)), end="") print("containing {:d} boxes".format(cumSumArr[-1])) n = int((nTarget - curBoxCnt) / cumSumArr[-1]) r = nTarget - cumSumArr[-1] * (1 + n) if r <= 0: r = 0 indx = np.argwhere(cumSumArr >= r) if len(indx) == 0: indx = 0 else: indx = indx[0, 0] repLst = [] if n > 0: imgSel = imgCntTab["imageName"] selAnnTab = annLocTab[annLocTab["imageName"].isin(imgSel)] .reset_index(drop=True) tmpGrpLst = [df for _, df in selAnnTab.groupby("imageName")] print("[INFO] > replicating whole table x {:d}".format(n)) repLst = tmpGrpLst * n random.shuffle(repLst) if indx > 0: imgSel = imgCntTab["imageName"].iloc[:indx+1] selAnnTab = annLocTab[annLocTab["imageName"].isin(imgSel)] .reset_index(drop=True) tmpGrpLst = [df for _, df in selAnnTab.groupby("imageName")] print("[INFO] > replicating to index {:d}".format(indx)) repLst += tmpGrpLst random.shuffle(repLst) if len(repLst)>1: selGrpLst += repLst trainGrpLst += selGrpLst selTab = pd.concat(selGrpLst, ignore_index=True) labSelGroups = selTab.groupby("label") splitCntSer = labSelGroups.count()["imageName"] splitCntSer.name = "splitCount" splitSer = splitSer.add(splitCntSer, fill_value=0) if args["debug"]: print(splitSer) random.shuffle(validGrpLst) random.shuffle(trainGrpLst) print("[INFO] merging master tables (make take a little while) ... ") trainTab =
pd.concat(trainGrpLst)
pandas.concat
# -*- coding: utf-8 -*- """Data_Analysis.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/106zvCx_5_p0TlKI3zkCcEb0VbnWwdahx """ # -*- coding: utf-8 -*- import numpy as np import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm from sklearn.linear_model import LinearRegression from sklearn.linear_model import SGDRegressor import xgboost as xgb from sklearn.metrics import mean_squared_error, r2_score import re from nltk.sentiment.vader import SentimentIntensityAnalyzer from sklearn.ensemble import RandomForestRegressor from sklearn.svm import SVR """ 1. Preprocessing Functions: """ def calc_change_sentiment(data): change_in_sent = [] change_in_sent.append(data['compound'][0]) for i in range(1,len(data['compound'])): if data['compound'][i] == 0: change_in_sent.append(0) elif data['compound'][i] < 0 or data['compound'][i] > 0: dif = data['compound'][i] - data['compound'][(i-1)] change_in_sent.append(dif) return change_in_sent def remove_pattern(input_txt, pattern): r = re.findall(pattern, input_txt) for i in r: input_txt = re.sub(i, '', input_txt) return input_txt def clean_tweets(tweets): tweets = np.vectorize(remove_pattern)(tweets, "RT @[\w]*:") tweets = np.vectorize(remove_pattern)(tweets, "@[\w]*") tweets = np.vectorize(remove_pattern)(tweets, "https?://[A-Za-z0-9./]*") tweets = np.core.defchararray.replace(tweets, "[^a-zA-Z]", " ") return tweets def classify_news(dataframe): day23, day24, day25, day26, day27, day28, day29, day30, day31, day32, day33, day34, day35, day36, day37, day38 = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[] for i in range(len(dataframe['timestamp'])): if dataframe['timestamp'][i].day == 23 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day23.append(i) elif dataframe['timestamp'][i].day == 24 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day24.append(i) elif dataframe['timestamp'][i].day == 25 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day25.append(i) elif dataframe['timestamp'][i].day == 26 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day26.append(i) elif dataframe['timestamp'][i].day == 27 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day27.append(i) elif dataframe['timestamp'][i].day == 28 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day28.append(i) elif dataframe['timestamp'][i].day == 29 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day29.append(i) elif dataframe['timestamp'][i].day == 30 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day30.append(i) elif dataframe['timestamp'][i].day == 1 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day31.append(i) elif dataframe['timestamp'][i].day == 2 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day32.append(i) elif dataframe['timestamp'][i].day == 3 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day33.append(i) elif dataframe['timestamp'][i].day == 4 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day34.append(i) elif dataframe['timestamp'][i].day == 5 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day35.append(i) elif dataframe['timestamp'][i].day == 6 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day36.append(i) elif dataframe['timestamp'][i].day == 7 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day37.append(i) elif dataframe['timestamp'][i].day == 8 and (dataframe['timestamp'][i].hour <= 15 and dataframe['timestamp'][i].hour >= 9): day38.append(i) else: pass news_d23,news_d24,news_d25,news_d26,news_d27,news_d28,news_d29,news_d30,news_d31,news_d32,news_d33,news_d34,news_d35,news_d36,news_d37,news_d38 = dataframe.iloc[day23],dataframe.iloc[day24],dataframe.iloc[day25], dataframe.iloc[day26], dataframe.iloc[day27],dataframe.iloc[day28],dataframe.iloc[day29],dataframe.iloc[day30],dataframe.iloc[day31], dataframe.iloc[day32],dataframe.iloc[day33],dataframe.iloc[day34],dataframe.iloc[day35],dataframe.iloc[day36],dataframe.iloc[day37],dataframe.iloc[day38] return news_d23,news_d24,news_d25,news_d26,news_d27,news_d28,news_d29,news_d30,news_d31,news_d32,news_d33,news_d34,news_d35,news_d36,news_d37,news_d38 def preprocess_headlines(data): data.drop_duplicates(subset='headline',keep=False, inplace=True) data.drop(['ticker','neg','neu','pos'], axis=1, inplace=True) data.rename(columns={'date_time':'timestamp'},inplace=True) data.set_index('timestamp', inplace=True) data_30m = data.resample('30min').median().ffill().reset_index() headline_sma = data_30m['compound'].rolling(3).mean() data_30m['Compound SMA(3) Headlines'] = headline_sma change_in_sent=calc_change_sentiment(data_30m) data_30m['change in sentiment headlines'] = change_in_sent data_30m['change in sentiment headlines (t-1)'] = data_30m['change in sentiment headlines'].shift(1) news_d23,news_d24,news_d25,news_d26,news_d27,news_d28,news_d29,news_d30,news_d31,news_d32,news_d33,news_d34,news_d35,news_d36,news_d37,news_d38 = classify_news(data_30m) news_d23_red,news_d24_red, news_d25_red, news_d28_red,news_d29_red,news_d30_red,news_d31_red,news_d32_red,news_d35_red,news_d36_red,news_d37_red,news_d38_red = news_d23.iloc[4:],news_d24.iloc[1:],news_d25.iloc[1:],news_d28.iloc[1:],news_d29.iloc[1:],news_d30.iloc[1:],news_d31.iloc[1:],news_d32.iloc[1:],news_d35.iloc[1:],news_d36.iloc[1:],news_d37.iloc[1:],news_d38.iloc[1:] frames_news = [news_d23_red,news_d24_red, news_d25_red, news_d28_red,news_d29_red,news_d30_red,news_d31_red,news_d32_red,news_d35_red,news_d36_red,news_d37_red,news_d38_red] processed_headlines = pd.concat(frames_news) return processed_headlines def preprocess_posts(dataframe): dataframe.drop(['neg','neu','pos','followers_count'],axis=1,inplace=True) dataframe['timestamp'] = dataframe['timestamp'].dt.tz_localize('UTC').dt.tz_convert('America/Montreal').dt.tz_localize(None) dataframe.set_index('timestamp', inplace=True) twitter_df_30m = dataframe.resample('30min').median().ffill().reset_index() change_in_sent = calc_change_sentiment(twitter_df_30m) twitter_sma = twitter_df_30m['compound'].rolling(3).mean() twitter_df_30m['Compound SMA(3) Twitter'] = twitter_sma twitter_df_30m['change in sentiment twitter'] = change_in_sent twitter_df_30m['change in sentiment twitter (t-1)'] = twitter_df_30m['change in sentiment twitter'].shift(1) tw_news_d23,tw_news_d24,tw_news_d25,tw_news_d26,tw_news_d27,tw_news_d28,tw_news_d29,tw_news_d30,tw_news_d31,tw_news_d32,tw_news_d33,tw_news_d34,tw_news_d35,tw_news_d36,tw_news_d37,tw_news_d38 = classify_news(twitter_df_30m) tw_news_d23_30m,tw_news_d24_30m,tw_news_d25_30m, tw_news_d28_30m,tw_news_d29_30m,tw_news_d30_30m,tw_news_d31_30m,tw_news_d32_30m,tw_news_d35_30m,tw_news_d36_30m,tw_news_d37_30m,tw_news_d38_30m = tw_news_d23.iloc[4:],tw_news_d24.iloc[1:],tw_news_d25.iloc[1:],tw_news_d28.iloc[1:],tw_news_d29.iloc[1:],tw_news_d30.iloc[1:],tw_news_d31.iloc[1:],tw_news_d32.iloc[1:],tw_news_d35.iloc[1:],tw_news_d36.iloc[1:],tw_news_d37.iloc[1:],tw_news_d38.iloc[1:] frames = [tw_news_d23_30m,tw_news_d24_30m,tw_news_d25_30m,tw_news_d28_30m,tw_news_d29_30m,tw_news_d30_30m,tw_news_d31_30m,tw_news_d32_30m,tw_news_d35_30m,tw_news_d36_30m,tw_news_d37_30m,tw_news_d38_30m] processed_tweets = pd.concat(frames) return processed_tweets """2 Modeling Functions:""" def baseline_model(data): pred = data['SMA(3)'][3:] actu = data['Adj Close'][3:] rmse = np.sqrt(mean_squared_error(actu,pred)) r2_sco = r2_score(actu,pred) return rmse, r2_sco def linear_modeling_no_sentiment(dataframe): x_var = ['Adj Close','Scaled Volume','SMA(3)'] i = len(dataframe['Percent Price Change Within Period (t+1)'])-4 y_train, y_test = dataframe['Percent Price Change Within Period (t+1)'][3:i], dataframe['Percent Price Change Within Period (t+1)'][i:-1] X_train, X_test = dataframe[x_var][3:i], dataframe[x_var][i:-1] lm = LinearRegression() lm.fit(X_train,y_train) predictions = lm.predict(X_test) rmse = np.sqrt(mean_squared_error(y_test,predictions)) r2_sco = r2_score(y_test,predictions) reg = SGDRegressor(random_state=42) reg.fit(X_train, y_train) predictions2 = reg.predict(X_test) rmse2 = np.sqrt(mean_squared_error(y_test,predictions2)) r2_sco2 = r2_score(y_test,predictions2) return rmse,r2_sco,rmse2,r2_sco2 def linear_modeling_headlines(dataframe): x_var = ['Adj Close','Scaled Volume','compound','Compound SMA(3) Headlines','SMA(3)','change in sentiment headlines','change in sentiment headlines (t-1)'] i = len(dataframe['Percent Price Change Within Period (t+1)'])-4 y_train, y_test = dataframe['Percent Price Change Within Period (t+1)'][:i], dataframe['Percent Price Change Within Period (t+1)'][i:-1] X_train, X_test = dataframe[x_var][:i], dataframe[x_var][i:-1] lm = LinearRegression() lm.fit(X_train,y_train) predictions = lm.predict(X_test) rmse = np.sqrt(mean_squared_error(y_test,predictions)) r2_sco = r2_score(y_test,predictions) reg = SGDRegressor(random_state=42) reg.fit(X_train, y_train) predictions2 = reg.predict(X_test) rmse2 = np.sqrt(mean_squared_error(y_test,predictions2)) r2_sco2 = r2_score(y_test,predictions2) xg_reg = xgb.XGBRegressor(colsample_bytree= 0.3, gamma= 0.0, learning_rate= 0.2, max_depth= 5, n_estimators= 20000) # xg_reg = xgb.XGBRegressor(colsample_bytree= 0.4, gamma= 0.4, learning_rate= 0.05, max_depth= 4, n_estimators= 10000) xg_reg.fit(X_train,y_train) preds3 = xg_reg.predict(X_test) rmse3 = np.sqrt(mean_squared_error(y_test, preds3)) r2_sco3 = r2_score(y_test,preds3) svr = SVR(kernel='rbf', C=0.01, epsilon=0.001) svr.fit(X_train,y_train) preds4 = svr.predict(X_test) rmse4 = np.sqrt(mean_squared_error(y_test,preds4)) r2_sco4 = r2_score(y_test,preds4) return rmse,r2_sco,rmse2,r2_sco2,rmse3,r2_sco3,rmse4,r2_sco4 def linear_model_twitter(dataframe): x_var = ['Adj Close','Scaled Volume','compound','Compound SMA(3) Twitter','SMA(3)','change in sentiment twitter','change in sentiment twitter (t-1)'] i = len(dataframe['Percent Price Change Within Period (t+1)'])-4 y_train, y_test = dataframe['Percent Price Change Within Period (t+1)'][:i], dataframe['Percent Price Change Within Period (t+1)'][i:-1] X_train, X_test = dataframe[x_var][:i], dataframe[x_var][i:-1] lm = LinearRegression() lm.fit(X_train,y_train) predictions = lm.predict(X_test) rmse = np.sqrt(mean_squared_error(y_test,predictions)) r2_sco = r2_score(y_test,predictions) reg = SGDRegressor(random_state=42) reg.fit(X_train, y_train) predictions2 = reg.predict(X_test) rmse2 = np.sqrt(mean_squared_error(y_test,predictions2)) r2_sco2 = r2_score(y_test,predictions2) xg_reg = xgb.XGBRegressor(colsample_bytree= 0.3, gamma= 0.0, learning_rate= 0.2, max_depth= 5, n_estimators= 20000) # xg_reg = xgb.XGBRegressor(colsample_bytree= 0.4, gamma= 0.4, learning_rate= 0.05, max_depth= 4, n_estimators= 10000) xg_reg.fit(X_train,y_train) preds3 = xg_reg.predict(X_test) rmse3 = np.sqrt(mean_squared_error(y_test, preds3)) r2_sco3 = r2_score(y_test,preds3) svr = SVR(kernel='rbf', C=0.01, epsilon=0.001) svr.fit(X_train,y_train) preds4 = svr.predict(X_test) rmse4 = np.sqrt(mean_squared_error(y_test,preds4)) r2_sco4 = r2_score(y_test,preds4) return rmse,r2_sco,rmse2,r2_sco2,rmse3,r2_sco3,rmse4,r2_sco4 def multi_model_full(dataframe): x_var = ['Adj Close','Scaled Volume','compound_y','compound_x','Compound SMA(3) Headlines','Compound SMA(3) Twitter','SMA(3)','change in sentiment headlines','change in sentiment headlines (t-1)','change in sentiment twitter','change in sentiment twitter (t-1)'] i = len(dataframe['Percent Price Change Within Period (t+1)'])-4 y_train, y_test = dataframe['Percent Price Change Within Period (t+1)'][:i], dataframe['Percent Price Change Within Period (t+1)'][i:-1] X_train, X_test = dataframe[x_var][:i], dataframe[x_var][i:-1] lm = LinearRegression() lm.fit(X_train,y_train) predictions = lm.predict(X_test) rmse = np.sqrt(mean_squared_error(y_test,predictions)) r2_sco = r2_score(y_test,predictions) reg = SGDRegressor(random_state=42) reg.fit(X_train, y_train) predictions2 = reg.predict(X_test) rmse2 = np.sqrt(mean_squared_error(y_test,predictions2)) r2_sco2 = r2_score(y_test,predictions2) xg_reg = xgb.XGBRegressor(colsample_bytree= 0.3, gamma= 0.0, learning_rate= 0.2, max_depth= 5, n_estimators= 20000) xg_reg.fit(X_train,y_train) preds3 = xg_reg.predict(X_test) rmse3 = np.sqrt(mean_squared_error(y_test, preds3)) r2_sco3 = r2_score(y_test,preds3) rf_regr = RandomForestRegressor(n_estimators=20, max_depth=600, random_state=42) rf_regr.fit(X_train,y_train) preds4 = rf_regr.predict(X_test) rmse4 = np.sqrt(mean_squared_error(y_test, preds4)) r2_sco4 = r2_score(y_test,preds4) svr = SVR(kernel='rbf', C=0.01, epsilon=0.001) svr.fit(X_train,y_train) preds5 = svr.predict(X_test) rmse5 = np.sqrt(mean_squared_error(y_test,preds5)) r2_sco5 = r2_score(y_test,preds5) return rmse,r2_sco,rmse2,r2_sco2,rmse3,r2_sco3,rmse4,r2_sco4,rmse5,r2_sco5 """## 2. Evaluate Model with Individual Stocks:""" def import_data(ticker): # 1. Historical Stock Data: stock_df = pd.read_csv('Dataset/1.Stock_Data/'+ticker+'_data.csv', index_col=0, parse_dates=['Datetime']) stock_df['Percent Price Change Within Period (t+1)'] = stock_df['Percent Price Change Within Period'].shift(-1) # 2. Headline Data: headlines1 = pd.read_csv('Dataset/2.FinViz_Headline_Data/'+ticker+'_2020-09-23_2020-10-07.csv', index_col=0, parse_dates=['date_time']) frames = [headlines1] headlines_df = pd.concat(frames) headlines_df.drop_duplicates(subset='headline',keep='first',inplace=True) # 3. Twitter Data: twitter1 = pd.read_csv('Dataset/3.Twitter_Data/'+ticker+'_2020-09-23_2020-10-07.csv', index_col=0, parse_dates=['timestamp']) # twitter2 = pd.read_csv('3.Twitter_Data/'+ticker+'_2020-10-07.csv',index_col=0, parse_dates=['timestamp']) # twitter3 = pd.read_csv('Dataset/3.Twitter_Data/'+ticker+'_2020-10-07_2.csv',index_col=0, parse_dates=['timestamp']) frames = [twitter1] twitter_df = pd.concat(frames) twitter_df.drop_duplicates(subset='tweet_text',keep='first', inplace=True) twitter_df.sort_values('timestamp',ascending=False,inplace=True) twitter_df.reset_index(drop=True) # twitter_df.to_csv('Dataset/3.Twitter_Data/'+ticker+'_2020-09-23_2020-10-07.csv') return stock_df,headlines_df,twitter_df def evaluate_models(baseline_df, headline_df, twitter_df): #1. Baseline: baseline_rmse, baseline_r2 = baseline_model(baseline_df) baseline_df2 = baseline_df baseline_df2['Percent Price Change Within Period (t+1)'] = baseline_df2['Percent Price Change Within Period'].shift(-1) lm_baseline_rmse, lm_baseline_r2, sgd_baseline_rmse, sgd_baseline_r2 = linear_modeling_no_sentiment(baseline_df2) #2. Headline Final Merge: headlines_final = preprocess_headlines(headline_df) with_headlines_df = stock_df.merge(headlines_final, left_on='Datetime', right_on='timestamp').drop('timestamp',axis=1) with_headlines_df['Percent Price Change Within Period (t+1)'] = with_headlines_df['Percent Price Change Within Period'].shift(-1) #3. Twitter Final Merge: final_twitter = preprocess_posts(twitter_df) with_twitter_df = stock_df.merge(final_twitter, left_on='Datetime', right_on='timestamp').drop('timestamp',axis=1) with_twitter_df['Percent Price Change Within Period (t+1)'] = with_twitter_df['Percent Price Change Within Period'].shift(-1) full_df = with_twitter_df.merge(headlines_final, left_on='Datetime', right_on='timestamp').drop('timestamp',axis=1) full_df['Percent Price Change Within Period (t+1)'] = full_df['Percent Price Change Within Period'].shift(-1) #5. Evaluating Models: lm_headlines_rmse, lm_headlines_r2, sgd_headlines_rmse, sgd_headlines_r2,xgb_headlines_rmse,xgb_headlines_r2,svr_headlines_rmse,svr_headlines_r2 = linear_modeling_headlines(with_headlines_df) lm_twitter_rmse, lm_twitter_r2, sgd_twitter_rmse, sgd_twitter_r2,xgb_twitter_rmse,xgb_twitter_r2,svr_twitter_rmse,svr_twitter_r2 = linear_model_twitter(with_twitter_df) lm_all_rmse, lm_all_r2, sgd_all_rmse, sgd_all_r2, xgb_all_rmse, xgb_all_r2, rf_all_rmse, rf_all_r2,svr_all_rmse,svr_all_r2 = multi_model_full(full_df) result_dict = { 'RMSE - Baseline':baseline_rmse, 'R2 - Baseline':baseline_r2, 'Linear RMSE - Baseline':lm_baseline_rmse, 'SGD RMSE - Baseline':sgd_baseline_rmse, 'Linear RMSE - Only Headlines': lm_headlines_rmse, 'SGD RMSE - Only Headlines':sgd_headlines_rmse, 'XGB RMSE - Only Headlines':xgb_headlines_rmse, 'SVR RMSE - Only Headlines':svr_headlines_rmse, 'Linear RMSE - Only Twitter':lm_twitter_rmse, 'SGD RMSE - Only Twitter':sgd_twitter_rmse, 'XGB RMSE - Only Twitter':xgb_twitter_rmse, 'SVR RMSE - Only Twitter':svr_twitter_rmse, 'Linear RMSE - All':lm_all_rmse, 'SGD RMSE - All':sgd_all_rmse, 'XGB RMSE - All':xgb_all_rmse, 'RF RMSE - All':rf_all_rmse,'SVR RMSE - All':svr_all_rmse } #7. Convert to DataFrame: result_df = pd.DataFrame.from_dict(result_dict, orient='index', columns=['Values']) #result_df.to_csv('~/LighthouseLabs-Final/Report_Analysis/AAPL_complete_analysis.csv') return result_df, full_df stock_df,headlines_df,twitter_df = import_data('AAPL') result_df, full_df = evaluate_models(stock_df,headlines_df,twitter_df) import seaborn as sn from matplotlib.pyplot import figure corrMatrix = full_df.corr() plt.figure(figsize=(20,15)) sn.heatmap(corrMatrix, annot=True) plt.show() i = round(len(full_df['t+1'])*0.6) x_var_base=['Adj Close','Scaled Volume','SMA(3)'] x_var_headlines=['Adj Close','Scaled Volume','compound_y','Compound SMA(3) Headlines','SMA(3)','change in sentiment headlines','change in sentiment headlines (t-1)'] x_var_twitter=['Adj Close','Scaled Volume','compound_x','Compound SMA(3) Twitter','SMA(3)','change in sentiment twitter','change in sentiment twitter (t-1)'] x_var_full=['Adj Close','Scaled Volume','compound_y','compound_x','Compound SMA(3) Headlines','Compound SMA(3) Twitter','SMA(3)','change in sentiment headlines','change in sentiment headlines (t-1)','change in sentiment twitter','change in sentiment twitter (t-1)'] X_train_base,X_test_base=full_df[x_var_base][:i],full_df[x_var_base][i:-1] X_predic_base = full_df[x_var_base][:-1] X_train_headlines,X_test_headlines=full_df[x_var_headlines][:i],full_df[x_var_headlines][i:-1] X_predic_headlines = full_df[x_var_headlines][:-1] X_train_twitter,X_test_twitter=full_df[x_var_twitter][:i],full_df[x_var_twitter][i:-1] X_predic_twitter = full_df[x_var_twitter][:-1] X_train_full, X_test_full = full_df[x_var_full][:i], full_df[x_var_full][i:-1] X_predic_full = full_df[x_var_full][:-1] y_train, y_test = full_df['Percent Price Change Within Period (t+1)'][:i], full_df['t+1'][i:-1] lm = LinearRegression() lm.fit(X_train_base,y_train) preds1 = lm.predict(X_predic_base) preds1 = np.append(preds1,np.NaN) full_df['base price predictions linear'] = ((preds1/100) * full_df['Adj Close']) + full_df['Adj Close'] svr = SVR(kernel='rbf', C=0.2, epsilon=0.0001) svr.fit(X_train_headlines,y_train) preds3 = svr.predict(X_predic_headlines) preds3 = np.append(preds3,np.NaN) full_df['headlines price predictions svr'] = ((preds3/100) * full_df['Adj Close']) + full_df['Adj Close'] svr = SVR(kernel='rbf', C=0.2, epsilon=0.0001) svr.fit(X_train_twitter,y_train) preds4 = svr.predict(X_predic_twitter) preds4 = np.append(preds4,np.NaN) full_df['twitter price predictions svr'] = ((preds4/100) * full_df['Adj Close']) + full_df['Adj Close'] # lm = LinearRegression() # lm.fit(X_train_full,y_train) # preds5 = lm.predict(X_predic_full) svr = SVR(kernel='rbf', C=0.2, epsilon=0.0001) svr.fit(X_train_full,y_train) preds5 = svr.predict(X_predic_full) preds5 = np.append(preds5,np.NaN) full_df['full price predictions linear'] = ((preds5/100) * full_df['Adj Close']) + full_df['Adj Close'] fig = plt.figure(figsize=(20,30)) price_ax = plt.subplot(2,1,1) price_ax.plot(full_df.index[:-1], full_df['Adj Close'][:-1], label='Adj Close') price_ax.plot(full_df.index[:-1], full_df['SMA(3)'][:-1], label='SMA(3)') price_ax.plot(full_df.index[:-1], full_df['base price predictions linear'][:-1], label='Predictions (base)',linewidth=2) # price_ax.plot(full_df.index[:-1], full_df['full price predictions svr'][:-1], label='Full SVR training fit') price_ax.plot(full_df.index[:-1], full_df['full price predictions linear'][:-1], label='Predictions (Full)',linewidth=2) # price_ax.plot(full_df.index[:-1], full_df['headlines price predictions svr'][:-1], label='Headlines SVR training fit') # price_ax.plot(full_df.index[i:-1], full_df['headlines price predictions svr'][i:-1], label='Predictions (Headlines)',linewidth=2) # price_ax.plot(full_df.index[:-1], full_df['twitter price predictions svr'][:-1], label='Twitter SVR training fit') # price_ax.plot(full_df.index[i:-1], full_df['twitter price predictions svr'][i:-1], label='Predictions (Twitter)',linewidth=2) price_ax.set_ylabel('Price ($)') price_ax.grid(which='major', color='k', linestyle='-.', linewidth=0.5) price_ax.minorticks_on() price_ax.grid(which='minor', color='k', linestyle=':', linewidth=0.3) price_ax.legend() roc_ax = plt.subplot(2,1,2, sharex=price_ax) roc_ax.plot(full_df.index, full_df['Compound SMA(3) Headlines'],label='Headline') roc_ax.plot(full_df.index, full_df['Compound SMA(3) Twitter'],label='Twitter') roc_ax.set_xlabel('Time Period (30 minutes)') roc_ax.set_ylabel('Headline Sentiment') roc_ax.grid(which="major", color='k', linestyle='-.', linewidth=0.5) roc_ax.minorticks_on() roc_ax.grid(which='minor', color='k', linestyle=':', linewidth=0.3) roc_ax.legend() fig.subplots_adjust(hspace=0.1) """## 3. Evaluate Model with Multiple Stocks:""" def import_data2(ticker,ticker2,ticker3,ticker4,ticker5,ticker6,ticker7,ticker8,ticker9,ticker10,ticker11,ticker12,ticker13): stock_path = 'Dataset/1.Stock_Data/' headline_path = 'Dataset/2.FinViz_Headline_Data/' twitter_path = '3.Twitter_Data/' latest_headlines='10-07' # 1. Historical Stock Data:------------------------------------------------------------------------------------------ stock_df1 = pd.read_csv(stock_path+ticker+'_data.csv', index_col=0,parse_dates=['Datetime']) stock_df2 = pd.read_csv(stock_path+ticker2+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df3 = pd.read_csv(stock_path+ticker3+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df4 = pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df5 = pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df6 = pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df7 = pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df8 = pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime']) stock_df9 =
pd.read_csv(stock_path+ticker4+'_data.csv',index_col=0, parse_dates=['Datetime'])
pandas.read_csv
#!/usr/bin/env python3 -u # -*- coding: utf-8 -*- __author__ = ["<NAME>"] __all__ = [] import numpy as np import pandas as pd from sklearn.utils import check_random_state def _make_series( n_timepoints=50, n_columns=1, all_positive=True, index_type=None, return_numpy=False, random_state=None, add_nan=False, ): """Helper function to generate univariate or multivariate time series""" rng = check_random_state(random_state) data = rng.normal(size=(n_timepoints, n_columns)) if add_nan: # add some nan values data[int(len(data) / 2)] = np.nan data[0] = np.nan data[-1] = np.nan if all_positive: data -= np.min(data, axis=0) - 1 if return_numpy: if n_columns == 1: data = data.ravel() return data else: index = _make_index(n_timepoints, index_type) if n_columns == 1: return pd.Series(data.ravel(), index) else: return pd.DataFrame(data, index) def _make_index(n_timepoints, index_type=None): """Helper function to make indices for unit testing""" if index_type == "period": start = "2000-01" freq = "M" return pd.period_range(start=start, periods=n_timepoints, freq=freq) elif index_type == "datetime" or index_type is None: start = "2000-01-01" freq = "D" return
pd.date_range(start=start, periods=n_timepoints, freq=freq)
pandas.date_range
import pandas as pd import numpy as np from dateutil.relativedelta import relativedelta #### Utilities def get_first_visit_date(data_patient): ''' Determines the first visit for a given patient''' #IDEA Could be parallelized in Dask data_patient['first_visit_date'] = min(data_patient.visit_date) return data_patient def subset_analysis_data(data, date_analysis): ''' Function that subsets the full dataset to only the data available for a certain analysis date''' if type(data.date_entered.iloc[0]) is str : data.date_entered = pd.to_datetime(data.date_entered) data = data[data.date_entered < date_analysis] return data def subset_cohort(data, horizon_date, horizon_time, bandwidth): ''' Function that subsets data from a cohort that has initiated care a year before the horizon_date, and after a year + bandwith''' horizon_date = pd.to_datetime(horizon_date) data['first_visit_date'] = pd.to_datetime(data['first_visit_date']) cohort_data = data[(data['first_visit_date'] >= horizon_date - relativedelta(days=horizon_time + bandwidth)) & (data['first_visit_date'] < horizon_date - relativedelta(days=horizon_time))] return cohort_data #### Standard reporting def status_patient(data_patient, reference_date, grace_period): ''' Determines the status of a patient at a given reference_date, given the data available at a given analysis_date TODO Also select the available data for Death and Transfer and other outcomes based on data entry time ''' #IDEA Could be parallelized in Dask data_patient = get_first_visit_date(data_patient) date_out = pd.NaT date_last_appointment = pd.to_datetime(max(data_patient.next_visit_date)) late_time = reference_date - date_last_appointment if late_time.days > grace_period: status = 'LTFU' date_out = date_last_appointment if late_time.days <= grace_period: status = 'Followed' if (data_patient.reasonDescEn.iloc[0] is not np.nan) & (pd.to_datetime(data_patient.discDate.iloc[0]) < reference_date): status = data_patient.reasonDescEn.iloc[0] date_out =
pd.to_datetime(data_patient.discDate.iloc[0])
pandas.to_datetime
import pandas as pd import numpy as np import matplotlib.pyplot as plt import copy import sys import pickle from collections import defaultdict from itertools import islice, combinations from datetime import datetime as dt import warnings warnings.filterwarnings("ignore") from utils import timer_func @timer_func def merge_attributes(df: pd.DataFrame, *args: str) -> None: """ dtype df: dataframe dtype *args: strings (attribute names that want to be combined) """ iterables = [df[arg].astype(str) for arg in args] columnName = '&'.join([*args]) fs = [''.join([v for v in var]) for var in zip(*iterables)] df.loc[:, columnName] = fs class Import_declarations(): """ Class for dataset engineering """ def __init__(self, path): self.path = path self.df = pd.read_csv(self.path, encoding = "ISO-8859-1") self.profile_candidates = None def firstCheck(self): """ Sorting and indexing necessary for data preparation """ self.df = self.df.dropna(subset=["illicit"]) self.df = self.df[~self.df.isin({'quantity': [0], 'gross.weight': [0], 'cif.value': [0]}).any(1)] self.df = self.df.sort_values("sgd.date") self.df = self.df.reset_index(drop=True) @timer_func def mask_labels(self, df: pd.DataFrame, ir_init: float, initial_masking: str) -> pd.DataFrame: """ Masking certain amount of data for semi-supervised learning by specific strategy - This function is used for masking initial training set. initial_masking = importer Masking certain amount of importer_id, to mimic the situation that not all imports are inspected. initial_masking = = random Masking transactions by random sampling. ir_init is the inspection ratio at the beginning. """ print('Before masking:\n', df['illicit'].value_counts()) # To do: For more consistent results, we can control the random seed while selecting inspected_id. if initial_masking == "importer": if self.args.data in ['synthetic-k']: importer_id = '신고인부호' else: importer_id = 'importer.id' inspected_id = {} train_id = list(set(df[importer_id])) inspected_id[ir_init] = np.random.choice(train_id, size= int(len(train_id) * ir_init / 100), replace=False) d = {} for id in train_id: d[id] = float('nan') for id in inspected_id[ir_init]: d[id] = 1 df['illicit'] = df[importer_id].apply(lambda x: d[x]) * df['illicit'] df['revenue'] = df[importer_id].apply(lambda x: d[x]) * df['revenue'] elif initial_masking == "random": sampled_idx = list(df.sample(frac=1 - ir_init / 100, replace=False).index) df.loc[sampled_idx,"illicit"] = df.loc[sampled_idx,"illicit"]* np.nan df.loc[sampled_idx,"revenue"] = df.loc[sampled_idx,"revenue"]* np.nan else: return df print('After masking:\n', df['illicit'].value_counts()) return df @timer_func def split(self, train_start_day, valid_start_day, test_start_day, test_end_day, valid_length, test_length, args): """ Split data into train / valid / test """ self.train_start_day = train_start_day.strftime('%y-%m-%d') self.valid_start_day = valid_start_day.strftime('%y-%m-%d') self.test_start_day = test_start_day.strftime('%y-%m-%d') self.test_end_day = test_end_day.strftime('%y-%m-%d') self.valid_length = valid_length self.test_length = test_length self.args = args self.train = self.df[(self.df["sgd.date"] >= self.train_start_day) & (self.df["sgd.date"] < self.valid_start_day)] self.valid = self.df[(self.df["sgd.date"] >= self.valid_start_day) & (self.df["sgd.date"] < self.test_start_day)] self.test = self.df[(self.df["sgd.date"] >= self.test_start_day) & (self.df["sgd.date"] < self.test_end_day)] if len(self.train) == 0: print('Training data is unavailable - Set the parameter \'train_from\' according to the dataset you are using and running main.py') sys.exit() # Intentionally masking datasets to simulate partially labeled scenario, note that our dataset is 100% inspected. # If your dataset is partially labeled already, does not need this procedure. if args.data in ['synthetic', 'synthetic-k', 'real-n', 'real-m', 'real-t', 'real-c']: self.train = self.mask_labels(self.train, args.initial_inspection_rate, args.initial_masking) self.train_lab = self.train[self.train['illicit'].notna()] self.valid_lab = self.valid[self.valid['illicit'].notna()] if self.args.semi_supervised == 1: self.train_unlab = self.train[self.train['illicit'].isna()] self.valid_unlab = self.valid[self.valid['illicit'].isna()] # save labels self.train_cls_label = self.train_lab["illicit"].values self.valid_cls_label = self.valid_lab["illicit"].values self.test_cls_label = self.test["illicit"].values self.train_reg_label = self.train_lab['revenue'].values self.valid_reg_label = self.valid_lab['revenue'].values self.test_reg_label = self.test['revenue'].values # Normalize revenue labels for later model fitting self.norm_revenue_train, self.norm_revenue_valid, self.norm_revenue_test = np.log(self.train_reg_label+1), np.log(self.valid_reg_label+1), np.log(self.test_reg_label+1) global_max = max(self.norm_revenue_train) self.norm_revenue_train = self.norm_revenue_train/global_max self.norm_revenue_valid = self.norm_revenue_valid/global_max self.norm_revenue_test = self.norm_revenue_test/global_max self.train_valid_lab = pd.concat([self.train_lab, self.valid_lab]) if self.args.semi_supervised == 1: self.train_valid_unlab = pd.concat([self.train_unlab, self.valid_unlab]) @timer_func def find_risk_profile(self, df: pd.DataFrame, feature: str, topk_ratio: float, adj: float, option: str) -> list or dict: """ dtype feature: str dtype topk_ratio: float (range: 0-1) dtype adj: float (to modify the mean) dtype option: str ('topk', 'ratio') rtype: list(option='topk') or dict(option='ratio') The option topk is usually better than the ratio because of overfitting. """ # Top-k suspicious item flagging if option == 'topk': total_cnt = df.groupby([feature])['illicit'] nrisky_profile = int(topk_ratio*len(total_cnt))+1 # prob_illicit = total_cnt.mean() # Simple mean adj_prob_illicit = total_cnt.sum() / (total_cnt.count()+adj) # Smoothed mean return list(adj_prob_illicit.sort_values(ascending=False).head(nrisky_profile).index) # Illicit-ratio encoding (Mean target encoding) # Refer: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-munging/target-encoding.html # Refer: https://towardsdatascience.com/why-you-should-try-mean-encoding-17057262cd0 elif option == 'ratio': # For target encoding, we just use 70% of train data to avoid overfitting (otherwise, test AUC drops significantly) total_cnt = df.sample(frac=0.7).groupby([feature])['illicit'] nrisky_profile = int(topk_ratio*len(total_cnt))+1 # prob_illicit = total_cnt.mean() # Simple mean adj_prob_illicit = total_cnt.sum() / (total_cnt.count()+adj) # Smoothed mean return adj_prob_illicit.to_dict() @timer_func def tag_risky_profiles(self, df: pd.DataFrame, profile: str, profiles: list or dict, option: str) -> pd.DataFrame: """ dtype df: dataframe dtype profile: str dtype profiles: list(option='topk') or dictionary(option='ratio') dtype option: str ('topk', 'ratio') rtype: dataframe The option topk is usually better than the ratio because of overfitting. """ if len(df) == 0: return df # Top-k suspicious item flagging if option == 'topk': d = defaultdict(int) for id in profiles: d[id] = 1 # print(list(islice(d.items(), 10))) # For debugging df.loc[:, 'RiskH.'+profile] = df[profile].apply(lambda x: d[x]) # Illicit-ratio encoding elif option == 'ratio': # overall_ratio_train = 0 overall_ratio_train = np.mean(self.train_lab.illicit) # When scripting, saving it as a class variable is clearer. df.loc[:, 'RiskH.'+profile] = df[profile].apply(lambda x: profiles.get(x), overall_ratio_train) return df @timer_func def preprocess(self, df: pd.DataFrame) -> pd.DataFrame: """ dtype df: dataframe rtype df: dataframe """ if len(df) == 0: return df if self.args.data in ['synthetic', 'real-n', 'real-m', 'real-t', 'real-c']: df = df.dropna(subset=['cif.value', 'total.taxes', 'quantity']) df.loc[:, ['cif.value', 'total.taxes', 'quantity', 'gross.weight']] = np.log(df.loc[:, ['cif.value', 'total.taxes', 'quantity', 'gross.weight']] + 1) df.loc[:, 'Unitprice'] = df['cif.value']/df['quantity'] df.loc[:, 'WUnitprice'] = df['cif.value']/df['gross.weight'] df.loc[:, 'TaxRatio'] = df['total.taxes'] / df['cif.value'] df.loc[:, 'TaxUnitquantity'] = df['total.taxes'] / df['quantity'] df.loc[:, 'HS6'] = df['tariff.code'] // 10000 df.loc[:, 'HS4'] = df['HS6'] // 100 df.loc[:, 'HS2'] = df['HS4'] // 100 # candFeaturesCombine = ['office.id','importer.id','country','HS6','declarant.id'] # for subset in combinations(candFeaturesCombine, 2): # merge_attributes(df, *subset) # for subset in combinations(candFeaturesCombine, 3): # merge_attributes(df, *subset) merge_attributes(df, 'office.id','importer.id') merge_attributes(df, 'office.id','HS6') merge_attributes(df, 'office.id','country') merge_attributes(df, 'HS6','country') df['sgd.date'] = df['sgd.date'].apply(lambda x: dt.strptime(x, '%y-%m-%d')) df.loc[:, 'SGD.DayofYear'] = df['sgd.date'].dt.dayofyear df.loc[:, 'SGD.WeekofYear'] = df['sgd.date'].dt.weekofyear df.loc[:, 'SGD.MonthofYear'] = df['sgd.date'].dt.month elif self.args.data in ['synthetic-k', 'synthetic-k-partial', 'real-k']: df.loc[:, 'WUnitprice'] = df['과세가격원화금액']/df['신고중량(KG)'] df.loc[:, 'HS6'] = df['HS10단위부호'] // 10000 df.loc[:, 'HS4'] = df['HS6'] // 100 df.loc[:, 'HS2'] = df['HS4'] // 100 return df @timer_func def featureEngineering(self): """ Feature engineering, """ try: self.offset = self.test.index[0] except IndexError: pass # If sampler requires preprocessing for unlabeled data if self.args.semi_supervised == 1: # Preprocessing self.train_lab = self.preprocess(self.train_lab) self.train_unlab = self.preprocess(self.train_unlab) self.valid_lab = self.preprocess(self.valid_lab) self.valid_unlab = self.preprocess(self.valid_unlab) self.test = self.preprocess(self.test) # Add a few more risky profiles risk_profiles = {} profile_candidates = self.profile_candidates + [col for col in self.train_lab.columns if '&' in col] for profile in profile_candidates: option = self.args.risk_profile # topk or ratio risk_profiles[profile] = self.find_risk_profile(self.train_lab, profile, 0.1, 10, option=option) self.train_lab = self.tag_risky_profiles(self.train_lab, profile, risk_profiles[profile], option=option) self.train_unlab = self.tag_risky_profiles(self.train_unlab, profile, risk_profiles[profile], option=option) self.valid_lab = self.tag_risky_profiles(self.valid_lab, profile, risk_profiles[profile], option=option) self.valid_unlab = self.tag_risky_profiles(self.valid_unlab, profile, risk_profiles[profile], option=option) self.test = self.tag_risky_profiles(self.test, profile, risk_profiles[profile], option=option) # If sampler does not require preprocessing for unlabeled data elif self.args.semi_supervised == 0: self.train_lab = self.preprocess(self.train_lab) self.valid_lab = self.preprocess(self.valid_lab) self.test = self.preprocess(self.test) risk_profiles = {} profile_candidates = self.profile_candidates + [col for col in self.train_lab.columns if '&' in col] for profile in profile_candidates: option = self.args.risk_profile # topk or ratio risk_profiles[profile] = self.find_risk_profile(self.train_lab, profile, 0.1, 10, option=option) self.train_lab = self.tag_risky_profiles(self.train_lab, profile, risk_profiles[profile], option=option) self.valid_lab = self.tag_risky_profiles(self.valid_lab, profile, risk_profiles[profile], option=option) self.test = self.tag_risky_profiles(self.test, profile, risk_profiles[profile], option=option) # Features to use in a classifier numeric_variables = ['cif.value', 'total.taxes', 'gross.weight', 'quantity', 'Unitprice', 'WUnitprice', 'TaxRatio', 'TaxUnitquantity', 'tariff.code', 'HS6', 'HS4', 'HS2', 'SGD.DayofYear', 'SGD.WeekofYear', 'SGD.MonthofYear'] flagged_variables = [col for col in self.train_lab.columns if 'RiskH' in col] if self.args.data in ['synthetic-k', 'synthetic-k-partial', 'real-k']: numeric_variables = ['신고중량(KG)', '관세율'] self.column_to_use = numeric_variables + flagged_variables self.X_train_lab = self.train_lab[self.column_to_use].values if not self.valid_lab.empty: self.X_valid_lab = self.valid_lab[self.column_to_use].values else: self.X_valid_lab = np.asarray([]) if not self.test.empty: self.X_test = self.test[self.column_to_use].values else: self.X_test = np.asarray([]) if self.args.semi_supervised == 1: if not self.train_unlab.empty: self.X_train_unlab = self.train_unlab[self.column_to_use].values else: self.X_train_unlab = np.asarray([]) if not self.valid_unlab.empty: self.X_valid_unlab = self.valid_unlab[self.column_to_use].values else: self.X_valid_unlab = np.asarray([]) if self.args.semi_supervised == 1: print(f'Data size - Train labeled: {self.train_lab.shape}, Train unlabeled: {self.train_unlab.shape}, Valid labeled: {self.valid_lab.shape}, Valid unlabeled: {self.valid_unlab.shape}, Test: {self.test.shape}') elif self.args.semi_supervised == 0: print(f'Data size - Train labeled: {self.train_lab.shape}, Valid labeled: {self.valid_lab.shape}, Test: {self.test.shape}') # impute nan self.X_train_lab = np.nan_to_num(self.X_train_lab, 0) self.X_valid_lab = np.nan_to_num(self.X_valid_lab, 0) self.X_test = np.nan_to_num(self.X_test, 0) if self.args.semi_supervised == 1: self.X_train_unlab = np.nan_to_num(self.X_train_unlab, 0) self.X_valid_unlab = np.nan_to_num(self.X_valid_unlab, 0) # from collections import Counter # print("Checking illicit rate: ") # cnt = Counter(self.train_cls_label) # print("Training:",round(cnt[1]/(cnt[0]+cnt[1]), 3)) # cnt = Counter(self.valid_cls_label) # try: # print("Validation:",round(cnt[1]/(cnt[0]+cnt[1]), 3)) # except ZeroDivisionError: # print("No validation set") # cnt = Counter(self.test_cls_label) # try: # print("Testing:", round(cnt[1]/(cnt[0]+cnt[1]), 3)) # except ZeroDivisionError: # print("No test set") self.dftrainx_lab =
pd.DataFrame(self.X_train_lab,columns=self.column_to_use)
pandas.DataFrame
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ High level functions for multiresolution analysis of spectrograms Code licensed under both GPL and BSD licenses Authors: <NAME> <<EMAIL>> <NAME> <<EMAIL>> """ # Load required modules from __future__ import print_function import numpy as np import pandas as pd from scipy import ndimage as ndi import itertools as it import matplotlib.pyplot as plt from skimage.io import imsave from skimage import transform, measure from scipy import ndimage from maad import sound from skimage.filters import gaussian from maad.util import format_rois, rois_to_imblobs, normalize_2d def _sigma_prefactor(bandwidth): """ Function from skimage. Parameters ---------- Returns ------- """ b = bandwidth # See http://www.cs.rug.nl/~imaging/simplecell.html return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \ (2.0 ** b + 1) / (2.0 ** b - 1) def gabor_kernel_nodc(frequency, theta=0, bandwidth=1, gamma=1, n_stds=3, offset=0): """ Return complex 2D Gabor filter kernel with no DC offset. This function is a modification of the gabor_kernel function of scikit-image Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation. Parameters ---------- frequency : float Spatial frequency of the harmonic function. Specified in pixels. theta : float, optional Orientation in radians. If 0, the harmonic is in the x-direction. bandwidth : float, optional The bandwidth captured by the filter. For fixed bandwidth, `sigma_x` and `sigma_y` will decrease with increasing frequency. This value is ignored if `sigma_x` and `sigma_y` are set by the user. gamma : float, optional gamma changes the aspect ratio (ellipsoidal) of the gabor filter. By default, gamma=1 which means no aspect ratio (circle) if gamma>1, the filter is larger (x-dir) if gamma<1, the filter is higher (y-dir) This value is ignored if `sigma_x` and `sigma_y` are set by the user. sigma_x, sigma_y : float, optional Standard deviation in x- and y-directions. These directions apply to the kernel *before* rotation. If `theta = pi/2`, then the kernel is rotated 90 degrees so that `sigma_x` controls the *vertical* direction. n_stds : scalar, optional The linear size of the kernel is n_stds (3 by default) standard deviations offset : float, optional Phase offset of harmonic function in radians. Returns ------- g_nodc : complex 2d array A single gabor kernel (complex) with no DC offset References ---------- .. [1] http://en.wikipedia.org/wiki/Gabor_filter .. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf Examples -------- >>> from skimage.filters import gabor_kernel >>> from skimage import io >>> from matplotlib import pyplot as plt # doctest: +SKIP >>> gk = gabor_kernel(frequency=0.2) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP >>> # more ripples (equivalent to increasing the size of the >>> # Gaussian spread) >>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP """ # set gaussian parameters b = bandwidth sigma_pref = 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * (2.0 ** b + 1) / (2.0 ** b - 1) sigma_y = sigma_pref / frequency sigma_x = sigma_y/gamma # meshgrid x0 = np.ceil(max(np.abs(n_stds * sigma_x * np.cos(theta)), np.abs(n_stds * sigma_y * np.sin(theta)), 1)) y0 = np.ceil(max(np.abs(n_stds * sigma_y * np.cos(theta)), np.abs(n_stds * sigma_x * np.sin(theta)), 1)) y, x = np.mgrid[-y0:y0 + 1, -x0:x0 + 1] # rotation matrix rotx = x * np.cos(theta) + y * np.sin(theta) roty = -x * np.sin(theta) + y * np.cos(theta) # combine gambor and g = np.zeros(y.shape, dtype=np.complex) g[:] = np.exp(-0.5 * (rotx ** 2 / sigma_x ** 2 + roty ** 2 / sigma_y ** 2)) g /= 2 * np.pi * sigma_x * sigma_y # gaussian envelope oscil = np.exp(1j * (2 * np.pi * frequency * rotx + offset)) # harmonic / oscilatory function g_dc = g*oscil # remove dc component by subtracting the envelope weighted by K K = np.sum(g_dc)/np.sum(g) g_nodc = g_dc - K*g return g_nodc def _plot_filter_bank(kernels, frequency, ntheta, bandwidth, gamma, **kwargs): """ Display filter bank Parameters ---------- kernels: list List of kernels from filter_bank_2d_nodc() frequency: 1d ndarray of scalars Spatial frequencies used to built the Gabor filters. Values should be in [0;1] ntheta: int Number of angular steps between 0° to 90° bandwidth: scalar, optional, default is 1 This parameter modifies the frequency of the Gabor filter gamma: scalar, optional, default is 1 This parameter change the Gaussian window that modulates the continuous sine. 1 => same gaussian window in x and y direction (circle) <1 => elongation of the filter size in the y direction (elipsoid) >1 => reduction of the filter size in the y direction (elipsoid) **kwargs, optional. This parameter is used by plt.plot and savefig functions figsize : tuple of integers, optional, default: (13,13) width, height in inches. dpi : integer, optional Dot per inch. For printed version, choose high dpi (i.e. dpi=300) => slow For screen version, choose low dpi (i.e. dpi=96) => fast interpolation : string, optional, default is 'nearest' Pixels interpolation aspect : string, optional, default is 'auto' fontsize : scalar, optional, default is 8/0.22*hmax*100/dpi) size of the font use to print the parameters of each filter ... and more, see matplotlib Returns ------- fig : Figure The Figure instance ax : Axis The Axis instance """ params = [] for theta in range(ntheta): theta = theta/ntheta * np.pi for freq in frequency: params.append([freq, theta, bandwidth, gamma]) w = [] h = [] for kernel in kernels: ylen, xlen = kernel.shape w.append(xlen) h.append(ylen) plt.gray() fig = plt.figure() dpi =kwargs.pop('dpi',fig.get_dpi()) figsize =kwargs.pop('figsize',(13,13)) interpolation =kwargs.pop('interpolation','nearest') aspect =kwargs.pop('aspect','auto') fig.set_figwidth(figsize[0]) fig.set_figheight(figsize[1]) w = np.asarray(w)/dpi h = np.asarray(h)/dpi wmax = np.max(w)*1.25 hmax = np.max(h)*1.05 fontsize =kwargs.pop('fontsize',8/0.22*hmax*100/dpi) params_label = [] for param in params: params_label.append('theta=%d f=%.2f \n bandwidth=%.1f \n gamma=%.1f' % (param[1] * 180 / np.pi, param[0], param[2], param[3])) n = len(frequency) for ii, kernel in enumerate(kernels): ax = plt.axes([(ii%n)*wmax + (wmax-w[ii])/2,(ii//n)*hmax + (hmax-h[ii])/2,w[ii],h[ii]]) ax.imshow(np.real(kernel),interpolation=interpolation, aspect =aspect, **kwargs) ax.set_xticks([]) ax.set_yticks([]) ax.set_ylabel(params_label[ii],fontsize=fontsize) ax.axis('tight') plt.show() return ax, fig def _plot_filter_results(im_ref, im_list, kernels, params, m, n): """ Display the result after filtering Parameters ---------- im_ref : 2D array Reference image im_list : list List of filtered images kernels: list List of kernels from filter_bank_2d_nodc() m: int number of columns n: int number of rows Returns ------- Returns ------- fig : Figure The Figure instance ax : Axis The Axis instance """ ncols = m nrows = n fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 5)) plt.gray() fig.suptitle('Image responses for Gabor filter kernels', fontsize=12) axes[0][0].axis('off') # Plot original images axes[0][1].imshow(im_ref, origin='lower') axes[0][1].set_title('spectrogram', fontsize=9) axes[0][1].axis('off') plt.tight_layout params_label = [] for param in params: params_label.append('theta=%d,\nf=%.2f' % (param[1] * 180 / np.pi, param[0])) ii = 0 for ax_row in axes[1:]: plotGabor = True for ax in ax_row: if plotGabor == True: # Plot Gabor kernel print(params_label[ii]) ax.imshow(np.real(kernels[ii]), interpolation='nearest') ax.set_ylabel(params_label[ii], fontsize=7) ax.set_xticks([]) ax.set_yticks([]) plotGabor = False else: im_filtered = im_list[ii] ax.imshow(im_filtered, origin='lower') ax.axis('off') plotGabor = True ii=ii+1 plt.show() return ax, fig def filter_mag(im, kernel): """ Normalizes the image and computes im and real part of filter response using the complex kernel and the modulus operation Parameters ---------- im: 2D array Input image to process kernel: 2D array Complex kernel (or filter) Returns ------- im_out: Modulus operand on filtered image """ im = (im - im.mean()) / im.std() im_out = np.sqrt(ndi.convolve(im, np.real(kernel), mode='reflect')**2 + ndi.convolve(im, np.imag(kernel), mode='reflect')**2) return im_out def filter_multires(im_in, kernels, npyr=4, rescale=True): """ Computes 2D wavelet coefficients at multiple octaves/pyramids Parameters ---------- im_in: list of 2D arrays List of input images to process kernels: list of 2D arrays List of 2D wavelets to filter the images npyr: int Number of pyramids to compute rescale: boolean Indicates if the reduced images should be rescaled Returns ------- im_out: list of 2D arrays List of images filtered by each 2D kernel """ # Downscale image using gaussian pyramid if npyr<2: print('Warning: npyr should be int and larger than 2 for multiresolution') im_pyr = tuple(transform.pyramid_gaussian(im_in, downscale=2, max_layer=1, multichannel=False)) else: im_pyr = tuple(transform.pyramid_gaussian(im_in, downscale=2, max_layer=npyr-1, multichannel=False)) # filter 2d array at multiple resolutions using gabor kernels im_filt=[] for im in im_pyr: # for each pyramid for kernel, param in kernels: # for each kernel im_filt.append(filter_mag(im, kernel)) # magnitude response of filter # Rescale image using gaussian pyramid if rescale: dims_raw = im_in.shape im_out=[] for im in im_filt: ratio = np.array(dims_raw)/np.array(im.shape) if ratio[0] > 1: im = transform.rescale(im, scale = ratio, mode='reflect', multichannel=False, anti_aliasing=True) else: pass im_out.append(im) else: pass return im_out def filter_bank_2d_nodc(frequency, ntheta, bandwidth=1, gamma=1, display=False, savefig=None, **kwargs): """ Build a Gabor filter bank with no offset component Parameters ---------- frequency: 1d ndarray of scalars Spatial frequencies used to built the Gabor filters. Values should be in [0;1] ntheta: int Number of angular steps between 0° to 90° bandwidth: scalar, optional, default is 1 This parameter modifies the frequency of the Gabor filter gamma: scalar, optional, default is 1 This parameter change the Gaussian window that modulates the continuous sine. 1 => same gaussian window in x and y direction (circle) <1 => elongation of the filter size in the y direction (elipsoid) >1 => reduction of the filter size in the y direction (elipsoid) Returns ------- params: 2d structured array Parameters used to calculate 2D gabor kernels. Params array has 4 fields (theta, freq, bandwidth, gamma) kernels: 2d ndarray of scalars Gabor kernels """ theta = np.arange(ntheta) theta = theta / ntheta * np.pi params=[i for i in it.product(theta,frequency)] kernels = [] for param in params: kernel = gabor_kernel_nodc(frequency=param[1], theta=param[0], bandwidth=bandwidth, gamma=gamma, offset=0, n_stds=3) kernels.append((kernel, param)) if display: _, fig = _plot_filter_bank(kernels, frequency, ntheta, bandwidth, gamma, **kwargs) if savefig is not None : dpi =kwargs.pop('dpi',96) format=kwargs.pop('format','png') filename = savefig+'_filter_bank2D.'+format fig.savefig(filename, bbox_inches='tight', dpi=dpi, format=format, **kwargs) return params, kernels def shape_features(im, im_blobs=None, resolution='low', opt_shape=None): """ Computes shape of 2D signal (image or spectrogram) at multiple resolutions using 2D Gabor filters Parameters ---------- im: 2D array Input image to process im_blobs: 2D array, optional Optional binary array with '1' on the region of interest and '0' otherwise opt: dictionary options for the filter bank (kbank_opt) and the number of scales (npyr) Returns ------- shape: 1D array Shape coeficients of each filter params: 2D numpy structured array Corresponding parameters of the 2D fileters used to calculate the shape coefficient. Params has 4 fields (theta, freq, pyr_level, scale) bbox: If im_blobs provided, corresponding bounding box """ # unpack settings opt_shape = opt_shape_presets(resolution, opt_shape) npyr = opt_shape['npyr'] # build filterbank params, kernels = filter_bank_2d_nodc(ntheta=opt_shape['ntheta'], bandwidth=opt_shape['bandwidth'], frequency=opt_shape['frequency'], gamma=opt_shape['gamma']) # filter images im_rs = filter_multires(im, kernels, npyr, rescale=True) # Get mean intensity shape = [] if im_blobs is None: for im in im_rs: shape.append(np.mean(im)) rois_bbox=None shape = [shape] # for dataframe formating below else: for im in im_rs: labels = measure.label(im_blobs) rprops = measure.regionprops(labels, intensity_image=im) roi_mean = [roi.mean_intensity for roi in rprops] shape.append(roi_mean) rois_bbox = [roi.bbox for roi in rprops] shape = list(map(list, zip(*shape))) # transpose shape # organise parameters params = np.asarray(params) orient = params[:,0]*180/np.pi orient = orient.tolist()*npyr pyr_level = np.sort(np.arange(npyr).tolist()*len(params))+1 freq = params[:,1].tolist()*npyr #params_multires = np.vstack((np.asarray(orient), freq, pyr_level)) nparams = len(params)*npyr params_multires = np.zeros(nparams, dtype={'names':('theta', 'freq', 'pyr_level','scale'), 'formats':('f8', 'f8', 'f8','f8')}) params_multires['theta'] = orient params_multires['freq'] = freq params_multires['scale'] = 1/np.asarray(freq) params_multires['pyr_level'] = pyr_level params_multires = pd.DataFrame(params_multires) # format shape into dataframe cols=['shp_' + str(idx).zfill(3) for idx in range(1,len(shape[0])+1)] shape = pd.DataFrame(data=np.asarray(shape),columns=cols) # format rois into dataframe rois_bbox = pd.DataFrame(rois_bbox, columns=['min_y','min_x', 'max_y','max_x']) # compensate half-open interval of bbox from skimage rois_bbox.max_y = rois_bbox.max_y - 1 rois_bbox.max_x = rois_bbox.max_x - 1 return rois_bbox, params_multires, shape def centroid(im, im_blobs=None): """ Computes intensity centroid of the 2D signal (usually time-frequency representation) along a margin, frequency (0) or time (1). Parameters ---------- im: 2D array Input image to process im_blobs: 2D array, optional Optional binary array with '1' on the region of interest and '0' otherwise margin: 0 or 1 Margin of the centroid, frequency=1, time=0 Returns ------- centroid: 1D array centroid of image. If im_blobs provided, centroid for each region of interest """ centroid=[] rois_bbox=[] if im_blobs is None: centroid = ndimage.center_of_mass(im) else: labels = measure.label(im_blobs) rprops = measure.regionprops(labels, intensity_image=im) centroid = [roi.weighted_centroid for roi in rprops] rois_bbox = [roi.bbox for roi in rprops] # variables to dataframes centroid =
pd.DataFrame(centroid, columns=['y', 'x'])
pandas.DataFrame
from __future__ import print_function, unicode_literals from PyInquirer import style_from_dict, Token, prompt, Separator from pprint import pprint import os import tarfile from art import * from colorama import Fore, Back, Style from colorama import init import re import pandas as pd import openpyxl from itertools import groupby import matplotlib.pyplot as plt from pathlib import Path import shutil import easygui ################################################################# init(convert=True) #pint program name tprint('<<<Tar Analysis 2.0>>>') #ask for folder name print(Fore.CYAN) #name = input("Please enter the Tar folder name:") print(Style.RESET_ALL) name = easygui.fileopenbox() name = name[-12:-7] ##thammarak get home directory home = str(Path.home()) download_path = os.path.join(home, "Downloads\\") #so we dont have to tipe .tar.gz namet = name + ".tar.gz" path = os.path.join(download_path, namet) nameU = name+'unzip' #open and unzip tar folder tar = tarfile.open(path,"r:gz") tar.extractall() tar.close() os.mkdir(nameU) #### to unzip files inside zip folder:) for i in os.listdir(name): foldername = os.path.join(name, i) os.makedirs(i) y = tarfile.open(foldername,"r:gz") y.extractall(i) y.close() shutil.move(i,nameU) ################################################################ #CLI to ask what errors to look for style = style_from_dict({ Token.Separator: '#cc5454', Token.QuestionMark: '#673ab7', Token.Selected: '#cc5454', # default Token.Pointer: '#673ab7', Token.Instruction: '', # default Token.Answer: '#f44336', Token.Question: '', }) questions = [ { 'type': 'checkbox', 'message': 'Please select errors to look for', 'name' : 'variables', 'choices': [ Separator('= Errors to look for ='), { 'name': 'FAILED VALIDATION!!', 'checked': True }, { 'name': 'FAILED VALIDATION while executing command', }, { 'name': 'FAILED VALIDATION - Reported', }, { 'name': 'FAILED - COMMAND TIMED OUT', }, { 'name': 'Test(s) failed:', }, { 'name': '***err', }, { 'name': 'FAIL**', }, { 'name': 'err-disable', }, { 'name': 'Write your own', }, { 'name': 'RegEx query', }, { 'name': '->Make Excel report', }, Separator('= Enter a Command and print switch log for it ='), { 'name': 'Enter Command :', }, ], 'validate': lambda answer: 'You must choose at least one error to look for.' \ if len(answer) == 0 else True } ] answers = prompt(questions, style=style) ################################################################### #reads for new entry "write yout own" if 'Write your own' in answers["variables"]: own = input("Please enter the error you are looking for: ") answers["variables"] = [own if i=='Write your own' else i for i in answers["variables"]] ################################################################### #reads for new entry "write yout own" if 'RegEx query' in answers["variables"]: regex = input("Please enter the regex pattern to search for: ") regex = r"{}".format(regex) answers["variables"] = [regex if i=='RegEx query' else i for i in answers["variables"]] regex_flag = True else: regex_flag = False ################################################################### #opens all the files and writes line by line in log.txt document logs = open('logs.txt',"w+") for path,subdirs,files in os.walk(nameU): for i in files: filename = os.path.join(path, i) ### writing to text file with open(filename) as infile: for line in infile: logs.write(line) #################################################################### #reads text file to find corners cornerCount1 = 0 corner1=[] with open("logs.txt") as L: for line in L: if 'Corner Name :' in line and 'PST' not in line and 'PDT' not in line: cornerCount1 +=1 ################################################################### #reads for new entry "Enter Command" cmdDo = 0 if 'Enter Command :' in answers["variables"]: cmd = input("please enter Command to output log:") print ('There are ' ,cornerCount1,'corners, Example for entry : 1 2 3 ') cornerPrint1 = input("Write the number of corner separated by a space(type 0 for all):") cornerPrint = cornerPrint1.split() #makes list int for i in range(0, len(cornerPrint)): cornerPrint[i] = int(cornerPrint[i]) ##looks for next comand to know where to stop with open("logs.txt") as C: for line in C: line = line.split(",",1) if cmdDo == 1: cmdEnd = line[0] cmdDo = 0 if cmd in line: cmdDo = 1 ################################################################### #look up for errors line by line #varibles count = 0 corner = '' fails = [] switch = [] switchNumber = 'first777#$' #opens text file to read with open("logs.txt") as L: for line in L: # look for corner if 'Corner Name :' in line and 'PST' not in line and 'PDT' not in line and line not in corner: corner = line count = 0 print(Fore.BLACK) print (Back.WHITE+' > '+corner) print(Style.RESET_ALL) #Looks for Testcase number if 'TESTCASE START -' in line : testn = line # using re.py to search for switch number if 'TESTCASE START -' in line and switchNumber not in line: count = 0 switchNumber = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) print (Fore.GREEN+(switchNumber)) print(Style.RESET_ALL) #adding to list with errors for i in answers["variables"]: if i in line and i not in fails: fails.append(line) elif regex_flag and re.search(i, line): fails.append(line) if len(fails) > 0 and 'TESTCASE END -' in line: count += 1 print(Fore.YELLOW +str(count)+"--"+(testn)) print(Style.RESET_ALL) print (*fails, sep = "\n") #clearing error list if 'TESTCASE END -' in line: fails.clear() ################################################################# #looking for command output log cmdLog = [] cmdStart = 10000000 ii = 0 full = 0 cornerCount = 0 switchNumber1 = 'first777#$' if 'Enter Command :' in answers["variables"]: corner = '' cmd = cmd + ' ' print(Fore.BLACK) print (Back.RED+'Command Log Output') print(Style.RESET_ALL) with open("logs.txt") as B: for line in B: ii += 1 # look for corner if 'Corner Name :' in line and 'PST' not in line and 'PDT' not in line and line not in corner: corner = line print(Fore.BLACK) print (Back.WHITE+' > '+corner) print(Style.RESET_ALL) cornerCount += 1 #Looks for Testcase number if 'TESTCASE START -' in line : testn = line # using re.py to search for switch number if 'TESTCASE START -' in line and switchNumber1 not in line: switchNumber1 = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) print (Fore.GREEN+(switchNumber1)) print(Style.RESET_ALL) # looking for comand output if line.startswith(cmd) and line not in cmdLog: cmdLog.append(line) cmdStart = ii cmdStop = 10000000 full = 1 if ii >= cmdStart and ii < cmdStop and line not in cmdLog: cmdLog.append(line) if cmdEnd in line or 'Done executing all the given commands' in line: cmdStop = ii #print command output if 'TESTCASE END' and full == 1: for i in cornerPrint: if i == cornerCount or i == 0: cmdLog = list(filter(None, cmdLog)) print(*cmdLog, sep = "\n") cmdLog.clear() full = 0 ################################################################# #this part is to make an excel report on test ##variables testName = [] jobID = [] nameEx = name + ".xlsx" one = [] two = [] three = [] testExcel = [] switchExcel = 'first777#$' failsE = [] cornerE = '' #### four = [] fourCorner = '' fourSwitch = 'first777#$' fourFails = 0 do = 0 sfp = [] sfpee = str("sfpee ") sfpSwitch = 'first777#$' InfoSwitch = 'first777#$' sfpCorner = 0 sfpCount2 = 0 switch_count = [] countE=0 #makes first sheet in excel - Test info if '->Make Excel report' in answers["variables"]: with open("logs.txt") as E: for line in E: if "Starting Job Id " in line and len(one)<1: one.append(line) if "job_name" in line and len(one)<2: one.append(line) if 'TESTCASE START -' in line and InfoSwitch not in line: InfoSwitch = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) InfoSwitch = '---------------------->' + InfoSwitch if InfoSwitch not in one: one.append(InfoSwitch) if "MODEL_NUM" in line and line not in one: one.append(line) if "MOTHERBOARD_SERIAL_NUM" in line and line not in one: one.append(line) if "SYSTEM_SERIAL_NUM" in line and line not in one: one.append(line) # look for corner ############ add sfp info to SFP sheet in excel if 'TESTCASE START -' in line and sfpSwitch not in line: sfpSwitch = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) if sfpSwitch not in switch_count: switch_count.append(sfpSwitch) if '{sfpee}' in line: sfpCount2 += 1 if sfpCount2 <= len(switch_count): sfp.append('*************************************************') sfp.append(sfpSwitch) sfp.append('*************************************************') if "EEPROM in port" in line and sfpCount2 <= len(switch_count): sfp.append(line) if " Transceiver" in line and sfpCount2 <= len(switch_count): sfp.append(line) if " Vendor PN" in line and sfpCount2 <= len(switch_count): sfp.append(line) if " Vendor SN" in line and sfpCount2 <= len(switch_count): sfp.append(line) if " Extended ID" in line and sfpCount2 <= len(switch_count): sfp.append(line) ############ add errors to second sheet in excel if 'Corner Name :' in line and 'PST' not in line and 'PDT' not in line and line not in corner: cornerE = line countE = 0 two.append(cornerE) fourCorner = re.search(r"\{.*?}", line).group(0) #Looks for Testcase number if 'TESTCASE START -' in line : testExcel = line # using re.py to search for switch number if 'TESTCASE START -' in line and switchExcel not in line: switchExcel = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) fourSwitch = switchExcel two.append(switchExcel) #adding to list with errors for i in answers["variables"]: if i in line and i not in failsE: failsE.append(line) fourFails += 1 elif regex_flag and re.search(i, line): failsE.append(line) fourFails += 1 if len(failsE) > 0 and 'TESTCASE END -' in line: countE += 1 testExcel = str(countE) + "--" + testExcel two.append(testExcel) two.extend(failsE) ### to make a graph four.append(fourCorner) four.append(fourSwitch) four.append(countE) four.append(fourFails) #clearing error list if 'TESTCASE END -' in line: failsE.clear() ########################################################### #### graph to excel #varibles count_graph = 0 corner = '' fails = [] switch = [] testcase_graph = 'first777#$' switchNumber_graph = 'first777#$' switch_graph1 = [] group_graph = [] #opens text file to read with open("logs.txt") as L: for line in L: # look for corner if 'TESTCASE START ' in line and 'Testcase' in line and 'PDT' in line or 'PST' in line: if count_graph >= 1: switch_graph1 = [switchNumber_graph +" - "+ testcase_graph,'Failed'] else: switch_graph1 = [switchNumber_graph +" - "+ testcase_graph,'Passed'] switchNumber_graph = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) testcase_graph = line[line.index('{') + len('{'):] testcase_graph = testcase_graph.replace('}\n',"") switchNumber_graph = re.sub("[^0123456789\.]","",switchNumber_graph) count_graph = 0 group_graph.append(switch_graph1) for i in answers["variables"]: if i in line: count_graph += 1 ################################################################# ## makes the data for the graph nicer ## makes the data for the graph nicer group_graph.pop(0) group_graphD = pd.DataFrame(group_graph, columns = ['switch - Testcase','error']) group_graph_count = group_graphD.pivot_table(index=['switch - Testcase','error'], aggfunc='size') ########################################################### ############add commannd log if any to third sheet in excel #variables cmdLogE = [] cmdStartE = 10000000 iE = 0 fullE = 0 cornerLE = '' switchNumber1E = 'first777#$' three = [] cornerCountE = 0 if 'Enter Command :' in answers["variables"]: with open("logs.txt") as B: for line in B: iE += 1 # look for corner if 'Corner Name :' in line and 'PST' not in line and 'PDT' not in line and line not in cornerLE: cornerLE = line cornerLE = '---------------'+ cornerLE countLogE = 0 cornerCountE += 1 three.append(cornerLE) #Looks for Testcase number if 'TESTCASE START -' in line : testLog = line # using re.py to search for switch number if 'TESTCASE START -' in line and switchNumber1E not in line: countLogE = 0 switchNumber1E = re.search(r'\w\w\w\w\w\w\d(\d)?', line).group(0) three.append(switchNumber1E) # looking for comand output if line.startswith(cmd) and line not in cmdLogE: cmdLogE.append(line) cmdStartE = iE cmdStopE = 10000000 fullE = 1 if iE >= cmdStartE and iE < cmdStopE and line not in cmdLogE: cmdLogE.append(line) if cmdEnd in line or 'Done executing all the given commands' in line: cmdStopE = iE #print command output if 'TESTCASE END' and fullE == 1: for i in cornerPrint: if i == cornerCountE or i == 0: cmdLogE = list(filter(None, cmdLogE)) three.extend(cmdLogE) cmdLogE.clear() fullE = 0 ################################################################ #print to excel if len(one)>0: one = pd.Series(one) if len(two)>0: two = pd.Series(two) if 'Enter Command :' in answers["variables"]: three = pd.Series(three) if len(four)>0: four = pd.Series(four) if len(sfp)>0: sfp = pd.Series(sfp) w =
pd.ExcelWriter(nameEx)
pandas.ExcelWriter
# # Copyright (c) 2020 IBM Corp. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import pandas as pd import os import tempfile import unittest # noinspection PyPackageRequirements import pytest from pandas.tests.extension import base from text_extensions_for_pandas.array.test_span import ArrayTestBase from text_extensions_for_pandas.array.span import * from text_extensions_for_pandas.array.token_span import * class TokenSpanTest(ArrayTestBase): def test_create(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 1) self.assertEqual(s1.covered_text, "This") # Begin too small with self.assertRaises(ValueError): TokenSpan(toks, -2, 4) # End too small with self.assertRaises(ValueError): TokenSpan(toks, 1, -1) # End too big with self.assertRaises(ValueError): TokenSpan(toks, 1, 10) # Begin null, end not null with self.assertRaises(ValueError): TokenSpan(toks, TokenSpan.NULL_OFFSET_VALUE, 0) def test_repr(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 2) self.assertEqual(repr(s1), "[0, 7): 'This is'") toks2 = SpanArray( "This is a really really really really really really really really " "really long string.", np.array([0, 5, 8, 10, 17, 24, 31, 38, 45, 52, 59, 66, 73, 78, 84]), np.array([4, 7, 9, 16, 23, 30, 37, 44, 51, 58, 65, 72, 77, 84, 85]), ) self._assertArrayEquals( toks2.covered_text, [ "This", "is", "a", "really", "really", "really", "really", "really", "really", "really", "really", "really", "long", "string", ".", ], ) s2 = TokenSpan(toks2, 0, 4) self.assertEqual(repr(s2), "[0, 16): 'This is a really'") s2 = TokenSpan(toks2, 0, 15) self.assertEqual( repr(s2), "[0, 85): 'This is a really really really really really really " "really really really [...]'" ) def test_equals(self): toks = self._make_spans_of_tokens() other_toks = toks[:-1].copy() s1 = TokenSpan(toks, 0, 2) s2 = TokenSpan(toks, 0, 2) s3 = TokenSpan(toks, 0, 3) s4 = TokenSpan(other_toks, 0, 2) s5 = Span(toks.target_text, s4.begin, s4.end) s6 = Span(toks.target_text, s4.begin, s4.end + 1) self.assertEqual(s1, s2) self.assertNotEqual(s1, s3) self.assertEqual(s1, s4) self.assertEqual(s1, s5) self.assertEqual(s5, s1) self.assertNotEqual(s1, s6) def test_less_than(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 3) s2 = TokenSpan(toks, 2, 3) s3 = TokenSpan(toks, 3, 4) self.assertLess(s1, s3) self.assertLessEqual(s1, s3) self.assertFalse(s1 < s2) def test_add(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 3) s2 = TokenSpan(toks, 2, 3) s3 = TokenSpan(toks, 3, 4) char_s1 = Span(s1.target_text, s1.begin, s1.end) char_s2 = Span(s2.target_text, s2.begin, s2.end) self.assertEqual(s1 + s2, s1) self.assertEqual(char_s1 + s2, char_s1) self.assertEqual(s2 + char_s1, char_s1) self.assertEqual(char_s2 + char_s1, char_s1) self.assertEqual(s2 + s3, TokenSpan(toks, 2, 4)) def test_hash(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 3) s2 = TokenSpan(toks, 0, 3) s3 = TokenSpan(toks, 3, 4) d = {s1: "foo"} self.assertEqual(d[s1], "foo") self.assertEqual(d[s2], "foo") d[s2] = "bar" d[s3] = "fab" self.assertEqual(d[s1], "bar") self.assertEqual(d[s2], "bar") self.assertEqual(d[s3], "fab") class TokenSpanArrayTest(ArrayTestBase): def _make_spans(self): toks = self._make_spans_of_tokens() return TokenSpanArray(toks, [0, 1, 2, 3, 0, 2, 0], [1, 2, 3, 4, 2, 4, 4]) def test_create(self): arr = self._make_spans() self._assertArrayEquals( arr.covered_text, ["This", "is", "a", "test", "This is", "a test", "This is a test"], ) with self.assertRaises(TypeError): TokenSpanArray(self._make_spans_of_tokens(), "Not a valid begins list", [42]) def test_dtype(self): arr = self._make_spans() self.assertTrue(isinstance(arr.dtype, TokenSpanDtype)) def test_len(self): self.assertEqual(len(self._make_spans()), 7) def test_getitem(self): arr = self._make_spans() self.assertEqual(arr[2].covered_text, "a") self._assertArrayEquals(arr[2:4].covered_text, ["a", "test"]) def test_setitem(self): arr = self._make_spans() arr[1] = arr[2] self._assertArrayEquals(arr.covered_text[0:4], ["This", "a", "a", "test"]) arr[3] = None self._assertArrayEquals(arr.covered_text[0:4], ["This", "a", "a", None]) with self.assertRaises(ValueError): arr[0] = "Invalid argument for __setitem__()" arr[0:2] = arr[0] self._assertArrayEquals(arr.covered_text[0:4], ["This", "This", "a", None]) arr[[0, 1, 3]] = None self._assertArrayEquals(arr.covered_text[0:4], [None, None, "a", None]) arr[[2, 1, 3]] = arr[[4, 5, 6]] self._assertArrayEquals( arr.covered_text[0:4], [None, "a test", "This is", "This is a test"] ) def test_equals(self): arr = self._make_spans() self._assertArrayEquals(arr[0:4] == arr[1], [False, True, False, False]) arr2 = self._make_spans() self._assertArrayEquals(arr == arr, [True] * 7) self._assertArrayEquals(arr == arr2, [True] * 7) self._assertArrayEquals(arr[0:3] == arr[3:6], [False, False, False]) arr3 = SpanArray(arr.target_text, arr.begin, arr.end) self._assertArrayEquals(arr == arr3, [True] * 7) self._assertArrayEquals(arr3 == arr, [True] * 7) def test_not_equals(self): arr = self._make_spans() arr2 = self._make_spans() self._assertArrayEquals(arr[0:4] != arr[1], [True, False, True, True]) self._assertArrayEquals(arr != arr2, [False] * 7) self._assertArrayEquals(arr[0:3] != arr[3:6], [True, True, True]) def test_concat_same_type(self): arr = self._make_spans() arr2 = self._make_spans() # Type: TokenSpanArray arr3 = TokenSpanArray._concat_same_type((arr, arr2)) self._assertArrayEquals(arr3.covered_text, np.tile(arr2.covered_text, 2)) def test_from_factorized(self): arr = self._make_spans() spans_list = [arr[i] for i in range(len(arr))] arr2 = TokenSpanArray._from_factorized(spans_list, arr) self._assertArrayEquals(arr.covered_text, arr2.covered_text) def test_from_sequence(self): arr = self._make_spans() spans_list = [arr[i] for i in range(len(arr))] arr2 = TokenSpanArray._from_sequence(spans_list) self._assertArrayEquals(arr.covered_text, arr2.covered_text) def test_nulls(self): arr = self._make_spans() self._assertArrayEquals(arr.isna(), [False] * 7) self.assertFalse(arr.have_nulls) arr[2] = TokenSpan.make_null(arr.tokens) self.assertIsNone(arr.covered_text[2]) self._assertArrayEquals(arr[0:4].covered_text, ["This", "is", None, "test"]) self._assertArrayEquals(arr[0:4].isna(), [False, False, True, False]) self.assertTrue(arr.have_nulls) def test_copy(self): arr = self._make_spans() arr2 = arr.copy() self._assertArrayEquals(arr.covered_text, arr2.covered_text) self.assertEqual(arr[1], arr2[1]) arr[1] = TokenSpan.make_null(arr.tokens) self.assertNotEqual(arr[1], arr2[1]) # Double underscore because you can't call a test case "test_take" def test_take(self): arr = self._make_spans() arr2 = arr.take([1, 1, 2, 3, 5, -1]) self._assertArrayEquals( arr2.covered_text, ["is", "is", "a", "test", "a test", "This is a test"] ) arr3 = arr.take([1, 1, 2, 3, 5, -1], allow_fill=True) self._assertArrayEquals( arr3.covered_text, ["is", "is", "a", "test", "a test", None] ) def test_less_than(self): tokens = self._make_spans_of_tokens() arr1 = TokenSpanArray(tokens, [0, 2], [4, 3]) s1 = TokenSpan(tokens, 0, 1) s2 = TokenSpan(tokens, 3, 4) arr2 = TokenSpanArray(tokens, [0, 3], [0, 4]) self._assertArrayEquals(s1 < arr1, [False, True]) self._assertArrayEquals(s2 > arr1, [False, True]) self._assertArrayEquals(arr1 < s1, [False, False]) self._assertArrayEquals(arr1 < arr2, [False, True]) def test_add(self): toks = self._make_spans_of_tokens() s1 = TokenSpan(toks, 0, 3) s2 = TokenSpan(toks, 2, 3) s3 = TokenSpan(toks, 3, 4) s4 = TokenSpan(toks, 2, 4) s5 = TokenSpan(toks, 0, 3) char_s1 = Span(s1.target_text, s1.begin, s1.end) char_s2 = Span(s2.target_text, s2.begin, s2.end) char_s3 = Span(s3.target_text, s3.begin, s3.end) char_s4 = Span(s4.target_text, s4.begin, s4.end) char_s5 = Span(s5.target_text, s5.begin, s5.end) # TokenSpanArray + TokenSpanArray self._assertArrayEquals( TokenSpanArray._from_sequence([s1, s2, s3]) + TokenSpanArray._from_sequence([s2, s3, s3]), TokenSpanArray._from_sequence([s1, s4, s3]), ) # SpanArray + TokenSpanArray self._assertArrayEquals( SpanArray._from_sequence([char_s1, char_s2, char_s3]) + TokenSpanArray._from_sequence([s2, s3, s3]), SpanArray._from_sequence([char_s1, char_s4, char_s3]), ) # TokenSpanArray + SpanArray self._assertArrayEquals( TokenSpanArray._from_sequence([s1, s2, s3]) + SpanArray._from_sequence([char_s2, char_s3, char_s3]), SpanArray._from_sequence([char_s1, char_s4, char_s3]), ) # TokenSpanArray + TokenSpan self._assertArrayEquals( TokenSpanArray._from_sequence([s1, s2, s3]) + s2, TokenSpanArray._from_sequence([s5, s2, s4]), ) # TokenSpan + TokenSpanArray self._assertArrayEquals( s2 + TokenSpanArray._from_sequence([s1, s2, s3]), TokenSpanArray._from_sequence([s5, s2, s4]), ) # TokenSpanArray + Span self._assertArrayEquals( TokenSpanArray._from_sequence([s1, s2, s3]) + char_s2, SpanArray._from_sequence([char_s5, char_s2, char_s4]), ) # Span + SpanArray self._assertArrayEquals( char_s2 + SpanArray._from_sequence([char_s1, char_s2, char_s3]), SpanArray._from_sequence([char_s5, char_s2, char_s4]), ) def test_reduce(self): arr = self._make_spans() self.assertEqual(arr._reduce("sum"), TokenSpan(arr.tokens, 0, 4)) # Remind ourselves to modify this test after implementing min and max with self.assertRaises(TypeError): arr._reduce("min") def test_make_array(self): arr = self._make_spans() arr_series = pd.Series(arr) toks_list = [arr[0], arr[1], arr[2], arr[3]] self._assertArrayEquals( TokenSpanArray.make_array(arr).covered_text, ["This", "is", "a", "test", "This is", "a test", "This is a test"], ) self._assertArrayEquals( TokenSpanArray.make_array(arr_series).covered_text, ["This", "is", "a", "test", "This is", "a test", "This is a test"], ) self._assertArrayEquals( TokenSpanArray.make_array(toks_list).covered_text, ["This", "is", "a", "test"], ) def test_begin_and_end(self): arr = self._make_spans() self._assertArrayEquals(arr.begin, [0, 5, 8, 10, 0, 8, 0]) self._assertArrayEquals(arr.end, [4, 7, 9, 14, 7, 14, 14]) def test_normalized_covered_text(self): arr = self._make_spans() self._assertArrayEquals( arr.normalized_covered_text, ["this", "is", "a", "test", "this is", "a test", "this is a test"], ) def test_as_frame(self): arr = self._make_spans() df = arr.as_frame() self._assertArrayEquals( df.columns, ["begin", "end", "begin_token", "end_token", "covered_text"] ) self.assertEqual(len(df), len(arr)) class TokenSpanArrayIOTests(ArrayTestBase): def do_roundtrip(self, df): with tempfile.TemporaryDirectory() as dirpath: filename = os.path.join(dirpath, 'token_span_array_test.feather') df.to_feather(filename) df_read = pd.read_feather(filename) pd.testing.assert_frame_equal(df, df_read) def test_feather(self): toks = self._make_spans_of_tokens() # Equal token spans to tokens ts1 = TokenSpanArray(toks, np.arange(len(toks)), np.arange(len(toks)) + 1) df1 = pd.DataFrame({"ts1": ts1}) self.do_roundtrip(df1) # More token spans than tokens ts2 = TokenSpanArray(toks, [0, 1, 2, 3, 0, 2, 0], [1, 2, 3, 4, 2, 4, 4]) df2 = pd.DataFrame({"ts2": ts2}) self.do_roundtrip(df2) # Less token spans than tokens, 2 splits no padding ts3 = TokenSpanArray(toks, [0, 3], [3, 4]) df3 =
pd.DataFrame({"ts3": ts3})
