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import sys
import os
import yaml
import random
from typing import Any, BinaryIO, Callable, Dict, List, Optional, Sequence, Tuple, Union
from pathlib import Path
import numpy as np
from scipy import stats
import pandas as pd
import darts
from darts import models
from darts import metrics
from darts import TimeSeries
from darts.dataprocessing.transformers import Scaler
from pytorch_lightning.callbacks import Callback
from sympy import pprint
# import data formatter
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from data_formatter.base import *
pd.set_option('display.width', None) # Set display width to None to avoid truncation
pd.set_option('display.max_columns', None) # Display all columns
def make_series(data: Dict[str, pd.DataFrame],
time_col: str,
group_col: str,
value_cols: Dict[str, List[str]],
include_sid: bool = False,
verbose: bool = False
) -> Dict[str, darts.TimeSeries]:
"""
Makes the TimeSeries from the data.
Parameters
----------
data
dict of train, val, test dataframes
time_col
name of time column
group_col
name of group column
value_cols
dict with key specifying the type of covariate and value specifying the list of columns.
include_sid
whether to include segment id as static covariate
Returns
-------
series: Dict[str, Dict[str, darts.TimeSeries]]
dict of train, val, test splits of target and covariates TimeSeries objects
scalers: Dict[str, darts.preprocessing.Scaler]
dict of scalers for target and covariates
"""
series = {i: {j: None for j in value_cols} for i in data.keys()}
scalers = {}
for key, df in data.items():
for name, cols in value_cols.items():
# Adjust display settings
if verbose:
print(f"DATAFRAME for key {key} in NAME {name} and COLS {cols} and GROUP_COL {group_col}")
pprint(df.head(1))
series[key][name] = TimeSeries.from_group_dataframe(df = df,
group_cols = group_col,
time_col = time_col,
value_cols = cols) if cols is not None else None
if series[key][name] is not None and include_sid is False:
for i in range(len(series[key][name])):
series[key][name][i] = series[key][name][i].with_static_covariates(None)
if cols is not None:
if key == 'train':
scalers[name] = ScalerCustom()
series[key][name] = scalers[name].fit_transform(series[key][name])
else:
series[key][name] = scalers[name].transform(series[key][name])
else:
scalers[name] = None
return series, scalers
def load_data(url: str,
config_path: Path,
#df: pd.DataFrame,
use_covs: bool = False,
cov_type: str = 'past',
use_static_covs: bool = False, seed = 0):
"""
Load data according to the specified config file and covert to Darts TimeSeries objects.
Parameters
----------
seed: int
Random seed for data splitting.
study_file: str
Path to the study file.
dataset: str
Name of the dataset.
use_covs: bool
Whether to use covariates.
cov_type: str
Type of covariates to use. Can be 'past' or 'mixed' or 'dual'.
use_static_covs: bool
Whether to use static covariates.
Returns
-------
formatter: DataFormatter
Data formatter object.
series: Dict[str, Dict[str, TimeSeries]]
First dictionary specified the split, second dictionary specifies the type of series (target or covariate).
scalers: Dict[str, Scaler]
Dictionary of scalers with key indicating the type of series (target or covariate).
