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run_experiments_lear_lars.py
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import pandas as pd
import numpy as np
import datetime as dt
import pickle
from copy import copy
import os
from adaptive_standardisation import adaptive_standardisation
from epftoolbox.models._lear_original import LEAR
from epftoolbox.models._lear_original import LEAR_adaptive_normalization as LEAR_as
from epftoolbox.evaluation import MAE, sMAPE
import concurrent.futures
# import warnings
# warnings.filterwarnings(action='ignore')
def process_combination(combination):
print(combination)
apply_adaptive_standardisation = combination[0]
dataset = combination[1]
calibration_window = combination[2]
df = pd.read_csv(f"Data//{dataset}")
df['Date'] = pd.to_datetime(df.Date)
if dataset in ["BE.csv", "FR.csv"]:
date_test = dt.datetime(2015, 1, 4)
elif dataset in ["DE_2023.csv", "SP_2023.csv"] :
date_test = dt.datetime(2022, 1, 1)
elif dataset in ["NP.csv"] :
date_test = dt.datetime(2016, 12, 27)
df.columns = ['Date', 'Price', 'Exogenous 1', 'Exogenous 2']
if apply_adaptive_standardisation:
original_df = copy(df)
df['Simple Date'] = df.Date.dt.strftime("%Y-%m-%d")
df['Hour'] = df.Date.dt.hour
df.columns = ['Date', 'Price', 'Exogenous 1', 'Exogenous 2', 'Simple Date', 'Hour']
try:
with open(f'dicts_as_py//dataset_{dataset.replace(".csv", "")}.pkl', 'rb') as f:
dict_new_df = pickle.load(f)
except:
dict_new_df = adaptive_standardisation(df, window_size=7)
with open(f'dicts_as_py//dataset_{dataset.replace(".csv", "")}.pkl', 'wb') as f:
pickle.dump(dict_new_df, f)
df = pd.DataFrame(dict_new_df)[['Date', 'Price', 'Exogenous 1', 'Exogenous 2']]
df['Date'] = pd.to_datetime(df.Date)
df_scalers = pd.DataFrame({'Date':dict_new_df['Date'], 'scaler':dict_new_df['scaler']})
df = df.set_index('Date')
if apply_adaptive_standardisation:
original_df = original_df.set_index('Date')
original_df.columns = ['Price', 'Exogenous 1', 'Exogenous 2']
df_train = df[df.index < date_test]
df_test= df[df.index >= date_test]
forecast = pd.DataFrame(index=df_test.index[::24], columns=['h' + str(k) for k in range(24)])
if apply_adaptive_standardisation:
real_values = original_df[original_df.index >= date_test].loc[:, ['Price']].values.reshape(-1, 24)
else:
real_values = df_test.loc[:, ['Price']].values.reshape(-1, 24)
real_values = pd.DataFrame(real_values, index=forecast.index, columns=forecast.columns)
forecast_dates = forecast.index
if apply_adaptive_standardisation:
model = LEAR_as(calibration_window=calibration_window)
else:
model = LEAR(calibration_window=calibration_window)
# For loop over the recalibration dates
for date in forecast_dates: #tqdm(forecast_dates[:10], file=sys.stdout):
# For simulation purposes, we assume that the available data is
# the data up to current date where the prices of current date are not known
data_available = pd.concat([df_train, df_test.loc[:date + pd.Timedelta(hours=23), :]], axis=0)
# We set the real prices for current date to NaN in the dataframe of available data
data_available.loc[date:date + pd.Timedelta(hours=23), 'Price'] = np.NaN
# Recalibrating the model with the most up-to-date available data and making a prediction
# for the next day
if apply_adaptive_standardisation:
scalers = df_scalers[(df_scalers.Date >= date) & (df_scalers.Date <= date + pd.Timedelta(hours=23))].scaler.to_numpy()
Yp = model.recalibrate_and_forecast_next_day(df=data_available, next_day_date=date,
calibration_window=calibration_window, scalers=scalers)
else:
Yp = model.recalibrate_and_forecast_next_day(df=data_available, next_day_date=date,
calibration_window=calibration_window)
# Saving the current prediction
forecast.loc[date, :] = Yp