pandas.DataFrame
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 mouse=a import matplotlib matplotlib.rcParams['figure.facecolor'] = '1.' matplotlib.use('Agg') import ants import numpy as np import pandas as pd import os import imageio import nipype.pipeline.engine as pe import nipype.interfaces.utility as niu import nibabel as nib import shutil import ntpath import nipype.pipeline.engine as pe import nipype.interfaces.utility as niu import nipype.interfaces.io as nio import matplotlib.pyplot as plt import seaborn as sns import inspect import json import re import time import matplotlib.animation as animation from skimage.feature import canny from nibabel.processing import resample_to_output from sklearn.metrics import normalized_mutual_info_score from sklearn.ensemble import IsolationForest from sklearn.cluster import DBSCAN from sklearn.neighbors import LocalOutlierFactor from sklearn.svm import OneClassSVM from skimage.filters import threshold_otsu from math import sqrt, log, ceil from os import getcwd from os.path import basename from sys import argv, exit from glob import glob from src.outlier import kde, MAD from sklearn.neighbors import LocalOutlierFactor from src.utils import concat_df from nipype.interfaces.base import (TraitedSpec, File, traits, InputMultiPath, BaseInterface, OutputMultiPath, BaseInterfaceInputSpec, isdefined) from scipy.ndimage.filters import gaussian_filter from nipype.utils.filemanip import (load_json, save_json, split_filename, fname_presuffix, copyfile) _file_dir, fn =os.path.split( os.path.abspath(__file__) ) def load_3d(fn, t=0): print('Reading Frame %d'%t,'from', fn) img = nib.load(fn) vol = img.get_fdata() hd = img.header if len(vol.shape) == 4 : vol = vol[:,:,:,t] vol = vol.reshape(vol.shape[0:3] ) img = nib.Nifti1Image(vol, img.affine) return img, vol def get_spacing(aff, i) : return aff[i, np.argmax(np.abs(aff[i,0:3]))] ###################### # Group-level QC # ###################### #datasink for dist metrics #check how the calc outlier measure node is implemented, may need to be reimplemented final_dir="qc" def group_level_qc(opts, args): #setup workflow workflow = pe.Workflow(name=qc_err+opts.preproc_dir) workflow.base_dir = opts.targetDir #Datasink datasink=pe.Node(interface=nio.DataSink(), name=qc_err+"output") datasink.inputs.base_directory= opts.targetDir +os.sep +"qc" datasink.inputs.substitutions = [('_cid_', ''), ('sid_', '')] outfields=['coreg_metrics','tka_metrics','pvc_metrics'] paths={'coreg_metrics':"*/coreg_qc_metrics/*_metric.csv", 'tka_metrics':"*/results_tka/*_3d.csv",'pvc_metrics':"*/pvc_qc_metrics/*qc_metric.csv"} #If any one of the sets of metrics does not exist because it has not been run at the scan level, then #remove it from the list of outfields and paths that the datagrabber will look for. for outfield, path in paths.items(): # zip(paths, outfields): full_path = opts.targetDir + os.sep + opts.preproc_dir + os.sep + path print(full_path) if len(glob(full_path)) == 0 : outfields.remove(outfield) paths.pop(outfield) #Datagrabber datasource = pe.Node( interface=nio.DataGrabber( outfields=outfields, raise_on_empty=True, sort_filelist=False), name=qc_err+"datasource") datasource.inputs.base_directory = opts.targetDir + os.sep +opts.preproc_dir datasource.inputs.template = '*' datasource.inputs.field_template = paths #datasource.inputs.template_args = dict( coreg_metrics = [['preproc_dir']] ) ################## # Coregistration # ################## qc_err='' if opts.pvc_label_name != None : qc_err += "_"+opts.pvc_label_name if opts.quant_label_name != None : qc_err += "_"+opts.quant_label_name if opts.results_label_name != None : qc_err += "_"+opts.results_label_name qc_err += "_" if 'coreg_metrics' in outfields: #Concatenate distance metrics concat_coreg_metricsNode=pe.Node(interface=concat_df(), name=qc_err+"concat_coreg_metrics") concat_coreg_metricsNode.inputs.out_file="coreg_qc_metrics.csv" workflow.connect(datasource, 'coreg_metrics', concat_coreg_metricsNode, 'in_list') workflow.connect(concat_coreg_metricsNode, "out_file", datasink, 'coreg/metrics') #Plot Coregistration Metrics plot_coreg_metricsNode=pe.Node(interface=plot_qcCommand(), name=qc_err+"plot_coreg_metrics") workflow.connect(concat_coreg_metricsNode, "out_file", plot_coreg_metricsNode, 'in_file') workflow.connect(plot_coreg_metricsNode, "out_file", datasink, 'coreg/metrics_plot') #Calculate Coregistration outlier measures outlier_measureNode = pe.Node(interface=outlier_measuresCommand(), name=qc_err+"coregistration_outlier_measure") workflow.connect(concat_coreg_metricsNode, 'out_file', outlier_measureNode, 'in_file') workflow.connect(outlier_measureNode, "out_file", datasink, 'coreg/outlier') #Plot coregistration outlier measures plot_coreg_measuresNode=pe.Node(interface=plot_qcCommand(),name=qc_err+"plot_coreg_measures") workflow.connect(outlier_measureNode,"out_file",plot_coreg_measuresNode,'in_file') workflow.connect(plot_coreg_measuresNode,"out_file",datasink,'coreg/measures_plot') ####### # PVC # ####### if 'pvc_metrics' in outfields: #Concatenate PVC metrics concat_pvc_metricsNode=pe.Node(interface=concat_df(), name=qc_err+"concat_pvc_metrics") concat_pvc_metricsNode.inputs.out_file="pvc_qc_metrics.csv" workflow.connect(datasource, 'pvc_metrics', concat_pvc_metricsNode, 'in_list') workflow.connect(concat_pvc_metricsNode, "out_file", datasink, 'pvc/metrics') #Plot PVC Metrics plot_pvc_metricsNode=pe.Node(interface=plot_qcCommand(), name=qc_err+"plot_pvc_metrics") workflow.connect(concat_pvc_metricsNode, "out_file", plot_pvc_metricsNode, 'in_file') workflow.connect(plot_pvc_metricsNode, "out_file", datasink, 'pvc/metrics_plot') #Calculate PVC outlier measures pvc_outlier_measureNode = pe.Node(interface=outlier_measuresCommand(), name=qc_err+"pvc_outlier_measure") workflow.connect(concat_pvc_metricsNode, 'out_file', pvc_outlier_measureNode, 'in_file') workflow.connect(pvc_outlier_measureNode, "out_file", datasink, 'pvc/outlier') #Plot PVC outlier measures plot_pvc_measuresNode=pe.Node(interface=plot_qcCommand(), name=qc_err+"plot_pvc_measures") workflow.connect(pvc_outlier_measureNode,"out_file",plot_pvc_measuresNode,'in_file') workflow.connect(plot_pvc_measuresNode, "out_file", datasink, 'pvc/measures_plot') ####### # TKA # ####### if 'tka_metrics' in outfields: #Concatenate TKA metrics concat_tka_metricsNode=pe.Node(interface=concat_df(), name=qc_err+"concat_tka_metrics") concat_tka_metricsNode.inputs.out_file="tka_qc_metrics.csv" workflow.connect(datasource, 'tka_metrics', concat_tka_metricsNode, 'in_list') workflow.connect(concat_tka_metricsNode, "out_file", datasink, 'tka/metrics') #Plot TKA Metrics plot_tka_metricsNode=pe.Node(interface=plot_qcCommand(), name=qc_err+"plot_tka_metrics") workflow.connect(concat_tka_metricsNode, "out_file", plot_tka_metricsNode, 'in_file') workflow.connect(plot_tka_metricsNode, "out_file", datasink, 'tka/metrics_plot') #Calculate TKA outlier measures tka_outlier_measureNode = pe.Node(interface=outlier_measuresCommand(), name=qc_err+"tka_outlier_measure") workflow.connect(concat_tka_metricsNode, 'out_file', tka_outlier_measureNode, 'in_file') workflow.connect(tka_outlier_measureNode, "out_file", datasink, 'tka/outlier') #Plot PVC outlier measures plot_tka_measuresNode=pe.Node(interface=plot_qcCommand(), name=qc_err+"plot_tka_measures") workflow.connect(tka_outlier_measureNode,"out_file",plot_tka_measuresNode,'in_file') workflow.connect(plot_tka_measuresNode, "out_file", datasink, 'tka/measures_plot') workflow.run() #################### # Distance Metrics # #################### __NBINS=-1 import copy def pvc_mse(pvc_fn, pve_fn, fwhm): pvc = nib.load(pvc_fn) pvc.data = pvc.get_data() pve = nib.load(pve_fn) pve.data = pve.get_data() mse = 0 if len(pvc.data.shape) > 3 :#if volume has more than 3 dimensions t = int(pvc.data.shape[3]/2) #for t in range(pvc.sizes[0]): pve_frame = pve.data[:,:,:,t] pvc_frame = pvc.data[:,:,:,t] n = np.sum(pve.data[t,:,:,:]) # np.prod(pve.data.shape[0:4]) pvc_blur = gaussian_filter(pvc_frame,fwhm) m = np.sum(np.sqrt((pve_frame - pvc_blur)**2)) mse += m print(t, m) else : #volume has 3 dimensions n = np.sum(pve.data) # np.prod(pve.data.shape[0:3]) pvc_blur = gaussian_filter(pvc.data,fwhm) m = np.sum(np.sqrt((pve.data - pvc_blur)**2)) mse += m mse = -mse / n #np.sum(pve.data) print("PVC MSE:", mse) return mse #################### # Outlier Measures # #################### def _IsolationForest(X): X = np.array(X) if len(X.shape) == 1 : X=X.reshape(-1,1) rng = np.random.RandomState(42) clf = IsolationForest(max_samples=X.shape[0], random_state=rng) return clf.fit(X).predict(X) def _LocalOutlierFactor(X): X = np.array(X) if len(X.shape) == 1 : X=X.reshape(-1,1) n=int(round(X.shape[0]*0.2)) clf = LocalOutlierFactor(n_neighbors=n) clf.fit_predict(X) return clf.negative_outlier_factor_ def _OneClassSVM(X): clf = OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1) clf.fit(X) return clf.predict(X) def _dbscan(X): db = DBSCAN(eps=0.3) return db.fit_predict(X) ########### # Globals # ########### global distance_metrics global outlier_measures global metric_columns global outlier_columns outlier_measures={"KDE":kde, "LOF": _LocalOutlierFactor, "IsolationForest":_IsolationForest, "MAD":MAD} #, "DBSCAN":_dbscan, "OneClassSVM":_OneClassSVM } metric_columns = ['analysis', 'sub','ses','task','run','acq','rec','roi','metric','value'] outlier_columns = ['analysis', 'sub','ses','task','roi','metric','measure','value'] ####################### ### Outlier Metrics ### ####################### ### PVC Metrics class pvc_qc_metricsOutput(TraitedSpec): out_file = traits.File(desc="Output file") class pvc_qc_metricsInput(BaseInterfaceInputSpec): pve = traits.File(exists=True, mandatory=True, desc="Input PVE PET image") pvc = traits.File(exists=True, mandatory=True, desc="Input PVC PET") fwhm = traits.List(desc='FWHM of the scanner') sub = traits.Str("Subject ID") task = traits.Str("Task") ses = traits.Str("Ses") run = traits.Str("Run") rec = traits.Str("Reconstruction") acq = traits.Str("Acquisition") out_file = traits.File(desc="Output file") class pvc_qc_metrics(BaseInterface): input_spec = pvc_qc_metricsInput output_spec = pvc_qc_metricsOutput def _gen_output(self, sid, ses, task,run,acq,rec, fname ="pvc_qc_metric.csv"): dname = os.getcwd() fn = dname+os.sep+'sub-'+sid+'_ses-'+ses+'_task-'+task if isdefined(run) : fn += '_run-'+str(run) fn += "_acq-"+str(acq)+"_rec-"+str(rec)+fname return fn def _run_interface(self, runtime): sub = self.inputs.sub ses = self.inputs.ses task = self.inputs.task fwhm = self.inputs.fwhm run = self.inputs.run rec = self.inputs.rec acq = self.inputs.acq df = pd.DataFrame([], columns=metric_columns) pvc_metrics={'mse':pvc_mse } for metric_name, metric_function in pvc_metrics.items(): mse = pvc_mse(self.inputs.pvc, self.inputs.pve, fwhm) temp = pd.DataFrame([['pvc', sub,ses,task,run,acq,rec,'02',metric_name,mse]], columns=metric_columns) df = pd.concat([df, temp]) df.fillna(0, inplace=True) if not isdefined(self.inputs.out_file): self.inputs.out_file = self._gen_output(self.inputs.sub, self.inputs.ses, self.inputs.task, self.inputs.run, self.inputs.acq, self.inputs.rec) df.to_csv(self.inputs.out_file, index=False) return runtime def _list_outputs(self): outputs = self.output_spec().get() if not isdefined(self.inputs.out_file): self.inputs.out_file = self.inputs._gen_output(self.inputs.sid,self.inputs.ses, self.inputs.task, self.inputs.run, self.inputs.acq, self.inputs.rec) outputs["out_file"] = self.inputs.out_file return outputs ### Coregistration Metrics class coreg_qc_metricsOutput(TraitedSpec): out_file = traits.File(desc="Output file") class coreg_qc_metricsInput(BaseInterfaceInputSpec): pet = traits.File(exists=True, mandatory=True, desc="Input PET image") t1 = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") brain_mask_space_mri = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") pet_brain_mask = traits.File(exists=True, mandatory=True, desc="Input T1 MRI") sid = traits.Str(desc="Subject") ses = traits.Str(desc="Session") task = traits.Str(desc="Task") run = traits.Str(desc="Run") rec = traits.Str(desc="Reconstruction") acq = traits.Str(desc="Acquisition") study_prefix = traits.Str(desc="Study Prefix") out_file = traits.File(desc="Output file") clobber = traits.Bool(desc="Overwrite output file", default=False) class coreg_qc_metricsCommand(BaseInterface): input_spec = coreg_qc_metricsInput output_spec = coreg_qc_metricsOutput def _gen_output(self, sid, ses, task, run, rec, acq, fname ="distance_metric.csv"): dname = os.getcwd() fn = dname+os.sep+'sub-'+sid+'_ses-'+ses+'_task-'+task if isdefined(run) : fn += '_run-'+str(run) fn += "_acq-"+str(acq)+"_rec-"+str(rec)+fname return fn def _run_interface(self, runtime): sub_df=pd.DataFrame(columns=metric_columns ) pet = self.inputs.pet t1 = self.inputs.t1 sid = self.inputs.sid ses = self.inputs.ses task = self.inputs.task run = self.inputs.run rec = self.inputs.rec acq = self.inputs.acq brain_mask_space_mri = self.inputs.brain_mask_space_mri pet_brain_mask = self.inputs.pet_brain_mask coreg_metrics=['MattesMutualInformation'] path, ext = os.path.splitext(pet) base=basename(path) param=base.split('_')[-1] param_type=base.split('_')[-2] df=pd.DataFrame(columns=metric_columns ) def image_read(fn) : img, vol = load_3d(fn) vol = vol.astype(float) aff = img.affine origin = [ aff[0,3], aff[1,3], aff[2,3]] spacing = [ get_spacing(aff, 0), get_spacing(aff, 1), get_spacing(aff, 2) ] return ants.from_numpy( vol, origin=origin, spacing=spacing ) for metric in coreg_metrics : print("t1 ",t1) fixed = image_read( t1 ) moving = image_read( pet ) try : metric_val = ants.create_ants_metric( fixed = fixed, moving= moving, fixed_mask=ants.image_read( brain_mask_space_mri ), moving_mask=ants.image_read( pet_brain_mask ), metric_type=metric ).get_value() except RuntimeError : metric_val = np.NaN temp = pd.DataFrame([['coreg',sid,ses,task,run,acq,rec,'01',metric,metric_val]],columns=df.columns ) sub_df = pd.concat([sub_df, temp]) if not isdefined( self.inputs.out_file) : self.inputs.out_file = self._gen_output(self.inputs.sid, self.inputs.ses, self.inputs.task,self.inputs.run,self.inputs.rec,self.inputs.acq) sub_df.to_csv(self.inputs.out_file, index=False) return runtime def _list_outputs(self): outputs = self.output_spec().get() if not isdefined( self.inputs.out_file) : self.inputs.out_file = self._gen_output(self.inputs.sid, self.inputs.ses, self.inputs.task,self.inputs.run,self.inputs.rec,self.inputs.acq) outputs["out_file"] = self.inputs.out_file return outputs ### Plot Metrics # analysis sub ses task metric roi value # 0 coreg 19 F 1 CC 1 0.717873 class plot_qcOutput(TraitedSpec): out_file = traits.File(desc="Output file") class plot_qcInput(BaseInterfaceInputSpec): in_file = traits.File(desc="Input file") out_file = traits.File(desc="Output file") class plot_qcCommand (BaseInterface): input_spec = plot_qcInput output_spec = plot_qcOutput def _gen_output(self, basefile="metrics.png"): fname = ntpath.basename(basefile) dname = os.getcwd() return dname+ os.sep+fname def _parse_inputs(self, skip=None): if skip is None: skip = [] if not isdefined(self.inputs.out_file): self.inputs.out_file = self._gen_output(self.inputs.in_file, self._suffix) return super(plot_qcCommand, self)._parse_inputs(skip=skip) def _run_interface(self, runtime): df = pd.read_csv( self.inputs.in_file ) if "measure" in df.columns: plot_type="measure" elif "metric" in df.columns : plot_type = "metric" else: print("Unrecognized data frame") exit(1) df["sub"]="sub: "+df["sub"].map(str)+" task: "+df["task"].map(str)+" ses: "+df["ses"].map(str) print(df) plt.clf() fig, ax = plt.subplots() plt.figure(1) nROI = len(np.unique(df.roi)) if plot_type == "measure" : unique_measure =np.unique(df.measure) nMeasure = np.unique(unique_measure) unique_metric = np.unique(df.metric) nMetric = len(unique_metric) for roi, i in zip(np.unique(df.roi), range(nROI)): df0=df[ (df.roi==roi) ] for metric in unique_metric : x=df0.value[df.metric == metric] if plot_type == "measure" : sns.factorplot(x="metric", col="measure", y="value", kind="swarm", data=df0, legend=False, hue="sub") else : sns.factorplot(x="metric", y="value", data=df0, kind="swarm", hue="sub") plt.ylabel('') plt.xlabel('') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.ylim([-0.05,1.05]) plt.legend(bbox_to_anchor=(1.05, 1), loc="upper right", ncol=1, prop={'size': 6}) if not isdefined( self.inputs.out_file) : self.inputs.out_file = self._gen_output() print('Out file:', self.inputs.out_file) #plt.tight_layout() plt.savefig(self.inputs.out_file, bbox_inches="tight", dpi=300, width=2000) plt.clf() return runtime def _list_outputs(self): outputs = self.output_spec().get() if not isdefined( self.inputs.out_file) : self.inputs.out_file = self._gen_output() outputs["out_file"] = self.inputs.out_file return outputs ######################### ### Outlier measures ### ######################### class outlier_measuresOutput(TraitedSpec): out_file = traits.File(desc="Output file") class outlier_measuresInput(BaseInterfaceInputSpec): in_file = traits.File(desc="Input file") out_file = traits.File(desc="Output file") clobber = traits.Bool(desc="Overwrite output file", default=False) class outlier_measuresCommand(BaseInterface): input_spec = outlier_measuresInput output_spec = outlier_measuresOutput def _gen_output(self, fname ="measures.csv"): dname = os.getcwd() + os.sep + fname return dname def _run_interface(self, runtime): df = pd.read_csv( self.inputs.in_file ) out_columns=['sub','ses','task','roi','metric','measure', 'value'] df_out =
pd.DataFrame(columns=out_columns)
pandas.DataFrame
""" MicroGridsPy - Multi-year capacity-expansion (MYCE) Linear Programming framework for microgrids least-cost sizing, able to account for time-variable load demand evolution and capacity expansion. Authors: <NAME> - Department of Energy, Politecnico di Milano <NAME> - Department of Energy, Politecnico di Milano <NAME> - Department of Energy, Politecnico di Milano / Fondazione Eni Enrico Mattei <NAME> - Department of Energy, Politecnico di Milano <NAME> - Department of Energy, Politecnico di Milano Based on the original model by: <NAME> - Department of Mechanical and Aerospace Engineering, University of Liège / San Simon University, Centro Universitario de Investigacion en Energia <NAME> - Department of Mechanical Engineering Technology, KU Leuven """ import pandas as pd import re #%% This section extracts the values of Scenarios, Periods, Years from data.dat and creates ranges for them Data_file = "Inputs/data.dat" Data_import = open(Data_file).readlines() for i in range(len(Data_import)): if "param: Scenarios" in Data_import[i]: n_scenarios = int((re.findall('\d+',Data_import[i])[0])) if "param: Years" in Data_import[i]: n_years = int((re.findall('\d+',Data_import[i])[0])) if "param: Periods" in Data_import[i]: n_periods = int((re.findall('\d+',Data_import[i])[0])) if "param: Generator_Types" in Data_import[i]: n_generators = int((re.findall('\d+',Data_import[i])[0])) scenario = [i for i in range(1,n_scenarios+1)] year = [i for i in range(1,n_years+1)] period = [i for i in range(1,n_periods+1)] generator = [i for i in range(1,n_generators+1)] #%% This section is useful to define the number of investment steps as well as to assign each year to its corresponding step def Initialize_Upgrades_Number(model): Data_file = "Inputs/data.dat" Data_import = open(Data_file).readlines() for i in range(len(Data_import)): if "param: Years" in Data_import[i]: n_years = int((re.findall('\d+',Data_import[i])[0])) if "param: Step_Duration" in Data_import[i]: step_duration = int((re.findall('\d+',Data_import[i])[0])) if "param: Min_Last_Step_Duration" in Data_import[i]: min_last_step_duration = int((re.findall('\d+',Data_import[i])[0])) if n_years % step_duration == 0: n_upgrades = n_years/step_duration return n_upgrades else: n_upgrades = 1 for y in range(1, n_years + 1): if y % step_duration == 0 and n_years - y > min_last_step_duration: n_upgrades += 1 return int(n_upgrades) def Initialize_YearUpgrade_Tuples(model): upgrade_years_list = [1 for i in range(len(model.steps))] s_dur = model.Step_Duration for i in range(1, len(model.steps)): upgrade_years_list[i] = upgrade_years_list[i-1] + s_dur yu_tuples_list = [0 for i in model.years] if model.Steps_Number == 1: for y in model.years: yu_tuples_list[y-1] = (y, 1) else: for y in model.years: for i in range(len(upgrade_years_list)-1): if y >= upgrade_years_list[i] and y < upgrade_years_list[i+1]: yu_tuples_list[y-1] = (y, model.steps[i+1]) elif y >= upgrade_years_list[-1]: yu_tuples_list[y-1] = (y, len(model.steps)) print('\nTime horizon (year,investment-step): ' + str(yu_tuples_list)) return yu_tuples_list #%% This section imports the multi-year Demand and Renewable-Energy output and creates a Multi-indexed DataFrame for it Demand = pd.read_excel('Inputs/Demand.xls') Energy_Demand_Series = pd.Series() for i in range(1,n_years*n_scenarios+1): dum = Demand[i][:] Energy_Demand_Series =
pd.concat([Energy_Demand_Series,dum])
pandas.concat
import pandas as pd from scripts.python.routines.manifest import get_manifest import numpy as np import os from scripts.python.pheno.datasets.filter import filter_pheno, get_passed_fields from scipy.stats import spearmanr import matplotlib.pyplot as plt from scripts.python.pheno.datasets.features import get_column_name, get_status_dict, get_sex_dict from matplotlib import colors from scipy.stats import mannwhitneyu import plotly.graph_objects as go from scripts.python.routines.plot.save import save_figure from scripts.python.routines.plot.violin import add_violin_trace from scripts.python.routines.plot.box import add_box_trace from scripts.python.routines.plot.layout import add_layout import pathlib import seaborn as sns dataset = "GSEUNN" path = f"E:/YandexDisk/Work/pydnameth/datasets" datasets_info = pd.read_excel(f"{path}/datasets.xlsx", index_col='dataset') platform = datasets_info.loc[dataset, 'platform'] manifest = get_manifest(platform) features = { 'biomarkers3_milli_Age_Control_Acc': 'EstimatedAgeAcc', 'FGF21_milli': 'FGF21', 'GDF15_milli': 'GDF15', 'CXCL9_milli': 'CXCL9', 'biomarkers3_milli_Age_Control': 'EstimatedAge', } feat_ranges = { 'biomarkers3_milli_Age_Control_Acc': [-40, 400], 'FGF21_milli': [0, 1.2], 'GDF15_milli': [0, 7], 'CXCL9_milli': [-2, 35], 'biomarkers3_milli_Age_Control': [0, 400], } status_col = get_column_name(dataset, 'Status').replace(' ','_') age_col = get_column_name(dataset, 'Age').replace(' ','_') sex_col = get_column_name(dataset, 'Sex').replace(' ','_') status_dict = get_status_dict(dataset) status_passed_fields = status_dict['Control'] + status_dict['Case'] sex_dict = get_sex_dict(dataset) path_save = f"{path}/{platform}/{dataset}/special/002_disease_groups_statistic" if not os.path.exists(f"{path_save}/figs/vio"): pathlib.Path(f"{path_save}/figs/vio").mkdir(parents=True, exist_ok=True) pathlib.Path(f"{path_save}/figs/box").mkdir(parents=True, exist_ok=True) continuous_vars = {v: k for k, v in features.items()} categorical_vars = {status_col: [x.column for x in status_passed_fields], sex_col: list(sex_dict.values())} pheno =
pd.read_pickle(f"{path}/{platform}/{dataset}/pheno_xtd.pkl")
pandas.read_pickle
# random forest regression tutorial at: # https://github.com/WillKoehrsen/Data-Analysis/blob/master/random_forest_explained/Random%20Forest%20Explained.ipynb import argparse import os import sys from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split from sklearn.tree import export_graphviz import numpy as np import pandas as pd import pydot # args parser = argparse.ArgumentParser() parser.add_argument("filename", help="CSV file") args = parser.parse_args() # data df =
pd.read_csv(args.filename)
pandas.read_csv
#!/usr/bin/env python # -*- coding: utf-8 -*- # # <NAME> # Modified and updated to process finngen dataset by <NAME> <EMAIL> # # import sys import os import pandas as pd import json import subprocess as sp def main(): # # Args -------------------------------------------------------------------- # study_prefix = 'FINNGEN_R5_' # Manifest files from Finngen R5 in_finngen = 'inputs/r5_finngen.json' in_snp_path_list = 'inputs/input_paths_finngen.txt' # Path to write main manifest file out_manifest = 'finngen.manifest.json' # Output directory for individual study fine-mapping results output_path = 'output/' keep_columns = [ 'code', 'trait', 'trait_category', 'n_cases', 'n_controls' ] finngen = ( pd.read_json(path_or_buf=in_finngen, lines=True) .rename( columns={ 'phenocode': 'code', 'phenostring': 'trait', 'category': 'trait_category', 'num_cases': 'n_cases', 'num_controls': 'n_controls' } ) ) finngen = finngen[keep_columns] finngen['code'] = study_prefix + finngen['code'] finngen['n_total'] = finngen['n_cases'] + finngen['n_controls'] gcs =
pd.read_csv(in_snp_path_list, sep='\t', header=None, names=['in_path'])
pandas.read_csv
import re import sys import numpy as np import pytest from pandas.compat import PYPY from pandas import Categorical, Index, NaT, Series, date_range import pandas._testing as tm from pandas.api.types import is_scalar class TestCategoricalAnalytics: @pytest.mark.parametrize("aggregation", ["min", "max"]) def test_min_max_not_ordered_raises(self, aggregation): # unordered cats have no min/max cat = Categorical(["a", "b", "c", "d"], ordered=False) msg = f"Categorical is not ordered for operation {aggregation}" agg_func = getattr(cat, aggregation) with pytest.raises(TypeError, match=msg): agg_func() def test_min_max_ordered(self): cat = Categorical(["a", "b", "c", "d"], ordered=True) _min = cat.min() _max = cat.max() assert _min == "a" assert _max == "d" cat = Categorical( ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True ) _min = cat.min() _max = cat.max() assert _min == "d" assert _max == "a" @pytest.mark.parametrize( "categories,expected", [ (list("ABC"), np.NaN), ([1, 2, 3], np.NaN), pytest.param( Series(date_range("2020-01-01", periods=3), dtype="category"), NaT, marks=pytest.mark.xfail( reason="https://github.com/pandas-dev/pandas/issues/29962" ), ), ], ) @pytest.mark.parametrize("aggregation", ["min", "max"]) def test_min_max_ordered_empty(self, categories, expected, aggregation): # GH 30227 cat = Categorical([], categories=categories, ordered=True) agg_func = getattr(cat, aggregation) result = agg_func() assert result is expected @pytest.mark.parametrize( "values, categories", [(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])], ) @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.parametrize("function", ["min", "max"]) def test_min_max_with_nan(self, values, categories, function, skipna): # GH 25303 cat = Categorical(values, categories=categories, ordered=True) result = getattr(cat, function)(skipna=skipna) if skipna is False: assert result is np.nan else: expected = categories[0] if function == "min" else categories[2] assert result == expected @pytest.mark.parametrize("function", ["min", "max"]) @pytest.mark.parametrize("skipna", [True, False]) def test_min_max_only_nan(self, function, skipna): # https://github.com/pandas-dev/pandas/issues/33450 cat = Categorical([np.nan], categories=[1, 2], ordered=True) result = getattr(cat, function)(skipna=skipna) assert result is np.nan @pytest.mark.parametrize("method", ["min", "max"]) def test_deprecate_numeric_only_min_max(self, method): # GH 25303 cat = Categorical( [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True ) with tm.assert_produces_warning(expected_warning=FutureWarning): getattr(cat, method)(numeric_only=True) @pytest.mark.parametrize("method", ["min", "max"]) def test_numpy_min_max_raises(self, method): cat = Categorical(["a", "b", "c", "b"], ordered=False) msg = ( f"Categorical is not ordered for operation {method}\n" "you can use .as_ordered() to change the Categorical to an ordered one" ) method = getattr(np, method) with pytest.raises(TypeError, match=re.escape(msg)): method(cat) @pytest.mark.parametrize("kwarg", ["axis", "out", "keepdims"]) @pytest.mark.parametrize("method", ["min", "max"]) def test_numpy_min_max_unsupported_kwargs_raises(self, method, kwarg): cat = Categorical(["a", "b", "c", "b"], ordered=True) msg = ( f"the '{kwarg}' parameter is not supported in the pandas implementation " f"of {method}" ) if kwarg == "axis": msg = r"`axis` must be fewer than the number of dimensions \(1\)" kwargs = {kwarg: 42} method = getattr(np, method) with pytest.raises(ValueError, match=msg): method(cat, **kwargs) @pytest.mark.parametrize("method, expected", [("min", "a"), ("max", "c")]) def test_numpy_min_max_axis_equals_none(self, method, expected): cat = Categorical(["a", "b", "c", "b"], ordered=True) method = getattr(np, method) result = method(cat, axis=None) assert result == expected @pytest.mark.parametrize( "values,categories,exp_mode", [ ([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]), ([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]), ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]), ([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]), ([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), ([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), ], ) def test_mode(self, values, categories, exp_mode): s = Categorical(values, categories=categories, ordered=True) res = s.mode() exp = Categorical(exp_mode, categories=categories, ordered=True) tm.assert_categorical_equal(res, exp) def test_searchsorted(self, ordered): # https://github.com/pandas-dev/pandas/issues/8420 # https://github.com/pandas-dev/pandas/issues/14522 cat = Categorical( ["cheese", "milk", "apple", "bread", "bread"], categories=["cheese", "milk", "apple", "bread"], ordered=ordered, ) ser = Series(cat) # Searching for single item argument, side='left' (default) res_cat = cat.searchsorted("apple") assert res_cat == 2 assert is_scalar(res_cat) res_ser = ser.searchsorted("apple") assert res_ser == 2 assert is_scalar(res_ser) # Searching for single item array, side='left' (default) res_cat = cat.searchsorted(["bread"]) res_ser = ser.searchsorted(["bread"]) exp = np.array([3], dtype=np.intp) tm.assert_numpy_array_equal(res_cat, exp) tm.assert_numpy_array_equal(res_ser, exp) # Searching for several items array, side='right' res_cat = cat.searchsorted(["apple", "bread"], side="right") res_ser = ser.searchsorted(["apple", "bread"], side="right") exp = np.array([3, 5], dtype=np.intp) tm.assert_numpy_array_equal(res_cat, exp) tm.assert_numpy_array_equal(res_ser, exp) # Searching for a single value that is not from the Categorical with pytest.raises(KeyError, match="cucumber"): cat.searchsorted("cucumber") with pytest.raises(KeyError, match="cucumber"): ser.searchsorted("cucumber") # Searching for multiple values one of each is not from the Categorical with pytest.raises(KeyError, match="cucumber"): cat.searchsorted(["bread", "cucumber"]) with pytest.raises(KeyError, match="cucumber"): ser.searchsorted(["bread", "cucumber"]) def test_unique(self): # categories are reordered based on value when ordered=False cat = Categorical(["a", "b"]) exp = Index(["a", "b"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) tm.assert_categorical_equal(res, cat) cat = Categorical(["a", "b", "a", "a"], categories=["a", "b", "c"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) tm.assert_categorical_equal(res, Categorical(exp)) cat = Categorical(["c", "a", "b", "a", "a"], categories=["a", "b", "c"]) exp = Index(["c", "a", "b"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) exp_cat = Categorical(exp, categories=["c", "a", "b"]) tm.assert_categorical_equal(res, exp_cat) # nan must be removed cat = Categorical(["b", np.nan, "b", np.nan, "a"], categories=["a", "b", "c"]) res = cat.unique() exp = Index(["b", "a"]) tm.assert_index_equal(res.categories, exp) exp_cat = Categorical(["b", np.nan, "a"], categories=["b", "a"]) tm.assert_categorical_equal(res, exp_cat) def test_unique_ordered(self): # keep categories order when ordered=True cat = Categorical(["b", "a", "b"], categories=["a", "b"], ordered=True) res = cat.unique() exp_cat = Categorical(["b", "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical( ["c", "b", "a", "a"], categories=["a", "b", "c"], ordered=True ) res = cat.unique() exp_cat = Categorical(["c", "b", "a"], categories=["a", "b", "c"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical(["b", "a", "a"], categories=["a", "b", "c"], ordered=True) res = cat.unique() exp_cat = Categorical(["b", "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical( ["b", "b", np.nan, "a"], categories=["a", "b", "c"], ordered=True ) res = cat.unique() exp_cat = Categorical(["b", np.nan, "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) def test_unique_index_series(self): c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1]) # Categorical.unique sorts categories by appearance order # if ordered=False exp = Categorical([3, 1, 2], categories=[3, 1, 2]) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) c = Categorical([1, 1, 2, 2], categories=[3, 2, 1]) exp = Categorical([1, 2], categories=[1, 2]) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1], ordered=True) # Categorical.unique keeps categories order if ordered=True exp = Categorical([3, 1, 2], categories=[3, 2, 1], ordered=True) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) def test_shift(self): # GH 9416 cat = Categorical(["a", "b", "c", "d", "a"]) # shift forward sp1 = cat.shift(1) xp1 = Categorical([np.nan, "a", "b", "c", "d"]) tm.assert_categorical_equal(sp1, xp1)
tm.assert_categorical_equal(cat[:-1], sp1[1:])
pandas._testing.assert_categorical_equal
import streamlit as st import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB, MultinomialNB from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from openpyxl import load_workbook def proccesClassification(allData,split_tt): for index, row in allData.iterrows(): if row ['PRODI'] == "TEKNIK ELEKTRO": rs = 15 piechartName = "TEKNIK ELEKTRO" elif row ['PRODI'] == "TEKNIK INDUSTRI": rs = 37 piechartName = "TEKNIK INDUSTRI" elif row ['PRODI'] == "TEKNIK INFORMATIKA": rs = 32 piechartName = "TEKNIK INFORMATIKA" elif row ['PRODI'] == "TEKNIK KIMIA": rs = 13 piechartName = "TEKNIK KIMIA" model = GaussianNB() st.write("***Data Training dan Testing***") x = allData[['ASAL SEKOLAH', 'PROVINSI', 'KUANT. MATE']] st.write(x) st.write("***Data Target***") y = allData['STATUS KELULUSAN'] st.write(y) st.write(y.shape) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = split_tt, random_state=rs) # 52019, 1230, 7, 12/13/14/15, industri = 37, elektro = 15, informatika = 32, kimia = 13, test = 230 nbtrain = model.fit(x_train, y_train) st.markdown('***DATA TRAINING : ***') st.write(x_train) st.write(x_train.shape) st.markdown('***TARGET TRAINING : ***') st.write(y_train) st.write(y_train.shape) st.markdown('***DATA TESTING : ***') st.write(x_test) st.write(x_test.shape) st.markdown('***TARGET TESTING : ***') st.write(y_test) st.write(y_test.shape) y_pred = nbtrain.predict(x_test) st.write("***Hasil Prediksi***") st.write(y_pred) st.write(y_pred.shape) st.write("***Data Porbabilitas Prediksi***") predic_prob = nbtrain.predict_proba(x_test) st.write(predic_prob) # Confusion matrix st.write("***Confusion Matrix***") df_confusion = pd.crosstab(y_test, y_pred) st.write(df_confusion) report = classification_report(y_test, y_pred) st.text(report) hasilTest = pd.DataFrame(y_pred) testTepat = hasilTest.apply(lambda x: True if x[0] == "TEPAT" else False , axis=1) testTidakTepat = hasilTest.apply(lambda x: True if x[0] == "TIDAK TEPAT" else False , axis=1) jumlahTestTepat = len(testTepat[testTepat == True].index) jumlahTestTidakTepat = len(testTidakTepat[testTidakTepat == True].index) Data = {piechartName: [jumlahTestTepat,jumlahTestTidakTepat]} df = pd.DataFrame(Data,columns=[piechartName],index = ['Tepat','Tidak Tepat']) df.plot.pie(y=piechartName,figsize=(10,6), autopct='%1.0f%%', startangle=60) TestChart = plt.show() st.pyplot(TestChart) uploadDataUji = st.file_uploader("Choose a Excel file", type=['csv','xlsx'], key = 'b') if uploadDataUji is not None: st.write("***Data Mahasiswa***") wb = load_workbook(uploadDataUji) sheet_ranges = wb["Sheet1"] model = GaussianNB() datamhs = pd.DataFrame(sheet_ranges.values) datamhs = datamhs[datamhs != 0] jml_row = datamhs[0].count() cleaning_mhs = datamhs[1:jml_row][[1,2,3,4,5,6]] cleaning_mhs.columns = ['NIM', 'NAMA', 'ASAL SEKOLAH', 'PRODI', 'PROVINSI', 'RATA MATE'] # menghapus data noise cleaning_mhs = cleaning_mhs.dropna(axis=0, how='any') # mengubah tipe data dari object ke float cleaning_mhs['RATA MATE'] = cleaning_mhs['RATA MATE'].apply(str) cleaning_mhs['RATA MATE'] = cleaning_mhs['RATA MATE'].str.replace(',','.').apply(float) for index, row in cleaning_mhs.iterrows(): # RATA MATE MATIKA if row['RATA MATE'] >= 93 and row['RATA MATE'] <= 100: cleaning_mhs.loc[index,'KUANT. MATE'] = 'SANGAT BAIK' elif row['RATA MATE'] >= 84 and row['RATA MATE'] <= 92: cleaning_mhs.loc[index,'KUANT. MATE'] = 'BAIK' elif row['RATA MATE'] >= 75 and row['RATA MATE'] <= 83: cleaning_mhs.loc[index,'KUANT. MATE'] = 'CUKUP' elif row['RATA MATE'] >= 0 and row['RATA MATE'] <= 74: cleaning_mhs.loc[index,'KUANT. MATE'] = 'PERLU DIMAKSIMALKAN' cleaning_mhs transformasi_mhs = cleaning_mhs[['NIM', 'NAMA','ASAL SEKOLAH', 'PROVINSI', 'KUANT. MATE']] for index, row in transformasi_mhs.iterrows(): if 'Maluku Utara' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '1' elif 'Kalimantan Tengah' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '1' elif 'Banten' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Yogyakarta' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Gorontalo' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Bengkulu'in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Kalimantan Selatan' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Lampung' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Sumatera' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Riau' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Sulawesi' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Nusa Tenggara' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Aceh' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Bangka' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Kalimantan Barat' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Jawa' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'jawa' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Jambi' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Jakarta' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Bali' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Kalimantan Utara' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '2' elif 'Papua' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '3' elif 'Kalimantan Timur' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '3' elif 'Maluku' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '3' elif 'lain' in row['PROVINSI']: transformasi_mhs.loc[index, 'PROVINSI'] = '3' #ASAL SEOKLAH if 'SMA' in row['ASAL SEKOLAH'] or 'sma' in row['ASAL SEKOLAH'] or 'Sma' in row['ASAL SEKOLAH'] or 'SMTA' in row['ASAL SEKOLAH']: transformasi_mhs.loc[index, 'ASAL SEKOLAH'] = '1' elif 'SMK' in row['ASAL SEKOLAH'] or 'smk' in row['ASAL SEKOLAH'] or 'Smk' in row['ASAL SEKOLAH'] or 'STM' in row['ASAL SEKOLAH'] or 'SMF' in row['ASAL SEKOLAH']: transformasi_mhs.loc[index, 'ASAL SEKOLAH'] = '2' elif 'MA' in row['ASAL SEKOLAH'] or 'Ma' in row['ASAL SEKOLAH']: transformasi_mhs.loc[index, 'ASAL SEKOLAH'] = '3' # RATA MATEMATIKA if row['KUANT. MATE'] == "SANGAT BAIK": transformasi_mhs.loc[index,'KUANT. MATE'] = 4 elif row['KUANT. MATE'] == "BAIK": transformasi_mhs.loc[index,'KUANT. MATE'] = 3 elif row['KUANT. MATE'] == "CUKUP": transformasi_mhs.loc[index,'KUANT. MATE'] = 2 elif row['KUANT. MATE'] == "PERLU DIMAKSIMALKAN": transformasi_mhs.loc[index,'KUANT. MATE'] = 1 dataPrediksi = transformasi_mhs[['ASAL SEKOLAH', 'PROVINSI', 'KUANT. MATE']] ass = cleaning_mhs[['NIM', 'NAMA','ASAL SEKOLAH', 'PROVINSI', 'KUANT. MATE']] model.fit(x_train, y_train) nbtrain = model.fit(x_train, y_train) st.write(pd.DataFrame(transformasi_mhs)) dataPrediksi = pd.DataFrame(dataPrediksi) # lbr = len(ddd.columns) # st.write(lbr) pjg = len(dataPrediksi) dindex = [] for i in range(pjg): dindex.append(int(i+1)) # ddd.set_axis(dindex,axis='index') # st.write(ddd) prediksi = model.predict(dataPrediksi) prediksi = pd.DataFrame(prediksi) prediksi.set_axis(dindex,axis='index') prediksi_prob = nbtrain.predict_proba(dataPrediksi) prediksi_prob = pd.DataFrame(prediksi_prob) prediksi_prob.set_axis(dindex,axis='index') df_index =
pd.merge(ass, prediksi_prob, right_index=True, left_index=True)
pandas.merge
""" Test output formatting for Series/DataFrame, including to_string & reprs """ from datetime import datetime from io import StringIO import itertools from operator import methodcaller import os from pathlib import Path import re from shutil import get_terminal_size import sys import textwrap import dateutil import numpy as np import pytest import pytz from pandas.compat import ( IS64, is_platform_windows, ) import pandas.util._test_decorators as td import pandas as pd from pandas import ( DataFrame, Index, MultiIndex, NaT, Series, Timestamp, date_range, get_option, option_context, read_csv, reset_option, set_option, ) import pandas._testing as tm import pandas.io.formats.format as fmt import pandas.io.formats.printing as printing use_32bit_repr = is_platform_windows() or not IS64 @pytest.fixture(params=["string", "pathlike", "buffer"]) def filepath_or_buffer_id(request): """ A fixture yielding test ids for filepath_or_buffer testing. """ return request.param @pytest.fixture def filepath_or_buffer(filepath_or_buffer_id, tmp_path): """ A fixture yielding a string representing a filepath, a path-like object and a StringIO buffer. Also checks that buffer is not closed. """ if filepath_or_buffer_id == "buffer": buf = StringIO() yield buf assert not buf.closed else: assert isinstance(tmp_path, Path) if filepath_or_buffer_id == "pathlike": yield tmp_path / "foo" else: yield str(tmp_path / "foo") @pytest.fixture def assert_filepath_or_buffer_equals( filepath_or_buffer, filepath_or_buffer_id, encoding ): """ Assertion helper for checking filepath_or_buffer. """ def _assert_filepath_or_buffer_equals(expected): if filepath_or_buffer_id == "string": with open(filepath_or_buffer, encoding=encoding) as f: result = f.read() elif filepath_or_buffer_id == "pathlike": result = filepath_or_buffer.read_text(encoding=encoding) elif filepath_or_buffer_id == "buffer": result = filepath_or_buffer.getvalue() assert result == expected return _assert_filepath_or_buffer_equals def curpath(): pth, _ = os.path.split(os.path.abspath(__file__)) return pth def has_info_repr(df): r = repr(df) c1 = r.split("\n")[0].startswith("<class") c2 = r.split("\n")[0].startswith(r"&lt;class") # _repr_html_ return c1 or c2 def has_non_verbose_info_repr(df): has_info = has_info_repr(df) r = repr(df) # 1. <class> # 2. Index # 3. Columns # 4. dtype # 5. memory usage # 6. trailing newline nv = len(r.split("\n")) == 6 return has_info and nv def has_horizontally_truncated_repr(df): try: # Check header row fst_line = np.array(repr(df).splitlines()[0].split()) cand_col = np.where(fst_line == "...")[0][0] except IndexError: return False # Make sure each row has this ... in the same place r = repr(df) for ix, l in enumerate(r.splitlines()): if not r.split()[cand_col] == "...": return False return True def has_vertically_truncated_repr(df): r = repr(df) only_dot_row = False for row in r.splitlines(): if re.match(r"^[\.\ ]+$", row): only_dot_row = True return only_dot_row def has_truncated_repr(df): return has_horizontally_truncated_repr(df) or has_vertically_truncated_repr(df) def has_doubly_truncated_repr(df): return has_horizontally_truncated_repr(df) and has_vertically_truncated_repr(df) def has_expanded_repr(df): r = repr(df) for line in r.split("\n"): if line.endswith("\\"): return True return False @pytest.mark.filterwarnings("ignore::FutureWarning:.*format") class TestDataFrameFormatting: def test_eng_float_formatter(self, float_frame): df = float_frame df.loc[5] = 0 fmt.set_eng_float_format() repr(df) fmt.set_eng_float_format(use_eng_prefix=True) repr(df) fmt.set_eng_float_format(accuracy=0) repr(df) tm.reset_display_options() def test_show_null_counts(self): df = DataFrame(1, columns=range(10), index=range(10)) df.iloc[1, 1] = np.nan def check(show_counts, result): buf = StringIO() df.info(buf=buf, show_counts=show_counts) assert ("non-null" in buf.getvalue()) is result with option_context( "display.max_info_rows", 20, "display.max_info_columns", 20 ): check(None, True) check(True, True) check(False, False) with option_context("display.max_info_rows", 5, "display.max_info_columns", 5): check(None, False) check(True, False) check(False, False) # GH37999 with tm.assert_produces_warning( FutureWarning, match="null_counts is deprecated.+" ): buf = StringIO() df.info(buf=buf, null_counts=True) assert "non-null" in buf.getvalue() # GH37999 with pytest.raises(ValueError, match=r"null_counts used with show_counts.+"): df.info(null_counts=True, show_counts=True) def test_repr_truncation(self): max_len = 20 with option_context("display.max_colwidth", max_len): df = DataFrame( { "A": np.random.randn(10), "B": [ tm.rands(np.random.randint(max_len - 1, max_len + 1)) for i in range(10) ], } ) r = repr(df) r = r[r.find("\n") + 1 :] adj = fmt.get_adjustment() for line, value in zip(r.split("\n"), df["B"]): if adj.len(value) + 1 > max_len: assert "..." in line else: assert "..." not in line with option_context("display.max_colwidth", 999999): assert "..." not in repr(df) with option_context("display.max_colwidth", max_len + 2): assert "..." not in repr(df) def test_repr_deprecation_negative_int(self): # TODO(2.0): remove in future version after deprecation cycle # Non-regression test for: # https://github.com/pandas-dev/pandas/issues/31532 width = get_option("display.max_colwidth") with tm.assert_produces_warning(FutureWarning): set_option("display.max_colwidth", -1) set_option("display.max_colwidth", width) def test_repr_chop_threshold(self): df = DataFrame([[0.1, 0.5], [0.5, -0.1]]) reset_option("display.chop_threshold") # default None assert repr(df) == " 0 1\n0 0.1 0.5\n1 0.5 -0.1" with option_context("display.chop_threshold", 0.2): assert repr(df) == " 0 1\n0 0.0 0.5\n1 0.5 0.0" with option_context("display.chop_threshold", 0.6): assert repr(df) == " 0 1\n0 0.0 0.0\n1 0.0 0.0" with option_context("display.chop_threshold", None): assert repr(df) == " 0 1\n0 0.1 0.5\n1 0.5 -0.1" def test_repr_chop_threshold_column_below(self): # GH 6839: validation case df = DataFrame([[10, 20, 30, 40], [8e-10, -1e-11, 2e-9, -2e-11]]).T with option_context("display.chop_threshold", 0): assert repr(df) == ( " 0 1\n" "0 10.0 8.000000e-10\n" "1 20.0 -1.000000e-11\n" "2 30.0 2.000000e-09\n" "3 40.0 -2.000000e-11" ) with option_context("display.chop_threshold", 1e-8): assert repr(df) == ( " 0 1\n" "0 10.0 0.000000e+00\n" "1 20.0 0.000000e+00\n" "2 30.0 0.000000e+00\n" "3 40.0 0.000000e+00" ) with option_context("display.chop_threshold", 5e-11): assert repr(df) == ( " 0 1\n" "0 10.0 8.000000e-10\n" "1 20.0 0.000000e+00\n" "2 30.0 2.000000e-09\n" "3 40.0 0.000000e+00" ) def test_repr_obeys_max_seq_limit(self): with option_context("display.max_seq_items", 2000): assert len(printing.pprint_thing(list(range(1000)))) > 1000 with option_context("display.max_seq_items", 5): assert len(printing.pprint_thing(list(range(1000)))) < 100 with option_context("display.max_seq_items", 1): assert len(printing.pprint_thing(list(range(1000)))) < 9 def test_repr_set(self): assert printing.pprint_thing({1}) == "{1}" def test_repr_is_valid_construction_code(self): # for the case of Index, where the repr is traditional rather than # stylized idx = Index(["a", "b"]) res = eval("pd." + repr(idx)) tm.assert_series_equal(Series(res), Series(idx)) def test_repr_should_return_str(self): # https://docs.python.org/3/reference/datamodel.html#object.__repr__ # "...The return value must be a string object." # (str on py2.x, str (unicode) on py3) data = [8, 5, 3, 5] index1 = ["\u03c3", "\u03c4", "\u03c5", "\u03c6"] cols = ["\u03c8"] df = DataFrame(data, columns=cols, index=index1) assert type(df.__repr__()) == str # both py2 / 3 def test_repr_no_backslash(self): with option_context("mode.sim_interactive", True): df = DataFrame(np.random.randn(10, 4)) assert "\\" not in repr(df) def test_expand_frame_repr(self): df_small = DataFrame("hello", index=[0], columns=[0]) df_wide = DataFrame("hello", index=[0], columns=range(10)) df_tall = DataFrame("hello", index=range(30), columns=range(5)) with option_context("mode.sim_interactive", True): with option_context( "display.max_columns", 10, "display.width", 20, "display.max_rows", 20, "display.show_dimensions", True, ): with option_context("display.expand_frame_repr", True): assert not has_truncated_repr(df_small) assert not has_expanded_repr(df_small) assert not has_truncated_repr(df_wide) assert has_expanded_repr(df_wide) assert has_vertically_truncated_repr(df_tall) assert has_expanded_repr(df_tall) with option_context("display.expand_frame_repr", False): assert not has_truncated_repr(df_small) assert not has_expanded_repr(df_small) assert not has_horizontally_truncated_repr(df_wide) assert not has_expanded_repr(df_wide) assert has_vertically_truncated_repr(df_tall) assert not has_expanded_repr(df_tall) def test_repr_non_interactive(self): # in non interactive mode, there can be no dependency on the # result of terminal auto size detection df = DataFrame("hello", index=range(1000), columns=range(5)) with option_context( "mode.sim_interactive", False, "display.width", 0, "display.max_rows", 5000 ): assert not has_truncated_repr(df) assert not has_expanded_repr(df) def test_repr_truncates_terminal_size(self, monkeypatch): # see gh-21180 terminal_size = (118, 96) monkeypatch.setattr( "pandas.io.formats.format.get_terminal_size", lambda: terminal_size ) index = range(5) columns = MultiIndex.from_tuples( [ ("This is a long title with > 37 chars.", "cat"), ("This is a loooooonger title with > 43 chars.", "dog"), ] ) df = DataFrame(1, index=index, columns=columns) result = repr(df) h1, h2 = result.split("\n")[:2] assert "long" in h1 assert "loooooonger" in h1 assert "cat" in h2 assert "dog" in h2 # regular columns df2 = DataFrame({"A" * 41: [1, 2], "B" * 41: [1, 2]}) result = repr(df2) assert df2.columns[0] in result.split("\n")[0] def test_repr_truncates_terminal_size_full(self, monkeypatch): # GH 22984 ensure entire window is filled terminal_size = (80, 24) df = DataFrame(np.random.rand(1, 7)) monkeypatch.setattr( "pandas.io.formats.format.get_terminal_size", lambda: terminal_size ) assert "..." not in str(df) def test_repr_truncation_column_size(self): # dataframe with last column very wide -> check it is not used to # determine size of truncation (...) column df = DataFrame( { "a": [108480, 30830], "b": [12345, 12345], "c": [12345, 12345], "d": [12345, 12345], "e": ["a" * 50] * 2, } ) assert "..." in str(df) assert " ... " not in str(df) def test_repr_max_columns_max_rows(self): term_width, term_height = get_terminal_size() if term_width < 10 or term_height < 10: pytest.skip(f"terminal size too small, {term_width} x {term_height}") def mkframe(n): index = [f"{i:05d}" for i in range(n)] return DataFrame(0, index, index) df6 = mkframe(6) df10 = mkframe(10) with option_context("mode.sim_interactive", True): with option_context("display.width", term_width * 2): with option_context("display.max_rows", 5, "display.max_columns", 5): assert not has_expanded_repr(mkframe(4)) assert not has_expanded_repr(mkframe(5)) assert not has_expanded_repr(df6) assert has_doubly_truncated_repr(df6) with option_context("display.max_rows", 20, "display.max_columns", 10): # Out off max_columns boundary, but no extending # since not exceeding width assert not has_expanded_repr(df6) assert not has_truncated_repr(df6) with option_context("display.max_rows", 9, "display.max_columns", 10): # out vertical bounds can not result in expanded repr assert not has_expanded_repr(df10) assert has_vertically_truncated_repr(df10) # width=None in terminal, auto detection with option_context( "display.max_columns", 100, "display.max_rows", term_width * 20, "display.width", None, ): df = mkframe((term_width // 7) - 2) assert not has_expanded_repr(df) df = mkframe((term_width // 7) + 2) printing.pprint_thing(df._repr_fits_horizontal_()) assert has_expanded_repr(df) def test_repr_min_rows(self): df = DataFrame({"a": range(20)}) # default setting no truncation even if above min_rows assert ".." not in repr(df) assert ".." not in df._repr_html_() df = DataFrame({"a": range(61)}) # default of max_rows 60 triggers truncation if above assert ".." in repr(df) assert ".." in df._repr_html_() with option_context("display.max_rows", 10, "display.min_rows", 4): # truncated after first two rows assert ".." in repr(df) assert "2 " not in repr(df) assert "..." in df._repr_html_() assert "<td>2</td>" not in df._repr_html_() with option_context("display.max_rows", 12, "display.min_rows", None): # when set to None, follow value of max_rows assert "5 5" in repr(df) assert "<td>5</td>" in df._repr_html_() with option_context("display.max_rows", 10, "display.min_rows", 12): # when set value higher as max_rows, use the minimum assert "5 5" not in repr(df) assert "<td>5</td>" not in df._repr_html_() with option_context("display.max_rows", None, "display.min_rows", 12): # max_rows of None -> never truncate assert ".." not in repr(df) assert ".." not in df._repr_html_() def test_str_max_colwidth(self): # GH 7856 df = DataFrame( [ { "a": "foo", "b": "bar", "c": "uncomfortably long line with lots of stuff", "d": 1, }, {"a": "foo", "b": "bar", "c": "stuff", "d": 1}, ] ) df.set_index(["a", "b", "c"]) assert str(df) == ( " a b c d\n" "0 foo bar uncomfortably long line with lots of stuff 1\n" "1 foo bar stuff 1" ) with option_context("max_colwidth", 20): assert str(df) == ( " a b c d\n" "0 foo bar uncomfortably lo... 1\n" "1 foo bar stuff 1" ) def test_auto_detect(self): term_width, term_height = get_terminal_size() fac = 1.05 # Arbitrary large factor to exceed term width cols = range(int(term_width * fac)) index = range(10) df = DataFrame(index=index, columns=cols) with option_context("mode.sim_interactive", True): with option_context("display.max_rows", None): with option_context("display.max_columns", None): # Wrap around with None assert has_expanded_repr(df) with option_context("display.max_rows", 0): with option_context("display.max_columns", 0): # Truncate with auto detection. assert has_horizontally_truncated_repr(df) index = range(int(term_height * fac)) df = DataFrame(index=index, columns=cols) with option_context("display.max_rows", 0): with option_context("display.max_columns", None): # Wrap around with None assert has_expanded_repr(df) # Truncate vertically assert has_vertically_truncated_repr(df) with option_context("display.max_rows", None): with option_context("display.max_columns", 0): assert has_horizontally_truncated_repr(df) def test_to_string_repr_unicode(self): buf = StringIO() unicode_values = ["\u03c3"] * 10 unicode_values = np.array(unicode_values, dtype=object) df = DataFrame({"unicode": unicode_values}) df.to_string(col_space=10, buf=buf) # it works! repr(df) idx = Index(["abc", "\u03c3a", "aegdvg"]) ser = Series(np.random.randn(len(idx)), idx) rs = repr(ser).split("\n") line_len = len(rs[0]) for line in rs[1:]: try: line = line.decode(get_option("display.encoding")) except AttributeError: pass if not line.startswith("dtype:"): assert len(line) == line_len # it works even if sys.stdin in None _stdin = sys.stdin try: sys.stdin = None repr(df) finally: sys.stdin = _stdin def test_east_asian_unicode_false(self): # not aligned properly because of east asian width # mid col df = DataFrame( {"a": ["あ", "いいい", "う", "ええええええ"], "b": [1, 222, 33333, 4]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\na あ 1\n" "bb いいい 222\nc う 33333\n" "ddd ええええええ 4" ) assert repr(df) == expected # last col df = DataFrame( {"a": [1, 222, 33333, 4], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\na 1 あ\n" "bb 222 いいい\nc 33333 う\n" "ddd 4 ええええええ" ) assert repr(df) == expected # all col df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\na あああああ あ\n" "bb い いいい\nc う う\n" "ddd えええ ええええええ" ) assert repr(df) == expected # column name df = DataFrame( {"b": ["あ", "いいい", "う", "ええええええ"], "あああああ": [1, 222, 33333, 4]}, index=["a", "bb", "c", "ddd"], ) expected = ( " b あああああ\na あ 1\n" "bb いいい 222\nc う 33333\n" "ddd ええええええ 4" ) assert repr(df) == expected # index df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["あああ", "いいいいいい", "うう", "え"], ) expected = ( " a b\nあああ あああああ あ\n" "いいいいいい い いいい\nうう う う\n" "え えええ ええええええ" ) assert repr(df) == expected # index name df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=Index(["あ", "い", "うう", "え"], name="おおおお"), ) expected = ( " a b\n" "おおおお \n" "あ あああああ あ\n" "い い いいい\n" "うう う う\n" "え えええ ええええええ" ) assert repr(df) == expected # all df = DataFrame( {"あああ": ["あああ", "い", "う", "えええええ"], "いいいいい": ["あ", "いいい", "う", "ええ"]}, index=Index(["あ", "いいい", "うう", "え"], name="お"), ) expected = ( " あああ いいいいい\n" "お \n" "あ あああ あ\n" "いいい い いいい\n" "うう う う\n" "え えええええ ええ" ) assert repr(df) == expected # MultiIndex idx = MultiIndex.from_tuples( [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")] ) df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=idx, ) expected = ( " a b\n" "あ いい あああああ あ\n" "う え い いいい\n" "おおお かかかか う う\n" "き くく えええ ええええええ" ) assert repr(df) == expected # truncate with option_context("display.max_rows", 3, "display.max_columns", 3): df = DataFrame( { "a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"], "c": ["お", "か", "ききき", "くくくくくく"], "ああああ": ["さ", "し", "す", "せ"], }, columns=["a", "b", "c", "ああああ"], ) expected = ( " a ... ああああ\n0 あああああ ... さ\n" ".. ... ... ...\n3 えええ ... せ\n" "\n[4 rows x 4 columns]" ) assert repr(df) == expected df.index = ["あああ", "いいいい", "う", "aaa"] expected = ( " a ... ああああ\nあああ あああああ ... さ\n" ".. ... ... ...\naaa えええ ... せ\n" "\n[4 rows x 4 columns]" ) assert repr(df) == expected def test_east_asian_unicode_true(self): # Enable Unicode option ----------------------------------------- with option_context("display.unicode.east_asian_width", True): # mid col df = DataFrame( {"a": ["あ", "いいい", "う", "ええええええ"], "b": [1, 222, 33333, 4]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\na あ 1\n" "bb いいい 222\nc う 33333\n" "ddd ええええええ 4" ) assert repr(df) == expected # last col df = DataFrame( {"a": [1, 222, 33333, 4], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\na 1 あ\n" "bb 222 いいい\nc 33333 う\n" "ddd 4 ええええええ" ) assert repr(df) == expected # all col df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["a", "bb", "c", "ddd"], ) expected = ( " a b\n" "a あああああ あ\n" "bb い いいい\n" "c う う\n" "ddd えええ ええええええ" ) assert repr(df) == expected # column name df = DataFrame( {"b": ["あ", "いいい", "う", "ええええええ"], "あああああ": [1, 222, 33333, 4]}, index=["a", "bb", "c", "ddd"], ) expected = ( " b あああああ\n" "a あ 1\n" "bb いいい 222\n" "c う 33333\n" "ddd ええええええ 4" ) assert repr(df) == expected # index df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=["あああ", "いいいいいい", "うう", "え"], ) expected = ( " a b\n" "あああ あああああ あ\n" "いいいいいい い いいい\n" "うう う う\n" "え えええ ええええええ" ) assert repr(df) == expected # index name df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=Index(["あ", "い", "うう", "え"], name="おおおお"), ) expected = ( " a b\n" "おおおお \n" "あ あああああ あ\n" "い い いいい\n" "うう う う\n" "え えええ ええええええ" ) assert repr(df) == expected # all df = DataFrame( {"あああ": ["あああ", "い", "う", "えええええ"], "いいいいい": ["あ", "いいい", "う", "ええ"]}, index=Index(["あ", "いいい", "うう", "え"], name="お"), ) expected = ( " あああ いいいいい\n" "お \n" "あ あああ あ\n" "いいい い いいい\n" "うう う う\n" "え えええええ ええ" ) assert repr(df) == expected # MultiIndex idx = MultiIndex.from_tuples( [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")] ) df = DataFrame( {"a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"]}, index=idx, ) expected = ( " a b\n" "あ いい あああああ あ\n" "う え い いいい\n" "おおお かかかか う う\n" "き くく えええ ええええええ" ) assert repr(df) == expected # truncate with option_context("display.max_rows", 3, "display.max_columns", 3): df = DataFrame( { "a": ["あああああ", "い", "う", "えええ"], "b": ["あ", "いいい", "う", "ええええええ"], "c": ["お", "か", "ききき", "くくくくくく"], "ああああ": ["さ", "し", "す", "せ"], }, columns=["a", "b", "c", "ああああ"], ) expected = ( " a ... ああああ\n" "0 あああああ ... さ\n" ".. ... ... ...\n" "3 えええ ... せ\n" "\n[4 rows x 4 columns]" ) assert repr(df) == expected df.index = ["あああ", "いいいい", "う", "aaa"] expected = ( " a ... ああああ\n" "あああ あああああ ... さ\n" "... ... ... ...\n" "aaa えええ ... せ\n" "\n[4 rows x 4 columns]" ) assert repr(df) == expected # ambiguous unicode df = DataFrame( {"b": ["あ", "いいい", "¡¡", "ええええええ"], "あああああ": [1, 222, 33333, 4]}, index=["a", "bb", "c", "¡¡¡"], ) expected = ( " b あああああ\n" "a あ 1\n" "bb いいい 222\n" "c ¡¡ 33333\n" "¡¡¡ ええええええ 4" ) assert repr(df) == expected def test_to_string_buffer_all_unicode(self): buf = StringIO() empty = DataFrame({"c/\u03c3": Series(dtype=object)}) nonempty = DataFrame({"c/\u03c3": Series([1, 2, 3])}) print(empty, file=buf) print(nonempty, file=buf) # this should work buf.getvalue() def test_to_string_with_col_space(self): df = DataFrame(np.random.random(size=(1, 3))) c10 = len(df.to_string(col_space=10).split("\n")[1]) c20 = len(df.to_string(col_space=20).split("\n")[1]) c30 = len(df.to_string(col_space=30).split("\n")[1]) assert c10 < c20 < c30 # GH 8230 # col_space wasn't being applied with header=False with_header = df.to_string(col_space=20) with_header_row1 = with_header.splitlines()[1] no_header = df.to_string(col_space=20, header=False) assert len(with_header_row1) == len(no_header) def test_to_string_with_column_specific_col_space_raises(self): df = DataFrame(np.random.random(size=(3, 3)), columns=["a", "b", "c"]) msg = ( "Col_space length\\(\\d+\\) should match " "DataFrame number of columns\\(\\d+\\)" ) with pytest.raises(ValueError, match=msg): df.to_string(col_space=[30, 40]) with pytest.raises(ValueError, match=msg): df.to_string(col_space=[30, 40, 50, 60]) msg = "unknown column" with pytest.raises(ValueError, match=msg): df.to_string(col_space={"a": "foo", "b": 23, "d": 34}) def test_to_string_with_column_specific_col_space(self): df = DataFrame(np.random.random(size=(3, 3)), columns=["a", "b", "c"]) result = df.to_string(col_space={"a": 10, "b": 11, "c": 12}) # 3 separating space + each col_space for (id, a, b, c) assert len(result.split("\n")[1]) == (3 + 1 + 10 + 11 + 12) result = df.to_string(col_space=[10, 11, 12]) assert len(result.split("\n")[1]) == (3 + 1 + 10 + 11 + 12) def test_to_string_truncate_indices(self): for index in [ tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeIntIndex, tm.makeDateIndex, tm.makePeriodIndex, ]: for column in [tm.makeStringIndex]: for h in [10, 20]: for w in [10, 20]: with option_context("display.expand_frame_repr", False): df = DataFrame(index=index(h), columns=column(w)) with option_context("display.max_rows", 15): if h == 20: assert has_vertically_truncated_repr(df) else: assert not has_vertically_truncated_repr(df) with option_context("display.max_columns", 15): if w == 20: assert has_horizontally_truncated_repr(df) else: assert not (has_horizontally_truncated_repr(df)) with option_context( "display.max_rows", 15, "display.max_columns", 15 ): if h == 20 and w == 20: assert has_doubly_truncated_repr(df) else: assert not has_doubly_truncated_repr(df) def test_to_string_truncate_multilevel(self): arrays = [ ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ["one", "two", "one", "two", "one", "two", "one", "two"], ] df = DataFrame(index=arrays, columns=arrays) with option_context("display.max_rows", 7, "display.max_columns", 7): assert has_doubly_truncated_repr(df) def test_truncate_with_different_dtypes(self): # 11594, 12045 # when truncated the dtypes of the splits can differ # 11594 import datetime s = Series( [datetime.datetime(2012, 1, 1)] * 10 + [datetime.datetime(1012, 1, 2)] + [datetime.datetime(2012, 1, 3)] * 10 ) with option_context("display.max_rows", 8): result = str(s) assert "object" in result # 12045 df = DataFrame({"text": ["some words"] + [None] * 9}) with option_context("display.max_rows", 8, "display.max_columns", 3): result = str(df) assert "None" in result assert "NaN" not in result def test_truncate_with_different_dtypes_multiindex(self): # GH#13000 df = DataFrame({"Vals": range(100)}) frame = pd.concat([df], keys=["Sweep"], names=["Sweep", "Index"]) result = repr(frame) result2 = repr(frame.iloc[:5]) assert result.startswith(result2) def test_datetimelike_frame(self): # GH 12211 df = DataFrame({"date": [Timestamp("20130101").tz_localize("UTC")] + [NaT] * 5}) with option_context("display.max_rows", 5): result = str(df) assert "2013-01-01 00:00:00+00:00" in result assert "NaT" in result assert "..." in result assert "[6 rows x 1 columns]" in result dts = [Timestamp("2011-01-01", tz="US/Eastern")] * 5 + [NaT] * 5 df = DataFrame({"dt": dts, "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}) with option_context("display.max_rows", 5): expected = ( " dt x\n" "0 2011-01-01 00:00:00-05:00 1\n" "1 2011-01-01 00:00:00-05:00 2\n" ".. ... ..\n" "8 NaT 9\n" "9 NaT 10\n\n" "[10 rows x 2 columns]" ) assert repr(df) == expected dts = [NaT] * 5 + [Timestamp("2011-01-01", tz="US/Eastern")] * 5 df = DataFrame({"dt": dts, "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}) with option_context("display.max_rows", 5): expected = ( " dt x\n" "0 NaT 1\n" "1 NaT 2\n" ".. ... ..\n" "8 2011-01-01 00:00:00-05:00 9\n" "9 2011-01-01 00:00:00-05:00 10\n\n" "[10 rows x 2 columns]" ) assert repr(df) == expected dts = [Timestamp("2011-01-01", tz="Asia/Tokyo")] * 5 + [ Timestamp("2011-01-01", tz="US/Eastern") ] * 5 df = DataFrame({"dt": dts, "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}) with option_context("display.max_rows", 5): expected = ( " dt x\n" "0 2011-01-01 00:00:00+09:00 1\n" "1 2011-01-01 00:00:00+09:00 2\n" ".. ... ..