"""
"""
config={
'data_csv_path':f'{url}',
'drop': None,
'ds_name': 'livia_mini',
'index_col': -1,
'observation_interval': '5min',
'column_definition': {
{'data_type': 'categorical',
'input_type':'id',
'name':'id'
},
{'date_type':'date',
'input_type':'time',
'name':'time'
},
{'date_type':'real_valued',
'input_type':'target',
'name':'gl'
}
},
'encoding_params':{'date':['day','month','year','hour','minute','second']
},
'nan_vals':None,
'interpolation_params':{'gap_threshold': 45,
'min_drop_length': 240
},
'scaling_params':{'scaler':None
},
'split_params':{'length_segment': 13,
'random_state':seed,
'test_percent_subjects': 0.1
},
'max_length_input': 192,
'length_pred': 12,
'params':{
'gluformer':{'in_len': 96,
'd_model': 512,
'n_heads': 10,
'd_fcn': 1024,
'num_enc_layers': 2,
'num_dec_layers': 2,
'length_pred': 12
}
}
}
"""
with config_path.open("r") as f:
config = yaml.safe_load(f)
config["data_csv_path"] = url
formatter = DataFormatter(config
#,df
)
assert use_covs is not None, 'use_covs must be specified in the load_data call'
# convert to series
time_col = formatter.get_column('time')
group_col = formatter.get_column('sid')
target_col = formatter.get_column('target')
static_cols = formatter.get_column('static_covs')
static_cols = static_cols + [formatter.get_column('id')] if static_cols is not None else [formatter.get_column('id')]
dynamic_cols = formatter.get_column('dynamic_covs')
future_cols = formatter.get_column('future_covs')
data = {'train': formatter.train_data,
'val': formatter.val_data,
'test': formatter.test_data.loc[~formatter.test_data.index.isin(formatter.test_idx_ood)],
'test_ood': formatter.test_data.loc[formatter.test_data.index.isin(formatter.test_idx_ood)]}
value_cols = {'target': target_col,
'static': static_cols,
'dynamic': dynamic_cols,
'future': future_cols}
# build series
series, scalers = make_series(data,
time_col,
group_col,
value_cols)
if not use_covs:
# set dynamic and future covariates to None
for split in ['train', 'val', 'test', 'test_ood']:
for cov in ['dynamic', 'future']:
series[split][cov] = None
elif use_covs and cov_type == 'mixed':
pass # this is the default for make_series()
elif use_covs and cov_type == 'past':
# use future covariates as dynamic (past) covariates
if series['train']['dynamic'] is None:
for split in ['train', 'val', 'test', 'test_ood']:
series[split]['dynamic'] = series[split]['future']
else:
for split in ['train', 'val', 'test', 'test_ood']:
for i in range(len(series[split]['future'])):
series[split]['dynamic'][i] = series[split]['dynamic'][i].concatenate(series[split]['future'][i], axis=1)
# erase future covariates
for split in ['train', 'val', 'test', 'test_ood']:
series[split]['future'] = None
elif use_covs and cov_type == 'dual':
# erase dynamic (past) covariates
for split in ['train', 'val', 'test', 'test_ood']:
series[split]['dynamic'] = None
if use_static_covs:
# attach static covariates to series
for split in ['train', 'val', 'test', 'test_ood']:
for i in range(len(series[split]['target'])):
static_covs = series[split]['static'][i][0].pd_dataframe()
series[split]['target'][i] = series[split]['target'][i].with_static_covariates(static_covs)
return formatter, series, scalers
def reshuffle_data(formatter: DataFormatter,
seed: int = 0,
use_covs: bool = None,
cov_type: str = 'past',
use_static_covs: bool = False,):
"""
Reshuffle data according to the seed and covert to Darts TimeSeries objects.
Parameters
----------
formatter: DataFormatter
Data formatter object containing the data
seed: int
Random seed for data splitting.
use_covs: bool
Whether to use covariates.
cov_type: str
Type of covariates to use. Can be 'past' or 'mixed' or 'dual'.
use_static_covs: bool
Whether to use static covariates.
Returns
-------
formatter: DataFormatter
Reshuffled data formatter object.
series: Dict[str, Dict[str, TimeSeries]]
First dictionary specified the split, second dictionary specifies the type of series (target or covariate).
scalers: Dict[str, Scaler]
Dictionary of scalers with key indicating the type of series (target or covariate).
