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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import random | |
| import sys | |
| import openpyxl | |
| import re | |
| import time | |
| import streamlit as st | |
| import matplotlib | |
| from matplotlib.colors import LinearSegmentedColormap | |
| import json | |
| import requests | |
| import gspread | |
| import plotly.figure_factory as ff | |
| scope = ['https://www.googleapis.com/auth/spreadsheets', | |
| "https://www.googleapis.com/auth/drive"] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| gc = gspread.service_account_from_dict(credentials) | |
| st.set_page_config(layout="wide") | |
| dk_player_url = 'PGA_Basic_ROO' | |
| CSV_URL = 'https://sheetdb.io/api/v1/ckjq8yp37qxly?sheet=DK_CSV' | |
| def load_dk_player_model(URL): | |
| sh = gc.open(URL) | |
| worksheet = sh.get_worksheet(0) | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float) | |
| raw_display['Top_finish'] = raw_display['Top_finish'].str.replace('%', '').astype(float)/100 | |
| raw_display['Top_5_finish'] = raw_display['Top_5_finish'].str.replace('%', '').astype(float)/100 | |
| raw_display['Top_10_finish'] = raw_display['Top_10_finish'].str.replace('%', '').astype(float)/100 | |
| raw_display['100+%'] = raw_display['100+%'].str.replace('%', '').astype(float)/100 | |
| raw_display['10x%'] = raw_display['10x%'].str.replace('%', '').astype(float)/100 | |
| raw_display['11x%'] = raw_display['11x%'].str.replace('%', '').astype(float)/100 | |
| raw_display['12x%'] = raw_display['12x%'].str.replace('%', '').astype(float)/100 | |
| raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100 | |
| return raw_display | |
| def grab_csv_data(URL): | |
| draftkings_data = pd.read_json(URL) | |
| draftkings_data.rename(columns={"Name": "Player"}, inplace = True) | |
| return draftkings_data | |
| tab1, tab2 = st.tabs(["Player Overall Projections", "Optimizer"]) | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| lineup_display = [] | |
| check_list = [] | |
| rand_player = 0 | |
| boost_player = 0 | |
| salaryCut = 0 | |
| with tab1: | |
| if st.button("Reset Data", key='reset1'): | |
| # Clear values from *all* all in-memory and on-disk data caches: | |
| # i.e. clear values from both square and cube | |
| st.cache_data.clear() | |
| hold_display = load_dk_player_model(dk_player_url) | |
| csv_data = grab_csv_data(CSV_URL) | |
| csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player']) | |
| id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID'])) | |
| hold_container = st.empty() | |
| display = hold_display.set_index('Player') | |
| st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(display), | |
| file_name='PGA_DFS_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| col1, col2 = st.columns([1, 4]) | |
| with col1: | |
| max_sal = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100) | |
| min_sal = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100) | |
| proj_cut = st.number_input('Lowest median allowed', min_value = 0, max_value = 100, value = 50, step = 1) | |
| slack_var = st.number_input('Median randomness', min_value = 0, max_value = 5, value = 0, step = 1) | |
| totalRuns_raw = st.number_input('How many Lineups', min_value = 1, max_value = 1000, value = 5, step = 1) | |
| totalRuns = totalRuns_raw | |
| cut_group_1 = [] | |
| cut_group_2 = [] | |
| force_group_1 = [] | |
| force_group_2 = [] | |
| avoid_players = [] | |
| lock_player = [] | |
| lineups = [] | |
| player_pool_raw = [] | |
| player_pool = [] | |
| player_count = [] | |
| player_trim_pool = [] | |
| portfolio = pd.DataFrame() | |
| x = 1 | |
| if st.button('Optimize'): | |
| max_proj = 1000 | |
| max_own = 1000 | |
| total_proj = 0 | |
| total_own = 0 | |
| with col2: | |
| with st.spinner('Wait for it...'): | |
| with hold_container.container(): | |
| while x <= totalRuns: | |
| raw_proj_file = hold_display | |
| raw_flex_file = raw_proj_file.dropna(how='all') | |
| raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] | |
| raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > proj_cut] | |
| flex_file = raw_flex_file | |
| flex_file = flex_file[['Player', 'Salary', 'Median', 'Own', 'LevX']] | |
| flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) | |
| flex_file['name_var'] = flex_file['Player'] | |
| flex_file['lock'] = flex_file['Player'].isin(lock_player)*1 | |
| flex_file['force_group_1'] = flex_file['Player'].isin(force_group_1)*1 | |
| flex_file['force_group_2'] = flex_file['Player'].isin(force_group_2)*1 | |
| flex_file['cut_group_1'] = flex_file['Player'].isin(cut_group_1)*1 | |
| flex_file['cut_group_2'] = flex_file['Player'].isin(cut_group_2)*1 | |
| chalk_file = flex_file.sort_values(by='Proj DK Own%', ascending=False) | |
| chalk_group_df = chalk_file.sample(n=10) | |
| chalk_group = chalk_group_df['Player'].tolist() | |
