From 50a4d2f8b52fc0cfcfc46a8b20f4a585d26edf07 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 14 Nov 2023 12:45:24 +0000 Subject: [PATCH 01/19] result loading and evaluation v1 --- conftest.py | 21 + pyproject.toml | 6 +- .../classification/convolution_based/hydra.py | 2 +- .../regression/convolution_based/arsenal.py | 2 +- .../{benchmark.py => efficiency_benchmark.py} | 31 +- tsml_eval/evaluation/metrics.py | 23 +- .../multiple_estimator_evaluation.py | 539 +++++++++ tsml_eval/evaluation/storage/__init__.py | 29 + .../evaluation/storage/classifier_results.py | 353 ++++++ .../evaluation/storage/clusterer_results.py | 251 ++++ .../evaluation/storage/estimator_results.py | 107 ++ .../evaluation/storage/forecaster_results.py | 192 +++ .../evaluation/storage/regressor_results.py | 233 ++++ .../evaluation/storage/tests/__init__.py | 0 tsml_eval/evaluation/storage/tests/test_io.py | 68 ++ tsml_eval/evaluation/tests/test_metrics.py | 4 +- .../test_multiple_estimator_evaluation.py | 67 ++ .../experiments/classification_experiments.py | 7 +- .../experiments/clustering_experiments.py | 7 +- tsml_eval/experiments/experiments.py | 97 +- .../experiments/forecasting_experiments.py | 8 +- .../experiments/regression_experiments.py | 7 +- tsml_eval/experiments/set_clusterer.py | 24 +- tsml_eval/experiments/tests/__init__.py | 20 +- .../experiments/tests/test_classification.py | 7 +- .../experiments/tests/test_clustering.py | 15 +- .../experiments/tests/test_experiments.py | 15 +- .../experiments/tests/test_forecasting.py | 7 +- .../experiments/tests/test_regression.py | 6 +- .../threaded_classification_experiments.py | 5 +- .../threaded_clustering_experiments.py | 6 +- .../threaded_forecasting_experiments.py | 5 +- .../threaded_regression_experiments.py | 5 +- .../run_distance_experiments.py | 5 +- .../set_distance_clusterer.py | 22 +- .../tests/test_run_experiments.py | 2 +- .../tests/test_set_distance_clusterer.py | 2 +- .../run_classification_experiments.py | 5 +- .../run_regression_experiments.py | 7 +- .../tests/test_run_experiments.py | 2 +- .../rist_pipeline/tests/test_set_estimator.py | 2 +- .../y2023/tsc_bakeoff/run_experiments.py | 7 +- .../tsc_bakeoff/tests/test_run_experiments.py | 2 +- .../tsc_bakeoff/tests/test_set_classifier.py | 2 +- .../tser_archive_expansion/run_experiments.py | 7 +- .../tests/test_run_experiments.py | 2 +- .../tests/test_set_regressor.py | 2 +- tsml_eval/testing/__init__.py | 0 .../brokenClassificationResultsFile.csv | 0 .../broken}/brokenRegressionResultsFile.csv | 0 .../broken}/brokenResultsFile.csv | 0 .../broken}/brokenResultsFileLine3.csv | 0 .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/trainResample0.csv | 70 ++ .../ItalyPowerDemand/trainResample1.csv | 70 ++ .../ItalyPowerDemand/trainResample2.csv | 70 ++ .../Predictions/Trace/testResample0.csv | 103 ++ .../Predictions/Trace/testResample1.csv | 103 ++ .../Predictions/Trace/testResample2.csv | 103 ++ .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/trainResample0.csv | 70 ++ .../ItalyPowerDemand/trainResample1.csv | 70 ++ .../ItalyPowerDemand/trainResample2.csv | 70 ++ .../MinimalChinatown/testResample0.csv | 23 + .../Predictions/Trace/testResample0.csv | 103 ++ .../Predictions/Trace/testResample1.csv | 103 ++ .../Predictions/Trace/testResample2.csv | 103 ++ .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../TSF/Predictions/Trace/testResample0.csv | 103 ++ .../TSF/Predictions/Trace/testResample1.csv | 103 ++ .../TSF/Predictions/Trace/testResample2.csv | 103 ++ .../classificationResultsFile1.csv | 0 .../classificationResultsFile2.csv | 0 .../classificationResultsFile3.csv | 0 .../MinimalChinatown/trainResample0.csv | 23 + .../ShampooSales/testResample0.csv | 15 + .../MinimalGasPrices/testResample0.csv | 23 + .../regression}/regressionResultsFile.csv | 0 tsml_eval/{utils => testing}/test_utils.py | 7 + tsml_eval/testing/tests/__init__.py | 0 .../tests/test_test_utils.py | 2 +- tsml_eval/utils/arguments.py | 223 ++++ tsml_eval/utils/experiments.py | 494 ++------ tsml_eval/utils/functions.py | 74 ++ tsml_eval/utils/memory_recorder.py | 49 + tsml_eval/utils/tests/test_args.py | 4 +- tsml_eval/utils/tests/test_functions.py | 73 +- .../utils/tests/test_misc_experiments.py | 1 - tsml_eval/utils/tests/test_resampling.py | 26 +- .../test_results_validation_and_repair.py | 55 +- tsml_eval/utils/tests/test_results_writing.py | 20 +- tsml_eval/utils/validation.py | 148 ++- 113 files changed, 16720 insertions(+), 665 deletions(-) create mode 100644 conftest.py rename tsml_eval/evaluation/{benchmark.py => efficiency_benchmark.py} (88%) create mode 100644 tsml_eval/evaluation/multiple_estimator_evaluation.py create mode 100644 tsml_eval/evaluation/storage/__init__.py create mode 100644 tsml_eval/evaluation/storage/classifier_results.py create mode 100644 tsml_eval/evaluation/storage/clusterer_results.py create mode 100644 tsml_eval/evaluation/storage/estimator_results.py create mode 100644 tsml_eval/evaluation/storage/forecaster_results.py create mode 100644 tsml_eval/evaluation/storage/regressor_results.py create mode 100644 tsml_eval/evaluation/storage/tests/__init__.py create mode 100644 tsml_eval/evaluation/storage/tests/test_io.py create mode 100644 tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py create mode 100644 tsml_eval/testing/__init__.py rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/broken}/brokenClassificationResultsFile.csv (100%) rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/broken}/brokenRegressionResultsFile.csv (100%) rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/broken}/brokenResultsFile.csv (100%) rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/broken}/brokenResultsFileLine3.csv (100%) create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/MinimalChinatown/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/classification}/classificationResultsFile1.csv (100%) rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/classification}/classificationResultsFile2.csv (100%) rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/classification}/classificationResultsFile3.csv (100%) create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/MinimalChinatown/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/MinimalGasPrices/testResample0.csv rename tsml_eval/{utils/tests/test_files => testing/_test_result_files/regression}/regressionResultsFile.csv (100%) rename tsml_eval/{utils => testing}/test_utils.py (92%) create mode 100644 tsml_eval/testing/tests/__init__.py rename tsml_eval/{utils => testing}/tests/test_test_utils.py (81%) create mode 100644 tsml_eval/utils/arguments.py create mode 100644 tsml_eval/utils/memory_recorder.py diff --git a/conftest.py b/conftest.py new file mode 100644 index 00000000..7ee30853 --- /dev/null +++ b/conftest.py @@ -0,0 +1,21 @@ +"""Main configuration file for pytest.""" + +__author__ = ["MatthewMiddlehurst"] + +from tsml_eval.experiments import experiments + + +def pytest_addoption(parser): + """Pytest command line parser options