"""
# reshuffle
formatter.reshuffle(seed)
assert use_covs is not None, 'use_covs must be specified in the reshuffle_data call'
# convert to series
time_col = formatter.get_column('time')
group_col = formatter.get_column('sid')
target_col = formatter.get_column('target')
static_cols = formatter.get_column('static_covs')
static_cols = static_cols + [formatter.get_column('id')] if static_cols is not None else [formatter.get_column('id')]
dynamic_cols = formatter.get_column('dynamic_covs')
future_cols = formatter.get_column('future_covs')
# build series
series, scalers = make_series({'train': formatter.train_data,
'val': formatter.val_data,
'test': formatter.test_data.loc[~formatter.test_data.index.isin(formatter.test_idx_ood)],
'test_ood': formatter.test_data.loc[formatter.test_data.index.isin(formatter.test_idx_ood)]},
time_col,
group_col,
{'target': target_col,
'static': static_cols,
'dynamic': dynamic_cols,
'future': future_cols})
if not use_covs:
# set dynamic and future covariates to None
for split in ['train', 'val', 'test', 'test_ood']:
for cov in ['dynamic', 'future']:
series[split][cov] = None
elif use_covs and cov_type == 'past':
# use future covariates as dynamic covariates
if series['train']['dynamic'] is None:
for split in ['train', 'val', 'test', 'test_ood']:
series[split]['dynamic'] = series[split]['future']
# or attach them to dynamic covariates
else:
for split in ['train', 'val', 'test', 'test_ood']:
for i in range(len(series[split]['future'])):
series[split]['dynamic'][i] = series[split]['dynamic'][i].concatenate(series[split]['future'][i], axis=1)
elif use_covs and cov_type == 'dual':
# set dynamic covariates to None, because they are not supported
for split in ['train', 'val', 'test', 'test_ood']:
series[split]['dynamic'] = None
if use_static_covs:
# attach static covariates to series
for split in ['train', 'val', 'test', 'test_ood']:
for i in range(len(series[split]['target'])):
static_covs = series[split]['static'][i][0].pd_dataframe()
series[split]['target'][i] = series[split]['target'][i].with_static_covariates(static_covs)
return formatter, series, scalers
class ScalerCustom:
'''
Min-max scaler for TimeSeries that fits on all sequences simultaenously.
Default Darts scaler fits one scaler per sequence in the list.
Attributes
----------
scaler: Scaler
Darts scaler object.
min_: np.ndarray
Per feature adjustment for minimum (see Scikit-learn).
scale_: np.ndarray
Per feature relative scaling of the data (see Scikit-learn).
'''
def __init__(self):
self.scaler = Scaler()
self.min_ = None
self.scale_ = None
def fit(self, time_series: Union[List[TimeSeries], TimeSeries]) -> None:
if isinstance(time_series, list):
# extract series as Pandas dataframe
df = pd.concat([ts.pd_dataframe() for ts in time_series])
value_cols = df.columns
df.reset_index(inplace=True)
# create new equally spaced time grid
df['new_time'] = pd.date_range(start=df['time'].min(), periods=len(df), freq='1h')
# fit scaler
series = TimeSeries.from_dataframe(df, time_col='new_time', value_cols=value_cols)
series = self.scaler.fit(series)
else:
series = self.scaler.fit(time_series)
# extract min and scale
self.min_ = self.scaler._fitted_params[0].min_
self.scale_ = self.scaler._fitted_params[0].scale_
def transform(self, time_series: Union[List[TimeSeries], TimeSeries]) -> Union[List[TimeSeries], TimeSeries]:
if isinstance(time_series, list):
# transform one by one
series = [self.scaler.transform(ts) for ts in time_series]
else:
series = self.scaler.transform(time_series)
return series
def inverse_transform(self, time_series: Union[List[TimeSeries], TimeSeries]) -> Union[List[TimeSeries], TimeSeries]:
if isinstance(time_series, list):
# transform one by one
series = [self.scaler.inverse_transform(ts) for ts in time_series]
else:
series = self.scaler.inverse_transform(time_series)
return series
def fit_transform(self, time_series: Union[List[TimeSeries], TimeSeries]) -> Union[List[TimeSeries], TimeSeries]:
self.fit(time_series)
series = self.transform(time_series)
return series |