\n" "8 2011-01-01 00:00:00-05:00 9\n" "9 2011-01-01 00:00:00-05:00 10\n\n" "[10 rows x 2 columns]" ) assert repr(df) == expected @pytest.mark.parametrize( "start_date", [ "2017-01-01 23:59:59.999999999", "2017-01-01 23:59:59.99999999", "2017-01-01 23:59:59.9999999", "2017-01-01 23:59:59.999999", "2017-01-01 23:59:59.99999", "2017-01-01 23:59:59.9999", ], ) def test_datetimeindex_highprecision(self, start_date): # GH19030 # Check that high-precision time values for the end of day are # included in repr for DatetimeIndex df = DataFrame({"A": date_range(start=start_date, freq="D", periods=5)}) result = str(df) assert start_date in result dti = date_range(start=start_date, freq="D", periods=5) df = DataFrame({"A": range(5)}, index=dti) result = str(df.index) assert start_date in result def test_nonunicode_nonascii_alignment(self): df = DataFrame([["aa\xc3\xa4\xc3\xa4", 1], ["bbbb", 2]]) rep_str = df.to_string() lines = rep_str.split("\n") assert len(lines[1]) == len(lines[2]) def test_unicode_problem_decoding_as_ascii(self): dm = DataFrame({"c/\u03c3": Series({"test": np.nan})}) str(dm.to_string()) def test_string_repr_encoding(self, datapath): filepath = datapath("io", "parser", "data", "unicode_series.csv") df = read_csv(filepath, header=None, encoding="latin1") repr(df) repr(df[1]) def test_repr_corner(self): # representing infs poses no problems df = DataFrame({"foo": [-np.inf, np.inf]}) repr(df) def test_frame_info_encoding(self): index = ["'Til There Was You (1997)", "ldum klaka (Cold Fever) (1994)"] fmt.set_option("display.max_rows", 1) df = DataFrame(columns=["a", "b", "c"], index=index) repr(df) repr(df.T) fmt.set_option("display.max_rows", 200) def test_wide_repr(self): with option_context( "mode.sim_interactive", True, "display.show_dimensions", True, "display.max_columns", 20, ): max_cols = get_option("display.max_columns") df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) set_option("display.expand_frame_repr", False) rep_str = repr(df) assert f"10 rows x {max_cols - 1} columns" in rep_str set_option("display.expand_frame_repr", True) wide_repr = repr(df) assert rep_str != wide_repr with option_context("display.width", 120): wider_repr = repr(df) assert len(wider_repr) < len(wide_repr) reset_option("display.expand_frame_repr") def test_wide_repr_wide_columns(self): with option_context("mode.sim_interactive", True, "display.max_columns", 20): df = DataFrame( np.random.randn(5, 3), columns=["a" * 90, "b" * 90, "c" * 90] ) rep_str = repr(df) assert len(rep_str.splitlines()) == 20 def test_wide_repr_named(self): with option_context("mode.sim_interactive", True, "display.max_columns", 20): max_cols = get_option("display.max_columns") df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) df.index.name = "DataFrame Index" set_option("display.expand_frame_repr", False) rep_str = repr(df) set_option("display.expand_frame_repr", True) wide_repr = repr(df) assert rep_str != wide_repr with option_context("display.width", 150): wider_repr = repr(df) assert len(wider_repr) < len(wide_repr) for line in wide_repr.splitlines()[1::13]: assert "DataFrame Index" in line reset_option("display.expand_frame_repr") def test_wide_repr_multiindex(self): with option_context("mode.sim_interactive", True, "display.max_columns", 20): midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10))) max_cols = get_option("display.max_columns") df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)), index=midx) df.index.names = ["Level 0", "Level 1"] set_option("display.expand_frame_repr", False) rep_str = repr(df) set_option("display.expand_frame_repr", True) wide_repr = repr(df) assert rep_str != wide_repr with option_context("display.width", 150): wider_repr = repr(df) assert len(wider_repr) < len(wide_repr) for line in wide_repr.splitlines()[1::13]: assert "Level 0 Level 1" in line reset_option("display.expand_frame_repr") def test_wide_repr_multiindex_cols(self): with option_context("mode.sim_interactive", True, "display.max_columns", 20): max_cols = get_option("display.max_columns") midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10))) mcols = MultiIndex.from_arrays(tm.rands_array(3, size=(2, max_cols - 1))) df = DataFrame( tm.rands_array(25, (10, max_cols - 1)), index=midx, columns=mcols ) df.index.names = ["Level 0", "Level 1"] set_option("display.expand_frame_repr", False) rep_str = repr(df) set_option("display.expand_frame_repr", True) wide_repr = repr(df) assert rep_str != wide_repr with option_context("display.width", 150, "display.max_columns", 20): wider_repr = repr(df) assert len(wider_repr) < len(wide_repr) reset_option("display.expand_frame_repr") def test_wide_repr_unicode(self): with option_context("mode.sim_interactive", True, "display.max_columns", 20): max_cols = 20 df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) set_option("display.expand_frame_repr", False) rep_str = repr(df) set_option("display.expand_frame_repr", True) wide_repr = repr(df) assert rep_str != wide_repr with option_context("display.width", 150): wider_repr = repr(df) assert len(wider_repr) < len(wide_repr) reset_option("display.expand_frame_repr") def test_wide_repr_wide_long_columns(self): with option_context("mode.sim_interactive", True): df = DataFrame({"a": ["a" * 30, "b" * 30], "b": ["c" * 70, "d" * 80]}) result = repr(df) assert "ccccc" in result assert "ddddd" in result def test_long_series(self): n = 1000 s = Series( np.random.randint(-50, 50, n), index=[f"s{x:04d}" for x in range(n)], dtype="int64", ) import re str_rep = str(s) nmatches = len(re.findall("dtype", str_rep)) assert nmatches == 1 def test_index_with_nan(self): # GH 2850 df = DataFrame( { "id1": {0: "1a3", 1: "9h4"}, "id2": {0: np.nan, 1: "d67"}, "id3": {0: "78d", 1: "79d"}, "value": {0: 123, 1: 64}, } ) # multi-index y = df.set_index(["id1", "id2", "id3"]) result = y.to_string() expected = ( " value\nid1 id2 id3 \n" "1a3 NaN 78d 123\n9h4 d67 79d 64" ) assert result == expected # index y = df.set_index("id2") result = y.to_string() expected = ( " id1 id3 value\nid2 \n" "NaN 1a3 78d 123\nd67 9h4 79d 64" ) assert result == expected # with append (this failed in 0.12) y = df.set_index(["id1", "id2"]).set_index("id3", append=True) result = y.to_string() expected = ( " value\nid1 id2 id3 \n" "1a3 NaN 78d 123\n9h4 d67 79d 64" ) assert result == expected # all-nan in mi df2 = df.copy() df2.loc[:, "id2"] = np.nan y = df2.set_index("id2") result = y.to_string() expected = ( " id1 id3 value\nid2 \n" "NaN 1a3 78d 123\nNaN 9h4 79d 64" ) assert result == expected # partial nan in mi df2 = df.copy() df2.loc[:, "id2"] = np.nan y = df2.set_index(["id2", "id3"]) result = y.to_string() expected = ( " id1 value\nid2 id3 \n" "NaN 78d 1a3 123\n 79d 9h4 64" ) assert result == expected df = DataFrame( { "id1": {0: np.nan, 1: "9h4"}, "id2": {0: np.nan, 1: "d67"}, "id3": {0: np.nan, 1: "79d"}, "value": {0: 123, 1: 64}, } ) y = df.set_index(["id1", "id2", "id3"]) result = y.to_string() expected = ( " value\nid1 id2 id3 \n" "NaN NaN NaN 123\n9h4 d67 79d 64" ) assert result == expected def test_to_string(self): # big mixed biggie = DataFrame( {"A": np.random.randn(200), "B": tm.makeStringIndex(200)}, index=np.arange(200), ) biggie.loc[:20, "A"] = np.nan biggie.loc[:20, "B"] = np.nan s = biggie.to_string() buf = StringIO() retval = biggie.to_string(buf=buf) assert retval is None assert buf.getvalue() == s assert isinstance(s, str) # print in right order result = biggie.to_string( columns=["B", "A"], col_space=17, float_format="%.5f".__mod__ ) lines = result.split("\n") header = lines[0].strip().split() joined = "\n".join([re.sub(r"\s+", " ", x).strip() for x in lines[1:]]) recons = read_csv(StringIO(joined), names=header, header=None, sep=" ") tm.assert_series_equal(recons["B"], biggie["B"]) assert recons["A"].count() == biggie["A"].count() assert (np.abs(recons["A"].dropna() - biggie["A"].dropna()) < 0.1).all() # expected = ['B', 'A'] # assert header == expected result = biggie.to_string(columns=["A"], col_space=17) header = result.split("\n")[0].strip().split() expected = ["A"] assert header == expected biggie.to_string(columns=["B", "A"], formatters={"A": lambda x: f"{x:.1f}"}) biggie.to_string(columns=["B", "A"], float_format=str) biggie.to_string(columns=["B", "A"], col_space=12, float_format=str) frame = DataFrame(index=np.arange(200)) frame.to_string() def test_to_string_no_header(self): df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) df_s = df.to_string(header=False) expected = "0 1 4\n1 2 5\n2 3 6" assert df_s == expected def test_to_string_specified_header(self): df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) df_s = df.to_string(header=["X", "Y"]) expected = " X Y\n0 1 4\n1 2 5\n2 3 6" assert df_s == expected msg = "Writing 2 cols but got 1 aliases" with pytest.raises(ValueError, match=msg): df.to_string(header=["X"]) def test_to_string_no_index(self): # GH 16839, GH 13032 df = DataFrame({"x": [11, 22], "y": [33, -44], "z": ["AAA", " "]}) df_s = df.to_string(index=False) # Leading space is expected for positive numbers. expected = " x y z\n11 33 AAA\n22 -44 " assert df_s == expected df_s = df[["y", "x", "z"]].to_string(index=False) expected = " y x z\n 33 11 AAA\n-44 22 " assert df_s == expected def test_to_string_line_width_no_index(self): # GH 13998, GH 22505 df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) df_s = df.to_string(line_width=1, index=False) expected = " x \\\n 1 \n 2 \n 3 \n\n y \n 4 \n 5 \n 6 " assert df_s == expected df = DataFrame({"x": [11, 22, 33], "y": [4, 5, 6]}) df_s = df.to_string(line_width=1, index=False) expected = " x \\\n11 \n22 \n33 \n\n y \n 4 \n 5 \n 6 " assert df_s == expected df = DataFrame({"x": [11, 22, -33], "y": [4, 5, -6]}) df_s = df.to_string(line_width=1, index=False) expected = " x \\\n 11 \n 22 \n-33 \n\n y \n 4 \n 5 \n-6 " assert df_s == expected def test_to_string_float_formatting(self): tm.reset_display_options() fmt.set_option( "display.precision", 5, "display.column_space", 12, "display.notebook_repr_html", False, ) df = DataFrame( {"x": [0, 0.25, 3456.000, 12e45, 1.64e6, 1.7e8, 1.253456, np.pi, -1e6]} ) df_s = df.to_string() if _three_digit_exp(): expected = ( " x\n0 0.00000e+000\n1 2.50000e-001\n" "2 3.45600e+003\n3 1.20000e+046\n4 1.64000e+006\n" "5 1.70000e+008\n6 1.25346e+000\n7 3.14159e+000\n" "8 -1.00000e+006" ) else: expected = ( " x\n0 0.00000e+00\n1 2.50000e-01\n" "2 3.45600e+03\n3 1.20000e+46\n4 1.64000e+06\n" "5 1.70000e+08\n6 1.25346e+00\n7 3.14159e+00\n" "8 -1.00000e+06" ) assert df_s == expected df = DataFrame({"x": [3234, 0.253]}) df_s = df.to_string() expected = " x\n0 3234.000\n1 0.253" assert df_s == expected tm.reset_display_options() assert get_option("display.precision") == 6 df = DataFrame({"x": [1e9, 0.2512]}) df_s = df.to_string() if _three_digit_exp(): expected = " x\n0 1.000000e+009\n1 2.512000e-001" else: expected = " x\n0 1.000000e+09\n1 2.512000e-01" assert df_s == expected def test_to_string_float_format_no_fixed_width(self): # GH 21625 df = DataFrame({"x": [0.19999]}) expected = " x\n0 0.200" assert df.to_string(float_format="%.3f") == expected # GH 22270 df = DataFrame({"x": [100.0]}) expected = " x\n0 100" assert df.to_string(float_format="%.0f") == expected def test_to_string_small_float_values(self): df = DataFrame({"a": [1.5, 1e-17, -5.5e-7]}) result = df.to_string() # sadness per above if _three_digit_exp(): expected = ( " a\n" "0 1.500000e+000\n" "1 1.000000e-017\n" "2 -5.500000e-007" ) else: expected = ( " a\n" "0 1.500000e+00\n" "1 1.000000e-17\n" "2 -5.500000e-07" ) assert result == expected # but not all exactly zero df = df * 0 result = df.to_string() expected = " 0\n0 0\n1 0\n2 -0" def test_to_string_float_index(self): index = Index([1.5, 2, 3, 4, 5]) df = DataFrame(np.arange(5), index=index) result = df.to_string() expected = " 0\n1.5 0\n2.0 1\n3.0 2\n4.0 3\n5.0 4" assert result == expected def test_to_string_complex_float_formatting(self): # GH #25514, 25745 with option_context("display.precision", 5): df = DataFrame( { "x": [ (0.4467846931321966 + 0.0715185102060818j), (0.2739442392974528 + 0.23515228785438969j), (0.26974928742135185 + 0.3250604054898979j), (-1j), ] } ) result = df.to_string() expected = ( " x\n0 0.44678+0.07152j\n" "1 0.27394+0.23515j\n" "2 0.26975+0.32506j\n" "3 -0.00000-1.00000j" ) assert result == expected def test_to_string_ascii_error(self): data = [ ( "0 ", " .gitignore ", " 5 ", " \xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2", ) ] df = DataFrame(data) # it works! repr(df) def test_to_string_int_formatting(self): df = DataFrame({"x": [-15, 20, 25, -35]}) assert issubclass(df["x"].dtype.type, np.integer) output = df.to_string() expected = " x\n0 -15\n1 20\n2 25\n3 -35" assert output == expected def test_to_string_index_formatter(self): df = DataFrame([range(5), range(5, 10), range(10, 15)]) rs = df.to_string(formatters={"__index__": lambda x: "abc"[x]}) xp = """\ 0 1 2 3 4 a 0 1 2 3 4 b 5 6 7 8 9 c 10 11 12 13 14\ """ assert rs == xp def test_to_string_left_justify_cols(self): tm.reset_display_options() df = DataFrame({"x": [3234, 0.253]}) df_s = df.to_string(justify="left") expected = " x \n0 3234.000\n1 0.253" assert df_s == expected def test_to_string_format_na(self): tm.reset_display_options() df = DataFrame( { "A": [np.nan, -1, -2.1234, 3, 4], "B": [np.nan, "foo", "foooo", "fooooo", "bar"], } ) result = df.to_string() expected = ( " A B\n" "0 NaN NaN\n" "1 -1.0000 foo\n" "2 -2.1234 foooo\n" "3 3.0000 fooooo\n" "4 4.0000 bar" ) assert result == expected df = DataFrame( { "A": [np.nan, -1.0, -2.0, 3.0, 4.0], "B": [np.nan, "foo", "foooo", "fooooo", "bar"], } ) result = df.to_string() expected = ( " A B\n" "0 NaN NaN\n" "1 -1.0 foo\n" "2 -2.0 foooo\n" "3 3.0 fooooo\n" "4 4.0 bar" ) assert result == expected def test_to_string_format_inf(self): # Issue #24861 tm.reset_display_options() df = DataFrame( { "A": [-np.inf, np.inf, -1, -2.1234, 3, 4], "B": [-np.inf, np.inf, "foo", "foooo", "fooooo", "bar"], } ) result = df.to_string() expected = ( " A B\n" "0 -inf -inf\n" "1 inf inf\n" "2 -1.0000 foo\n" "3 -2.1234 foooo\n" "4 3.0000 fooooo\n" "5 4.0000 bar" ) assert result == expected df = DataFrame( { "A": [-np.inf, np.inf, -1.0, -2.0, 3.0, 4.0], "B": [-np.inf, np.inf, "foo", "foooo", "fooooo", "bar"], } ) result = df.to_string() expected = ( " A B\n" "0 -inf -inf\n" "1 inf inf\n" "2 -1.0 foo\n" "3 -2.0 foooo\n" "4 3.0 fooooo\n" "5 4.0 bar" ) assert result == expected def test_to_string_decimal(self): # Issue #23614 df = DataFrame({"A": [6.0, 3.1, 2.2]}) expected = " A\n0 6,0\n1 3,1\n2 2,2" assert df.to_string(decimal=",") == expected def test_to_string_line_width(self): df = DataFrame(123, index=range(10, 15), columns=range(30)) s = df.to_string(line_width=80) assert max(len(line) for line in s.split("\n")) == 80 def test_show_dimensions(self): df = DataFrame(123, index=range(10, 15), columns=range(30)) with option_context( "display.max_rows", 10, "display.max_columns", 40, "display.width", 500, "display.expand_frame_repr", "info", "display.show_dimensions", True, ): assert "5 rows" in str(df) assert "5 rows" in df._repr_html_() with option_context( "display.max_rows", 10, "display.max_columns", 40, "display.width", 500, "display.expand_frame_repr", "info", "display.show_dimensions", False, ): assert "5 rows" not in str(df) assert "5 rows" not in df._repr_html_() with option_context( "display.max_rows", 2, "display.max_columns", 2, "display.width", 500, "display.expand_frame_repr", "info", "display.show_dimensions", "truncate", ): assert "5 rows" in str(df) assert "5 rows" in df._repr_html_() with option_context( "display.max_rows", 10, "display.max_columns", 40, "display.width", 500, "display.expand_frame_repr", "info", "display.show_dimensions", "truncate", ): assert "5 rows" not in str(df) assert "5 rows" not in df._repr_html_() def test_repr_html(self, float_frame): df = float_frame df._repr_html_() fmt.set_option("display.max_rows", 1, "display.max_columns", 1) df._repr_html_() fmt.set_option("display.notebook_repr_html", False) df._repr_html_() tm.reset_display_options() df = DataFrame([[1, 2], [3, 4]]) fmt.set_option("display.show_dimensions", True) assert "2 rows" in df._repr_html_() fmt.set_option("display.show_dimensions", False) assert "2 rows" not in df._repr_html_() tm.reset_display_options() def test_repr_html_mathjax(self): df = DataFrame([[1, 2], [3, 4]]) assert "tex2jax_ignore" not in df._repr_html_() with option_context("display.html.use_mathjax", False): assert "tex2jax_ignore" in df._repr_html_() def test_repr_html_wide(self): max_cols = 20 df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) with option_context("display.max_rows", 60, "display.max_columns", 20): assert "..." not in df._repr_html_() wide_df = DataFrame(tm.rands_array(25, size=(10, max_cols + 1))) with option_context("display.max_rows", 60, "display.max_columns", 20): assert "..." in wide_df._repr_html_() def test_repr_html_wide_multiindex_cols(self): max_cols = 20 mcols = MultiIndex.from_product( [np.arange(max_cols // 2), ["foo", "bar"]], names=["first", "second"] ) df = DataFrame(tm.rands_array(25, size=(10, len(mcols))), columns=mcols) reg_repr = df._repr_html_() assert "..." not in reg_repr mcols = MultiIndex.from_product( (np.arange(1 + (max_cols // 2)), ["foo", "bar"]), names=["first", "second"] ) df = DataFrame(tm.rands_array(25, size=(10, len(mcols))), columns=mcols) with option_context("display.max_rows", 60, "display.max_columns", 20): assert "..." in df._repr_html_() def test_repr_html_long(self): with option_context("display.max_rows", 60): max_rows = get_option("display.max_rows") h = max_rows - 1 df = DataFrame({"A": np.arange(1, 1 + h), "B": np.arange(41, 41 + h)}) reg_repr = df._repr_html_() assert ".." not in reg_repr assert str(41 + max_rows // 2) in reg_repr h = max_rows + 1 df = DataFrame({"A": np.arange(1, 1 + h), "B": np.arange(41, 41 + h)}) long_repr = df._repr_html_() assert ".." in long_repr assert str(41 + max_rows // 2) not in long_repr assert f"{h} rows " in long_repr assert "2 columns" in long_repr def test_repr_html_float(self): with option_context("display.max_rows", 60): max_rows = get_option("display.max_rows") h = max_rows - 1 df = DataFrame( { "idx": np.linspace(-10, 10, h), "A": np.arange(1, 1 + h), "B": np.arange(41, 41 + h), } ).set_index("idx") reg_repr = df._repr_html_() assert ".." not in reg_repr assert f"<td>{40 + h}</td>" in reg_repr h = max_rows + 1 df = DataFrame( { "idx": np.linspace(-10, 10, h), "A": np.arange(1, 1 + h), "B": np.arange(41, 41 + h), } ).set_index("idx") long_repr = df._repr_html_() assert ".." in long_repr assert "<td>31</td>" not in long_repr assert f"{h} rows " in long_repr assert "2 columns" in long_repr def test_repr_html_long_multiindex(self): max_rows = 60 max_L1 = max_rows // 2 tuples = list(itertools.product(np.arange(max_L1), ["foo", "bar"])) idx = MultiIndex.from_tuples(tuples, names=["first", "second"]) df = DataFrame(np.random.randn(max_L1 * 2, 2), index=idx, columns=["A", "B"]) with option_context("display.max_rows", 60, "display.max_columns", 20): reg_repr = df._repr_html_() assert "..." not in reg_repr tuples = list(itertools.product(np.arange(max_L1 + 1), ["foo", "bar"])) idx = MultiIndex.from_tuples(tuples, names=["first", "second"]) df = DataFrame( np.random.randn((max_L1 + 1) * 2, 2), index=idx, columns=["A", "B"] ) long_repr = df._repr_html_() assert "..." in long_repr def test_repr_html_long_and_wide(self): max_cols = 20 max_rows = 60 h, w = max_rows - 1, max_cols - 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) with option_context("display.max_rows", 60, "display.max_columns", 20): assert "..." not in df._repr_html_() h, w = max_rows + 1, max_cols + 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) with option_context("display.max_rows", 60, "display.max_columns", 20): assert "..." in df._repr_html_() def test_info_repr(self): # GH#21746 For tests inside a terminal (i.e. not CI) we need to detect # the terminal size to ensure that we try to print something "too big" term_width, term_height = get_terminal_size() max_rows = 60 max_cols = 20 + (max(term_width, 80) - 80) // 4 # Long h, w = max_rows + 1, max_cols - 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) assert has_vertically_truncated_repr(df) with option_context("display.large_repr", "info"): assert has_info_repr(df) # Wide h, w = max_rows - 1, max_cols + 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) assert has_horizontally_truncated_repr(df) with option_context( "display.large_repr", "info", "display.max_columns", max_cols ): assert has_info_repr(df) def test_info_repr_max_cols(self): # GH #6939 df = DataFrame(np.random.randn(10, 5)) with option_context( "display.large_repr", "info", "display.max_columns", 1, "display.max_info_columns", 4, ): assert has_non_verbose_info_repr(df) with option_context( "display.large_repr", "info", "display.max_columns", 1, "display.max_info_columns", 5, ): assert not has_non_verbose_info_repr(df) # test verbose overrides # fmt.set_option('display.max_info_columns', 4) # exceeded def test_info_repr_html(self): max_rows = 60 max_cols = 20 # Long h, w = max_rows + 1, max_cols - 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) assert r"&lt;class" not in df._repr_html_() with option_context("display.large_repr", "info"): assert r"&lt;class" in df._repr_html_() # Wide h, w = max_rows - 1, max_cols + 1 df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)}) assert "<class" not in df._repr_html_() with option_context( "display.large_repr", "info", "display.max_columns", max_cols ): assert "&lt;class" in df._repr_html_() def test_fake_qtconsole_repr_html(self, float_frame): df = float_frame def get_ipython(): return {"config": {"KernelApp": {"parent_appname": "ipython-qtconsole"}}} repstr = df._repr_html_() assert repstr is not None fmt.set_option("display.max_rows", 5, "display.max_columns", 2) repstr = df._repr_html_() assert "class" in repstr # info fallback tm.reset_display_options() def test_pprint_pathological_object(self): """ If the test fails, it at least won't hang. """ class A: def __getitem__(self, key): return 3 # obviously simplified df = DataFrame([A()]) repr(df) # just don't die def test_float_trim_zeros(self): vals = [ 2.08430917305e10, 3.52205017305e10, 2.30674817305e10, 2.03954217305e10, 5.59897817305e10, ] skip = True for line in repr(DataFrame({"A": vals})).split("\n")[:-2]: if line.startswith("dtype:"): continue if _three_digit_exp(): assert ("+010" in line) or skip else: assert ("+10" in line) or skip skip = False @pytest.mark.parametrize( "data, expected", [ (["3.50"], "0 3.50\ndtype: object"), ([1.20, "1.00"], "0 1.2\n1 1.00\ndtype: object"), ([np.nan], "0 NaN\ndtype: float64"), ([None], "0 None\ndtype: object"), (["3.50", np.nan], "0 3.50\n1 NaN\ndtype: object"), ([3.50, np.nan], "0 3.5\n1 NaN\ndtype: float64"), ([3.50, np.nan, "3.50"], "0 3.5\n1 NaN\n2 3.50\ndtype: object"), ([3.50, None, "3.50"], "0 3.5\n1 None\n2 3.50\ndtype: object"), ], ) def test_repr_str_float_truncation(self, data, expected): # GH#38708 series = Series(data) result = repr(series) assert result == expected @pytest.mark.parametrize( "float_format,expected", [ ("{:,.0f}".format, "0 1,000\n1 test\ndtype: object"), ("{:.4f}".format, "0 1000.0000\n1 test\ndtype: object"), ], ) def test_repr_float_format_in_object_col(self, float_format, expected): # GH#40024 df = Series([1000.0, "test"]) with option_context("display.float_format", float_format): result = repr(df) assert result == expected def test_dict_entries(self): df = DataFrame({"A": [{"a": 1, "b": 2}]}) val = df.to_string() assert "'a': 1" in val assert "'b': 2" in val def test_categorical_columns(self): # GH35439 data = [[4, 2], [3, 2], [4, 3]] cols = ["aaaaaaaaa", "b"] df = DataFrame(data, columns=cols) df_cat_cols = DataFrame(data, columns=pd.CategoricalIndex(cols)) assert df.to_string() == df_cat_cols.to_string() def test_period(self): # GH 12615 df = DataFrame( { "A": pd.period_range("2013-01", periods=4, freq="M"), "B": [ pd.Period("2011-01", freq="M"), pd.Period("2011-02-01", freq="D"), pd.Period("2011-03-01 09:00", freq="H"), pd.Period("2011-04", freq="M"), ], "C": list("abcd"), } ) exp = ( " A B C\n" "0 2013-01 2011-01 a\n" "1 2013-02 2011-02-01 b\n" "2 2013-03 2011-03-01 09:00 c\n" "3 2013-04 2011-04 d" ) assert str(df) == exp @pytest.mark.parametrize( "length, max_rows, min_rows, expected", [ (10, 10, 10, 10), (10, 10, None, 10), (10, 8, None, 8), (20, 30, 10, 30), # max_rows > len(frame), hence max_rows (50, 30, 10, 10), # max_rows < len(frame), hence min_rows (100, 60, 10, 10), # same (60, 60, 10, 60), # edge case (61, 60, 10, 10), # edge case ], ) def test_max_rows_fitted(self, length, min_rows, max_rows, expected): """Check that display logic is correct. GH #37359 See description here: https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options """ formatter = fmt.DataFrameFormatter( DataFrame(np.random.rand(length, 3)), max_rows=max_rows, min_rows=min_rows, ) result = formatter.max_rows_fitted assert result == expected def gen_series_formatting(): s1 = Series(["a"] * 100) s2 = Series(["ab"] * 100) s3 = Series(["a", "ab", "abc", "abcd", "abcde", "abcdef"]) s4 = s3[::-1] test_sers = {"onel": s1, "twol": s2, "asc": s3, "desc": s4} return test_sers class TestSeriesFormatting: def setup_method(self, method): self.ts = tm.makeTimeSeries() def test_repr_unicode(self): s = Series(["\u03c3"] * 10) repr(s) a = Series(["\u05d0"] * 1000) a.name = "title1" repr(a) def test_to_string(self): buf = StringIO() s = self.ts.to_string() retval = self.ts.to_string(buf=buf) assert retval is None assert buf.getvalue().strip() == s # pass float_format format = "%.4f".__mod__ result = self.ts.to_string(float_format=format) result = [x.split()[1] for x in result.split("\n")[:-1]] expected = [format(x) for x in self.ts] assert result == expected # empty string result = self.ts[:0].to_string() assert result == "Series([], Freq: B)" result = self.ts[:0].to_string(length=0) assert result == "Series([], Freq: B)" # name and length cp = self.ts.copy() cp.name = "foo" result = cp.to_string(length=True, name=True, dtype=True) last_line = result.split("\n")[-1].strip() assert last_line == (f"Freq: B, Name: foo, Length: {len(cp)}, dtype: float64") def test_freq_name_separation(self): s = Series( np.random.randn(10), index=date_range("1/1/2000", periods=10), name=0 ) result = repr(s) assert "Freq: D, Name: 0" in result def test_to_string_mixed(self): s = Series(["foo", np.nan, -1.23, 4.56]) result = s.to_string() expected = "0 foo\n" + "1 NaN\n" + "2 -1.23\n" + "3 4.56" assert result == expected # but don't count NAs as floats s = Series(["foo", np.nan, "bar", "baz"]) result = s.to_string() expected = "0 foo\n" + "1 NaN\n" + "2 bar\n" + "3 baz" assert result == expected s = Series(["foo", 5, "bar", "baz"]) result = s.to_string() expected = "0 foo\n" + "1 5\n" + "2 bar\n" + "3 baz" assert result == expected def test_to_string_float_na_spacing(self): s = Series([0.0, 1.5678, 2.0, -3.0, 4.0]) s[::2] = np.nan result = s.to_string() expected = ( "0 NaN\n" + "1 1.5678\n" + "2 NaN\n" + "3 -3.0000\n" + "4 NaN" ) assert result == expected def test_to_string_without_index(self): # GH 11729 Test index=False option s = Series([1, 2, 3, 4]) result = s.to_string(index=False) expected = "1\n" + "2\n" + "3\n" + "4" assert result == expected def test_unicode_name_in_footer(self): s = Series([1, 2], name="\u05e2\u05d1\u05e8\u05d9\u05ea") sf = fmt.SeriesFormatter(s, name="\u05e2\u05d1\u05e8\u05d9\u05ea") sf._get_footer() # should not raise exception def test_east_asian_unicode_series(self): # not aligned properly because of east asian width # unicode index s = Series(["a", "bb", "CCC", "D"], index=["あ", "いい", "ううう", "ええええ"]) expected = "あ a\nいい bb\nううう CCC\nええええ D\ndtype: object" assert repr(s) == expected # unicode values s = Series(["あ", "いい", "ううう", "ええええ"], index=["a", "bb", "c", "ddd"]) expected = "a あ\nbb いい\nc ううう\nddd ええええ\ndtype: object" assert repr(s) == expected # both s = Series(["あ", "いい", "ううう", "ええええ"], index=["ああ", "いいいい", "う", "えええ"]) expected = ( "ああ あ\nいいいい いい\nう ううう\nえええ ええええ\ndtype: object" ) assert repr(s) == expected # unicode footer s = Series( ["あ", "いい", "ううう", "ええええ"], index=["ああ", "いいいい", "う", "えええ"], name="おおおおおおお" ) expected = ( "ああ あ\nいいいい いい\nう ううう\n" "えええ ええええ\nName: おおおおおおお, dtype: object" ) assert repr(s) == expected # MultiIndex idx = MultiIndex.from_tuples( [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")] ) s = Series([1, 22, 3333, 44444], index=idx) expected = ( "あ いい 1\n" "う え 22\n" "おおお かかかか 3333\n" "き くく 44444\ndtype: int64" ) assert repr(s) == expected # object dtype, shorter than unicode repr s = Series([1, 22, 3333, 44444], index=[1, "AB", np.nan, "あああ"]) expected = ( "1 1\nAB 22\nNaN 3333\nあああ 44444\ndtype: int64" ) assert repr(s) == expected # object dtype, longer than unicode repr s = Series( [1, 22, 3333, 44444], index=[1, "AB", Timestamp("2011-01-01"), "あああ"] ) expected = ( "1 1\n" "AB 22\n" "2011-01-01 00:00:00 3333\n" "あああ 44444\ndtype: int64" ) assert repr(s) == expected # truncate with option_context("display.max_rows", 3): s = Series(["あ", "いい", "ううう", "ええええ"], name="おおおおおおお") expected = ( "0 あ\n ... \n" "3 ええええ\n" "Name: おおおおおおお, Length: 4, dtype: object" ) assert repr(s) == expected s.index = ["ああ", "いいいい", "う", "えええ"] expected = ( "ああ あ\n ... \n" "えええ ええええ\n" "Name: おおおおおおお, Length: 4, dtype: object" ) assert repr(s) == expected # Enable Unicode option ----------------------------------------- with option_context("display.unicode.east_asian_width", True): # unicode index s = Series(["a", "bb", "CCC", "D"], index=["あ", "いい", "ううう", "ええええ"]) expected = ( "あ a\nいい bb\nううう CCC\n" "ええええ D\ndtype: object" ) assert repr(s) == expected # unicode values s = Series(["あ", "いい", "ううう", "ええええ"], index=["a", "bb", "c", "ddd"]) expected = ( "a あ\nbb いい\nc ううう\n" "ddd ええええ\ndtype: object" ) assert repr(s) == expected # both s = Series(["あ", "いい", "ううう", "ええええ"], index=["ああ", "いいいい", "う", "えええ"]) expected = ( "ああ あ\n" "いいいい いい\n" "う ううう\n" "えええ ええええ\ndtype: object" ) assert repr(s) == expected # unicode footer s = Series( ["あ", "いい", "ううう", "ええええ"], index=["ああ", "いいいい", "う", "えええ"], name="おおおおおおお", ) expected = ( "ああ あ\n" "いいいい いい\n" "う ううう\n" "えええ ええええ\n" "Name: おおおおおおお, dtype: object" ) assert repr(s) == expected # MultiIndex idx = MultiIndex.from_tuples( [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")] ) s = Series([1, 22, 3333, 44444], index=idx) expected = ( "あ いい 1\n" "う え 22\n" "おおお かかかか 3333\n" "き くく 44444\n" "dtype: int64" ) assert repr(s) == expected # object dtype, shorter than unicode repr s = Series([1, 22, 3333, 44444], index=[1, "AB", np.nan, "あああ"]) expected = ( "1 1\nAB 22\nNaN 3333\n" "あああ 44444\ndtype: int64" ) assert repr(s) == expected # object dtype, longer than unicode repr s = Series( [1, 22, 3333, 44444], index=[1, "AB", Timestamp("2011-01-01"), "あああ"], ) expected = ( "1 1\n" "AB 22\n" "2011-01-01 00:00:00 3333\n" "あああ 44444\ndtype: int64" ) assert repr(s) == expected # truncate with option_context("display.max_rows", 3): s = Series(["あ", "いい", "ううう", "ええええ"], name="おおおおおおお") expected = ( "0 あ\n ... \n" "3 ええええ\n" "Name: おおおおおおお, Length: 4, dtype: object" ) assert repr(s) == expected s.index = ["ああ", "いいいい", "う", "えええ"] expected = ( "ああ あ\n" " ... \n" "えええ ええええ\n" "Name: おおおおおおお, Length: 4, dtype: object" ) assert repr(s) == expected # ambiguous unicode s = Series( ["¡¡", "い¡¡", "ううう", "ええええ"], index=["ああ", "¡¡¡¡いい", "¡¡", "えええ"] ) expected = ( "ああ ¡¡\n" "¡¡¡¡いい い¡¡\n" "¡¡ ううう\n" "えええ ええええ\ndtype: object" ) assert repr(s) == expected def test_float_trim_zeros(self): vals = [ 2.08430917305e10, 3.52205017305e10, 2.30674817305e10, 2.03954217305e10, 5.59897817305e10, ] for line in repr(Series(vals)).split("\n"): if line.startswith("dtype:"): continue if _three_digit_exp(): assert "+010" in line else: assert "+10" in line def test_datetimeindex(self): index = date_range("20130102", periods=6) s = Series(1, index=index) result = s.to_string() assert "2013-01-02" in result # nat in index s2 = Series(2, index=[Timestamp("20130111"), NaT]) s = s2.append(s) result = s.to_string() assert "NaT" in result # nat in summary result = str(s2.index) assert "NaT" in result @pytest.mark.parametrize( "start_date", [ "2017-01-01 23:59:59.999999999", "2017-01-01 23:59:59.99999999", "2017-01-01 23:59:59.9999999", "2017-01-01 23:59:59.999999", "2017-01-01 23:59:59.99999", "2017-01-01 23:59:59.9999", ], ) def test_datetimeindex_highprecision(self, start_date): # GH19030 # Check that high-precision time values for the end of day are # included in repr for DatetimeIndex s1 = Series(date_range(start=start_date, freq="D", periods=5)) result = str(s1) assert start_date in result dti = date_range(start=start_date, freq="D", periods=5) s2 = Series(3, index=dti) result = str(s2.index) assert start_date in result def test_timedelta64(self): from datetime import ( datetime, timedelta, ) Series(np.array([1100, 20], dtype="timedelta64[ns]")).to_string() s = Series(date_range("2012-1-1", periods=3, freq="D")) # GH2146 # adding NaTs y = s - s.shift(1) result = y.to_string() assert "1 days" in result assert "00:00:00" not in result assert "NaT" in result # with frac seconds o = Series([datetime(2012, 1, 1, microsecond=150)] * 3) y = s - o result = y.to_string() assert "-1 days +23:59:59.999850" in result # rounding? o = Series([datetime(2012, 1, 1, 1)] * 3) y = s - o result = y.to_string() assert "-1 days +23:00:00" in result assert "1 days 23:00:00" in result o = Series([datetime(2012, 1, 1, 1, 1)] * 3) y = s - o result = y.to_string() assert "-1 days +22:59:00" in result assert "1 days 22:59:00" in result o = Series([datetime(2012, 1, 1, 1, 1, microsecond=150)] * 3) y = s - o result = y.to_string() assert "-1 days +22:58:59.999850" in result assert "0 days 22:58:59.999850" in result # neg time td = timedelta(minutes=5, seconds=3) s2 = Series(date_range("2012-1-1", periods=3, freq="D")) + td y = s - s2 result = y.to_string() assert "-1 days +23:54:57" in result td = timedelta(microseconds=550) s2 = Series(date_range("2012-1-1", periods=3, freq="D")) + td y = s - td result = y.to_string() assert "2012-01-01 23:59:59.999450" in result # no boxing of the actual elements td = Series(pd.timedelta_range("1 days", periods=3)) result = td.to_string() assert result == "0 1 days\n1 2 days\n2 3 days" def test_mixed_datetime64(self): df = DataFrame({"A": [1, 2], "B": ["2012-01-01", "2012-01-02"]}) df["B"] = pd.to_datetime(df.B) result = repr(df.loc[0]) assert "2012-01-01" in result def test_period(self): # GH 12615 index = pd.period_range("2013-01", periods=6, freq="M") s = Series(np.arange(6, dtype="int64"), index=index) exp = ( "2013-01 0\n" "2013-02 1\n" "2013-03 2\n" "2013-04 3\n" "2013-05 4\n" "2013-06 5\n" "Freq: M, dtype: int64" ) assert str(s) == exp s = Series(index) exp = ( "0 2013-01\n" "1 2013-02\n" "2 2013-03\n" "3 2013-04\n" "4 2013-05\n" "5 2013-06\n" "dtype: period[M]" ) assert str(s) == exp # periods with mixed freq s = Series( [ pd.Period("2011-01", freq="M"), pd.Period("2011-02-01", freq="D"), pd.Period("2011-03-01 09:00", freq="H"), ] ) exp = ( "0 2011-01\n1 2011-02-01\n" "2 2011-03-01 09:00\ndtype: object" ) assert str(s) == exp def test_max_multi_index_display(self): # GH 7101 # doc example (indexing.rst) # multi-index arrays = [ ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ["one", "two", "one", "two", "one", "two", "one", "two"], ] tuples = list(zip(*arrays)) index = MultiIndex.from_tuples(tuples, names=["first", "second"]) s = Series(np.random.randn(8), index=index) with option_context("display.max_rows", 10): assert len(str(s).split("\n")) == 10 with option_context("display.max_rows", 3): assert len(str(s).split("\n")) == 5 with option_context("display.max_rows", 2): assert len(str(s).split("\n")) == 5 with option_context("display.max_rows", 1): assert len(str(s).split("\n")) == 4 with option_context("display.max_rows", 0): assert len(str(s).split("\n")) == 10 # index s = Series(np.random.randn(8), None) with option_context("display.max_rows", 10): assert len(str(s).split("\n")) == 9 with option_context("display.max_rows", 3): assert len(str(s).split("\n")) == 4 with option_context("display.max_rows", 2): assert len(str(s).split("\n")) == 4 with option_context("display.max_rows", 1): assert len(str(s).split("\n")) == 3 with option_context("display.max_rows", 0): assert len(str(s).split("\n")) == 9 # Make sure #8532 is fixed def test_consistent_format(self): s = Series([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9999, 1, 1] * 10) with option_context("display.max_rows", 10, "display.show_dimensions", False): res = repr(s) exp = ( "0 1.0000\n1 1.0000\n2 1.0000\n3 " "1.0000\n4 1.0000\n ... \n125 " "1.0000\n126 1.0000\n127 0.9999\n128 " "1.0000\n129 1.0000\ndtype: float64" ) assert res == exp def chck_ncols(self, s): with option_context("display.max_rows", 10): res = repr(s) lines = res.split("\n") lines = [ line for line in repr(s).split("\n") if not re.match(r"[^\.]*\.+", line) ][:-1] ncolsizes = len({len(line.strip()) for line in lines}) assert ncolsizes == 1 def test_format_explicit(self): test_sers = gen_series_formatting() with option_context("display.max_rows", 4, "display.show_dimensions", False): res = repr(test_sers["onel"]) exp = "0 a\n1 a\n ..\n98 a\n99 a\ndtype: object" assert exp == res res = repr(test_sers["twol"]) exp = "0 ab\n1 ab\n ..\n98 ab\n99 ab\ndtype: object" assert exp == res res = repr(test_sers["asc"]) exp = ( "0 a\n1 ab\n ... \n4 abcde\n5 " "abcdef\ndtype: object" ) assert exp == res res = repr(test_sers["desc"]) exp = ( "5 abcdef\n4 abcde\n ... \n1 ab\n0 " "a\ndtype: object" ) assert exp == res def test_ncols(self): test_sers = gen_series_formatting() for s in test_sers.values(): self.chck_ncols(s) def test_max_rows_eq_one(self): s = Series(range(10), dtype="int64") with option_context("display.max_rows", 1): strrepr = repr(s).split("\n") exp1 = ["0", "0"] res1 = strrepr[0].split() assert exp1 == res1 exp2 = [".."] res2 = strrepr[1].split() assert exp2 == res2 def test_truncate_ndots(self): def getndots(s): return len(re.match(r"[^\.]*(\.*)", s).groups()[0]) s = Series([0, 2, 3, 6]) with option_context("display.max_rows", 2): strrepr = repr(s).replace("\n", "") assert getndots(strrepr) == 2 s = Series([0, 100, 200, 400]) with option_context("display.max_rows", 2): strrepr = repr(s).replace("\n", "") assert getndots(strrepr) == 3 def test_show_dimensions(self): # gh-7117 s = Series(range(5)) assert "Length" not in repr(s) with option_context("display.max_rows", 4): assert "Length" in repr(s) with option_context("display.show_dimensions", True): assert "Length" in repr(s) with option_context("display.max_rows", 4, "display.show_dimensions", False): assert "Length" not in repr(s) def test_repr_min_rows(self): s = Series(range(20)) # default setting no truncation even if above min_rows assert ".." not in repr(s) s = Series(range(61)) # default of max_rows 60 triggers truncation if above assert ".." in repr(s) with option_context("display.max_rows", 10, "display.min_rows", 4): # truncated after first two rows assert ".." in repr(s) assert "2 " not in repr(s) with option_context("display.max_rows", 12, "display.min_rows", None): # when set to None, follow value of max_rows assert "5 5" in repr(s) with option_context("display.max_rows", 10, "display.min_rows", 12): # when set value higher as max_rows, use the minimum assert "5 5" not in repr(s) with option_context("display.max_rows", None, "display.min_rows", 12): # max_rows of None -> never truncate assert ".." not in repr(s) def test_to_string_name(self): s = Series(range(100), dtype="int64") s.name = "myser" res = s.to_string(max_rows=2, name=True) exp = "0 0\n ..\n99 99\nName: myser" assert res == exp res = s.to_string(max_rows=2, name=False) exp = "0 0\n ..\n99 99" assert res == exp def test_to_string_dtype(self): s = Series(range(100), dtype="int64") res = s.to_string(max_rows=2, dtype=True) exp = "0 0\n ..\n99 99\ndtype: int64" assert res == exp res = s.to_string(max_rows=2, dtype=False) exp = "0 0\n ..\n99 99" assert res == exp def test_to_string_length(self): s = Series(range(100), dtype="int64") res = s.to_string(max_rows=2, length=True) exp = "0 0\n ..\n99 99\nLength: 100" assert res == exp def test_to_string_na_rep(self): s = Series(index=range(100), dtype=np.float64) res = s.to_string(na_rep="foo", max_rows=2) exp = "0 foo\n ..\n99 foo" assert res == exp def test_to_string_float_format(self): s = Series(range(10), dtype="float64") res = s.to_string(float_format=lambda x: f"{x:2.1f}", max_rows=2) exp = "0 0.0\n ..\n9 9.0" assert res == exp def test_to_string_header(self): s = Series(range(10), dtype="int64") s.index.name = "foo" res = s.to_string(header=True, max_rows=2) exp = "foo\n0 0\n ..\n9 9" assert res == exp res = s.to_string(header=False, max_rows=2) exp = "0 0\n ..\n9 9" assert res == exp def test_to_string_multindex_header(self): # GH 16718 df = DataFrame({"a": [0], "b": [1], "c": [2], "d": [3]}).set_index(["a", "b"]) res = df.to_string(header=["r1", "r2"]) exp = " r1 r2\na b \n0 1 2 3" assert res == exp def test_to_string_empty_col(self): # GH 13653 s = Series(["", "Hello", "World", "", "", "Mooooo", "", ""]) res = s.to_string(index=False) exp = " \n Hello\n World\n \n \nMooooo\n \n " assert re.match(exp, res) class TestGenericArrayFormatter: def test_1d_array(self): # GenericArrayFormatter is used on types for which there isn't a dedicated # formatter. np.bool_ is one of those types. obj = fmt.GenericArrayFormatter(np.array([True, False])) res = obj.get_result() assert len(res) == 2 # Results should be right-justified. assert res[0] == " True" assert res[1] == " False" def test_2d_array(self): obj = fmt.GenericArrayFormatter(np.array([[True, False], [False, True]])) res = obj.get_result() assert len(res) == 2 assert res[0] == " [True, False]" assert res[1] == " [False, True]" def test_3d_array(self): obj = fmt.GenericArrayFormatter( np.array([[[True, True], [False, False]], [[False, True], [True, False]]]) ) res = obj.get_result() assert len(res) == 2 assert res[0] == " [[True, True], [False, False]]" assert res[1] == " [[False, True], [True, False]]" def test_2d_extension_type(self): # GH 33770 # Define a stub extension type with just enough code to run Series.__repr__() class DtypeStub(pd.api.extensions.ExtensionDtype): @property def type(self): return np.ndarray @property def name(self): return "DtypeStub" class ExtTypeStub(pd.api.extensions.ExtensionArray): def __len__(self): return 2 def __getitem__(self, ix): return [ix == 1, ix == 0] @property def dtype(self): return DtypeStub() series = Series(ExtTypeStub()) res = repr(series) # This line crashed before #33770 was fixed. expected = "0 [False True]\n" + "1 [ True False]\n" + "dtype: DtypeStub" assert res == expected def _three_digit_exp(): return f"{1.7e8:.4g}" == "1.7e+008" class TestFloatArrayFormatter: def test_misc(self): obj = fmt.FloatArrayFormatter(np.array([], dtype=np.float64)) result = obj.get_result() assert len(result) == 0 def test_format(self): obj = fmt.FloatArrayFormatter(np.array([12, 0], dtype=np.float64)) result = obj.get_result() assert result[0] == " 12.0" assert result[1] == " 0.0" def test_output_display_precision_trailing_zeroes(self): # Issue #20359: trimming zeros while there is no decimal point # Happens when display precision is set to zero with option_context("display.precision", 0): s = Series([840.0, 4200.0]) expected_output = "0 840\n1 4200\ndtype: float64" assert str(s) == expected_output def test_output_significant_digits(self): # Issue #9764 # In case default display precision changes: with option_context("display.precision", 6): # DataFrame example from issue #9764 d = DataFrame( { "col1": [ 9.999e-8, 1e-7, 1.0001e-7, 2e-7, 4.999e-7, 5e-7, 5.0001e-7, 6e-7, 9.999e-7, 1e-6, 1.0001e-6, 2e-6, 4.999e-6, 5e-6, 5.0001e-6, 6e-6, ] } ) expected_output = { (0, 6): " col1\n" "0 9.999000e-08\n" "1 1.000000e-07\n" "2 1.000100e-07\n" "3 2.000000e-07\n" "4 4.999000e-07\n" "5 5.000000e-07", (1, 6): " col1\n" "1 1.000000e-07\n" "2 1.000100e-07\n" "3 2.000000e-07\n" "4 4.999000e-07\n" "5 5.000000e-07", (1, 8): " col1\n" "1 1.000000e-07\n" "2 1.000100e-07\n" "3 2.000000e-07\n" "4 4.999000e-07\n" "5 5.000000e-07\n" "6 5.000100e-07\n" "7 6.000000e-07", (8, 16): " col1\n" "8 9.999000e-07\n" "9 1.000000e-06\n" "10 1.000100e-06\n" "11 2.000000e-06\n" "12 4.999000e-06\n" "13 5.000000e-06\n" "14 5.000100e-06\n" "15 6.000000e-06", (9, 16): " col1\n" "9 0.000001\n" "10 0.000001\n" "11 0.000002\n" "12 0.000005\n" "13 0.000005\n" "14 0.000005\n" "15 0.000006", } for (start, stop), v in expected_output.items(): assert str(d[start:stop]) == v def test_too_long(self): # GH 10451 with option_context("display.precision", 4): # need both a number > 1e6 and something that normally formats to # having length > display.precision + 6 df = DataFrame({"x": [12345.6789]}) assert str(df) == " x\n0 12345.6789" df = DataFrame({"x": [2e6]}) assert str(df) == " x\n0 2000000.0" df = DataFrame({"x": [12345.6789, 2e6]}) assert str(df) == " x\n0 1.2346e+04\n1 2.0000e+06" class TestRepr_timedelta64: def test_none(self): delta_1d = pd.to_timedelta(1, unit="D") delta_0d = pd.to_timedelta(0, unit="D") delta_1s = pd.to_timedelta(1, unit="s") delta_500ms = pd.to_timedelta(500, unit="ms") drepr = lambda x: x._repr_base() assert drepr(delta_1d) == "1 days" assert drepr(-delta_1d) == "-1 days" assert drepr(delta_0d) == "0 days" assert drepr(delta_1s) == "0 days 00:00:01" assert drepr(delta_500ms) == "0 days 00:00:00.500000" assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" def test_sub_day(self): delta_1d = pd.to_timedelta(1, unit="D") delta_0d = pd.to_timedelta(0, unit="D") delta_1s = pd.to_timedelta(1, unit="s") delta_500ms = pd.to_timedelta(500, unit="ms") drepr = lambda x: x._repr_base(format="sub_day") assert drepr(delta_1d) == "1 days" assert drepr(-delta_1d) == "-1 days" assert drepr(delta_0d) == "00:00:00" assert drepr(delta_1s) == "00:00:01" assert drepr(delta_500ms) == "00:00:00.500000" assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" def test_long(self): delta_1d = pd.to_timedelta(1, unit="D") delta_0d = pd.to_timedelta(0, unit="D") delta_1s = pd.to_timedelta(1, unit="s") delta_500ms = pd.to_timedelta(500, unit="ms") drepr = lambda x: x._repr_base(format="long") assert drepr(delta_1d) == "1 days 00:00:00" assert drepr(-delta_1d) == "-1 days +00:00:00" assert drepr(delta_0d) == "0 days 00:00:00" assert drepr(delta_1s) == "0 days 00:00:01" assert drepr(delta_500ms) == "0 days 00:00:00.500000" assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" def test_all(self): delta_1d = pd.to_timedelta(1, unit="D") delta_0d = pd.to_timedelta(0, unit="D") delta_1ns = pd.to_timedelta(1, unit="ns") drepr = lambda x: x._repr_base(format="all") assert drepr(delta_1d) == "1 days 00:00:00.000000000" assert drepr(-delta_1d) == "-1 days +00:00:00.000000000" assert drepr(delta_0d) == "0 days 00:00:00.000000000" assert drepr(delta_1ns) == "0 days 00:00:00.000000001" assert drepr(-delta_1d + delta_1ns) == "-1 days +00:00:00.000000001" class TestTimedelta64Formatter: def test_days(self): x = pd.to_timedelta(list(range(5)) + [NaT], unit="D") result = fmt.Timedelta64Formatter(x, box=True).get_result() assert result[0].strip() == "'0 days'" assert result[1].strip() == "'1 days'" result = fmt.Timedelta64Formatter(x[1:2], box=True).get_result() assert result[0].strip() == "'1 days'" result = fmt.Timedelta64Formatter(x, box=False).get_result() assert result[0].strip() == "0 days" assert result[1].strip() == "1 days" result = fmt.Timedelta64Formatter(x[1:2], box=False).get_result() assert result[0].strip() == "1 days" def test_days_neg(self): x = pd.to_timedelta(list(range(5)) + [NaT], unit="D") result = fmt.Timedelta64Formatter(-x, box=True).get_result() assert result[0].strip() == "'0 days'" assert result[1].strip() == "'-1 days'" def test_subdays(self): y = pd.to_timedelta(list(range(5)) + [NaT], unit="s") result = fmt.Timedelta64Formatter(y, box=True).get_result() assert result[0].strip() == "'0 days 00:00:00'" assert result[1].strip() == "'0 days 00:00:01'" def test_subdays_neg(self): y = pd.to_timedelta(list(range(5)) + [NaT], unit="s") result = fmt.Timedelta64Formatter(-y, box=True).get_result() assert result[0].strip() == "'0 days 00:00:00'" assert result[1].strip() == "'-1 days +23:59:59'" def test_zero(self): x = pd.to_timedelta(list(range(1)) + [NaT], unit="D") result = fmt.Timedelta64Formatter(x, box=True).get_result() assert result[0].strip() == "'0 days'" x = pd.to_timedelta(list(range(1)), unit="D") result = fmt.Timedelta64Formatter(x, box=True).get_result() assert result[0].strip() == "'0 days'" class TestDatetime64Formatter: def test_mixed(self): x = Series([datetime(2013, 1, 1), datetime(2013, 1, 1, 12), NaT]) result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 00:00:00" assert result[1].strip() == "2013-01-01 12:00:00" def test_dates(self): x = Series([datetime(2013, 1, 1), datetime(2013, 1, 2), NaT]) result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01" assert result[1].strip() == "2013-01-02" def test_date_nanos(self): x = Series([Timestamp(200)]) result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "1970-01-01 00:00:00.000000200" def test_dates_display(self): # 10170 # make sure that we are consistently display date formatting x = Series(date_range("20130101 09:00:00", periods=5, freq="D")) x.iloc[1] = np.nan result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 09:00:00" assert result[1].strip() == "NaT" assert result[4].strip() == "2013-01-05 09:00:00" x = Series(date_range("20130101 09:00:00", periods=5, freq="s")) x.iloc[1] = np.nan result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 09:00:00" assert result[1].strip() == "NaT" assert result[4].strip() == "2013-01-01 09:00:04" x = Series(date_range("20130101 09:00:00", periods=5, freq="ms")) x.iloc[1] = np.nan result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 09:00:00.000" assert result[1].strip() == "NaT" assert result[4].strip() == "2013-01-01 09:00:00.004" x = Series(date_range("20130101 09:00:00", periods=5, freq="us")) x.iloc[1] = np.nan result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 09:00:00.000000" assert result[1].strip() == "NaT" assert result[4].strip() == "2013-01-01 09:00:00.000004" x = Series(date_range("20130101 09:00:00", periods=5, freq="N")) x.iloc[1] = np.nan result = fmt.Datetime64Formatter(x).get_result() assert result[0].strip() == "2013-01-01 09:00:00.000000000" assert result[1].strip() == "NaT" assert result[4].strip() == "2013-01-01 09:00:00.000000004" def test_datetime64formatter_yearmonth(self): x = Series([datetime(2016, 1, 1), datetime(2016, 2, 2)]) def format_func(x): return x.strftime("%Y-%m") formatter = fmt.Datetime64Formatter(x, formatter=format_func) result = formatter.get_result() assert result == ["2016-01", "2016-02"] def test_datetime64formatter_hoursecond(self): x = Series( pd.to_datetime(["10:10:10.100", "12:12:12.120"], format="%H:%M:%S.%f") ) def format_func(x): return x.strftime("%H:%M") formatter = fmt.Datetime64Formatter(x, formatter=format_func) result = formatter.get_result() assert result == ["10:10", "12:12"] class TestNaTFormatting: def test_repr(self): assert repr(NaT) == "NaT" def test_str(self): assert str(NaT) == "NaT" class TestDatetimeIndexFormat: def test_datetime(self): formatted = pd.to_datetime([datetime(2003, 1, 1, 12), NaT]).format() assert formatted[0] == "2003-01-01 12:00:00" assert formatted[1] == "NaT" def test_date(self): formatted = pd.to_datetime([datetime(2003, 1, 1), NaT]).format() assert formatted[0] == "2003-01-01" assert formatted[1] == "NaT" def test_date_tz(self): formatted = pd.to_datetime([datetime(2013, 1, 1)], utc=True).format() assert formatted[0] == "2013-01-01 00:00:00+00:00" formatted = pd.to_datetime([datetime(2013, 1, 1), NaT], utc=True).format() assert formatted[0] == "2013-01-01 00:00:00+00:00" def test_date_explicit_date_format(self): formatted = pd.to_datetime([datetime(2003, 2, 1), NaT]).format( date_format="%m-%d-%Y", na_rep="UT" ) assert formatted[0] == "02-01-2003" assert formatted[1] == "UT" class TestDatetimeIndexUnicode: def test_dates(self): text = str(pd.to_datetime([datetime(2013, 1, 1), datetime(2014, 1, 1)])) assert "['2013-01-01'," in text assert ", '2014-01-01']" in text def test_mixed(self): text = str( pd.to_datetime( [datetime(2013, 1, 1), datetime(2014, 1, 1, 12), datetime(2014, 1, 1)] ) ) assert "'2013-01-01 00:00:00'," in text assert "'2014-01-01 00:00:00']" in text class TestStringRepTimestamp: def test_no_tz(self): dt_date = datetime(2013, 1, 2) assert str(dt_date) == str(Timestamp(dt_date)) dt_datetime = datetime(2013, 1, 2, 12, 1, 3) assert str(dt_datetime) == str(Timestamp(dt_datetime)) dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45) assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us)) ts_nanos_only = Timestamp(200) assert str(ts_nanos_only) == "1970-01-01 00:00:00.000000200" ts_nanos_micros = Timestamp(1200) assert str(ts_nanos_micros) == "1970-01-01 00:00:00.000001200" def test_tz_pytz(self): dt_date = datetime(2013, 1, 2, tzinfo=pytz.utc) assert str(dt_date) == str(Timestamp(dt_date)) dt_datetime = datetime(2013, 1, 2, 12, 1, 3, tzinfo=pytz.utc) assert str(dt_datetime) == str(
Timestamp(dt_datetime)
pandas.Timestamp
""" Script goal, Test out the google earth engine to see what i can do - find a landsat collection for a single point """ #============================================================================== __title__ = "GEE Movie Maker" __author__ = "<NAME>" __version__ = "v1.0(04.04.2019)" __email__ = "<EMAIL>" #============================================================================== # +++++ Check the paths and set ex path to fireflies folder +++++ import os import sys if not os.getcwd().endswith("fireflies"): if "fireflies" in os.getcwd(): p1, p2, _ = os.getcwd().partition("fireflies") os.chdir(p1+p2) else: raise OSError( "This script was called from an unknown path. CWD can not be set" ) sys.path.append(os.getcwd()) #============================================================================== # Import packages import numpy as np import pandas as pd import geopandas as gpd import argparse import datetime as dt import warnings as warn import xarray as xr import bottleneck as bn import scipy as sp import glob import time from collections import OrderedDict from scipy import stats from numba import jit # Import the Earth Engine Python Package import ee import ee.mapclient from ee import batch from geetools import batch as gee_batch # from netCDF4 import Dataset, num2date, date2num # from scipy import stats # import statsmodels.stats.multitest as smsM # Import plotting and colorpackages import matplotlib.pyplot as plt import matplotlib.colors as mpc import matplotlib as mpl import palettable import fiona fiona.drvsupport.supported_drivers['kml'] = 'rw' # enable KML support which is disabled by default fiona.drvsupport.supported_drivers['KML'] = 'rw' # enable KML support which is disabled by default # import seaborn as sns # import cartopy.crs as ccrs # import cartopy.feature as cpf # from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER import geopy.distance as geodis import myfunctions.corefunctions as cf # # Import debugging packages # import socket # print(socket.gethostname()) import ipdb print("numpy version : ", np.__version__) print("pandas version : ", pd.__version__) print("xarray version : ", xr.__version__) #============================================================================== def main(args): # ========== Initialize the Earth Engine object ========== ee.Initialize() # ========== Set an overwrite ========= force = False cordf = True #force the creation of a new maskter coord list tsite = args.site cordg = True # ========== Create the system specific paths ========== sysname = os.uname()[1] if sysname == 'DESKTOP-CSHARFM': # LAPTOP spath = "/mnt/c/Users/arden/Google Drive/UoL/FIREFLIES/VideoExports/" elif sysname == "owner": spath = "/mnt/c/Users/user/Google Drive/UoL/FIREFLIES/VideoExports/" elif sysname == "ubuntu": # Work PC spath = "/media/ubuntu/Seagate Backup Plus Drive/Data51/VideoExports/" else: warn.warn("Paths not created for this computer") # spath = "/media/ubuntu/Seagate Backup Plus Drive" ipdb.set_trace() cf.pymkdir(spath) # ========== create the geometery ========== cordname = "./data/other/GEE_sitelist.csv" if not os.path.isfile(cordname) or cordf: print("Generating and saving a new master coord table") site_coords = geom_builder() for col in site_coords.columns[1:]: site_coords = site_coords.astype({col:float}) site_coords.to_csv(cordname) else: print("Loading master coord table") site_coords = pd.read_csv(cordname, index_col=0)#, parse_dates=True # warn.warn("THere is some form of bug here, going interactive. Look at the dataframe") # ipdb.set_trace() program = "LANDSAT" cordf = True # ========== Loop over each site ========== for index, coords in site_coords.iterrows(): # ========== Check if the pathe and file exists ========== checkfile = "%s%s/%s_%s_gridinfo.csv" % (spath, coords["name"], program, coords["name"]) if not args.site is None: # check is the site is correct if tsite == coords["name"]: # ========== Get the start time ========== t0 =
pd.Timestamp.now()
pandas.Timestamp.now
import streamlit as st import numpy as np import pandas as pd import sqlite3 conn=sqlite3.connect('data.db') c=conn.cursor() import os import warnings warnings.filterwarnings('ignore') import tensorflow.keras as tf import joblib import base64 from io import BytesIO ratings_1=pd.read_csv("ratings_1.csv") ratings_2=pd.read_csv("ratings_2.csv") ratings_3=pd.read_csv("ratings_3.csv") ratings_4=pd.read_csv("ratings_4.csv") ratings_5=pd.read_csv("ratings_5.csv") ratings_df_list=[ratings_1,ratings_2,ratings_3,ratings_4,ratings_5] ratings_df=pd.concat(ratings_df_list) del ratings_1,ratings_2,ratings_3,ratings_4,ratings_5,ratings_df_list new_model=tf.models.load_model("modelrecsys.h5") co=joblib.load("contentsfile.joblib") titlefile=joblib.load('title.joblib') ####To download dataframe recommondations def to_excel(df): output = BytesIO() writer = pd.ExcelWriter(output, engine='xlsxwriter') df.to_excel(writer, sheet_name='Sheet1') writer.save() processed_data = output.getvalue() return processed_data def get_table_download_link(df): #Generates a link allowing the data in a given panda dataframe to be downloaded #in: dataframe #out: href string val = to_excel(df) b64 = base64.b64encode(val) # val looks like b'...' return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="extract.xlsx">Download csv file</a>' # decode b'abc' => abc ##df = ... # your dataframe ##st.markdown(get_table_download_link(df), unsafe_allow_html=True) def create_usertable(): c.execute('CREATE TABLE IF NOT EXISTS userstable(username TEXT, password TEXT)') def add_userdata(username,password): c.execute('INSERT INTO userstable(username, password) VALUES(?,?)',(username,password)) conn.commit() def login_user(username,password): c.execute('SELECT * FROM userstable WHERE username=? AND password=?',(username,password)) data=c.fetchall() return data def view_all_users(): c.execute('SELECT * FROM userstable') data=c.fetchall() return data st.title("...WELCOME...") st.title("HYBRID BOOK RECOMMENDATION SYSTEM") menu=["Home","Login", "Sign up","Book"] choice=st.sidebar.selectbox("Menu",menu) if choice=="Home": st.subheader("HOME") elif choice=="Login": st.subheader("Login Section") username=st.sidebar.text_input("username") password=st.sidebar.text_input("password",type='password') if st.sidebar.checkbox("Login"): # if password=="<PASSWORD>": create_usertable() result=login_user(username,password) if result: st.success("LOGGED IN SUCCESSFULLY AS {} ".format(username)) task=st.selectbox("Task",["Help","Start-Analytics","Profile"]) if task=="Help": st.subheader("use Start-Analytics for Reccomondations") elif task=="Start-Analytics": st.subheader("Top N number of Book Recommondations predicted realtime") #user_id = st.number_input('user_id', min_value=1, max_value=53424, value=1) user_id=st.text_input("Enter user_id {1-53424} default 1") if user_id!="": user_id=int(user_id) if user_id<1 or user_id>53424: user_id=1 else: user_id=1 us_id_temp=[user_id for i in range(len(co['book_id']))] reccom = new_model.predict([pd.Series(us_id_temp),co['book_id'],co.iloc[:,1:]]) recc_df=pd.DataFrame(reccom,columns=["rating"]) recc_df["book_id"]=co['book_id'].values df_new=ratings_df.where(ratings_df["user_id"]==user_id) df_new.dropna(inplace=True) list_books_seen=df_new['book_id'].tolist() del df_new recc_df_table = recc_df[~recc_df.book_id.isin(list_books_seen)] recc_df.sort_values(by="rating",ascending=False,inplace=True) recc_df=recc_df.iloc[6:36].reset_index(drop=True) #num= st.number_input('required_reccomondation_count', min_value=2, max_value=30, value=5) num=st.text_input("Enter required_reccomondation_count (2-30) default 2") if num!="": num=int(num) if num<2 or num>30: num=2 else: num=2 recc_df_table =recc_df.iloc[:num] recc_df_table=pd.merge(recc_df_table,titlefile,left_on="book_id",right_on="book_id") recc_df_table_new = recc_df_table.iloc[:,:6].reset_index(drop=True) st.write(recc_df_table_new) st.markdown(get_table_download_link(recc_df_table_new), unsafe_allow_html=True) for i in range(len(recc_df_table_new.index)): st.image( recc_df_table.iloc[i,7], width=200, # Manually Adjust the width of the image as per requirement caption=recc_df_table.iloc[i,4] ) elif task=="Profile": st.subheader("User Profiles") user_result=view_all_users() clean_db=
pd.DataFrame(user_result,columns=["Username","Password"])
pandas.DataFrame
# -*- coding: utf-8 -*- import scrapy # needed to scrape import xlrd # used to easily import xlsx file import json import re import pandas as pd import numpy as np from openpyxl import load_workbook import datetime from datetime import timedelta ##### NOTE # PART 1: This script writes all monthly Ercot data to its own tab without affecting the "Master Data" tab # PART 2: This script will perform analysis on the monthly data ### PART 1 - Clean Data ########################################################################################## ########################################################################################## ########################################################################################## file_path = r"/Users/YoungFreeesh/Visual Studio Code/_Python/Web Scraping/Ercot/MASTER-Ercot.xlsx" df = pd.read_excel(file_path, sheet_name = 'Master Data') # read all data from "Master Data" tab in the "MASTER-Ercot" workbook headers = list(df.columns.values) # get the headers of "Master Data" df = pd.DataFrame(df) # convert df to a Date Frame print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') ### Get all Unique Months in the data frame dateArray = np.array(df.iloc[:, 0]) # convert dates column in df to a numpy array monthsArray = [] # initialize array for x in range(dateArray.shape[0]): # change format of dates: 05/10/2018 --> 05-2018 temp = dateArray[x] #print(str(temp[:2]) + '-' + str(temp[6:])) #print(str(temp)) #monthsArrayUnique = np.unique(monthsArray) # Unique Months, hence get all unique strings of the form: 'mm-yyyy' #print("Unique Months: ", monthsArrayUnique) # if (str(temp[:2]) + '-' + str(temp[6:]) == "08-2018"): # monthsArray.append("08-2018") monthsArray.append(str(temp[:2]) + '-' + str(temp[6:])) #print(monthsArray) monthsArrayUnique = np.unique(monthsArray) # Unique Months, hence get all unique strings of the form: 'mm-yyyy' print("Unique Months: ", monthsArrayUnique) ##### Use PANDAS to write to an Excel file and create tabs ########################################################################################## #file_path_HardDrive = r"/Users/YoungFreeesh/Visual Studio Code/_Python/Web Scraping/Ercot/Test-Ercot-Scrape.xlsx" #file_path_Dropbox = r"/Users/YoungFreeesh/Dropbox/Ercot Data/Test-Ercot-Scrape.xlsx" file_path_HardDrive = r"/Users/YoungFreeesh/Visual Studio Code/_Python/Web Scraping/Ercot/MASTER-Ercot.xlsx" file_path_Dropbox = r"/Users/YoungFreeesh/Dropbox/Ercot Data/MASTER-Ercot.xlsx" ### For Ercot Summary Page - Calculations (LZ_SOUTH) # read all data from "Master Data" tab from "MASTER-Ercot" dfMASTER = pd.read_excel(file_path_HardDrive, sheet_name = 'Master Data') writer_HardDrive = pd.ExcelWriter(file_path_HardDrive, engine='openpyxl') writer_Dropbox = pd.ExcelWriter(file_path_Dropbox , engine='openpyxl') book_HardDrive = load_workbook(file_path_HardDrive) book_Dropbox = load_workbook(file_path_Dropbox) writer_HardDrive.book = book_HardDrive writer_Dropbox.book = book_Dropbox writer_HardDrive.sheets = dict((ws.title, ws) for ws in book_HardDrive.worksheets) writer_Dropbox.sheets = dict((ws.title, ws) for ws in book_Dropbox.worksheets) ### Create a unique Excel Worksheet/tab for each month of data in the Master Date tab # This tab will contain all the price data for that particular month # This loop will create a tab for the month if it doesn't alreay exist # This loop will overwrite any data already in the months tab in columns A-P # This loop will not affect and data or formulas or graphs Beyond column Q # This loop will not affect any other tabs monthsArray = np.array(monthsArray) # convert monthsArray to a numpy array for month in monthsArrayUnique: print("Tab Created: ", month) indices = [i for i, x in enumerate(monthsArray) if x == month] # get indices for month monthDF = pd.DataFrame(data = np.array(df.iloc[indices, :]), columns = headers) # create data frame monthDF.to_excel(writer_HardDrive, startrow= 0 , index=False, sheet_name=str(month)) # write to "MASTER-Ercot.xlsx" spreadsheet monthDF.to_excel(writer_Dropbox , startrow= 0 , index=False, sheet_name=str(month)) # write to "MASTER-Ercot.xlsx" spreadsheet print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') # End of PART 1 ########################################################################################## ### PART 2 - Analyze Data ########################################################################################## ########################################################################################## ########################################################################################## ###Ercot Summary Page - Calculations (LZ_SOUTH) ### Refine the DataFrame #Only Take LZ_SOUTH dfMASTER_LZ_SOUTH = dfMASTER[['Oper Day', 'Interval Ending', 'LZ_SOUTH']].copy(deep=True) dfMASTER_LZ_SOUTH['Oper Day'] =
pd.to_datetime(dfMASTER_LZ_SOUTH['Oper Day'])
pandas.to_datetime
# -*- coding: utf-8 -*- """Module for analyzing sentiment regarding financial markets. Includes one class: 1. SentimentAnalyzer """ import os import pickle import re import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer import pandas as pd from socfin.parsers import TextParser class SentimentAnalyzer(TextParser): """Class for analyzing sentiment of text. Inherits from `TextParser`. Args: **kwargs: Arbitrary keyword arguments. """ def __init__(self, classifier='classifier.pickle', **kwargs): super().__init__(**kwargs) with open(os.path.join('socfin', 'data', classifier), 'rb') as file: self._sia = pickle.load(file) def sentiment(self, text): """Calucates sentiment scores for text. Args: text (str): Text to calcuate sentiment for. Returns: `Pandas`_ dataframe of sentiment scores for the `text` parameter. Columns include neg, neu, pos, and compound. Compound is the overall sentiment score. .. _Pandas: https://pandas.pydata.org/ """ text = self.replace_emojis(text) text = self.alpha(text) sentiment = self._sia.polarity_scores(text) return pd.DataFrame(sentiment, index=[0]) def ticker_sentiment(self, text, ticker): """Calucates sentiment scores for text regarding a stock ticker. Args: text (str): Text to calcuate sentiment for. ticker (str): Ticker to calculate sentiment for. Returns: `Pandas`_ dataframe of sentiment scores for the `text` parameter, specifically regarding the `ticker` parameter. Columns include ticker, neg, neu, pos, and compound. Compound is the overall sentiment score. .. _Pandas: https://pandas.pydata.org/ """ words = self.words(text) if ticker not in words: sentiment = {'ticker': ticker, 'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0} return
pd.DataFrame(sentiment, index=[0])
pandas.DataFrame
import pandas as pd if __name__ == '__main__': output = [] for f in snakemake.input: output.append(pd.read_csv(f, sep="\t", index_col=0))
pd.concat(output)
pandas.concat
import pytz from pandas import to_datetime from smrf.framework.model_framework import SMRF from smrf.tests.smrf_test_case import SMRFTestCase class TestModelFramework(SMRFTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.smrf = SMRF(cls.config_file) def test_start_date(self): self.assertEqual( self.smrf.start_date, to_datetime(self.smrf.config['time']['start_date'], utc=True) ) def test_end_date(self): self.assertEqual( self.smrf.end_date, to_datetime(self.smrf.config['time']['end_date'], utc=True) ) def test_time_zone(self): self.assertEqual(self.smrf.time_zone, pytz.UTC) def test_date_time(self): self.assertEqual( self.smrf.date_time[0],
to_datetime('1998-01-14 15:00:00', utc=True)
pandas.to_datetime
# Copyright 1999-2021 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import pandas as pd from mars.dataframe.datasource.dataframe import from_pandas from mars.dataframe.datasource.series import from_pandas as series_from_pandas from mars.dataframe.merge import concat from mars.dataframe.utils import sort_dataframe_inplace def test_merge(setup): df1 = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e']) df2 = pd.DataFrame(np.arange(20).reshape((5, 4)) + 1, columns=['a', 'b', 'x', 'y']) df3 = df1.copy() df3.index = pd.RangeIndex(2, 6, name='index') df4 = df1.copy() df4.index = pd.MultiIndex.from_tuples([(i, i + 1) for i in range(4)], names=['i1', 'i2']) mdf1 = from_pandas(df1, chunk_size=2) mdf2 = from_pandas(df2, chunk_size=2) mdf3 = from_pandas(df3, chunk_size=3) mdf4 = from_pandas(df4, chunk_size=2) # Note [Index of Merge] # # When `left_index` and `right_index` of `merge` is both false, pandas will generate an RangeIndex to # the final result dataframe. # # We chunked the `left` and `right` dataframe, thus every result chunk will have its own RangeIndex. # When they are contenated we don't generate a new RangeIndex for the result, thus we cannot obtain the # same index value with pandas. But we guarantee that the content of dataframe is correct. # merge on index expected0 = df1.merge(df2) jdf0 = mdf1.merge(mdf2) result0 = jdf0.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected0, 0), sort_dataframe_inplace(result0, 0)) # merge on left index and `right_on` expected1 = df1.merge(df2, how='left', right_on='x', left_index=True) jdf1 = mdf1.merge(mdf2, how='left', right_on='x', left_index=True) result1 = jdf1.execute().fetch() expected1.set_index('a_x', inplace=True) result1.set_index('a_x', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected1, 0), sort_dataframe_inplace(result1, 0)) # merge on `left_on` and right index expected2 = df1.merge(df2, how='right', left_on='a', right_index=True) jdf2 = mdf1.merge(mdf2, how='right', left_on='a', right_index=True) result2 = jdf2.execute().fetch() expected2.set_index('a', inplace=True) result2.set_index('a', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected2, 0), sort_dataframe_inplace(result2, 0)) # merge on `left_on` and `right_on` expected3 = df1.merge(df2, how='left', left_on='a', right_on='x') jdf3 = mdf1.merge(mdf2, how='left', left_on='a', right_on='x') result3 = jdf3.execute().fetch() expected3.set_index('a_x', inplace=True) result3.set_index('a_x', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected3, 0), sort_dataframe_inplace(result3, 0)) # merge on `on` expected4 = df1.merge(df2, how='right', on='a') jdf4 = mdf1.merge(mdf2, how='right', on='a') result4 = jdf4.execute().fetch() expected4.set_index('a', inplace=True) result4.set_index('a', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected4, 0), sort_dataframe_inplace(result4, 0)) # merge on multiple columns expected5 = df1.merge(df2, how='inner', on=['a', 'b']) jdf5 = mdf1.merge(mdf2, how='inner', on=['a', 'b']) result5 = jdf5.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected5, 0), sort_dataframe_inplace(result5, 0)) # merge when some on is index expected6 = df3.merge(df2, how='inner', left_on='index', right_on='a') jdf6 = mdf3.merge(mdf2, how='inner', left_on='index', right_on='a') result6 = jdf6.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected6, 0), sort_dataframe_inplace(result6, 0)) # merge when on is in MultiIndex expected7 = df4.merge(df2, how='inner', left_on='i1', right_on='a') jdf7 = mdf4.merge(mdf2, how='inner', left_on='i1', right_on='a') result7 = jdf7.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected7, 0), sort_dataframe_inplace(result7, 0)) # merge when on is in MultiIndex, and on not in index expected8 = df4.merge(df2, how='inner', on=['a', 'b']) jdf8 = mdf4.merge(mdf2, how='inner', on=['a', 'b']) result8 = jdf8.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected8, 0), sort_dataframe_inplace(result8, 0)) def test_join(setup): df1 = pd.DataFrame([[1, 3, 3], [4, 2, 6], [7, 8, 9]], index=['a1', 'a2', 'a3']) df2 = pd.DataFrame([[1, 2, 3], [1, 5, 6], [7, 8, 9]], index=['a1', 'b2', 'b3']) + 1 df2 = pd.concat([df2, df2 + 1]) mdf1 = from_pandas(df1, chunk_size=2) mdf2 = from_pandas(df2, chunk_size=2) # default `how` expected0 = df1.join(df2, lsuffix='l_', rsuffix='r_') jdf0 = mdf1.join(mdf2, lsuffix='l_', rsuffix='r_') result0 = jdf0.execute().fetch() pd.testing.assert_frame_equal(expected0.sort_index(), result0.sort_index()) # how = 'left' expected1 = df1.join(df2, how='left', lsuffix='l_', rsuffix='r_') jdf1 = mdf1.join(mdf2, how='left', lsuffix='l_', rsuffix='r_') result1 = jdf1.execute().fetch() pd.testing.assert_frame_equal(expected1.sort_index(), result1.sort_index()) # how = 'right' expected2 = df1.join(df2, how='right', lsuffix='l_', rsuffix='r_') jdf2 = mdf1.join(mdf2, how='right', lsuffix='l_', rsuffix='r_') result2 = jdf2.execute().fetch() pd.testing.assert_frame_equal(expected2.sort_index(), result2.sort_index()) # how = 'inner' expected3 = df1.join(df2, how='inner', lsuffix='l_', rsuffix='r_') jdf3 = mdf1.join(mdf2, how='inner', lsuffix='l_', rsuffix='r_') result3 = jdf3.execute().fetch() pd.testing.assert_frame_equal(expected3.sort_index(), result3.sort_index()) # how = 'outer' expected4 = df1.join(df2, how='outer', lsuffix='l_', rsuffix='r_') jdf4 = mdf1.join(mdf2, how='outer', lsuffix='l_', rsuffix='r_') result4 = jdf4.execute().fetch() pd.testing.assert_frame_equal(expected4.sort_index(), result4.sort_index()) def test_join_on(setup): df1 = pd.DataFrame([[1, 3, 3], [4, 2, 6], [7, 8, 9]], columns=['a1', 'a2', 'a3']) df2 = pd.DataFrame([[1, 2, 3], [1, 5, 6], [7, 8, 9]], columns=['a1', 'b2', 'b3']) + 1 df2 = pd.concat([df2, df2 + 1]) mdf1 = from_pandas(df1, chunk_size=2) mdf2 = from_pandas(df2, chunk_size=2) expected0 = df1.join(df2, on=None, lsuffix='_l', rsuffix='_r') jdf0 = mdf1.join(mdf2, on=None, lsuffix='_l', rsuffix='_r') result0 = jdf0.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected0, 0), sort_dataframe_inplace(result0, 0)) expected1 = df1.join(df2, how='left', on='a1', lsuffix='_l', rsuffix='_r') jdf1 = mdf1.join(mdf2, how='left', on='a1', lsuffix='_l', rsuffix='_r') result1 = jdf1.execute().fetch() # Note [Columns of Left Join] # # I believe we have no chance to obtain the entirely same result with pandas here: # # Look at the following example: # # >>> df1 # a1 a2 a3 # 0 1 3 3 # >>> df2 # a1 b2 b3 # 1 2 6 7 # >>> df3 # a1 b2 b3 # 1 2 6 7 # 1 2 6 7 # # >>> df1.merge(df2, how='left', left_on='a1', left_index=False, right_index=True) # a1_x a2 a3 a1_y b2 b3 # 0 1 3 3 2 6 7 # >>> df1.merge(df3, how='left', left_on='a1', left_index=False, right_index=True) # a1 a1_x a2 a3 a1_y b2 b3 # 0 1 1 3 3 2 6 7 # 0 1 1 3 3 2 6 7 # # Note that the result of `df1.merge(df3)` has an extra column `a` compared to `df1.merge(df2)`. # The value of column `a` is the same of `a1_x`, just because `1` occurs twice in index of `df3`. # I haven't invistagated why pandas has such behaviour... # # We cannot yield the same result with pandas, because, the `df3` is chunked, then some of the # result chunk has 6 columns, others may have 7 columns, when concatenated into one DataFrame # some cells of column `a` will have value `NaN`, which is different from the result of pandas. # # But we can guarantee that other effective columns have absolutely same value with pandas. columns_to_compare = jdf1.columns_value.to_pandas() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected1[columns_to_compare], 0, 1), sort_dataframe_inplace(result1[columns_to_compare], 0, 1)) # Note [Index of Join on EmptyDataFrame] # # It is tricky that it is non-trivial to get the same `index` result with pandas. # # Look at the following example: # # >>> df1 # a1 a2 a3 # 1 4 2 6 # >>> df2 # a1 b2 b3 # 1 2 6 7 # 2 8 9 10 # >>> df3 # Empty DataFrame # Columns: [a1, a2, a3] # Index: [] # >>> df1.join(df2, how='right', on='a2', lsuffix='_l', rsuffix='_r') # a1_l a2 a3 a1_r b2 b3 # 1.0 4.0 2 6.0 8 9 10 # NaN NaN 1 NaN 2 6 7 # >>> df3.join(df2, how='right', on='a2', lsuffix='_l', rsuffix='_r') # a1_l a2 a3 a1_r b2 b3 # 1 NaN 1 NaN 2 6 7 # 2 NaN 2 NaN 8 9 10 # # When the `left` dataframe is not empty, the mismatched rows in `right` will have index value `NaN`, # and the matched rows have index value from `right`. When the `left` dataframe is empty, the mismatched # rows have index value from `right`. # # Since we chunked the `left` dataframe, it is uneasy to obtain the same index value with pandas in the # final result dataframe, but we guaranteed that the dataframe content is correctly. expected2 = df1.join(df2, how='right', on='a2', lsuffix='_l', rsuffix='_r') jdf2 = mdf1.join(mdf2, how='right', on='a2', lsuffix='_l', rsuffix='_r') result2 = jdf2.execute().fetch() expected2.set_index('a2', inplace=True) result2.set_index('a2', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected2, 0), sort_dataframe_inplace(result2, 0)) expected3 = df1.join(df2, how='inner', on='a2', lsuffix='_l', rsuffix='_r') jdf3 = mdf1.join(mdf2, how='inner', on='a2', lsuffix='_l', rsuffix='_r') result3 = jdf3.execute().fetch() pd.testing.assert_frame_equal(sort_dataframe_inplace(expected3, 0), sort_dataframe_inplace(result3, 0)) expected4 = df1.join(df2, how='outer', on='a2', lsuffix='_l', rsuffix='_r') jdf4 = mdf1.join(mdf2, how='outer', on='a2', lsuffix='_l', rsuffix='_r') result4 = jdf4.execute().fetch() expected4.set_index('a2', inplace=True) result4.set_index('a2', inplace=True) pd.testing.assert_frame_equal(sort_dataframe_inplace(expected4, 0), sort_dataframe_inplace(result4, 0)) def test_merge_one_chunk(setup): df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]}, index=['a1', 'a2', 'a3', 'a4']) df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]}, index=['a1', 'a2', 'a3', 'a4']) # all have one chunk mdf1 = from_pandas(df1) mdf2 = from_pandas(df2) expected = df1.merge(df2, left_on='lkey', right_on='rkey') jdf = mdf1.merge(mdf2, left_on='lkey', right_on='rkey') result = jdf.execute().fetch() pd.testing.assert_frame_equal(expected.sort_values(by=expected.columns[1]).reset_index(drop=True), result.sort_values(by=result.columns[1]).reset_index(drop=True)) # left have one chunk mdf1 = from_pandas(df1) mdf2 = from_pandas(df2, chunk_size=2) expected = df1.merge(df2, left_on='lkey', right_on='rkey') jdf = mdf1.merge(mdf2, left_on='lkey', right_on='rkey') result = jdf.execute().fetch() pd.testing.assert_frame_equal(expected.sort_values(by=expected.columns[1]).reset_index(drop=True), result.sort_values(by=result.columns[1]).reset_index(drop=True)) # right have one chunk mdf1 = from_pandas(df1, chunk_size=3) mdf2 = from_pandas(df2) expected = df1.merge(df2, left_on='lkey', right_on='rkey') jdf = mdf1.merge(mdf2, left_on='lkey', right_on='rkey') result = jdf.execute().fetch() pd.testing.assert_frame_equal(expected.sort_values(by=expected.columns[1]).reset_index(drop=True), result.sort_values(by=result.columns[1]).reset_index(drop=True)) def test_merge_on_duplicate_columns(setup): raw1 = pd.DataFrame([['foo', 1, 'bar'], ['bar', 2, 'foo'], ['baz', 3, 'foo']], columns=['lkey', 'value', 'value'], index=['a1', 'a2', 'a3']) raw2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]}, index=['a1', 'a2', 'a3', 'a4']) df1 = from_pandas(raw1, chunk_size=2) df2 = from_pandas(raw2, chunk_size=3) r = df1.merge(df2, left_on='lkey', right_on='rkey') result = r.execute().fetch() expected = raw1.merge(raw2, left_on='lkey', right_on='rkey') pd.testing.assert_frame_equal(expected, result) def test_append_execution(setup): df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD')) df2 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD')) mdf1 = from_pandas(df1, chunk_size=3) mdf2 = from_pandas(df2, chunk_size=3) adf = mdf1.append(mdf2) expected = df1.append(df2) result = adf.execute().fetch() pd.testing.assert_frame_equal(expected, result) adf = mdf1.append(mdf2, ignore_index=True) expected = df1.append(df2, ignore_index=True) result = adf.execute(extra_config={'check_index_value': False}).fetch() pd.testing.assert_frame_equal(expected, result) mdf1 = from_pandas(df1, chunk_size=3) mdf2 = from_pandas(df2, chunk_size=2) adf = mdf1.append(mdf2) expected = df1.append(df2) result = adf.execute().fetch() pd.testing.assert_frame_equal(expected, result) adf = mdf1.append(mdf2, ignore_index=True) expected = df1.append(df2, ignore_index=True) result = adf.execute(extra_config={'check_index_value': False}).fetch() pd.testing.assert_frame_equal(expected, result) df3 = pd.DataFrame(np.random.rand(8, 4), columns=list('ABCD')) mdf3 = from_pandas(df3, chunk_size=3) expected = df1.append([df2, df3]) adf = mdf1.append([mdf2, mdf3]) result = adf.execute().fetch() pd.testing.assert_frame_equal(expected, result) adf = mdf1.append(dict(A=1, B=2, C=3, D=4), ignore_index=True) expected = df1.append(dict(A=1, B=2, C=3, D=4), ignore_index=True) result = adf.execute(extra_config={'check_index_value': False}).fetch() pd.testing.assert_frame_equal(expected, result) # test for series series1 = pd.Series(np.random.rand(10,)) series2 = pd.Series(np.random.rand(10,)) mseries1 = series_from_pandas(series1, chunk_size=3) mseries2 = series_from_pandas(series2, chunk_size=3) aseries = mseries1.append(mseries2) expected = series1.append(series2) result = aseries.execute().fetch()
pd.testing.assert_series_equal(expected, result)