| flex_file['chalk_group'] = flex_file['Player'].isin(chalk_group)*1 | |
| flex_file['Pos'] = 'G' | |
| flex_file = flex_file[['Player', 'name_var', 'Pos', 'Salary', 'Median', 'Proj DK Own%', 'lock', 'force_group_1', 'force_group_2', 'cut_group_1', 'cut_group_2', 'chalk_group', 'LevX']] | |
| if x > 1: | |
| if slack_var > 0: | |
| flex_file['randNumCol'] = np.random.randint(-int(slack_var),int(slack_var), flex_file.shape[0]) | |
| elif slack_var ==0: | |
| flex_file['randNumCol'] = 0 | |
| elif x == 1: | |
| flex_file['randNumCol'] = 0 | |
| flex_file['Median'] = flex_file['Median'] + flex_file['randNumCol'] | |
| flex_file_check = flex_file | |
| check_list.append(flex_file['Median'][4]) | |
| player_ids = flex_file.index | |
| overall_players = flex_file[['Player']] | |
| overall_players['player_var_add'] = flex_file.index | |
| overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) | |
| player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) | |
| total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) | |
| player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) | |
| player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) | |
| player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) | |
| player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) | |
| player_lev = dict(zip(flex_file['Player'], flex_file['LevX'])) | |
| player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) | |
| obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} | |
| total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) | |
| obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} | |
| obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} | |
| obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} | |
| total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal | |
| total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal | |
| for flex in flex_file['Pos'].unique(): | |
| sub_idx = flex_file[flex_file['Pos'] != "Var"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 6 | |
| player_count = [] | |
| player_trim = [] | |
| lineup_list = [] | |
| total_score += pulp.lpSum([player_vars[idx]*obj_points_max[idx] for idx in flex_file.index]) <= max_proj - .01 | |
| total_score.solve() | |
| for v in total_score.variables(): | |
| if v.varValue > 0: | |
| lineup_list.append(v.name) | |
| df = pd.DataFrame(lineup_list) | |
| df['Names'] = df[0].map(player_match) | |
| df['Cost'] = df['Names'].map(player_sal) | |
| df['Proj'] = df['Names'].map(player_proj) | |
| df['Own'] = df['Names'].map(player_own) | |
| total_cost = sum(df['Cost']) | |
| total_own = sum(df['Own']) | |
| total_proj = sum(df['Proj']) | |
| lineup_raw = pd.DataFrame(lineup_list) | |
| lineup_raw['Names'] = lineup_raw[0].map(player_match) | |
| lineup_raw['value'] = lineup_raw[0].map(player_index_match) | |
| lineup_final = lineup_raw.sort_values(by=['value']) | |
| del lineup_final[lineup_final.columns[0]] | |
| del lineup_final[lineup_final.columns[1]] | |
| lineup_final = lineup_final.reset_index(drop=True) | |
| lineup_test = lineup_final | |
| lineup_final = lineup_final.T | |
| lineup_final['Cost'] = total_cost | |
| lineup_final['Proj'] = total_proj | |
| lineup_final['Own'] = total_own | |
| if total_cost < 50001: | |
| lineups.append(lineup_final) | |
| lineup_test['Salary'] = lineup_test['Names'].map(player_sal) | |
| lineup_test['Proj'] = lineup_test['Names'].map(player_proj) | |
| lineup_test['Own'] = lineup_test['Names'].map(player_own) | |
| lineup_test['LevX'] = lineup_test['Names'].map(player_lev) | |
| lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) | |
| lineup_display.append(lineup_test) | |
| with col2: | |
| with st.container(): | |
| st.table(lineup_test) | |
| max_proj = total_proj | |
| max_own = total_own | |
| check_list.append(total_proj) | |
| portfolio = portfolio.append(lineup_final, ignore_index = True) | |
| x += 1 | |
| portfolio.rename(columns={0: "Player_1", 1: "Player_2", 2: "Player_3", 3: "Player_4", 4: "Player_5", 5: "Player_6"}, inplace = True) | |
| portfolio = portfolio.dropna() | |
| final_outcomes = portfolio | |
| final_outcomes['p1 id'] = final_outcomes['Player_1'].map(id_dict) | |
| final_outcomes['p2 id'] = final_outcomes['Player_2'].map(id_dict) | |
| final_outcomes['p3 id'] = final_outcomes['Player_3'].map(id_dict) | |
| final_outcomes['p4 id'] = final_outcomes['Player_4'].map(id_dict) | |
| final_outcomes['p5 id'] = final_outcomes['Player_5'].map(id_dict) | |
| final_outcomes['p6 id'] = final_outcomes['Player_6'].map(id_dict) | |
| final_outcomes = final_outcomes[['p1 id', 'p2 id', 'p3 id', 'p4 id', 'p5 id', 'p6 id']] | |
| with col1: | |
| st.download_button( | |
| label="Export Lineups", | |
| data=convert_df_to_csv(final_outcomes), | |
| file_name='PGA_DFS_export.csv', | |
| mime='text/csv', | |
| ) | |
| with hold_container: | |
| hold_container = st.empty() |