adder.""" + parser.addoption( + "--meminterval", + type=float, + default=5.0, + help="Set the time interval in seconds for recording memory usage " + "(default: %(default)s).", + ) + + +def pytest_configure(config): + """Pytest configuration preamble.""" + experiments.MEMRECORD_INTERVAL = config.getoption("--meminterval") diff --git a/pyproject.toml b/pyproject.toml index 3c1a4a08..82924e0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,9 +37,10 @@ classifiers = [ "Programming Language :: Python :: 3.11", ] dependencies = [ - "aeon>=0.4.0,<0.5.0", + "aeon>=0.5.0,<0.6.0", "scikit-learn>=1.0.2,<=1.2.2", - "tsml>=0.2.0,<0.3.0", + "tsml>=0.2.1,<0.3.0", + "matplotlib", "gpustat", "psutil", ] @@ -142,4 +143,5 @@ addopts = ''' --showlocals --doctest-modules --numprocesses auto + --meminterval 0.1 ''' diff --git a/tsml_eval/estimators/classification/convolution_based/hydra.py b/tsml_eval/estimators/classification/convolution_based/hydra.py index 7fa53bd8..b48b3d7a 100644 --- a/tsml_eval/estimators/classification/convolution_based/hydra.py +++ b/tsml_eval/estimators/classification/convolution_based/hydra.py @@ -10,7 +10,7 @@ import numpy as np from aeon.classification.base import BaseClassifier -from aeon.transformations.collection.rocket import MultiRocket +from aeon.transformations.collection.convolution_based import MultiRocket from aeon.utils.validation import check_n_jobs from aeon.utils.validation._dependencies import _check_soft_dependencies from sklearn.linear_model import RidgeClassifierCV diff --git a/tsml_eval/estimators/regression/convolution_based/arsenal.py b/tsml_eval/estimators/regression/convolution_based/arsenal.py index 2b7171b9..72a8ef04 100644 --- a/tsml_eval/estimators/regression/convolution_based/arsenal.py +++ b/tsml_eval/estimators/regression/convolution_based/arsenal.py @@ -11,7 +11,7 @@ import numpy as np from aeon.base._base import _clone_estimator from aeon.regression.base import BaseRegressor -from aeon.transformations.collection.rocket import ( +from aeon.transformations.collection.convolution_based import ( MiniRocket, MiniRocketMultivariate, MultiRocket, diff --git a/tsml_eval/evaluation/benchmark.py b/tsml_eval/evaluation/efficiency_benchmark.py similarity index 88% rename from tsml_eval/evaluation/benchmark.py rename to tsml_eval/evaluation/efficiency_benchmark.py index ac5c24d2..7e8fc818 100644 --- a/tsml_eval/evaluation/benchmark.py +++ b/tsml_eval/evaluation/efficiency_benchmark.py @@ -2,10 +2,8 @@ from dataclasses import dataclass from math import floor -from time import perf_counter import numpy as np -import psutil from sklearn.base import clone from sklearn.model_selection import train_test_split from sklearn.utils.validation import check_random_state @@ -18,16 +16,15 @@ "compare_estimators", ] +from tsml_eval.utils.memory_recorder import record_max_memory + @dataclass class BenchmarkResult: - """Aggregates runtimes (seconds) and memory usage (bytes).""" + """Aggregates runtimes (milliseconds) and memory usage (bytes).""" - total_runtime: float fit_runtime: float predict_runtime: float - - total_memory_usage: int fit_memory_usage: int predict_memory_usage: int @@ -88,20 +85,18 @@ def benchmark_estimator( random_state=rng, ) - runtime_fit, memory_fit, _ = _benchmark_function_wrapper( - estimator.fit, args=[X_train, y_train], kwargs={} + memory_fit, runtime_fit = record_max_memory( + estimator.fit, args=(X_train, y_train), return_func_time=True ) - runtime_predict, memory_predict, _ = _benchmark_function_wrapper( - estimator.predict, args=[X_test], kwargs={} + memory_predict, runtime_predict = record_max_memory( + estimator.predict, args=(X_test,), return_func_time=True ) return BenchmarkResult( fit_runtime=runtime_fit, predict_runtime=runtime_predict, - total_runtime=runtime_fit + runtime_predict, fit_memory_usage=memory_fit, predict_memory_usage=memory_predict, - total_memory_usage=memory_fit + memory_predict, ) @@ -237,15 +232,3 @@ def compare_estimators( f"Invalid varying method: {varying}. Allowed values" + " are {'total', 'train', 'test'}." ) - - -def _benchmark_function_wrapper(func, args, kwargs): - process = psutil.Process() - - mem_before = process.memory_info().vms - clock_start = perf_counter() - func_output = func(*args, **kwargs) - clock_end = perf_counter() - mem_after = process.memory_info().vms - - return clock_end - clock_start, mem_after - mem_before, func_output diff --git a/tsml_eval/evaluation/metrics.py b/tsml_eval/evaluation/metrics.py index d208adc7..17d259c7 100644 --- a/tsml_eval/evaluation/metrics.py +++ b/tsml_eval/evaluation/metrics.py @@ -2,33 +2,16 @@ __author__ = ["MatthewMiddlehurst"] -__all__ = ["clustering_accuracy", "davies_bouldin_score_from_file"] +__all__ = ["clustering_accuracy_score"] -import sys -import numpy as np from scipy.optimize import linear_sum_assignment -from sklearn.metrics import confusion_matrix, davies_bouldin_score +from sklearn.metrics import confusion_matrix -def clustering_accuracy(y_true, y_pred): +def clustering_accuracy_score(y_true, y_pred): """Calculate clustering accuracy.""" matrix = confusion_matrix(y_true, y_pred) row, col = linear_sum_assignment(matrix.max() - matrix) s = sum([matrix[row[i], col[i]] for i in range(len(row))]) return s / y_pred.size - - -def davies_bouldin_score_from_file(X, file_path): - """Calculate Davies-Bouldin score from a results file.""" - y = np.zeros(len(X)) - with open(file_path, "r") as f: - lines = f.readlines() - for i, line in enumerate(lines[3:]): - y[i] = float(line.split(",")[1]) - - clusters = len(np.unique(y)) - if clusters <= 1: - return sys.float_info.max - else: - return davies_bouldin_score(X, y) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py new file mode 100644 index 00000000..117c17b6 --- /dev/null +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -0,0 +1,539 @@ +import os +from datetime import datetime + +import numpy as np +from aeon.benchmarking import plot_critical_difference + +from tsml_eval.evaluation.storage import ( + ClassifierResults, + ClustererResults, + ForecasterResults, + RegressorResults, +) +from tsml_eval.utils.functions import rank_array + + +def evaluate_classifiers( + classifier_results, save_path, error_on_missing=True, eval_name=None +): + _evaluate_estimators( + classifier_results, + ClassifierResults.statistics, + save_path, + error_on_missing, + eval_name, + ) + + +def evaluate_classifiers_from_file( + load_paths, save_path, error_on_missing=True, eval_name=None +): + classifier_results = [] + for load_path in load_paths: + classifier_results.append(ClassifierResults().load_from_file(load_path)) + + evaluate_classifiers( + classifier_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_classifiers_by_problem( + load_path, + classifier_names, + dataset_names, + save_path, + resamples=None, + load_train_results=False, + error_on_missing=True, + eval_name=None, +): + if resamples is None: + resamples = [""] + elif isinstance(resamples, int): + resamples = [str(i) for i in range(resamples)] + else: + resamples = [str(resample) for resample in