pandas.testing.assert_series_equal
# Copyright (c) 2019-2020, NVIDIA CORPORATION. """ Test related to MultiIndex """ import re import cupy as cp import numpy as np import pandas as pd import pytest import cudf from cudf.core.column import as_column from cudf.core.index import as_index from cudf.tests.utils import assert_eq, assert_neq def test_multiindex_levels_codes_validation(): levels = [["a", "b"], ["c", "d"]] # Codes not a sequence of sequences with pytest.raises(TypeError): pd.MultiIndex(levels, [0, 1]) with pytest.raises(TypeError): cudf.MultiIndex(levels, [0, 1]) # Codes don't match levels with pytest.raises(ValueError): pd.MultiIndex(levels, [[0], [1], [1]]) with pytest.raises(ValueError): cudf.MultiIndex(levels, [[0], [1], [1]]) # Largest code greater than number of levels with pytest.raises(ValueError): pd.MultiIndex(levels, [[0, 1], [0, 2]]) with pytest.raises(ValueError): cudf.MultiIndex(levels, [[0, 1], [0, 2]]) # Unequal code lengths with pytest.raises(ValueError): pd.MultiIndex(levels, [[0, 1], [0]]) with pytest.raises(ValueError): cudf.MultiIndex(levels, [[0, 1], [0]]) # Didn't pass levels and codes with pytest.raises(TypeError): pd.MultiIndex() with pytest.raises(TypeError): cudf.MultiIndex() # Didn't pass non zero levels and codes with pytest.raises(ValueError): pd.MultiIndex([], []) with pytest.raises(ValueError): cudf.MultiIndex([], []) def test_multiindex_construction(): levels = [["a", "b"], ["c", "d"]] codes = [[0, 1], [1, 0]] pmi = pd.MultiIndex(levels, codes) mi = cudf.MultiIndex(levels, codes) assert_eq(pmi, mi) pmi = pd.MultiIndex(levels, codes) mi = cudf.MultiIndex(levels=levels, codes=codes) assert_eq(pmi, mi) def test_multiindex_types(): codes = [[0, 1], [1, 0]] levels = [[0, 1], [2, 3]] pmi = pd.MultiIndex(levels, codes) mi = cudf.MultiIndex(levels, codes) assert_eq(pmi, mi) levels = [[1.2, 2.1], [1.3, 3.1]] pmi = pd.MultiIndex(levels, codes) mi = cudf.MultiIndex(levels, codes) assert_eq(pmi, mi) levels = [["a", "b"], ["c", "d"]] pmi = pd.MultiIndex(levels, codes) mi = cudf.MultiIndex(levels, codes) assert_eq(pmi, mi) def test_multiindex_df_assignment(): pdf = pd.DataFrame({"x": [1, 2, 3]}) gdf = cudf.from_pandas(pdf) pdf.index = pd.MultiIndex([["a", "b"], ["c", "d"]], [[0, 1, 0], [1, 0, 1]]) gdf.index = cudf.MultiIndex( levels=[["a", "b"], ["c", "d"]], codes=[[0, 1, 0], [1, 0, 1]] ) assert_eq(pdf, gdf) def test_multiindex_series_assignment(): ps = pd.Series([1, 2, 3]) gs = cudf.from_pandas(ps) ps.index = pd.MultiIndex([["a", "b"], ["c", "d"]], [[0, 1, 0], [1, 0, 1]]) gs.index = cudf.MultiIndex( levels=[["a", "b"], ["c", "d"]], codes=[[0, 1, 0], [1, 0, 1]] ) assert_eq(ps, gs) def test_string_index(): from cudf.core.index import StringIndex pdf = pd.DataFrame(np.random.rand(5, 5)) gdf = cudf.from_pandas(pdf) stringIndex = ["a", "b", "c", "d", "e"] pdf.index = stringIndex gdf.index = stringIndex assert_eq(pdf, gdf) stringIndex = np.array(["a", "b", "c", "d", "e"]) pdf.index = stringIndex gdf.index = stringIndex assert_eq(pdf, gdf) stringIndex = StringIndex(["a", "b", "c", "d", "e"], name="name") pdf.index = stringIndex.to_pandas() gdf.index = stringIndex assert_eq(pdf, gdf) stringIndex = as_index(as_column(["a", "b", "c", "d", "e"]), name="name") pdf.index = stringIndex.to_pandas() gdf.index = stringIndex assert_eq(pdf, gdf) def test_multiindex_row_shape(): pdf = pd.DataFrame(np.random.rand(0, 5)) gdf = cudf.from_pandas(pdf) pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]]) pdfIndex.names = ["alpha"] gdfIndex = cudf.from_pandas(pdfIndex) assert_eq(pdfIndex, gdfIndex) with pytest.raises(ValueError): pdf.index = pdfIndex with pytest.raises(ValueError): gdf.index = gdfIndex @pytest.fixture def pdf(): return pd.DataFrame(np.random.rand(7, 5)) @pytest.fixture def gdf(pdf): return cudf.from_pandas(pdf) @pytest.fixture def pdfIndex(): pdfIndex = pd.MultiIndex( [ ["a", "b", "c"], ["house", "store", "forest"], ["clouds", "clear", "storm"], ["fire", "smoke", "clear"], [ np.datetime64("2001-01-01", "ns"), np.datetime64("2002-01-01", "ns"), np.datetime64("2003-01-01", "ns"), ], ], [ [0, 0, 0, 0, 1, 1, 2], [1, 1, 1, 1, 0, 0, 2], [0, 0, 2, 2, 2, 0, 1], [0, 0, 0, 1, 2, 0, 1], [1, 0, 1, 2, 0, 0, 1], ], ) pdfIndex.names = ["alpha", "location", "weather", "sign", "timestamp"] return pdfIndex @pytest.fixture def pdfIndexNulls(): pdfIndex = pd.MultiIndex( [ ["a", "b", "c"], ["house", "store", "forest"], ["clouds", "clear", "storm"], ], [ [0, 0, 0, -1, 1, 1, 2], [1, -1, 1, 1, 0, 0, -1], [-1, 0, 2, 2, 2, 0, 1], ], ) pdfIndex.names = ["alpha", "location", "weather"] return pdfIndex def test_from_pandas(pdf, pdfIndex): pdf.index = pdfIndex gdf = cudf.from_pandas(pdf) assert_eq(pdf, gdf) def test_multiindex_transpose(pdf, pdfIndex): pdf.index = pdfIndex gdf = cudf.from_pandas(pdf) assert_eq(pdf.transpose(), gdf.transpose()) def test_from_pandas_series(): pdf = pd.DataFrame( {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} ).set_index(["a", "b"]) result = cudf.from_pandas(pdf) assert_eq(pdf, result) test_pdf = pdf["c"] result = cudf.from_pandas(test_pdf) assert_eq(test_pdf, result) def test_series_multiindex(pdfIndex): ps = pd.Series(np.random.rand(7)) gs = cudf.from_pandas(ps) ps.index = pdfIndex gs.index = cudf.from_pandas(pdfIndex) assert_eq(ps, gs) def test_multiindex_take(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex assert_eq(pdf.index.take([0]), gdf.index.take([0])) assert_eq(pdf.index.take(np.array([0])), gdf.index.take(np.array([0]))) from cudf import Series assert_eq(pdf.index.take(pd.Series([0])), gdf.index.take(Series([0]))) assert_eq(pdf.index.take([0, 1]), gdf.index.take([0, 1])) assert_eq( pdf.index.take(np.array([0, 1])), gdf.index.take(np.array([0, 1])) ) assert_eq( pdf.index.take(pd.Series([0, 1])), gdf.index.take(Series([0, 1])) ) def test_multiindex_getitem(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex assert_eq(pdf.index[0], gdf.index[0]) @pytest.mark.parametrize( "key_tuple", [ # return 2 rows, 0 remaining keys = dataframe with entire index ("a", "store", "clouds", "fire"), (("a", "store", "clouds", "fire"), slice(None)), # return 2 rows, 1 remaining key = dataframe with n-k index columns ("a", "store", "storm"), (("a", "store", "storm"), slice(None)), # return 2 rows, 2 remaining keys = dataframe with n-k index columns ("a", "store"), (("a", "store"), slice(None)), # return 2 rows, n-1 remaining keys = dataframe with n-k index columns ("a",), (("a",), slice(None)), # return 1 row, 0 remaining keys = dataframe with entire index ("a", "store", "storm", "smoke"), (("a", "store", "storm", "smoke"), slice(None)), # return 1 row and 1 remaining key = series ("c", "forest", "clear"), (("c", "forest", "clear"), slice(None)), ], ) def test_multiindex_loc(pdf, gdf, pdfIndex, key_tuple): gdfIndex = cudf.from_pandas(pdfIndex) assert_eq(pdfIndex, gdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex assert_eq(pdf.loc[key_tuple], gdf.loc[key_tuple]) def test_multiindex_loc_slice(pdf, gdf, pdfIndex): gdf = cudf.from_pandas(pdf) gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex assert_eq( pdf.loc[("a", "store"):("b", "house")], gdf.loc[("a", "store"):("b", "house")], ) def test_multiindex_loc_then_column(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) assert_eq(pdfIndex, gdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex assert_eq( pdf.loc[("a", "store", "clouds", "fire"), :][0], gdf.loc[("a", "store", "clouds", "fire"), :][0], ) def test_multiindex_loc_rows_0(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex with pytest.raises(KeyError): print(pdf.loc[("d",), :].to_pandas()) with pytest.raises(KeyError): print(gdf.loc[("d",), :].to_pandas()) assert_eq(pdf, gdf) def test_multiindex_loc_rows_1_2_key(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex print(pdf.loc[("c", "forest"), :]) print(gdf.loc[("c", "forest"), :].to_pandas()) assert_eq(pdf.loc[("c", "forest"), :], gdf.loc[("c", "forest"), :]) def test_multiindex_loc_rows_1_1_key(pdf, gdf, pdfIndex): gdfIndex = cudf.from_pandas(pdfIndex) pdf.index = pdfIndex gdf.index = gdfIndex print(pdf.loc[("c",), :]) print(gdf.loc[("c",), :].to_pandas()) assert_eq(pdf.loc[("c",), :], gdf.loc[("c",), :]) def test_multiindex_column_shape(): pdf = pd.DataFrame(np.random.rand(5, 0)) gdf = cudf.from_pandas(pdf) pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]]) pdfIndex.names = ["alpha"] gdfIndex = cudf.from_pandas(pdfIndex) assert_eq(pdfIndex, gdfIndex) with pytest.raises(ValueError): pdf.columns = pdfIndex with pytest.raises(ValueError): gdf.columns = gdfIndex @pytest.mark.parametrize( "query", [ ("a", "store", "clouds", "fire"), ("a", "store", "storm", "smoke"), ("a", "store"), ("b", "house"), ("a", "store", "storm"), ("a",), ("c", "forest", "clear"), ], ) def test_multiindex_columns(pdf, gdf, pdfIndex, query): pdf = pdf.T gdf = cudf.from_pandas(pdf) gdfIndex = cudf.from_pandas(pdfIndex) assert_eq(pdfIndex, gdfIndex) pdf.columns = pdfIndex gdf.columns = gdfIndex assert_eq(pdf[query], gdf[query]) def test_multiindex_from_tuples(): arrays = [["a", "a", "b", "b"], ["house", "store", "house", "store"]] tuples = list(zip(*arrays)) pmi =
pd.MultiIndex.from_tuples(tuples)
pandas.MultiIndex.from_tuples
import numpy as np import pandas as pd import pytest from hypothesis import given, settings from pandas.testing import assert_frame_equal from janitor.testing_utils.strategies import ( conditional_df, conditional_right, conditional_series, ) @pytest.mark.xfail(reason="empty object will pass thru") @given(s=conditional_series()) def test_df_empty(s): """Raise ValueError if `df` is empty.""" df = pd.DataFrame([], dtype="int", columns=["A"]) with pytest.raises(ValueError): df.conditional_join(s, ("A", "non", "==")) @pytest.mark.xfail(reason="empty object will pass thru") @given(df=conditional_df()) def test_right_empty(df): """Raise ValueError if `right` is empty.""" s = pd.Series([], dtype="int", name="A") with pytest.raises(ValueError): df.conditional_join(s, ("A", "non", "==")) @given(df=conditional_df()) def test_right_df(df): """Raise TypeError if `right` is not a Series/DataFrame.""" with pytest.raises(TypeError): df.conditional_join({"non": [2, 3, 4]}, ("A", "non", "==")) @given(df=conditional_df(), s=conditional_series()) def test_right_series(df, s): """Raise ValueError if `right` is not a named Series.""" with pytest.raises(ValueError): df.conditional_join(s, ("A", "non", "==")) @given(df=conditional_df()) def test_df_MultiIndex(df): """Raise ValueError if `df` columns is a MultiIndex.""" with pytest.raises(ValueError): df.columns = [list("ABCDE"), list("FGHIJ")] df.conditional_join( pd.Series([2, 3, 4], name="A"), (("A", "F"), "non", "==") ) @given(df=conditional_df()) def test_right_MultiIndex(df): """Raise ValueError if `right` columns is a MultiIndex.""" with pytest.raises(ValueError): right = df.copy() right.columns = [list("ABCDE"), list("FGHIJ")] df.conditional_join(right, (("A", "F"), "non", ">=")) @given(df=conditional_df(), s=conditional_series()) def test_check_conditions_exist(df, s): """Raise ValueError if no condition is provided.""" with pytest.raises(ValueError): s.name = "B" df.conditional_join(s) @given(df=conditional_df(), s=conditional_series()) def test_check_condition_type(df, s): """Raise TypeError if any condition in conditions is not a tuple.""" with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, ("A", "B", ""), ["A", "B"]) @given(df=conditional_df(), s=conditional_series()) def test_check_condition_length(df, s): """Raise ValueError if any condition is not length 3.""" with pytest.raises(ValueError): s.name = "B" df.conditional_join(s, ("A", "B", "C", "<")) df.conditional_join(s, ("A", "B", ""), ("A", "B")) @given(df=conditional_df(), s=conditional_series()) def test_check_left_on_type(df, s): """Raise TypeError if left_on is not a string.""" with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, (1, "B", "<")) @given(df=conditional_df(), s=conditional_series()) def test_check_right_on_type(df, s): """Raise TypeError if right_on is not a string.""" with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, ("B", 1, "<")) @given(df=conditional_df(), s=conditional_series()) def test_check_op_type(df, s): """Raise TypeError if the operator is not a string.""" with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, ("B", "B", 1)) @given(df=conditional_df(), s=conditional_series()) def test_check_column_exists_df(df, s): """ Raise ValueError if `left_on` can not be found in `df`. """ with pytest.raises(ValueError): s.name = "B" df.conditional_join(s, ("C", "B", "<")) @given(df=conditional_df(), s=conditional_series()) def test_check_column_exists_right(df, s): """ Raise ValueError if `right_on` can not be found in `right`. """ with pytest.raises(ValueError): s.name = "B" df.conditional_join(s, ("B", "A", ">=")) @given(df=conditional_df(), s=conditional_series()) def test_check_op_correct(df, s): """ Raise ValueError if `op` is not any of `!=`, `<`, `>`, `>=`, `<=`. """ with pytest.raises(ValueError): s.name = "B" df.conditional_join(s, ("B", "B", "=!")) @given(df=conditional_df(), s=conditional_series()) def test_check_how_type(df, s): """ Raise TypeError if `how` is not a string. """ with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, ("B", "B", "<"), how=1) @given(df=conditional_df(), s=conditional_series()) def test_check_how_value(df, s): """ Raise ValueError if `how` is not one of `inner`, `left`, or `right`. """ with pytest.raises(ValueError): s.name = "B" df.conditional_join(s, ("B", "B", "<"), how="INNER") @given(df=conditional_df(), right=conditional_right()) def test_dtype_strings_non_equi(df, right): """ Raise ValueError if the dtypes are both strings on a non-equi operator. """ with pytest.raises(ValueError): df.conditional_join(right, ("C", "Strings", "<")) @given(df=conditional_df(), s=conditional_series()) def test_dtype_not_permitted(df, s): """ Raise ValueError if dtype of column in `df` is not an acceptable type. """ df["F"] = pd.Timedelta("1 days") with pytest.raises(ValueError): s.name = "A" df.conditional_join(s, ("F", "A", "<")) @given(df=conditional_df(), s=conditional_series()) def test_dtype_str(df, s): """ Raise ValueError if dtype of column in `df` does not match the dtype of column from `right`. """ with pytest.raises(ValueError): s.name = "A" df.conditional_join(s, ("C", "A", "<")) @given(df=conditional_df(), s=conditional_series()) def test_dtype_category_non_equi(df, s): """ Raise ValueError if dtype is category, and op is non-equi. """ with pytest.raises(ValueError): s.name = "A" s = s.astype("category") df["C"] = df["C"].astype("category") df.conditional_join(s, ("C", "A", "<")) @given(df=conditional_df(), s=conditional_series()) def test_check_sort_by_appearance_type(df, s): """ Raise TypeError if `sort_by_appearance` is not a boolean. """ with pytest.raises(TypeError): s.name = "B" df.conditional_join(s, ("B", "B", "<"), sort_by_appearance="True") @given(df=conditional_df(), right=conditional_right()) def test_single_condition_less_than_floats(df, right): """Test output for a single condition. "<".""" left_on, right_on = ["B", "Numeric"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "<"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_less_than_ints(df, right): """Test output for a single condition. "<".""" left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1) .merge(right.assign(t=1, C="2"), on="t") .query(f"{left_on} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "<"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_less_than_ints_extension_array(df, right): """Test output for a single condition. "<".""" df = df.assign(A=df["A"].astype("Int64")) right = right.assign(Integers=right["Integers"].astype(pd.Int64Dtype())) left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "<"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_less_than_equal(df, right): """Test output for a single condition. "<=". DateTimes""" left_on, right_on = ["E", "Dates"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "<="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_less_than_date(df, right): """Test output for a single condition. "<". Dates""" left_on, right_on = ["E", "Dates"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "<"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_greater_than_datetime(df, right): """Test output for a single condition. ">". Datetimes""" left_on, right_on = ["E", "Dates"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} > {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, ">"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_greater_than_ints(df, right): """Test output for a single condition. ">=".""" left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} >= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, ">="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_greater_than_floats_floats(df, right): """Test output for a single condition. ">".""" left_on, right_on = ["B", "Numeric"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} > {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, ">"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_greater_than_ints_extension_array(df, right): """Test output for a single condition. ">=".""" left_on, right_on = ["A", "Integers"] df = df.assign(A=df["A"].astype("Int64")) right = right.assign(Integers=right["Integers"].astype(pd.Int64Dtype())) expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} > {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, ">"), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_not_equal_numeric(df, right): """Test output for a single condition. "!=".""" left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .dropna(subset=["A", "Integers"]) .query(f"{left_on} != {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "!="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_not_equal_ints_only(df, right): """Test output for a single condition. "!=".""" left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .dropna(subset=["A", "Integers"]) .query(f"{left_on} != {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "!="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_not_equal_floats_only(df, right): """Test output for a single condition. "!=".""" left_on, right_on = ["B", "Numeric"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .dropna(subset=["B", "Numeric"]) .query(f"{left_on} != {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "!="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_not_equal_datetime(df, right): """Test output for a single condition. "!=".""" left_on, right_on = ["E", "Dates"] expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .dropna(subset=["E", "Dates"]) .query(f"{left_on} != {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "!="), how="inner", sort_by_appearance=True ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_equality_string(df, right): """Test output for a single condition. "==".""" left_on, right_on = ["C", "Strings"] expected = df.dropna(subset=[left_on]).merge( right.dropna(subset=[right_on]), left_on=left_on, right_on=right_on ) expected = expected.reset_index(drop=True) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "=="), how="inner", sort_by_appearance=False ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @pytest.mark.xfail( reason="""sometimes, categories are coerced to objects; might be a pandas version issue. """ ) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_equality_category(df, right): """Test output for a single condition. "==".""" left_on, right_on = ["C", "Strings"] df = df.assign(C=df["C"].astype("category")) right = right.assign(Strings=right["Strings"].astype("category")) expected = df.dropna(subset=[left_on]).merge( right.dropna(subset=[right_on]), left_on=left_on, right_on=right_on ) expected = expected.reset_index(drop=True) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "=="), how="inner", sort_by_appearance=False ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_equality_numeric(df, right): """Test output for a single condition. "==".""" left_on, right_on = ["A", "Integers"] df = df.assign(A=df["A"].astype("Int64")) right = right.assign(Integers=right["Integers"].astype(pd.Int64Dtype())) df.loc[0, "A"] = pd.NA right.loc[0, "Integers"] = pd.NA expected = df.dropna(subset=[left_on]).merge( right.dropna(subset=[right_on]), left_on=left_on, right_on=right_on ) expected = expected.reset_index(drop=True) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "=="), how="inner", sort_by_appearance=False ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_single_condition_equality_datetime(df, right): """Test output for a single condition. "==".""" left_on, right_on = ["E", "Dates"] expected = df.dropna(subset=[left_on]).merge( right.dropna(subset=[right_on]), left_on=left_on, right_on=right_on ) expected = expected.reset_index(drop=True) expected = expected.filter([left_on, right_on]) actual = df.conditional_join( right, (left_on, right_on, "=="), how="inner", sort_by_appearance=False ) actual = actual.filter([left_on, right_on]) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_how_left(df, right): """Test output when `how==left`. "<=".""" left_on, right_on = ["A", "Integers"] expected = ( df.assign(t=1, index=np.arange(len(df))) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {right_on}") ) expected = expected.set_index("index") expected.index.name = None expected = df.join( expected.filter(right.columns), how="left", sort=False ).reset_index(drop=True) actual = df.conditional_join( right, (left_on, right_on, "<="), how="left", sort_by_appearance=True ) assert_frame_equal(expected, actual) @given(df=conditional_df(), right=conditional_right()) def test_how_right(df, right): """Test output when `how==right`. ">".""" left_on, right_on = ["E", "Dates"] expected = ( df.assign(t=1) .merge(right.assign(t=1, index=np.arange(len(right))), on="t") .query(f"{left_on} > {right_on}") ) expected = expected.set_index("index") expected.index.name = None expected = ( expected.filter(df.columns) .join(right, how="right", sort=False) .reset_index(drop=True) ) actual = df.conditional_join( right, (left_on, right_on, ">"), how="right", sort_by_appearance=True ) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_gt_and_lt_dates(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("E", "Dates", "Dates_Right") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} < {middle} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, left_on, ">"), (middle, right_on, "<"), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on]) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_ge_and_le_dates(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("E", "Dates", "Dates_Right") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {middle} <= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, left_on, ">="), (middle, right_on, "<="), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on]) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_le_and_ge_dates(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("E", "Dates", "Dates_Right") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {middle} <= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, right_on, "<="), (middle, left_on, ">="), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on]) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_ge_and_le_numbers(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("B", "Numeric", "Floats") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {middle} <= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, left_on, ">="), (middle, right_on, "<="), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on]) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_le_and_ge_numbers(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("B", "Numeric", "Floats") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} <= {middle} <= {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, right_on, "<="), (middle, left_on, ">="), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on]) assert_frame_equal(expected, actual) @settings(deadline=None) @given(df=conditional_df(), right=conditional_right()) def test_dual_conditions_gt_and_lt_numbers(df, right): """Test output for interval conditions.""" middle, left_on, right_on = ("B", "Numeric", "Floats") expected = ( df.assign(t=1) .merge(right.assign(t=1), on="t") .query(f"{left_on} < {middle} < {right_on}") .reset_index(drop=True) ) expected = expected.filter([left_on, middle, right_on]) actual = df.conditional_join( right, (middle, left_on, ">"), (middle, right_on, "<"), how="inner", sort_by_appearance=True, ) actual = actual.filter([left_on, middle, right_on])
assert_frame_equal(expected, actual)
pandas.testing.assert_frame_equal
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. # A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is distributed # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. from typing import TYPE_CHECKING, Callable, Dict, Iterable, Iterator import numpy as np import pandas as pd from autogluon import TabularPrediction as task from pandas.tseries.holiday import USFederalHolidayCalendar as calendar from gluonts.dataset.common import DataEntry, Dataset from gluonts.dataset.field_names import FieldName from gluonts.dataset.util import to_pandas from gluonts.model.estimator import Estimator from gluonts.model.forecast import SampleForecast from gluonts.model.predictor import Localizer, Predictor if TYPE_CHECKING: # avoid circular import from gluonts.model.estimator import Estimator OutputTransform = Callable[[DataEntry, np.ndarray], np.ndarray] def get_prediction_dataframe(series): hour_of_day = series.index.hour month_of_year = series.index.month day_of_week = series.index.dayofweek year_idx = series.index.year target = series.values cal = calendar() holidays = cal.holidays(start=series.index.min(), end=series.index.max()) df = pd.DataFrame( zip( year_idx, month_of_year, day_of_week, hour_of_day, series.index.isin(holidays), target, ), columns=[ "year_idx", "month_of_year", "day_of_week", "hour_of_day", "holiday", "target", ], ) convert_type = {x: "category" for x in df.columns.values[:4]} df = df.astype(convert_type) return df class TabularPredictor(Predictor): def __init__( self, ag_model, freq: str, prediction_length: int, ) -> None: self.ag_model = ag_model # task? self.freq = freq self.prediction_length = prediction_length def predict(self, dataset: Iterable[Dict]) -> Iterator[SampleForecast]: for entry in dataset: ts = to_pandas(entry) start = ts.index[-1] +
pd.tseries.frequencies.to_offset(self.freq)
pandas.tseries.frequencies.to_offset
import numpy as np np.warnings.filterwarnings('ignore') #to not display numpy warnings... be careful import pandas as pd from mpi4py import MPI import matplotlib.pyplot as plt import numpy as np import pandas as pd from subprocess import call from orca import * from orca.data import * from datetime import datetime import warnings from ptreeopt.tree import PTree warnings.filterwarnings('ignore') # this whole script will run on all processors requested by the job script with open('orca/data/scenario_names_all.txt') as f: scenarios = f.read().splitlines() with open('orca/data/demand_scenario_names_all.txt') as f: demand_scenarios = f.read().splitlines() calc_indices = False climate_forecasts = False simulation = True tree_input_files = False indicator_data_file = False window_type = 'rolling' window_length = 40 index_exceedence_sac = 8 shift = 0 SHA_shift = shift ORO_shift = shift FOL_shift = shift SHA_baseline = pd.read_csv('orca/data/baseline_storage/SHA_storage.csv',parse_dates = True, index_col = 0) SHA_baseline = SHA_baseline[(SHA_baseline.index >= '2006-09-30') & (SHA_baseline.index <= '2099-10-01')] ORO_baseline = pd.read_csv('orca/data/baseline_storage/ORO_storage.csv',parse_dates = True, index_col = 0) ORO_baseline = ORO_baseline[(ORO_baseline.index >= '2006-09-30') & (ORO_baseline.index <= '2099-10-01')] FOL_baseline = pd.read_csv('orca/data/baseline_storage/FOL_storage.csv',parse_dates = True, index_col = 0) FOL_baseline = FOL_baseline[(FOL_baseline.index >= '2006-09-30') & (FOL_baseline.index <= '2099-10-01')] features = json.load(open('orca/data/json_files/indicators_whole_bounds.json')) feature_names = [] feature_bounds = [] indicator_codes = [] min_depth = 4 for k,v in features.items(): indicator_codes.append(k) feature_names.append(v['name']) feature_bounds.append(v['bounds']) action_dict = json.load(open('orca/data/json_files/action_list.json')) actions = action_dict['actions'] snapshots = pickle.load(open('snapshots/training_scenarios_seed_2.pkl', 'rb')) P = snapshots['best_P'][-1][0] demand_indicators = {} for D in demand_scenarios: dfdemand = pd.read_csv('orca/data/demand_files/%s.csv'%D, index_col = 0, parse_dates = True) dfdemand['demand_multiplier'] = dfdemand['combined_demand'] dfd_ind = pd.DataFrame(index = dfdemand.index) for i in features: #indicators ind = features[i] if ind['type'] == 'demand': if ind['delta'] == 'no': if ind['stat'] == 'mu': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).mean()*100 elif ind['stat'] == 'sig': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).std()*100 elif ind['stat'] == 'max': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).max()*100 else: if ind['stat'] == 'mu': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).mean().pct_change(periods=ind['delta'])*100 elif ind['stat'] == 'sig': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).std().pct_change(periods=ind['delta'])*100 elif ind['stat'] == 'max': dfd_ind[i] = dfdemand.demand_multiplier.resample('AS-OCT').first().rolling(ind['window']).max().pct_change(periods=ind['delta'])*100 elif ind['type'] == "discount": discount_indicator = i demand_indicators[D] = dfd_ind indicator_columns = [] comm = MPI.COMM_WORLD # communication object rank = comm.rank # what number processor am I? sc = scenarios[rank] call(['mkdir', 'orca/data/scenario_runs/%s'%sc]) if calc_indices: gains_loop_df = pd.read_csv('orca/data/historical_runs_data/gains_loops.csv', index_col = 0, parse_dates = True) OMR_loop_df = pd.read_csv('orca/data/historical_runs_data/OMR_loops.csv', index_col = 0, parse_dates = True) input_df = pd.read_csv('orca/data/input_climate_files/%s_input_data.csv'%sc, index_col = 0, parse_dates = True) proj_ind_df, ind_df = process_projection(input_df,gains_loop_df,OMR_loop_df,'orca/data/json_files/gains_regression.json','orca/data/json_files/inf_regression.json',window = window_type) proj_ind_df.to_csv('orca/data/scenario_runs/%s/orca-data-processed-%s.csv'%(sc,sc)) ind_df.to_csv('orca/data/scenario_runs/%s/hydrologic-indicators-%s.csv'%(sc,sc)) # proj_ind_df = pd.read_csv('orca/data/scenario_runs/%s/orca-data-processed-%s.csv'%(sc,sc),index_col = 0, parse_dates = True) WYI_stats_file =
pd.read_csv('orca/data/forecast_regressions/WYI_forcasting_regression_stats.csv', index_col = 0, parse_dates = True)
pandas.read_csv
# -*- coding: utf-8 -*- """ Created on Thu Mar 26 10:52:43 2020 @author: Celina """ import pandas as pd import outdoor.excel_wrapper.Wrapping_Functions as WF def wrapp_GeneralData(obj, df1): """ Description ----------- Get general Process Data: Lifetime and Group Context ---------- Function is called in Wrapp_ProcessUnits Parameters ---------- df1 : Dataframe which holds information of LT and Group """ Name = df1.iloc[0,0] LifeTime = df1.iloc[4,0] ProcessGroup = df1.iloc[3,0] if not pd.isnull(df1.iloc[12,0]): emissions = df1.iloc[12,0] else: emissions = 0 if not pd.isnull(df1.iloc[13,0]): maintenance_factor = df1.iloc[13,0] else: maintenance_factor = 0.044875 cost_percentage = None time_span = None time_mode = 'No Mode' if not pd.isnull(df1.iloc[14,0]): cost_percentage = df1.iloc[14,0] time_span = df1.iloc[15,0] if df1.iloc[16,0] == 'Yearly': time_mode = 'Yearly' else: time_mode = 'Hourly' if not pd.isnull(df1.iloc[17,0]): full_load_hours = df1.iloc[17,0] else: full_load_hours = None obj.set_generalData(ProcessGroup, LifeTime, emissions, full_load_hours, maintenance_factor, cost_percentage, time_span, time_mode) def wrapp_ReacionData(obj, df1, df2 = None): """ Description ----------- Get Reaction Data (Stoichiometric or Yield Function) from Excel sheet Context ---------- Function is called in Wrapp_ProcessUnits Parameters ---------- df1 : Dataframe which either holds Stoichiometric or Yield Coefficents df2: Dataframe which is either empty or holds conversion factors """ if obj.Type == "Yield-Reactor": dict1 = WF.read_type1(df1,0,1) obj.set_xiFactors(dict1) list1 = WF.read_list_new(df1, 2, 0) obj.set_inertComponents(list1) else: dict1 = WF.read_type2(df1,0,1,2) obj.set_gammaFactors(dict1) dict2 = WF.read_type2(df2,0,1,2) obj.set_thetaFactors(dict2) def wrapp_EnergyData(obj, df, df2, df3): """ Description ----------- Define specific columns from the spreadsheet to set the energydatas. Sets Demands, ReferenceFlow Types and Components for El, Heat1 and Heat2. But only if there are values in the Excel, if not, than these values are left as None Also: Calls wrapp_Temperatures, which sets Temperature and Tau for Heat Context ---------- Function is called in Wrapp.ProcessUnits Parameters ---------- df : Dataframe which holds inforation of energy demand and reference flow type df2 : Dataframe which holds information of reference flow components df3 : Dataframe which holds information on heat temperatures """ # Set Reference Flow Type: if not pd.isnull(df.iloc[0,1]): ProcessElectricityDemand = df.iloc[0,1] ProcessElectricityReferenceFlow = df.iloc[1,1] ProcessElectricityReferenceComponentList = WF.read_list_new(df2, 1, 2) else: ProcessElectricityDemand = 0 ProcessElectricityReferenceFlow = None ProcessElectricityReferenceComponentList = [] if not pd.isnull(df.iloc[0,2]): ProcessHeatDemand = df.iloc[0,2] ProcessHeatReferenceFlow = df.iloc[1,2] ProcessHeatReferenceComponentList = WF.read_list_new(df2, 2, 2) else: ProcessHeatDemand = None ProcessHeatReferenceFlow = None ProcessHeatReferenceComponentList = [] if not pd.isnull(df.iloc[0,3]): ProcessHeat2Demand = df.iloc[0,3] ProcessHeat2ReferenceFlow = df.iloc[1,3] ProcessHeat2ReferenceComponentList = WF.read_list_new(df2, 3, 2) else: ProcessHeat2Demand = None ProcessHeat2ReferenceFlow = None ProcessHeat2ReferenceComponentList = [] wrapp_Temperatures(obj, df3, df) obj.set_energyData(None, None, ProcessElectricityDemand, ProcessHeatDemand, ProcessHeat2Demand, ProcessElectricityReferenceFlow, ProcessElectricityReferenceComponentList, ProcessHeatReferenceFlow, ProcessHeatReferenceComponentList, ProcessHeat2ReferenceFlow, ProcessHeat2ReferenceComponentList ) def wrapp_Temperatures(obj, df1, df2): """ Description ----------- Set Process Temperatures and specific energy demand (tau) from Excel file If no Temperatures and tau are defined everything is set to None Sets Tau1 and Tau2 only if the values are really available, otherwise Temperatures and Tau values are set to None Parameters ---------- obj : Process unit object df1 : Dataframe holding the information about the Temperatures needed df2 : Dataframe holding the inforamation about specific energy damand """ obj.set_Temperatures() if not pd.isnull(df2.iloc[0,2]): TIN1 = df1.iloc[7,0] TOUT1 = df1.iloc[8,0] tau1 = df2.iloc[0,2] obj.set_Temperatures(TIN1, TOUT1, tau1) if not pd.isnull(df2.iloc[0,3]): tau2 = df2.iloc[0,3] TIN2 = df1.iloc[9,0] TOUT2 = df1.iloc[10,0] obj.set_Temperatures(TIN1, TOUT1, tau1, TIN2, TOUT2, tau2) def wrapp_AdditivesData(obj,df1, df2, df3): """ Description ----------- Define specific columns from the spreadsheet to set the added Input-flows Define specific columns from the spreadsheet to set the concentration datas Context ---------- function is called in Wrapp.ProcessUnits Parameters ---------- df1 : Dataframe df2 : Dataframe """ req_concentration = None lhs_comp_list = WF.read_list (df2,1) rhs_comp_list = WF.read_list (df2,3) lhs_ref_flow = df2.iloc[0,0] rhs_ref_flow = df2.iloc[0,2] if not pd.isnull(df2.iloc[0,4]): req_concentration = df2.iloc[0,4] myu_dict = WF.read_type2 (df3,0,1,2) obj.set_flowData(req_concentration, rhs_ref_flow, lhs_ref_flow, rhs_comp_list, lhs_comp_list, myu_dict, ) sourceslist = WF.read_list(df1,0) obj.set_possibleSources(sourceslist) def wrapp_EconomicData(obj, df, df2): """ Description ----------- Get Economic information from Excel Sheet Colomns defined in df and df2 Context ----------- Function is called in Wrapp.ProcessUnits Parameters ---------- df : Dataframe with economic CAPEX Factors and Components List df2 : Dataframe with General Factors for Direct and Indirect Costs """ ReferenceCosts = df.iloc[0,1] ReferenceFlow = df.iloc[1,1] CostExponent = df.iloc[2,1] ReferenceYear= df.iloc[3,1] DirectCostFactor = df2.iloc[5,0] IndirectCostFactor = df2.iloc[6,0] ReferenceFlowType = df.iloc[4,1] ReferenceFlowComponentList = WF.read_list_new(df, 1, 5) # Set Economic Data in Process Unit Object obj.set_economicData(DirectCostFactor, IndirectCostFactor, ReferenceCosts, ReferenceFlow, CostExponent, ReferenceYear, ReferenceFlowType, ReferenceFlowComponentList ) def wrapp_ProductpoolData(obj, series): """ Description ----------- Define specific columns from the spreadsheet Productpool to set Productname, Productprice and Producttype Context ---------- function is called in Wrapp_ProcessUnits Parameters ---------- df : Dataframe """ obj.ProductName= series[4] obj.set_productPrice(series[8]) obj.ProductType = series[9] obj.set_group(series[7]) EmissionCredits = 0 if not pd.isnull(series[10]): EmissionCredits = series[10] minp = 0 maxp = 10000000 if not
pd.isnull(series[11])
pandas.isnull
# -*- coding: utf-8 -*- import re import numpy as np import pytest from pandas.core.dtypes.common import ( is_bool_dtype, is_categorical, is_categorical_dtype, is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_interval_dtype, is_period, is_period_dtype, is_string_dtype) from pandas.core.dtypes.dtypes import ( CategoricalDtype, DatetimeTZDtype, IntervalDtype, PeriodDtype, registry) import pandas as pd from pandas import ( Categorical, CategoricalIndex, IntervalIndex, Series, date_range) from pandas.core.sparse.api import SparseDtype import pandas.util.testing as tm @pytest.fixture(params=[True, False, None]) def ordered(request): return request.param class Base(object): def setup_method(self, method): self.dtype = self.create() def test_hash(self): hash(self.dtype) def test_equality_invalid(self): assert not self.dtype == 'foo' assert not is_dtype_equal(self.dtype, np.int64) def test_numpy_informed(self): pytest.raises(TypeError, np.dtype, self.dtype) assert not self.dtype == np.str_ assert not np.str_ == self.dtype def test_pickle(self): # make sure our cache is NOT pickled # clear the cache type(self.dtype).reset_cache() assert not len(self.dtype._cache) # force back to the cache result = tm.round_trip_pickle(self.dtype) assert not len(self.dtype._cache) assert result == self.dtype class TestCategoricalDtype(Base): def create(self): return CategoricalDtype() def test_pickle(self): # make sure our cache is NOT pickled # clear the cache type(self.dtype).reset_cache() assert not len(self.dtype._cache) # force back to the cache result = tm.round_trip_pickle(self.dtype) assert result == self.dtype def test_hash_vs_equality(self): dtype = self.dtype dtype2 = CategoricalDtype() assert dtype == dtype2 assert dtype2 == dtype assert hash(dtype) == hash(dtype2) def test_equality(self): assert is_dtype_equal(self.dtype, 'category') assert is_dtype_equal(self.dtype, CategoricalDtype()) assert not is_dtype_equal(self.dtype, 'foo') def test_construction_from_string(self): result = CategoricalDtype.construct_from_string('category') assert is_dtype_equal(self.dtype, result) pytest.raises( TypeError, lambda: CategoricalDtype.construct_from_string('foo')) def test_constructor_invalid(self): msg = "Parameter 'categories' must be list-like" with pytest.raises(TypeError, match=msg): CategoricalDtype("category") dtype1 = CategoricalDtype(['a', 'b'], ordered=True) dtype2 = CategoricalDtype(['x', 'y'], ordered=False) c = Categorical([0, 1], dtype=dtype1, fastpath=True) @pytest.mark.parametrize('values, categories, ordered, dtype, expected', [ [None, None, None, None, CategoricalDtype()], [None, ['a', 'b'], True, None, dtype1], [c, None, None, dtype2, dtype2], [c, ['x', 'y'], False, None, dtype2], ]) def test_from_values_or_dtype( self, values, categories, ordered, dtype, expected): result = CategoricalDtype._from_values_or_dtype(values, categories, ordered, dtype) assert result == expected @pytest.mark.parametrize('values, categories, ordered, dtype', [ [None, ['a', 'b'], True, dtype2], [None, ['a', 'b'], None, dtype2], [None, None, True, dtype2], ]) def test_from_values_or_dtype_raises(self, values, categories, ordered, dtype): msg = "Cannot specify `categories` or `ordered` together with `dtype`." with pytest.raises(ValueError, match=msg): CategoricalDtype._from_values_or_dtype(values, categories, ordered, dtype) def test_is_dtype(self): assert CategoricalDtype.is_dtype(self.dtype) assert CategoricalDtype.is_dtype('category') assert CategoricalDtype.is_dtype(CategoricalDtype()) assert not CategoricalDtype.is_dtype('foo') assert not CategoricalDtype.is_dtype(np.float64) def test_basic(self): assert is_categorical_dtype(self.dtype) factor = Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']) s = Series(factor, name='A') # dtypes assert is_categorical_dtype(s.dtype) assert is_categorical_dtype(s) assert not is_categorical_dtype(np.dtype('float64')) assert is_categorical(s.dtype) assert is_categorical(s) assert not is_categorical(np.dtype('float64')) assert not is_categorical(1.0) def test_tuple_categories(self): categories = [(1, 'a'), (2, 'b'), (3, 'c')] result = CategoricalDtype(categories) assert all(result.categories == categories) @pytest.mark.parametrize("categories, expected", [ ([True, False], True), ([True, False, None], True), ([True, False, "a", "b'"], False), ([0, 1], False), ]) def test_is_boolean(self, categories, expected): cat = Categorical(categories) assert cat.dtype._is_boolean is expected assert is_bool_dtype(cat) is expected assert is_bool_dtype(cat.dtype) is expected class TestDatetimeTZDtype(Base): def create(self): return DatetimeTZDtype('ns', 'US/Eastern') def test_alias_to_unit_raises(self): # 23990 with tm.assert_produces_warning(FutureWarning): DatetimeTZDtype('datetime64[ns, US/Central]') def test_alias_to_unit_bad_alias_raises(self): # 23990 with pytest.raises(TypeError, match=''): DatetimeTZDtype('this is a bad string') with pytest.raises(TypeError, match=''): DatetimeTZDtype('datetime64[ns, US/NotATZ]') def test_hash_vs_equality(self): # make sure that we satisfy is semantics dtype = self.dtype dtype2 = DatetimeTZDtype('ns', 'US/Eastern') dtype3 = DatetimeTZDtype(dtype2) assert dtype == dtype2 assert dtype2 == dtype assert dtype3 == dtype assert hash(dtype) == hash(dtype2) assert hash(dtype) == hash(dtype3) dtype4 = DatetimeTZDtype("ns", "US/Central") assert dtype2 != dtype4 assert hash(dtype2) != hash(dtype4) def test_construction(self): pytest.raises(ValueError, lambda: DatetimeTZDtype('ms', 'US/Eastern')) def test_subclass(self): a = DatetimeTZDtype.construct_from_string('datetime64[ns, US/Eastern]') b =
DatetimeTZDtype.construct_from_string('datetime64[ns, CET]')
pandas.core.dtypes.dtypes.DatetimeTZDtype.construct_from_string
# -*- coding: utf-8 -*- """ Created on Mon Feb 5 12:27:07 2018 @author: djk """ import os import pandas as pd import numpy as np r2d = np.rad2deg d2r = np.deg2rad def IAGA2002_Header_Reader(IAGA2002_file): """ This function counts the header and comment rows in an IAGA 2002 format file. It is designed to cope with the number of header lines being either 12 or 13, and an arbitrary number of comment lines (including none). (The IAGA2002 format was last revised in June 2015 to allow an optional thirteenth header line 'Publication date'. Ref: https://www.ngdc.noaa.gov/IAGA/vdat/IAGA2002/iaga2002format.html) The rows of data are preceded by a row of column headers starting with "DATE" in columns 0:3. This string cannot occur earlier in the file, so detecting the first occurence of this string may be used to count the total number of header and comment lines. This function may be useful to define the number of rows to skip (n_header + n_comment) in another function designed to read in the data. While it is rather cumbersome, when reading in a long sequence of IAGA2002 files, the 'safety first' approach would be to call this function for each file in case the number of header lines changes within the sequence of files. Input parameter --------------- IAGA2002_file: string the full path and file name for the IAGA2002 data file Output ------ A tuple: with integer number of header rows (n_header), integer number of comment rows (n_comment), and headers, a dictionary containing the information in the headers. Dependencies ------------ pandas BGS Dependencies ---------------- None Revision date ------------- 5 Feb 2018 """ COMMENT_STR = '#' DATE_STR = 'DATE' head = ' ' n_header = 0 n_lines = 0 headers = {} with open(IAGA2002_file) as ofile: while head[0:4] != DATE_STR: head = next(ofile) if head[1] != COMMENT_STR: key = head[0:24].strip() val = head[24:69].strip() headers[key] = val n_header += 1 n_lines += 1 headers.pop(key) # Remove the data column header line from the dictionary n_comment = n_lines-n_header # The number of comment lines n_header -= 1 # The number of header lines return (n_header, n_comment, headers) def IAGA2002_Data_Reader(IAGA2002_file): """ This function reads the data in an IAGA 2002 format file into a pandas dataframe. Input parameter --------------- IAGA2002_file: string the full path and file name for the IAGA2002 data file Output ------ A pandas dataframe: vals - has the data with a datetime index and the column labels from the IAGA2002 file Dependencies ------------ pandas BGS Dependencies ---------------- IAGA2002_Header_Reader Revision date ------------- 5 Feb 2018 """ # Read the header and comment lines at the top of the file to get the number # of rows to skip before reading the data header = IAGA2002_Header_Reader(IAGA2002_file) nskip = header[0]+header[1] # Read the data into a pandas dataframe (an IAGA2002 file has 'DATE' and 'TIME' # as the first two column labels.) There's a trailing '|' on the column header # line which is interpreted as the header for a column of nans and this # property is used to delete it. DT_INDEX = 'DATE_TIME' vals = pd.read_csv(IAGA2002_file, delim_whitespace=True, skiprows=nskip, parse_dates=[DT_INDEX.split('_')], index_col=DT_INDEX) vals.dropna(inplace=True, axis=1) return(vals) def load_year(observatory=None, year=None, path=None): """Read in the daily 1-min files from a whole year. Parameters ---------- observatory: string Observatory code e.g. ESK year: int/string Desired year to load path: string Directory containing the files for that year Returns ------- DataFrame """ dates_in_year = pd.date_range( start=f'{year}-01-01', end=f'{year}-12-31', freq='D' ) df = pd.DataFrame() for date in dates_in_year: ymd = date.strftime('%Y%m%d') file_name = f'{observatory}{ymd}dmin.min' file_path = os.path.join(path, file_name) df = df.append(IAGA2002_Data_Reader(file_path)) return df def read_obs_hmv(obscode, year_st, year_fn, folder): """Read in observatory annual mean files in IAGA2002 format. This function reads the hourly mean value data in yearly IAGA2002 format files into a pandas dataframe. (Note: The data may be reported in different ways in different years (e.g. DFHZ, FXYZ).) Input parameters --------------- obscode: the IAGA observatory code: string (3 or 4 characters) year_st: the start year for the data request year_fn: the final year for the data request folder : the location of the yearly hmv files Output ------ A pandas dataframe: datareq This has columns of X, Y and Z data (only) and keeps the datetime index from the IAGA2002 files Dependencies ------------ pandas Local Dependencies ---------------- none Revision date ------------- 30 Jan 2019 """ OBSY = obscode.upper() obsy = obscode.lower() # Read in the observatory data one year file at a time and construct filenames datareq = pd.DataFrame() for year in range(year_st, year_fn+1): ystr = str(year) file = obsy + ystr + 'dhor.hor' fpf = folder + file tmp = IAGA2002_Data_Reader(fpf) tmp.columns = [col.strip(OBSY) for col in tmp.columns] if('D' in tmp.columns): xvals, yvals = dh2xy(tmp['D'], tmp['H']) tmp['X'] = xvals.round(decimals=1) tmp['Y'] = yvals.round(decimals=1) datareq = datareq.append(tmp[['X','Y', 'Z']]) return(datareq) def read_obs_hmv_declination(obscode, year_st, year_fn, folder): """Read (or calculate) the declination from hourly mean files in IAGA2002 format. This function reads the hourly mean value data in yearly IAGA2002 format files into a pandas dataframe for the specified observatory between year_st and year_fn. Note that D is reported in angular units of minutes of arc (and not degrees) in this file format. Input parameters --------------- obscode: the IAGA observatory code: string (3 or 4 characters) year_st: the start year for the data request year_fn: the final year for the data request folder : the location of the yearly hmv files Output ------ A pandas dataframe: datareq This has columns for datetime and declination Dependencies ------------ pandas Local Dependencies ---------------- none Revision date ------------- 24/06/19 (<NAME>) """ OBSY = obscode.upper() obsy = obscode.lower() # Read in the observatory data one year file at a time and construct filenames datareq =
pd.DataFrame()
pandas.DataFrame
from pathlib import Path import numpy as np import pandas as pd from dstools.pipeline.clients import SQLAlchemyClient from dstools import testing def test_can_check_nulls(tmp_directory): client = SQLAlchemyClient('sqlite:///' + str(Path(tmp_directory, 'db.db'))) df =
pd.DataFrame({'no_nas': [1, 2, 1], 'nas': [1, np.nan, 1]})
pandas.DataFrame
#!/usr/bin/env python # -*- coding: utf-8 -*- from geoedfframework.utils.GeoEDFError import GeoEDFError from geoedfframework.GeoEDFPlugin import GeoEDFPlugin import pandas as pd import requests from cdo_api_py import Client """ Module for implementing the GHCND input connector plugin. This plugin will retrieve data for five specific meterological parameters for a given station ID and date range. The plugin returns a CSV file for each parameter with data records for each intervening date. The CSV file is named based on the station and parameter. The new NOAA API is used that does not require tokens. """ class GHCNDInput(GeoEDFPlugin): # auth is also required by GHCNDInput __optional_params = [] __required_params = ['start_date','end_date','station_id'] # we use just kwargs since we need to be able to process the list of attributes # and their values to create the dependency graph in the GeoEDFInput super class def __init__(self, **kwargs): # list to hold all the parameter names; will be accessed in super to # construct dependency graph self.provided_params = self.__required_params + self.__optional_params # check that all required params have been provided for param in self.__required_params: if param not in kwargs: raise GeoEDFError('Required parameter %s for GHCNDInput not provided' % param) # set all required parameters for key in self.__required_params: setattr(self,key,kwargs.get(key)) # set optional parameters for key in self.__optional_params: # if key not provided in optional arguments, defaults value to None setattr(self,key,kwargs.get(key,None)) # set the hardcoded set of meterological params # can possibly generalize to fetch any list of params in the future self.met_params = ['SNOW','SNWD','TMAX','TMIN','PRCP'] # class super class init super().__init__() # each Input plugin needs to implement this method # if error, raise exception; if not, return True def get(self): # semantic checking of parameters # process dates try: startdate = pd.to_datetime(self.start_date,format='%m/%d/%Y') enddate = pd.to_datetime(self.end_date,format='%m/%d/%Y') except: raise GeoEDFError("Error parsing dates provided to GHCNDInput, please ensure format is mm/dd/YYYY") # param checks complete try: # parse out station_id station_id = self.station_id.split(':')[1] # use new API # construct URL station_data_url = "https://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=SNOW,PRCP,SNWD,TMIN,TMAX&stations=%s&startDate=2010-08-30&endDate=2020-09-30&format=json" % station_id res = requests.get(station_data_url) res.raise_for_status() station_data =
pd.read_json(res.text)
pandas.read_json
import pandas as pd def load_data(): df = pd.read_csv('./assets/BankChurners.csv') df.drop(['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2', 'CLIENTNUM'], axis=1, inplace=True) return df def balance_labels(df, random_state=42): attrited = df.loc[df['Attrition_Flag'] == 'Attrited Customer'] existing = df.loc[df['Attrition_Flag'] == 'Existing Customer'] balanced_df = pd.concat([attrited.reset_index(drop=True), existing.sample(n=len(attrited), replace=False, random_state=random_state).reset_index(drop=True)]) return balanced_df def get_data_target(df): target = df['Attrition_Flag'] data = df.drop(['Attrition_Flag'], axis=1) data =
pd.get_dummies(data, drop_first=False)
pandas.get_dummies
# -*- coding: utf-8 -*- # %% import pandas as pd import numpy as np import tkinter as tk class package: def __init__(self): # elements defined C = 12 H = 1.007825 N = 14.003074 O = 15.994915 P = 30.973763 S = 31.972072 Na = 22.98977 Cl = 34.968853 self.elements = [C,H,N,O,P,S,Na,Cl] self.elementsymbol = ['C','H','N','O','P','S','Na','Cl'] ionname = ['M','M+H','M+2H','M+H-H2O','M+2H-H2O','M+Na','M+2Na','M+2Na-H','M+NH4', 'M-H','M-2H','M-3H','M-4H','M-5H','M-H-H2O','M-2H-H2O','M-CH3','M+Cl','M+HCOO','M+OAc'] ionfunc = [] ionfunc.append(lambda ms: ms) ionfunc.append(lambda ms: ms+package().elements[1]) ionfunc.append(lambda ms: (ms+2*package().elements[1])/2) ionfunc.append(lambda ms: ms-package().elements[1]-package().elements[3]) ionfunc.append(lambda ms: (ms-package().elements[3])/2) ionfunc.append(lambda ms: ms+package().elements[6]) ionfunc.append(lambda ms: (ms+2*package().elements[6])/2) ionfunc.append(lambda ms: ms-package().elements[1]+2*package().elements[6]) ionfunc.append(lambda ms: ms+4*package().elements[1]+package().elements[2]) ionfunc.append(lambda ms: ms-package().elements[1]) ionfunc.append(lambda ms: (ms-2*package().elements[1])/2) ionfunc.append(lambda ms: (ms-3*package().elements[1])/3) ionfunc.append(lambda ms: (ms-4*package().elements[1])/4) ionfunc.append(lambda ms: (ms-5*package().elements[1])/5) ionfunc.append(lambda ms: ms-3*package().elements[1]-package().elements[3]) ionfunc.append(lambda ms: (ms-4*package().elements[1]-package().elements[3])/2) ionfunc.append(lambda ms: ms-package().elements[0]-3*package().elements[1]) ionfunc.append(lambda ms: ms+package().elements[7]) ionfunc.append(lambda ms: ms+package().elements[0]+package().elements[1]+2*package().elements[3]) ionfunc.append(lambda ms: ms+2*package().elements[0]+3*package().elements[1]+2*package().elements[3]) self.ion = {} for i,j in enumerate(ionname): self.ion[j] = ionfunc[i] # %% [markdown] # Package for Sphingolipids # %% class package_sl(package): def __init__(self): # base structure defined self.base = {'Cer': np.array([0,3,1,0]+[0]*(len(package().elements)-4)), 'Sphingosine': np.array([0,3,1,0]+[0]*(len(package().elements)-4)), 'Sphinganine': np.array([0,3,1,0]+[0]*(len(package().elements)-4))} # headgroups defined headgroup = ['Pi','Choline','Ethanolamine','Inositol','Glc','Gal','GalNAc','NeuAc','Fuc','NeuGc'] formula = [] formula.append(np.array([0,3,0,4,1]+[0]*(len(package().elements)-5))) formula.append(np.array([5,13,1,1]+[0]*(len(package().elements)-4))) formula.append(np.array([2,7,1,1]+[0]*(len(package().elements)-4))) formula.append(np.array([6,12,0,6]+[0]*(len(package().elements)-4))) formula.append(np.array([6,12,0,6]+[0]*(len(package().elements)-4))) formula.append(np.array([6,12,0,6]+[0]*(len(package().elements)-4))) formula.append(np.array([8,15,1,6]+[0]*(len(package().elements)-4))) formula.append(np.array([11,19,1,9]+[0]*(len(package().elements)-4))) formula.append(np.array([6,12,0,5]+[0]*(len(package().elements)-4))) formula.append(np.array([11,19,1,10]+[0]*(len(package().elements)-4))) self.components = self.base.copy() for i,j in enumerate(headgroup): self.components[j] = formula[i] # sn type defined sntype = ['none','d','t'] snformula = [] snformula.append(lambda carbon,db: np.array([carbon,2*carbon-2*db,0,2]+[0]*(len(package().elements)-4))) snformula.append(lambda carbon,db: np.array([carbon,2*carbon+2-2*db,0,3]+[0]*(len(package().elements)-4))) snformula.append(lambda carbon,db: np.array([carbon,2*carbon+2-2*db,0,4]+[0]*(len(package().elements)-4))) self.sn = {} for i,j in enumerate(sntype): self.sn[j] = snformula[i] # extended structure nana = ['M','D','T','Q','P'] iso = ['1a','1b','1c'] namedf =
pd.DataFrame({'0-series': ['LacCer'],'a-series': ['GM3'],'b-series': ['GD3'],'c-series': ['GT3']})
pandas.DataFrame
from functools import lru_cache from operator import itemgetter from typing import Union import pandas as pd import pycep_correios from geopy import distance from geopy.geocoders import Photon from pymongo import MongoClient def a() -> float: collection = db["estabelecimentos"] total_empresas = collection.count_documents({}) # Total de 31,564,677 empresas no banco de dados """ Cria um pipeline que separa os todas as empresas por grupos situação cadastral e depois calcula a porcentagem em cima do total de empresas no banco de dados """ results = collection.aggregate([{ "$group": { "_id": {"situacao_cadastral": "$situacao_cadastral"}, "count": {"$sum": 1} } # Agrupa por situacao_cadastral }, { "$project": { "count": 1, "percentage": { "$multiply": [{"$divide": [100, total_empresas]}, "$count"] # Calcula porcentagem } } }]) result_list = [(x["_id"], x["total"]) for x in results] # Cria uma lista de tuplas com (situação, porcentagem) for situacao in result_list: if situacao[0] == "02": # Ativas print(f"Porcentagem de empresas ativas: {situacao[0]:.2f}%") # Resultado: 42.04% return f"{situacao[0]:.2f}" def b() -> list[tuple[float, float]]: collection = db["estabelecimentos"] results = collection.aggregate([ {'$match': { 'cnae_fiscal_principal': {'$regex': '^561'} } }, {'$group': { '_id': {'$substrBytes': ['$data_inicio_atividades', 0, 4]}, # Pega os primeiros 4 dígitos (ano) 'total': {'$sum': 1} } }]) # Agrupa os que tem o início do CNAE Principal 561 pela data que iniciou as atividades result_list = [(x["_id"], x["total"]) for x in results] # Cria uma lista de tuplas com (ano, total) result_list.sort(key=itemgetter(0), reverse=True) # Ordena as tuplas de forma decrescente pelo ano return result_list def c(): collection = db["estabelecimentos"] # Como muito dos dados possuem CEP e endereço repetido, utilizei o LRU Cache # para evitar processamento e chamada de API desnecessárias. @lru_cache(maxsize=None) def get_address_from_cep(cep: str) -> Union[str, None]: """Utiliza a API do VIACEP para tentar obter o endereço do CEP""" try: endereco = pycep_correios.get_address_from_cep(cep, webservice=pycep_correios.WebService.VIACEP) return endereco['logradouro'] + ", " + endereco['bairro'] + ", " + endereco['cidade'] + " - " + endereco['uf'] except pycep_correios.exceptions.InvalidCEP: print(f"Cep Inválido: {cep}") except pycep_correios.exceptions.CEPNotFound: print(f"Cep Não Encontrado: {cep}") return None @lru_cache(maxsize=None) def address_to_coordinates(address: str) -> tuple[float, float]: """Utiliza a API do Photon para tentar pegar a coordenada do endereço""" geolocator = Photon(user_agent="parmenas_dataops_desafio") result = geolocator.geocode(address) if result: return (result.latitude, result.longitude) else: print(f"Não foi possível encontrar o endereço {address}") return None, None @lru_cache(maxsize=None) def calculate_distance(coordinates_start: tuple, coordinates_end: tuple) -> float: """Retorna a disância em kilometros entre duas coordenadas""" result = distance.distance(coordinates_start, coordinates_end) return result.kilometers """ Buscar mais de 30 milhões de empresas e testar CEP por CEP é loucura, como eu precisava somente os do São Paulo-SP, que é a cidade do CEP 01422000 Eu puxei somente os ceps que começam em 0 e trabalhei somente com eles. """ result_db = collection.find({ '$and': [ {'cep': {'$regex': '^0'}}, {'cep': {'$not': {'$regex': '^0$'}}}, # Não pega caso tenha cep = "0" {'cep': {'$not': {'$regex': '00000000'}}} # Não pega caso tenha cep = "00000000" ] }, projection={"_id": 1, "cep": 1}) # Retorna só o _id e o CEP result_raw = [x for x in result_db] """ Como seria ainda mais de 4 milhões de CEPs pra testar, resolvi tirar a precisão dos últimos 3 números do CEP, assim consigo agrupar melhor""" lista_cep_tratado = [] for doc in result_raw: doc["cep"] = f"{doc['cep'][:-0]}000" lista_cep_tratado.append(doc) del result_raw lista_com_endereco = [] for doc in lista_cep_tratado: address = get_address_from_cep(doc["cep"]) if address: doc["endereco"] = address lista_com_endereco.append(doc) del lista_cep_tratado get_address_from_cep.cache_clear() # Limpando o cache já que não vamos mais precisar lista_com_coordenadas = [] for doc in lista_com_coordenadas: if doc["endereco"]: doc["latitude"], doc["longitude"] = address_to_coordinates(doc["endereco"]) if doc["latitude"]: lista_com_coordenadas.append(doc) del lista_com_endereco address_to_coordinates.cache_clear() # Calcula a distância dos CEPs com início nos coordenadas do CEP 01422000: (-23.5648196, -46.6600444) list_com_distancias = [] for cep in list_com_distancias: start = (-23.5648196, -46.6600444) end = (float(cep["latitude"]), float(cep["longitude"])) distancia = calculate_distance(start, end) if distancia <= 5: list_com_distancias.append(cep["_id"]) calculate_distance.cache_clear() return len(list_com_distancias) def exportar_respostas(): resultado_a = a() resultado_b = b() resultado_c = c() df = pd.DataFrame([("a", resultado_a), ("c", resultado_c)], columns=['Letra', 'Resultado']) df.to_excel('resultados/resultados_a_c.xlsx', index=False) df_b =
pd.DataFrame(resultado_b, columns=['Ano', 'Total'])
pandas.DataFrame
import pandas as pd import numpy as np ''' DataFrame A DataFrame represents a rectangular table of data and contains an ordered collec‐ tion of columns, each of which can be a different value type (numeric, string, boolean, etc.). The DataFrame has both a row and column index; it can be thought of as a dict of Series all sharing the same index. Under the hood, the data is stored as one or more two-dimensional blocks rather than a list, dict, or some other collection of one-dimensional arrays. While a DataFrame is physically two-dimensional, you can use it to represent higher dimensional data in a tabular format using hierarchical indexing. ''' ''' There are many ways to construct a DataFrame, though one of the most common is from a dict of equal-length lists or NumPy arrays ''' data = {'state': ['Oshiwara', 'Oshiwara', 'Oshiwara', 'Navapura', 'Navapura', 'Navapura'], 'year': [2000, 2001, 2002, 2001, 2002, 2003], 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]} frame = pd.DataFrame(data) print(frame) ''' For large DataFrames, the head method selects only the first five rows ''' print(frame.head()) ''' If you specify a sequence of columns, the DataFrame’s columns will be arranged in that order ''' print(pd.DataFrame(data, columns=['year', 'state', 'pop'])) ''' If you pass a column that isn’t contained in the dict, it will appear with missing values in the result ''' print(pd.DataFrame(data, columns=['year', 'state', 'pop','clean'])) ''' A column in a DataFrame can be retrieved as a Series either by dict-like notation or by attribute. frame2[column] works for any column name, but frame2.column only works when the column name is a valid Python variable name ''' frame2 =
pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],index=['one', 'two', 'three', 'four','five', 'six'])
pandas.DataFrame
from typing import Type import numpy as np import pandas as pd import pytest from vivid.featureset.encodings import CountEncodingAtom, OneHotEncodingAtom, InnerMergeAtom class BaseTestCase: def setup_method(self): data = [ [1, 2.1, 'hoge'], [1, 1.01, 'spam'], [2, 10.001, 'ham'], [1, 1.1, 'spam'], [3, 2.5, 'spam'], [1, None, None] ] self.train_df = pd.DataFrame(data, columns=['int1', 'float1', 'str1']) self.y = [1] * len(self.train_df) test_data = [ data[0], data[2] ] self.test_df = pd.DataFrame(test_data, columns=self.train_df.columns) def is_generate_idempotency(self, atom): """atom のべき等チェック""" feat_1 = atom.generate(self.train_df, self.y) feat_2 = atom.generate(self.train_df) return feat_1.equals(feat_2) class TestCountEncodingAtom(BaseTestCase): def setup_method(self): super(TestCountEncodingAtom, self).setup_method() class IrisCountEncodingAtom(CountEncodingAtom): use_columns = ['int1', 'str1'] self.atom = IrisCountEncodingAtom() def test_generate_data(self): feat_train = self.atom.generate(self.train_df, self.y) assert len(self.train_df) == len(feat_train) def test_output_values(self): """出力データが正しいことの確認""" # 学習データで学習済み self.atom.generate(self.train_df, self.y) test_data = [ [1, 'spam'], # 対応関係があるもの [2, 'ham'], [None, None] # レコードにない or None ] ground_truth = [ [4, 3], [1, 1], [np.nan, np.nan] ] test_df = pd.DataFrame(test_data, columns=self.atom.use_columns) feat_test = self.atom.generate(test_df) assert len(test_df) == len(feat_test) assert pd.DataFrame(ground_truth).equals(
pd.DataFrame(feat_test.values)
pandas.DataFrame
