API

This page contains the auto-generated API documentation for tsml-eval package functions and classes.

Estimators: tsml_eval.estimators

Wrappers and misc estimators for use with tsml-eval.

estimators.SklearnToTsmlClassifier([...])	Wrapper for sklearn estimators to use the tsml base class.
estimators.SklearnToTsmlClusterer([...])	Wrapper for sklearn estimators to use the tsml base class.
estimators.SklearnToTsmlRegressor([...])	Wrapper for sklearn estimators to use the tsml base class.
estimators.classification.hybrid.FromFileHIVECOTE(...)	HIVE-COTE from file.
estimators.clustering.consensus.IterativeVotingClustering([...])	IVC (Iterative Voting Clustering) Consensus Clusterer.
estimators.clustering.consensus.FromFileIterativeVotingClustering(...)	IVC (Iterative Voting Clustering) Consensus Clusterer.
estimators.clustering.consensus.SimpleVote([...])	SimpleVote clustering ensemble.
estimators.clustering.consensus.FromFileSimpleVote(...)	SimpleVote clustering ensemble.

Evaluation: tsml_eval.evaluation

Functions for evaluating the performance of a model.

evaluation.evaluate_classifiers(...[, ...])	Evaluate multiple classifiers on multiple datasets.
evaluation.evaluate_classifiers_from_file(...)	Evaluate multiple classifiers on multiple datasets from file.
evaluation.evaluate_classifiers_by_problem(...)	Evaluate multiple classifiers on multiple datasets from file using standard paths.
evaluation.evaluate_clusterers(...[, ...])	Evaluate multiple clusterers on multiple datasets.
evaluation.evaluate_clusterers_from_file(...)	Evaluate multiple clusterers on multiple datasets from file.
evaluation.evaluate_clusterers_by_problem(...)	Evaluate multiple clusterers on multiple datasets from file using standard paths.
evaluation.evaluate_regressors(...[, ...])	Evaluate multiple regressors on multiple datasets.
evaluation.evaluate_regressors_from_file(...)	Evaluate multiple regressors on multiple datasets from file.
evaluation.evaluate_regressors_by_problem(...)	Evaluate multiple regressors on multiple datasets from file using standard paths.
evaluation.storage.ClassifierResults([...])	A class for storing and managing results from classification experiments.
evaluation.storage.ClustererResults([...])	A class for storing and managing results from clustering experiments.
evaluation.storage.RegressorResults([...])	A class for storing and managing results from regression experiments.
evaluation.storage.load_classifier_results(...)	Load and return classifier results from a specified file.
evaluation.storage.load_clusterer_results(...)	Load and return clusterer results from a specified file.
evaluation.storage.load_regressor_results(...)	Load and return regressor results from a specified file.

Experiments: tsml_eval.experiments

Functions for running experiments.

experiments.run_classification_experiment(...)	Run a classification experiment and save the results to file.
experiments.load_and_run_classification_experiment(...)	Load a dataset and run a classification experiment.
experiments.run_clustering_experiment(...[, ...])	Run a clustering experiment and save the results to file.
experiments.load_and_run_clustering_experiment(...)	Load a dataset and run a clustering experiment.
experiments.run_regression_experiment(...[, ...])	Run a regression experiment and save the results to file.
experiments.load_and_run_regression_experiment(...)	Load a dataset and run a regression experiment.
experiments.get_classifier_by_name(...[, ...])	Return a classifier matching a given input name.
experiments.get_clusterer_by_name(clusterer_name)	Return a clusterer matching a given input name.
experiments.get_regressor_by_name(regressor_name)	Return a regressor matching a given input name.
experiments.run_timing_experiment(estimators)	Return the time taken to run estimator functions for randomly generated data.
experiments.classification_cross_validation(X, ...)	Run a classification experiment using cross-validation.
experiments.classification_cross_validation_folds(X, y)	Get the folds for a classification cross-validation experiment.
experiments.regression_cross_validation(X, ...)	Run a regression experiment using cross-validation.
experiments.regression_cross_validation_folds(X, y)	Get the folds for a regression cross-validation experiment.

Utilities: tsml_eval.utils

Public utility functions used elsewhere in the package.

utils.arguments.parse_args(args)	Parse the command line arguments for tsml_eval.
utils.datasets.load_experiment_data(...)	Load data for experiments.
utils.datasets.copy_dataset_ts_files(...)	Copy the TRAIN and TEST .ts files of the datasets to the destination path.
utils.datasets.save_merged_dataset_splits(...)	Merge the TRAIN and TEST .ts files of a dataset and save the merged file.
utils.estimator_validation.is_sklearn_estimator(...)	Check if estimator is a scikit-learn estimator.
utils.estimator_validation.is_sklearn_classifier(...)	Check if estimator is a scikit-learn classifier.
utils.estimator_validation.is_sklearn_regressor(...)	Check if estimator is a scikit-learn regressor.
utils.estimator_validation.is_sklearn_clusterer(...)	Check if estimator is a scikit-learn clusterer.
utils.experiments.assign_gpu([set_environ])	Assign a GPU to the current process.
utils.experiments.timing_benchmark([...])	Measures the time taken to sort a given number of numpy arrays of a specified size.
utils.experiments.estimator_attributes_to_file(...)	Write the attributes of an estimator to file(s).
utils.functions.str_in_nested_list(...)	Find an item in a nested list.
utils.functions.pair_list_to_dict(pl)	Convert a 2d list of pairs to a dict.
utils.functions.time_to_milliseconds(...)	Convert a time value from the given time unit to milliseconds.
utils.functions.rank_array(arr[, higher_better])	Assign a rank to each value in a 1D numpy array.
utils.memory_recorder.record_max_memory(function)	Record the maximum memory usage of a function.
utils.publications.extract_publication_csv_from_evaluation(...)	Extract the CSV files from the evaluation directory to a new directory.
utils.publications.parameter_table_from_estimator_selector(...)	Create a table of estimator names and their parameters.
utils.resampling.resample_data(X_train, ...)	Resample data without replacement using a random state.
utils.resampling.resample_data_indices(...)	Return data resample indices without replacement using a random state.
utils.resampling.stratified_resample_data(...)	Stratified resample data without replacement using a random state.
utils.resampling.stratified_resample_data_indices(...)	Return stratified data resample indices without replacement using a random state.
utils.results_loading.load_estimator_results(...)	Load and return estimator results from a specified file.
utils.results_loading.estimator_results_to_dict(...)	Convert a list of EstimatorResults objects to a dictionary of metrics.
utils.results_loading.load_estimator_results_to_dict(...)	Load and convert EstimatorResults objects to a dictionary of metrics.
utils.results_loading.estimator_results_to_array(...)	Convert a list of EstimatorResults objects to an array of metrics.
utils.results_loading.load_estimator_results_to_array(...)	Load and convert EstimatorResults objects to an array of metrics.
utils.results_repair.fix_broken_second_line(...)	Fix a results while where the written second line has line breaks.
utils.results_validation.validate_results_file(...)	Validate that a results file is in the correct format.
utils.results_validation.compare_result_file_resample(...)	Validate that two results files use the same data resample.
utils.results_writing.write_classification_results(...)	Write the predictions for a classification experiment in the format used by tsml.
utils.results_writing.write_regression_results(...)	Write the predictions for a regression experiment in the format used by tsml.
utils.results_writing.write_clustering_results(...)	Write the predictions for a clustering experiment in the format used by tsml.
utils.results_writing.write_results_to_tsml_format(...)	Write the predictions for an experiment in the standard format used by tsml.