resamples] + + if load_train_results: + splits = ["test", "train"] + else: + splits = ["test"] + + classifier_results = [] + for classifier_name in classifier_names: + for dataset_name in dataset_names: + for resample in resamples: + for split in splits: + classifier_results.append( + ClassifierResults().load_from_file( + f"{load_path}/{classifier_name}/Predictions/{dataset_name}" + f"/{split}Resample{resample}.csv" + ) + ) + + evaluate_classifiers( + classifier_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_clusterers( + clusterer_results, save_path, error_on_missing=True, eval_name=None +): + _evaluate_estimators( + clusterer_results, + ClustererResults.statistics, + save_path, + error_on_missing, + eval_name, + ) + + +def evaluate_clusterers_from_file( + load_paths, save_path, error_on_missing=True, eval_name=None +): + clusterer_results = [] + for load_path in load_paths: + clusterer_results.append(ClustererResults().load_from_file(load_path)) + + evaluate_classifiers( + clusterer_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_clusterers_by_problem( + load_path, + clusterer_names, + dataset_names, + save_path, + resamples=None, + load_test_results=True, + error_on_missing=True, + eval_name=None, +): + if resamples is None: + resamples = [""] + elif isinstance(resamples, int): + resamples = [str(i) for i in range(resamples)] + else: + resamples = [str(resample) for resample in resamples] + + if load_test_results: + splits = ["test", "train"] + else: + splits = ["train"] + + clusterer_results = [] + for clusterer_name in clusterer_names: + for dataset_name in dataset_names: + for resample in resamples: + for split in splits: + clusterer_results.append( + ClustererResults().load_from_file( + f"{load_path}/{clusterer_name}/Predictions/{dataset_name}" + f"/{split}Resample{resample}.csv" + ) + ) + + evaluate_clusterers( + clusterer_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_regressors( + regressor_results, save_path, error_on_missing=True, eval_name=None +): + _evaluate_estimators( + regressor_results, + RegressorResults.statistics, + save_path, + error_on_missing, + eval_name, + ) + + +def evaluate_regressors_from_file( + load_paths, save_path, error_on_missing=True, eval_name=None +): + regressor_results = [] + for load_path in load_paths: + regressor_results.append(RegressorResults().load_from_file(load_path)) + + evaluate_classifiers( + regressor_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_regressors_by_problem( + load_path, + regressor_names, + dataset_names, + save_path, + resamples=None, + load_train_results=False, + error_on_missing=True, + eval_name=None, +): + if resamples is None: + resamples = [""] + elif isinstance(resamples, int): + resamples = [str(i) for i in range(resamples)] + else: + resamples = [str(resample) for resample in resamples] + + if load_train_results: + splits = ["test", "train"] + else: + splits = ["test"] + + regressor_results = [] + for regressor_name in regressor_names: + for dataset_name in dataset_names: + for resample in resamples: + for split in splits: + regressor_results.append( + RegressorResults().load_from_file( + f"{load_path}/{regressor_name}/Predictions/{dataset_name}" + f"/{split}Resample{resample}.csv" + ) + ) + + evaluate_regressors( + regressor_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_forecasters( + forecaster_results, save_path, error_on_missing=True, eval_name=None +): + _evaluate_estimators( + forecaster_results, + ForecasterResults.statistics, + save_path, + error_on_missing, + eval_name, + ) + + +def evaluate_forecasters_from_file( + load_paths, save_path, error_on_missing=True, eval_name=None +): + forecaster_results = [] + for load_path in load_paths: + forecaster_results.append(ForecasterResults().load_from_file(load_path)) + + evaluate_classifiers( + forecaster_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def evaluate_forecasters_by_problem( + load_path, + forecaster_names, + dataset_names, + save_path, + resamples=None, + error_on_missing=True, + eval_name=None, +): + if resamples is None: + resamples = [""] + elif isinstance(resamples, int): + resamples = [str(i) for i in range(resamples)] + else: + resamples = [str(resample) for resample in resamples] + + forecaster_results = [] + for forecaster_name in forecaster_names: + for dataset_name in dataset_names: + for resample in resamples: + forecaster_results.append( + ForecasterResults().load_from_file( + f"{load_path}/{forecaster_name}/Predictions/{dataset_name}" + f"/resample{resample}.csv" + ) + ) + + evaluate_forecasters( + forecaster_results, + save_path, + error_on_missing=error_on_missing, + eval_name=eval_name, + ) + + +def _evaluate_estimators( + estimator_results, statistics, save_path, error_on_missing, eval_name +): + save_path = save_path + "/" + eval_name + "/" + + estimators = set() + datasets = set() + resamples = set() + has_test = False + has_train = False + + results_dict = _create_results_dictionary(estimator_results) + + if eval_name is None: + dt = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + eval_name = f"{estimator_results[0].__class__.__name__}Evaluation {dt}" + + for estimator_name in results_dict: + estimators.add(estimator_name) + for dataset_name in results_dict[estimator_name]: + datasets.add(dataset_name) + for split in results_dict[estimator_name][dataset_name]: + split_fail = False + if split == "train": + has_train = True + elif split == "test": + has_test = True + else: + split_fail = True + + for resample in results_dict[estimator_name][dataset_name][split]: + if split_fail: + raise ValueError( + "Results must have a split of either 'train' or 'test' " + f"to be evaluated. Missing for {estimator_name} on " + f"{dataset_name} resample {resample}." + ) + + if resample is not None: + resamples.add(resample) + else: + raise ValueError( + "Results must have a resample_id to be evaluated. " + f"Missing for {estimator_name} on {dataset_name} " + f"{split} resample {resample}." + ) + + estimators = sorted(list(estimators)) + datasets = sorted(list(datasets)) + resamples = sorted(list(resamples)) + has_dataset_train = np.zeros( + (len(estimators), len(datasets), len(resamples)), dtype=bool + ) + has_dataset_test = np.zeros( + (len(estimators), len(datasets), len(resamples)), dtype=bool + ) + + for estimator_name in results_dict: + for dataset_name in results_dict[estimator_name]: + for split in results_dict[estimator_name][dataset_name]: + for resample in results_dict[estimator_name][dataset_name][split]: + if split == "train": + has_dataset_train[estimators.index(estimator_name)][ + datasets.index(dataset_name) + ][resamples.index(resample)] = True + elif split == "test": + has_dataset_test[estimators.index(estimator_name)][ + datasets.index(dataset_name) + ][resamples.index(resample)] = True + + msg = "\n\n" + missing = False + splits = [] + + if has_train: + splits.append("train") + for (i, n, j), present in np.ndenumerate(has_dataset_train): + if not present: + msg += ( + f"Estimator {estimators[i]} is missing train results for " + f"{datasets[n]} resample {resamples[j]}.