""" Module for integration testing. """ import os from pathlib import Path from dask.distributed import Client, LocalCluster import numpy as np import pandas as pd import pytest from ..unlikely.engine import abc_smc from ..unlikely.models import Models, Model from ..unlikely.misc import create_images_from_data from ..unlikely.priors import Beta, Uniform from .conftest import assert_similar_enough_distribution def test_beta_binomial_1(): # A 1 is a "success", and a 0 is a "failure" obs = np.array([1, 0, 1, 1, 1, 0, 1, 0, 1]) # Number of particles to sample per epoch num_particles = 2000 # The cutoff(s) that decide whether or not to accept a particle. epsilon_sets = [[0], [1, 0], [3, 2, 1, 0]] column = [ { 'title': 'Posterior after 1 success out of 1', 'data': [ pd.DataFrame( { 'reference_posterior': np.random.beta( 2, 1, num_particles) } ), pd.DataFrame( { 'prior': np.random.beta( 1, 1, num_particles) } ) ] }, { 'title': 'Full update with 6 successes out of 9', 'data': [ pd.DataFrame( { 'reference_posterior': np.random.beta( obs.sum() + 1, len(obs) - obs.sum() + 1, num_particles ) } ), pd.DataFrame( { 'prior': np.random.beta( 1, 1, num_particles) } ) ] } ] def distance(x, y): """ For binomially distributed data, this essentially counts the number of "successes". We do that for both the observed and the simulated data sets and find the absolute distance between the two of them. This is for illustrative purposes only. You could write a more complex one that suits your own problem. Parameters: x: np.array y: np.array Returns: numeric """ return abs(x.sum() - y.sum()) def simulate(priors, actual_data): """ Used by a model to simulate data. This is for illustrative purposes only. You could write a more complex one that suits your own problem. Parameters: priors: unlikely.priors.Priors Acts like a dict. Keys should be names of priors of a model. actual_data: Some data Returns: integer A number between 0 and 1. """ return np.random.binomial( n=1, p=priors['beta'], size=len(actual_data) ) models_list = [] for i, epsilons in enumerate(epsilon_sets): # Create a model. A model is a set of priors, plus a simulator models = Models( [ Model( name='flat prior', priors=[ Beta(alpha=1, beta=1, name="beta"), ], simulate=simulate, prior_model_proba=1 ), ], perturbation_param=0.9 ) models_list.append(models) # Compute the posterior distribution. abc_smc( num_particles, epsilons, models, np.array([obs[0]]), distance, ) column[0]['data'].append( models[0].prev_accepted_proposals.rename( columns={'beta': f'eps: {epsilons}'} ) ) # Create a model that uses the full data set models_more_data = Models( [ Model( name='flat prior', priors=[ Beta(alpha=1, beta=1, name="beta"), ], simulate=simulate, prior_model_proba=1 ), ], ) models_list.append(models_more_data) # Compute the posterior distribution for the models object with all the # data. abc_smc( num_particles, epsilons=epsilons, models=models_more_data, obs=obs, distance=distance, ) column[1]['data'].append( models_more_data[0].prev_accepted_proposals.rename( columns={'beta': f'eps: {epsilons}'} ) ) # The posterior distribution (i.e. accepted particles that are compatible # "enough" with the data and model) are stored in # models[0].prev_accepted_proposals # Assuming you have an "images" folder in your current working directory: create_images_from_data( save_path=Path( os.getenv("PWD")) / "images" / "beta_binomial_example.png", data={ 'title': "Comparison of Prior & Posterior of a Beta-Binomial", 'data': [ column ] }, xlim=(0, 1), figsize_mult=(5, 5) ) for i in range(len(models_list)): if i % 2 == 0: assert_similar_enough_distribution( models_list[i][0].prev_accepted_proposals, pd.DataFrame({'beta': np.random.beta(2, 1, num_particles)}) ) else: assert_similar_enough_distribution( models_list[i][0].prev_accepted_proposals, pd.DataFrame( { 'beta': np.random.beta( obs.sum() + 1, len(obs) - obs.sum() + 1, num_particles ) } ) ) def test_uniform_binomial_1(): # A 1 is a "success", and a 0 is a "failure" obs = np.array([1, 0, 1, 1, 1, 0, 1, 0, 1]) # Number of particles to sample per epoch num_particles = 2000 # The cutoff(s) that decide whether or not to accept a particle. epsilon_sets = [[0], [1, 0], [3, 2, 1, 0]] column = [ { 'title': 'Posterior after 1 success out of 1', 'data': [ pd.DataFrame( { 'reference_posterior': np.random.beta( 2, 1, num_particles) } ), pd.DataFrame( { 'prior': np.random.uniform( 0, 1, num_particles) } ) ] }, { 'title': 'Full update with 6 successes out of 9', 'data': [ pd.DataFrame( { 'reference_posterior': np.random.beta( obs.sum() + 1, len(obs) - obs.sum() + 1, num_particles ) } ), pd.DataFrame( { 'prior': np.random.uniform( 0, 1, num_particles) } ) ] } ] def distance(x, y): """ For binomially distributed data, this essentially counts the number of "successes". We do that for both the observed and the simulated data sets and find the absolute distance between the two of them. This is for illustrative purposes only. You could write a more complex one that suits your own problem. Parameters: x: np.array y: np.array Returns: numeric """ return abs(x.sum() - y.sum()) def simulate(priors, actual_data): """ Used by a model to simulate data. This is for illustrative purposes only. You could write a more complex one that suits your own problem. Parameters: priors: unlikely.priors.Priors Acts like a dict. Keys should be names of priors of a model. actual_data: Some data Returns: integer A number between 0 and 1. """ return np.random.binomial( n=1, p=priors['uniform'], size=len(actual_data) ) models_list = [] for i, epsilons in enumerate(epsilon_sets): # Create a model. A model is a set of priors, plus a simulator models = Models( [ Model( name='flat prior', priors=[ Uniform(alpha=0, beta=1, name="uniform"), ], simulate=simulate, prior_model_proba=1 ), ], perturbation_param=0.9 ) models_list.append(models) # Compute the posterior distribution. abc_smc( num_particles, epsilons, models, np.array([obs[0]]), distance, ) column[0]['data'].append( models[0].prev_accepted_proposals.rename( columns={'uniform': f'eps: {epsilons}'} ) ) # Create a model that uses the full data set models_more_data = Models( [ Model( name='flat prior', priors=[ Uniform(alpha=0, beta=1, name="uniform"), ], simulate=simulate, prior_model_proba=1 ), ], ) models_list.append(models_more_data) # Compute the posterior distribution for the models object with all the # data. abc_smc( num_particles, epsilons=epsilons, models=models_more_data, obs=obs, distance=distance, ) column[1]['data'].append( models_more_data[0].prev_accepted_proposals.rename( columns={'uniform': f'eps: {epsilons}'} ) ) # The posterior distribution (i.e. accepted particles that are compatible # "enough" with the data and model) are stored in # models[0].prev_accepted_proposals # Assuming you have an "images" folder in your current working directory: create_images_from_data( save_path=Path( os.getenv("PWD")) / "images" / "uniform_binomial_example.png", data={ 'title': "Comparison of Prior & Posterior of a Uniform-Binomial", 'data': [ column ] }, xlim=(0, 1), figsize_mult=(5, 5) ) for i in range(len(models_list)): if i % 2 == 0: assert_similar_enough_distribution( models_list[i][0].prev_accepted_proposals, pd.DataFrame({'uniform': np.random.beta(2, 1, num_particles)}) ) else: assert_similar_enough_distribution( models_list[i][0].prev_accepted_proposals, pd.DataFrame( { 'uniform': np.random.beta( obs.sum() + 1, len(obs) - obs.sum() + 1, num_particles ) } ) ) def test_uniform_binomial_2(): num_particles = 2000 obs = np.array([ 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1 ]) epsilons_list = [[0], [3, 2, 1, 0]] local_cluster = LocalCluster(threads_per_worker=1) client = Client(local_cluster) def distance(x, y): """ Compare the number of ones in one vs. the other. """ return abs(x.sum() - y.sum()) def simulate(priors, obs): """ Data is binomially distributed. """ return np.random.binomial(n=1, p=priors['uniform'], size=len(obs)) data_to_display = [ [ { 'title': f"obs: {obs[:3]}", 'data': [] }, { 'title': f"after {obs[3:7]}", 'data': [] }, { 'title': f"after {obs[7:]}", 'data': [] }, { 'title': "Full batch", 'data': [] } ] ] for row, epsilons in enumerate(epsilons_list): models = Models( [ Model( name='Uniform over (0.5, 1)', priors=[ Uniform(alpha=0.5, beta=1, name="uniform"), ], simulate=simulate, prior_model_proba=1, ), ] ) # Update with 1st batch abc_smc( num_particles=num_particles, epsilons=epsilons, models=models, obs=obs[:3], distance=distance, client=client ) data_to_display[0][0]['data'].append( pd.DataFrame(models[0].prev_accepted_proposals).rename( columns={'uniform': f'eps: {epsilons}'} ) ) # The posterior distribution becomes the prior models.use_distribution_from_samples() # Update with 2nd batch abc_smc( num_particles=num_particles, epsilons=epsilons, models=models, obs=obs[3:7], distance=distance, client=client ) # The posterior distribution becomes the prior models.use_distribution_from_samples() data_to_display[0][1]['data'].append( pd.DataFrame( models[0].prev_accepted_proposals).rename( columns={'uniform': f'eps: {epsilons}'} ) ) # Update with 3rd batch abc_smc( num_particles=num_particles, epsilons=epsilons, models=models, obs=obs[7:], distance=distance, client=client ) data_to_display[0][2]['data'].append( pd.DataFrame( models[0].prev_accepted_proposals).rename( columns={'uniform': f'eps: {epsilons}'} ) ) models_full_batch = Models( [ Model( name='flat prior', priors=[ Uniform(alpha=0.5, beta=1, name="uniform"), ], simulate=simulate, prior_model_proba=1, ), ] ) # Update full batch abc_smc( num_particles=num_particles, epsilons=epsilons, models=models_full_batch, obs=obs, distance=distance, client=client ) data_to_display[0][3]['data'].append( pd.DataFrame( models_full_batch[0].prev_accepted_proposals ).rename(columns={'uniform': f'eps: {epsilons}'}) ) create_images_from_data( data={ 'title': '3 batch updates', 'data': data_to_display }, xlim=(0, 1), figsize_mult=(2, 8), save_path=Path( os.getenv("PWD") ) / "images" / "uniform_half_to_1_binomial_mini_batch.png", ) (models[0].prev_accepted_proposals < 0.5).sum()['uniform'] == 0 (models_full_batch[0].prev_accepted_proposals < 0.5).sum()['uniform'] == 0 assert (models[0].prev_accepted_proposals < 0.5).sum()['uniform'] == 0 assert (models_full_batch[0].prev_accepted_proposals < 0.5)\ .sum()['uniform'] == 0 assert (models[0].prev_accepted_proposals > 1)\ .sum()['uniform'] == 0 assert (models_full_batch[0].prev_accepted_proposals > 1)\ .sum()['uniform'] == 0 def test_beta_binomial_non_abc_rejection_sampling(): """ To see how settings affect the dispersion of the posterior distribution, here we vary a bunch of settings. We vary the number of epsilons, whether or not to use a constant standard deviation for the perturbation process within one abc_smc run, and if not using a constant standard deviation, varying how thin the adaptive standard deviation. """ num_particles = 2000 obs = np.array([ 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1 ]) def distance(x, y): """ Compare the number of ones in one vs. the other. """ return abs(x.sum() - y.sum()) def simulate(priors, obs): """ Data is binomially distributed. """ return np.random.binomial(n=1, p=priors['beta'], size=len(obs)) data_to_display = [] constant_devs = [True, True, True] beta_std_divs = [1.0, 2.0, 3.0] cols = list(range(len(beta_std_divs))) for col, beta_std_div, use_constant_dev in zip( cols, beta_std_divs, constant_devs ): data_to_display.append([ { 'title': f"batch 1: {obs[:3]}" + f", std_div: {beta_std_div}," + f" constant_dev: {use_constant_dev}", 'data': [] }, { 'title': f"batch 2: {obs[3:7]}," + f" std_div: {beta_std_div}," + f" constant_dev: {use_constant_dev}", 'data': [] }, { 'title': f"batch 3: {obs[7:]}," + f" std_div: {beta_std_div}," + f" constant_dev: {use_constant_dev}", 'data': [] }, { 'title': "full batch," + f" std_div: {beta_std_div}," + f" constant_dev: {use_constant_dev}", 'data': [] } ]) obs_indices = [(0, 3), (3, 7), (7, len(obs))] epsilon_sets = [[0], [0], [1, 0], [3, 2, 1, 0]] perturbations_config = [False, True, True, True] for i, (epsilons, use_perturbation) in enumerate( zip(epsilon_sets, perturbations_config) ): models = Models( [ Model( name='flat prior', priors=[ Beta( alpha=1, beta=1, name="beta", ) ], simulate=simulate, prior_model_proba=1, perturb=use_perturbation ), ], use_constant_dev=use_constant_dev ) # Loop through the rows for j, (start_index, end_index) in enumerate(obs_indices): obs_batch = obs[start_index:end_index] if i == 0: data_to_display[col][j]['data'].append( pd.DataFrame( { 'target': np.random.beta( obs[:end_index].sum() + 1, len(obs[:end_index]) - obs[:end_index].sum() + 1, num_particles ) } ) ) # Update with 1st batch abc_smc( num_particles=num_particles, epsilons=epsilons, models=models, obs=obs_batch, distance=distance, ) data_to_display[col][j]['data'].append(
pd.DataFrame(models[0].prev_accepted_proposals)
pandas.DataFrame
__author__ = '<NAME>' __email__ = '<EMAIL>' __status__ = 'Development' import numpy as np import pandas as pd import random import math import os from scipy.spatial.distance import cdist from sklearn import metrics from sklearn.cluster import KMeans OUTPUTPATH = os.path.join(os.path.dirname(__file__), '../data/') # apply K-means def mat_kmeans(df_Feat_Norm, k, outputname, init='k-means++'): mydata = pd.DataFrame() mydata = df_Feat_Norm.copy() kmeans = KMeans(init=init, n_clusters=k, n_init=10, max_iter=300, random_state=0).fit(mydata) pred = kmeans.labels_ represent = pd.DataFrame() for j in np.unique(pred): df_dist = pd.DataFrame(metrics.pairwise.euclidean_distances(mydata[pred == j], mydata[pred == j]), index=mydata[pred == j].index.values, columns=mydata[pred == j].index.values) reptiv = df_dist.sum().idxmin() represent = represent.append({'representative':reptiv, 'label':j}, ignore_index=True) # save hillslope groups as csv file mydata['pred'] = pred for r in np.unique(pred): mydata.loc[mydata['pred'] == r,'rep'] = represent.loc[represent['label'] == r, 'representative'].values[0] # mydata[['pred', 'rep']].to_csv(OUTPUTPATH + outputname + str(k) + '.csv', header=['label', 'representative']) # represent.to_csv(OUTPUTPATH + outputname + str(k) + 'rep.csv', index=False, header=True) mydata_dict = mydata[['pred', 'rep']].to_dict() mydata_list = [mydata.index.values.tolist()] + [mydata['rep'].tolist()] + [mydata['pred'].tolist()] return [mydata_list, represent['representative'].tolist(), mydata_dict, kmeans.cluster_centers_] # calculate RMSE def mat_rmse(o_path, c_path, df_HS_runtime, out_count): df_rmse = pd.DataFrame() arr_ctime = np.empty([0]) output_all = pd.read_csv(c_path + 'output_all_.csv') output_all = output_all.fillna(0) # map representatives outputs into all hillslopes for k in range(1, out_count): ctime = 0.0 rmse_ = pd.DataFrame() clust_data = pd.read_csv(c_path + 'mat_kmeans' + str(k) + '.csv') clust_data.columns = ['hsname', 'label', 'rep'] # output_data = pd.read_csv(o_path + 'output_' + str(k) + '/output_all_.csv') # output_data = output_data.fillna(0) output_mapped =
pd.read_csv(c_path + 'output_names.csv')
pandas.read_csv
import numpy as np import scipy as sp import pandas as pd from sklearn.base import BaseEstimator, RegressorMixin from modules.KernelRegWrapper import KernelRegWrapper class DependentKernelReg(BaseEstimator, RegressorMixin): """ A sklearn-style NW kernel regression with dependent bandwidth matrix and multi-variate normal kernel. """ def __init__(self, kernel="exp", bw_init="scott"): self.outp = None self.regressors = None self.kernel = kernel self.bw_init = bw_init self.kernel_params = None self.params = [] def fit(self, X, y): self.regressors = X self.outp = y cov_mat = np.cov(X, rowvar=False) n = X.shape[0] d = X.shape[1] H_init = 1 if self.bw_init == "scott": H_init = 1.06 * n ** (-1. / (d + 4)) elif self.bw_init == "silverman": H_init = (n * (d+2) / 4.) ** (-1. / (d + 4)) if self.regressors.shape[1] == 1: H = H_init * np.sqrt(cov_mat) else: H = H_init * sp.linalg.sqrtm(cov_mat) self.params = {"sample_size": n, "no_of_regressors": d, "bw_matrix": H} if self.kernel == "exp": if self.regressors.shape[1] == 1: H_inv = H**(-1) H_det = H else: H_inv = np.linalg.inv(H) H_det = np.linalg.det(H) H_const = 1. / ((np.sqrt(2 * np.pi) ** d) * H_det) #0.5)) self.kernel_params = {"H": H, "invH": H_inv, "detH": H_det, "dim": d, "const": H_const} return self def exp_kernel(self, X): if isinstance(X, pd.DataFrame): X = X.to_numpy() if self.regressors.shape[1] == 1: powa = -0.5 * (self.kernel_params["invH"] ** 2) * (X * X).sum(-1) else: xtimesH = np.matmul(X, self.kernel_params["invH"]) powa = -0.5 * (xtimesH * xtimesH).sum(-1) # equiv. to (invH * X)^T (invH *X) # powa = np.expand_dims(powa, axis=0) weight = self.kernel_params["const"] * np.exp(powa) return weight def predict(self, X): # preds = np.empty(X.shape[0]) if isinstance(X, pd.DataFrame): X = X.to_numpy() def predict_step(i): x_shifted = np.subtract(self.regressors, i) k_weights = self.exp_kernel(x_shifted) pred = np.matmul(k_weights, np.squeeze(self.outp)) pred /= np.sum(k_weights) return pred preds = np.apply_along_axis(predict_step, axis=1, arr=X) if isinstance(self.outp, pd.Series): preds = pd.Series(preds, name=self.outp.name) elif isinstance(self.outp, pd.DataFrame): preds =
pd.DataFrame(preds, columns=self.outp.columns)
pandas.DataFrame
# -*- coding: utf-8 -*- # # License: This module is released under the terms of the LICENSE file # contained within this applications INSTALL directory """ Utility functions for model generation """ # -- Coding Conventions # http://www.python.org/dev/peps/pep-0008/ - Use the Python style guide # http://sphinx.pocoo.org/rest.html - Use Restructured Text for # docstrings # -- Public Imports import logging import math import numpy as np import pandas as pd from datetime import datetime # -- Private Imports # -- Globals logger = logging.getLogger(__name__) dict_wday_name = { 0: 'W-MON', 1: 'W-TUE', 2: 'W-WED', 3: 'W-THU', 4: 'W-FRI', 5: 'W-SAT', 6: 'W-SUN', } # -- Exception classes # -- Functions def logger_info(msg, data): # Convenience function for easier log typing logger.info(msg + '\n%s', data) def array_transpose(a): """ Transpose a 1-D numpy array :param a: An array with shape (n,) :type a: numpy.Array :return: The original array, with shape (n,1) :rtype: numpy.Array """ return a[np.newaxis, :].T # TODO: rework to support model composition def model_requires_scaling(model): """ Given a :py:class:`anticipy.forecast_models.ForecastModel` return True if the function requires scaling a_x :param model: A get_model_<modeltype> function from :py:mod:`anticipy.model.periodic_models` or :py:mod:`anticipy.model.aperiodic_models` :type model: function :return: True if function is logistic or sigmoidal :rtype: bool """ requires_scaling = model is not None and model.name in [ 'logistic', 'sigmoid' ] return requires_scaling def apply_a_x_scaling(a_x, model=None, scaling_factor=100.0): """ Modify a_x for forecast_models that require it :param a_x: x axis of time series :type a_x: numpy array :param model: a :py:class:`anticipy.forecast_models.ForecastModel` :type model: function or None :param scaling_factor: Value used for scaling t_values for logistic models :type scaling_factor: float :return: a_x with scaling applied, if required :rtype: numpy array """ if model_requires_scaling(model): # todo: check that this is still useful a_x = a_x / scaling_factor return a_x dict_freq_units_per_year = dict( A=1.0, Y=1.0, D=365.0, W=52.0, M=12, Q=4, H=24 * 365.0 ) dict_dateoffset_input = dict( Y='years', A='years', M='months', W='weeks', D='days', H='hours' ) def get_normalized_x_from_date(s_date): """Get column of days since Monday of first date""" date_start = s_date.iloc[0] # Convert to Monday date_start = date_start - pd.to_timedelta(date_start.weekday(), unit='D') s_x = (s_date - date_start).dt.days return s_x def get_s_x_extrapolate( date_start_actuals, date_end_actuals, model=None, freq=None, extrapolate_years=2.5, scaling_factor=100.0, x_start_actuals=0.): """ Return a_x series with DateTimeIndex, covering the date range for the actuals, plus a forecast period. :param date_start_actuals: date or numeric index for first actuals sample :type date_start_actuals: str, datetime, int or float :param date_end_actuals: date or numeric index for last actuals sample :type date_end_actuals: str, datetime, int or float :param extrapolate_years: :type extrapolate_years: float :param model: :type model: function :param freq: Time unit between samples. Supported units are 'W' for weekly samples, or 'D' for daily samples. (untested) Any date unit or time unit accepted by numpy should also work, see https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.datetime.html#arrays-dtypes-dateunits # noqa :type freq: basestring :param shifted_origin: Offset to apply to a_x :type shifted_origin: int :param scaling_factor: Value used for scaling a_x for certain model functions :type scaling_factor: float :param x_start_actuals: numeric index for the first actuals sample :type x_start_actuals: int :return: Series of floats with DateTimeIndex. To be used as (a_date, a_x) input for a model function. :rtype: pandas.Series The returned series covers the actuals time domain plus a forecast period lasting extrapolate_years, in years. The number of additional samples for the forecast period is time_resolution * extrapolate_years, rounded down """ if isinstance(date_start_actuals, str) or \ isinstance(date_start_actuals, datetime): # Use dates if available date_start_actuals =
pd.to_datetime(date_start_actuals)
pandas.to_datetime
# Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership. The Modin Development Team licenses this file to you under the # Apache License, Version 2.0 (the "License"); you may not use this file except in # compliance with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software distributed under # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. import abc from modin.data_management.functions.default_methods import ( DataFrameDefault, SeriesDefault, DateTimeDefault, StrDefault, BinaryDefault, ResampleDefault, RollingDefault, CatDefault, GroupByDefault, ) from pandas.core.dtypes.common import is_scalar import pandas.core.resample import pandas import numpy as np def _get_axis(axis): def axis_getter(self): return self.to_pandas().axes[axis] return axis_getter def _set_axis(axis): def axis_setter(self, labels): new_qc = DataFrameDefault.register(pandas.DataFrame.set_axis)( self, axis=axis, labels=labels ) self.__dict__.update(new_qc.__dict__) return axis_setter class BaseQueryCompiler(abc.ABC): """Abstract Class that handles the queries to Modin dataframes. Note: See the Abstract Methods and Fields section immediately below this for a list of requirements for subclassing this object. """ @abc.abstractmethod def default_to_pandas(self, pandas_op, *args, **kwargs): """Default to pandas behavior. Parameters ---------- pandas_op : callable The operation to apply, must be compatible pandas DataFrame call args The arguments for the `pandas_op` kwargs The keyword arguments for the `pandas_op` Returns ------- BaseQueryCompiler The result of the `pandas_op`, converted back to BaseQueryCompiler """ pass # Abstract Methods and Fields: Must implement in children classes # In some cases, there you may be able to use the same implementation for # some of these abstract methods, but for the sake of generality they are # treated differently. lazy_execution = False # Metadata modification abstract methods def add_prefix(self, prefix, axis=1): if axis: return DataFrameDefault.register(pandas.DataFrame.add_prefix)( self, prefix=prefix ) else: return SeriesDefault.register(pandas.Series.add_prefix)(self, prefix=prefix) def add_suffix(self, suffix, axis=1): if axis: return DataFrameDefault.register(pandas.DataFrame.add_suffix)( self, suffix=suffix ) else: return SeriesDefault.register(pandas.Series.add_suffix)(self, suffix=suffix) # END Metadata modification abstract methods # Abstract copy # For copy, we don't want a situation where we modify the metadata of the # copies if we end up modifying something here. We copy all of the metadata # to prevent that. def copy(self): return DataFrameDefault.register(pandas.DataFrame.copy)(self) # END Abstract copy # Abstract join and append helper functions def concat(self, axis, other, **kwargs): """Concatenates two objects together. Args: axis: The axis index object to join (0 for columns, 1 for index). other: The other_index to concat with. Returns: Concatenated objects. """ concat_join = ["inner", "outer"] def concat(df, axis, other, **kwargs): kwargs.pop("join_axes", None) ignore_index = kwargs.get("ignore_index", False) if kwargs.get("join", "outer") in concat_join: if not isinstance(other, list): other = [other] other = [df] + other result = pandas.concat(other, axis=axis, **kwargs) else: if isinstance(other, (list, np.ndarray)) and len(other) == 1: other = other[0] how = kwargs.pop("join", None) ignore_index = kwargs.pop("ignore_index", None) kwargs["how"] = how result = df.join(other, **kwargs) if ignore_index: if axis == 0: result = result.reset_index(drop=True) else: result.columns = pandas.RangeIndex(len(result.columns)) return result return DataFrameDefault.register(concat)(self, axis=axis, other=other, **kwargs) # END Abstract join and append helper functions # Data Management Methods @abc.abstractmethod def free(self): """In the future, this will hopefully trigger a cleanup of this object.""" # TODO create a way to clean up this object. pass # END Data Management Methods # To/From Pandas @abc.abstractmethod def to_pandas(self): """Converts Modin DataFrame to Pandas DataFrame. Returns: Pandas DataFrame of the QueryCompiler. """ pass @classmethod @abc.abstractmethod def from_pandas(cls, df, data_cls): """Improve simple Pandas DataFrame to an advanced and superior Modin DataFrame. Parameters ---------- df: pandas.DataFrame The pandas DataFrame to convert from. data_cls : Modin DataFrame object to convert to. Returns ------- BaseQueryCompiler QueryCompiler containing data from the Pandas DataFrame. """ pass # END To/From Pandas # From Arrow @classmethod @abc.abstractmethod def from_arrow(cls, at, data_cls): """Improve simple Arrow Table to an advanced and superior Modin DataFrame. Parameters ---------- at : Arrow Table The Arrow Table to convert from. data_cls : Modin DataFrame object to convert to. Returns ------- BaseQueryCompiler QueryCompiler containing data from the Pandas DataFrame. """ pass # END From Arrow # To NumPy def to_numpy(self, **kwargs): """ Converts Modin DataFrame to NumPy array. Returns ------- NumPy array of the QueryCompiler. """ return DataFrameDefault.register(pandas.DataFrame.to_numpy)(self, **kwargs) # END To NumPy # Abstract inter-data operations (e.g. add, sub) # These operations require two DataFrames and will change the shape of the # data if the index objects don't match. An outer join + op is performed, # such that columns/rows that don't have an index on the other DataFrame # result in NaN values. def add(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.add)(self, other=other, **kwargs) def combine(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.combine)( self, other=other, **kwargs ) def combine_first(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.combine_first)( self, other=other, **kwargs ) def eq(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.eq)(self, other=other, **kwargs) def floordiv(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.floordiv)( self, other=other, **kwargs ) def ge(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.ge)(self, other=other, **kwargs) def gt(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.gt)(self, other=other, **kwargs) def le(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.le)(self, other=other, **kwargs) def lt(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.lt)(self, other=other, **kwargs) def mod(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.mod)(self, other=other, **kwargs) def mul(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.mul)(self, other=other, **kwargs) def corr(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.corr)(self, **kwargs) def cov(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.cov)(self, **kwargs) def dot(self, other, **kwargs): if kwargs.get("squeeze_self", False): applyier = pandas.Series.dot else: applyier = pandas.DataFrame.dot return BinaryDefault.register(applyier)(self, other=other, **kwargs) def ne(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.ne)(self, other=other, **kwargs) def pow(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.pow)(self, other=other, **kwargs) def rfloordiv(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.rfloordiv)( self, other=other, **kwargs ) def rmod(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.rmod)( self, other=other, **kwargs ) def rpow(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.rpow)( self, other=other, **kwargs ) def rsub(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.rsub)( self, other=other, **kwargs ) def rtruediv(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.rtruediv)( self, other=other, **kwargs ) def sub(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.sub)(self, other=other, **kwargs) def truediv(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.truediv)( self, other=other, **kwargs ) def __and__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__and__)( self, other=other, **kwargs ) def __or__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__or__)( self, other=other, **kwargs ) def __rand__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__rand__)( self, other=other, **kwargs ) def __ror__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__ror__)( self, other=other, **kwargs ) def __rxor__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__rxor__)( self, other=other, **kwargs ) def __xor__(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.__xor__)( self, other=other, **kwargs ) def df_update(self, other, **kwargs): return BinaryDefault.register(pandas.DataFrame.update, inplace=True)( self, other=other, **kwargs ) def series_update(self, other, **kwargs): return BinaryDefault.register(pandas.Series.update, inplace=True)( self, other=other, squeeze_self=True, squeeze_other=True, **kwargs ) def clip(self, lower, upper, **kwargs): return DataFrameDefault.register(pandas.DataFrame.clip)( self, lower=lower, upper=upper, **kwargs ) def where(self, cond, other, **kwargs): """Gets values from this manager where cond is true else from other. Args: cond: Condition on which to evaluate values. Returns: New QueryCompiler with updated data and index. """ return DataFrameDefault.register(pandas.DataFrame.where)( self, cond=cond, other=other, **kwargs ) def merge(self, right, **kwargs): """ Merge DataFrame or named Series objects with a database-style join. Parameters ---------- right : BaseQueryCompiler The query compiler of the right DataFrame to merge with. Returns ------- BaseQueryCompiler A new query compiler that contains result of the merge. Notes ----- See pd.merge or pd.DataFrame.merge for more info on kwargs. """ return DataFrameDefault.register(pandas.DataFrame.merge)( self, right=right, **kwargs ) def join(self, right, **kwargs): """ Join columns of another DataFrame. Parameters ---------- right : BaseQueryCompiler The query compiler of the right DataFrame to join with. Returns ------- BaseQueryCompiler A new query compiler that contains result of the join. Notes ----- See pd.DataFrame.join for more info on kwargs. """ return DataFrameDefault.register(pandas.DataFrame.join)(self, right, **kwargs) # END Abstract inter-data operations # Abstract Transpose def transpose(self, *args, **kwargs): """Transposes this QueryCompiler. Returns: Transposed new QueryCompiler. """ return DataFrameDefault.register(pandas.DataFrame.transpose)( self, *args, **kwargs ) def columnarize(self): """ Transposes this QueryCompiler if it has a single row but multiple columns. This method should be called for QueryCompilers representing a Series object, i.e. self.is_series_like() should be True. Returns ------- BaseQueryCompiler Transposed new QueryCompiler or self. """ if len(self.columns) != 1 or ( len(self.index) == 1 and self.index[0] == "__reduced__" ): return self.transpose() return self def is_series_like(self): """Return True if QueryCompiler has a single column or row""" return len(self.columns) == 1 or len(self.index) == 1 # END Abstract Transpose # Abstract reindex/reset_index (may shuffle data) def reindex(self, axis, labels, **kwargs): """Fits a new index for this Manger. Args: axis: The axis index object to target the reindex on. labels: New labels to conform 'axis' on to. Returns: New QueryCompiler with updated data and new index. """ return DataFrameDefault.register(pandas.DataFrame.reindex)( self, axis=axis, labels=labels, **kwargs ) def reset_index(self, **kwargs): """Removes all levels from index and sets a default level_0 index. Returns: New QueryCompiler with updated data and reset index. """ return DataFrameDefault.register(pandas.DataFrame.reset_index)(self, **kwargs) # END Abstract reindex/reset_index # Full Reduce operations # # These operations result in a reduced dimensionality of data. # Currently, this means a Pandas Series will be returned, but in the future # we will implement a Distributed Series, and this will be returned # instead. def is_monotonic(self): """Return boolean if values in the object are monotonic_increasing. Returns ------- bool """ return SeriesDefault.register(pandas.Series.is_monotonic)(self) def is_monotonic_decreasing(self): """Return boolean if values in the object are monotonic_decreasing. Returns ------- bool """ return SeriesDefault.register(pandas.Series.is_monotonic_decreasing)(self) def count(self, **kwargs): """Counts the number of non-NaN objects for each column or row. Return: Pandas series containing counts of non-NaN objects from each column or row. """ return DataFrameDefault.register(pandas.DataFrame.count)(self, **kwargs) def max(self, **kwargs): """Returns the maximum value for each column or row. Return: Pandas series with the maximum values from each column or row. """ return DataFrameDefault.register(pandas.DataFrame.max)(self, **kwargs) def mean(self, **kwargs): """Returns the mean for each numerical column or row. Return: Pandas series containing the mean from each numerical column or row. """ return DataFrameDefault.register(pandas.DataFrame.mean)(self, **kwargs) def min(self, **kwargs): """Returns the minimum from each column or row. Return: Pandas series with the minimum value from each column or row. """ return DataFrameDefault.register(pandas.DataFrame.min)(self, **kwargs) def prod(self, **kwargs): """Returns the product of each numerical column or row. Return: Pandas series with the product of each numerical column or row. """ return DataFrameDefault.register(pandas.DataFrame.prod)(self, **kwargs) def sum(self, **kwargs): """Returns the sum of each numerical column or row. Return: Pandas series with the sum of each numerical column or row. """ return DataFrameDefault.register(pandas.DataFrame.sum)(self, **kwargs) def to_datetime(self, *args, **kwargs): return SeriesDefault.register(pandas.to_datetime)(self, *args, **kwargs) # END Abstract full Reduce operations # Abstract map partitions operations # These operations are operations that apply a function to every partition. def abs(self): return DataFrameDefault.register(pandas.DataFrame.abs)(self) def applymap(self, func): return DataFrameDefault.register(pandas.DataFrame.applymap)(self, func=func) def conj(self, **kwargs): """ Return the complex conjugate, element-wise. The complex conjugate of a complex number is obtained by changing the sign of its imaginary part. """ def conj(df, *args, **kwargs): return pandas.DataFrame(np.conj(df)) return DataFrameDefault.register(conj)(self, **kwargs) def isin(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.isin)(self, **kwargs) def isna(self): return DataFrameDefault.register(pandas.DataFrame.isna)(self) def negative(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.__neg__)(self, **kwargs) def notna(self): return DataFrameDefault.register(pandas.DataFrame.notna)(self) def round(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.round)(self, **kwargs) def replace(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.replace)(self, **kwargs) def series_view(self, **kwargs): return SeriesDefault.register(pandas.Series.view)(self, **kwargs) def to_numeric(self, *args, **kwargs): return SeriesDefault.register(pandas.to_numeric)(self, *args, **kwargs) def unique(self, **kwargs): return SeriesDefault.register(pandas.Series.unique)(self, **kwargs) def searchsorted(self, **kwargs): return SeriesDefault.register(pandas.Series.searchsorted)(self, **kwargs) # END Abstract map partitions operations def value_counts(self, **kwargs): return SeriesDefault.register(pandas.Series.value_counts)(self, **kwargs) def stack(self, level, dropna): return DataFrameDefault.register(pandas.DataFrame.stack)( self, level=level, dropna=dropna ) # Abstract map partitions across select indices def astype(self, col_dtypes, **kwargs): """Converts columns dtypes to given dtypes. Args: col_dtypes: Dictionary of {col: dtype,...} where col is the column name and dtype is a numpy dtype. Returns: DataFrame with updated dtypes. """ return DataFrameDefault.register(pandas.DataFrame.astype)( self, dtype=col_dtypes, **kwargs ) @property def dtypes(self): return self.to_pandas().dtypes # END Abstract map partitions across select indices # Abstract column/row partitions reduce operations # # These operations result in a reduced dimensionality of data. # Currently, this means a Pandas Series will be returned, but in the future # we will implement a Distributed Series, and this will be returned # instead. def all(self, **kwargs): """Returns whether all the elements are true, potentially over an axis. Return: Pandas Series containing boolean values or boolean. """ return DataFrameDefault.register(pandas.DataFrame.all)(self, **kwargs) def any(self, **kwargs): """Returns whether any the elements are true, potentially over an axis. Return: Pandas Series containing boolean values or boolean. """ return DataFrameDefault.register(pandas.DataFrame.any)(self, **kwargs) def first_valid_index(self): """Returns index of first non-NaN/NULL value. Return: Scalar of index name. """ return ( DataFrameDefault.register(pandas.DataFrame.first_valid_index)(self) .to_pandas() .squeeze() ) def idxmax(self, **kwargs): """Returns the first occurance of the maximum over requested axis. Returns: Series containing the maximum of each column or axis. """ return DataFrameDefault.register(pandas.DataFrame.idxmax)(self, **kwargs) def idxmin(self, **kwargs): """Returns the first occurance of the minimum over requested axis. Returns: Series containing the minimum of each column or axis. """ return DataFrameDefault.register(pandas.DataFrame.idxmin)(self, **kwargs) def last_valid_index(self): """Returns index of last non-NaN/NULL value. Return: Scalar of index name. """ return ( DataFrameDefault.register(pandas.DataFrame.last_valid_index)(self) .to_pandas() .squeeze() ) def median(self, **kwargs): """Returns median of each column or row. Returns: Series containing the median of each column or row. """ return DataFrameDefault.register(pandas.DataFrame.median)(self, **kwargs) def memory_usage(self, **kwargs): """Returns the memory usage of each column. Returns: Series containing the memory usage of each column. """ return DataFrameDefault.register(pandas.DataFrame.memory_usage)(self, **kwargs) def nunique(self, **kwargs): """Returns the number of unique items over each column or row. Returns: Series of ints indexed by column or index names. """ return DataFrameDefault.register(pandas.DataFrame.nunique)(self, **kwargs) def quantile_for_single_value(self, **kwargs): """Returns quantile of each column or row. Returns: Series containing the quantile of each column or row. """ return DataFrameDefault.register(pandas.DataFrame.quantile)(self, **kwargs) def skew(self, **kwargs): """Returns skew of each column or row. Returns: Series containing the skew of each column or row. """ return DataFrameDefault.register(pandas.DataFrame.skew)(self, **kwargs) def sem(self, **kwargs): """ Returns standard deviation of the mean over requested axis. Returns ------- BaseQueryCompiler QueryCompiler containing the standard deviation of the mean over requested axis. """ return DataFrameDefault.register(pandas.DataFrame.sem)(self, **kwargs) def std(self, **kwargs): """Returns standard deviation of each column or row. Returns: Series containing the standard deviation of each column or row. """ return DataFrameDefault.register(pandas.DataFrame.std)(self, **kwargs) def var(self, **kwargs): """Returns variance of each column or row. Returns: Series containing the variance of each column or row. """ return DataFrameDefault.register(pandas.DataFrame.var)(self, **kwargs) # END Abstract column/row partitions reduce operations # Abstract column/row partitions reduce operations over select indices # # These operations result in a reduced dimensionality of data. # Currently, this means a Pandas Series will be returned, but in the future # we will implement a Distributed Series, and this will be returned # instead. def describe(self, **kwargs): """Generates descriptive statistics. Returns: DataFrame object containing the descriptive statistics of the DataFrame. """ return DataFrameDefault.register(pandas.DataFrame.describe)(self, **kwargs) # END Abstract column/row partitions reduce operations over select indices # Map across rows/columns # These operations require some global knowledge of the full column/row # that is being operated on. This means that we have to put all of that # data in the same place. def cumsum(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.cumsum)(self, **kwargs) def cummax(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.cummax)(self, **kwargs) def cummin(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.cummin)(self, **kwargs) def cumprod(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.cumprod)(self, **kwargs) def diff(self, **kwargs): return DataFrameDefault.register(pandas.DataFrame.diff)(self, **kwargs) def dropna(self, **kwargs): """Returns a new QueryCompiler with null values dropped along given axis. Return: New QueryCompiler """ return DataFrameDefault.register(pandas.DataFrame.dropna)(self, **kwargs) def nlargest(self, n=5, columns=None, keep="first"): if columns is None: return SeriesDefault.register(pandas.Series.nlargest)(self, n=n, keep=keep) else: return DataFrameDefault.register(pandas.DataFrame.nlargest)( self, n=n, columns=columns, keep=keep ) def nsmallest(self, n=5, columns=None, keep="first"): if columns is None: return SeriesDefault.register(pandas.Series.nsmallest)(self, n=n, keep=keep) else: return DataFrameDefault.register(pandas.DataFrame.nsmallest)( self, n=n, columns=columns, keep=keep ) def eval(self, expr, **kwargs): """Returns a new QueryCompiler with expr evaluated on columns. Args: expr: The string expression to evaluate. Returns: A new QueryCompiler with new columns after applying expr. """ return DataFrameDefault.register(pandas.DataFrame.eval)( self, expr=expr, **kwargs ) def mode(self, **kwargs): """Returns a new QueryCompiler with modes calculated for each label along given axis. Returns: A new QueryCompiler with modes calculated. """ return DataFrameDefault.register(pandas.DataFrame.mode)(self, **kwargs) def fillna(self, **kwargs): """Replaces NaN values with the method provided. Returns: A new QueryCompiler with null values filled. """ return DataFrameDefault.register(pandas.DataFrame.fillna)(self, **kwargs) def query(self, expr, **kwargs): """Query columns of the QueryCompiler with a boolean expression. Args: expr: Boolean expression to query the columns with. Returns: QueryCompiler containing the rows where the boolean expression is satisfied. """ return DataFrameDefault.register(pandas.DataFrame.query)( self, expr=expr, **kwargs ) def rank(self, **kwargs): """Computes numerical rank along axis. Equal values are set to the average. Returns: QueryCompiler containing the ranks of the values along an axis. """ return DataFrameDefault.register(pandas.DataFrame.rank)(self, **kwargs) def sort_index(self, **kwargs): """Sorts the data with respect to either the columns or the indices. Returns: QueryCompiler containing the data sorted by columns or indices. """ return DataFrameDefault.register(pandas.DataFrame.sort_index)(self, **kwargs) def melt(self, *args, **kwargs): return DataFrameDefault.register(pandas.DataFrame.melt)(self, *args, **kwargs) def sort_columns_by_row_values(self, rows, ascending=True, **kwargs): return DataFrameDefault.register(pandas.DataFrame.sort_values)( self, by=rows, axis=1, ascending=ascending, **kwargs ) def sort_rows_by_column_values(self, rows, ascending=True, **kwargs): return DataFrameDefault.register(pandas.DataFrame.sort_values)( self, by=rows, axis=0, ascending=ascending, **kwargs ) # END Abstract map across rows/columns # Map across rows/columns # These operations require some global knowledge of the full column/row # that is being operated on. This means that we have to put all of that # data in the same place. def quantile_for_list_of_values(self, **kwargs): """Returns Manager containing quantiles along an axis for numeric columns. Returns: QueryCompiler containing quantiles of original QueryCompiler along an axis. """ return DataFrameDefault.register(pandas.DataFrame.quantile)(self, **kwargs) # END Abstract map across rows/columns # Abstract __getitem__ methods def getitem_array(self, key): """ Get column or row data specified by key. Parameters ---------- key : BaseQueryCompiler, numpy.ndarray, pandas.Index or list Target numeric indices or labels by which to retrieve data. Returns ------- BaseQueryCompiler A new Query Compiler. """ def getitem_array(df, key): return df[key] return DataFrameDefault.register(getitem_array)(self, key) def getitem_column_array(self, key, numeric=False): """Get column data for target labels. Args: key: Target labels by which to retrieve data. numeric: A boolean representing whether or not the key passed in represents the numeric index or the named index. Returns: A new Query Compiler. """ def get_column(df, key): if numeric: return df.iloc[:, key] else: return df[key] return DataFrameDefault.register(get_column)(self, key=key) def getitem_row_array(self, key): """Get row data for target labels. Args: key: Target numeric indices by which to retrieve data. Returns: A new Query Compiler. """ def get_row(df, key): return df.iloc[key] return DataFrameDefault.register(get_row)(self, key=key) # END Abstract __getitem__ methods # Abstract insert # This method changes the shape of the resulting data. In Pandas, this # operation is always inplace, but this object is immutable, so we just # return a new one from here and let the front end handle the inplace # update. def insert(self, loc, column, value): """Insert new column data. Args: loc: Insertion index. column: Column labels to insert. value: Dtype object values to insert. Returns: A new QueryCompiler with new data inserted. """ return DataFrameDefault.register(pandas.DataFrame.insert, inplace=True)( self, loc=loc, column=column, value=value ) # END Abstract insert # Abstract drop def drop(self, index=None, columns=None): """Remove row data for target index and columns. Args: index: Target index to drop. columns: Target columns to drop. Returns: A new QueryCompiler. """ if index is None and columns is None: return self else: return DataFrameDefault.register(pandas.DataFrame.drop)( self, index=index, columns=columns ) # END drop # UDF (apply and agg) methods # There is a wide range of behaviors that are supported, so a lot of the # logic can get a bit convoluted. def apply(self, func, axis, *args, **kwargs): """Apply func across given axis. Args: func: The function to apply. axis: Target axis to apply the function along. Returns: A new QueryCompiler. """ return DataFrameDefault.register(pandas.DataFrame.apply)( self, func=func, axis=axis, *args, **kwargs ) # END UDF # Manual Partitioning methods (e.g. merge, groupby) # These methods require some sort of manual partitioning due to their # nature. They require certain data to exist on the same partition, and # after the shuffle, there should be only a local map required. def groupby_count( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby count. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.count)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_any( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby any. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.any)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_min( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby min. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.min)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_prod( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby prod. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.prod)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_max( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby max. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.max)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_all( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby all. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.all)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_sum( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby sum. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.sum)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_size( self, by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False, ): """Perform a groupby size. Parameters ---------- by : BaseQueryCompiler The query compiler object to groupby. axis : 0 or 1 The axis to groupby. Must be 0 currently. groupby_args : dict The arguments for the groupby component. map_args : dict The arguments for the `map_func`. reduce_args : dict The arguments for `reduce_func`. numeric_only : bool Whether to drop non-numeric columns. drop : bool Whether the data in `by` was dropped. Returns ------- BaseQueryCompiler """ return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.size)( self, by=by, axis=axis, groupby_args=groupby_args, map_args=map_args, reduce_args=reduce_args, numeric_only=numeric_only, drop=drop, ) def groupby_agg(self, by, axis, agg_func, groupby_args, agg_args, drop=False): return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.aggregate)( self, by=by, axis=axis, agg_func=agg_func, groupby_args=groupby_args, agg_args=agg_args, drop=drop, ) def groupby_dict_agg(self, by, func_dict, groupby_args, agg_args, drop=False): return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.aggregate)( self, by=by, func_dict=func_dict, groupby_args=groupby_args, agg_args=agg_args, drop=drop, ) # END Manual Partitioning methods def unstack(self, level, fill_value): return DataFrameDefault.register(pandas.DataFrame.unstack)( self, level=level, fill_value=fill_value ) def pivot(self, index, columns, values): return DataFrameDefault.register(pandas.DataFrame.pivot)( self, index=index, columns=columns, values=values ) def pivot_table( self, index, values, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, ): return DataFrameDefault.register(pandas.DataFrame.pivot_table)( self, index=index, values=values, columns=columns, aggfunc=aggfunc, fill_value=fill_value, margins=margins, dropna=dropna, margins_name=margins_name, observed=observed, ) def get_dummies(self, columns, **kwargs): """Convert categorical variables to dummy variables for certain columns. Args: columns: The columns to convert. Returns: A new QueryCompiler. """ def get_dummies(df, columns, **kwargs): return
pandas.get_dummies(df, columns=columns, **kwargs)
pandas.get_dummies
import glob, os import pandas as pd from Exp_Main.models import OCA, ExpBase, ExpPath, RSD from Analysis.models import OszAnalysis from Exp_Sub.models import LSP, MFR from dbfread import DBF from Lab_Misc import General import datetime from django.apps import apps import numpy as np from django.utils import timezone cwd = os.getcwd() rel_path = General.get_BasePath() def Load_from_Model(ModelName, pk): if ModelName == 'OCA': return Load_OCA(pk) if ModelName == 'RSD': return Load_RSD(pk) if ModelName == 'LSP': return Load_LSP(pk) if ModelName == 'MFL': return Load_MFL(pk) if ModelName == 'MFR': return Load_MFR(pk) if ModelName == 'HME': return Load_HME(pk) if ModelName == 'SEL': return Load_SEL(pk) def Load_SEL(pk): entry = General.get_in_full_model(pk) file = os.path.join( rel_path, entry.Link_XLSX) df = pd.read_excel(file, 'Tabelle1') new_vals = df[df>1]/1000000#correct for wrong format Curr_Dash = entry.Dash df.update(new_vals) df["Time (min.)"] = Curr_Dash.Start_datetime_elli + pd.TimedeltaIndex(df["Time (min.)"], unit='m') df["time"] = df["Time (min.)"].dt.tz_convert(timezone.get_current_timezone()) df['time_loc'] = df["time"] return df def get_subs_in_dic(pk): main_entry = General.get_in_full_model(pk) Sub_Exps = main_entry.Sub_Exp.all() data = {} for Sub_Exp in Sub_Exps: Sub_Exp = General.get_in_full_model_sub(Sub_Exp.pk) data_sub = Load_from_Model(Sub_Exp.Device.Abbrev, Sub_Exp.id) try: Sub_Exp.Gas.first().Name data[Sub_Exp.Name + '_' + Sub_Exp.Gas.first().Name] = data_sub except: data[Sub_Exp.Name] = data_sub return data def get_subs_by_model(pk, sub_model): # sub_model = 'mfr' main_entry = General.get_in_full_model(pk) main_model = str.lower(main_entry.Device.Abbrev) model = apps.get_model('Exp_Sub', sub_model) data = {} mfrs = model.objects.filter(**{main_model: ExpBase.objects.get(id = pk)}).all() for mfr in mfrs: try: mfr.Gas.first().Name data[mfr.Name + '_' + mfr.Gas.first().Name] = Load_from_Model(mfr.Device.Abbrev, mfr.id) except: data[mfr.Name] = Load_from_Model(mfr.Device.Abbrev, mfr.id) return data def Load_RSD_subs(pk): Gases = {} mfrs = MFR.objects.filter(rsd = ExpBase.objects.get(id = pk)).all() for mfr in mfrs: Gases[mfr.Gas.first().Name] = Load_MFR(mfr.id) Pump = {} lsps = LSP.objects.filter(rsd = ExpBase.objects.get(id = pk)).all() for lsp in lsps: Pump[lsp.Name] = Load_LSP(lsp.id) if len(Gases)>0: Gases = pd.concat(Gases) if len(Pump)>0: Pump = pd.concat(Pump) return Gases, Pump def Load_RSD(pk): cwd = os.getcwd() entry = General.get_in_full_model(pk) os.chdir(os.path.join(General.get_BasePath(),entry.Link_Data)) Drops = {} Drops_names = [] for file in glob.glob("*.xlsx"): if file[0:4] == 'Drop': Drops[file[:-5]] = pd.read_excel(file) Drops_names.append(file[:-5]) os.chdir(cwd) dropss = pd.concat(Drops, keys=Drops_names) dropss['time_loc'] = dropss['abs_time'].dt.tz_localize(timezone.get_current_timezone()) return dropss def Load_sliced_RSD(Main_id): data = Load_RSD(Main_id) entry = General.get_in_full_model(Main_id) DashTab = entry.Dash return Slice_data(data, DashTab) def Load_MFL(pk): entry = General.get_in_full_model_sub(pk) MFL_N2_data = Load_csv(entry) MFL_N2_data['Date_Time'] = pd.to_datetime(MFL_N2_data['Date_Time'], format='%d.%m.%Y %H:%M:%S', errors="coerce") MFL_N2_data['time'] = MFL_N2_data['Date_Time'].dt.tz_localize(timezone.get_current_timezone()) return MFL_N2_data def Load_MFR(pk): entry = General.get_in_full_model_sub(pk) file = os.path.join( rel_path, entry.Link) data = pd.read_csv(file, sep=' ', error_bad_lines=False) data['date_time'] =
pd.to_datetime(data['date'] + '_' + data['time'], format='%Y-%m-%d_%H:%M:%S.%f', errors="coerce")
pandas.to_datetime
# Import relevant modules from agent.agent_DDQNN import RNNAgent, GTNAgent, TTNNAgent, GNNAgent from matplotlib import pyplot as plt import pandas as pd import numpy as np import random # Set random seed random.seed(0) np.random.seed(0) # Initialize Agent variables trading_currency = 'USDSEK' window_size = 30 episode_count = 15 batch_size = 64 # batch size for replaying/training the agent agent_type = 'GNN' # RNN or GTN or TTNN or GNN # Initialize training variables total_rewards_df = pd.DataFrame(dtype=float) # Get returns data rs_types = ['open', 'high', 'low', 'last'] file_names = [f'g10_minute_{t}_rs_2019-10-01.csv' for t in rs_types] rs_data = dict(zip(rs_types, [pd.read_csv(f'data/{f}', index_col=0, header=0) for f in file_names])) rs_y = rs_data['last'][trading_currency] # Get graphs data A_t =
pd.read_csv('data/A_t_22.csv', index_col=0, header=0)
pandas.read_csv
#!/usr/bin/env python # -*- coding: utf-8 -*- import pytest import pandas as pd import pandas_should # noqa class TestEqualAccessorMixin(object): def test_equal_true(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3], columns=['id']) assert df1.should.equal(df2) def test_equal_false(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3, 4], columns=['id']) assert not df1.should.equal(df2) @pytest.mark.parametrize('alias_name', [ 'be_equal_to', 'be_equals_to', 'be_eq_to', 'eq', ]) def test_qeual_aliases(self, alias_name): df = pd.DataFrame([1, 2, 3], columns=['id']) assert hasattr(df.should, alias_name) def test_not_equal_true(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3, 4], columns=['id']) assert df1.should.not_equal(df2) def test_not_equal_false(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3], columns=['id']) assert not df1.should.not_equal(df2) @pytest.mark.parametrize('alias_name', [ 'be_not_equal_to', 'be_not_equals_to', 'be_neq_to', 'neq', ]) def test_not_qeual_aliases(self, alias_name): df = pd.DataFrame([1, 2, 3], columns=['id']) assert hasattr(df.should, alias_name) def test_have_same_length_true(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3], columns=['id']) assert df1.should.have_same_length(df2) def test_have_same_length_false(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2, 3, 4], columns=['id']) assert not df1.should.have_same_length(df2) def test_have_same_length_multiple(self): df1 = pd.DataFrame([1, 2, 3], columns=['id']) df2 = pd.DataFrame([1, 2], columns=['id']) df3 = pd.DataFrame([3], columns=['id']) assert df1.should.have_same_length(df2, df3) def test_have_same_width_true(self): data1 = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df1 = pd.DataFrame(data1, columns=['id', 'name', 'age']) data2 = [ ('apple', 198, 'red'), ('banana', 128, 'yellow'), ] df2 = pd.DataFrame(data2, columns=['fruit', 'price', 'color']) assert df1.should.have_same_width(df2) def test_have_same_width_false(self): data1 = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df1 = pd.DataFrame(data1, columns=['id', 'name', 'age']) data2 = [ ('apple', 198), ('banana', 128), ] df2 = pd.DataFrame(data2, columns=['fruit', 'price']) assert not df1.should.have_same_width(df2) def test_have_same_width_multiple(self): data1 = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df1 = pd.DataFrame(data1, columns=['id', 'name', 'age']) data2 = [ ('apple', 198), ('banana', 128), ] df2 = pd.DataFrame(data2, columns=['fruit', 'price']) df3 = pd.DataFrame(['red', 'blue', 'green']) assert df1.should.have_same_width(df2, df3) class TestNullAccessorMixin(object): def test_have_null_true(self): data = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df = pd.DataFrame(data, columns=['id', 'name', 'age']) assert df.should.have_null() def test_have_null_false(self): data = [ (1, 'alice', 20), (2, 'bob', 30), (3, 'carol', 40), ] df = pd.DataFrame(data, columns=['id', 'name', 'age']) assert not df.should.have_null() def test_have_null_count(self): data = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df = pd.DataFrame(data, columns=['id', 'name', 'age']) assert df.should.have_null(count=True) == (True, {'age': 1, 'id': 0, 'name': 0}) def test_have_not_null_true(self): data = [ (1, 'alice', 20), (2, 'bob', 30), (3, 'carol', 40), ] df = pd.DataFrame(data, columns=['id', 'name', 'age']) assert df.should.have_not_null() def test_have_not_null_false(self): data = [ (1, 'alice', 20), (2, 'bob', None), (3, 'carol', 40), ] df = pd.DataFrame(data, columns=['id', 'name', 'age']) assert not df.should.have_not_null() @pytest.mark.parametrize('alias_name', ['havent_null']) def test_have_not_null_aliases(self, alias_name): df = pd.DataFrame([1, 2, 3], columns=['id']) assert hasattr(df.should, alias_name) class TestShapeAccessorMixin(object): @pytest.mark.parametrize('df1, df2', [ (pd.DataFrame([1, 2, 3], columns=['id']), pd.DataFrame(['a', 'b', 'c'], columns=['name'])), (pd.DataFrame([(1, 'a'), (2, 'b')], columns=['id', 'name']), pd.DataFrame([(-2, True), (-1, False)], columns=['a', 'b'])) ]) def test_be_shaped_like_df(self, df1, df2): assert df1.should.be_shaped_like(df2) @pytest.mark.parametrize('df, shape', [ (pd.DataFrame([1, 2, 3], columns=['id']), (3, 1)), (pd.DataFrame([(1, 'a'), (2, 'b')], columns=['id', 'name']), (2, 2)), ]) def test_be_shaped_like_tuple(self, df, shape): assert df.should.be_shaped_like(shape) @pytest.mark.parametrize('df, rows, columns', [ (pd.DataFrame([1, 2, 3], columns=['id']), 3, 1), (pd.DataFrame([(1, 'a'), (2, 'b')], columns=['id', 'name']), 2, 2), ]) def test_be_shaped_like(self, df, rows, columns): assert df.should.be_shaped_like(rows, columns) @pytest.mark.parametrize('alias_name', ['shape']) def test_be_shaped_like_aliases(self, alias_name): df = pd.DataFrame([1, 2, 3], columns=['id']) assert hasattr(df.should, alias_name) @pytest.mark.parametrize('df, length', [ (pd.DataFrame([1, 2, 3], columns=['id']), 1), (pd.DataFrame([(1, 'a'), (2, 'b')], columns=['id', 'name']), 2), ]) def test_have_width(self, df, length): assert df.should.have_width(length) @pytest.mark.parametrize('alias_name', ['columns', 'columns_len', 'have_length_of_columns']) def test_have_length_of_columns_aliases(self, alias_name): df =
pd.DataFrame([1, 2, 3], columns=['id'])
pandas.DataFrame
""" Download, transform and simulate various datasets. """ # Author: <NAME> <<EMAIL>> # License: MIT from os.path import join from urllib.parse import urljoin from string import ascii_lowercase from sqlite3 import connect from rich.progress import track import numpy as np import pandas as pd from .base import Datasets, FETCH_URLS class ContinuousCategoricalDatasets(Datasets): """Class to download, transform and save datasets with both continuous and categorical features.""" @staticmethod def _modify_columns(data, categorical_features): """Rename and reorder columns of dataframe.""" X, y = data.drop(columns="target"), data.target X.columns = range(len(X.columns)) return pd.concat([X, y], axis=1), categorical_features def download(self): """Download the datasets.""" if self.names == "all": func_names = [func_name for func_name in dir(self) if "fetch_" in func_name] else: func_names = [ f"fetch_{name}".lower().replace(" ", "_") for name in self.names ] self.content_ = [] for func_name in track(func_names, description="Datasets"): name = func_name.replace("fetch_", "").upper().replace("_", " ") fetch_data = getattr(self, func_name) data, categorical_features = self._modify_columns(*fetch_data()) self.content_.append((name, data, categorical_features)) return self def save(self, path, db_name): """Save datasets.""" with connect(join(path, f"{db_name}.db")) as connection: for name, data in self.content_: data.to_sql(name, connection, index=False, if_exists="replace") def fetch_adult(self): """Download and transform the Adult Data Set. https://archive.ics.uci.edu/ml/datasets/Adult """ data = pd.read_csv(FETCH_URLS["adult"], header=None, na_values=" ?").dropna() data.rename(columns={data.columns[-1]: "target"}, inplace=True) categorical_features = [1, 3, 5, 6, 7, 8, 9, 13] return data, categorical_features def fetch_abalone(self): """Download and transform the Abalone Data Set. https://archive.ics.uci.edu/ml/datasets/Abalone """ data = pd.read_csv(FETCH_URLS["abalone"], header=None) data.rename(columns={data.columns[-1]: "target"}, inplace=True) categorical_features = [0] return data, categorical_features def fetch_acute(self): """Download and transform the Acute Inflammations Data Set. https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations """ data = pd.read_csv( FETCH_URLS["acute"], header=None, sep="\t", decimal=",", encoding="UTF-16" ) data["target"] = data[6].str[0] + data[7].str[0] data.drop(columns=[6, 7], inplace=True) categorical_features = list(range(1, 6)) return data, categorical_features def fetch_annealing(self): """Download and transform the Annealing Data Set. https://archive.ics.uci.edu/ml/datasets/Annealing """ data = pd.read_csv(FETCH_URLS["annealing"], header=None, na_values="?") # some features are dropped; they have too many missing values missing_feats = (data.isnull().sum(0) / data.shape[0]) < 0.1 data = data.iloc[:, missing_feats.values] data[2].fillna(data[2].mode().squeeze(), inplace=True) data = data.T.reset_index(drop=True).T data.rename(columns={data.columns[-1]: "target"}, inplace=True) categorical_features = [0, 1, 5, 9] return data, categorical_features def fetch_census(self): """Download and transform the Census-Income (KDD) Data Set. https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29 """ data = pd.read_csv(FETCH_URLS["census"], header=None) categorical_features = ( list(range(1, 5)) + list(range(6, 16)) + list(range(19, 29)) + list(range(30, 38)) + [39] ) # some features are dropped; they have too many missing values cols_ids = [1, 6, 9, 13, 14, 20, 21, 29, 31, 37] categorical_features = np.argwhere( np.delete( data.rename(columns={k: f"nom_{k}" for k in categorical_features}) .columns.astype("str") .str.startswith("nom_"), cols_ids, ) ).squeeze() data = data.drop(columns=cols_ids).T.reset_index(drop=True).T # some rows are dropped; they have rare missing values data = data.iloc[ data.applymap(lambda x: x != " Not in universe").all(1).values, : ] data.rename(columns={data.columns[-1]: "target"}, inplace=True) return data, categorical_features def fetch_contraceptive(self): """Download and transform the Contraceptive Method Choice Data Set. https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice """ data =
pd.read_csv(FETCH_URLS["contraceptive"], header=None)
pandas.read_csv
import pandas as pd import os from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification from sklearn import metrics from sklearn.metrics import classification_report from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score # from IPython.display import display currentDirectory = os.getcwd() final_train = pd.read_csv(currentDirectory+"/final_train.csv") train =
pd.read_csv('./unbalanced/train.csv')
pandas.read_csv
# -*- coding: utf-8 -*- # This file is part of CbM (https://github.com/ec-jrc/cbm). __author__ = ["<NAME>"] __copyright__ = "Copyright 2021, European Commission Joint Research Centre" __credits__ = ["GTCAP Team"] __license__ = "3-Clause BSD" __version__ = "" __maintainer__ = [""] __status__ = "Development" import pandas as pd import requests import datetime pd.options.mode.chained_assignment = None # default='warn' def create_list_of_tiles_to_be_downloaded_from_RESTful(MS, year, parcel_id, search_window_start_date, search_window_end_date, cloud_categories, api_user, api_pass, tstype, ptype): was_error = False ms = MS.lower() if ms == "be-wa": ms = "bewa" url_base = "https://cap.users.creodias.eu" if ptype == "": url = url_base + "/query/parcelTimeSeries?aoi=" + ms + "&year=" + str(year) + "&pid=" + str(parcel_id) + "&tstype=" + tstype + "&scl=True&ref=True" else: url = url_base + "/query/parcelTimeSeries?aoi=" + ms + "&year=" + str(year) + "&pid=" + str(parcel_id) + "&ptype=" + ptype + "&tstype=" + tstype + "&scl=True&ref=True" print(url) try: response = requests.get(url, auth=(api_user, api_pass)) print(response) if response.status_code == 404 or response.status_code == 500 or response.status_code == 401: was_error = True if response.status_code == 401: print("Please, provide valid credentials to access the RESTFul server") tiles_to_download = [] else: df =
pd.read_json(response.text)
pandas.read_json