# Computing metrics up-to-current-date
mae = np.mean(MAE(forecast.loc[:date].values.squeeze(), real_values.loc[:date].values))
smape = np.mean(sMAPE(forecast.loc[:date].values.squeeze(), real_values.loc[:date].values)) * 100
# Pringint information
print('{} - sMAPE: {:.2f}% | MAE: {:.3f}'.format(str(date)[:10], smape, mae))
if apply_adaptive_standardisation:
forecast.to_csv(f"Results_py//dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_as_calibration_window{calibration_window}.csv")
else:
forecast.to_csv(f"Results_py//dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_calibration_window{calibration_window}.csv")
forecast = pd.DataFrame(index=df_test.index[::24], columns=['h' + str(k) for k in range(24)])
if apply_adaptive_standardisation and not os.path.isfile(f"Results_py//dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_as_calibration_window_None.csv"):
model = LEAR_as(calibration_window=None)
# For loop over the recalibration dates
for date in forecast_dates: #tqdm(forecast_dates, desc='Calibration Window None'):
# For simulation purposes, we assume that the available data is
# the data up to current date where the prices of current date are not known
data_available = pd.concat([df_train, df_test.loc[:date + pd.Timedelta(hours=23), :]], axis=0)
# We set the real prices for current date to NaN in the dataframe of available data
data_available.loc[date:date + pd.Timedelta(hours=23), 'Price'] = np.NaN
# Recalibrating the model with the most up-to-date available data and making a prediction
# for the next day
scalers = df_scalers[(df_scalers.Date >= date) & (df_scalers.Date <= date + pd.Timedelta(hours=23))].scaler.to_numpy()
Yp = model.recalibrate_and_forecast_next_day(df=data_available, next_day_date=date,
calibration_window=calibration_window, scalers=scalers)
# Saving the current prediction
forecast.loc[date, :] = Yp
# Computing metrics up-to-current-date
mae = np.mean(MAE(forecast.loc[:date].values.squeeze(), real_values.loc[:date].values))
smape = np.mean(sMAPE(forecast.loc[:date].values.squeeze(), real_values.loc[:date].values)) * 100
# Pringint information
print('{} - sMAPE: {:.2f}% | MAE: {:.3f}'.format(str(date)[:10], smape, mae))
forecast.to_csv(f"Results_py//dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_as_calibration_window_None.csv")
forecast = pd.DataFrame(index=df_test.index[::24], columns=['h' + str(k) for k in range(24)])
else:
print("Combination already processed: ", apply_adaptive_standardisation, dataset, None)
# datasets = ["SP_2023.csv", "DE_2023.csv", "BE.csv", "FR.csv", "NP.csv"]
datasets = ["SP_2023.csv", "DE_2023.csv"]
apply_adaptive_standardisation_list = [False]
calibration_windows_1 = [1092, 1456]
# calibration_windows_2 = [56, 84, 364, 728]
calibration_windows_2 = [364, 728]
combinations = []
for apply_adaptive_standardisation in apply_adaptive_standardisation_list:
for dataset in datasets:
if dataset in ["BE.csv", "FR.csv", "NP.csv"]:
calibration_windows = calibration_windows_1
elif dataset in ["DE_2023.csv", "SP_2023.csv"]:
calibration_windows = calibration_windows_2
for calibration_window in calibration_windows:
if apply_adaptive_standardisation:
file_name = f"dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_as_calibration_window{calibration_window}.csv"
else:
file_name = f"dataset_{dataset.replace('.csv', '')}_larsmodel_LEAR_calibration_window{calibration_window}.csv"
if not os.path.isfile("Results_py//" + file_name):
combinations.append([apply_adaptive_standardisation, dataset, calibration_window])
else:
print("Combination already processed: ", apply_adaptive_standardisation, dataset, calibration_window)
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
executor.map(process_combination, combinations)
# for combination in combinations:
# process_combination(combination)