epftoolbox.models.evaluate_lear_in_test_dataset

epftoolbox.models.evaluate_lear_in_test_dataset(path_datasets_folder='./datasets', path_recalibration_folder='./experimental_files', dataset='PJM', years_test=2, calibration_window=1092, begin_test_date=None, end_test_date=None)[source]

Function for easy evaluation of the LEAR model in a test dataset using daily recalibration.

The test dataset is defined by a market name and the test dates dates. The function generates the test and training datasets, and evaluates a LEAR model considering daily recalibration.

An example on how to use this function is provided here.

Parameters:
  • path_datasets_folder (str, optional) – path where the datasets are stored or, if they do not exist yet, the path where the datasets are to be stored.
  • path_recalibration_folder (str, optional) – path to save the files of the experiment dataset.
  • dataset (str, optional) – Name of the dataset/market under study. If it is one one of the standard markets, i.e. "PJM", "NP", "BE", "FR", or "DE", the dataset is automatically downloaded. If the name is different, a dataset with a csv format should be place in the path_datasets_folder.
  • years_test (int, optional) – Number of years (a year is 364 days) in the test dataset. It is only used if the arguments begin_test_date and end_test_date are not provided.
  • calibration_window (int, optional) – Number of days used in the training dataset for recalibration.
  • begin_test_date (datetime/str, optional) – Optional parameter to select the test dataset. Used in combination with the argument end_test_date. If either of them is not provided, the test dataset is built using the years_test argument. begin_test_date should either be a string with the following format "%d/%m/%Y %H:%M", or a datetime object.
  • end_test_date (datetime/str, optional) – Optional parameter to select the test dataset. Used in combination with the argument begin_test_date. If either of them is not provided, the test dataset is built using the years_test argument. end_test_date should either be a string with the following format "%d/%m/%Y %H:%M", or a datetime object.
Returns:

A dataframe with all the predictions in the test dataset. The dataframe is also written to path_recalibration_folder.
Return type:

pandas.DataFrame