#!/usr/bin/env python # -*- coding=utf-8 -*- ########################################################################### # Copyright (C) 2013-2016 by Caspar. All rights reserved. # File Name: txtclf.py # Author: <NAME> # E-mail: <EMAIL> # Created Time: 2016-07-05 14:39:18 ########################################################################### # import os, sys, difflib, itertools from time import time import numpy as np import scipy as sp import scipy.stats as stats import pandas as pd from sklearn.base import clone from sklearn.preprocessing import MinMaxScaler, LabelBinarizer, label_binarize, normalize from sklearn.multiclass import OneVsRestClassifier from sklearn.pipeline import Pipeline from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold, KFold, GridSearchCV, RandomizedSearchCV from sklearn import metrics from .util import io, func, plot from .util import math as imath common_cfg = {} def init(plot_cfg={}, plot_common={}): if (len(plot_cfg) > 0 and plot_cfg['MON'] is not None): plot.MON = plot_cfg['MON'] global common_cfg if (len(plot_common) > 0): common_cfg = plot_common def get_featw(pipeline, feat_num): feat_w_dict, sub_feat_w = [{} for i in range(2)] filt_feat_idx = feature_idx = np.arange(feat_num) for component in ('featfilt', 'clf'): if (type(pipeline) != Pipeline): if (component == 'featfilt'): continue else: cmpn = pipeline elif (component in pipeline.named_steps): cmpn = pipeline.named_steps[component] else: continue if (hasattr(cmpn, 'estimators_')): for i, estm in enumerate(cmpn.estimators_): filt_subfeat_idx = feature_idx[:] if (hasattr(estm, 'get_support')): filt_subfeat_idx = feature_idx[estm.get_support()] for measure in ('feature_importances_', 'coef_', 'scores_'): if (hasattr(estm, measure)): filt_subfeat_w = getattr(estm, measure) subfeat_w = (filt_subfeat_w.min() - 1) * np.ones_like(feature_idx) # subfeat_w[filt_subfeat_idx] = normalize(estm.feature_importances_, norm='l1') subfeat_w[filt_subfeat_idx] = filt_subfeat_w # print 'Sub FI shape: (%s)' % ','.join([str(x) for x in filt_subfeat_w.shape]) # print 'Feature Importance inside %s Ensemble Method: %s' % (component, filt_subfeat_w) sub_feat_w[(component, i)] = subfeat_w if (hasattr(component, 'get_support')): filt_feat_idx = feature_idx[component.get_support()] for measure in ('feature_importances_', 'coef_', 'scores_'): if (hasattr(cmpn, measure)): filt_feat_w = getattr(cmpn, measure) # print '*' * 80 + '\n%s\n'%filt_feat_w + '*' * 80 feat_w = (filt_feat_w.min() - 1) * np.ones_like(feature_idx) # feat_w[filt_feat_idx] = normalize(filt_feat_w, norm='l1') feat_w[filt_feat_idx] = filt_feat_w # print '*' * 80 + '\n%s\n'%feat_w + '*' * 80 feat_w_dict[(component, measure)] = feat_w print('FI shape: (%s)' % ','.join([str(x) for x in feat_w_dict[(component, measure)].shape])) print('Sample 10 Feature from %s.%s: %s' % (component, measure, feat_w[feat_w > 0][:10])) # print 'Feature Importance from %s.%s: %s' % (component, measure, feat_w) return feat_w_dict, sub_feat_w def get_score(pipeline, X_test, mltl=False): if ((not isinstance(pipeline, Pipeline) and hasattr(pipeline, 'predict_proba')) or(isinstance(pipeline.named_steps['clf'], OneVsRestClassifier) and hasattr(pipeline.named_steps['clf'].estimators_[0], 'predict_proba')) or (not isinstance(pipeline.named_steps['clf'], OneVsRestClassifier) and hasattr(pipeline, 'predict_proba'))): if (mltl): return pipeline.predict_proba(X_test) else: # return pipeline.predict_proba(X_test)[:, 1] return pipeline.predict_proba(X_test) elif (hasattr(pipeline, 'decision_function')): return pipeline.decision_function(X_test) else: print('Neither probability estimate nor decision function is supported in the classification model!') return [0] * Y_test.shape[0] # Benchmark def benchmark(pipeline, X_train, Y_train, X_test, Y_test, mltl=False, signed=False, average='micro'): print('+' * 80) print('Training Model: ') print(pipeline) t0 = time() pipeline.fit(X_train, Y_train) train_time = time() - t0 print('train time: %0.3fs' % train_time) t0 = time() orig_pred = pred = pipeline.predict(X_test) orig_prob = prob = pipeline.predict_proba(X_test) if hasattr(pipeline, 'predict_proba') else pipeline.decision_function(X_test) test_time = time() - t0 print('+' * 80) print('Testing: ') print('test time: %0.3fs' % test_time) is_mltl = mltl if (signed): Y_test = np.column_stack([np.abs(Y_test).reshape((Y_test.shape[0],-1))] + [label_binarize(lb, classes=[-1,1,0])[:,1] for lb in (np.sign(Y_test).astype('int8').reshape((Y_test.shape[0],-1))).T]) if (len(Y_test.shape) < 2 or Y_test.shape[1] == 1 or np.where(Y_test<0)[0].shape[0]>0) else Y_test pred = np.column_stack([np.abs(pred).reshape((pred.shape[0],-1))] + [label_binarize(lb, classes=[-1,1,0])[:,1] for lb in (np.sign(pred).astype('int8').reshape((pred.shape[0],-1))).T]) if (len(pred.shape) < 2 or pred.shape[1] == 1 or np.where(pred<0)[0].shape[0]>0) else pred is_mltl = True try: accuracy = metrics.accuracy_score(Y_test, pred) except ValueError as e: print(e) Y_test, pred = Y_test.ravel(), pred.ravel() accuracy = metrics.accuracy_score(Y_test, pred) print('accuracy: %0.3f' % accuracy) if (is_mltl and average == 'all'): micro_precision = metrics.precision_score(Y_test, pred, average='micro') print('micro-precision: %0.3f' % micro_precision) micro_recall = metrics.recall_score(Y_test, pred, average='micro') print('micro-recall: %0.3f' % micro_recall) micro_fscore = metrics.fbeta_score(Y_test, pred, beta=1, average='micro') print('micro-fscore: %0.3f' % micro_fscore) macro_precision = metrics.precision_score(Y_test, pred, average='macro') print('macro-precision: %0.3f' % macro_precision) macro_recall = metrics.recall_score(Y_test, pred, average='macro') print('macro-recall: %0.3f' % macro_recall) macro_fscore = metrics.fbeta_score(Y_test, pred, beta=1, average='macro') print('macro-fscore: %0.3f' % macro_fscore) else: precision = metrics.precision_score(Y_test, pred, average=average if is_mltl else 'binary') print('precision: %0.3f' % precision) recall = metrics.recall_score(Y_test, pred, average=average if is_mltl else 'binary') print('recall: %0.3f' % recall) fscore = metrics.fbeta_score(Y_test, pred, beta=1, average=average if is_mltl else 'binary') print('fscore: %0.3f' % fscore) print('classification report:') # print metrics.classification_report(Y_test, pred) metric_df = pd.DataFrame(metrics.classification_report(Y_test, pred, output_dict=True)).T[['precision', 'recall', 'f1-score', 'support']] print(metric_df) print('confusion matrix:') if (is_mltl): pass else: print(metrics.confusion_matrix(Y_test, pred)) print('+' * 80) clf = pipeline.named_steps['clf'] if (type(pipeline) is Pipeline) else pipeline if ((isinstance(clf, OneVsRestClassifier) and hasattr(clf.estimators_[0], 'predict_proba')) or (not isinstance(clf, OneVsRestClassifier) and hasattr(pipeline, 'predict_proba'))): if (mltl): scores = pipeline.predict_proba(X_test) if (type(scores) == list): scores = np.concatenate([score[:, -1].reshape((-1, 1)) for score in scores], axis=1) else: scores = pipeline.predict_proba(X_test)[:, -1] elif (hasattr(pipeline, 'decision_function')): scores = pipeline.decision_function(X_test) else: print('Neither probability estimate nor decision function is supported in the classification model! ROC and PRC figures will be invalid.') scores = [0] * Y_test.shape[0] if (signed and (len(scores.shape) < 2 or scores.shape[1] < pred.shape[1])): scores = np.concatenate([np.abs(scores).reshape((scores.shape[0],-1))] + [label_binarize(lb, classes=[-1,1,0])[:,:2] for lb in (np.sign(scores).astype('int8').reshape((scores.shape[0],-1))).T], axis=1) if (is_mltl): if ((len(Y_test.shape) == 1 or Y_test.shape[1] == 1) and len(np.unique(Y_test)) > 2): lbz = LabelBinarizer() Y_test = lbz.fit_transform(Y_test) def micro(): # Micro-average ROC curve y_true = np.array(Y_test) s_array = np.array(scores) if (len(s_array.shape) == 3): s_array = s_array[:,:,1].reshape((s_array.shape[0],s_array.shape[1],)) if (y_true.shape[0] == s_array.shape[1] and y_true.shape[1] == s_array.shape[0]): s_array = s_array.T return metrics.roc_curve(y_true.ravel(), s_array.ravel()) def macro(): # Macro-average ROC curve n_classes = Y_test.shape[1] fpr, tpr = [dict() for i in range(2)] for i in range(n_classes): fpr[i], tpr[i], _ = metrics.roc_curve(Y_test[:, i], scores[:, i]) # First aggregate all false positive rates all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += np.interp(all_fpr, fpr[i], tpr[i]) # Finally average it and compute AUC mean_tpr /= n_classes return all_fpr, mean_tpr, _ if (average == 'micro'): roc = micro() elif (average == 'macro'): roc = macro() elif (average == 'all'): micro_roc = micro() macro_roc = macro() if (type(scores) == list): scores = np.array(scores)[:,:,0] prc = metrics.precision_recall_curve(Y_test.ravel(), scores.ravel()) # Only micro-prc is supported else: roc = metrics.roc_curve(Y_test, scores) prc = metrics.precision_recall_curve(Y_test, scores) # print 'ROC:\n%s\n%s' % (roc[0], roc[1]) # print 'PRC:\n%s\n%s' % (prc[0], prc[1]) print('Training and Testing X shape: %s; %s' % (', '.join(['(%s)' % ','.join([str(x) for x in X.shape]) for X in X_train]) if type(X_train) is list else '(%s)' % ','.join([str(x) for x in X_train.shape]), ', '.join(['(%s)' % ','.join([str(x) for x in X.shape]) for X in X_test]) if type(X_test) is list else '(%s)' % ','.join([str(x) for x in X_test.shape]))) feat_w_dict, sub_feat_w = [{} for i in range(2)] filt_feat_idx = feature_idx = np.arange(X_train[0].shape[1] if type(X_train) is list else X_train.shape[1]) for component in ('featfilt', 'clf'): if (type(pipeline) != Pipeline): if (component == 'featfilt'): continue else: cmpn = pipeline elif (component in pipeline.named_steps): cmpn = pipeline.named_steps[component] else: continue if (hasattr(cmpn, 'estimators_')): for i, estm in enumerate(cmpn.estimators_): filt_subfeat_idx = filt_feat_idx[:] if (hasattr(estm, 'get_support')): filt_subfeat_idx = filt_feat_idx[estm.get_support()] for measure in ('feature_importances_', 'coef_', 'scores_'): if (hasattr(estm, measure)): filt_subfeat_w = getattr(estm, measure) subfeat_w = (filt_subfeat_w.min() - 1) * np.ones_like(feature_idx) # subfeat_w[filt_subfeat_idx][:len(estm.feature_importances_)] = normalize(estm.feature_importances_, norm='l1') subfeat_w[filt_subfeat_idx][:len(filt_subfeat_w)] = filt_subfeat_w # print 'Sub FI shape: (%s)' % ','.join([str(x) for x in filt_subfeat_w.shape]) # print 'Feature Importance inside %s Ensemble Method: %s' % (component, filt_subfeat_w) sub_feat_w[(component, i)] = subfeat_w for measure in ('feature_importances_', 'coef_', 'scores_'): if (hasattr(cmpn, measure)): filt_feat_w = getattr(cmpn, measure) # print '*' * 80 + '\n%s\n'%filt_feat_w + '*' * 80 feat_w = (filt_feat_w.min() - 1) * np.ones_like(feature_idx) # feat_w[filt_feat_idx][:filt_feat_w.shape[1] if len(filt_feat_w.shape) > 1 else len(filt_feat_w)] = normalize(filt_feat_w[1,:] if len(filt_feat_w.shape) > 1 else filt_feat_w, norm='l1') feat_w[filt_feat_idx][:filt_feat_w.shape[1] if len(filt_feat_w.shape) > 1 else len(filt_feat_w)] = filt_feat_w[1,:] if len(filt_feat_w.shape) > 1 else filt_feat_w # print '*' * 80 + '\n%s\n'%feat_w + '*' * 80 feat_w_dict[(component, measure)] = feat_w print('FI shape: (%s)' % ','.join([str(x) for x in feat_w_dict[(component, measure)].shape])) print('Sample 10 Feature from %s.%s: %s' % (component, measure, feat_w[feat_w > 0][:10])) # print 'Feature Importance from %s.%s: %s' % (component, measure, feat_w) if (hasattr(cmpn, 'get_support')): filt_feat_idx = filt_feat_idx[cmpn.get_support()] print('\n') if (is_mltl and average == 'all'): return {'accuracy':accuracy, 'micro-precision':micro_precision, 'micro-recall':micro_recall, 'micro-fscore':micro_fscore, 'macro-precision':macro_precision, 'macro-recall':macro_recall, 'macro-fscore':macro_fscore, 'train_time':train_time, 'test_time':test_time, 'micro-roc':micro_roc, 'macro-roc':macro_roc, 'prc':prc, 'feat_w':feat_w_dict, 'sub_feat_w':sub_feat_w, 'pred_lb':orig_pred, 'metrics':metric_df} else: return {'accuracy':accuracy, 'precision':precision, 'recall':recall, 'fscore':fscore, 'train_time':train_time, 'test_time':test_time, 'roc':roc, 'prc':prc, 'feat_w':feat_w_dict, 'sub_feat_w':sub_feat_w, 'pred_lb':orig_pred, 'pred_prob':orig_prob, 'metrics':metric_df} # Calculate the venn digram overlaps def pred_ovl(preds, pred_true=None, axis=1): if (axis == 0): preds = preds.T if (pred_true is not None): pred_true = pred_true.reshape((-1,)) # Row represents feature, column represents instance var_num, dim = preds.shape[0], preds.shape[1] orig_idx = np.arange(var_num) if (len(preds.shape) < 2 or preds.shape[1] == 1): if (pred_true is None): return np.ones(shape=(1,), dtype='int') else: overlap_mt = np.ones(shape=(1,2), dtype='int') overlap_mt[0,1] = orig_idx[preds.reshape((-1,)) == pred_true].shape[0] return overlap_mt # Calculate possible subsets of all the instance indices subset_idx = list(imath.subset(list(range(dim)), min_crdnl=1)) # Initialize result matrix if (pred_true is None): overlap_mt = np.zeros(shape=(len(subset_idx),), dtype='int') else: overlap_mt = np.zeros(shape=(len(subset_idx), 2), dtype='int') # Calculate overlap for each subset for i, idx in enumerate(subset_idx): rmn_idx = set(range(dim)) - set(idx) # Select the positions of the target instance that without any overlap with other instances pred_sum, chsn_sum, rmn_sum = preds.sum(axis=1), preds[:,idx].sum(axis=1), preds[:,list(rmn_idx)].sum(axis=1) condition = np.all([np.logical_or(chsn_sum == 0, chsn_sum == len(idx)), np.logical_or(rmn_sum == 0, rmn_sum == len(rmn_idx)), np.logical_or(pred_sum == len(idx), pred_sum == len(rmn_idx))], axis=0) if (pred_true is None): overlap_mt[i] = orig_idx[condition].shape[0] else: # And the selected positions should be true true_cond = np.logical_and(condition, preds[:,idx[0]] == pred_true) overlap_mt[i,0] = orig_idx[condition].shape[0] overlap_mt[i,1] = orig_idx[true_cond].shape[0] return overlap_mt def save_featw(features, crsval_featw, crsval_subfeatw, cfg_param={}, lbid=''): lbidstr = ('_' + (str(lbid) if lbid != -1 else 'all')) if lbid is not None and lbid != '' else '' for k, v in crsval_featw.items(): measure_str = k.replace(' ', '_').strip('_').lower() feat_w_mt = np.column_stack(v) mms = MinMaxScaler() feat_w_mt = mms.fit_transform(feat_w_mt) feat_w_avg = feat_w_mt.mean(axis=1) feat_w_std = feat_w_mt.std(axis=1) sorted_idx = np.argsort(feat_w_avg, axis=-1)[::-1] # sorted_idx = sorted(range(feat_w_avg.shape[0]), key=lambda k: feat_w_avg[k])[::-1] sorted_feat_w = np.column_stack((features[sorted_idx], feat_w_avg[sorted_idx], feat_w_std[sorted_idx])) feat_w_df = pd.DataFrame(sorted_feat_w, index=sorted_idx, columns=['Feature Name', 'Importance Mean', 'Importance Std']) if (cfg_param.setdefault('save_featw', False)): feat_w_df.to_excel('featw%s_%s.xlsx' % (lbidstr, measure_str)) if (cfg_param.setdefault('save_featw_npz', False)): io.write_df(feat_w_df, 'featw%s_%s' % (lbidstr, measure_str), with_idx=True) if (cfg_param.setdefault('plot_featw', False)): plot.plot_bar(feat_w_avg[sorted_idx[:10]].reshape((1,-1)), feat_w_std[sorted_idx[:10]].reshape((1,-1)), features[sorted_idx[:10]], labels=None, title='Feature importances', fname='fig_featw%s_%s' % (lbidstr, measure_str), plot_cfg=common_cfg) for k, v in crsval_subfeatw.items(): measure_str = k.replace(' ', '_').strip('_').lower() subfeat_w_mt = np.column_stack(v) mms = MinMaxScaler() subfeat_w_mt = mms.fit_transform(subfeat_w_mt) subfeat_w_avg = subfeat_w_mt.mean(axis=1) subfeat_w_std = subfeat_w_mt.std(axis=1) sorted_idx = np.argsort(subfeat_w_avg, axis=-1)[::-1] sorted_subfeat_w = np.column_stack((features[sorted_idx], subfeat_w_avg[sorted_idx], subfeat_w_std[sorted_idx])) subfeat_w_df = pd.DataFrame(sorted_subfeat_w, index=sorted_idx, columns=['Feature Name', 'Importance Mean', 'Importance Std']) if (cfg_param.setdefault('save_subfeatw', False)): subfeat_w_df.to_excel('subfeatw%s_%s.xlsx' % (lbidstr, measure_str)) if (cfg_param.setdefault('save_subfeatw_npz', False)): io.write_df(subfeat_w_df, 'subfeatw%s_%s' % (lbidstr, measure_str), with_idx=True) if (cfg_param.setdefault('plot_subfeatw', False)): plot.plot_bar(subfeat_w_avg[sorted_idx[:10]].reshape((1,-1)), subfeat_w_std[sorted_idx[:10]].reshape((1,-1)), features[sorted_idx[:10]], labels=None, title='Feature importances', fname='fig_subfeatw_%s' % measure_str, plot_cfg=common_cfg) # Classification def classification(X_train, Y_train, X_test, model_iter, model_param={}, cfg_param={}, global_param={}, lbid=''): print('Classifing...') global common_cfg FILT_NAMES, CLF_NAMES, PL_NAMES, PL_SET = model_param['glb_filtnames'], model_param['glb_clfnames'], global_param['pl_names'], global_param['pl_set'] lbidstr = ('_' + (str(lbid) if lbid != -1 else 'all')) if lbid is not None and lbid != '' else '' to_hdf, hdf5_fpath = cfg_param.setdefault('to_hdf', False), '%s' % 'crsval_dataset.h5' if cfg_param.setdefault('hdf5_fpath', 'crsval_dataset.h5') is None else cfg_param['hdf5_fpath'] # Format the data if (type(X_train) == list): assert all([len(x) == len(X_train[0]) for x in X_train[1:]]) X_train = [pd.DataFrame(x) if (type(x) != pd.io.parsers.TextFileReader and type(x) != pd.DataFrame) else x for x in X_train] X_train = [pd.concat(x) if (type(x) == pd.io.parsers.TextFileReader and not to_hdf) else x for x in X_train] else: if (type(X_train) != pd.io.parsers.TextFileReader and type(X_train) != pd.DataFrame): X_train = pd.DataFrame(X_train) X_train = pd.concat(X_train) if (type(X_train) == pd.io.parsers.TextFileReader and not to_hdf) else X_train if (type(X_test) == list): assert all([len(x) == len(X_test[0]) for x in X_test[1:]]) X_test = [pd.DataFrame(x) if (type(x) != pd.io.parsers.TextFileReader and type(x) != pd.DataFrame) else x for x in X_test] X_test = [pd.concat(x) if (type(x) == pd.io.parsers.TextFileReader and not to_hdf) else x for x in X_test] else: if (type(X_test) != pd.io.parsers.TextFileReader and type(X_test) != pd.DataFrame): X_test = pd.DataFrame(X_test) X_test = pd.concat(X_test) if (type(X_test) == pd.io.parsers.TextFileReader and not to_hdf) else X_test if (type(Y_train) != pd.io.parsers.TextFileReader and type(Y_train) != pd.DataFrame): Y_train = pd.DataFrame(Y_train) Y_train_mt = Y_train.values.reshape((Y_train.shape[0],)) if (len(Y_train.shape) == 1 or Y_train.shape[1] == 1) else Y_train.values mltl=True if len(Y_train_mt.shape) > 1 and Y_train_mt.shape[1] > 1 or 2 in Y_train_mt else False print('Classification is starting...') preds, probs, scores = [[] for i in range(3)] crsval_featw, crsval_subfeatw = [{} for i in range(2)] for vars in model_iter(**model_param): if (global_param['comb']): mdl_name, mdl = [vars[x] for x in range(2)] else: filt_name, filter, clf_name, clf= [vars[x] for x in range(4)] print('#' * 80) # Assemble a pipeline if ('filter' in locals() and filter != None): model_name = '%s [Ft Filt] & %s [CLF]' % (filt_name, clf_name) pipeline = Pipeline([('featfilt', clone(filter)), ('clf', clf)]) elif ('clf' in locals() and clf != None): model_name = '%s [CLF]' % clf_name pipeline = Pipeline([('clf', clf)]) else: model_name = mdl_name pipeline = mdl if (type(mdl) is Pipeline) else Pipeline([('clf', mdl)]) if (model_name in PL_SET): continue PL_NAMES.append(model_name) PL_SET.add(model_name) print(model_name) # Build the model print('+' * 80) print('Training Model: ') print(pipeline) t0 = time() pipeline.fit(X_train, Y_train_mt) train_time = time() - t0 print('train time: %0.3fs' % train_time) t0 = time() pred = pipeline.predict(X_test) prob = pipeline.predict_proba(X_test) test_time = time() - t0 print('+' * 80) print('Testing: ') print('test time: %0.3fs' % test_time) preds.append(pred) probs.append(prob) scores.append(get_score(pipeline, X_test, mltl)) # Save predictions and model if (cfg_param.setdefault('save_pred', True)): io.write_npz(dict(pred_lb=pred, pred_prob=prob), 'clf_pred_%s%s' % (model_name.replace(' ', '_').lower(), lbidstr)) if (cfg_param.setdefault('save_model', True)): mdl_name = '%s' % model_name.replace(' ', '_').lower() if (all([hasattr(pipeline.steps[i][1], 'save') for i in range(len(pipeline.steps))])): for sub_mdl_name, mdl in pipeline.steps: mdl.save('%s_%s%s' % (mdl_name, sub_mdl_name.replace(' ', '_').lower(), lbidstr), **global_param.setdefault('mdl_save_kwargs', {})) else: io.write_obj(pipeline, '%s%s' % (mdl_name, lbidstr)) # Feature importances feat_w, sub_feat_w = get_featw(pipeline, X_train[0].shape[1] if (type(X_train) is list) else X_train.shape[1]) for k, v in feat_w.items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) crsval_featw.setdefault(key, []).append(v) for k, v in sub_feat_w.items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) crsval_subfeatw.setdefault(key, []).append(v) print('\n') if (len(preds) > 1): # Prediction overlap preds_mt = np.column_stack([x.ravel() for x in preds]) povl = np.array(pred_ovl(preds_mt)) # Spearman's rank correlation spmnr, spmnr_pval = stats.spearmanr(preds_mt) # Kendall rank correlation # kendalltau = stats.kendalltau(preds_mt)[0] # Pearson correlation # pearson = tats.pearsonr(preds_mt)[0] ## Save performance data povl_idx = [' & '.join(x) for x in imath.subset(PL_NAMES, min_crdnl=1)] povl_df = pd.DataFrame(povl, index=povl_idx, columns=['pred_ovl']) spmnr_df = pd.DataFrame(spmnr, index=PL_NAMES, columns=PL_NAMES) spmnr_pval_df = pd.DataFrame(spmnr_pval, index=PL_NAMES, columns=PL_NAMES) if (cfg_param.setdefault('save_povl', False)): povl_df.to_excel('cpovl_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_povl_npz', False)): io.write_df(povl_df, 'povl_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr', False)): spmnr_df.to_excel('spmnr_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_npz', False)): io.write_df(spmnr_df, 'spmnr_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr_pval', False)): spmnr_pval_df.to_excel('spmnr_pval_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_pval_npz', False)): io.write_df(spmnr_pval_df, 'spmnr_pval_clf%s.npz' % lbidstr, with_idx=True) save_featw(X_train[0].columns.values if (type(X_train) is list) else X_train.columns.values, crsval_featw, crsval_subfeatw, cfg_param=cfg_param, lbid=lbid) return preds, scores def kf2data(kf, X, Y, to_hdf=False, hdf5_fpath='crsval_dataset.h5'): if (to_hdf): import h5py from keras.utils.io_utils import HDF5Matrix hdf5_fpath = hdf5_fpath if hdf5_fpath else os.path.abspath('crsval_dataset.h5') for i, (train_idx, test_idx) in enumerate(kf): if (type(X)==list): if (type(X[0]) == pd.io.parsers.TextFileReader): pass assert all([len(x) == len(X[0]) for x in X[1:]]) X_train, X_test = [x[train_idx,:] for x in X] if to_hdf and type(X[0]) == HDF5Matrix or type(X[0]) != pd.DataFrame else [x.iloc[train_idx,:] for x in X], [x[test_idx,:] for x in X] if to_hdf and type(X[0]) == HDF5Matrix or type(X[0]) != pd.DataFrame else [x.iloc[test_idx,:] for x in X] train_idx_df, test_idx_df = pd.DataFrame(np.arange(X_train[0].shape[0]), index=X[0].index[train_idx]), pd.DataFrame(np.arange(X_test[0].shape[0]), index=X[0].index[test_idx]) else: if (type(X) == pd.io.parsers.TextFileReader): pass X_train, X_test = X[train_idx] if to_hdf and type(X) == HDF5Matrix or type(X) != pd.DataFrame else X.iloc[train_idx,:], X[test_idx] if to_hdf and type(X) == HDF5Matrix or type(X) != pd.DataFrame else X.iloc[test_idx,:] train_idx_df, test_idx_df = pd.DataFrame(np.arange(X_train.shape[0]), index=None if to_hdf and type(X) == HDF5Matrix or type(X) != pd.DataFrame else X.index[train_idx]), pd.DataFrame(np.arange(X_test.shape[0]), index=None if to_hdf and type(X) == HDF5Matrix or type(X) != pd.DataFrame else X.index[test_idx]) Y_train, Y_test = Y[train_idx], Y[test_idx] # Y_train = Y_train.reshape((Y_train.shape[0],)) if (len(Y_train.shape) > 1 and Y_train.shape[1] == 1) else Y_train # Y_test = Y_test.reshape((Y_test.shape[0],)) if (len(Y_test.shape) > 1 and Y_test.shape[1] == 1) else Y_test if (to_hdf): with h5py.File(hdf5_fpath, 'w') as hf: if (type(X_train) == list): for idx, x_train in enumerate(X_train): hf.create_dataset('X_train%i' % idx, data=x_train.values if type(X) != HDF5Matrix else x_train[:]) else: hf.create_dataset('X_train', data=X_train.values if type(X) != HDF5Matrix else X_train[:]) if (type(X_test) == list): for idx, x_test in enumerate(X_test): hf.create_dataset('X_test%i' % idx, data=x_test.values if type(X) != HDF5Matrix else x_test[:]) else: hf.create_dataset('X_test', data=X_test.values if type(X) != HDF5Matrix else X_test[:]) hf.create_dataset('Y_train', data=Y_train if type(Y) != HDF5Matrix else Y_train[:]) hf.create_dataset('Y_test', data=Y_test if type(Y) != HDF5Matrix else Y_test[:]) yield i, [HDF5Matrix(hdf5_fpath, 'X_train%i' % idx) for idx in range(len(X_train))] if (type(X_train) == list) else HDF5Matrix(hdf5_fpath, 'X_train'), [HDF5Matrix(hdf5_fpath, 'X_test%i' % idx) for idx in range(len(X_test))] if (type(X_test) == list) else HDF5Matrix(hdf5_fpath, 'X_test'), HDF5Matrix(hdf5_fpath, 'Y_train'), HDF5Matrix(hdf5_fpath, 'Y_test'), train_idx_df, test_idx_df # The implementation of HDF5Matrix is not good since it keep all the hdf5 file opened, so we need to manually close them. remove_hfps = [] for hfpath, hf in HDF5Matrix.refs.items(): if (hfpath.startswith(hdf5_fpath)): hf.close() remove_hfps.append(hfpath) for hfpath in remove_hfps: HDF5Matrix.refs.pop(hfpath, None) else: yield i, [x.values for x in X_train] if (type(X_train) == list) else X_train.values, [x.values for x in X_test] if (type(X_test) == list) else X_test.values, Y_train, Y_test, train_idx_df, test_idx_df # Evaluation def evaluate(X_train, Y_train, X_test, Y_test, model_iter, model_param={}, avg='micro', kfold=5, cfg_param={}, global_param={}, lbid=''): print('Evaluating...') from keras.utils.io_utils import HDF5Matrix global common_cfg FILT_NAMES, CLF_NAMES, PL_NAMES, PL_SET = model_param['glb_filtnames'], model_param['glb_clfnames'], global_param['pl_names'], global_param['pl_set'] lbidstr = ('_' + (str(lbid) if lbid != -1 else 'all')) if lbid is not None and lbid != '' else '' # Format the data if (type(X_train) == list): assert all([len(x) == len(X_train[0]) for x in X_train[1:]]) X_train = [pd.DataFrame(x) if (type(x) != pd.io.parsers.TextFileReader and type(x) != pd.DataFrame) else x for x in X_train] X_train = [pd.concat(x) if (type(x) == pd.io.parsers.TextFileReader and not to_hdf) else x for x in X_train] else: if (type(X_train) != pd.io.parsers.TextFileReader and type(X_train) != pd.DataFrame): X_train = pd.DataFrame(X_train) if type(X_train) != HDF5Matrix else X_train X_train = pd.concat(X_train) if (type(X_train) == pd.io.parsers.TextFileReader and not to_hdf) else X_train if (type(Y_train) != pd.io.parsers.TextFileReader and type(Y_train) != pd.DataFrame): Y_train = pd.DataFrame(Y_train) if (type(Y_train) == pd.io.parsers.TextFileReader and not to_hdf) else Y_train if (type(Y_train) != HDF5Matrix): Y_train = Y_train.values.reshape((Y_train.shape[0],)) if (len(Y_train.shape) == 1 or Y_train.shape[1] == 1) else Y_train.values else: Y_train = Y_train if (type(X_test) == list): assert all([len(x) == len(X_test[0]) for x in X_test[1:]]) X_test = [pd.DataFrame(x) if (type(x) != pd.io.parsers.TextFileReader and type(x) != pd.DataFrame) else x for x in X_test] X_test = [pd.concat(x) if (type(x) == pd.io.parsers.TextFileReader and not to_hdf) else x for x in X_test] else: if (type(X_test) != pd.io.parsers.TextFileReader and type(X_test) != pd.DataFrame): X_test = pd.DataFrame(X_test) if type(X_test) != HDF5Matrix else X_test X_test = pd.concat(X_test) if (type(X_test) == pd.io.parsers.TextFileReader and not to_hdf) else X_test if (type(Y_test) != pd.io.parsers.TextFileReader and type(Y_test) != pd.DataFrame): Y_test = pd.DataFrame(Y_test) if (type(Y_test) == pd.io.parsers.TextFileReader and not to_hdf) else Y_test if (type(Y_test) != HDF5Matrix): Y_test = Y_test.values.reshape((Y_test.shape[0],)) if (len(Y_test.shape) == 1 or Y_test.shape[1] == 1) else Y_test.values else: Y_test = Y_test is_mltl = True if len(Y_train.shape) > 1 and Y_train.shape[1] > 1 or 2 in Y_train else False print('Benchmark is starting...') mean_fpr = np.linspace(0, 1, 100) mean_recall = np.linspace(0, 1, 100) xdf = X_train[0] if type(X_train)==list else X_train roc_dict, prc_dict, featw_data, subfeatw_data = [{} for i in range(4)] ## Copy from cross_validate function Start ## del PL_NAMES[:] PL_SET.clear() if (cfg_param.setdefault('npg_ratio', None) is not None): npg_ratio = cfg_param['npg_ratio'] Y_train = np.array(Y_train) # HDF5Matrix is not working in matrix slicing and boolean operation y = Y_train[:,0] if (len(Y_train.shape) > 1) else Y_train if (1.0 * np.abs(y).sum() / Y_train.shape[0] < 1.0 / (npg_ratio + 1)): all_true = np.arange(Y_train.shape[0])[y > 0].tolist() all_false = np.arange(Y_train.shape[0])[y <= 0].tolist() true_id = np.random.choice(len(all_true), size=int(1.0 / npg_ratio * len(all_false)), replace=True) true_idx = [all_true[i] for i in true_id] all_train_idx = sorted(set(true_idx + all_false)) X_train = [x.iloc[all_train_idx] if type(x) != HDF5Matrix else x[all_train_idx] for x in X_train] if (type(X_train) is list) else X_train.iloc[all_train_idx] if type(x) != HDF5Matrix else X_train[all_train_idx] Y_train = Y_train[all_train_idx,:] if (len(Y_train.shape) > 1) else Y_train[all_train_idx] results, preds = [[] for x in range(2)] # Y_test = np.column_stack([np.abs(Y_test).reshape((Y_test.shape[0],-1))] + [label_binarize(lb, classes=[-1,1,0])[:,1] for lb in (np.sign(Y_test).astype('int8').reshape((Y_test.shape[0],-1))).T]) if (len(Y_test.shape) < 2 or Y_test.shape[1] == 1 or np.where(Y_test<0)[0].shape[0]>0) else Y_test for vars in model_iter(**model_param): if (global_param['comb']): mdl_name, mdl = [vars[x] for x in range(2)] else: filt_name, filter, clf_name, clf= [vars[x] for x in range(4)] print('#' * 80) # Assemble a pipeline if ('filter' in locals() and filter != None): model_name = '%s [Ft Filt] & %s [CLF]' % (filt_name, clf_name) pipeline = Pipeline([('featfilt', clone(filter)), ('clf', clf)]) elif ('clf' in locals() and clf != None): model_name = '%s [CLF]' % clf_name pipeline = Pipeline([('clf', clf)]) else: model_name = mdl_name pipeline = mdl if (model_name in PL_SET): continue PL_NAMES.append(model_name) PL_SET.add(model_name) print(model_name) # Benchmark results bm_results = benchmark(pipeline, X_train, Y_train, X_test, Y_test, mltl=is_mltl, signed=global_param.setdefault('signed', True if np.where(Y_train<0)[0].shape[0]>0 else False), average=avg) # Clear the model environment (e.g. GPU resources) del pipeline # if (type(pipeline) is Pipeline): # for cmpn in pipeline.named_steps.values(): # if (getattr(cmpn, "clear", None)): cmpn.clear() # else: # if (getattr(pipeline, "clear", None)): # pipeline.clear() # Obtain the results if (is_mltl and avg == 'all'): results.append([bm_results[x] for x in ['accuracy', 'micro-precision', 'micro-recall', 'micro-fscore', 'macro-precision', 'macro-recall', 'macro-fscore', 'train_time', 'test_time']]) else: results.append([bm_results[x] for x in ['accuracy', 'precision', 'recall', 'fscore', 'train_time', 'test_time']]) preds.append(bm_results['pred_lb']) if (cfg_param.setdefault('save_pred', False)): io.write_npz(dict(pred_lb=bm_results['pred_lb'], pred_prob=bm_results['pred_prob'], true_lb=Y_test), 'pred_%s%s' % (model_name.replace(' ', '_').lower(), lbidstr)) if (is_mltl and avg == 'all'): micro_id, macro_id = '-'.join([model_name,'micro']), '-'.join([model_name,'macro']) roc_dict[micro_id] = roc_dict.setdefault(micro_id, 0) + np.interp(mean_fpr, bm_results['micro-roc'][0], bm_results['micro-roc'][1]) roc_dict[macro_id] = roc_dict.setdefault(macro_id, 0) + np.interp(mean_fpr, bm_results['macro-roc'][0], bm_results['macro-roc'][1]) else: roc_dict[model_name] = roc_dict.setdefault(model_name, 0) + np.interp(mean_fpr, bm_results['roc'][0], bm_results['roc'][1]) prc_dict[model_name] = prc_dict.setdefault(model_name, 0) + np.interp(mean_recall, bm_results['prc'][0], bm_results['prc'][1]) for k, v in bm_results['feat_w'].items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) featw_data[key] = v for k, v in bm_results['sub_feat_w'].items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) subfeatw_data[key] = v print('\n') # Prediction overlap if (True if len(Y_train.shape) > 1 and Y_train.shape[1] > 1 else False): preds_mt = np.column_stack([x.ravel() for x in preds]) else: preds_mt = np.column_stack(preds) preds.append(Y_test) tpreds_mt = np.column_stack([x.ravel() for x in preds]) ## Copy from cross_validate function End ## povl = pred_ovl(preds_mt, Y_test) # Spearman's rank correlation spearman = stats.spearmanr(tpreds_mt) # Kendall rank correlation # kendalltau = stats.kendalltau(preds_mt) # Pearson correlation # pearson = stats.pearsonr(preds_mt) ## Save performance data if (is_mltl and avg == 'all'): metric_idx = ['Accuracy', 'Micro Precision', 'Micro Recall', 'Micro F score', 'Macro Precision', 'Macro Recall', 'Macro F score', 'Train time', 'Test time'] else: metric_idx = ['Accuracy', 'Precision', 'Recall', 'F score', 'Train time', 'Test time'] perf_df = pd.DataFrame(np.array(results).T, index=metric_idx, columns=PL_NAMES) povl_idx = [' & '.join(x) for x in imath.subset(PL_NAMES, min_crdnl=1)] povl_df = pd.DataFrame(np.array(povl), index=povl_idx, columns=['pred_ovl', 'tpred_ovl']) spmnr_val_df = pd.DataFrame(spearman[0], index=PL_NAMES+['Annotations'], columns=PL_NAMES+['Annotations']) spmnr_pval_df = pd.DataFrame(spearman[1], index=PL_NAMES+['Annotations'], columns=PL_NAMES+['Annotations']) if (cfg_param.setdefault('save_tpred', True)): io.write_npz(tpreds_mt, 'tpred_clf%s' % lbidstr) if (cfg_param.setdefault('save_perf', True)): perf_df.to_excel('perf_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_perf_npz', False)): io.write_df(perf_df, 'perf_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_povl', False)): povl_df.to_excel('povl_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_povl_npz', False)): io.write_df(povl_df, 'povl_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr', False)): spmnr_val_df.to_excel('spmnr_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_npz', False)): io.write_df(spmnr_val_df, 'spmnr_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr_pval', False)): spmnr_pval_df.to_excel('spmnr_pval_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_pval_npz', False)): io.write_df(spmnr_pval_df, 'spmnr_pval_clf%s.npz' % lbidstr, with_idx=True) # Feature importances try: save_featw(xdf.columns.values if type(xdf) != HDF5Matrix else np.arange(xdf.shape[1]), featw_data, subfeatw_data, cfg_param=cfg_param, lbid=lbid) except Exception as e: print(e) ## Plot figures if (is_mltl and avg == 'all'): micro_roc_data, micro_roc_labels, micro_roc_aucs, macro_roc_data, macro_roc_labels, macro_roc_aucs = [[] for i in range(6)] else: roc_data, roc_labels, roc_aucs = [[] for i in range(3)] prc_data, prc_labels, prc_aucs = [[] for i in range(3)] for pl in PL_NAMES: if (is_mltl and avg == 'all'): micro_id, macro_id = '-'.join([pl,'micro']), '-'.join([pl,'macro']) micro_mean_tpr, macro_mean_tpr = roc_dict[micro_id], roc_dict[macro_id] micro_roc_auc = metrics.auc(mean_fpr, micro_mean_tpr) macro_roc_auc = metrics.auc(mean_fpr, macro_mean_tpr) micro_roc_data.append([mean_fpr, micro_mean_tpr]) micro_roc_aucs.append(micro_roc_auc) micro_roc_labels.append('%s (AUC=%0.2f)' % (pl, micro_roc_auc)) macro_roc_data.append([mean_fpr, macro_mean_tpr]) macro_roc_aucs.append(macro_roc_auc) macro_roc_labels.append('%s (AUC=%0.2f)' % (pl, macro_roc_auc)) else: mean_tpr = roc_dict[pl] mean_roc_auc = metrics.auc(mean_fpr, mean_tpr) roc_data.append([mean_fpr, mean_tpr]) roc_aucs.append(mean_roc_auc) roc_labels.append('%s (AUC=%0.2f)' % (pl, mean_roc_auc)) mean_prcn = prc_dict[pl] mean_prc_auc = metrics.auc(mean_recall, mean_prcn) prc_data.append([mean_recall, mean_prcn]) prc_aucs.append(mean_prc_auc) prc_labels.append('%s (AUC=%0.2f)' % (pl, mean_prc_auc)) group_dict = {} for i, pl in enumerate(PL_NAMES): group_dict.setdefault(tuple(set(difflib.get_close_matches(pl, PL_NAMES))), []).append(i) if (not cfg_param.setdefault('group_by_name', False) or len(group_dict) == len(PL_NAMES)): groups = None else: group_array = np.array(group_dict.values()) group_array.sort() groups = group_array.tolist() if (is_mltl and avg == 'all'): aucs_df = pd.DataFrame([micro_roc_aucs, macro_roc_aucs, prc_aucs], index=['Micro ROC AUC', 'Macro ROC AUC', 'PRC AUC'], columns=PL_NAMES) if (cfg_param.setdefault('plot_roc', True)): plot.plot_roc(micro_roc_data, micro_roc_labels, groups=groups, fname='micro_roc%s'%lbidstr, plot_cfg=common_cfg) plot.plot_roc(macro_roc_data, macro_roc_labels, groups=groups, fname='macro_roc%s'%lbidstr, plot_cfg=common_cfg) else: aucs_df = pd.DataFrame([roc_aucs, prc_aucs], index=['ROC AUC', 'PRC AUC'], columns=PL_NAMES) if (cfg_param.setdefault('plot_roc', True)): plot.plot_roc(roc_data, roc_labels, groups=groups, fname='roc%s'%lbidstr, plot_cfg=common_cfg) if (cfg_param.setdefault('plot_prc', True)): plot.plot_prc(prc_data, prc_labels, groups=groups, fname='prc%s'%lbidstr, plot_cfg=common_cfg) if (cfg_param.setdefault('save_auc', False)): aucs_df.to_excel('auc%s.xlsx' % lbidstr) filt_num, clf_num = len(FILT_NAMES), len(CLF_NAMES) if (cfg_param.setdefault('plot_metric', False)): for mtrc in metric_idx: mtrc_avg_list, mtrc_std_list = [[] for i in range(2)] if (global_param['comb']): mtrc_avg = perf_avg_df.ix[mtrc,:].values.reshape((1,-1)) mtrc_std = perf_std_df.ix[mtrc,:].values.reshape((1,-1)) plot.plot_bar(mtrc_avg, mtrc_std, xlabels=PL_NAMES, labels=None, title='%s by Classifier and Feature Selection' % mtrc, fname='%s_clf_ft%s' % (mtrc.replace(' ', '_').lower(), lbidstr), plot_cfg=common_cfg) else: for i in range(filt_num): offset = i * clf_num mtrc_avg_list.append(perf_avg_df.ix[mtrc,offset:offset+clf_num].values.reshape((1,-1))) mtrc_std_list.append(perf_std_df.ix[mtrc,offset:offset+clf_num].values.reshape((1,-1))) mtrc_avg = np.concatenate(mtrc_avg_list) mtrc_std = np.concatenate(mtrc_std_list) plot.plot_bar(mtrc_avg, mtrc_std, xlabels=CLF_NAMES, labels=FILT_NAMES, title='%s by Classifier and Feature Selection' % mtrc, fname='%s_clf_ft%s' % (mtrc.replace(' ', '_').lower(), lbidstr), plot_cfg=common_cfg) # Cross validation def cross_validate(X, Y, model_iter, model_param={}, avg='micro', kfold=5, cfg_param={}, split_param={}, global_param={}, lbid=''): print('Cross validating...') from keras.utils.io_utils import HDF5Matrix global common_cfg FILT_NAMES, CLF_NAMES, PL_NAMES, PL_SET = model_param['glb_filtnames'], model_param['glb_clfnames'], global_param['pl_names'], global_param['pl_set'] lbidstr = ('_' + (str(lbid) if lbid != -1 else 'all')) if lbid is not None and lbid != '' else '' to_hdf, hdf5_fpath = cfg_param.setdefault('to_hdf', False), 'crsval_dataset%s.h5' % lbidstr if cfg_param.setdefault('hdf5_fpath', 'crsval_dataset%s.h5' % lbidstr) is None else cfg_param['hdf5_fpath'] # Format the data if (type(X) == list): assert all([len(x) == len(X[0]) for x in X[1:]]) X = [pd.DataFrame(x) if (type(x) != pd.io.parsers.TextFileReader and type(x) != pd.DataFrame) else x for x in X] X = [pd.concat(x) if (type(x) == pd.io.parsers.TextFileReader and not to_hdf) else x for x in X] else: if (type(X) != pd.io.parsers.TextFileReader and type(X) != pd.DataFrame): X = pd.DataFrame(X) if type(X) != HDF5Matrix else X X = pd.concat(X) if (type(X) == pd.io.parsers.TextFileReader and not to_hdf) else X if (type(Y) != pd.io.parsers.TextFileReader and type(Y) != pd.DataFrame): Y = pd.DataFrame(Y) if (type(Y) == pd.io.parsers.TextFileReader and not to_hdf) else Y if (type(Y) != HDF5Matrix): Y_mt = Y.values.reshape((Y.shape[0],)) if (len(Y.shape) == 1 or Y.shape[1] == 1) else Y.values else: Y_mt = Y is_mltl = True if len(Y_mt.shape) > 1 and Y_mt.shape[1] > 1 or 2 in Y_mt else False print('Benchmark is starting...') mean_fpr = np.linspace(0, 1, 100) mean_recall = np.linspace(0, 1, 100) xdf = X[0] if type(X)==list else X if (len(split_param) == 0): if (type(xdf) != HDF5Matrix): kf = list(KFold(n_splits=kfold, shuffle=True, random_state=0).split(xdf, Y_mt)) if (len(Y_mt.shape) == 1) else list(KFold(n_splits=kfold, shuffle=True, random_state=0).split(xdf, Y_mt[:,0].reshape((Y_mt.shape[0],)))) else: kf = list(KFold(n_splits=kfold, shuffle=False, random_state=0).split(xdf[:], Y_mt[:])) if (len(Y_mt.shape) == 1) else list(KFold(n_splits=kfold, shuffle=False, random_state=0).split(xdf[:], Y_mt[:].reshape((-1,)))) # HDF5Matrix is not working in shuffle indices else: split_param['shuffle'] = True if type(xdf) != HDF5Matrix else False # To-do: implement the split method for multi-label data if ('train_size' in split_param and 'test_size' in split_param): kf = list(StratifiedShuffleSplit(n_splits=kfold, train_size=split_param['train_size'], test_size=split_param['test_size'], random_state=0).split(xdf, Y_mt)) if (len(Y_mt.shape) == 1) else list(StratifiedShuffleSplit(n_splits=kfold, train_size=split_param['train_size'], test_size=split_param['test_size'], random_state=0).split(xdf, Y_mt[:,0].reshape((Y_mt.shape[0],)))) else: kf = list(StratifiedKFold(n_splits=kfold, shuffle=split_param.setdefault('shuffle', True), random_state=0).split(xdf, Y_mt)) if (len(Y_mt.shape) == 1) else list(StratifiedKFold(n_splits=kfold, shuffle=split_param.setdefault('shuffle', True), random_state=0).split(xdf, Y_mt[:,0].reshape((Y_mt.shape[0],)))) crsval_results, crsval_tpreds, crsval_povl, crsval_spearman, crsval_kendalltau, crsval_pearson = [[] for i in range(6)] crsval_roc, crsval_prc, crsval_featw, crsval_subfeatw = [{} for i in range(4)] # for i, (train_idx, test_idx) in enumerate(kf): for i, X_train, X_test, Y_train, Y_test, train_idx_df, test_idx_df in kf2data(kf, X, Y_mt, to_hdf=to_hdf, hdf5_fpath=hdf5_fpath): del PL_NAMES[:] PL_SET.clear() print('\n' + '-' * 80 + '\n' + '%s time validation' % imath.ordinal(i+1) + '\n' + '-' * 80 + '\n') if (cfg_param.setdefault('save_crsval_idx', False)): io.write_df(train_idx_df, 'train_idx_crsval_%s%s.npz' % (i, lbidstr), with_idx=True) io.write_df(test_idx_df, 'test_idx_crsval_%s%s.npz' % (i, lbidstr), with_idx=True) if (cfg_param.setdefault('npg_ratio', None) is not None): npg_ratio = cfg_param['npg_ratio'] Y_train = np.array(Y_train) # HDF5Matrix is not working in matrix slicing and boolean operation y = Y_train[:,0] if (len(Y_train.shape) > 1) else Y_train if (1.0 * np.abs(y).sum() / Y_train.shape[0] < 1.0 / (npg_ratio + 1)): all_true = np.arange(Y_train.shape[0])[y > 0].tolist() all_false = np.arange(Y_train.shape[0])[y <= 0].tolist() true_id = np.random.choice(len(all_true), size=int(1.0 / npg_ratio * len(all_false)), replace=True) true_idx = [all_true[i] for i in true_id] all_train_idx = sorted(set(true_idx + all_false)) X_train = [x.iloc[all_train_idx] if type(x) != HDF5Matrix else x[all_train_idx] for x in X_train] if (type(X_train) is list) else X_train.iloc[all_train_idx] if type(x) != HDF5Matrix else X_train[all_train_idx] Y_train = Y_train[all_train_idx,:] if (len(Y_train.shape) > 1) else Y_train[all_train_idx] results, preds = [[] for x in range(2)] Y_test = np.array(Y_test) for vars in model_iter(**model_param): if (global_param['comb']): mdl_name, mdl = [vars[x] for x in range(2)] else: filt_name, filter, clf_name, clf= [vars[x] for x in range(4)] print('#' * 80) # Assemble a pipeline if ('filter' in locals() and filter != None): model_name = '%s [Ft Filt] & %s [CLF]' % (filt_name, clf_name) pipeline = Pipeline([('featfilt', clone(filter)), ('clf', clf)]) elif ('clf' in locals() and clf != None): model_name = '%s [CLF]' % clf_name pipeline = Pipeline([('clf', clf)]) else: model_name = mdl_name pipeline = mdl if (model_name in PL_SET): continue PL_NAMES.append(model_name) PL_SET.add(model_name) print(model_name) # Benchmark results bm_results = benchmark(pipeline, X_train, Y_train, X_test, Y_test, mltl=is_mltl, signed=global_param.setdefault('signed', True if np.where(Y_mt<0)[0].shape[0]>0 else False), average=avg) # Clear the model environment (e.g. GPU resources) del pipeline # if (type(pipeline) is Pipeline): # for cmpn in pipeline.named_steps.values(): # if (getattr(cmpn, "clear", None)): cmpn.clear() # else: # if (getattr(pipeline, "clear", None)): # pipeline.clear() # Obtain the results if (is_mltl and avg == 'all'): results.append([bm_results[x] for x in ['accuracy', 'micro-precision', 'micro-recall', 'micro-fscore', 'macro-precision', 'macro-recall', 'macro-fscore', 'train_time', 'test_time']]) else: # for k, v in zip(['precision', 'recall', 'fscore'], bm_results['metrics'].loc['weighted avg',['precision', 'recall', 'f1-score']]): # bm_results[k] = v results.append([bm_results[x] for x in ['accuracy', 'precision', 'recall', 'fscore', 'train_time', 'test_time']]) preds.append(bm_results['pred_lb']) if (cfg_param.setdefault('save_crsval_pred', False)): io.write_npz(dict(pred_lb=bm_results['pred_lb'], true_lb=Y_test), 'pred_crsval_%s_%s%s' % (i, model_name.replace(' ', '_').lower(), lbidstr)) if (is_mltl and avg == 'all'): micro_id, macro_id = '-'.join([model_name,'micro']), '-'.join([model_name,'macro']) crsval_roc[micro_id] = crsval_roc.setdefault(micro_id, 0) + np.interp(mean_fpr, bm_results['micro-roc'][0], bm_results['micro-roc'][1]) crsval_roc[macro_id] = crsval_roc.setdefault(macro_id, 0) + np.interp(mean_fpr, bm_results['macro-roc'][0], bm_results['macro-roc'][1]) else: crsval_roc[model_name] = crsval_roc.setdefault(model_name, 0) + np.interp(mean_fpr, bm_results['roc'][0], bm_results['roc'][1]) crsval_prc[model_name] = crsval_prc.setdefault(model_name, 0) + np.interp(mean_recall, bm_results['prc'][0], bm_results['prc'][1]) for k, v in bm_results['feat_w'].items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) crsval_featw.setdefault(key, []).append(v) for k, v in bm_results['sub_feat_w'].items(): key = '%s_%s_%s' % (model_name, k[0], k[1]) crsval_subfeatw.setdefault(key, []).append(v) print('\n') # Cross validation results crsval_results.append(results) # Prediction overlap if (True if len(Y_mt.shape) > 1 and Y_mt.shape[1] > 1 else False): preds_mt = np.column_stack([x.ravel() for x in preds]) else: preds_mt = np.column_stack(preds) preds.append(Y_test) tpreds_mt = np.column_stack([x.ravel() for x in preds]) crsval_tpreds.append(tpreds_mt) crsval_povl.append(pred_ovl(preds_mt, Y_test)) # Spearman's rank correlation crsval_spearman.append(stats.spearmanr(tpreds_mt)) # Kendall rank correlation # crsval_kendalltau.append(stats.kendalltau(preds_mt)) # Pearson correlation # crsval_pearson.append(stats.pearsonr(preds_mt)) del X_train, X_test, Y_train, Y_test print('\n') perf_avg = np.array(crsval_results).mean(axis=0) perf_std = np.array(crsval_results).std(axis=0) povl_avg = np.array(crsval_povl).mean(axis=0).round() spmnr_avg = np.array([crsp[0] for crsp in crsval_spearman]).mean(axis=0) spmnr_pval = np.array([crsp[1] for crsp in crsval_spearman]).mean(axis=0) # kndtr_avg = np.array([crkdt[0] for crkdt in crsval_kendalltau).mean(axis=0) # kndtr_pval = np.array([crkdt[1] for crkdt in crsval_kendalltau]).mean(axis=0) # prsnr_avg = np.array([crprs[0] for crprs in crsval_pearson).mean(axis=0) # prsnr_pval = np.array([crprs[1] for crprs in crsval_pearson]).mean(axis=0) ## Save performance data if (is_mltl and avg == 'all'): metric_idx = ['Accuracy', 'Micro Precision', 'Micro Recall', 'Micro F score', 'Macro Precision', 'Macro Recall', 'Macro F score', 'Train time', 'Test time'] else: metric_idx = ['Accuracy', 'Precision', 'Recall', 'F score', 'Train time', 'Test time'] perf_avg_df = pd.DataFrame(perf_avg.T, index=metric_idx, columns=PL_NAMES) perf_std_df = pd.DataFrame(perf_std.T, index=metric_idx, columns=PL_NAMES) povl_idx = [' & '.join(x) for x in imath.subset(PL_NAMES, min_crdnl=1)] povl_avg_df = pd.DataFrame(povl_avg, index=povl_idx, columns=['pred_ovl', 'tpred_ovl']) spmnr_avg_df = pd.DataFrame(spmnr_avg, index=PL_NAMES+['Annotations'], columns=PL_NAMES+['Annotations']) spmnr_pval_df = pd.DataFrame(spmnr_pval, index=PL_NAMES+['Annotations'], columns=PL_NAMES+['Annotations']) if (cfg_param.setdefault('save_tpred', True)): io.write_npz(crsval_tpreds, 'tpred_clf%s' % lbidstr) if (cfg_param.setdefault('save_perf_avg', True)): perf_avg_df.to_excel('perf_avg_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_perf_avg_npz', False)): io.write_df(perf_avg_df, 'perf_avg_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_perf_std', True)): perf_std_df.to_excel('perf_std_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_perf_std_npz', False)): io.write_df(perf_std_df, 'perf_std_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_povl', False)): povl_avg_df.to_excel('cpovl_avg_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_povl_npz', False)): io.write_df(povl_avg_df, 'povl_avg_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr_avg', False)): spmnr_avg_df.to_excel('spmnr_avg_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_avg_npz', False)): io.write_df(spmnr_avg_df, 'spmnr_avg_clf%s.npz' % lbidstr, with_idx=True) if (cfg_param.setdefault('save_spmnr_pval', False)): spmnr_pval_df.to_excel('spmnr_pval_clf%s.xlsx' % lbidstr) if (cfg_param.setdefault('save_spmnr_pval_npz', False)): io.write_df(spmnr_pval_df, 'spmnr_pval_clf%s.npz' % lbidstr, with_idx=True) # Feature importances try: save_featw(xdf.columns.values if type(xdf) != HDF5Matrix else np.arange(xdf.shape[1]), crsval_featw, crsval_subfeatw, cfg_param=cfg_param, lbid=lbid) except Exception as e: print(e) ## Plot figures if (is_mltl and avg == 'all'): micro_roc_data, micro_roc_labels, micro_roc_aucs, macro_roc_data, macro_roc_labels, macro_roc_aucs = [[] for i in range(6)] else: roc_data, roc_labels, roc_aucs = [[] for i in range(3)] prc_data, prc_labels, prc_aucs = [[] for i in range(3)] for pl in PL_NAMES: if (is_mltl and avg == 'all'): micro_id, macro_id = '-'.join([pl,'micro']), '-'.join([pl,'macro']) micro_mean_tpr, macro_mean_tpr = crsval_roc[micro_id], crsval_roc[macro_id] micro_mean_tpr, macro_mean_tpr = micro_mean_tpr / len(kf), macro_mean_tpr / len(kf) micro_roc_auc = metrics.auc(mean_fpr, micro_mean_tpr) macro_roc_auc = metrics.auc(mean_fpr, macro_mean_tpr) micro_roc_data.append([mean_fpr, micro_mean_tpr]) micro_roc_aucs.append(micro_roc_auc) micro_roc_labels.append('%s (AUC=%0.2f)' % (pl, micro_roc_auc)) macro_roc_data.append([mean_fpr, macro_mean_tpr]) macro_roc_aucs.append(macro_roc_auc) macro_roc_labels.append('%s (AUC=%0.2f)' % (pl, macro_roc_auc)) else: mean_tpr = crsval_roc[pl] mean_tpr /= len(kf) mean_roc_auc = metrics.auc(mean_fpr, mean_tpr) roc_data.append([mean_fpr, mean_tpr]) roc_aucs.append(mean_roc_auc) roc_labels.append('%s (AUC=%0.2f)' % (pl, mean_roc_auc)) mean_prcn = crsval_prc[pl] mean_prcn /= len(kf) mean_prc_auc = metrics.auc(mean_recall, mean_prcn) prc_data.append([mean_recall, mean_prcn]) prc_aucs.append(mean_prc_auc) prc_labels.append('%s (AUC=%0.2f)' % (pl, mean_prc_auc)) group_dict = {} for i, pl in enumerate(PL_NAMES): group_dict.setdefault(tuple(set(difflib.get_close_matches(pl, PL_NAMES))), []).append(i) if (not cfg_param.setdefault('group_by_name', False) or len(group_dict) == len(PL_NAMES)): groups = None else: group_array = np.array(group_dict.values()) group_array.sort() groups = group_array.tolist() if (is_mltl and avg == 'all'): aucs_df = pd.DataFrame([micro_roc_aucs, macro_roc_aucs, prc_aucs], index=['Micro ROC AUC', 'Macro ROC AUC', 'PRC AUC'], columns=PL_NAMES) if (cfg_param.setdefault('plot_roc', True)): plot.plot_roc(micro_roc_data, micro_roc_labels, groups=groups, fname='micro_roc%s'%lbidstr, plot_cfg=common_cfg) plot.plot_roc(macro_roc_data, macro_roc_labels, groups=groups, fname='macro_roc%s'%lbidstr, plot_cfg=common_cfg) else: aucs_df = pd.DataFrame([roc_aucs, prc_aucs], index=['ROC AUC', 'PRC AUC'], columns=PL_NAMES) if (cfg_param.setdefault('plot_roc', True)): plot.plot_roc(roc_data, roc_labels, groups=groups, fname='roc%s'%lbidstr, plot_cfg=common_cfg) if (cfg_param.setdefault('plot_prc', True)): plot.plot_prc(prc_data, prc_labels, groups=groups, fname='prc%s'%lbidstr, plot_cfg=common_cfg) if (cfg_param.setdefault('save_auc', False)): aucs_df.to_excel('auc%s.xlsx' % lbidstr) filt_num, clf_num = len(FILT_NAMES), len(CLF_NAMES) if (cfg_param.setdefault('plot_metric', False)): for mtrc in metric_idx: mtrc_avg_list, mtrc_std_list = [[] for i in range(2)] if (global_param['comb']): mtrc_avg = perf_avg_df.ix[mtrc,:].values.reshape((1,-1)) mtrc_std = perf_std_df.ix[mtrc,:].values.reshape((1,-1)) plot.plot_bar(mtrc_avg, mtrc_std, xlabels=PL_NAMES, labels=None, title='%s by Classifier and Feature Selection' % mtrc, fname='%s_clf_ft%s' % (mtrc.replace(' ', '_').lower(), lbidstr), plot_cfg=common_cfg) else: for i in range(filt_num): offset = i * clf_num mtrc_avg_list.append(perf_avg_df.ix[mtrc,offset:offset+clf_num].values.reshape((1,-1))) mtrc_std_list.append(perf_std_df.ix[mtrc,offset:offset+clf_num].values.reshape((1,-1))) mtrc_avg = np.concatenate(mtrc_avg_list) mtrc_std = np.concatenate(mtrc_std_list) plot.plot_bar(mtrc_avg, mtrc_std, xlabels=CLF_NAMES, labels=FILT_NAMES, title='%s by Classifier and Feature Selection' % mtrc, fname='%s_clf_ft%s' % (mtrc.replace(' ', '_').lower(), lbidstr), plot_cfg=common_cfg) def tune_param(mdl_name, mdl, X, Y, rdtune, params, mltl=False, avg='micro', n_jobs=-1): if (rdtune): param_dist, n_iter = [params[k] for k in ['param_dist', 'n_iter']] grid = RandomizedSearchCV(estimator=mdl, param_distributions=param_dist, n_iter=n_iter, scoring='f1_%s' % avg if mltl else 'f1', n_jobs=n_jobs, error_score=0) else: param_grid, cv = [params[k] for k in ['param_grid', 'cv']] grid = GridSearchCV(estimator=mdl, param_grid=param_grid, scoring='f1_micro' if mltl else 'f1', cv=cv, n_jobs=n_jobs, error_score=0) grid.fit(X, Y) print("The best parameters of [%s] are %s, with a score of %0.3f" % (mdl_name, grid.best_params_, grid.best_score_)) # Store all the parameter candidates into a dictionary of list if (rdtune): param_grid = {} for p_option in grid.cv_results_['params']: for p_name, p_val in p_option.items(): param_grid.setdefault(p_name, []).append(p_val) else: param_grid = grid.param_grid # Index the parameter names and valules dim_names = dict([(k, i) for i, k in enumerate(param_grid.keys())]) dim_vals = {} for pn in dim_names.keys(): dim_vals[pn] = dict([(k, i) for i, k in enumerate(param_grid[pn])]) # Create data cube score_avg_cube = np.ndarray(shape=[len(param_grid[k]) for k in param_grid.keys()], dtype='float') score_std_cube = np.ndarray(shape=[len(param_grid[k]) for k in param_grid.keys()], dtype='float') # Calculate the score list score_avg_list = (np.array(grid.cv_results_['mean_train_score']) + np.array(grid.cv_results_['mean_test_score'])) / 2 score_std_list = (np.array(grid.cv_results_['std_train_score']) + np.array(grid.cv_results_['std_test_score'])) / 2 # Fill in the data cube for i, p_option in enumerate(grid.cv_results_['params']): idx = np.zeros((len(dim_names),), dtype='int') for k, v in p_option.items(): idx[dim_names[k]] = dim_vals[k][v] score_avg_cube[tuple(idx)] = score_avg_list[i] score_std_cube[tuple(idx)] = score_std_list[i] return grid.best_params_, grid.best_score_, score_avg_cube, score_std_cube, dim_names, dim_vals def tune_param_optunity(mdl_name, mdl, X, Y, perf_func=None, scoring='f1', optfunc='max', solver='particle swarm', params={}, mltl=False, avg='micro', n_jobs=-1): import optunity struct, param_space, folds, n_iter = [params.setdefault(k, None) for k in ['struct', 'param_space', 'folds', 'n_iter']] ext_params = dict.fromkeys(param_space.keys()) if (not struct) else dict.fromkeys(params.setdefault('param_names', [])) kwargs = dict([('num_iter', n_iter), ('num_folds', folds)]) if (type(folds) is int) else dict([('num_iter', n_iter), ('num_folds', folds.get_n_splits()), ('folds', [list(folds.split(X))] * n_iter)]) @optunity.cross_validated(x=X, y=Y, **kwargs) def default_perf(x_train, y_train, x_test, y_test, **ext_params): mdl.fit(x_train, y_train) if (scoring == 'roc'): preds = get_score(mdl, x_test, mltl) if (mltl): from . import metric as imetric return imetric.mltl_roc(y_test, preds, average=avg) else: preds = mdl.predict(x_test) score_func = getattr(optunity, scoring) if (hasattr(optunity, scoring)) else None score_func = getattr(metrics, scoring+'_score') if (score_func is None and hasattr(metrics, scoring+'_score')) else score_func if (score_func is None): print('Score function %s is not supported!' % scoring) sys.exit(1) return score_func(y_test, preds, average=avg) perf = perf_func if callable(perf_func) else default_perf if (optfunc == 'max'): config, info, _ = optunity.maximize(perf, num_evals=n_iter, solver_name=solver, pmap=optunity.parallel.create_pmap(n_jobs), **param_space) if (not struct) else optunity.maximize_structured(perf, search_space=param_space, num_evals=n_iter, pmap=optunity.parallel.create_pmap(n_jobs)) elif (optfunc == 'min'): config, info, _ = optunity.minimize(perf, num_evals=n_iter, solver_name=solver, pmap=optunity.parallel.create_pmap(n_jobs), **param_space) if (not struct) else optunity.minimize_structured(perf, search_space=param_space, num_evals=n_iter, pmap=optunity.parallel.create_pmap(n_jobs)) print("The best parameters of [%s] are %s, with a score of %0.3f" % (mdl_name, config, info.optimum)) cl_df = optunity.call_log2dataframe(info.call_log) cl_df.to_csv('call_log.csv') # Store all the parameter candidates into a dictionary of list param_grid = dict([(x, sorted(set(cl_df[x]))) for x in cl_df.columns if x != 'value']) param_names = param_grid.keys() # Index the parameter names and valules dim_names = dict([(k, i) for i, k in enumerate(param_names)]) dim_vals = {} for pn in dim_names.keys(): dim_vals[pn] = dict([(k, i) for i, k in enumerate(param_grid[pn])]) # Create data cube score_avg_cube = np.ndarray(shape=[len(param_grid[k]) for k in param_names], dtype='float') * np.nan score_std_cube = np.ndarray(shape=[len(param_grid[k]) for k in param_names], dtype='float') * np.nan # Calculate the score list score_avg_list = cl_df['value'] score_std_list = np.zeros_like(cl_df['value']) # Fill in the data cube for i, p_option in cl_df[param_names].iterrows(): idx = np.zeros((len(dim_names),), dtype='int') for k, v in p_option.items(): idx[dim_names[k]] = dim_vals[k][v] score_avg_cube[tuple(idx)] = score_avg_list[i] score_std_cube[tuple(idx)] = score_std_list[i] return config, info.optimum, score_avg_cube, score_std_cube, dim_names, dim_vals def tune_param_hyperopt(mdl_name, mdl, X, Y, obj_func=None, scoring='f1', solver=None, params={}, mltl=False, avg='micro', n_jobs=-1): import hyperopt param_space, trials, folds, max_evals = [params.setdefault(k, v) for k, v in zip(['param_space', 'trials', 'folds', 'n_iter'], [{}, hyperopt.Trials(), 5, 500])] ext_params = dict.fromkeys(param_space.keys()) num_folds = folds if (type(folds) is int) else folds.get_n_splits() def default_obj(parameters): from sklearn.model_selection import cross_validate as cv cv_results = cv(mdl, X, Y, scoring=scoring, cv=num_folds, return_train_score=False) return {'loss': 1-cv_results['test_score'].mean(), 'params': parameters, 'status': hyperopt.STATUS_OK} objective = obj_func if callable(obj_func) else default_obj best_config = hyperopt.fmin(fn=objective, space=param_space, algo=solver if solver else hyperopt.tpe.suggest, max_evals=max_evals, trials=trials) best_trials = sorted(trials.results, key=lambda x: x['loss'], reverse=False) best_score = 1 - best_trials[0]['loss'] print("The best parameters of [%s] are %s, with a score of %0.3f" % (mdl_name, best_config, best_score)) params, losses = zip(*[(x['params'], x['loss']) for x in best_trials]) tune_df = pd.concat([
pd.DataFrame(params)
pandas.DataFrame
""" Tests the usecols functionality during parsing for all of the parsers defined in parsers.py """ from io import StringIO import numpy as np import pytest from pandas._libs.tslib import Timestamp from pandas import DataFrame, Index import pandas._testing as tm _msg_validate_usecols_arg = ( "'usecols' must either be list-like " "of all strings, all unicode, all " "integers or a callable." ) _msg_validate_usecols_names = ( "Usecols do not match columns, columns expected but not found: {0}" ) def test_raise_on_mixed_dtype_usecols(all_parsers): # See gh-12678 data = """a,b,c 1000,2000,3000 4000,5000,6000 """ usecols = [0, "b", 2] parser = all_parsers with pytest.raises(ValueError, match=_msg_validate_usecols_arg): parser.read_csv(StringIO(data), usecols=usecols) @pytest.mark.parametrize("usecols", [(1, 2), ("b", "c")]) def test_usecols(all_parsers, usecols): data = """\ a,b,c 1,2,3 4,5,6 7,8,9 10,11,12""" parser = all_parsers result = parser.read_csv(StringIO(data), usecols=usecols) expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"]) tm.assert_frame_equal(result, expected) def test_usecols_with_names(all_parsers): data = """\ a,b,c 1,2,3 4,5,6 7,8,9 10,11,12""" parser = all_parsers names = ["foo", "bar"] result = parser.read_csv(StringIO(data), names=names, usecols=[1, 2], header=0) expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=names) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "names,usecols", [(["b", "c"], [1, 2]), (["a", "b", "c"], ["b", "c"])] ) def test_usecols_relative_to_names(all_parsers, names, usecols): data = """\ 1,2,3 4,5,6 7,8,9 10,11,12""" parser = all_parsers result = parser.read_csv(StringIO(data), names=names, header=None, usecols=usecols) expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"]) tm.assert_frame_equal(result, expected) def test_usecols_relative_to_names2(all_parsers): # see gh-5766 data = """\ 1,2,3 4,5,6 7,8,9 10,11,12""" parser = all_parsers result = parser.read_csv( StringIO(data), names=["a", "b"], header=None, usecols=[0, 1] ) expected = DataFrame([[1, 2], [4, 5], [7, 8], [10, 11]], columns=["a", "b"]) tm.assert_frame_equal(result, expected) def test_usecols_name_length_conflict(all_parsers): data = """\ 1,2,3 4,5,6 7,8,9 10,11,12""" parser = all_parsers msg = "Number of passed names did not match number of header fields in the file" with pytest.raises(ValueError, match=msg): parser.read_csv(StringIO(data), names=["a", "b"], header=None, usecols=[1]) def test_usecols_single_string(all_parsers): # see gh-20558 parser = all_parsers data = """foo, bar, baz 1000, 2000, 3000 4000, 5000, 6000""" with pytest.raises(ValueError, match=_msg_validate_usecols_arg): parser.read_csv(StringIO(data), usecols="foo") @pytest.mark.parametrize( "data", ["a,b,c,d\n1,2,3,4\n5,6,7,8", "a,b,c,d\n1,2,3,4,\n5,6,7,8,"] ) def test_usecols_index_col_false(all_parsers, data): # see gh-9082 parser = all_parsers usecols = ["a", "c", "d"] expected = DataFrame({"a": [1, 5], "c": [3, 7], "d": [4, 8]}) result = parser.read_csv(StringIO(data), usecols=usecols, index_col=False) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("index_col", ["b", 0]) @pytest.mark.parametrize("usecols", [["b", "c"], [1, 2]]) def test_usecols_index_col_conflict(all_parsers, usecols, index_col): # see gh-4201: test that index_col as integer reflects usecols parser = all_parsers data = "a,b,c,d\nA,a,1,one\nB,b,2,two" expected = DataFrame({"c": [1, 2]}, index=Index(["a", "b"], name="b")) result = parser.read_csv(StringIO(data), usecols=usecols, index_col=index_col) tm.assert_frame_equal(result, expected) def test_usecols_index_col_conflict2(all_parsers): # see gh-4201: test that index_col as integer reflects usecols parser = all_parsers data = "a,b,c,d\nA,a,1,one\nB,b,2,two" expected = DataFrame({"b": ["a", "b"], "c": [1, 2], "d": ("one", "two")}) expected = expected.set_index(["b", "c"]) result = parser.read_csv( StringIO(data), usecols=["b", "c", "d"], index_col=["b", "c"] ) tm.assert_frame_equal(result, expected) def test_usecols_implicit_index_col(all_parsers): # see gh-2654 parser = all_parsers data = "a,b,c\n4,apple,bat,5.7\n8,orange,cow,10" result = parser.read_csv(StringIO(data), usecols=["a", "b"]) expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8]) tm.assert_frame_equal(result, expected) def test_usecols_regex_sep(all_parsers): # see gh-2733 parser = all_parsers data = "a b c\n4 apple bat 5.7\n8 orange cow 10" result = parser.read_csv(StringIO(data), sep=r"\s+", usecols=("a", "b")) expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8]) tm.assert_frame_equal(result, expected) def test_usecols_with_whitespace(all_parsers): parser = all_parsers data = "a b c\n4 apple bat 5.7\n8 orange cow 10" result = parser.read_csv(StringIO(data), delim_whitespace=True, usecols=("a", "b")) expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8]) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "usecols,expected", [ # Column selection by index. ([0, 1], DataFrame(data=[[1000, 2000], [4000, 5000]], columns=["2", "0"])), # Column selection by name. (["0", "1"], DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"])), ], ) def test_usecols_with_integer_like_header(all_parsers, usecols, expected): parser = all_parsers data = """2,0,1 1000,2000,3000 4000,5000,6000""" result = parser.read_csv(StringIO(data), usecols=usecols) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("usecols", [[0, 2, 3], [3, 0, 2]]) def test_usecols_with_parse_dates(all_parsers, usecols): # see gh-9755 data = """a,b,c,d,e 0,1,20140101,0900,4 0,1,20140102,1000,4""" parser = all_parsers parse_dates = [[1, 2]] cols = { "a": [0, 0], "c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")], } expected = DataFrame(cols, columns=["c_d", "a"]) result = parser.read_csv(StringIO(data), usecols=usecols, parse_dates=parse_dates) tm.assert_frame_equal(result, expected) def test_usecols_with_parse_dates2(all_parsers): # see gh-13604 parser = all_parsers data = """2008-02-07 09:40,1032.43 2008-02-07 09:50,1042.54 2008-02-07 10:00,1051.65""" names = ["date", "values"] usecols = names[:] parse_dates = [0] index = Index( [ Timestamp("2008-02-07 09:40"), Timestamp("2008-02-07 09:50"), Timestamp("2008-02-07 10:00"), ], name="date", ) cols = {"values": [1032.43, 1042.54, 1051.65]} expected = DataFrame(cols, index=index) result = parser.read_csv( StringIO(data), parse_dates=parse_dates, index_col=0, usecols=usecols, header=None, names=names, ) tm.assert_frame_equal(result, expected) def test_usecols_with_parse_dates3(all_parsers): # see gh-14792 parser = all_parsers data = """a,b,c,d,e,f,g,h,i,j 2016/09/21,1,1,2,3,4,5,6,7,8""" usecols = list("abcdefghij") parse_dates = [0] cols = { "a": Timestamp("2016-09-21"), "b": [1], "c": [1], "d": [2], "e": [3], "f": [4], "g": [5], "h": [6], "i": [7], "j": [8], } expected = DataFrame(cols, columns=usecols) result = parser.read_csv(StringIO(data), usecols=usecols, parse_dates=parse_dates) tm.assert_frame_equal(result, expected) def test_usecols_with_parse_dates4(all_parsers): data = "a,b,c,d,e,f,g,h,i,j\n2016/09/21,1,1,2,3,4,5,6,7,8" usecols = list("abcdefghij") parse_dates = [[0, 1]] parser = all_parsers cols = { "a_b": "2016/09/21 1", "c": [1], "d": [2], "e": [3], "f": [4], "g": [5], "h": [6], "i": [7], "j": [8], } expected = DataFrame(cols, columns=["a_b"] + list("cdefghij")) result = parser.read_csv(StringIO(data), usecols=usecols, parse_dates=parse_dates) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("usecols", [[0, 2, 3], [3, 0, 2]]) @pytest.mark.parametrize( "names", [ list("abcde"), # Names span all columns in original data. list("acd"), # Names span only the selected columns. ], ) def test_usecols_with_parse_dates_and_names(all_parsers, usecols, names): # see gh-9755 s = """0,1,20140101,0900,4 0,1,20140102,1000,4""" parse_dates = [[1, 2]] parser = all_parsers cols = { "a": [0, 0], "c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")], } expected = DataFrame(cols, columns=["c_d", "a"]) result = parser.read_csv( StringIO(s), names=names, parse_dates=parse_dates, usecols=usecols ) tm.assert_frame_equal(result, expected) def test_usecols_with_unicode_strings(all_parsers): # see gh-13219 data = """AAA,BBB,CCC,DDD 0.056674973,8,True,a 2.613230982,2,False,b 3.568935038,7,False,a""" parser = all_parsers exp_data = { "AAA": {0: 0.056674972999999997, 1: 2.6132309819999997, 2: 3.5689350380000002}, "BBB": {0: 8, 1: 2, 2: 7}, } expected =
DataFrame(exp_data)
pandas.DataFrame
import pandas as pd import numpy as np from torch.utils.data import Dataset from .electricity_utils import * from .utils import * class ElectricityDataset(Dataset): @staticmethod def get_split(data_folder, num_encoder_steps=7 * 24): data_path = os.path.join(data_folder, 'LD2011_2014.txt') if not os.path.isfile(data_path): ElectricityDataset.download(data_path) formatter = ElectricityFormatter() train, val, test = formatter.split_data( ElectricityDataset.aggregating_to_hourly_data( pd.read_csv(data_path, index_col=0, sep=';', decimal=',') ) ) return [ElectricityDataset(data, formatter, num_encoder_steps) for data in [train, val, test]] def __init__(self, data, formatter, num_encoder_steps): super().__init__() self.formatter = formatter self.num_encoder_steps = num_encoder_steps self.data = data.reset_index(drop=True) self.data_index, self.col_mappings = self.build_data_index(self.data) def __len__(self): return self.data_index.shape[0] def __getitem__(self, idx): _data_index = self.data.iloc[self.data_index.init_abs.iloc[idx]:self.data_index.end_abs.iloc[idx]] data_map = {} for k in self.col_mappings: cols = self.col_mappings[k] if k not in data_map: data_map[k] = [_data_index[cols].values] else: data_map[k].append(_data_index[cols].values) for k in data_map: data_map[k] = np.concatenate(data_map[k], axis=0) scaler = self.formatter._target_scaler[data_map["identifier"][0][0]] outputs = data_map['outputs'][self.num_encoder_steps:, 0] return data_map['inputs'], outputs, scaler.mean_, scaler.scale_ def build_data_index(self, data): column_definition = self.formatter._column_definition col_mappings = { 'identifier': [get_single_col_by_input_type(InputTypes.ID, column_definition)], 'time': [get_single_col_by_input_type(InputTypes.TIME, column_definition)], 'outputs': [get_single_col_by_input_type(InputTypes.TARGET, column_definition)], 'inputs': [tup[0] for tup in column_definition if tup[2] not in {InputTypes.ID, InputTypes.TIME}] } lookback = self.formatter.get_time_steps() data_index = self.get_index_filtering(data, col_mappings["identifier"], col_mappings["outputs"], lookback) group_size = data.groupby(col_mappings["identifier"]).apply(lambda x: x.shape[0]).mean() data_index = data_index[data_index.end_rel < group_size].reset_index() return data_index, col_mappings def get_index_filtering(self, data, id_col, target_col, lookback): g = data.groupby(id_col) df_index_abs = g[target_col].transform(lambda x: x.index+lookback) \ .reset_index() \ .rename(columns={'index': 'init_abs', target_col[0]: 'end_abs'}) df_index_rel_init = g[target_col].transform(lambda x: x.reset_index(drop=True).index) \ .rename(columns={target_col[0]: 'init_rel'}) df_index_rel_end = g[target_col].transform(lambda x: x.reset_index(drop=True).index+lookback) \ .rename(columns={target_col[0]: 'end_rel'}) df_total_count = g[target_col].transform(lambda x: x.shape[0] - lookback + 1) \ .rename(columns = {target_col[0]: 'group_count'}) return pd.concat([df_index_abs, df_index_rel_init, df_index_rel_end, data[id_col], df_total_count], axis = 1).reset_index(drop = True) @staticmethod def aggregating_to_hourly_data(df): df.index = pd.to_datetime(df.index) df.sort_index(inplace=True) # Used to determine the start and end dates of a series output = df.resample('1h').mean().replace(0., np.nan) earliest_time = output.index.min() df_list = [] for label in output: print('Processing {}'.format(label)) srs = output[label] start_date = min(srs.fillna(method='ffill').dropna().index) end_date = max(srs.fillna(method='bfill').dropna().index) active_range = (srs.index >= start_date) & (srs.index <= end_date) srs = srs[active_range].fillna(0.) tmp =
pd.DataFrame({'power_usage': srs})
pandas.DataFrame
from kernels import * from classifiers import * import time, os import pandas as pd import numpy as np def make_dataset_uniform_start(save_dir="../datasets"): Xtr0 = pd.read_csv(os.path.join(save_dir, "Xtr0.csv")) Xtr1 = pd.read_csv(os.path.join(save_dir, "Xtr1.csv")) Xtr2 = pd.read_csv(os.path.join(save_dir, "Xtr2.csv")) Ytr0 = pd.read_csv(os.path.join(save_dir, "Ytr0.csv")) Ytr1 = pd.read_csv(os.path.join(save_dir, "Ytr1.csv")) Ytr2 = pd.read_csv(os.path.join(save_dir, "Ytr2.csv")) Xte0 = pd.read_csv(os.path.join(save_dir, "Xte0.csv")) Xte1 = pd.read_csv(os.path.join(save_dir, "Xte1.csv")) Xte2 = pd.read_csv(os.path.join(save_dir, "Xte2.csv")) Xtr =
pd.concat([Xtr0, Xtr1, Xtr2])
pandas.concat
import os root_path = os.path.dirname(os.path.abspath('__file__')) import sys import glob import pandas as pd import numpy as np from sklearn import decomposition import deprecated import logging sys.path.append(root_path) from config.globalLog import logger def generate_monoscale_samples(source_file, save_path, lags_dict, column, test_len, lead_time=1,regen=False): """Generate learning samples for autoregression problem using original time series. Args: 'source_file' -- ['String'] The source data file path. 'save_path' --['String'] The path to restore the training, development and testing samples. 'lags_dict' -- ['int dict'] The lagged time for original time series. 'column' -- ['String']The column's name for read the source data by pandas. 'test_len' --['int'] The length of development and testing set. 'lead_time' --['int'] The lead time. """ logger.info('Generating muliti-step decomposition-ensemble hindcasting samples') save_path = save_path+'/'+str(lead_time)+'_ahead_pacf/' logger.info('Source file:{}'.format(source_file)) logger.info('Save path:{}'.format(save_path)) if not os.path.exists(save_path): os.makedirs(save_path) if len(os.listdir(save_path))>0 and not regen: logger.info('Learning samples have been generated!') else: # Load data from local dick if '.xlsx' in source_file: dataframe = pd.read_excel(source_file)[column] elif '.csv' in source_file: dataframe = pd.read_csv(source_file)[column] # convert pandas dataframe to numpy array nparr = np.array(dataframe) # Create an empty pandas Dataframe full_samples = pd.DataFrame() # Generate input series based on lag and add these series to full dataset lag = lags_dict['ORIG'] for i in range(lag): x = pd.DataFrame(nparr[i:dataframe.shape[0] - (lag - i)], columns=['X' + str(i + 1)]) x = x.reset_index(drop=True) full_samples = pd.concat([full_samples, x], axis=1, sort=False) # Generate label data label = pd.DataFrame(nparr[lag+lead_time-1:], columns=['Y']) label = label.reset_index(drop=True) full_samples = full_samples[:full_samples.shape[0]-(lead_time-1)] full_samples = full_samples.reset_index(drop=True) # Add labled data to full_data_set full_samples = pd.concat([full_samples, label], axis=1, sort=False) # Get the length of this series series_len = full_samples.shape[0] # Get the training and developing set train_dev_samples = full_samples[0:(series_len - test_len)] # Get the testing set. test_samples = full_samples[(series_len - test_len):series_len] # train_dev_len = train_dev_samples.shape[0] train_samples = full_samples[0:(series_len - test_len - test_len)] dev_samples = full_samples[( series_len - test_len - test_len):(series_len - test_len)] assert (train_samples.shape[0] + dev_samples.shape[0] + test_samples.shape[0]) == series_len # Get the max and min value of each series series_max = train_samples.max(axis=0) series_min = train_samples.min(axis=0) # Normalize each series to the range between -1 and 1 train_samples = 2 * (train_samples - series_min) / \ (series_max - series_min) - 1 dev_samples = 2 * (dev_samples - series_min) / \ (series_max - series_min) - 1 test_samples = 2 * (test_samples - series_min) / \ (series_max - series_min) - 1 logger.info('Series length:{}'.format(series_len)) logger.info('Series length:{}'.format(series_len)) logger.info( 'Training-development sample size:{}'.format(train_dev_samples.shape[0])) logger.info('Training sample size:{}'.format(train_samples.shape[0])) logger.info('Development sample size:{}'.format(dev_samples.shape[0])) logger.info('Testing sample size:{}'.format(test_samples.shape[0])) series_max = pd.DataFrame(series_max, columns=['series_max']) series_min = pd.DataFrame(series_min, columns=['series_min']) normalize_indicators = pd.concat([series_max, series_min], axis=1) normalize_indicators.to_csv(save_path+'norm_unsample_id.csv') train_samples.to_csv(save_path+'minmax_unsample_train.csv', index=None) dev_samples.to_csv(save_path+'minmax_unsample_dev.csv', index=None) test_samples.to_csv(save_path+'minmax_unsample_test.csv', index=None) def gen_one_step_hindcast_samples(station, decomposer, lags_dict, input_columns, output_column, test_len, wavelet_level="db10-2", lead_time=1,regen=False): """ Generate one step hindcast decomposition-ensemble learning samples. Args: 'station'-- ['string'] The station where the original time series come from. 'decomposer'-- ['string'] The decompositin algorithm used for decomposing the original time series. 'lags_dict'-- ['int dict'] The lagged time for each subsignal. 'input_columns'-- ['string list'] The input columns' name used for generating the learning samples. 'output_columns'-- ['string'] The output column's name used for generating the learning samples. 'test_len'-- ['int'] The size of development and testing samples (). """ logger.info('Generating one-step decomposition ensemble hindcasting samples') logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Lags_dict:{}'.format(lags_dict)) logger.info('Input columns:{}'.format(input_columns)) logger.info('Output column:{}'.format(output_column)) logger.info('Testing sample length:{}'.format(test_len)) logger.info( 'Mother wavelet and decomposition level:{}'.format(wavelet_level)) logger.info('Lead time:{}'.format(lead_time)) # Load data from local dick if decomposer == "dwt" or decomposer == 'modwt': data_path = root_path+"/"+station+"_"+decomposer+"/data/"+wavelet_level+"/" else: data_path = root_path+"/"+station+"_"+decomposer+"/data/" save_path = data_path+"one_step_"+str(lead_time)+"_ahead_hindcast_pacf/" if not os.path.exists(save_path): os.makedirs(save_path) if len(os.listdir(save_path))>0 and not regen: logger.info('Learning samples have been generated!') else: decompose_file = data_path+decomposer.upper()+"_FULL.csv" decompositions = pd.read_csv(decompose_file) # Drop NaN decompositions.dropna() # Get the input data (the decompositions) input_data = decompositions[input_columns] # Get the output data (the original time series) output_data = decompositions[output_column] # Get the number of input features subsignals_num = input_data.shape[1] # Get the data size data_size = input_data.shape[0] # Compute the samples size max_lag = max(lags_dict.values()) samples_size = data_size-max_lag # Generate feature columns samples_cols = [] for i in range(sum(lags_dict.values())): samples_cols.append('X'+str(i+1)) samples_cols.append('Y') # Generate input colmuns for each subsignal full_samples = pd.DataFrame() for i in range(subsignals_num): # Get one subsignal one_in = (input_data[input_columns[i]]).values oness = pd.DataFrame() lag = lags_dict[input_columns[i]] for j in range(lag): x = pd.DataFrame(one_in[j:data_size-(lag-j)], columns=['X' + str(j + 1)]) x = x.reset_index(drop=True) oness = pd.concat([oness, x], axis=1, sort=False) # make all sample size of each subsignal identical oness = oness.iloc[oness.shape[0]-samples_size:] oness = oness.reset_index(drop=True) full_samples = pd.concat([full_samples, oness], axis=1, sort=False) # Get the target target = (output_data.values)[max_lag+lead_time-1:] target = pd.DataFrame(target, columns=['Y']) full_samples = full_samples[:full_samples.shape[0]-(lead_time-1)] full_samples = full_samples.reset_index(drop=True) # Concat the features and target full_samples = pd.concat([full_samples, target], axis=1, sort=False) full_samples = pd.DataFrame(full_samples.values, columns=samples_cols) full_samples.to_csv(save_path+'full_samples.csv') assert samples_size == full_samples.shape[0] # Get the training and developing set train_dev_samples = full_samples[0:(samples_size - test_len)] # Get the testing set. test_samples = full_samples[(samples_size - test_len):samples_size] # train_dev_len = train_dev_samples.shape[0] train_samples = full_samples[0:(samples_size - test_len - test_len)] dev_samples = full_samples[( samples_size - test_len - test_len):(samples_size - test_len)] assert (train_samples['X1'].size + dev_samples['X1'].size + test_samples['X1'].size) == samples_size # Get the max and min value of training set series_max = train_samples.max(axis=0) series_min = train_samples.min(axis=0) # Normalize each series to the range between -1 and 1 train_samples = 2 * (train_samples - series_min) / \ (series_max - series_min) - 1 dev_samples = 2 * (dev_samples - series_min) / \ (series_max - series_min) - 1 test_samples = 2 * (test_samples - series_min) / \ (series_max - series_min) - 1 logger.info('Save path:{}'.format(save_path)) logger.info('Series length:{}'.format(samples_size)) logger.info('Training and development sample size:{}'.format( train_dev_samples.shape[0])) logger.info('Training sample size:{}'.format(train_samples.shape[0])) logger.info('Development sample size:{}'.format(dev_samples.shape[0])) logger.info('Testing sample size:{}'.format(test_samples.shape[0])) series_max = pd.DataFrame(series_max, columns=['series_max']) series_min = pd.DataFrame(series_min, columns=['series_min']) normalize_indicators = pd.concat([series_max, series_min], axis=1) normalize_indicators.to_csv(save_path+'norm_unsample_id.csv') train_samples.to_csv(save_path + 'minmax_unsample_train.csv', index=None) dev_samples.to_csv(save_path + 'minmax_unsample_dev.csv', index=None) test_samples.to_csv(save_path+'minmax_unsample_test.csv', index=None) def gen_one_step_forecast_samples_triandev_test(station, decomposer, lags_dict, input_columns, output_column, start, stop, test_len, wavelet_level="db10-2", lead_time=1,regen=False): """ Generate one step forecast decomposition-ensemble samples. Args: 'station'-- ['string'] The station where the original time series come from. 'decomposer'-- ['string'] The decompositin algorithm used for decomposing the original time series. 'lags_dict'-- ['int dict'] The lagged time for subsignals. 'input_columns'-- ['string lsit'] the input columns' name for read the source data by pandas. 'output_columns'-- ['string'] the output column's name for read the source data by pandas. 'start'-- ['int'] The start index of appended decomposition file. 'stop'-- ['int'] The stop index of appended decomposotion file. 'test_len'-- ['int'] The size of development and testing samples. """ logger.info( 'Generateing one-step decomposition ensemble forecasting samples (traindev-test pattern)') logger.info('Station:{}'.format(station)) logger.info('Decomposer:{}'.format(decomposer)) logger.info('Lags_dict:{}'.format(lags_dict)) logger.info('Input columns:{}'.format(input_columns)) logger.info('Output column:{}'.format(output_column)) logger.info('Validation start index:{}'.format(start)) logger.info('Validation stop index:{}'.format(stop)) logger.info('Testing sample length:{}'.format(test_len)) logger.info( 'Mother wavelet and decomposition level:{}'.format(wavelet_level)) logger.info('Lead time:{}'.format(lead_time)) # Load data from local dick if decomposer == "dwt" or decomposer == 'modwt': data_path = root_path+"/"+station+"_"+decomposer+"/data/"+wavelet_level+"/" else: data_path = root_path+"/"+station+"_"+decomposer+"/data/" save_path = data_path+"one_step_" + \ str(lead_time)+"_ahead_forecast_pacf_traindev_test/" if not os.path.exists(save_path): os.makedirs(save_path) if len(os.listdir(save_path))>0 and not regen: logger.info('Learning samples have been generated!') else: # !!!!!!Generate training samples traindev_decompose_file = data_path+decomposer.upper()+"_TRAINDEV.csv" traindev_decompositions = pd.read_csv(traindev_decompose_file) # Drop NaN traindev_decompositions.dropna() # Get the input data (the decompositions) traindev_input_data = traindev_decompositions[input_columns] # Get the output data (the original time series) traindev_output_data = traindev_decompositions[output_column] # Get the number of input features subsignals_num = traindev_input_data.shape[1] # Get the data size traindev_data_size = traindev_input_data.shape[0] # Compute the samples size max_lag = max(lags_dict.values()) traindev_samples_size = traindev_data_size-max_lag # Generate feature columns samples_cols = [] for i in range(sum(lags_dict.values())): samples_cols.append('X'+str(i+1)) samples_cols.append('Y') # Generate input colmuns for each input feature train_dev_samples = pd.DataFrame() for i in range(subsignals_num): # Get one input feature one_in = (traindev_input_data[input_columns[i]]).values # subsignal lag = lags_dict[input_columns[i]] oness = pd.DataFrame() # restor input features for j in range(lag): x = pd.DataFrame(one_in[j:traindev_data_size-(lag-j)], columns=['X' + str(j + 1)])['X' + str(j + 1)] x = x.reset_index(drop=True) oness = pd.DataFrame(pd.concat([oness, x], axis=1)) oness = oness.iloc[oness.shape[0]-traindev_samples_size:] oness = oness.reset_index(drop=True) train_dev_samples = pd.DataFrame( pd.concat([train_dev_samples, oness], axis=1)) # Get the target target = (traindev_output_data.values)[max_lag+lead_time-1:] target =
pd.DataFrame(target, columns=['Y'])
pandas.DataFrame