\n" + ) + missing = True + + if has_test: + splits.append("test") + for (i, n, j), present in np.ndenumerate(has_dataset_test): + if not present: + msg += ( + f"Estimator {estimators[i]} is missing test results for " + f"{datasets[n]} resample {resamples[j]}.\n" + ) + missing = True + + if missing: + if error_on_missing: + print(msg + "\n") # noqa: T201 + raise ValueError("Missing results, exiting evaluation.") + else: + if has_test and has_train: + datasets = datasets[ + has_dataset_train.any(axis=(0, 2)) + & has_dataset_test.any(axis=(0, 2)) + ] + elif has_test: + datasets = datasets[has_dataset_test.any(axis=(0, 2))] + else: + datasets = datasets[has_dataset_train.any(axis=(0, 2))] + + msg += "\nMissing results, continuing evaluation with available datasets.\n" + print(msg) # noqa: T201 + else: + msg += "All results present, continuing evaluation.\n" + print(msg) + + print(f"Estimators: {estimators}\n") # noqa: T201 + print(f"Datasets: {datasets}\n") # noqa: T201 + print(f"Resamples: {resamples}\n") # noqa: T201 + + stats = [] + for var, (stat, ascending) in statistics.items(): + for split in splits: + average, rank = _create_directory_for_statistic( + estimators, + datasets, + resamples, + split, + results_dict, + stat, + ascending, + var, + save_path, + ) + stats.append((average, rank, stat, split)) + + _summary_evaluation(stats, estimators, save_path, eval_name) + + +def _create_results_dictionary(estimator_results): + results_dict = {} + + for estimator_result in estimator_results: + if results_dict.get(estimator_result.estimator_name) is None: + results_dict[estimator_result.estimator_name] = {} + + if ( + results_dict[estimator_result.estimator_name].get( + estimator_result.dataset_name + ) + is None + ): + results_dict[estimator_result.estimator_name][ + estimator_result.dataset_name + ] = {} + + if ( + results_dict[estimator_result.estimator_name][ + estimator_result.dataset_name + ].get(estimator_result.split.lower()) + is None + ): + results_dict[estimator_result.estimator_name][ + estimator_result.dataset_name + ][estimator_result.split.lower()] = {} + + results_dict[estimator_result.estimator_name][estimator_result.dataset_name][ + estimator_result.split.lower() + ][estimator_result.resample_id] = estimator_result + + return results_dict + + +def _create_directory_for_statistic( + estimators, + datasets, + resamples, + split, + results_dict, + statistic_name, + higher_better, + variable_name, + save_path, +): + os.makedirs(f"{save_path}/{statistic_name}/all_folds/", exist_ok=True) + + average_stats = np.zeros((len(datasets), len(estimators))) + + for i, estimator_name in enumerate(estimators): + est_stats = np.zeros((len(datasets), len(resamples))) + + for n, dataset_name in enumerate(datasets): + for j, resample in enumerate(resamples): + er = results_dict[estimator_name][dataset_name][split][resample] + er.calculate_statistics() + est_stats[n, j] = er.__dict__[variable_name] + + average_stats[n, i] = np.mean(est_stats[n, :]) + + with open( + f"{save_path}/{statistic_name}/all_folds/{estimator_name}_" + f"{statistic_name}.csv", + "w", + ) as file: + file.write(f",{','.join([str(j) for j in resamples])}\n") + for n, dataset_name in enumerate(datasets): + file.write( + f"{dataset_name},{','.join([str(j) for j in est_stats[n]])}\n" + ) + + with open(f"{save_path}/{statistic_name}/{statistic_name}_mean.csv", "w") as file: + file.write(f",{','.join(estimators)}\n") + for i, dataset_name in enumerate(datasets): + file.write( + f"{dataset_name},{','.join([str(n) for n in average_stats[i]])}\n" + ) + + ranks = np.apply_along_axis( + lambda x: rank_array(x, higher_better=higher_better), 1, average_stats + ) + + with open(f"{save_path}/{statistic_name}/{statistic_name}_ranks.csv", "w") as file: + file.write(f",{','.join(estimators)}\n") + for i, dataset_name in enumerate(datasets): + file.write(f"{dataset_name},{','.join([str(n) for n in ranks[i]])}\n") + + _figures_for_statistic( + average_stats, estimators, statistic_name, higher_better, save_path + ) + + return average_stats, ranks + + +def _figures_for_statistic( + scores, estimators, statistic_name, higher_better, save_path +): + os.makedirs(f"{save_path}/{statistic_name}/figures/", exist_ok=True) + + cd = plot_critical_difference(scores, estimators, errors=not higher_better) + cd.savefig( + f"{save_path}/{statistic_name}/figures/{statistic_name}_critical_difference.png" + ) + + +def _summary_evaluation(stats, estimators, save_path, eval_name): + with open(f"{save_path}/{eval_name}_summary.csv", "w") as file: + for stat in stats: + file.write(f"{stat[3]}{stat[2]},{','.join(estimators)}\n") + file.write( + f"{stat[3]}{stat[2]}Mean," + f"{','.join([str(n) for n in np.mean(stat[0], axis=0)])}\n" + ) + file.write( + f"{stat[3]}{stat[2]}AvgRank," + f"{','.join([str(n) for n in np.mean(stat[1], axis=0)])}\n\n" + ) diff --git a/tsml_eval/evaluation/storage/__init__.py b/tsml_eval/evaluation/storage/__init__.py new file mode 100644 index 00000000..c05a0a0a --- /dev/null +++ b/tsml_eval/evaluation/storage/__init__.py @@ -0,0 +1,29 @@ +"""Storage for estimator results and result i/o""" + +__all__ = [ + "ClassifierResults", + "ClustererResults", + "ForecasterResults", + "RegressorResults", + "load_classifier_results", + "load_clusterer_results", + "load_forecaster_results", + "load_regressor_results", +] + +from tsml_eval.evaluation.storage.classifier_results import ( + ClassifierResults, + load_classifier_results, +) +from tsml_eval.evaluation.storage.clusterer_results import ( + ClustererResults, + load_clusterer_results, +) +from tsml_eval.evaluation.storage.forecaster_results import ( + ForecasterResults, + load_forecaster_results, +) +from tsml_eval.evaluation.storage.regressor_results import ( + RegressorResults, + load_regressor_results, +) diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py new file mode 100644 index 00000000..9415007e --- /dev/null +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -0,0 +1,353 @@ +"""Class for storing and loading results from a classification experiment.""" + +import numpy as np +from sklearn.metrics import ( + accuracy_score, + balanced_accuracy_score, + f1_score, + log_loss, + roc_auc_score, +) + +import tsml_eval.evaluation.storage as storage +from tsml_eval.evaluation.storage.estimator_results import EstimatorResults +from tsml_eval.utils.experiments import write_classification_results + + +class ClassifierResults(EstimatorResults): + """ + A class for storing and managing results from classification experiments. + + This class provides functionalities for storing classification results, + including predictions, probabilities, and various performance metrics. + It extends the `EstimatorResults` class, inheriting its base functionalities. + + Parameters + ---------- + dataset_name : str, optional + Name of the dataset used, by default "N/A". + classifier_name : str, optional + Name of the classifier used, by default "N/A". + split : str, optional + Type of data split