:py:mod:`ForeTiS.optim_pipeline`
================================

.. py:module:: ForeTiS.optim_pipeline


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   ForeTiS.optim_pipeline.run



.. py:function:: run(data_dir, save_dir, datasplit = 'timeseries-cv', test_set_size_percentage = 20, val_set_size_percentage = 20, n_splits = 3, imputation_method = None, windowsize_current_statistics = 3, windowsize_lagged_statistics = 3, models = None, n_trials = 200, pca_transform = False, save_final_model = False, periodical_refit_frequency = None, refit_drops = 0, data = None, config_file_path = None, config_file_section = None, refit_window = 5, intermediate_results_interval = None, batch_size = 32, n_epochs = 100000, event_lags = None, optimize_featureset = False, scale_thr = 0.1, scale_seasons = 2, cf_thr_perc = 70, scale_window_factor = 0.1, cf_r = 0.4, cf_order = 1, cf_smooth = 4, scale_window_minimum = 2, max_samples_factor = 10, valtest_seasons = 1, seasonal_valtest = True)

   Run the whole optimization pipeline

   :param data_dir: data directory where the phenotype and genotype matrix are stored
   :param save_dir: directory for saving the results. Default is None, so same directory as data_dir
   :param datasplit: datasplit to use. Options are: nested-cv, cv-test, train-val-test
   :param test_set_size_percentage: size of the test set relevant for cv-test and train-val-test
   :param val_set_size_percentage: size of the validation set relevant for train-val-test
   :param n_splits: splits to use for 'timeseries-cv' or 'cv'
   :param imputation_method: the imputation method to use. Options are: 'mean' , 'knn' , 'iterative'
   :param windowsize_current_statistics: the windowsize for the feature engineering of the current statistic
   :param windowsize_lagged_statistics: the windowsize for the feature engineering of the lagged statistics
   :param models: list of models that should be optimized
   :param n_trials: number of trials for optuna
   :param pca_transform: whether pca dimensionality reduction will be optimized or not
   :param save_final_model: specify if the final model should be saved
   :param periodical_refit_frequency: if and for which intervals periodical refitting should be performed
   :param refit_drops: after how many periods the model should get updated
   :param data: the dataset that you want to use
   :param config_file_path: the path of the config file
   :param config_file_section: the section of the config file for the used dataset
   :param refit_window: seasons get used for refitting
   :param intermediate_results_interval: number of trials after which intermediate results will be saved
   :param batch_size: batch size for neural network models
   :param n_epochs: number of epochs for neural network models
   :param event_lags: the event lags for the counters
   :param optimize_featureset: whether feature set will be optimized or not output scale threshold
   :param scale_thr: only relevant for evars-gpr: output scale threshold
   :param scale_seasons: only relevant for evars-gpr: output scale seasons taken into account
   :param cf_thr_perc: only relevant for evars-gpr: percentile of train set anomaly factors as threshold for cpd with changefinder
   :param scale_window_factor: only relevant for evars-gpr: scale window factor based on seasonal periods
   :param cf_r: only relevant for evars-gpr: changefinders r param (decay factor older values)
   :param cf_order: only relevant for evars-gpr: changefinders SDAR model order param
   :param cf_smooth: only relevant for evars-gpr: changefinders smoothing param
   :param scale_window_minimum: only relevant for evars-gpr: scale window minimum
   :param max_samples_factor: only relevant for evars-gpr: max samples factor of seasons to keep for gpr pipeline
   :param valtest_seasons: define the number of seasons to be used when seasonal_valtest is True
   :param seasonal_valtest: whether validation and test sets should be a multiple of the season length