import plotly.graph_objects as go import plotly.io as pio import numpy as np import pandas as pd import math from static_data import K_value_ranges,condition_number_ranges,generalized_condition_number_ranges,ARR_ranges, on_plot_shown_label,fig_size,color_schemes,themes from preprocess_util import * def define_theme(): # naming a layout theme for future reference pio.templates["encode"] = go.layout.Template( layout_colorway=color_schemes, data_scatter=[dict(line=dict(width=5))] ) pio.templates["large"] = go.layout.Template( layout_font=dict(family="Helvetica", size=16), layout_title_font = dict(family="Helvetica", size=19), ) pio.templates["ultralarge"] = go.layout.Template( layout_font=dict(family="Helvetica", size=16), layout_title_font = dict(family="Helvetica", size=19), ) pio.templates["medium"] = go.layout.Template( layout_font=dict(family="Helvetica", size=16), layout_title_font = dict(family="Helvetica", size=19), ) # pio.templates.default = "encode" pio.templates.default = "presentation+encode" def define_write_to_file_theme(): # naming a layout theme for future reference pio.templates["encode"] = go.layout.Template( layout_colorway=color_schemes, layout_font=dict(family="Arial Black", size=22), layout_title_font = dict(family="Arial Black", size=27), data_scatter=[dict(line=dict(width=5))] ) pio.templates["large"] = go.layout.Template( layout_font=dict(family="Arial Black", size=35), layout_title_font = dict(family="Arial Black", size=40), ) pio.templates["ultralarge"] = go.layout.Template( layout_font=dict(family="Arial Black", size=35), layout_title_font = dict(family="Arial Black", size=40), ) pio.templates["medium"] = go.layout.Template( layout_font=dict(family="Arial Black", size=25), layout_title_font = dict(family="Arial Black", size=30), ) # pio.templates.default = "encode" pio.templates.default = "presentation+encode" def filter_by_scale(scale, plot_df): if scale == 'k>=1': plot_df = plot_df[plot_df['K_value'] >= 1] elif scale == 'k<1': plot_df = plot_df[plot_df['K_value'] < 1] elif scale == 'All': plot_df = plot_df return plot_df def get_k_val_dist(plot_df,groupby): if (plot_df[groupby].median()>np.median(condition_number_ranges)): ranges = condition_number_ranges elif (plot_df[groupby].median()>np.median(generalized_condition_number_ranges)): ranges = generalized_condition_number_ranges else: ranges = K_value_ranges plot_df.loc[:, 'group_range'] = pd.cut( plot_df[groupby], ranges).astype(str) plot_df.loc[plot_df[groupby] > ranges[-1], 'group_range'] = '>{}'.format(ranges[-1]) plot_df.loc[plot_df[groupby] == ranges[0], 'group_range'] = ' {}'.format(ranges[0]) plot_df.loc[plot_df[groupby] < ranges[0], 'group_range'] = '<{}'.format(ranges[0]) def custom_sort(col): vals = [] for val in col.tolist(): if ',' in val: vals.append(float(val.split(',')[1][1:-1])) else: vals.append(float(val[1:])) return pd.Series(vals) return plot_df, custom_sort def get_group_range(plot_df, groupby): if groupby == 'K_value': # ranges = K_value_ranges # plot_df.loc[:, 'group_range'] = pd.cut( # plot_df[groupby], ranges).astype(str) # plot_df.loc[plot_df[groupby] > ranges[-1], # 'group_range'] = '>{}'.format(ranges[-1]) # plot_df.loc[plot_df[groupby] == ranges[0], # 'group_range'] = ' {}'.format(ranges[0]) # plot_df.loc[plot_df[groupby] < ranges[0], # 'group_range'] = '<{}'.format(ranges[0]) # qcutted = pd.qcut(plot_df[plot_df[groupby]<1][groupby], 9,duplicates='drop') # categories = qcutted.cat.categories # qcutted_str = qcutted.astype(str) # qcutted_str[qcutted_str == str(categories[0])] = '(0, {}]'.format(categories[0].right) # qcutted_str[qcutted_str == str(categories[-1])] = '({}, 1)'.format(categories[-1].left) # plot_df.loc[plot_df[groupby]<1, 'group_range'] = qcutted_str # plot_df.loc[plot_df[groupby]>=1, 'group_range'] = '>= 1' qcutted = pd.qcut(plot_df[groupby], 9,duplicates='drop') categories = qcutted.cat.categories qcutted_str = qcutted.astype(str) # qcutted_str[qcutted_str == str(categories[0])] = '(1, {}]'.format(categories[0].right) plot_df['group_range'] = qcutted_str plot_df = plot_df[plot_df['group_range']!='nan'] # plot_df.loc[plot_df[groupby]>=1, 'group_range'] = '>= 1' def custom_sort(col): vals = [] for val in col.tolist(): if ',' in val: vals.append(float(val.split(',')[1][1:-1])) else: # vals.append(float(val[2:])) vals.append(float('inf')) return pd.Series(vals) return plot_df,custom_sort elif groupby == 'arr': def custom_sort(col, ranges): vals = [] for val in col.tolist(): if val == '<={:.0%}'.format(ranges[1]): vals.append('0') else: vals.append(val) return pd.Series(vals) ranges = ARR_ranges plot_df.loc[:, 'group_range'] = pd.cut(plot_df[groupby], ranges).apply( lambda x: '{:.0%}-{:.0%}'.format(x.left, x.right) if x is not None else 'nan').astype(str) plot_df.loc[plot_df[groupby] > ranges[-1], 'group_range'] = '>{:.0%}'.format(ranges[-1]) plot_df.loc[plot_df[groupby] <= ranges[1], 'group_range'] = '<={:.0%}'.format(ranges[1]) plot_df = plot_df.dropna() return plot_df,custom_sort elif groupby in ['ave_true_abund','log2_true_abund']: def custom_sort(col): vals = [] for val in col.tolist(): if ',' in val: vals.append(float(val.split(',')[1][1:-1])) else: # vals.append(float(val[1:])) vals.append(float('inf')) return
pd.Series(vals)
pandas.Series
import pytest from datetime import datetime, timedelta import pytz import numpy as np from pandas import (NaT, Index, Timestamp, Timedelta, Period, DatetimeIndex, PeriodIndex, TimedeltaIndex, Series, isna) from pandas.util import testing as tm from pandas._libs.tslib import iNaT @pytest.mark.parametrize('nat, idx', [(Timestamp('NaT'), DatetimeIndex), (Timedelta('NaT'), TimedeltaIndex), (Period('NaT', freq='M'), PeriodIndex)]) def test_nat_fields(nat, idx): for field in idx._field_ops: # weekday is a property of DTI, but a method # on NaT/Timestamp for compat with datetime if field == 'weekday': continue result = getattr(NaT, field) assert np.isnan(result) result = getattr(nat, field) assert np.isnan(result) for field in idx._bool_ops: result = getattr(NaT, field) assert result is False result = getattr(nat, field) assert result is False def test_nat_vector_field_access(): idx = DatetimeIndex(['1/1/2000', None, None, '1/4/2000']) for field in DatetimeIndex._field_ops: # weekday is a property of DTI, but a method # on NaT/Timestamp for compat with datetime if field == 'weekday': continue result = getattr(idx, field) expected = Index([getattr(x, field) for x in idx]) tm.assert_index_equal(result, expected) s = Series(idx) for field in DatetimeIndex._field_ops: # weekday is a property of DTI, but a method # on NaT/Timestamp for compat with datetime if field == 'weekday': continue result = getattr(s.dt, field) expected = [getattr(x, field) for x in idx] tm.assert_series_equal(result, Series(expected)) for field in DatetimeIndex._bool_ops: result = getattr(s.dt, field) expected = [getattr(x, field) for x in idx] tm.assert_series_equal(result, Series(expected)) @pytest.mark.parametrize('klass', [Timestamp, Timedelta, Period]) def test_identity(klass): assert klass(None) is NaT result = klass(np.nan) assert result is NaT result = klass(None) assert result is NaT result = klass(iNaT) assert result is NaT result = klass(np.nan) assert result is NaT result = klass(float('nan')) assert result is NaT result = klass(NaT) assert result is NaT result = klass('NaT') assert result is NaT assert isna(klass('nat')) @pytest.mark.parametrize('klass', [Timestamp, Timedelta, Period]) def test_equality(klass): # nat if klass is not Period: klass('').value == iNaT klass('nat').value == iNaT klass('NAT').value == iNaT klass(None).value == iNaT klass(np.nan).value == iNaT assert isna(klass('nat')) @pytest.mark.parametrize('klass', [Timestamp, Timedelta]) def test_round_nat(klass): # GH14940 ts = klass('nat') for method in ["round", "floor", "ceil"]: round_method = getattr(ts, method) for freq in ["s", "5s", "min", "5min", "h", "5h"]: assert round_method(freq) is ts def test_NaT_methods(): # GH 9513 raise_methods = ['astimezone', 'combine', 'ctime', 'dst', 'fromordinal', 'fromtimestamp', 'isocalendar', 'strftime', 'strptime', 'time', 'timestamp', 'timetuple', 'timetz', 'toordinal', 'tzname', 'utcfromtimestamp', 'utcnow', 'utcoffset', 'utctimetuple'] nat_methods = ['date', 'now', 'replace', 'to_datetime', 'today', 'tz_convert', 'tz_localize'] nan_methods = ['weekday', 'isoweekday'] for method in raise_methods: if hasattr(NaT, method): with pytest.raises(ValueError): getattr(NaT, method)() for method in nan_methods: if hasattr(NaT, method): assert np.isnan(getattr(NaT, method)()) for method in nat_methods: if hasattr(NaT, method): # see gh-8254 exp_warning = None if method == 'to_datetime': exp_warning = FutureWarning with tm.assert_produces_warning( exp_warning, check_stacklevel=False): assert getattr(NaT, method)() is NaT # GH 12300 assert
NaT.isoformat()
pandas.NaT.isoformat
#!/usr/bin/env python # coding: utf-8 # # GenCode Explore # # Explore the human RNA sequences from GenCode. # # Assume user downloaded files from GenCode 38 [FTP](http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/) # to a subdirectory called data. # # Exclude mitochondrial genes because many have non-standard start and stop codons. # In[1]: import time def show_time(): t = time.time() s = time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)) print(s) show_time() # In[2]: import numpy as np import pandas as pd import gzip import sys try: from google.colab import drive IN_COLAB = True print("On Google CoLab, mount cloud-local file, get our code from GitHub.") PATH='/content/drive/' #drive.mount(PATH,force_remount=True) # hardly ever need this drive.mount(PATH) # Google will require login credentials DATAPATH=PATH+'My Drive/data/' # must end in "/" import requests s = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_describe.py') with open('RNA_describe.py', 'w') as f: f.write(s.text) # writes to cloud local, delete the file later? s = requests.get('https://raw.githubusercontent.com/ShepherdCode/Soars2021/master/SimTools/RNA_special.py') with open('RNA_special.py', 'w') as f: f.write(s.text) # writes to cloud local, delete the file later? from RNA_describe import * from RNA_special import * except: print("CoLab not working. On my PC, use relative paths.") IN_COLAB = False DATAPATH='../data/' # must end in "/" sys.path.append("..") # append parent dir in order to use sibling dirs from SimTools.RNA_describe import * from SimTools.RNA_special import * MODELPATH="BestModel" # saved on cloud instance and lost after logout #MODELPATH=DATAPATH+MODELPATH # saved on Google Drive but requires login if not assert_imported_RNA_describe(): print("ERROR: Cannot use RNA_describe.") # In[3]: PC_FILENAME='gencode.v38.pc_transcripts.fa.gz' NC_FILENAME='gencode.v38.lncRNA_transcripts.fa.gz' TEST_FILENAME='test.fa.gz' # In[4]: def load_gencode(filename,label): DEFLINE='>' # start of line with ids in a FASTA FILE DELIM='|' # character between ids VERSION='.' # character between id and version EMPTY='' # use this to avoid saving "previous" sequence in first iteration labels=[] # usually 1 for protein-coding or 0 for non-coding seqs=[] # usually strings of ACGT lens=[] # sequence length ids=[] # GenCode transcript ID, always starts ENST, excludes version one_seq = EMPTY one_id = None special = RNA_Special_Cases() special.mitochondria() # Use gzip 'r' mode to open file in read-only mode. # Use gzip 't' mode to read each line of text as type string. with gzip.open (filename,'rt') as infile: for line in infile: if line[0]==DEFLINE: # Save the previous sequence (if previous exists). if not one_seq == EMPTY and not special.is_special(one_id): labels.append(label) seqs.append(one_seq) lens.append(len(one_seq)) ids.append(one_id) # Get ready to read the next sequence. # Parse a GenCode defline that is formatted like this: # >ENST0001234.5|gene_ID|other_fields other_info|other_info # Use the following if ever GenCode includes an ID with no version # one_id = line[1:].split(DELIM)[0].split(VERSION)[0] one_id = line[1:].split(VERSION)[0] one_seq = EMPTY else: # Continue loading sequence lines till next defline. additional = line.rstrip() one_seq = one_seq + additional # Don't forget to save the last sequence after end-of-file. if not one_seq == EMPTY and not special.is_special(one_id): labels.append(label) seqs.append(one_seq) lens.append(len(one_seq)) ids.append(one_id) df1=pd.DataFrame(ids,columns=['tid']) df2=
pd.DataFrame(labels,columns=['class'])
pandas.DataFrame
import pandas as pd import numpy as np from statsmodels.formula.api import ols from swstats import * from scipy.stats import ttest_ind import xlsxwriter from statsmodels.stats.multitest import multipletests from statsmodels.stats.proportion import proportions_ztest debugging = False def pToSign(pval): if pval < .001: return "***" elif pval < .01: return "**" elif pval < .05: return "*" elif pval < .1: return "+" else: return "" def analyzeExperiment_ContinuousVar(dta, varName): order_value_control_group = dta.loc[dta.surveyArm == "arm1_control", varName] order_value_arm2_group = dta.loc[dta.surveyArm == "arm2_written_techniques", varName] order_value_arm3_group = dta.loc[dta.surveyArm == "arm3_existingssa", varName] order_value_arm4_group = dta.loc[dta.surveyArm == "arm4_interactive_training", varName] # Arm 1 arm1mean = np.mean(order_value_control_group) arm1sd = np.std(order_value_control_group) arm1text = "" + "{:.2f}".format(arm1mean) + " (" + "{:.2f}".format(arm1sd) + ")" # Effect of Arm 2 arm2mean = np.mean(order_value_arm2_group) arm2sd = np.std(order_value_arm2_group) tscore, pval2 = ttest_ind(order_value_control_group, order_value_arm2_group) arm2sign = pToSign(pval2) arm2text = "" + "{:.2f}".format(arm2mean) + " (" + "{:.2f}".format(arm2sd) + ")" + arm2sign + " p:" + "{:.3f}".format(pval2) # Effect of Arm 3 arm3mean = np.mean(order_value_arm3_group) arm3sd = np.std(order_value_arm3_group) tscore, pval3 = ttest_ind(order_value_control_group, order_value_arm3_group) arm3sign = pToSign(pval3) arm3text = "" + "{:.2f}".format(arm3mean) + " (" + "{:.2f}".format(arm3sd) + ")" + arm3sign + " p:" + "{:.3f}".format(pval3) # Effect of Arm 4 arm4mean = np.mean(order_value_arm4_group) arm4sd = np.std(order_value_arm4_group) tscore, pval4 = ttest_ind(order_value_control_group, order_value_arm4_group) arm4sign = pToSign(pval4) arm4text = "" + "{:.2f}".format(arm4mean) + " (" + "{:.2f}".format(arm4sd) + ")" + arm4sign + " p:" + "{:.3f}".format(pval4) # Correct P-values y = multipletests(pvals=[pval2, pval3, pval4], alpha=0.05, method="holm") # print(len(y[1][np.where(y[1] < 0.05)])) # y[1] returns corrected P-vals (array) sigWithCorrection = y[1] < 0.05 if sigWithCorrection[0]: arm2text = arm2text + ",#" if sigWithCorrection[1]: arm3text = arm3text + ",#" if sigWithCorrection[2]: arm4text = arm4text + ",#" # Additional checks tscore, pval2to4 = ttest_ind(order_value_arm2_group, order_value_arm4_group) arm2to4sign = pToSign(pval2to4) arm2to4text = "" + "{:.2f}".format(arm4mean - arm2mean) + " " + arm2to4sign + " p:" + "{:.3f}".format(pval2to4) tscore, pval3to4 = ttest_ind(order_value_arm3_group, order_value_arm4_group) arm3to4sign = pToSign(pval3to4) arm3to4text = "" + "{:.2f}".format(arm4mean - arm3mean) + " " + arm3to4sign + " p:" + "{:.3f}".format(pval3to4) results = {"Outcome": varName, "Arm1": arm1text, "Arm2": arm2text, "Arm3": arm3text, "Arm4": arm4text, "Arm2To4": arm2to4text, "Arm3To4": arm3to4text, } return results def analyzeExperiment_BinaryVar(dta, varName): order_value_control_group = dta.loc[dta.surveyArm == "arm1_control", varName] order_value_arm2_group = dta.loc[dta.surveyArm == "arm2_written_techniques", varName] order_value_arm3_group = dta.loc[dta.surveyArm == "arm3_existingssa", varName] order_value_arm4_group = dta.loc[dta.surveyArm == "arm4_interactive_training", varName] # Arm 1 arm1Successes = sum(order_value_control_group.isin([True, 1])) arm1Count = sum(order_value_control_group.isin([True, False, 1, 0])) arm1PercentSuccess = arm1Successes/arm1Count arm1text = "" + "{:.2f}".format(arm1PercentSuccess) + " (" + "{:.0f}".format(arm1Successes) + ")" # Effect of Arm 2 arm2Successes = sum(order_value_arm2_group.isin([True, 1])) arm2Count = sum(order_value_arm2_group.isin([True, False, 1, 0])) arm2PercentSuccess = arm2Successes/arm2Count zstat, pval2 = proportions_ztest(count=[arm1Successes,arm2Successes], nobs=[arm1Count,arm2Count], alternative='two-sided') arm2sign = pToSign(pval2) arm2text = "" + "{:.2f}".format(arm2PercentSuccess) + " (" + "{:.0f}".format(arm2Successes) + ")" + arm2sign + " p:" + "{:.3f}".format(pval2) # Effect of Arm 3 arm3Successes = sum(order_value_arm3_group.isin([True, 1])) arm3Count = sum(order_value_arm3_group.isin([True, False, 1, 0])) arm3PercentSuccess = arm3Successes/arm3Count zstat, pval3 = proportions_ztest(count=[arm1Successes,arm3Successes], nobs=[arm1Count,arm3Count], alternative='two-sided') arm3sign = pToSign(pval3) arm3text = "" + "{:.2f}".format(arm3PercentSuccess) + " (" + "{:.0f}".format(arm3Successes) + ")" + arm3sign + " p:" + "{:.3f}".format(pval3) # Effect of Arm 4 arm4Successes = sum(order_value_arm4_group.isin([True, 1])) arm4Count = sum(order_value_arm4_group.isin([True, False, 1, 0])) arm4PercentSuccess = arm4Successes/arm4Count zstat, pval4 = proportions_ztest(count=[arm1Successes,arm4Successes], nobs=[arm1Count,arm4Count], alternative='two-sided') arm4sign = pToSign(pval4) arm4text = "" + "{:.2f}".format(arm4PercentSuccess) + " (" + "{:.0f}".format(arm4Successes) + ")" + arm4sign + " p:" + "{:.3f}".format(pval4) # Correct P-values y = multipletests(pvals=[pval2, pval3, pval4], alpha=0.05, method="holm") # print(len(y[1][np.where(y[1] < 0.05)])) # y[1] returns corrected P-vals (array) sigWithCorrection = y[1] < 0.05 if sigWithCorrection[0]: arm2text = arm2text + ",#" if sigWithCorrection[1]: arm3text = arm3text + ",#" if sigWithCorrection[2]: arm4text = arm4text + ",#" # Additional checks zstat, pval2to4 = proportions_ztest(count=[arm2Successes,arm4Successes], nobs=[arm2Count,arm4Count], alternative='two-sided') arm2to4sign = pToSign(pval2to4) arm2to4text = "" + "{:.2f}".format(arm4PercentSuccess - arm2PercentSuccess) + " " + arm2to4sign + " p:" + "{:.3f}".format(pval2to4) zstat, pval3to4 = proportions_ztest(count=[arm3Successes,arm4Successes], nobs=[arm3Count,arm4Count], alternative='two-sided') arm3to4sign = pToSign(pval3to4) arm3to4text = "" + "{:.2f}".format(arm4PercentSuccess - arm3PercentSuccess) + " " + arm3to4sign + " p:" + "{:.3f}".format(pval3to4) results = {"Outcome": varName, "Arm1": arm1text, "Arm2": arm2text, "Arm3": arm3text, "Arm4": arm4text, "Arm2To4": arm2to4text, "Arm3To4": arm3to4text, } return results def analyzeResults(dta, outputFileName, scoringVars, surveyVersion, primaryOnly=True): if primaryOnly: dta = dta[dta.IsPrimaryWave].copy() dataDir = "C:/Dev/src/ssascams/data/" ''' Analyze the answers''' writer = pd.ExcelWriter(dataDir + 'RESULTS_' + outputFileName + '.xlsx', engine='xlsxwriter') # ############### # Export summary stats # ############### demographicVars = ['trustScore', 'TotalIncome', 'incomeAmount', 'Race', 'race5', 'employment3', 'educYears', 'Married', 'marriedI', 'Age', 'ageYears', 'Gender', 'genderI'] allSummaryVars = ["percentCorrect", "surveyArm", "Wave", "daysFromTrainingToTest"] + scoringVars + demographicVars summaryStats = dta[allSummaryVars].describe() summaryStats.to_excel(writer, sheet_name="summary_FullPop", startrow=0, header=True, index=True) grouped = dta[allSummaryVars].groupby(["surveyArm"]) summaryStats = grouped.describe().unstack().transpose().reset_index() summaryStats.rename(columns={'level_0' :'VarName', 'level_1' :'Metric'}, inplace=True) summaryStats.sort_values(['VarName', 'Metric'], inplace=True) summaryStats.to_excel(writer, sheet_name="summary_ByArm", startrow=0, header=True, index=False) if ~primaryOnly: grouped = dta[allSummaryVars].groupby(["surveyArm", "Wave"]) summaryStats = grouped.describe().unstack().transpose().reset_index() summaryStats.rename(columns={'level_0' :'VarName', 'level_1' :'Metric'}, inplace=True) summaryStats.sort_values(['Wave','VarName', 'Metric'], inplace=True) # grouped.describe().reset_index().pivot(index='name', values='score', columns='level_1') summaryStats.to_excel(writer, sheet_name="summary_ByArmAndWave", startrow=0, header=True, index=False) # summaryStats.to_csv(dataDir + "RESULTS_" + outputFileName + '.csv') # ############### # RQ1: What is the effect? # ############### row1 = analyzeExperiment_ContinuousVar(dta, "numCorrect") row2 = analyzeExperiment_ContinuousVar(dta, "numFakeLabeledReal") row3 = analyzeExperiment_ContinuousVar(dta, "numRealLabeledFake") row4 = analyzeExperiment_ContinuousVar(dta, "percentCorrect") pd.DataFrame([row1, row2, row3, row4]).to_excel(writer, sheet_name="r1", startrow=1, header=True, index=True) ############## # RQ1* Robustness check on result: is the experiment randomized correctly? ############## # NumCorrect Regression resultTables = ols('numCorrect ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' 'C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r1_reg", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r1_reg", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # ############### # RQ2: Communication Type # ############### row1 = analyzeExperiment_ContinuousVar(dta, "numEmailsCorrect") row2 = analyzeExperiment_ContinuousVar(dta, "numSMSesCorrect") row3 = analyzeExperiment_ContinuousVar(dta, "numLettersCorrect") pd.DataFrame([row1, row2, row3]).to_excel(writer, sheet_name="r2", startrow=1, header=True, index=True) ############## # RQ2* Robustness check on Emails result: is the experiment randomized correctly? ############## # NumEmailsCorrect Regression resultTables = ols('numEmailsCorrect ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' 'C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r2_reg", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r2_reg", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # ############### # RQ3: Time Delay # ############### resultTables = ols('numCorrect ~ C(surveyArm)*Wave + daysFromTrainingToTest', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r3a_CorrectWaveAndDay_Simple", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r3a_CorrectWaveAndDay_Simple", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) resultTables = ols('numEmailsCorrect ~ C(surveyArm)*Wave + daysFromTrainingToTest', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r3b_EmailWaveAndDay_Simple", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r3b_EmailWaveAndDay_Simple", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # ############### # RQ4: Rainloop # ############### if surveyVersion == '6': resultTables = ols('NumHeadersOpened ~ C(surveyArm)', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r4_HeadersOpened", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r4_HeadersOpened",startrow=1 + len(resultTables[0]) + 2, header=False, index=False) ######################## # R5a: What determines fraud susceptibility (whether people get tricked or not)? # Ie, false negatives ######################## # First Try on Regression # resultTables = ols('numFakeLabeledReal ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' # 'C(race5) + C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables # pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="reg_numFakeLabeledReal_WRace", startrow=1, header=False, index=False) # pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="reg_numFakeLabeledReal_WRace", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # Remove race - many variables, small counts - likely over specifying resultTables = ols('numFakeLabeledReal ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' 'C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r5a_numFakeLabeledReal", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r5a_numFakeLabeledReal", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) resultTables = ols('numLabeledReal ~ C(surveyArm) + trustScore + lIncomeAmount + C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="reg_numLabeledReal", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="reg_numLabeledReal", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) ######################## # R5b: What determines lack of trust? ######################## # Ie, false positive resultTables = ols('numRealLabeledFake ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' 'C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="r5b_numRealLabeledFake", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="r5b_numRealLabeledFake", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) resultTables = ols('numLabeledFake ~ C(surveyArm) + daysFromTrainingToTest + trustScore + lIncomeAmount + ' 'C(employment3) + educYears + marriedI + ageYears + ageYearsSq + genderI + lose_moneyYN + duration_p2_Quantile ', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="reg_numLabeledFake", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="reg_numLabeledFake", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # ############### # RQ6: Impostor Type # ############### row1 = analyzeExperiment_ContinuousVar(dta, "numCorrect_SSA") row2 = analyzeExperiment_ContinuousVar(dta, "numCorrect_Other") row3 = analyzeExperiment_ContinuousVar(dta, "numEmailsCorrect_SSA") row4 = analyzeExperiment_ContinuousVar(dta, "numEmailsCorrect_Other") pd.DataFrame([row1, row2, row3, row4]).to_excel(writer, sheet_name="r6", startrow=1, header=True, index=True) # ############### # RQ7: Likelihood of being tricked # ############### dta['isTrickedByFraud'] = dta.numFakeLabeledReal > 0 dta['isTrickedByAnySSAEmail'] = dta.numEmailsCorrect_SSA < max(dta.numEmailsCorrect_SSA) dta['isTrickedByAnyNonSSAEmail'] = dta.numEmailsCorrect_Other < max(dta.numEmailsCorrect_Other) row1 = analyzeExperiment_BinaryVar(dta, "isTrickedByFraud") row2 = analyzeExperiment_BinaryVar(dta, "isTrickedByAnySSAEmail") row3 = analyzeExperiment_BinaryVar(dta, "isTrickedByAnyNonSSAEmail") pd.DataFrame([row1, row2, row3]).to_excel(writer, sheet_name="r7", startrow=1, header=True, index=True) # ############### # RQ8: Every Email # ############### filter_cols = [col for col in dta.columns if col.startswith('Correct_')] theRows = [] for filter_col in filter_cols: arow = analyzeExperiment_BinaryVar(dta, filter_col) theRows = theRows + [arow] pd.DataFrame(theRows).to_excel(writer, sheet_name="r8", startrow=1, header=True, index=True) # ############## # Correlations ################ indepVars = ['surveyArm', 'daysFromTrainingToTest', 'Wave', 'trustScore', 'incomeAmount', 'race5', 'employment3', 'educYears', 'marriedI', 'ageYears','Gender', 'previousFraudYN', 'lose_moneyYN', 'duration_p1', 'duration_p1_Quantile', 'duration_p2', 'duration_p2_Quantile', 'Employment'] depVars = ['numCorrect', 'numFakeLabeledReal', 'numRealLabeledFake'] dta.Wave = dta.Wave.astype('float64') # Look at Correlations among variables allVarsToCorr = depVars + indepVars corrMatrix = dta[allVarsToCorr].corr() pd.DataFrame(corrMatrix).to_excel(writer, sheet_name="corrMatrix", startrow=1, header=True, index=True) # duration_p1 is a proxy for arm, so strange results there. # we'd need a fine-tuned var. Let's use p2 instead. Also, the Quantile shows a much stronger relationship than the raw values (likely since it is not linear in the depvars) # Losing money and income and age show a moderate relationship # ############## # Scatter Plots ################ import seaborn as sns sns.set_theme(style="ticks") toPlot = dta[['numCorrect', 'surveyArm', 'daysFromTrainingToTest', 'Wave', 'trustScore', 'lose_moneyYN', 'duration_p2_Quantile']] sns.pairplot(toPlot, hue="surveyArm") # ############## # Regressions # ############## # Sanity Check regression resultTables = ols('lIncomeAmount ~ageYears + ageYearsSq + educYears + marriedI + genderI', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="reg_Sanity", startrow=1, header=False, index=False) pd.DataFrame(resultTables[1]).to_excel(writer, sheet_name="reg_Sanity", startrow=1 + len(resultTables[0]) + 2, header=False, index=False) # Simple Experiment-Only test resultTables = ols('numCorrect ~ C(surveyArm)', data=dta).fit().summary().tables pd.DataFrame(resultTables[0]).to_excel(writer, sheet_name="numCorrect_ByArm", startrow=1, header=False, index=False)
pd.DataFrame(resultTables[1])
pandas.DataFrame
from collections import OrderedDict from typing import Any, Dict, List, Tuple, Union, cast import pandas as pd from the_census._api.models import GeographyItem from the_census._config import Config from the_census._data_transformation.interface import ICensusDataTransformer from the_census._geographies.models import GeoDomain from the_census._utils.timer import timer from the_census._variables.models import Group, GroupVariable, VariableCode class CensusDataTransformer(ICensusDataTransformer[pd.DataFrame]): _config: Config def __init__(self, config: Config) -> None: self._config = config @timer def supported_geographies( self, supported_geos: OrderedDict[str, GeographyItem] ) -> pd.DataFrame: values_flattened: List[Dict[str, str]] = [] for geo_item in supported_geos.values(): for clause in geo_item.clauses: values_flattened.append( { "name": geo_item.name, "hierarchy": geo_item.hierarchy, "for": clause.for_clause, "in": ",".join(clause.in_clauses), } ) return
pd.DataFrame(values_flattened)
pandas.DataFrame
import os.path import glob import re from typing import Dict, List, Optional from bokeh.charts import TimeSeries from bokeh.models import Range1d import bokeh.plotting import json_lines import pandas as pd from scrapy.commands import ScrapyCommand from scrapy.exceptions import UsageError from dd_crawler.utils import get_domain class Command(ScrapyCommand): requires_project = True def syntax(self): return '<files>' def add_options(self, parser): ScrapyCommand.add_options(self, parser) arg = parser.add_option arg('-o', '--output', help='prefix for charts (without ".html")') arg('--step', type=float, default=30, help='time step, s') arg('--smooth', type=int, default=50, help='smooth span') arg('--top', type=int, default=30, help='top domains to show') arg('--no-show', action='store_true', help='don\'t show charts') def short_desc(self): return 'Print short speed summary, save charts to a file' def run(self, args, opts): if not args: raise UsageError() if len(args) == 1 and '*' in args[0]: # paths were not expanded (docker) filenames = glob.glob(args[0]) else: filenames = args del args filtered_filenames = [ f for f in filenames if re.match(r'[a-z0-9]{12}\.csv$', os.path.basename(f))] filenames = filtered_filenames or filenames if not filenames: raise UsageError() response_logs = [] for filename in filenames: with json_lines.open(filename) as f: response_logs.append(pd.DataFrame(f)) print('Read data from {} files'.format(len(filenames))) all_rpms = [rpms for rpms in ( get_rpms(name, rlog, step=opts.step, smooth=opts.smooth) for name, rlog in zip(filenames, response_logs)) if rpms is not None] if all_rpms: print_rpms(all_rpms, opts) print_scores(response_logs, opts) def get_rpms(filename: str, response_log: pd.DataFrame, step: float, smooth: int) -> Optional[pd.DataFrame]: timestamps = response_log['time'] buffer = [] if len(timestamps) == 0: return get_t0 = lambda t: int(t / step) * step t0 = get_t0(timestamps[0]) rpms = [] for ts in timestamps: if get_t0(ts) != t0: rpms.append((t0, len(buffer) / (ts - buffer[0]) * 60)) t0 = get_t0(ts) buffer = [] buffer.append(ts) if rpms: name = os.path.basename(filename) rpms =
pd.DataFrame(rpms, columns=['time', name])
pandas.DataFrame
#!/usr/bin/env python # -*- coding: utf-8 -*- import pandas as pd import numpy as np import matplotlib.pyplot as plt from cvxopt import matrix, solvers from datetime import datetime, date import quandl assets = ['AAPL', # Apple 'KO', # Coca-Cola 'DIS', # Disney 'XOM', # Exxon Mobil 'JPM', # JPMorgan Chase 'MCD', # McDonald's 'WMT'] # Walmart # download historical data from quandl hist_data = {} for asset in assets: data = quandl.get('wiki/'+asset, start_date='2015-01-01', end_date='2017-12-31', authtoken='<PASSWORD>') hist_data[asset] = data['Adj. Close'] hist_data = pd.concat(hist_data, axis=1) # calculate historical log returns hist_return = np.log(hist_data / hist_data.shift()) hist_return = hist_return.dropna() # find historical mean, covriance, and correlation hist_mean = hist_return.mean(axis=0).to_frame() hist_mean.columns = ['mu'] hist_cov = hist_return.cov() hist_corr = hist_return.corr() print(hist_mean.transpose()) print(hist_cov) print(hist_corr) # construct random portfolios n_portfolios = 5000 #set up array to hold results port_returns = np.zeros(n_portfolios) port_stdevs = np.zeros(n_portfolios) for i in range(n_portfolios): w = np.random.rand(len(assets)) # random weights w = w / sum(w) # weights sum to 1 port_return = np.dot(w.T, hist_mean.as_matrix()) * 250 # annualize; 250 business days port_stdev = np.sqrt(np.dot(w.T, np.dot(hist_cov, w))) * np.sqrt(250) # annualize; 250 business days port_returns[i] = port_return port_stdevs[i] = port_stdev plt.plot(port_stdevs, port_returns, 'o', markersize=6) plt.xlabel('Expected Volatility') plt.ylabel('Expected Return') plt.title('Return and Standard Deviation of Randomly Generated Portfolios') plt.show() # Global Minimum Variance (GMV) -- closed form hist_cov_inv = - np.linalg.inv(hist_cov) one_vec = np.ones(len(assets)) w_gmv = np.dot(hist_cov_inv, one_vec) / (np.dot(np.transpose(one_vec), np.dot(hist_cov_inv, one_vec))) w_gmv_df = pd.DataFrame(data = w_gmv).transpose() w_gmv_df.columns = assets stdev_gmv = np.sqrt(np.dot(w_gmv.T, np.dot(hist_cov, w_gmv))) * np.sqrt(250) print(w_gmv_df) print(stdev_gmv) # Global Minimum Variance (GMV) -- numerical P = matrix(hist_cov.as_matrix()) q = matrix(np.zeros((len(assets), 1))) A = matrix(1.0, (1, len(assets))) b = matrix(1.0) w_gmv_v2 = np.array(solvers.qp(P, q, A=A, b=b)['x']) w_gmv_df_v2 = pd.DataFrame(w_gmv_v2).transpose() w_gmv_df_v2.columns = assets stdev_gmv_v2 = np.sqrt(np.dot(w_gmv_v2.T, np.dot(hist_cov, w_gmv_v2))) * np.sqrt(250) print(w_gmv_df_v2) print(np.asscalar(stdev_gmv_v2)) # Maximum return -- closed form mu_o = np.asscalar(np.max(hist_mean)) # MCD A = np.matrix([[np.asscalar(np.dot(hist_mean.T,np.dot(hist_cov_inv,hist_mean))), np.asscalar(np.dot(hist_mean.T,np.dot(hist_cov_inv,one_vec)))], [np.asscalar(np.dot(hist_mean.T,np.dot(hist_cov_inv,one_vec))), np.asscalar(np.dot(one_vec.T,np.dot(hist_cov_inv,one_vec)))]]) B = np.hstack([np.array(hist_mean),one_vec.reshape(len(assets),1)]) y = np.matrix([mu_o, 1]).T w_max_ret = np.dot(np.dot(np.dot(hist_cov_inv, B), np.linalg.inv(A)),y) w_max_ret_df =
pd.DataFrame(w_max_ret)
pandas.DataFrame
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Feb 16 16:24:07 2017 @author: raimondas """ #%% imports import os, sys, glob import itertools from distutils.dir_util import mkpath from tqdm import tqdm import numpy as np #import matplotlib.pyplot as plt #plt.ion() sys.path.append('..') #import seaborn as sns #sns.set_style("ticks") ### import random import copy from collections import OrderedDict import pandas as pd import argparse #from utils import ETData, training_params import pickle from sklearn.model_selection import train_test_split import scipy.io as scio #from utils import eval_evt from etdata import ETData, get_px2deg #%% functions def augment_with_saccades(data, seq_len, histogram_eq = True): #debug # stop # data = data['unpaired_clean'] # seq_len=100 #augment by centering on saccades etdata = ETData() sacc = [] data_clean = [_d for (_i, _d) in data if len(_d) > seq_len] for i, _data in enumerate(data_clean): etdata.load(_data, **{'source':'array'}) etdata.calc_evt() evt = etdata.evt.loc[etdata.evt['evt']==2, :] evt = evt.assign(ind=i) sacc.append(evt) sacc_df = pd.concat(sacc).reset_index() seeds = range(len(sacc_df)) train_data_pick = [] for (_, sacc), seed in zip(sacc_df.iterrows(), seeds): np.random.seed(seed) i = np.random.randint(args.seq_len) trial_ind = int(sacc['ind']) s = np.maximum(0, sacc['s'] - i).astype(int) e = s + args.seq_len + 2 #because we will differentiate and predict next sample if e < len(data_clean[trial_ind]): #plt.plot(_train_data[trial_ind][s:e]['x']) #plt.plot(_train_data[trial_ind][s:e]['y']) train_data_pick.append(data_clean[trial_ind][s:e]) if histogram_eq: #augment with large saccades sacc=[] for i, _data in enumerate(train_data_pick): etdata.load(_data, **{'source':'array'}) etdata.calc_evt() evt = etdata.evt.loc[etdata.evt['evt']==2, :] evt = evt.assign(ind=i) sacc.append(evt) sacc_pick_df = pd.concat(sacc).reset_index() h, edges = np.histogram(sacc_pick_df['ampl'], bins='auto') p = (h.max()-h) sacc = [] seeds = range(len(h)) for _p, _es, _ee, seed in zip(p, edges[:-1], edges[1:], seeds): mask = (sacc_df['ampl'] > _es) & (sacc_df['ampl'] < _ee) if (len(sacc_df.loc[mask, :]) > 0) and (_p > 0): sacc.append(sacc_df.loc[mask, :].sample(n = _p, replace = True, random_state=seed)) sacc_ampl_df = pd.concat(sacc).reset_index() train_data_ampl = [] seeds = range(len(sacc_ampl_df)) for (_, sacc), seed in zip(sacc_ampl_df.iterrows(), seeds): np.random.seed(seed) i = np.random.randint(args.seq_len) trial_ind = int(sacc['ind']) s = np.maximum(0, sacc['s'] - i).astype(int) e = s + args.seq_len + 2 #because we will differentiate and predict next sample if e < len(data_clean[trial_ind]): #plt.plot(_train_data[trial_ind][s:e]['x']) #plt.plot(_train_data[trial_ind][s:e]['y']) train_data_ampl.append(data_clean[trial_ind][s:e]) train_data_pick +=train_data_ampl return train_data_pick #%% set parameters parser = argparse.ArgumentParser() parser.add_argument('--root', type=str, default='../../etdata', help='data root') parser.add_argument('--dataset', type=str, default='lund2013_npy', help='dataset') parser.add_argument('--seq_len', type=int, default=100, help='number of samples in data iterator') parser.add_argument('--events', default=[1, 2, 3], help='events') args = parser.parse_args() etdata = ETData() #%%data reader print ("Reading data") ddir = '%s/%s'%(args.root, args.dataset) if not os.path.exists(ddir): mkpath(ddir) #try to convert from mat fdir_mat = 'EyeMovementDetectorEvaluation/annotated_data/originally uploaded data/images' FILES_MAT = glob.glob('%s/%s/*.mat'% (args.root, fdir_mat)) for fpath in tqdm(FILES_MAT): fdir, fname = os.path.split(os.path.splitext(fpath)[0]) mat = scio.loadmat(fpath) fs = mat['ETdata']['sampFreq'][0][0][0][0] geom = { 'screen_width' :mat['ETdata']['screenDim'][0][0][0][0], 'screen_height': mat['ETdata']['screenDim'][0][0][0][1], 'display_width_pix' : mat['ETdata']['screenRes'][0][0][0][0], 'display_height_pix' :mat['ETdata']['screenRes'][0][0][0][1], 'eye_distance' : mat['ETdata']['viewDist'][0][0][0][0], } px2deg = get_px2deg(geom) data = mat['ETdata']['pos'][0][0] t = np.arange(0, len(data)).astype(np.float64)/fs status = (data[:,3] == 0) | (data[:,4] == 0) data = np.vstack((t, data[:,3], data[:,4], ~status, data[:,5])).T etdata.load(data, **{'source': 'np_array'}) etdata.data['x'] = (etdata.data['x'] - geom['display_width_pix']/2) / px2deg etdata.data['y'] = (etdata.data['y'] - geom['display_height_pix']/2) / px2deg etdata.data['x'][status] = np.nan etdata.data['y'][status] = np.nan #fix if 'UH29_img_Europe_labelled_MN' in fname: etdata.data['evt'][3197:3272] = 1 #set status one more time status = np.isnan(etdata.data['x']) | np.isnan(etdata.data['y']) |\ ~np.in1d(etdata.data['evt'], args.events) | ~etdata.data['status'] etdata.data['status'] = ~status etdata.save('%s/%s' % (ddir, fname)) FILES = glob.glob('%s/*.npy' % ddir) #for replication use following code with open('datalist', 'r') as f: FILES = ['%s/%s'%(ddir, _fname.strip()) for _fname in f.readlines()] #%%split based on trial print ("Train/test split") exp = [(fpath,) + tuple(os.path.split(os.path.splitext(fpath)[0])[-1].split('_labelled_')) +\ (os.path.split(os.path.splitext(fpath)[0])[-1].split('_')[0][:2], )+\ (os.path.split(os.path.splitext(fpath)[0])[-1].split('_')[2], ) for fpath in FILES] exp_df = pd.DataFrame(exp, columns=['fpath', 'flabel', 'coder', 'sub', 'img']) exp_gr = exp_df.groupby('flabel') exp_df['pair'] = False for _e, _d in exp_gr: if len(_d) >1: exp_df.loc[_d.index, 'pair'] = True print ('Number of trials: %d' %len(exp_df['flabel'].unique())) print ('Number of subjects: %d' %len(exp_df['sub'].unique())) print ('Number of images: %d' %len(exp_df['img'].unique())) #split on pairs exp_gr_pair = exp_df.groupby('pair') X_unpaired, X_paired = [_d for _, _d in exp_gr_pair] #%%load data print ("Cleaning data") data = OrderedDict() data_lens = [] #iterates through data and marks status as false for events other than [1, 2, 3] for df, part in zip([X_unpaired, X_paired, X_paired], [['unpaired'], ['paired', 'RA'], ['paired', 'MN']]): if len(part)==1: part = part[0] data[part] = [] for n, d in df.iterrows(): _data = np.load(d['fpath']) data_lens.append(len(_data)) mask = np.in1d(_data['evt'], args.events) _data['status'][~mask] = False data[part].append((d, _data)) else: coder = part[-1] part = part[-1] data[part] = [] for n, d in df.loc[df['coder']==coder,:].iterrows(): _data = np.load(d['fpath']) data_lens.append(len(_data)) mask = np.in1d(_data['evt'], args.events) _data['status'][~mask] = False data[part].append((d, _data)) #sort according to flabel labels_mn = pd.DataFrame([l['flabel'] for l, _ in data['MN']]) labels_ra =
pd.DataFrame([l['flabel'] for l, _ in data['RA']])
pandas.DataFrame
from datetime import ( datetime, timedelta, ) import re import numpy as np import pytest from pandas._libs import iNaT from pandas.errors import InvalidIndexError import pandas.util._test_decorators as td from pandas.core.dtypes.common import is_integer import pandas as pd from pandas import ( Categorical, DataFrame, DatetimeIndex, Index, MultiIndex, Series, Timestamp, date_range, isna, notna, ) import pandas._testing as tm import pandas.core.common as com # We pass through a TypeError raised by numpy _slice_msg = "slice indices must be integers or None or have an __index__ method" class TestDataFrameIndexing: def test_getitem(self, float_frame): # Slicing sl = float_frame[:20] assert len(sl.index) == 20 # Column access for _, series in sl.items(): assert len(series.index) == 20 assert tm.equalContents(series.index, sl.index) for key, _ in float_frame._series.items(): assert float_frame[key] is not None assert "random" not in float_frame with pytest.raises(KeyError, match="random"): float_frame["random"] def test_getitem2(self, float_frame): df = float_frame.copy() df["$10"] = np.random.randn(len(df)) ad = np.random.randn(len(df)) df["@awesome_domain"] = ad with pytest.raises(KeyError, match=re.escape("'df[\"$10\"]'")): df.__getitem__('df["$10"]') res = df["@awesome_domain"] tm.assert_numpy_array_equal(ad, res.values) def test_setitem_list(self, float_frame): float_frame["E"] = "foo" data = float_frame[["A", "B"]] float_frame[["B", "A"]] = data tm.assert_series_equal(float_frame["B"], data["A"], check_names=False) tm.assert_series_equal(float_frame["A"], data["B"], check_names=False) msg = "Columns must be same length as key" with pytest.raises(ValueError, match=msg): data[["A"]] = float_frame[["A", "B"]] newcolumndata = range(len(data.index) - 1) msg = ( rf"Length of values \({len(newcolumndata)}\) " rf"does not match length of index \({len(data)}\)" ) with pytest.raises(ValueError, match=msg): data["A"] = newcolumndata def test_setitem_list2(self): df = DataFrame(0, index=range(3), columns=["tt1", "tt2"], dtype=np.int_) df.loc[1, ["tt1", "tt2"]] = [1, 2] result = df.loc[df.index[1], ["tt1", "tt2"]] expected = Series([1, 2], df.columns, dtype=np.int_, name=1) tm.assert_series_equal(result, expected) df["tt1"] = df["tt2"] = "0" df.loc[df.index[1], ["tt1", "tt2"]] = ["1", "2"] result = df.loc[df.index[1], ["tt1", "tt2"]] expected = Series(["1", "2"], df.columns, name=1) tm.assert_series_equal(result, expected) def test_getitem_boolean(self, mixed_float_frame, mixed_int_frame, datetime_frame): # boolean indexing d = datetime_frame.index[10] indexer = datetime_frame.index > d indexer_obj = indexer.astype(object) subindex = datetime_frame.index[indexer] subframe = datetime_frame[indexer] tm.assert_index_equal(subindex, subframe.index) with pytest.raises(ValueError, match="Item wrong length"): datetime_frame[indexer[:-1]] subframe_obj = datetime_frame[indexer_obj] tm.assert_frame_equal(subframe_obj, subframe) with pytest.raises(ValueError, match="Boolean array expected"): datetime_frame[datetime_frame] # test that Series work indexer_obj = Series(indexer_obj, datetime_frame.index) subframe_obj = datetime_frame[indexer_obj] tm.assert_frame_equal(subframe_obj, subframe) # test that Series indexers reindex # we are producing a warning that since the passed boolean # key is not the same as the given index, we will reindex # not sure this is really necessary with tm.assert_produces_warning(UserWarning): indexer_obj = indexer_obj.reindex(datetime_frame.index[::-1]) subframe_obj = datetime_frame[indexer_obj] tm.assert_frame_equal(subframe_obj, subframe) # test df[df > 0] for df in [ datetime_frame, mixed_float_frame, mixed_int_frame, ]: data = df._get_numeric_data() bif = df[df > 0] bifw = DataFrame( {c: np.where(data[c] > 0, data[c], np.nan) for c in data.columns}, index=data.index, columns=data.columns, ) # add back other columns to compare for c in df.columns: if c not in bifw: bifw[c] = df[c] bifw = bifw.reindex(columns=df.columns) tm.assert_frame_equal(bif, bifw, check_dtype=False) for c in df.columns: if bif[c].dtype != bifw[c].dtype: assert bif[c].dtype == df[c].dtype def test_getitem_boolean_casting(self, datetime_frame): # don't upcast if we don't need to df = datetime_frame.copy() df["E"] = 1 df["E"] = df["E"].astype("int32") df["E1"] = df["E"].copy() df["F"] = 1 df["F"] = df["F"].astype("int64") df["F1"] = df["F"].copy() casted = df[df > 0] result = casted.dtypes expected = Series( [np.dtype("float64")] * 4 + [np.dtype("int32")] * 2 + [np.dtype("int64")] * 2, index=["A", "B", "C", "D", "E", "E1", "F", "F1"], ) tm.assert_series_equal(result, expected) # int block splitting df.loc[df.index[1:3], ["E1", "F1"]] = 0 casted = df[df > 0] result = casted.dtypes expected = Series( [np.dtype("float64")] * 4 + [np.dtype("int32")] + [np.dtype("float64")] + [np.dtype("int64")] + [np.dtype("float64")], index=["A", "B", "C", "D", "E", "E1", "F", "F1"], ) tm.assert_series_equal(result, expected) def test_getitem_boolean_list(self): df = DataFrame(np.arange(12).reshape(3, 4)) def _checkit(lst): result = df[lst] expected = df.loc[df.index[lst]] tm.assert_frame_equal(result, expected) _checkit([True, False, True]) _checkit([True, True, True]) _checkit([False, False, False]) def test_getitem_boolean_iadd(self): arr = np.random.randn(5, 5) df = DataFrame(arr.copy(), columns=["A", "B", "C", "D", "E"]) df[df < 0] += 1 arr[arr < 0] += 1 tm.assert_almost_equal(df.values, arr) def test_boolean_index_empty_corner(self): # #2096 blah = DataFrame(np.empty([0, 1]), columns=["A"], index=DatetimeIndex([])) # both of these should succeed trivially k = np.array([], bool) blah[k] blah[k] = 0 def test_getitem_ix_mixed_integer(self): df = DataFrame( np.random.randn(4, 3), index=[1, 10, "C", "E"], columns=[1, 2, 3] ) result = df.iloc[:-1] expected = df.loc[df.index[:-1]] tm.assert_frame_equal(result, expected) result = df.loc[[1, 10]] expected = df.loc[Index([1, 10])] tm.assert_frame_equal(result, expected) def test_getitem_ix_mixed_integer2(self): # 11320 df = DataFrame( { "rna": (1.5, 2.2, 3.2, 4.5), -1000: [11, 21, 36, 40], 0: [10, 22, 43, 34], 1000: [0, 10, 20, 30], }, columns=["rna", -1000, 0, 1000], ) result = df[[1000]] expected = df.iloc[:, [3]] tm.assert_frame_equal(result, expected) result = df[[-1000]] expected = df.iloc[:, [1]] tm.assert_frame_equal(result, expected) def test_getattr(self, float_frame): tm.assert_series_equal(float_frame.A, float_frame["A"]) msg = "'DataFrame' object has no attribute 'NONEXISTENT_NAME'" with pytest.raises(AttributeError, match=msg): float_frame.NONEXISTENT_NAME def test_setattr_column(self): df = DataFrame({"foobar": 1}, index=range(10)) df.foobar = 5 assert (df.foobar == 5).all() def test_setitem(self, float_frame): # not sure what else to do here series = float_frame["A"][::2] float_frame["col5"] = series assert "col5" in float_frame assert len(series) == 15 assert len(float_frame) == 30 exp = np.ravel(np.column_stack((series.values, [np.nan] * 15))) exp = Series(exp, index=float_frame.index, name="col5") tm.assert_series_equal(float_frame["col5"], exp) series = float_frame["A"] float_frame["col6"] = series tm.assert_series_equal(series, float_frame["col6"], check_names=False) # set ndarray arr = np.random.randn(len(float_frame)) float_frame["col9"] = arr assert (float_frame["col9"] == arr).all() float_frame["col7"] = 5 assert (float_frame["col7"] == 5).all() float_frame["col0"] = 3.14 assert (float_frame["col0"] == 3.14).all() float_frame["col8"] = "foo" assert (float_frame["col8"] == "foo").all() # this is partially a view (e.g. some blocks are view) # so raise/warn smaller = float_frame[:2] msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): smaller["col10"] = ["1", "2"] assert smaller["col10"].dtype == np.object_ assert (smaller["col10"] == ["1", "2"]).all() def test_setitem2(self): # dtype changing GH4204 df = DataFrame([[0, 0]]) df.iloc[0] = np.nan expected = DataFrame([[np.nan, np.nan]]) tm.assert_frame_equal(df, expected) df = DataFrame([[0, 0]]) df.loc[0] = np.nan tm.assert_frame_equal(df, expected) def test_setitem_boolean(self, float_frame): df = float_frame.copy() values = float_frame.values df[df["A"] > 0] = 4 values[values[:, 0] > 0] = 4 tm.assert_almost_equal(df.values, values) # test that column reindexing works series = df["A"] == 4 series = series.reindex(df.index[::-1]) df[series] = 1 values[values[:, 0] == 4] = 1 tm.assert_almost_equal(df.values, values) df[df > 0] = 5 values[values > 0] = 5 tm.assert_almost_equal(df.values, values) df[df == 5] = 0 values[values == 5] = 0 tm.assert_almost_equal(df.values, values) # a df that needs alignment first df[df[:-1] < 0] = 2 np.putmask(values[:-1], values[:-1] < 0, 2) tm.assert_almost_equal(df.values, values) # indexed with same shape but rows-reversed df df[df[::-1] == 2] = 3 values[values == 2] = 3 tm.assert_almost_equal(df.values, values) msg = "Must pass DataFrame or 2-d ndarray with boolean values only" with pytest.raises(TypeError, match=msg): df[df * 0] = 2 # index with DataFrame mask = df > np.abs(df) expected = df.copy() df[df > np.abs(df)] = np.nan expected.values[mask.values] = np.nan tm.assert_frame_equal(df, expected) # set from DataFrame expected = df.copy() df[df > np.abs(df)] = df * 2 np.putmask(expected.values, mask.values, df.values * 2) tm.assert_frame_equal(df, expected) def test_setitem_cast(self, float_frame): float_frame["D"] = float_frame["D"].astype("i8") assert float_frame["D"].dtype == np.int64 # #669, should not cast? # this is now set to int64, which means a replacement of the column to # the value dtype (and nothing to do with the existing dtype) float_frame["B"] = 0 assert float_frame["B"].dtype == np.int64 # cast if pass array of course float_frame["B"] = np.arange(len(float_frame)) assert issubclass(float_frame["B"].dtype.type, np.integer) float_frame["foo"] = "bar" float_frame["foo"] = 0 assert float_frame["foo"].dtype == np.int64 float_frame["foo"] = "bar" float_frame["foo"] = 2.5 assert float_frame["foo"].dtype == np.float64 float_frame["something"] = 0 assert float_frame["something"].dtype == np.int64 float_frame["something"] = 2 assert float_frame["something"].dtype == np.int64 float_frame["something"] = 2.5 assert float_frame["something"].dtype == np.float64 def test_setitem_corner(self, float_frame): # corner case df = DataFrame({"B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]}, index=np.arange(3)) del df["B"] df["B"] = [1.0, 2.0, 3.0] assert "B" in df assert len(df.columns) == 2 df["A"] = "beginning" df["E"] = "foo" df["D"] = "bar" df[datetime.now()] = "date" df[datetime.now()] = 5.0 # what to do when empty frame with index dm = DataFrame(index=float_frame.index) dm["A"] = "foo" dm["B"] = "bar" assert len(dm.columns) == 2 assert dm.values.dtype == np.object_ # upcast dm["C"] = 1 assert dm["C"].dtype == np.int64 dm["E"] = 1.0 assert dm["E"].dtype == np.float64 # set existing column dm["A"] = "bar" assert "bar" == dm["A"][0] dm = DataFrame(index=np.arange(3)) dm["A"] = 1 dm["foo"] = "bar" del dm["foo"] dm["foo"] = "bar" assert dm["foo"].dtype == np.object_ dm["coercible"] = ["1", "2", "3"] assert dm["coercible"].dtype == np.object_ def test_setitem_corner2(self): data = { "title": ["foobar", "bar", "foobar"] + ["foobar"] * 17, "cruft": np.random.random(20), } df = DataFrame(data) ix = df[df["title"] == "bar"].index df.loc[ix, ["title"]] = "foobar" df.loc[ix, ["cruft"]] = 0 assert df.loc[1, "title"] == "foobar" assert df.loc[1, "cruft"] == 0 def test_setitem_ambig(self): # Difficulties with mixed-type data from decimal import Decimal # Created as float type dm = DataFrame(index=range(3), columns=range(3)) coercable_series = Series([Decimal(1) for _ in range(3)], index=range(3)) uncoercable_series = Series(["foo", "bzr", "baz"], index=range(3)) dm[0] = np.ones(3) assert len(dm.columns) == 3 dm[1] = coercable_series assert len(dm.columns) == 3 dm[2] = uncoercable_series assert len(dm.columns) == 3 assert dm[2].dtype == np.object_ def test_setitem_None(self, float_frame): # GH #766 float_frame[None] = float_frame["A"] tm.assert_series_equal( float_frame.iloc[:, -1], float_frame["A"], check_names=False ) tm.assert_series_equal( float_frame.loc[:, None], float_frame["A"], check_names=False ) tm.assert_series_equal(float_frame[None], float_frame["A"], check_names=False) repr(float_frame) def test_loc_setitem_boolean_mask_allfalse(self): # GH 9596 df = DataFrame( {"a": ["1", "2", "3"], "b": ["11", "22", "33"], "c": ["111", "222", "333"]} ) result = df.copy() result.loc[result.b.isna(), "a"] = result.a tm.assert_frame_equal(result, df) def test_getitem_fancy_slice_integers_step(self): df = DataFrame(np.random.randn(10, 5)) # this is OK result = df.iloc[:8:2] # noqa df.iloc[:8:2] = np.nan assert isna(df.iloc[:8:2]).values.all() def test_getitem_setitem_integer_slice_keyerrors(self): df = DataFrame(np.random.randn(10, 5), index=range(0, 20, 2)) # this is OK cp = df.copy() cp.iloc[4:10] = 0 assert (cp.iloc[4:10] == 0).values.all() # so is this cp = df.copy() cp.iloc[3:11] = 0 assert (cp.iloc[3:11] == 0).values.all() result = df.iloc[2:6] result2 = df.loc[3:11] expected = df.reindex([4, 6, 8, 10]) tm.assert_frame_equal(result, expected) tm.assert_frame_equal(result2, expected) # non-monotonic, raise KeyError df2 = df.iloc[list(range(5)) + list(range(5, 10))[::-1]] with pytest.raises(KeyError, match=r"^3$"): df2.loc[3:11] with pytest.raises(KeyError, match=r"^3$"): df2.loc[3:11] = 0 @td.skip_array_manager_invalid_test # already covered in test_iloc_col_slice_view def test_fancy_getitem_slice_mixed(self, float_frame, float_string_frame): sliced = float_string_frame.iloc[:, -3:] assert sliced["D"].dtype == np.float64 # get view with single block # setting it triggers setting with copy sliced = float_frame.iloc[:, -3:] assert np.shares_memory(sliced["C"]._values, float_frame["C"]._values) msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): sliced.loc[:, "C"] = 4.0 assert (float_frame["C"] == 4).all() def test_getitem_setitem_non_ix_labels(self): df = tm.makeTimeDataFrame() start, end = df.index[[5, 10]] result = df.loc[start:end] result2 = df[start:end] expected = df[5:11] tm.assert_frame_equal(result, expected) tm.assert_frame_equal(result2, expected) result = df.copy() result.loc[start:end] = 0 result2 = df.copy() result2[start:end] = 0 expected = df.copy() expected[5:11] = 0 tm.assert_frame_equal(result, expected) tm.assert_frame_equal(result2, expected) def test_ix_multi_take(self): df = DataFrame(np.random.randn(3, 2)) rs = df.loc[df.index == 0, :] xp = df.reindex([0]) tm.assert_frame_equal(rs, xp) # GH#1321 df = DataFrame(np.random.randn(3, 2)) rs = df.loc[df.index == 0, df.columns == 1] xp = df.reindex(index=[0], columns=[1]) tm.assert_frame_equal(rs, xp) def test_getitem_fancy_scalar(self, float_frame): f = float_frame ix = f.loc # individual value for col in f.columns: ts = f[col] for idx in f.index[::5]: assert ix[idx, col] == ts[idx] @td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values def test_setitem_fancy_scalar(self, float_frame): f = float_frame expected = float_frame.copy() ix = f.loc # individual value for j, col in enumerate(f.columns): ts = f[col] # noqa for idx in f.index[::5]: i = f.index.get_loc(idx) val = np.random.randn() expected.values[i, j] = val ix[idx, col] = val tm.assert_frame_equal(f, expected) def test_getitem_fancy_boolean(self, float_frame): f = float_frame ix = f.loc expected = f.reindex(columns=["B", "D"]) result = ix[:, [False, True, False, True]] tm.assert_frame_equal(result, expected) expected = f.reindex(index=f.index[5:10], columns=["B", "D"]) result = ix[f.index[5:10], [False, True, False, True]] tm.assert_frame_equal(result, expected) boolvec = f.index > f.index[7] expected = f.reindex(index=f.index[boolvec]) result = ix[boolvec] tm.assert_frame_equal(result, expected) result = ix[boolvec, :] tm.assert_frame_equal(result, expected) result = ix[boolvec, f.columns[2:]] expected = f.reindex(index=f.index[boolvec], columns=["C", "D"]) tm.assert_frame_equal(result, expected) @td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values def test_setitem_fancy_boolean(self, float_frame): # from 2d, set with booleans frame = float_frame.copy() expected = float_frame.copy() mask = frame["A"] > 0 frame.loc[mask] = 0.0 expected.values[mask.values] = 0.0 tm.assert_frame_equal(frame, expected) frame = float_frame.copy() expected = float_frame.copy() frame.loc[mask, ["A", "B"]] = 0.0 expected.values[mask.values, :2] = 0.0 tm.assert_frame_equal(frame, expected) def test_getitem_fancy_ints(self, float_frame): result = float_frame.iloc[[1, 4, 7]] expected = float_frame.loc[float_frame.index[[1, 4, 7]]] tm.assert_frame_equal(result, expected) result = float_frame.iloc[:, [2, 0, 1]] expected = float_frame.loc[:, float_frame.columns[[2, 0, 1]]] tm.assert_frame_equal(result, expected) def test_getitem_setitem_boolean_misaligned(self, float_frame): # boolean index misaligned labels mask = float_frame["A"][::-1] > 1 result = float_frame.loc[mask] expected = float_frame.loc[mask[::-1]] tm.assert_frame_equal(result, expected) cp = float_frame.copy() expected = float_frame.copy() cp.loc[mask] = 0 expected.loc[mask] = 0 tm.assert_frame_equal(cp, expected) def test_getitem_setitem_boolean_multi(self): df = DataFrame(np.random.randn(3, 2)) # get k1 = np.array([True, False, True]) k2 = np.array([False, True]) result = df.loc[k1, k2] expected = df.loc[[0, 2], [1]] tm.assert_frame_equal(result, expected) expected = df.copy() df.loc[np.array([True, False, True]), np.array([False, True])] = 5 expected.loc[[0, 2], [1]] = 5 tm.assert_frame_equal(df, expected) def test_getitem_setitem_float_labels(self): index = Index([1.5, 2, 3, 4, 5]) df = DataFrame(np.random.randn(5, 5), index=index) result = df.loc[1.5:4] expected = df.reindex([1.5, 2, 3, 4]) tm.assert_frame_equal(result, expected) assert len(result) == 4 result = df.loc[4:5] expected = df.reindex([4, 5]) # reindex with int tm.assert_frame_equal(result, expected, check_index_type=False) assert len(result) == 2 result = df.loc[4:5] expected = df.reindex([4.0, 5.0]) # reindex with float tm.assert_frame_equal(result, expected) assert len(result) == 2 # loc_float changes this to work properly result = df.loc[1:2] expected = df.iloc[0:2] tm.assert_frame_equal(result, expected) df.loc[1:2] = 0 result = df[1:2] assert (result == 0).all().all() # #2727 index = Index([1.0, 2.5, 3.5, 4.5, 5.0]) df = DataFrame(np.random.randn(5, 5), index=index) # positional slicing only via iloc! msg = ( "cannot do positional indexing on Float64Index with " r"these indexers \[1.0\] of type float" ) with pytest.raises(TypeError, match=msg): df.iloc[1.0:5] result = df.iloc[4:5] expected = df.reindex([5.0]) tm.assert_frame_equal(result, expected) assert len(result) == 1 cp = df.copy() with pytest.raises(TypeError, match=_slice_msg): cp.iloc[1.0:5] = 0 with pytest.raises(TypeError, match=msg): result = cp.iloc[1.0:5] == 0 assert result.values.all() assert (cp.iloc[0:1] == df.iloc[0:1]).values.all() cp = df.copy() cp.iloc[4:5] = 0 assert (cp.iloc[4:5] == 0).values.all() assert (cp.iloc[0:4] == df.iloc[0:4]).values.all() # float slicing result = df.loc[1.0:5] expected = df tm.assert_frame_equal(result, expected) assert len(result) == 5 result = df.loc[1.1:5] expected = df.reindex([2.5, 3.5, 4.5, 5.0]) tm.assert_frame_equal(result, expected) assert len(result) == 4 result = df.loc[4.51:5] expected = df.reindex([5.0]) tm.assert_frame_equal(result, expected) assert len(result) == 1 result = df.loc[1.0:5.0] expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0]) tm.assert_frame_equal(result, expected) assert len(result) == 5 cp = df.copy() cp.loc[1.0:5.0] = 0 result = cp.loc[1.0:5.0] assert (result == 0).values.all() def test_setitem_single_column_mixed_datetime(self): df = DataFrame( np.random.randn(5, 3), index=["a", "b", "c", "d", "e"], columns=["foo", "bar", "baz"], ) df["timestamp"] = Timestamp("20010102") # check our dtypes result = df.dtypes expected = Series( [np.dtype("float64")] * 3 + [np.dtype("datetime64[ns]")], index=["foo", "bar", "baz", "timestamp"], ) tm.assert_series_equal(result, expected) # GH#16674 iNaT is treated as an integer when given by the user df.loc["b", "timestamp"] = iNaT assert not isna(df.loc["b", "timestamp"]) assert df["timestamp"].dtype == np.object_ assert df.loc["b", "timestamp"] == iNaT # allow this syntax (as of GH#3216) df.loc["c", "timestamp"] = np.nan assert isna(df.loc["c", "timestamp"]) # allow this syntax df.loc["d", :] = np.nan assert not isna(df.loc["c", :]).all() def test_setitem_mixed_datetime(self): # GH 9336 expected = DataFrame( { "a": [0, 0, 0, 0, 13, 14], "b": [ datetime(2012, 1, 1), 1, "x", "y", datetime(2013, 1, 1), datetime(2014, 1, 1), ], } ) df = DataFrame(0, columns=list("ab"), index=range(6)) df["b"] = pd.NaT df.loc[0, "b"] = datetime(2012, 1, 1) df.loc[1, "b"] = 1 df.loc[[2, 3], "b"] = "x", "y" A = np.array( [ [13, np.datetime64("2013-01-01T00:00:00")], [14, np.datetime64("2014-01-01T00:00:00")], ] ) df.loc[[4, 5], ["a", "b"]] = A tm.assert_frame_equal(df, expected) def test_setitem_frame_float(self, float_frame): piece = float_frame.loc[float_frame.index[:2], ["A", "B"]] float_frame.loc[float_frame.index[-2] :, ["A", "B"]] = piece.values result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values expected = piece.values tm.assert_almost_equal(result, expected) def test_setitem_frame_mixed(self, float_string_frame): # GH 3216 # already aligned f = float_string_frame.copy() piece = DataFrame( [[1.0, 2.0], [3.0, 4.0]], index=f.index[0:2], columns=["A", "B"] ) key = (f.index[slice(None, 2)], ["A", "B"]) f.loc[key] = piece tm.assert_almost_equal(f.loc[f.index[0:2], ["A", "B"]].values, piece.values) def test_setitem_frame_mixed_rows_unaligned(self, float_string_frame): # GH#3216 rows unaligned f = float_string_frame.copy() piece = DataFrame( [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], index=list(f.index[0:2]) + ["foo", "bar"], columns=["A", "B"], ) key = (f.index[slice(None, 2)], ["A", "B"]) f.loc[key] = piece tm.assert_almost_equal( f.loc[f.index[0:2:], ["A", "B"]].values, piece.values[0:2] ) def test_setitem_frame_mixed_key_unaligned(self, float_string_frame): # GH#3216 key is unaligned with values f = float_string_frame.copy() piece = f.loc[f.index[:2], ["A"]] piece.index = f.index[-2:] key = (f.index[slice(-2, None)], ["A", "B"]) f.loc[key] = piece piece["B"] = np.nan tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values) def test_setitem_frame_mixed_ndarray(self, float_string_frame): # GH#3216 ndarray f = float_string_frame.copy() piece = float_string_frame.loc[f.index[:2], ["A", "B"]] key = (f.index[slice(-2, None)], ["A", "B"]) f.loc[key] = piece.values tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values) def test_setitem_frame_upcast(self): # needs upcasting df = DataFrame([[1, 2, "foo"], [3, 4, "bar"]], columns=["A", "B", "C"]) df2 = df.copy() df2.loc[:, ["A", "B"]] = df.loc[:, ["A", "B"]] + 0.5 expected = df.reindex(columns=["A", "B"]) expected += 0.5 expected["C"] = df["C"] tm.assert_frame_equal(df2, expected) def test_setitem_frame_align(self, float_frame): piece = float_frame.loc[float_frame.index[:2], ["A", "B"]] piece.index = float_frame.index[-2:] piece.columns = ["A", "B"] float_frame.loc[float_frame.index[-2:], ["A", "B"]] = piece result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values expected = piece.values tm.assert_almost_equal(result, expected) def test_getitem_setitem_ix_duplicates(self): # #1201 df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"]) result = df.loc["foo"] expected = df[:2] tm.assert_frame_equal(result, expected) result = df.loc["bar"] expected = df.iloc[[2, 4]] tm.assert_frame_equal(result, expected) result = df.loc["baz"] expected = df.iloc[3] tm.assert_series_equal(result, expected) def test_getitem_ix_boolean_duplicates_multiple(self): # #1201 df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"]) result = df.loc[["bar"]] exp = df.iloc[[2, 4]] tm.assert_frame_equal(result, exp) result = df.loc[df[1] > 0] exp = df[df[1] > 0] tm.assert_frame_equal(result, exp) result = df.loc[df[0] > 0] exp = df[df[0] > 0] tm.assert_frame_equal(result, exp) @pytest.mark.parametrize("bool_value", [True, False]) def test_getitem_setitem_ix_bool_keyerror(self, bool_value): # #2199 df = DataFrame({"a": [1, 2, 3]}) message = f"{bool_value}: boolean label can not be used without a boolean index" with pytest.raises(KeyError, match=message): df.loc[bool_value] msg = "cannot use a single bool to index into setitem" with pytest.raises(KeyError, match=msg): df.loc[bool_value] = 0 # TODO: rename? remove? def test_single_element_ix_dont_upcast(self, float_frame): float_frame["E"] = 1 assert issubclass(float_frame["E"].dtype.type, (int, np.integer)) result = float_frame.loc[float_frame.index[5], "E"] assert is_integer(result) # GH 11617 df = DataFrame({"a": [1.23]}) df["b"] = 666 result = df.loc[0, "b"] assert is_integer(result) expected = Series([666], [0], name="b") result = df.loc[[0], "b"]
tm.assert_series_equal(result, expected)
pandas._testing.assert_series_equal
# Categorical Variables import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder ### Read the data data = pd.read_csv('./Data/melb_data.csv') ### Separate target from predictors y = data.Price X = data.drop(['Price'], axis=1) ### Divide data into training and validation subsets X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0) ### Drop columns with missing values (simplest approach) cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()] X_train_full.drop(cols_with_missing, axis=1, inplace=True) X_valid_full.drop(cols_with_missing, axis=1, inplace=True) ### "Cardinality" means the number of unique values in a column ### Select categorical columns with relatively low cardinality (convenient but arbitrary) low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == "object"] ### Select numerical columns numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']] ### Keep selected columns only my_cols = low_cardinality_cols + numerical_cols X_train = X_train_full[my_cols].copy() X_valid = X_valid_full[my_cols].copy() ### Get list of categorical variables s = (X_train.dtypes == 'object') object_cols = list(s[s].index) print("Categorical variables:") print(object_cols) ### Function for comparing different approaches def score_dataset(X_train, X_valid, y_train, y_valid): model = RandomForestRegressor(n_estimators=100, random_state=0) model.fit(X_train, y_train) preds = model.predict(X_valid) return mean_absolute_error(y_valid, preds) drop_X_train = X_train.select_dtypes(exclude=['object']) drop_X_valid = X_valid.select_dtypes(exclude=['object']) print("MAE from Approach 1 (Drop categorical variables):") print(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid)) ### Make copy to avoid changing original data label_X_train = X_train.copy() label_X_valid = X_valid.copy() ### Apply label encoder to each column with categorical data label_encoder = LabelEncoder() for col in object_cols: label_X_train[col] = label_encoder.fit_transform(X_train[col]) label_X_valid[col] = label_encoder.transform(X_valid[col]) print("MAE from Approach 2 (Label Encoding):") print(score_dataset(label_X_train, label_X_valid, y_train, y_valid)) ### Apply one-hot encoder to each column with categorical data OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False) OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols])) OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols])) ### One-hot encoding removed index; put it back OH_cols_train.index = X_train.index OH_cols_valid.index = X_valid.index ### Remove categorical columns (will replace with one-hot encoding) num_X_train = X_train.drop(object_cols, axis=1) num_X_valid = X_valid.drop(object_cols, axis=1) ### Add one-hot encoded columns to numerical features OH_X_train =
pd.concat([num_X_train, OH_cols_train], axis=1)
pandas.concat