used, by default "N/A". + resample_id : int or None, optional + Identifier for the resampling method, by default None. + time_unit : str, optional + Unit of time measurement, by default "nanoseconds". + description : str, optional + Description of the classification experiment, by default "". + parameters : str, optional + Information about parameters used, by default "No parameter info". + fit_time : float, optional + Time taken for fitting the model, by default -1.0. + predict_time : float, optional + Time taken for making predictions, by default -1.0. + benchmark_time : float, optional + Time taken for benchmarking, by default -1.0. + memory_usage : float, optional + Memory usage during the experiment, by default -1.0. + n_classes : int or None, optional + Number of classes in the classification task, by default None. + error_estimate_method : str, optional + Method used for error estimation, by default "N/A". + error_estimate_time : float, optional + Time taken for error estimation, by default -1.0. + build_plus_estimate_time : float, optional + Total time for building and estimating, by default -1.0. + class_labels : array-like or None, optional + Actual class labels, by default None. + predictions : array-like or None, optional + Predicted class labels, by default None. + probabilities : array-like or None, optional + Predicted class probabilities, by default None. + pred_times : array-like or None, optional + Prediction times for each instance, by default None. + pred_descriptions : list of str or None, optional + Descriptions for each prediction, by default None. + + """ + + + def __init__( + self, + dataset_name="N/A", + classifier_name="N/A", + split="N/A", + resample_id=None, + time_unit="nanoseconds", + description="", + parameters="No parameter info", + fit_time=-1.0, + predict_time=-1.0, + benchmark_time=-1.0, + memory_usage=-1.0, + n_classes=None, + error_estimate_method="N/A", + error_estimate_time=-1.0, + build_plus_estimate_time=-1.0, + class_labels=None, + predictions=None, + probabilities=None, + pred_times=None, + pred_descriptions=None, + ): + # Line 3 + self.n_classes = n_classes + self.train_estimate_method = error_estimate_method + self.train_estimate_time = error_estimate_time + self.fit_and_estimate_time = build_plus_estimate_time + + # Results + self.class_labels = class_labels + self.predictions = predictions + self.probabilities = probabilities + self.pred_times = pred_times + self.pred_descriptions = pred_descriptions + + self.n_cases = None + + self.accuracy = None + self.balanced_accuracy = None + self.f1_score = None + self.negative_log_likelihood = None + self.mean_auroc = None + + super(ClassifierResults, self).__init__( + dataset_name=dataset_name, + estimator_name=classifier_name, + split=split, + resample_id=resample_id, + time_unit=time_unit, + description=description, + parameters=parameters, + fit_time=fit_time, + predict_time=predict_time, + benchmark_time=benchmark_time, + memory_usage=memory_usage, + ) + + # var_name: (display_name, higher is better) + statistics = { + "accuracy": ("Accuracy", True), + "balanced_accuracy": ("BalAcc", True), + "f1_score": ("F1", True), + "negative_log_likelihood": ("NLL", False), + "mean_auroc": ("AUROC", True), + **EstimatorResults.statistics, + } + + def save_to_file(self, file_path, full_path=True): + """ + Save the classifier results to a specified file. + + This method serializes the results of the classifier and saves them to a file + in a chosen format. + + Parameters + ---------- + file_path : str + The path to the file where the results should be saved. + """ + self.infer_size() + + if self.accuracy is None: + self.accuracy = accuracy_score(self.class_labels, self.predictions) + + write_classification_results( + self.predictions, + self.probabilities, + self.class_labels, + self.estimator_name, + self.dataset_name, + file_path, + full_path=full_path, + split=self.split, + resample_id=self.resample_id, + time_unit=self.time_unit, + first_line_comment=self.description, + parameter_info=self.parameter_info, + accuracy=self.accuracy, + fit_time=self.fit_time, + predict_time=self.predict_time, + benchmark_time=self.benchmark_time, + memory_usage=self.memory_usage, + n_classes=self.n_classes, + train_estimate_method=self.train_estimate_method, + train_estimate_time=self.train_estimate_time, + fit_and_estimate_time=self.fit_and_estimate_time, + ) + + def load_from_file(self, file_path): + """ + Load classifier results from a specified file. + + This method deserializes classifier results from a given file, allowing for the + analysis and comparison of previously computed results. + + Parameters + ---------- + file_path : str + The path to the file from which the results should be loaded. + + Returns + ------- + self: ClassifierResults + The classifier results object loaded from the file. + """ + cr = storage.load_classifier_results(file_path) + self.__dict__.update(cr.__dict__) + return self + + def calculate_statistics(self, overwrite=False): + """ + Calculate and return various statistics based on the classifier results. + + This method computes various performance metrics, such as accuracy, F1 score, + and others, based on the classifier's output. + + Returns + ------- + dict + A dictionary containing the calculated statistics. Keys are the names of the + metrics, and values are their computed values. + """ + self.infer_size(overwrite=overwrite) + + if self.accuracy is None or overwrite: + self.accuracy = accuracy_score(self.class_labels, self.predictions) + if self.balanced_accuracy is None or overwrite: + self.balanced_accuracy = balanced_accuracy_score( + self.class_labels, self.predictions + ) + if self.f1_score is None or overwrite: + self.f1_score = f1_score( + self.class_labels, self.predictions, average="macro" + ) + if self.negative_log_likelihood is None or overwrite: + self.negative_log_likelihood = log_loss( + self.class_labels, self.probabilities + ) + if self.mean_auroc is None or overwrite: + self.mean_auroc = roc_auc_score( + self.class_labels, + self.predictions if self.n_classes == 2 else self.probabilities, + multi_class="ovr", + ) + + def infer_size(self, overwrite=False): + """ + Infer and return the size of the dataset used in the classifier. + + This method estimates the size of the dataset that was used for the classifier, based on the results data. + + Returns + ------- + int + The inferred size of the dataset. + + Notes + ----- + The accuracy of the inferred size may vary and should be validated with actual dataset parameters when possible. + """ + if self.n_cases is None or overwrite: + self.n_cases = len(self.class_labels) + if self.n_classes is None or overwrite: + self.n_classes = len(self.probabilities[0]) + + +def load_classifier_results(file_path, calculate_stats=True, verify_values=True): + """ + Load and return classifier results from a specified file. + + This function reads a file containing serialized classifier results and + deserializes it to reconstruct the classifier results object. It optionally + calculates