regression_cross_validation

tsml_eval.experiments.regression_cross_validation(X, y, estimator, results_path, cv=None, fold_ids=None, regressor_name=None, dataset_name='', build_test_file=True, build_train_file=False, ignore_custom_train_estimate=False, attribute_file_path=None, att_max_shape=0, benchmark_time=True)

Run a regression experiment using cross-validation.

Parameters:

Xarray-like

Feature data.

yarray-like

Target labels.

estimatorobject

The regressor to be evaluated.

results_pathstr

Path to save results.

cvobject, optional

Cross-validation strategy. If None, 10-fold cross-validation will be used.

fold_idslist, optional

List of fold ids to run. If None, all folds will be run. row_normalise : bool, default=False Whether to normalise the data rows (time series) prior to fitting and predicting.

regressor_namestr or None, default=None

Name of regressor used in writing results. If None, the name is taken from the regressor.

dataset_namestr, default=”N/A”

Name of dataset.

build_test_filebool, default=True:

Whether to generate test files or not. If the regressor can generate its own train predictions, the classifier will be built but no file will be output.

build_train_filebool, default=False

Whether to generate train files or not. If true, it performs a 10-fold cross-validation on the train data and saves. If the regressor can produce its own estimates, those are used instead.

ignore_custom_train_estimatebool, default=False

todo

attribute_file_pathstr or None, default=None

todo (only test)

att_max_shapeint, default=0

todo

benchmark_timebool, default=True

Whether to benchmark the hardware used with a simple function and write the results. This will typically take ~2 seconds, but is hardware dependent.