import time import logging from TwitterAPI import TwitterAPI from twython import Twython from twython import TwythonError, TwythonRateLimitError, TwythonAuthError import pandas as pd from datetime import datetime, timedelta from spikexplore.NodeInfo import NodeInfo from spikexplore.graph import add_node_attributes, add_edges_attributes logger = logging.getLogger(__name__) class TwitterCredentials: def __init__(self, app_key, access_token, consumer_key=None, consumer_secret=None): self.app_key = app_key self.access_token = access_token self.consumer_key = consumer_key self.consumer_secret = consumer_secret class TweetsGetterV1: def __init__(self, credentials, config): # Instantiate an object self.app_key = credentials.app_key self.access_token = credentials.access_token self.config = config self.twitter_handle = Twython(self.app_key, access_token=self.access_token) pass def _filter_old_tweets(self, tweets): max_day_old = self.config.max_day_old if not max_day_old: return tweets days_limit = datetime.now() - timedelta(days=max_day_old) tweets_filt = filter(lambda t: datetime.strptime(t['created_at'], '%a %b %d %H:%M:%S +0000 %Y') >= days_limit, tweets) return list(tweets_filt) def get_user_tweets(self, username): # Collect tweets from a username count = self.config.max_tweets_per_user # Test if ok try: user_tweets_raw = self.twitter_handle.get_user_timeline(screen_name=username, count=count, include_rts=True, tweet_mode='extended', exclude_replies=False) # remove old tweets user_tweets_filt = self._filter_old_tweets(user_tweets_raw) # make a dictionary user_tweets = {x['id']: x for x in user_tweets_filt} tweets_metadata = \ map(lambda x: (x[0], {'user': x[1]['user']['screen_name'], 'name': x[1]['user']['name'], 'user_details': x[1]['user']['description'], 'mentions': list( map(lambda y: y['screen_name'], x[1]['entities']['user_mentions'])), 'hashtags': list(map(lambda y: y['text'], x[1]['entities']['hashtags'])), 'retweet_count': x[1]['retweet_count'], 'favorite_count': x[1]['favorite_count'], 'created_at': x[1]['created_at'], 'account_creation': x[1]['user']['created_at'], 'account_followers': x[1]['user']['followers_count'], 'account_following': x[1]['user']['friends_count'], 'account_statuses': x[1]['user']['statuses_count'], 'account_favourites': x[1]['user']['favourites_count'], 'account_verified': x[1]['user']['verified'], 'account_default_profile': x[1]['user']['default_profile'], 'account_default_profile_image': x[1]['user']['default_profile_image']}), user_tweets.items()) return user_tweets, dict(tweets_metadata) except TwythonAuthError as e_auth: if e_auth.error_code == 401: logger.warning('Unauthorized access to user {}. Skipping.'.format(username)) return {}, {} else: logger.error('Cannot access to twitter API, authentification error. {}'.format(e_auth.error_code)) raise except TwythonRateLimitError as e_lim: logger.warning('API rate limit reached') logger.warning(e_lim) remainder = float(self.twitter_handle.get_lastfunction_header(header='x-rate-limit-reset')) - time.time() logger.warning('Retry after {} seconds.'.format(remainder)) time.sleep(remainder + 1) del self.twitter_handle self.twitter_handle = Twython(self.app_key, access_token=self.access_token) # seems you need this return {}, {} # best way to handle it ? except TwythonError as e: logger.error('Twitter API returned error {} for user {}.'.format(e.error_code, username)) return {}, {} def reshape_node_data(self, node_df): # user name user_details mentions hashtags retweet_count favorite_count # created_at account_creation account_followers account_following account_statuses account_favourites # account_verified account_default_profile account_default_profile_image spikyball_hop node_df = node_df[ ['user', 'name', 'user_details', 'spikyball_hop', 'account_creation', 'account_default_profile', 'account_default_profile_image', 'account_favourites', 'account_followers', 'account_following', 'account_statuses', 'account_verified']] node_df = node_df.reset_index().groupby('user').max().rename(columns={'index': 'max_tweet_id'}) return node_df class TweetsGetterV2: def __init__(self, credentials, config): self.twitter_handle = TwitterAPI(credentials.consumer_key, credentials.consumer_secret, api_version='2', auth_type='oAuth2') self.config = config self.start_time = None if config.max_day_old: days_limit = datetime.now() - timedelta(days=config.max_day_old) # date format: 2010-11-06T00:00:00Z self.start_time = days_limit.strftime('%Y-%m-%dT%H:%M:%SZ') self.user_cache = {} def _safe_twitter_request(self, request_str, params): res = self.twitter_handle.request(request_str, params) while res.status_code == 429: # rate limit reached logger.warning('API rate limit reached') remainder = float(res.headers['x-rate-limit-reset']) - time.time() logger.warning('Retry after {} seconds.'.format(remainder)) time.sleep(remainder + 1) res = self.twitter_handle.request(request_str, params) if res.status_code != 200: logger.warning('API returned with code {}'.format(res.status_code)) return res def _get_user_info(self, username): if username not in self.user_cache: params = {'user.fields': 'created_at,verified,description,public_metrics,protected,profile_image_url'} res = dict(self._safe_twitter_request('users/by/username/:{}'.format(username), params).json()) if 'errors' in res: self.user_cache[username] = None for e in res['errors']: logger.info(e['detail']) else: self.user_cache[username] = res['data'] return self.user_cache[username] def _fill_user_info(self, includes): if 'users' not in includes: return for u in includes['users']: if u['username'] not in self.user_cache: self.user_cache[u['username']] = u def _get_user_tweets(self, username, num_tweets, next_token): assert(num_tweets <= 100 and num_tweets > 0) params = {'max_results': num_tweets, 'expansions': 'author_id,entities.mentions.username,referenced_tweets.id', 'tweet.fields': 'entities,created_at,public_metrics,lang,referenced_tweets', 'user.fields': 'verified,description,created_at,public_metrics,protected,profile_image_url'} if self.start_time: params['start_time'] = self.start_time if next_token: params['pagination_token'] = next_token user_info = self._get_user_info(username) if not user_info: # not found return {}, {}, None if user_info['protected']: logger.info('Skipping user {} - protected account'.format(username)) return {}, {}, None tweets_raw = dict(self._safe_twitter_request('users/:{}/tweets'.format(user_info['id']), params).json()) if 'errors' in tweets_raw: err_details = set([e['detail'] for e in tweets_raw['errors']]) for e in err_details: logger.info(e) if 'data' not in tweets_raw: logger.info('Empty results for {}'.format(username)) return {}, {}, None user_tweets = {int(x['id']): x for x in tweets_raw['data']} referenced_tweets = {x['id']: x for x in tweets_raw['includes'].get('tweets', {})} # make the tweets dict similar to the one retrieved using APIv1 for k in user_tweets.keys(): user_tweets[k]['id_str'] = user_tweets[k]['id'] user_tweets[k]['id'] = k # preserve 'id' as int (used as index) user_tweets[k]['full_text'] = user_tweets[k].pop('text') user_tweets[k]['user'] = {'id': int(user_info['id']), 'id_str': user_info['id'], 'screen_name': user_info['username'], 'name': user_info['name'], 'description': user_info['description'], 'verified': user_info['verified'], 'protected': user_info['protected'], 'created_at': user_info['created_at'], 'followers_count': user_info['public_metrics']['followers_count'], 'friends_count': user_info['public_metrics']['following_count'], 'statuses_count': user_info['public_metrics']['tweet_count']} # handle retweet info if 'referenced_tweets' in user_tweets[k]: ref = list(filter(lambda x: x['type'] == 'quoted' or x['type'] == 'retweeted', user_tweets[k]['referenced_tweets'])) if ref: ref_type = ref[0]['type'] ref_txt = '' if ref_type == 'quoted': ref_txt = user_tweets[k]['full_text'] + " " ref_txt += referenced_tweets[ref[0]['id']]['text'] user_tweets[k]['retweeted_status'] = {'full_text': ref_txt} tweets_metadata = \ dict(map(lambda x: (x[0], {'user': user_info['username'], 'name': user_info['name'], 'user_details': user_info['description'], 'mentions': list( map(lambda y: y['username'], x[1].get('entities', {}).get('mentions', {}))), 'hashtags': list( map(lambda y: y['tag'], x[1].get('entities', {}).get('hashtags', {}))), 'retweet_count': x[1]['public_metrics']['retweet_count'], 'favorite_count': x[1]['public_metrics']['like_count'], 'created_at': x[1]['created_at'], 'account_creation': user_info['created_at'], 'account_followers': user_info['public_metrics']['followers_count'], 'account_following': user_info['public_metrics']['following_count'], 'account_statuses': user_info['public_metrics']['tweet_count'], 'account_verified': user_info['verified']}), user_tweets.items())) if 'includes' in tweets_raw: self._fill_user_info(tweets_raw['includes']) return user_tweets, tweets_metadata, tweets_raw['meta'].get('next_token', None) def get_user_tweets(self, username): remaining_number_of_tweets = self.config.max_tweets_per_user next_token = None user_tweets_acc = {} tweets_metadata_acc = {} while remaining_number_of_tweets > 0: number_of_tweets = 100 if remaining_number_of_tweets > 100 else remaining_number_of_tweets remaining_number_of_tweets -= number_of_tweets user_tweets, tweets_metadata, next_token = self._get_user_tweets(username, number_of_tweets, next_token) user_tweets_acc.update(user_tweets) tweets_metadata_acc.update(tweets_metadata) if not next_token: break return user_tweets_acc, tweets_metadata_acc def reshape_node_data(self, node_df): node_df = node_df[ ['user', 'name', 'user_details', 'spikyball_hop', 'account_creation', 'account_followers', 'account_following', 'account_statuses', 'account_verified']] node_df = node_df.reset_index().groupby('user').max().rename(columns={'index': 'max_tweet_id'}) return node_df class TwitterNetwork: class TwitterNodeInfo(NodeInfo): def __init__(self, user_hashtags=None, user_tweets=None, tweets_meta=pd.DataFrame()): self.user_hashtags = user_hashtags if user_hashtags else {} self.user_tweets = user_tweets if user_tweets else {} self.tweets_meta = tweets_meta def update(self, new_info): self.user_hashtags.update(new_info.user_hashtags) self.user_tweets.update(new_info.user_tweets) def get_nodes(self): return self.tweets_meta def __init__(self, credentials, config): if config.api_version == 1: self.tweets_getter = TweetsGetterV1(credentials, config) elif config.api_version == 2: self.tweets_getter = TweetsGetterV2(credentials, config) else: raise ValueError("Invalid api version") self.config = config def create_node_info(self): return self.TwitterNodeInfo() def get_neighbors(self, user): if not isinstance(user, str): return self.TwitterNodeInfo(), pd.DataFrame() tweets_dic, tweets_meta = self.tweets_getter.get_user_tweets(user) edges_df, node_info = self.edges_nodes_from_user(tweets_meta, tweets_dic) # replace user and mentions by source and target if not edges_df.empty: edges_df.index.names = ['source', 'target'] edges_df.reset_index(level=['source', 'target'], inplace=True) return node_info, edges_df def filter(self, node_info, edges_df): # filter edges according to node properties # filter according to edges properties edges_df = self.filter_edges(edges_df) return node_info, edges_df def filter_edges(self, edges_df): # filter edges according to their properties if edges_df.empty: return edges_df return edges_df[edges_df['weight'] >= self.config.min_mentions] def neighbors_list(self, edges_df): if edges_df.empty: return edges_df users_connected = edges_df['target'].tolist() return users_connected def neighbors_with_weights(self, edges_df): user_list = self.neighbors_list(edges_df) return dict.fromkeys(user_list, 1) ############################################################### # Functions for extracting tweet info from the twitter API ############################################################### def edges_nodes_from_user(self, tweets_meta, tweets_dic): # Make an edge and node property dataframes edges_df = self.get_edges(tweets_meta) user_info = self.get_nodes_properties(tweets_meta, tweets_dic) return edges_df, user_info def get_edges(self, tweets_meta): if not tweets_meta: return pd.DataFrame() # Create the user -> mention table with their properties fom the list of tweets of a user meta_df = pd.DataFrame.from_dict(tweets_meta, orient='index').explode('mentions').dropna() # Some bots to be removed from the collection users_to_remove = self.config.users_to_remove filtered_meta_df = meta_df[~meta_df['mentions'].isin(users_to_remove) & ~meta_df['mentions'].isin(meta_df['user'])] # group by mentions and keep list of tweets for each mention tmp = filtered_meta_df.groupby(['user', 'mentions']).apply(lambda x: (x.index.tolist(), len(x.index))) if tmp.empty: return tmp edge_df = pd.DataFrame(tmp.tolist(), index=tmp.index) \ .rename(columns={0: 'tweet_id', 1: 'weight'}) return edge_df def get_nodes_properties(self, tweets_meta, tweets_dic): if not tweets_meta: return self.TwitterNodeInfo({}, {},
pd.DataFrame()
pandas.DataFrame
# MLP import csv from itertools import islice import random import matplotlib.pyplot as plt import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import KFold, train_test_split import pandas as pd from sklearn.utils import shuffle import tensorflow as tf def bit2attr(bitstr) -> list: attr_vec = list() for i in range(len(bitstr)): attr_vec.append(int(bitstr[i])) return attr_vec def mean_relative_error(y_pred, y_test): assert len(y_pred) == len(y_test) mre = 0.0 for i in range(len(y_pred)): mre = mre + abs((y_pred[i] - y_test[i]) / y_test[i]) mre = mre * 100/ len(y_pred) return mre Large_MRE_points = pd.DataFrame() Large_MRE_X = [] Large_MRE_y_test = [] Large_MRE_y_pred = [] Large_MRE = [] ''' 1) 数据预处理 ''' # filepath = 'data/fp/sjn/R+B+Cmorgan_fp1202.csv' filepath = 'data/database/22-01-29-descriptor-train.csv' data = pd.read_csv(filepath, encoding='gb18030') print(data.shape) data = data.dropna() print(data.shape) data = shuffle(data) data_x_df = data.drop(['label'], axis=1) data_y_df = data[['label']] # 归一化 min_max_scaler_X = MinMaxScaler() min_max_scaler_X.fit(data_x_df) x_trans1 = min_max_scaler_X.transform(data_x_df) min_max_scaler_y = MinMaxScaler() min_max_scaler_y.fit(data_y_df) y_trans1 = min_max_scaler_y.transform(data_y_df) test_filepath = "data/database/22-01-29-descriptor-test-level-1.csv" test_data =
pd.read_csv(test_filepath, encoding='gb18030')
pandas.read_csv
import pandas as pd from pandas.api.types import is_numeric_dtype, is_categorical, infer_dtype from functools import reduce import warnings import weakref from itertools import combinations from scipy.stats import chi2_contingency import numpy as np from collections import Counter @pd.api.extensions.register_dataframe_accessor("cats") class CatsAccessor: """A class of useful categorical stuff to add to pandas """ def __init__(self, pandas_obj): self._finalizer = weakref.finalize(self, self._cleanup) self._validate(pandas_obj) self._obj = pandas_obj self._categorical_columns = None def _cleanup(self): del self._obj def remove(self): self._finalizer() @staticmethod def _validate(obj): # verify this is a DataFrame if not isinstance(obj, pd.DataFrame): raise AttributeError("Must be a pandas DataFrame") def _get_categorical_columns(self): result = [col for col in self._obj.columns if
infer_dtype(self._obj[col])
pandas.api.types.infer_dtype
from MP import MpFunctions import requests import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import pandas as pd import plotly.graph_objs as go import datetime as dt import numpy as np import warnings warnings.filterwarnings('ignore') app = dash.Dash(__name__) def get_ticksize(data, freq=30): # data = dflive30 numlen = int(len(data) / 2) # sample size for calculating ticksize = 50% of most recent data tztail = data.tail(numlen).copy() tztail['tz'] = tztail.Close.rolling(freq).std() # std. dev of 30 period rolling tztail = tztail.dropna() ticksize = np.ceil(tztail['tz'].mean() * 0.25) # 1/4 th of mean std. dev is our ticksize if ticksize < 0.2: ticksize = 0.2 # minimum ticksize limit return int(ticksize) def get_data(url): """ :param url: binance url :return: ohlcv dataframe """ response = requests.get(url) data = response.json() df = pd.DataFrame(data) df = df.apply(pd.to_numeric) df[0] = pd.to_datetime(df[0], unit='ms') df = df[[0, 1, 2, 3, 4, 5]] df.columns = ['datetime', 'Open', 'High', 'Low', 'Close', 'volume'] df = df.set_index('datetime', inplace=False, drop=False) return df url_30m = "https://www.binance.com/api/v1/klines?symbol=BTCBUSD&interval=30m" # 10 days history 30 min ohlcv df = get_data(url_30m) df.to_csv('btcusd30m.csv', index=False) # params context_days = len([group[1] for group in df.groupby(df.index.date)]) # Number of days used for context freq = 2 # for 1 min bar use 30 min frequency for each TPO, here we fetch default 30 min bars server avglen = context_days - 2 # num days to calculate average values mode = 'tpo' # for volume --> 'vol' trading_hr = 24 # Default for BTC USD or Forex day_back = 0 # -1 While testing sometimes maybe you don't want current days data then use -1 # ticksz = 28 # If you want to use manual tick size then uncomment this. Really small number means convoluted alphabets (TPO) ticksz = (get_ticksize(df.copy(), freq=freq))*2 # Algorithm will calculate the optimal tick size based on volatility textsize = 10 if day_back != 0: symbol = 'Historical Mode' else: symbol = 'BTC-USD Live' dfnflist = [group[1] for group in df.groupby(df.index.date)] # dates = [] for d in range(0, len(dfnflist)): dates.append(dfnflist[d].index[0]) date_time_close = dt.datetime.today().strftime('%Y-%m-%d') + ' ' + '23:59:59' append_dt = pd.Timestamp(date_time_close) dates.append(append_dt) date_mark = {str(h): {'label': str(h), 'style': {'color': 'blue', 'fontsize': '4', 'text-orientation': 'upright'}} for h in range(0, len(dates))} mp = MpFunctions(data=df.copy(), freq=freq, style=mode, avglen=avglen, ticksize=ticksz, session_hr=trading_hr) mplist = mp.get_context() app.layout = html.Div( html.Div([ dcc.Location(id='url', refresh=False), dcc.Link('Twitter', href='https://twitter.com/beinghorizontal'), html.Br(), dcc.Link('python source code', href='http://www.github.com/beinghorizontal'), html.H4('@beinghorizontal'), dcc.Graph(id='beinghorizontal'), dcc.Interval( id='interval-component', interval=5 * 1000, # Reduce the time if you want frequent updates 5000 = 5 sec n_intervals=0 ), html.P([ html.Label("Time Period"), dcc.RangeSlider(id='slider', pushable=1, marks=date_mark, min=0, max=len(dates), step=None, value=[len(dates) - 2, len(dates) - 1]) ], style={'width': '80%', 'fontSize': '14px', 'padding-left': '100px', 'display': 'inline-block'}) ]) ) @app.callback(Output(component_id='beinghorizontal', component_property='figure'), [Input('interval-component', 'n_intervals'), Input('slider', 'value') ]) def update_graph(n, value): listmp_hist = mplist[0] distribution_hist = mplist[1] url_1m = "https://www.binance.com/api/v1/klines?symbol=BTCBUSD&interval=1m" df_live1 = get_data(url_1m) # this line fetches new data for current day df_live1 = df_live1.dropna() dflive30 = df_live1.resample('30min').agg({'datetime': 'last', 'Open': 'first', 'High': 'max', 'Low': 'min', 'Close': 'last', 'volume': 'sum'}) df2 = pd.concat([df, dflive30]) df2 = df2.drop_duplicates('datetime') ticksz_live = (get_ticksize(dflive30.copy(), freq=2)) mplive = MpFunctions(data=dflive30.copy(), freq=freq, style=mode, avglen=avglen, ticksize=ticksz_live, session_hr=trading_hr) mplist_live = mplive.get_context() listmp_live = mplist_live[0] # it will be in list format so take [0] slice for current day MP data frame df_distribution_live = mplist_live[1] df_distribution_concat =
pd.concat([distribution_hist, df_distribution_live], axis=0)
pandas.concat
import numpy as np import pandas as pd from itertools import islice import multiprocessing from multiprocessing.pool import ThreadPool, Pool N_CPUS = multiprocessing.cpu_count() def batch_generator(iterable, n=1): if hasattr(iterable, '__len__'): # https://stackoverflow.com/questions/8290397/how-to-split-an-iterable-in-constant-size-chunks l = len(iterable) for ndx in range(0, l, n): yield iterable[ndx:min(ndx + n, l)] elif hasattr(iterable, '__next__'): # https://stackoverflow.com/questions/1915170/split-a-generator-iterable-every-n-items-in-python-splitevery i = iter(iterable) piece = list(islice(i, n)) while piece: yield piece piece = list(islice(i, n)) else: raise ValueError('Iterable is not iterable?') def map_batches_multiproc(func, iterable, chunksize, multiproc_mode='threads', n_threads=None, threads_per_cpu=1.0): if n_threads is None: n_threads = int(threads_per_cpu * N_CPUS) if hasattr(iterable, '__len__') and len(iterable) <= chunksize: return [func(iterable)] with pool_type(multiproc_mode)(n_threads) as pool: batches = batch_generator(iterable, n=chunksize) return list(pool.imap(func, batches)) def pool_type(parallelism_type): if 'process' in parallelism_type.lower(): return Pool elif 'thread' in parallelism_type.lower(): return ThreadPool else: raise ValueError('Unsupported value for "parallelism_type"') def parallelize_dataframe(df, func, n_partitions=N_CPUS, parallelism_type='process'): # with Pool(n_partitions) as pool: # return pd.concat(pool.map(func, np.array_split(df, n_partitions))) df_split = np.array_split(df, n_partitions) with pool_type(parallelism_type)(n_partitions) as pool: res = pool.map(func, df_split) df =
pd.concat(res, sort=False)
pandas.concat
#!/usr/bin/env python3 import sys import re import os import collections import pickle import pandas as pd from pandas import ExcelWriter from pandas import ExcelFile def unpack_layer_tops_product(layer_top): product = 1 for i in layer_top: product *= int(i) return product def calculate_mlu_ops_byte(layer_name, net_shape_dict, debug=True): flops = 0 mem_bytes = 0 if debug: print(net_shape_dict) if layer_name in net_shape_dict: v = net_shape_dict[layer_name] if v['type'] == 'Convolution': # ops = out_h*out_w*(2*in_c*k_s*k_s)*out_c*out_n # bytes = (k_s*k_s*in_c*out_c+out_h*out_w*out_c)*out_n*2 in_n, in_c, in_h, in_w = v['bottoms'][0] out_n, out_c, out_h, out_w = v['tops'][0] k_s = int(v['kernel_size']) in_n, in_c, in_h, in_w = int(in_n), int(in_c), int(in_h), int(in_w) out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = out_h * out_w * (2 * in_c * k_s * k_s) * out_c * out_n mem_bytes = (k_s * k_s * in_c * out_c + out_h * out_w * out_c) * out_n * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Pooling': in_n, in_c, in_h, in_w = v['bottoms'][0] out_n, out_c, out_h, out_w = v['tops'][0] k_s = int(v['kernel_size']) in_n, in_c, in_h, in_w = int(in_n), int(in_c), int(in_h), int(in_w) out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) if int(v['kernel_size']) == 0: # global pooling # ops = in_c*in_h*in_w*in_n or in_c*(in_h*in_w+1)*in_n # bytes = (in_c*in_h*in_w+out_c*out_h*out_w)*out_n*2 flops = in_c * (in_h * in_w + 1) * in_n mem_bytes = (in_c * in_h * in_w + out_c * out_h * out_w) * out_n * 4 else: # common pooling # ops = out_c*out_h*out_w*k_s*k_s*out_n # bytes = out_c*out_h*out_w*(k_s*k_s+1)*out_n*2 flops = out_c * out_h * out_w * k_s * k_s * out_n mem_bytes = out_c * out_h * out_w * (k_s * k_s + 1) * out_n * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'ReLU': # ops=2*N*C*H*W # bytes=2*N*C*H*W*2 out_n, out_c, out_h, out_w = v['tops'][0] out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = out_n * out_c * out_h * out_w mem_bytes = 2 * out_n * out_c * out_w * out_w * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Scale': # ops=2*N*C*H*W # bytes=3*N*C*H*W*2 #out_n, out_c, out_h, out_w = v['tops'][0] #out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) #flops = 2 * out_n * out_c * out_h * out_w #mem_bytes = 3 * out_n * out_c * out_w * out_w * 2 product = unpack_layer_tops_product(v['tops'][0]) flops = 2 * product mem_bytes = 3 * product * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Softmax': # ops=4*N*C*H*W # bytes=2*N*C*H*W*2 # Note: sometimes this layer only has two dimensions: product = unpack_layer_tops_product(v['tops'][0]) flops = 4 * product mem_bytes = 2 * product * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'BatchNorm': # ops=8*N*C*H*W # bytes=4*N*C*H*W*2 #out_n, out_c, out_h, out_w = v['tops'][0] #out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) #flops = 8 * out_n * out_c * out_h * out_w #mem_bytes = 4 * out_n * out_c * out_w * out_w * 2 product = unpack_layer_tops_product(v['tops'][0]) flops = 8 * product mem_bytes = 4 * product * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Dropout': # ops=3*N*C*H*W # bytes=3*N*C*H*W*2 out_n, out_c, out_h, out_w = v['tops'][0] out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = 3 * out_n * out_c * out_h * out_w mem_bytes = 3 * out_n * out_c * out_w * out_w * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Concat': # ops=0 # bytes=2*out_n*out*c*out*h*out*w*2 out_n, out_c, out_h, out_w = v['tops'][0] out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = 0 mem_bytes = 2 * out_n * out_c * out_w * out_w * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Eltwise': # ops=2*N*C*H*W # bytes=3*N*C*H*W*2 #out_n, out_c, out_h, out_w = v['tops'][0] #out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) #flops = 2 * out_n * out_c * out_h * out_w #mem_bytes = 3 * out_n * out_c * out_w * out_w * 2 product = unpack_layer_tops_product(v['tops'][0]) flops = 2 * product mem_bytes = 3 * product * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'InnerProduct': # ops = out_h*outw*(2*in_c*k_s*k_s)*out_c*out_n # bytes = (k_s*k_s*in_c*out_c+out_h*out_w*out_c)*in_n*2 if debug: print('bottoms: ', v['bottoms']) print('tops: ', v['tops']) in_n, in_c, in_h, in_w = v['bottoms'][0] #out_n, out_c, out_h, out_w = v['tops'][0] if len(v['tops'][0]) == 4: out_n, out_c, out_h, out_w = v['tops'][0] elif len(v['tops'][0]) == 3: out_n, out_c, out_h = v['tops'][0] out_w = 1 elif len(v['tops'][0]) == 2: out_n, out_c = v['tops'][0] out_h = 1 out_w = 1 elif len(v['tops'][0]) == 1: out_n = v['tops'][0] out_c = 1 out_h = 1 out_w = 1 if 'kernel_size' in v: k_s = int(v['kernel_size']) else: k_s = 1 in_n, in_c, in_h, in_w = int(in_n), int(in_c), int(in_h), int(in_w) flops = out_h * out_w * (2 * in_c * k_s * k_s) * out_c * out_n mem_bytes = (k_s * k_s * in_c * out_c + out_h * out_w * out_c) * in_n * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'Normalize': # ops=8*N*C*H*W # bytes=2*N*C*H*W*2 out_n, out_c, out_h, out_w = v['tops'][0] out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = 8 * out_n * out_c * out_h * out_w mem_bytes = 2 * out_n * out_c * out_w * out_w * 4 if debug: print(flops, mem_bytes) elif v['type'] == 'SsdDetection': # ops=4*N*C*H*W # bytes=(in_0+in_1+...+in_n+out)*2 out_n, out_c, out_h, out_w = v['tops'][0] out_n, out_c, out_h, out_w = int(out_n), int(out_c), int(out_h), int(out_w) flops = 4 * out_n * out_c * out_h * out_w mem_bytes = 0 for n, c, h, w in v['bottoms']: mem_bytes += n * c * h * w * 2 mem_bytes += out_n * out_c * out_h * out_w * 4 if debug: print(flops, mem_bytes) return flops, mem_bytes def extract_into_excel(layer_info_file='layer_info.txt', layer_shape_file='layer_shape.txt', debug=False): layer_info_reader = open(layer_info_file, 'rb') layer_info = pickle.load(layer_info_reader) net_name = layer_info['name'] print(net_name) layer_shape_reader = open(layer_shape_file, 'rb') layer_shape = pickle.load(layer_shape_reader) layer_name_time_reader = open(str(net_name) + '-each_layer_time.txt', 'rb') layer_type_time_reader = open(str(net_name) + '-layer_type_time.txt', 'rb') layer_name_record_dict = pickle.load(layer_name_time_reader) layer_type_record_dict = pickle.load(layer_type_time_reader) flops_membwd_reader = open(str(net_name) + '-flops_membwd.txt', 'rb') flops_membwd_dict = pickle.load(flops_membwd_reader) layer_name_list = [] layer_name_type_list = [] layer_name_time_list = [] layer_type_list = [] layer_type_time_list = [] flops_membwd_type_list = [] flops_membwd_values_list = [] max_bottoms_length = max_tops_length = 0 # multiple input and output shape info layer_shape_list_dict = {} kernel_size_list = [] stride_list = [] pad_list = [] try: for k, v in list(layer_shape.items()): if v['type'] == 'Input' or v['type'] == 'Accuracy': del layer_shape[k] continue max_bottoms_length = max(max_bottoms_length, len(v['bottoms'])) max_tops_length = max(max_tops_length, len(v['tops'])) # determine the input and output tuples length for i in range(0, max_bottoms_length): layer_shape_list_dict['Input' + str(i) + ' N'] = [] layer_shape_list_dict['Input' + str(i) + ' C'] = [] layer_shape_list_dict['Input' + str(i) + ' H'] = [] layer_shape_list_dict['Input' + str(i) + ' W'] = [] for i in range(0, max_tops_length): layer_shape_list_dict['Output' + str(i) + ' N'] = [] layer_shape_list_dict['Output' + str(i) + ' C'] = [] layer_shape_list_dict['Output' + str(i) + ' H'] = [] layer_shape_list_dict['Output' + str(i) + ' W'] = [] for k, v in layer_name_record_dict.items(): layer_name_list.append(str(k)) layer_name_type_list.append(str(layer_info[str(k)])) layer_name_time_list.append(float(v)) # 'kernel_size', 'stride', 'pad' if layer_info[str(k)] == 'Convolution' or layer_info[str(k)] == 'Pooling': kernel_size_list.append(int(layer_shape[str(k)]['kernel_size'])) stride_list.append(int(layer_shape[str(k)]['stride'])) pad_list.append(int(layer_shape[str(k)]['pad'])) else: kernel_size_list.append(float('nan')) stride_list.append(float('nan')) pad_list.append(float('nan')) # Input and output shape for i in range(0, len(layer_shape[str(k)]['bottoms'])): if debug: print('max tops len:', max_tops_length, 'max bottoms len:', max_bottoms_length) print('layer:', str(k), 'type:', layer_shape[str(k)]['type'], 'bottoms:', layer_shape[str(k)]['bottoms'][i]) if len(layer_shape[str(k)]['bottoms'][i]) == 1: layer_shape_list_dict['Input' + str(i) + ' N'].append(int(layer_shape[str(k)]['bottoms'][i][0])) layer_shape_list_dict['Input' + str(i) + ' C'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['bottoms'][i]) == 2: layer_shape_list_dict['Input' + str(i) + ' N'].append(int(layer_shape[str(k)]['bottoms'][i][0])) layer_shape_list_dict['Input' + str(i) + ' C'].append(int(layer_shape[str(k)]['bottoms'][i][1])) layer_shape_list_dict['Input' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['bottoms'][i]) == 3: layer_shape_list_dict['Input' + str(i) + ' N'].append(int(layer_shape[str(k)]['bottoms'][i][0])) layer_shape_list_dict['Input' + str(i) + ' C'].append(int(layer_shape[str(k)]['bottoms'][i][1])) layer_shape_list_dict['Input' + str(i) + ' H'].append(int(layer_shape[str(k)]['bottoms'][i][2])) layer_shape_list_dict['Input' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['bottoms'][i]) == 4: layer_shape_list_dict['Input' + str(i) + ' N'].append(int(layer_shape[str(k)]['bottoms'][i][0])) layer_shape_list_dict['Input' + str(i) + ' C'].append(int(layer_shape[str(k)]['bottoms'][i][1])) layer_shape_list_dict['Input' + str(i) + ' H'].append(int(layer_shape[str(k)]['bottoms'][i][2])) layer_shape_list_dict['Input' + str(i) + ' W'].append(int(layer_shape[str(k)]['bottoms'][i][3])) for i in range(len(layer_shape[str(k)]['bottoms']), max_bottoms_length): layer_shape_list_dict['Input' + str(i) + ' N'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' C'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Input' + str(i) + ' W'].append(float('nan')) for i in range(0, len(layer_shape[str(k)]['tops'])): if len(layer_shape[str(k)]['tops'][i]) == 1: layer_shape_list_dict['Output' + str(i) + ' N'].append(int(layer_shape[str(k)]['tops'][i][0])) layer_shape_list_dict['Output' + str(i) + ' C'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['tops'][i]) == 2: layer_shape_list_dict['Output' + str(i) + ' N'].append(int(layer_shape[str(k)]['tops'][i][0])) layer_shape_list_dict['Output' + str(i) + ' C'].append(int(layer_shape[str(k)]['tops'][i][1])) layer_shape_list_dict['Output' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['tops'][i]) == 3: layer_shape_list_dict['Output' + str(i) + ' N'].append(int(layer_shape[str(k)]['tops'][i][0])) layer_shape_list_dict['Output' + str(i) + ' C'].append(int(layer_shape[str(k)]['tops'][i][1])) layer_shape_list_dict['Output' + str(i) + ' H'].append(int(layer_shape[str(k)]['tops'][i][2])) layer_shape_list_dict['Output' + str(i) + ' W'].append(float('nan')) elif len(layer_shape[str(k)]['tops'][i]) == 4: layer_shape_list_dict['Output' + str(i) + ' N'].append(int(layer_shape[str(k)]['tops'][i][0])) layer_shape_list_dict['Output' + str(i) + ' C'].append(int(layer_shape[str(k)]['tops'][i][1])) layer_shape_list_dict['Output' + str(i) + ' H'].append(int(layer_shape[str(k)]['tops'][i][2])) layer_shape_list_dict['Output' + str(i) + ' W'].append(int(layer_shape[str(k)]['tops'][i][3])) for i in range(len(layer_shape[str(k)]['tops']), max_tops_length): if debug: print('max tops len:', max_tops_length, 'max bottoms len:', max_bottoms_length) print('layer:', str(k), 'type:', layer_shape[str(k)]['type'], 'tops:', layer_shape[str(k)]['tops']) layer_shape_list_dict['Output' + str(i) + ' N'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' C'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' H'].append(float('nan')) layer_shape_list_dict['Output' + str(i) + ' W'].append(float('nan')) for k, v in layer_type_record_dict.items(): layer_type_list.append(str(k)) layer_type_time_list.append(float(v)) for k, v in flops_membwd_dict.items(): flops_membwd_type_list.append(str(k)) flops_membwd_values_list.append(float(v)) finally: layer_info_reader.close() layer_shape_reader.close() layer_name_time_reader.close() layer_type_time_reader.close() flops_membwd_reader.close() assert len(layer_name_list) == len(layer_name_time_list) and \ len(layer_name_time_list) == len(kernel_size_list) and \ len(kernel_size_list) == len(stride_list) and \ len(stride_list) == len(pad_list) and \ len(layer_type_list) == len(layer_type_time_list) and \ len(flops_membwd_type_list) == len(flops_membwd_values_list), \ " Error! Must have same records length!" # calculate flops and memory accessing bytes ops_list = [] mem_bytes_list = [] for layer_name in layer_name_list: flops, mem_bytes = calculate_mlu_ops_byte(layer_name, layer_shape) ops_list.append(flops) mem_bytes_list.append(mem_bytes) gflops_list = [] intensity_list = [] total_model_ops = 0.0 total_model_mem_bytes = 0.0 for i, exe_time in enumerate(layer_name_time_list): gflops_list.append(ops_list[i] / 1e9 / (exe_time / 1e3)) intensity_list.append(float(ops_list[i] / mem_bytes_list[i])) total_model_ops += ops_list[i] total_model_mem_bytes += mem_bytes_list[i] avg_model_intensity = float(total_model_ops / total_model_mem_bytes) total_model_time = 0 for time in layer_type_time_list: total_model_time += time avg_model_gflops = total_model_ops / 1e9 / (total_model_time / 1e3) # for sheet4 columns value_list = [total_model_ops, total_model_mem_bytes, total_model_time, avg_model_gflops, avg_model_intensity] name_list = ['model ops', 'model bytes', 'model time(ms)', 'model GFLOPS', 'model intensity'] sheet1_od = collections.OrderedDict() sheet1_od['layer name'] = layer_name_list sheet1_od['layer type'] = layer_name_type_list sheet1_od['time(ms)'] = layer_name_time_list sheet1_od['Ops'] = ops_list sheet1_od['Bytes'] = mem_bytes_list sheet1_od['GFLOPS'] = gflops_list sheet1_od['Intensity'] = intensity_list for i in range(0, max_bottoms_length): sheet1_od['Input' + str(i) + ' N'] = layer_shape_list_dict['Input' + str(i) + ' N'] sheet1_od['Input' + str(i) + ' C'] = layer_shape_list_dict['Input' + str(i) + ' C'] sheet1_od['Input' + str(i) + ' H'] = layer_shape_list_dict['Input' + str(i) + ' H'] sheet1_od['Input' + str(i) + ' W'] = layer_shape_list_dict['Input' + str(i) + ' W'] sheet1_od['kernel size'] = kernel_size_list sheet1_od['stride'] = stride_list sheet1_od['pad'] = pad_list for i in range(0, max_tops_length): sheet1_od['Output' + str(i) + ' N'] = layer_shape_list_dict['Output' + str(i) + ' N'] sheet1_od['Output' + str(i) + ' C'] = layer_shape_list_dict['Output' + str(i) + ' C'] sheet1_od['Output' + str(i) + ' H'] = layer_shape_list_dict['Output' + str(i) + ' H'] sheet1_od['Output' + str(i) + ' W'] = layer_shape_list_dict['Output' + str(i) + ' W'] sheet1_df =
pd.DataFrame(sheet1_od)
pandas.DataFrame
""" contains various implementations for recommending movies """ import pandas as pd import numpy as np from utils import movies from utils import movies,ratings,df_mov_avg_cnt, search_title,movie_to_id,id_to_movie,get_movieId from utils import model_nmf,model_knn from scipy.sparse import csr_matrix # creating the matrix (filling in 0 when there is no entry) from sklearn.decomposition import NMF # Non matrix factorization for Recommneder system def recommend_random(query, k=5): """ Recommends a list of k random movie ids """ movies_rand =movies[~movies['movieId'].isin(query)] # drops all movies in the query rand_list=movies_rand.sample(k)['movieId'].to_list() rand_movie_title_list=[] for list in rand_list: movie_title = movies.loc[movies['movieId']==list,'title'].values[0] rand_movie_title_list.append(movie_title) df_rand_recommended=pd.DataFrame(rand_movie_title_list,rand_list) return df_rand_recommended def recommend_popular(query, k=5): """ Recommend a list of k movie ids that are from 40 most popular """ df_popular=df_mov_avg_cnt[~df_mov_avg_cnt['movieId'].isin(query)] popular=df_popular.sort_values('popular',ascending=False)['movieId'].head(40).to_list() rand_popular = np.random.randint(40,size =(1,5)) pop_movieId_list=[] for rand_nu in range(len(rand_popular[0])): pop_id = popular[rand_popular[0][rand_nu]] pop_movieId_list.append(pop_id) pop_movietitle_list=[] for list_pop in pop_movieId_list: movie_title_pop=movies.loc[movies['movieId']==list_pop,'title'].values[0] pop_movietitle_list.append(movie_title_pop) df_pop_recommended =pd.DataFrame(pop_movietitle_list,pop_movieId_list) return df_pop_recommended def recommend_nmf(query, k=5): """ Recommend a list of k movie ids based on a trained NMF model """ # 1. candiate generation # user_query = disney_movies # construct a user vector user_vec=np.repeat(0,193610) user_vec[query]=5 # 2. scoring model = model_nmf scores=model.inverse_transform(model.transform([user_vec])) # calculate the score with the NMF model # 3. ranking scores =pd.Series(scores[0]) scores[query]=0 # set zero score to movies allready seen by the user scores=scores.sort_values(ascending=False) # return the top-k highst rated movie ids or titles recommendations= scores.head(k).index moiveId_r=[] movieId_t=[] for recs in range(len(recommendations)): movieId_r_Var = recommendations[recs] movieId_t_Var= movies.set_index('movieId').loc[movieId_r_Var]['title'] moiveId_r.append(movieId_r_Var) movieId_t.append(movieId_t_Var) df_nmf=pd.DataFrame(movieId_t,moiveId_r) return df_nmf def recommend_neighbors(query, k=5): """ Recommend a list of k movie ids based on the most similar users """ # 1. candiate generation user_vec=np.repeat(0,193610) user_vec[query]=5 # construct a user vector # calculates the distances to all other users in the data! distances, userIds = model_knn.kneighbors( X=[user_vec], n_neighbors=10, return_distance=True ) # sklearn returns a list of predictions - extract the first and only value of the list distances = distances[0] userIds = userIds[0] # 2. scoring # find n neighbors neighborhood =ratings.loc[ratings['userId'].isin(userIds)] scores=neighborhood.groupby('movieId')['rating'].sum() # calculate their average rating # 3. ranking # filter out movies allready seen by the user # give a zero score to movies the user has allready seen scores.loc[scores.index.isin(query)]=0 scores = scores.sort_values(ascending=False) recommendations=scores.head(k).index # return the top-k highst rated movie ids or titles moiveId_r=[] movieId_t=[] for recs in range(len(recommendations)): movieId_r_Var = recommendations[recs] movieId_t_Var= movies.set_index('movieId').loc[movieId_r_Var]['title'] moiveId_r.append(movieId_r_Var) movieId_t.append(movieId_t_Var) df_knn=
pd.DataFrame(movieId_t,moiveId_r)
pandas.DataFrame
import argparse import pandas as pd import tensorflow as tf import dataset_ops import numpy as np import datetime import cuda import pandas_format # noqa from pathlib import Path from sklearn import linear_model from sklearn import tree from sklearn.model_selection import train_test_split try: import tqdm except ImportError: tqdm = None session_start = datetime.datetime.now().strftime(r"%Y%m%d-%H%M%S") parser = argparse.ArgumentParser() parser.add_argument('-w', nargs='+', type=int) parser.add_argument('-o', '--out', default=f'classic_learning_{session_start}.csv') parser.add_argument('models', nargs='+') args = parser.parse_args() print(args) cuda.initialize() # dataset_manager = dataset_ops.MicroPilotTestsManager(dataset_dir=Path('./h5'), runs_filename='runs.hdf') dataset_manager = dataset_ops.PaparazziTestManager(dataset_dir=Path('pprz_h5'), runs_filename='pprz_runs.hdf') all_runs = dataset_manager.get_all_available_tests() # selected_runs = all_runs.loc[(all_runs['Test Length'] > 200) & (all_runs['Test Length'] < 20000)] # selected_runs = selected_runs.iloc[:40] selected_runs = all_runs.sample(frac=1, axis=1, random_state=55) inputs = ('SpeedFts', 'Pitch', 'Roll', 'Yaw', 'current_altitude', ) outputs= ('elev', 'ai', 'rdr', 'throttle', 'Flaps') # max_length = selected_runs['Test Length'].max() # max_length = 18000 # dataset_manager.preload_data(selected_runs, max_length=max_length, features=inputs + outputs) # tfdataset = dataset_ops.TensorflowDataset(dataset_manager) # dataset = tfdataset.get_dataset(selected_runs, batch_size=25, features=inputs+outputs, max_length=max_length) dataset = dataset_manager.preload_data(selected_runs, features=inputs+outputs) N_s = dataset_manager.count_states() train, test = train_test_split(dataset, test_size=0.2, random_state=44) # %% # results_df = pd.DataFrame(columns=['Data Set', 'Name', 'regularization', 'd', 'w', 'Precision', 'Recall']) # results_df = results_df.set_index(['Data Set', 'Name', 'w']) # results_df = pd.read_csv('tree_results_20.csv', index_col=0).append(pd.read_csv('results_3_5_10_15.csv', index_col=0)) file_name = Path(args.out) if file_name.exists(): results_df = pd.read_csv(file_name, index_col=0) else: results_df = pd.DataFrame(columns=['Data Set', 'Name', 'regularization', 'w', 'Precision', 'Recall']) # %% def class_precision_recall(y_true, y_pred): # _, classes = y_pred.shape # y_true = tf.math.argmax(y_true, axis=-1) # batch x L # y_pred = tf.math.argmax(y_pred, axis=-1) y_true = tf.constant(y_true) y_pred = tf.constant(y_pred) classes = N_s y_pred.shape.assert_is_compatible_with(y_true.shape) if y_true.dtype != y_pred.dtype: y_pred = tf.cast(y_pred, y_true.dtype) recall_scores, precision_scores = [], [] for C in range(classes): C = tf.cast(C, 'int64') trueC = tf.equal(y_true, C) declaredC = tf.equal(y_pred, C) correctlyC = tf.logical_and(declaredC, trueC) trueC = tf.cast(tf.math.count_nonzero(trueC), 'float32') declaredC = tf.cast(tf.math.count_nonzero(declaredC), 'float32') correctlyC = tf.cast(tf.math.count_nonzero(correctlyC), 'float32') if declaredC > 0: precision_score = tf.math.divide_no_nan(correctlyC, declaredC) precision_scores.append(precision_score) if trueC > 0: recall_score = tf.math.divide_no_nan(correctlyC, trueC) recall_scores.append(recall_score) P = tf.reduce_mean(tf.stack(precision_scores)) R = tf.reduce_mean(tf.stack(recall_scores)) return P, R def iterate_window(dataframe, w): last = dataframe.shape[0] - w + 1 for index in range(last): yield dataframe[index:index + w] def convert_to_data_points(w): def _generator(data): signals = data[1].to_numpy() states = data[2].to_numpy() signals_iter = iterate_window(signals, w) # previous_iter = iterate_window(states, w) next_state_iter = states[w:] # for signals, previous, next_state in zip(signals_iter, previous_iter, next_state_iter): for signals, next_state in zip(signals_iter, next_state_iter): # X = np.concatenate((signals.flatten(), one_hotter[previous].flatten())) X = signals.flatten() # y = one_hotter[next_state] y = next_state yield X, y return _generator def create_allX_allY(dataset, w): converter = convert_to_data_points(w) allX, allY = [], [] for test in tqdm.tqdm(dataset): lX, ly = [], [] for X, y in converter(test): lX.append(X) ly.append(y) allX.append(np.stack(lX)) allY.append(np.stack(ly)) del lX, ly # return allX, allY return np.concatenate(allX), np.concatenate(allY) def evaluate(model, *, name, w, regularization): train_p, train_r = class_precision_recall(y_train, model.predict(X_train)) test_p, test_r = class_precision_recall(y_test, model.predict(X_test)) df = pd.DataFrame({ 'Data Set': ['Train', 'Test'], 'Precision': [float(train_p), float(test_p)], 'Recall': [float(train_r), float(test_r)], }) df['Name'] = name df['w'] = w df['regularization'] = regularization # df = df.set_index(['Data Set', 'Name', 'w']) return df # %% for w in args.w: print('Window size', w) X_train, y_train = create_allX_allY(train, w) X_test, y_test = create_allX_allY(test, w) # .set_index(['Data Set', 'Name', 'w']) if 'ridge' in args.models: print('Training Ridge') model_ridge = linear_model.RidgeClassifierCV(alphas=np.logspace(-6, 6, 13)) model_ridge.fit(X_train, y_train) results = evaluate(model_ridge, name='ridge', w=w, regularization=None) if file_name.exists(): results_df = pd.read_csv(file_name, index_col=0) results_df = results_df.append(results) print(results) try: results_df.to_csv(file_name) except: print('Failed to save') if 'tree1' in args.models or 'trees' in args.models: print('Training Tree') model_tree = tree.DecisionTreeClassifier(max_features=None) model_tree.fit(X_train, y_train) results = evaluate(model_tree, name='tree', regularization=None, w=w) if file_name.exists(): results_df =
pd.read_csv(file_name, index_col=0)
pandas.read_csv
import pytest import pandas as pd from model.bitcoin.BitcoinFileManager import BitcoinFileManager def test_create_tweet_file_manager(): # Given # When try: BitcoinFileManager() # Then except Exception: pytest.fail("Could not create BitcoinFileManager") def test_get_file_name(): # Given bitcoinFileManager = BitcoinFileManager() args = {'date': '2021-01-13'} # When file_name = bitcoinFileManager.get_file_name(args) # Then assert file_name == "data/bitcoin/2021-01-13\\bitcoin.csv" or file_name == "data/bitcoin/2021-01-13/bitcoin.csv" def test_get_file_name_fails_on_no_date(): # Given bitcoinFileManager = BitcoinFileManager() args = {} # When try: bitcoinFileManager.get_file_name(args) # Then pytest.fail("Should throw exception") except Exception: assert True def test_open_file(mocker): # When data =
pd.DataFrame(columns=['timestamp', 'Close'])
pandas.DataFrame
from __future__ import annotations import numpy as np import pandas as pd from sklearn import datasets from IMLearn.metrics import mean_square_error from IMLearn.utils import split_train_test from IMLearn.model_selection import cross_validate from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, RidgeRegression from sklearn.linear_model import Lasso from utils import * import plotly.graph_objects as go from plotly.subplots import make_subplots def select_polynomial_degree(n_samples: int = 100, noise: float = 5): """ Simulate data from a polynomial model and use cross-validation to select the best fitting degree Parameters ---------- n_samples: int, default=100 Number of samples to generate noise: float, default = 5 Noise level to simulate in responses """ # Question 1 - Generate dataset for model f(x)=(x+3)(x+2)(x+1)(x-1)(x-2) + eps for eps Gaussian noise # and split into training- and testing portions f = lambda x: (x + 3) * (x + 2) * (x + 1) * (x - 1) * (x - 2) X = np.linspace(-1.2, 2, n_samples) y = f(X) + np.random.normal(0, noise, size=n_samples) X_train, y_train, X_test, y_test = split_train_test(pd.DataFrame(X),
pd.Series(y)
pandas.Series
############################### # # ADD DATE FEATURES # ############################### import numpy as np import pandas as pd import re def add_date_features(df, date_vars, drop = True, time = False): ''' Adds basic date-based features based to the data frame. -------------------- Arguments: - df (pandas DF): dataset - date_var (str): name of the date feature - drop (bool): whether to drop the original date feature - time (bool): whether to include time-based features -------------------- Returns: - pandas DF with new features -------------------- Examples: # create data frame data = {'age': [27, np.nan, 30], 'height': [170, 168, 173], 'gender': ['female', 'male', np.nan], 'date_of_birth': [np.datetime64('1993-02-10'), np.nan, np.datetime64('1990-04-08')]} df = pd.DataFrame(data) # add date features from dptools import add_date_features df_new = add_date_features(df, date_vars = 'date_of_birth') ''' # copy df df_new = df.copy() # store no. features n_feats = df_new.shape[1] # convert to list if not isinstance(date_vars, list): date_vars = [date_vars] # feature engineering loop for date_var in date_vars: var = df_new[date_var] var_dtype = var.dtype if isinstance(var_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype): var_dtype = np.datetime64 if not np.issubdtype(var_dtype, np.datetime64): df_new[date_var] = var = pd.to_datetime(var, infer_datetime_format = True) targ_pre = re.sub('[Dd]ate$', '', date_var) # list of day attributes attributes = ['year', 'month', 'week', 'day', 'dayofweek', 'dayofyear', 'is_month_end', 'is_month_start', 'is_quarter_end', 'is_quarter_start', 'is_year_end', 'is_year_start'] # list of time attributes if time: attributes = attributes + ['Hour', 'Minute', 'Second'] # compute features for att in attributes: df_new[targ_pre + '_' + att.lower()] = getattr(var.dt, att) df_new[targ_pre + '_elapsed'] = var.astype(np.int64) // 10 ** 9 # remove original feature if drop: df_new.drop(date_var, axis = 1, inplace = True) # return results print('Added {} date-based features.'.format(df_new.shape[1] - n_feats + int(drop) * len(date_vars))) return df_new ############################### # # ADD TEXT FEATURES # ############################### import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer import scipy.sparse def add_text_features(df, text_vars, tf_idf_feats = 5, common_words = 0, rare_words = 0, ngram_range = (1, 1), drop = True): ''' Adds basic text-based features including word count, character count and TF-IDF based features to the data frame. -------------------- Arguments: - df (pandas DF): dataset - text_vars (list): list of textual features - tf_idf_feats (int): number of TF-IDF based features - common_words (int): number of the most common words to remove for TF-IDF - rare_words (int): number of the most rare words to remove for TF-IDF - ngram_range (int, int): range of n-grams for TF-IDF based features - drop (bool): whether to drop the original textual features -------------------- Returns: - pandas DF with new features -------------------- Examples: # import dependecies import pandas as pd import numpy as np # create data frame data = {'age': [27, np.nan, 30, 25, np.nan], 'height': [170, 168, 173, 177, 165], 'gender': ['female', 'male', np.nan, 'male', 'female'], 'income': ['high', 'medium', 'low', 'low', 'no income']} df = pd.DataFrame(data) # add text features from dptools import add_text_features df_new = add_text_features(df, text_vars = ['income', 'gender']) ''' # copy df df_new = df.copy() # store no. features n_feats = df_new.shape[1] # convert to list if not isinstance(text_vars, list): text_vars = [text_vars] # feature engineering loop for text_var in text_vars: # replace NA with empty string df_new[text_var].fillna('', inplace = True) # remove common and rare words freq = pd.Series(' '.join(df_new[text_var]).split()).value_counts()[:common_words] freq = pd.Series(' '.join(df_new[text_var]).split()).value_counts()[-rare_words:] # convert to lowercase df_new[text_var] = df_new[text_var].apply(lambda x: ' '.join(x.lower() for x in x.split())) # remove punctuation df_new[text_var] = df_new[text_var].str.replace('[^\w\s]','') # word count df_new[text_var + '_word_count'] = df_new[text_var].apply(lambda x: len(str(x).split(' '))) df_new[text_var + '_word_count'][df_new[text_var] == ''] = 0 # character count df_new[text_var + '_char_count'] = df_new[text_var].str.len().fillna(0).astype('int64') # import vectorizer tfidf = TfidfVectorizer(max_features = tf_idf_feats, lowercase = True, norm = 'l2', analyzer = 'word', stop_words = 'english', ngram_range = ngram_range) # compute TF-IDF vals = tfidf.fit_transform(df_new[text_var]) vals = pd.DataFrame.sparse.from_spmatrix(vals) vals.columns = [text_var + '_tfidf_' + str(p) for p in vals.columns] df_new = pd.concat([df_new, vals], axis = 1) # remove original feature if drop: df_new.drop(text_var, axis = 1, inplace = True) # return results print('Added {} text-based features.'.format(df_new.shape[1] - n_feats + int(drop) * len(text_vars))) return df_new ############################### # # AGGRGEATE DATA # ############################### import pandas as pd def aggregate_data(df, group_var, num_stats = ['mean', 'sum'], fac_stats = ['count', 'mode'], factors = None, var_label = None, sd_zeros = False): ''' Aggregates the data by a certain categorical feature. Continuous features are aggregated by computing summary statistcs by the grouping feature. Categorical features are aggregated by computing the most frequent values and number of unique value by the grouping feature. -------------------- Arguments: - df (pandas DF): dataset - group_var (str): grouping feature - num_stats (list): list of stats for aggregating numeric features - fac_stats (list): list of stats for aggregating categorical features - factors (list): list of categorical features names - var_label (str): prefix for feature names after aggregation - sd_zeros (bool): whether to replace NA with 0 for standard deviation -------------------- Returns - aggregated pandas DF -------------------- Examples: # import dependecies import pandas as pd import numpy as np # create data frame data = {'age': [27, np.nan, 30, 25, np.nan], 'height': [170, 168, 173, 177, 165], 'gender': ['female', 'male', np.nan, 'male', 'female'], 'income': ['high', 'medium', 'low', 'low', 'no income']} df = pd.DataFrame(data) # aggregate the data from dptools import aggregate_data df_new = aggregate_data(df, group_var = 'gender', num_stats = ['min', 'max'], fac_stats = 'mode') ''' ##### SEPARATE FEATURES # display info print('- Preparing the dataset...') # find factors if factors == None: df_factors = [f for f in df.columns if df[f].dtype == 'object'] factors = [f for f in df_factors if f != group_var] else: df_factors = factors df_factors.append(group_var) # partition subsets if type(group_var) == str: num_df = df[[group_var] + list(set(df.columns) - set(df_factors))] fac_df = df[df_factors] else: num_df = df[group_var + list(set(df.columns) - set(df_factors))] fac_df = df[df_factors] # display info n_facs = fac_df.shape[1] - 1 n_nums = num_df.shape[1] - 1 print('- Extracted %.0f factors and %.0f numerics...' % (n_facs, n_nums)) ##### AGGREGATION # aggregate numerics if n_nums > 0: print('- Aggregating numeric features...') num_df = num_df.groupby([group_var]).agg(num_stats) num_df.columns = ['_'.join(col).strip() for col in num_df.columns.values] num_df = num_df.sort_index() # aggregate factors if n_facs > 0: print('- Aggregating factor features...') if (fac_stats == ['count', 'mode']) or (fac_stats == ['mode', 'count']): fac_df = fac_df.groupby([group_var]).agg([('count'), ('mode', lambda x: pd.Series.mode(x)[0])]) if (fac_stats == 'count') or (fac_stats == ['count']): fac_df = fac_df.groupby([group_var]).agg([('count')]) if (fac_stats == 'mode') or (fac_stats == ['mode']): fac_df = fac_df.groupby([group_var]).agg([('mode', lambda x:
pd.Series.mode(x)
pandas.Series.mode
from datetime import timedelta import pandas as pd # Specify start and periods, the number of periods (days). # dRan1 = pd.date_range(start='2017-01-01', periods=13, inclusive='right') def test_something(a, b): return {b, a} print(test_something(1, 1)) exit(1) for d in pd.date_range(start='2022-02-01', periods=10, inclusive='left'): original_date = pd.to_datetime(d).date() next_date =
pd.to_datetime(d)
pandas.to_datetime
from __future__ import division import numpy as np import pandas as pd import json from .util import * class Delta(): def __init__(self, df, key, baseline_run = False): ################################################################################################### ################################ basic time and key parameters #################################### ################################################################################################### self.baseline_run = baseline_run T = len(df) self.key = key self.demand_multiplier = df.demand_multiplier.values ########################################################################################################################## ################################ Gains, Old and Middle River, and San Joaquin options #################################### ########################################################################################################################## self.OMR_sim = df.OMR_sim.values self.netgains = df.gains_sim.values # self.sanjoaquin = self.netgains - df.YRS_fnf.values - df.NML_fnf.values self.sanjoaquin = df.sanjoaquin.values self.san_joaquin_ie_amt = df.san_joaquin_ie_amt.values ############################################################################################################# ################################ extract Delta properties from json file #################################### ############################################################################################################# for k,v in json.load(open('orca/data/json_files/Delta_properties.json')).items(): setattr(self,k,v) ############################################################################################ ################################ initialize time series #################################### ############################################################################################ self.dmin = np.zeros(T) self.min_rule = np.zeros(T) self.gains = np.zeros(T) self.sodd_cvp = np.zeros(T) self.sodd_swp = np.zeros(T) self.cvp_max = np.zeros(T) self.swp_max = np.zeros(T) self.TRP_pump = np.zeros(T) self.HRO_pump = np.zeros(T) self.inflow = np.zeros(T) self.outflow = np.zeros(T) self.CVP_shortage = np.zeros(T) self.SWP_shortage = np.zeros(T) self.SWP_shortage = np.zeros(T) self.Delta_shortage = np.zeros(T) # self.x2 = np.zeros(T+1) # self.x2[1] = 82.0 # self.x2[0] = 82.0 ######################################################################################################### ################################ initialize arrays for interpolation #################################### ######################################################################################################### self.cvp_targetO = np.zeros(367) self.swp_targetO = np.zeros(367) self.cvp_pmaxO = np.zeros(367) self.swp_pmaxO = np.zeros(367) self.swp_intake_maxO = np.zeros(367) self.cvp_intake_maxO = np.zeros(367) self.san_joaquin_adj = np.zeros(367) self.D1641_on_off = np.zeros(367) self.san_joaquin_ie_used = np.zeros(367) # self.san_joaquin_ie_amt = np.zeros(T) self.omr_reqr_int = np.zeros(367) ############################################################################################ ################################ interpolation to fill arrays ############################## ############################################################################################ # for i in range(0,T): # self.san_joaquin_ie_amt[i] = np.interp(self.sanjoaquin[i]*tafd_cfs, self.san_joaquin_export_ratio['D1641_flow_target'],self.san_joaquin_export_ratio['D1641_export_limit']) * cfs_tafd for i in range(0,365): self.san_joaquin_adj[i] = np.interp(water_day(i), self.san_joaquin_add['d'], self.san_joaquin_add['mult']) * max(self.sanjoaquin[i] - 1000.0 * cfs_tafd, 0.0) self.san_joaquin_ie_used[i] = np.interp(water_day(i), self.san_joaquin_export_ratio['d'], self.san_joaquin_export_ratio['on_off']) self.omr_reqr_int[i] = np.interp(water_day(i), self.omr_reqr['d'], self.omr_reqr['flow']) * cfs_tafd self.cvp_targetO[i] = np.interp(i, self.pump_max['cvp']['d'], #calculate pumping target for day of year (based on target pumping for sodd) self.pump_max['cvp']['target']) * cfs_tafd self.swp_targetO[i] = np.interp(i, self.pump_max['swp']['d'], self.pump_max['swp']['target']) * cfs_tafd self.cvp_pmaxO[i] = np.interp(i, self.pump_max['cvp']['d'], self.pump_max['cvp']['pmax']) * cfs_tafd #calculate pumping targets (based on max allowed pumping) based on time of year self.swp_pmaxO[i] = np.interp(i, self.pump_max['swp']['d'], self.pump_max['swp']['pmax']) * cfs_tafd self.swp_intake_maxO[i] = np.interp(i, self.pump_max['swp']['d'], self.pump_max['swp']['intake_limit']) * cfs_tafd self.cvp_intake_maxO[i] = np.interp(i, self.pump_max['cvp']['d'],self.pump_max['cvp']['intake_limit']) * cfs_tafd def find_release(self, dowy, d, t, wyt, orovilleAS, shastaAS, folsomAS): ################################################################################################################# ################################ San Joaquin river import/export ratio constraints ############################## ################################################################################################################# san_joaquin_ie = self.san_joaquin_ie_amt[t] * self.san_joaquin_ie_used[dowy] swp_jas_stor = (self.pump_max['swp']['pmax'][5] * cfs_tafd)/self.export_ratio[wyt][8] cvp_jas_stor = (self.pump_max['cvp']['pmax'][5] * cfs_tafd)/self.export_ratio[wyt][8] if dowy <= 274: numdaysSave = 92 else: numdaysSave = 1 if orovilleAS > numdaysSave*swp_jas_stor: swp_max = min(max(self.swp_intake_max[d] + self.san_joaquin_adj[d], san_joaquin_ie * 0.45), self.swp_pmax[d]) else: swp_max = 0.0 if (shastaAS + folsomAS) > numdaysSave*cvp_jas_stor: cvp_max = min(max(self.cvp_intake_max[d], san_joaquin_ie * 0.55), self.cvp_pmax[d]) else: cvp_max = 0.0 return cvp_max, swp_max def calc_flow_bounds(self, t, d, m, wyt, dowy, orovilleAS, shastaAS, folsomAS): ####################################################################################################################################################### ################################ Initial flow constraints based on Delta export ration constraints and reservoir storage ############################## ####################################################################################################################################################### gains = self.netgains[t] self.min_rule[t] = self.min_outflow[wyt][m-1] * cfs_tafd export_ratio = self.export_ratio[wyt][m-1] self.cvp_max[t] = self.cvp_target[d-1]*self.demand_multiplier[t] self.swp_max[t] = self.swp_target[d-1]*self.demand_multiplier[t] if d == 366: self.cvp_max[t] = self.cvp_target[d-2]*self.demand_multiplier[t] self.swp_max[t] = self.swp_target[d-2]*self.demand_multiplier[t] '''the sodd_* variables tell the reservoirs how much to release for south of delta demands only (dmin is the reservoir release needed to meet delta outflows)''' if gains > self.min_rule[t]: # extra unstored water available for pumping. in this case dmin[t] is 0 self.sodd_cvp[t] = max((self.cvp_max[t] - 0.55*(gains - self.min_rule[t])) / export_ratio, 0) #implementing export ratio "tax" self.sodd_swp[t] = max((self.swp_max[t] - 0.45*(gains - self.min_rule[t])) / export_ratio, 0) else: # additional flow needed self.dmin[t] = self.min_rule[t] - gains '''amount of additional flow from reservoirs that does not need "export tax" because dmin release helps to meet the export ratio requirement''' Q = self.min_rule[t]*export_ratio/(1-export_ratio) if self.cvp_max[t] + self.swp_max[t] < Q: self.sodd_cvp[t] = self.cvp_max[t] self.sodd_swp[t] = self.swp_max[t] else: self.sodd_cvp[t] = 0.75*Q + (self.cvp_max[t] - 0.75*Q)/export_ratio #implementing export ratio "tax" self.sodd_swp[t] = 0.25*Q + (self.swp_max[t] - 0.25*Q)/export_ratio #determining percentage of CVP sodd demands from both Shasta and Folsom if folsomAS > 0.0 and shastaAS > 0.0: self.folsomSODDPCT = folsomAS/(folsomAS + shastaAS) elif folsomAS < 0.0: self.folsomSODDPCT = 0.0 else: self.folsomSODDPCT = 1.0 self.shastaSODDPCT = 1.0 - self.folsomSODDPCT def meet_OMR_requirement(self, Tracy, Banks, t): #old and middle river requirements (hence "OMR") ################################################################################################# ################################ Old and Middle river requirements ############################## ################################################################################################# if Tracy + Banks > self.maxTotPump: '''maxTotPump is calculated in calc_weekly_storage, before this OMR function is called. current simulated puming is more that the total allowed pumping based on Delta requirements Tracy (CVP) is allocated 55% of available flow for pumping, Banks (SWP) is allocated 45%. (assuming Delta outflow is greater than it's requirement- I still need to look into where that's determined)''' #Tracy is pumping less that it's maximum allocated flow. Harvery should pump less flow now. if Tracy < self.maxTotPump*0.55: Banks = self.maxTotPump - Tracy elif Banks < self.maxTotPump*0.45: #Banks is pumping less that it's maximum allocated flow. Tracy should pump less flow now. Tracy = self.maxTotPump - Banks '''in this case, both pumps would be taking their allocated percentage of flow, but the overall flow through the pumps is still greater than the maximum allowed''' else: Banks = self.maxTotPump*0.45 Tracy= self.maxTotPump*0.55 return Tracy, Banks def step_init(self, t, d, m, wyt, dowy, cvp_flows, swp_flows, orovilleAS, shastaAS, folsomAS): ################################################################################################## ################################ initial stimulation step at time t ############################## ################################################################################################## self.gains[t] = self.netgains[t] #+ sumnodds self.inflow[t] = max(self.gains[t] + cvp_flows + swp_flows, 0) # realinflow * cfs_tafd self.outflow_rule = self.min_outflow[wyt][m-1] * cfs_tafd self.min_rule[t] = max(self.outflow_rule, 0) export_ratio = self.export_ratio[wyt][m-1] self.cvp_max[t] = self.cvp_pmax[d-1] #max pumping allowed self.swp_max[t] = self.swp_pmax[d-1] omrNat = self.OMR_sim[t]* cfs_tafd maxTotPumpInt = omrNat - self.omr_reqr_int[dowy] #- fish_trigger_adj self.maxTotPump = max(maxTotPumpInt,0.0) self.cvp_max[t], self.swp_max[t] = self.find_release(dowy, d, t, wyt, orovilleAS, shastaAS, folsomAS) self.cvp_max[t], self.swp_max[t] = self.meet_OMR_requirement(self.cvp_max[t], self.swp_max[t], t) self.required_outflow = max(self.min_rule[t], (1-export_ratio)*self.inflow[t]) self.surplus = self.gains[t] - self.required_outflow return self.surplus def step_pump(self, t, d, m, wyt, dowy, cvp_flows, swp_flows,surplus): ################################################################################################## ################################ second stimulation step at time t ############################## ################################################################################################## if surplus >= 0: #gains cover both the min_rule and the export ratio requirement.so, pump the full cvp/swp inflows self.TRP_pump[t] = max(min(cvp_flows + 0.55 * surplus, self.cvp_max[t]),0) #Tracy pumping plant, for CVP exports self.HRO_pump[t] = max(min(swp_flows + 0.45 * surplus, self.swp_max[t]),0) #Harvey 0. Banks pumping plant, for SWP exports else: '''deficit must be made up from cvp/swp flows. Assume 75/25 responsibility for these (including meeting the export ratio requirement)''' deficit = -surplus cvp_pump = max(cvp_flows - 0.75 * deficit, 0) if cvp_pump == 0: swp_pump = max(swp_flows - (deficit - cvp_flows), 0) else: swp_pump = max(swp_flows - 0.25 * deficit, 0) self.TRP_pump[t] = max(min(cvp_pump, self.cvp_max[t]),0) #overall TRP pumping self.HRO_pump[t] = max(min(swp_pump, self.swp_max[t]),0) #overall HRO pumping if d >= 365: self.TRP_pump[t] = self.TRP_pump[t-1] self.HRO_pump[t] = self.HRO_pump[t-1] self.outflow[t] = self.inflow[t] - self.TRP_pump[t] - self.HRO_pump[t] self.CVP_shortage[t] = max(self.cvp_max[t] - self.TRP_pump[t],0) self.SWP_shortage[t] = max(self.swp_max[t] - self.HRO_pump[t],0) self.Delta_shortage[t] = max(self.min_rule[t] -self.outflow[t],0) def results_as_df(self, index): ########################################################################################## ################################ for generating output file ############################## ########################################################################################## df = pd.DataFrame() if self.baseline_run == False: names = ['SODD_CVP','SODD_SWP','SWP_shortage', 'CVP_shortage'] things = [self.cvp_max,self.swp_max,self.SWP_shortage, self.CVP_shortage] for n,t in zip(names,things): df['%s_%s' % (self.key,n)] =
pd.Series(t, index=index)
pandas.Series
# # # # # # # # # # # # # # # # # # # # # # # # # # # Module to run real time contingencies # # By: <NAME> and <NAME> # # 09-08-2018 # # Version Aplha-0. 1 # # # # Module inputs: # # -> File name # # # # # # # # # # # # # # # # # # # # # # # # # import pandapower as pp import pandas as pd import json import copy import calendar from time import time import datetime from inspyred import ec import inspyred import math from random import Random # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def Disconet_Asset(net,Asset_type,Asset_to_disc, Service=False): net_lf = copy.deepcopy(net) if Asset_type=='GEN': # Disconnect Generators index = net_lf.sgen.loc[net_lf.sgen['name'] == Asset_to_disc].index[0] net_lf.sgen.in_service[index] = Service elif Asset_type=='TR': # Disconnect Transformers index = net_lf.trafo.loc[net_lf.trafo['name'] == Asset_to_disc].index[0] net_lf.trafo.in_service[index] = Service elif Asset_type=='LN': # Disconnect Lines index = net_lf.line.loc[net_lf.line['name'] == Asset_to_disc].index[0] net_lf.line.in_service[index] = Service elif Asset_type=='SW': index = net_lf.switch.loc[net.switch['name'] == Asset_to_disc].index[0] net_lf.switch.closed[index] = not Service elif Asset_type=='LO': index = net_lf.load.loc[net.load['name'] == Asset_to_disc].index[0] net_lf.load.in_service[index] = Service elif Asset_type=='BUS': index = net_lf.bus.loc[net.bus['name'] == Asset_to_disc].index[0] net_lf.bus.in_service[index] = Service elif Asset_type=='ST': index = net_lf.storage.loc[net.storage['name'] == Asset_to_disc].index[0] net_lf.storage.in_service[index] = Service else: print('Asset to disconnet does not exist') return net_lf # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def Network_Reconfiguration(net,strategy): net_lf = copy.deepcopy(net) for step in strategy: l_sequence = strategy[step] asset_type = l_sequence['Element_Type'] asset_to_disc = l_sequence['Element_Name'] net_lf = Disconet_Asset(net_lf,asset_type,asset_to_disc) return net_lf # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def Load_Contingency_Strategies(File): with open(File) as json_file: data = json.load(json_file) return data # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def Load_AM_Plan(File): data = Load_Contingency_Strategies(File) #with open(File) as json_file: # data = json.load(json_file) df = pd.DataFrame.from_dict(data, orient='index') df['Date'] = pd.to_datetime(df['Date'])#pd.to_datetime(df['Date']) return df # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Funtion to return the daily load growth def Load_Growth_By_Day(L_growth): daily_growth = pow(1+L_growth, 1/365)-1 # Daily growth rate def f_Load_Daily_Growth(ndays): # Daily growth rate fuction return pow(1+daily_growth,ndays) return f_Load_Daily_Growth # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Risk assessment def Power_Risk_assessment(net,secure=1): assessment = {} load = net.res_load['p_mw'].fillna(0)*secure load_base = net.load['p_mw']*net.load.scaling assessment['Load'] = pd.DataFrame( {'name':net.load.name, 'ENS':load_base - load, 'ES': load}) assessment['T_ES'] = load.sum() assessment['T_ENS'] = load_base.sum()-load.sum() gen_name = pd.concat([net.sgen.name, net.storage.name,net.ext_grid.name], ignore_index=True) p_gen = pd.concat([net.res_sgen.p_mw, net.res_storage.p_mw,net.res_ext_grid.p_mw], ignore_index=True) p_gen = p_gen.fillna(0)*secure net.res_sgen['Type'] = 'D_Gen' net.res_storage['Type'] = 'Storage' net.res_ext_grid['Type'] = 'External' p_source = pd.concat([net.res_sgen.Type, net.res_storage.Type,net.res_ext_grid.Type], ignore_index=True) assessment['Gen'] = pd.DataFrame( {'name':gen_name, 'source': p_source, 'gen':p_gen}) assessment['purchased_E'] = secure*net.res_ext_grid['p_mw'].values[0] # Delta of energy suplied p_gen_base =
pd.concat([net.sgen.p_mw, net.storage.p_mw], ignore_index=True)
pandas.concat
import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize import numpy as np import pandas as pd train = pd.read_csv('./input/train.csv').fillna(' ') i=1 filltered_comment_list = [] for comment in train.comment_text: filtered_words = [w for w in word_tokenize(comment) if not w.lower() in stopwords.words('english')] sentence = ' '.join(word for word in filtered_words) filltered_comment_list.append(sentence) i = i + 1 print(i) output =
pd.DataFrame(columns=['comment_text'])
pandas.DataFrame
import numpy as np import pandas as pd import pytest import ray from ray.ml.preprocessor import PreprocessorNotFittedException from ray.ml.preprocessors import ( StandardScaler, MinMaxScaler, OrdinalEncoder, OneHotEncoder, LabelEncoder, SimpleImputer, Chain, ) def test_standard_scaler(): """Tests basic StandardScaler functionality.""" col_a = [-1, 0, 1, 2] col_b = [1, 1, 5, 5] col_c = [1, 1, 1, None] in_df = pd.DataFrame.from_dict({"A": col_a, "B": col_b, "C": col_c}) ds = ray.data.from_pandas(in_df) scaler = StandardScaler(["B", "C"]) # Transform with unfitted preprocessor. with pytest.raises(PreprocessorNotFittedException): scaler.transform(ds) # Fit data. scaler.fit(ds) assert scaler.stats_ == { "mean(B)": 3.0, "mean(C)": 1.0, "std(B)": 2.0, "std(C)": 0.0, } # Transform data. transformed = scaler.transform(ds) out_df = transformed.to_pandas() processed_col_a = col_a processed_col_b = [-1.0, -1.0, 1.0, 1.0] processed_col_c = [0.0, 0.0, 0.0, None] expected_df = pd.DataFrame.from_dict( {"A": processed_col_a, "B": processed_col_b, "C": processed_col_c} ) assert out_df.equals(expected_df) # Transform batch. pred_col_a = [1, 2, 3] pred_col_b = [3, 5, 7] pred_col_c = [0, 1, 2] pred_in_df = pd.DataFrame.from_dict( {"A": pred_col_a, "B": pred_col_b, "C": pred_col_c} ) pred_out_df = scaler.transform_batch(pred_in_df) pred_processed_col_a = pred_col_a pred_processed_col_b = [0.0, 1.0, 2.0] pred_processed_col_c = [-1.0, 0.0, 1.0] pred_expected_df = pd.DataFrame.from_dict( { "A": pred_processed_col_a, "B": pred_processed_col_b, "C": pred_processed_col_c, } ) assert pred_out_df.equals(pred_expected_df) def test_min_max_scaler(): """Tests basic MinMaxScaler functionality.""" col_a = [-1, 0, 1] col_b = [1, 3, 5] col_c = [1, 1, None] in_df =
pd.DataFrame.from_dict({"A": col_a, "B": col_b, "C": col_c})
pandas.DataFrame.from_dict
import pandas as pd import numpy as np import matplotlib.pylab as plt from matplotlib.pylab import rcParams from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.stattools import acf, pacf from statsmodels.tsa.arima_model import ARIMA def test_stationarity(timeseries,title,figure_nb): # 决定起伏统计 rolmean = timeseries.rolling(window=24).mean() # 对size个数据进行移动平均 #rol_weighted_mean = pd.ewma(timeseries, span=12) # 对size个数据进行加权移动平均 rolstd = timeseries.rolling(window=24).std() # 偏离原始值多少 # 画出起伏统计 plt.figure(figure_nb) orig = plt.plot(timeseries, color='blue', label='Original') mean = plt.plot(rolmean, color='red', label='Rolling Mean') #weighted_mean = plt.plot(rol_weighted_mean, color='green', label='weighted Mean') std = plt.plot(rolstd, color='black', label='Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean & Standard Deviation') plt.xticks(rotation=20) plt.savefig('./Rolling mean std'+title+'.jpg') # 进行df测试 print ('Result of Dickry-Fuller test') dftest = adfuller(timeseries, autolag='AIC') dfoutput = pd.Series(dftest[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of observations Used']) for key, value in dftest[4].items(): dfoutput['Critical value(%s)' % key] = value dfoutput.to_csv('result.csv',mode='a+') print (dfoutput) # 分解decomposing def decomp(ts): decomposition = seasonal_decompose(ts) trend = decomposition.trend # 趋势 seasonal = decomposition.seasonal # 季节性 residual = decomposition.resid # 剩余的 plt.figure(4) plt.subplot(411) plt.title('Decomposition') plt.plot(ts,label='Original') plt.legend(loc=1); plt.xticks(rotation=20) plt.subplot(412) plt.plot(trend,label='Trend') plt.legend(loc=1); plt.xticks(rotation=20) plt.subplot(413) plt.plot(seasonal,label='Seasonarity') plt.legend(loc=1); plt.xticks(rotation=20) plt.subplot(414) plt.plot(residual,label='Residual') plt.legend(loc=1); plt.xticks(rotation=20) plt.tight_layout() plt.savefig('decompo.jpg') def acf_pacf(ts): # 确定参数 lag_acf = acf(ts, nlags=20) lag_pacf = pacf(ts, nlags=20) # q的获取:ACF图中曲线第一次穿过上置信区间.这里q取2 plt.figure(5) plt.subplot(121) plt.plot(lag_acf) plt.axhline(y=0, linestyle='--', color='gray') plt.axhline(y=-1.96 / np.sqrt(len(ts)), linestyle='--', color='gray') # lowwer置信区间 plt.axhline(y=1.96 / np.sqrt(len(ts)), linestyle='--', color='gray') # upper置信区间 plt.title('Autocorrelation Function') # p的获取:PACF图中曲线第一次穿过上置信区间.这里p取2 plt.subplot(122) plt.plot(lag_pacf) plt.axhline(y=0, linestyle='--', color='gray') plt.axhline(y=-1.96 / np.sqrt(len(ts)), linestyle='--', color='gray') plt.axhline(y=1.96 / np.sqrt(len(ts)), linestyle='--', color='gray') plt.title('Partial Autocorrelation Function') plt.tight_layout() plt.savefig('ACF & PACF') def arma_models(ts): model = ARIMA(ts_log, order=(2, 1, 0)) result_AR = model.fit(disp=-1) #plt.figure(6) #plt.plot(ts) #plt.plot(result_AR.fittedvalues, color='red') #plt.title('AR model RSS:%.4f' % sum(result_AR.fittedvalues - ts) ** 2) model = ARIMA(ts_log, order=(0, 1, 5)) result_MA = model.fit(disp=-1) #plt.figure(7) #plt.plot(ts) #plt.plot(result_MA.fittedvalues, color='red') #plt.title('MA model RSS:%.4f' % sum(result_MA.fittedvalues - ts) ** 2) #model = ARIMA(ts_log, order=(2, 1, 3)) #result_ARIMA = model.fit() #plt.figure(8) #plt.plot(ts) #plt.plot(result_ARIMA.fittedvalues, color='red') #plt.title('ARIMA RSS:%.4f' % sum(result_ARIMA.fittedvalues - ts) ** 2) return result_AR, result_MA def predict_insample(model_result,figure_nb): predictions_diff = pd.Series(model_result.fittedvalues, copy=True) # print(predictions_ARIMA_diff.head())#发现数据是没有第一行的,因为有1的延迟 predictions_diff_cumsum = predictions_diff.cumsum() # print (predictions_ARIMA_diff_cumsum.head()) predictions_log = pd.Series(ts_log.ix[0], index=ts_log.index) predictions_log = predictions_log.add(predictions_diff_cumsum, fill_value=0) # print predictions_ARIMA_log.head() predictions = np.exp(predictions_log) plt.figure(figure_nb) plt.plot(ts,label='origin') plt.plot(predictions,label='prediction') plt.xticks(rotation=20) plt.legend(loc=0) plt.title('predictions_ARIMA RMSE: %.4f' % np.sqrt(sum((predictions - ts) ** 2) / len(ts))) def predict_future(result_model,start_val): predict_diff = result_model.predict('2017-9-16 00:00:00','2017-9-23 23:00:00') predict_diff_cumsum =predict_diff.cumsum() predict_log=pd.Series(start_val,index=predict_diff.index) predict_log=predict_log.add(predict_diff_cumsum,fill_value=0) predict = np.exp(predict_log) plt.figure(11) plt.plot(ts,color='blue') plt.plot(data0.loc[
pd.Timestamp(2017,9,16)
pandas.Timestamp