statistics and verifies values based on the loaded data. + + Parameters + ---------- + file_path : str + The path to the file from which classifier results should be loaded. The file should be in a format compatible with the serialization method used. + calculate_stats : bool, optional + A flag to indicate whether to calculate statistics from the loaded results. Default is True. + verify_values : bool, optional + A flag to determine if the function should perform verification of the loaded values. Default is True. + + Returns + ------- + ClassifierResults + A ClassifierResults object containing the results loaded from the file. + """ + with open(file_path, "r") as file: + lines = file.readlines() + + line1 = lines[0].split(",") + line3 = lines[2].split(",") + acc = float(line3[0]) + n_classes = int(line3[5]) + n_cases = len(lines) - 3 + + line_size = len(lines[3].split(",")) + + class_labels = np.zeros(n_cases) + predictions = np.zeros(n_cases) + probabilities = np.zeros((n_cases, n_classes)) + + if line_size > 3 + n_classes: + pred_times = np.zeros(n_cases) + else: + pred_times = None + + if line_size > 6 + n_classes: + pred_descriptions = [] + else: + pred_descriptions = None + + for i in range(0, n_cases): + line = lines[i + 3].split(",") + class_labels[i] = int(line[0]) + predictions[i] = int(line[1]) + + for j in range(0, n_classes): + probabilities[i, j] = float(line[3 + j]) + + if pred_times is not None: + pred_times[i] = float(line[5 + n_classes]) + + if pred_descriptions is not None: + pred_descriptions.append(",".join(line[6 + n_classes :]).strip()) + + cr = ClassifierResults( + dataset_name=line1[0], + classifier_name=line1[1], + split=line1[2], + resample_id=None if line1[3] == "None" else int(line1[3]), + time_unit=line1[4].lower(), + description=",".join(line1[5:]).strip(), + parameters=lines[1].strip(), + fit_time=float(line3[1]), + predict_time=float(line3[2]), + benchmark_time=float(line3[3]), + memory_usage=float(line3[4]), + n_classes=n_classes, + error_estimate_method=line3[6], + error_estimate_time=float(line3[7]), + build_plus_estimate_time=float(line3[8]), + class_labels=class_labels, + predictions=predictions, + probabilities=probabilities, + pred_times=pred_times, + pred_descriptions=pred_descriptions, + ) + + if calculate_stats: + cr.calculate_statistics() + + if verify_values: + cr.infer_size(overwrite=True) + assert cr.n_cases == n_cases + assert cr.n_classes == n_classes + + if calculate_stats: + assert cr.accuracy == acc + + return cr diff --git a/tsml_eval/evaluation/storage/clusterer_results.py b/tsml_eval/evaluation/storage/clusterer_results.py new file mode 100644 index 00000000..edea259f --- /dev/null +++ b/tsml_eval/evaluation/storage/clusterer_results.py @@ -0,0 +1,251 @@ +"""Class for storing and loading results from a clustering experiment.""" + +import numpy as np +from sklearn.metrics import ( + adjusted_mutual_info_score, + adjusted_rand_score, + mutual_info_score, + normalized_mutual_info_score, + rand_score, +) + +import tsml_eval.evaluation.storage as storage +from tsml_eval.evaluation.metrics import clustering_accuracy_score +from tsml_eval.evaluation.storage.estimator_results import EstimatorResults +from tsml_eval.utils.experiments import write_clustering_results + + +class ClustererResults(EstimatorResults): + def __init__( + self, + dataset_name="N/A", + clusterer_name="N/A", + split="N/A", + resample_id=None, + time_unit="nanoseconds", + description="", + parameters="No parameter info", + fit_time=-1.0, + predict_time=-1.0, + benchmark_time=-1.0, + memory_usage=-1.0, + n_classes=None, + n_clusters=None, + class_labels=None, + predictions=None, + probabilities=None, + pred_times=None, + pred_descriptions=None, + ): + # Line 3 + self.n_classes = n_classes + self.n_clusters = n_clusters + + # Results + self.class_labels = class_labels + self.predictions = predictions + self.probabilities = probabilities + self.pred_times = pred_times + self.pred_descriptions = pred_descriptions + + self.n_cases = None + + self.clustering_accuracy = None + self.rand_index = None + self.adjusted_rand_index = None + self.mutual_information = None + self.adjusted_mutual_information = None + self.normalised_mutual_information = None + + super(ClustererResults, self).__init__( + dataset_name=dataset_name, + estimator_name=clusterer_name, + split=split, + resample_id=resample_id, + time_unit=time_unit, + description=description, + parameters=parameters, + fit_time=fit_time, + predict_time=predict_time, + benchmark_time=benchmark_time, + memory_usage=memory_usage, + ) + + # var_name: (display_name, higher is better) + statistics = { + "clustering_accuracy": ("CLAcc", True), + "rand_index": ("RI", True), + "adjusted_rand_index": ("ARI", True), + "mutual_information": ("MI", True), + "adjusted_mutual_information": ("AMI", True), + "normalised_mutual_information": ("NMI", True), + **EstimatorResults.statistics, + } + + def save_to_file(self, file_path, full_path=True): + """ + Writes the full results to a file. + + Parameters + ---------- + file_path : str + The path of the file to write the results to. + full_path : boolean, default=True + If True, results are written directly to the directory passed in output_path. + If False, then a standard file structure using the classifier and dataset names + is created and used to write the results file. + """ + self.infer_size() + + if self.clustering_accuracy is None: + self.clustering_accuracy = clustering_accuracy_score( + self.class_labels, self.predictions + ) + + write_clustering_results( + self.predictions, + self.probabilities, + self.class_labels, + self.estimator_name, + self.dataset_name, + file_path, + full_path=full_path, + split=self.split, + resample_id=self.resample_id, + time_unit=self.time_unit, + first_line_comment=self.description, + parameter_info=self.parameter_info, + clustering_accuracy=self.clustering_accuracy, + fit_time=self.fit_time, + predict_time=self.predict_time, + benchmark_time=self.benchmark_time, + memory_usage=self.memory_usage, + n_classes=self.n_classes, + n_clusters=self.n_clusters, + ) + + def load_from_file(self, file_path): + """Load results from a specified file. + + Parameters + ---------- + file_path : str + The path to the file where the results will be loaded from. + """ + cr = storage.load_clusterer_results(file_path) + self.__dict__.update(cr.__dict__) + return self + + def calculate_statistics(self, overwrite=False): + """Calculate statistics from the results. + + This method should handle any necessary calculations to produce statistics + from the results data held within the object. + """ + self.infer_size(overwrite=overwrite) + + if self.clustering_accuracy is None or overwrite: + self.clustering_accuracy = clustering_accuracy_score( + self.class_labels, self.predictions + ) + if self.rand_index is None or overwrite: + self.rand_index = rand_score(self.class_labels, self.predictions) + if self.adjusted_rand_index is None or overwrite: + self.adjusted_rand_index = adjusted_rand_score( + self.class_labels, self.predictions + ) + if self.mutual_information is None or overwrite: + self.mutual_information = mutual_info_score( + self.class_labels, self.predictions + ) + if self.adjusted_mutual_information is None or overwrite: + self.adjusted_mutual_information = adjusted_mutual_info_score( + self.class_labels, self.predictions + ) + if self.normalised_mutual_information is None or overwrite: + self.normalised_mutual_information = normalized_mutual_info_score( + self.class_labels, self.predictions + ) + + def infer_size(self, overwrite=False): + if self.n_cases is None or overwrite: + self.n_cases = len(self.class_labels) + if self.n_clusters is None or overwrite: + self.n_clusters = len(self.probabilities[0]) + + +def load_clusterer_results(file_path, calculate_stats=True, verify_values=True): + """Load clusterer results from a file.""" + + with open(file_path, "r") as file: + lines = file.readlines() + + line1 = lines[0].split(",") + line3 = lines[2].split(",") + cl_acc = float(line3[0]) + n_clusters = int(line3[6]) + n_cases = len(lines) - 3 + + line_size = len(lines[3].split(",")) + + class_labels = np.zeros(n_cases) + cluster = np.zeros(n_cases) + probabilities = np.zeros((n_cases, n_clusters)) + + if line_size > 3 + n_clusters: + pred_times = np.zeros(n_cases) + else: + pred_times = None + + if line_size > 6 + n_clusters: + pred_descriptions = [] + else: + pred_descriptions = None + + for i in range(0, n_cases): + line = lines[i + 3].split(",") + class_labels[i] = int(line[0]) + cluster[i] = int(line[1]) + + for j in range(0, n_clusters): + probabilities[i, j] = float(line[3 + j]) + + if pred_times is not None: + pred_times[i] = float(line[5 + n_clusters]) + + if pred_descriptions is not None: + pred_descriptions.append(",".join(line[6 + n_clusters :]).strip()) + + cr = ClustererResults( + dataset_name=line1[0], + clusterer_name=line1[1], + split=line1[2], + resample_id=None if line1[3] == "None" else int(line1[3]), + time_unit=line1[4].lower(), + description=",".join(line1[5:]).strip(), + parameters=lines[1].strip(), + fit_time=float(line3[1]), + predict_time=float(line3[2]), + benchmark_time=float(line3[3]), + memory_usage=float(line3[4]), + n_classes=int(line3[5]), + n_clusters=n_clusters, + class_labels=class_labels, + predictions=cluster, + probabilities=probabilities, + pred_times=pred_times, + pred_descriptions=pred_descriptions, + ) + + if calculate_stats: + cr.calculate_statistics() + + if verify_values: + cr.infer_size(overwrite=True) + assert cr.n_cases == n_cases + assert cr.n_clusters == n_clusters + + if calculate_stats: + assert cr.clustering_accuracy == cl_acc + + return cr diff --git a/tsml_eval/evaluation/storage/estimator_results.py b/tsml_eval/evaluation/storage/estimator_results.py new file mode 100644 index 00000000..24bce6e1 --- /dev/null +++ b/tsml_eval/evaluation/storage/estimator_results.py @@ -0,0 +1,107 @@ +"""Abstract class for storing and loading results from an experiment.""" + +from abc import ABC, abstractmethod + + +class EstimatorResults(ABC): + """ + Abstract base class for storing estimator results. + + Parameters + ---------- + dataset_name : str, optional + Name of the dataset. + estimator_name : str, optional + Name of the estimator. + split : str, optional + Dataset split (e.g., 'train' or 'test'). + resample_id : int, optional + Identifier for the data fold. + time_unit : str, optional + Unit of time measurement, default is "nanoseconds". + description : str, optional + A human-friendly description of the estimator results. + parameters : str, optional + Estimator parameters and other related information as a string. + fit_time : float, optional + Time taken to build the estimator. + predict_time : float, optional + Time taken to test the estimator. + benchmark_time : float, optional + Time taken to benchmark the estimator. + memory_usage : float, optional + Memory usage of the estimator. + + """ + + def __init__( + self, + dataset_name="N/A", + estimator_name="N/A", + split="N/A", + resample_id=-1, + time_unit="nanoseconds", + description="", + parameters="No parameter info", + fit_time=-1.0, + predict_time=-1.0, + benchmark_time=-1.0, + memory_usage=-1.0, + ): + # Line 1 + self.dataset_name = dataset_name + self.estimator_name = estimator_name + self.split = split + self.resample_id = resample_id + self.time_unit = time_unit + self.description = description + + # Line 2 + self.parameter_info = parameters + + # Line 3 + self.fit_time = fit_time + self.predict_time = predict_time + self.benchmark_time = benchmark_time + self.memory_usage = memory_usage + + self.build_time_milli_ = None + self.median_pred_time_milli_ = None + + # var_name: (display_name, higher is better) + statistics = { + "fit_time": ("FitTime", False), + "predict_time": ("PredictTime", False), + "memory_usage": ("MemoryUsage", False), + } + + @abstractmethod + def save_to_file(self, file_path): + """Save results to a specified file. + + Parameters + ---------- + file_path : str + The path to the file where the results will be saved. + """ + pass + + @abstractmethod + def load_from_file(self, file_path): + """Load results from a specified file. + + Parameters + ---------- + file_path : str + The path to the file where the results will be loaded from. + """ + pass + + @abstractmethod + def calculate_statistics(self, overwrite=False): + """Calculate statistics from the results. + + This method should handle any necessary calculations to produce statistics + from the results data held within the object. + """ + pass diff --git a/tsml_eval/evaluation/storage/forecaster_results.py b/tsml_eval/evaluation/storage/forecaster_results.py new file mode 100644 index 00000000..ade227d0 --- /dev/null +++ b/tsml_eval/evaluation/storage/forecaster_results.py @@ -0,0 +1,192 @@ +"""Class for storing and loading results from a forecasting experiment.""" + +import numpy as np +from sklearn.metrics import mean_absolute_percentage_error + +import tsml_eval.evaluation.storage as storage +from tsml_eval.evaluation.storage.estimator_results import EstimatorResults +from tsml_eval.utils.experiments import write_forecasting_results + + +class ForecasterResults(EstimatorResults): + def __init__( + self, + dataset_name="N/A", + forecaster_name="N/A", + split="N/A", + random_seed=None, + time_unit="nanoseconds", + description="", + parameters="No parameter info", + fit_time=-1.0, + predict_time=-1.0, + benchmark_time=-1.0, + memory_usage=-1.0, + target_labels=None, + predictions=None, + pred_times=None, + pred_descriptions=None, + ): + # Results + self.target_labels = target_labels + self.predictions = predictions + self.pred_times = pred_times + self.pred_descriptions = pred_descriptions + + self.forecasting_horizon = None + + self.mean_absolute_percentage_error = None + + super(ForecasterResults, self).__init__( + dataset_name=dataset_name, + estimator_name=forecaster_name, + split=split, + resample_id=random_seed, + time_unit=time_unit, + description=description, + parameters=parameters, + fit_time=fit_time, + predict_time=predict_time, + benchmark_time=benchmark_time, + memory_usage=memory_usage, + ) + + # var_name: (display_name, higher is better) + statistics = { + "mean_absolute_percentage_error": ("MAPE", False), + **EstimatorResults.statistics, + } + + def save_to_file(self, file_path, full_path=True): + """ + Writes the full results to a file. + + Parameters + ---------- + file_path : str + The path of the file to write the results to. + full_path : boolean, default=True + If True, results are written directly to the directory passed in output_path. + If False, then a standard file structure using the classifier and dataset names + is created and used to write the results file. + """ + self.infer_size() + + if self.mean_absolute_percentage_error is None: + self.mean_absolute_percentage_error = mean_absolute_percentage_error( + self.target_labels, self.predictions + ) + + write_forecasting_results( + self.predictions, + self.target_labels, + self.estimator_name, + self.dataset_name, + file_path, + full_path=full_path, + split=self.split, + random_seed=self.resample_id, + time_unit=self.time_unit, + first_line_comment=self.description, + parameter_info=self.parameter_info, + mape=self.mean_absolute_percentage_error, + fit_time=self.fit_time, + predict_time=self.predict_time, + benchmark_time=self.benchmark_time, + memory_usage=self.memory_usage, + ) + + def load_from_file(self, file_path): + """Load results from a specified file. + + Parameters + ---------- + file_path : str + The path to the file where the results will be loaded from. + """ + fr = storage.load_forecaster_results(file_path) + self.__dict__.update(fr.__dict__) + return self + + def calculate_statistics(self, overwrite=False): + """Calculate statistics from the results. + + This method should handle any necessary calculations to produce statistics + from the results data held within the object. + """ + self.infer_size(overwrite=overwrite) + + if self.mean_absolute_percentage_error is None or overwrite: + self.mean_absolute_percentage_error = mean_absolute_percentage_error( + self.target_labels, self.predictions + ) + + def infer_size(self, overwrite=False): + if self.forecasting_horizon is None or overwrite: + self.forecasting_horizon = len(self.target_labels) + + +def load_forecaster_results(file_path, calculate_stats=True, verify_values=True): + with open(file_path, "r") as file: + lines = file.readlines() + + line1 = lines[0].split(",") + line3 = lines[2].split(",") + mape = float(line3[0]) + fh = len(lines) - 3 + + line_size = len(lines[3].split(",")) + + target_labels = np.zeros(fh) + predictions = np.zeros(fh) + + if line_size > 3: + pred_times = np.zeros(fh) + else: + pred_times = None + + if line_size > 5: + pred_descriptions = [] + else: + pred_descriptions = None + + for i in range(0, fh): + line = lines[i + 3].split(",") + target_labels[i] = float(line[0]) + predictions[i] = float(line[1]) + + if pred_times is not None: + pred_times[i] = float(line[4]) + + if pred_descriptions is not None: + pred_descriptions.append(",".join(line[6]).strip()) + + fr = ForecasterResults( + dataset_name=line1[0], + forecaster_name=line1[1], + split=line1[2], + random_seed=None if line1[3] == "None" else int(line1[3]), + time_unit=line1[4].lower(), + description=",".join(line1[5:]).strip(), + parameters=lines[1].strip(), + fit_time=float(line3[1]), + predict_time=float(line3[2]), + benchmark_time=float(line3[3]), + memory_usage=float(line3[4]), + target_labels=target_labels, + predictions=predictions, + pred_times=pred_times, + pred_descriptions=pred_descriptions, + ) + + if calculate_stats: + fr.calculate_statistics() + + if verify_values: + fr.infer_size(overwrite=True) + assert fr.forecasting_horizon == fh + + if calculate_stats: + assert fr.mean_absolute_percentage_error == mape + + return fr diff --git a/tsml_eval/evaluation/storage/regressor_results.py b/tsml_eval/evaluation/storage/regressor_results.py new file mode 100644 index 00000000..382eeb1e --- /dev/null +++ b/tsml_eval/evaluation/storage/regressor_results.py @@ -0,0 +1,233 @@ +"""Class for storing and loading results from a regression experiment.""" + +import numpy as np +from sklearn.metrics import ( + mean_absolute_error, + mean_absolute_percentage_error, + mean_squared_error, + r2_score, +) + +import tsml_eval.evaluation.storage as storage +from tsml_eval.evaluation.storage.estimator_results import EstimatorResults +from tsml_eval.utils.experiments import write_regression_results + + +class RegressorResults(EstimatorResults): + def __init__( + self, + dataset_name="N/A", + regressor_name="N/A", + split="N/A", + resample_id=None, + time_unit="nanoseconds", + description="", + parameters="No parameter info", + fit_time=-1.0, + predict_time=-1.0, + benchmark_time=-1.0, + memory_usage=-1.0, + error_estimate_method="N/A", + error_estimate_time=-1.0, + build_plus_estimate_time=-1.0, + target_labels=None, + predictions=None, + pred_times=None, + pred_descriptions=None, + ): + # Line 3 + self.train_estimate_method = error_estimate_method + self.train_estimate_time = error_estimate_time + self.fit_and_estimate_time = build_plus_estimate_time + + # Results + self.target_labels = target_labels + self.predictions = predictions + self.pred_times = pred_times + self.pred_descriptions = pred_descriptions + + self.n_cases = None + + self.mean_squared_error = None + self.root_mean_squared_error = None + self.mean_absolute_error = None + self.r2_score = None + self.mean_absolute_percentage_error = None + + super(RegressorResults, self).__init__( + dataset_name=dataset_name, + estimator_name=regressor_name, + split=split, + resample_id=resample_id, + time_unit=time_unit, + description=description, + parameters=parameters, + fit_time=fit_time, + predict_time=predict_time, + benchmark_time=benchmark_time, + memory_usage=memory_usage, + ) + + # var_name: (display_name, higher is better) + statistics = { + "mean_squared_error": ("MSE", False), + "root_mean_squared_error": ("RMSE", False), + "mean_absolute_error": ("MAE", False), + "r2_score": ("R2", True), + "mean_absolute_percentage_error": ("MAPE", False), + **EstimatorResults.statistics, + } + + def save_to_file(self, file_path, full_path=True): + """ + Writes the full results to a file. + + Parameters + ---------- + file_path : str + The path of the file to write the results to. + full_path : boolean, default=True + If True, results are written directly to the directory passed in output_path. + If False, then a standard file structure using the classifier and dataset names + is created and used to write the results file. + """ + self.infer_size() + + if self.mean_squared_error is None: + self.mean_squared_error = mean_squared_error( + self.target_labels, self.predictions + ) + + write_regression_results( + self.predictions, + self.target_labels, + self.estimator_name, + self.dataset_name, + file_path, + full_path=full_path, + split=self.split, + resample_id=self.resample_id, + time_unit=self.time_unit, + first_line_comment=self.description, + parameter_info=self.parameter_info, + mse=self.mean_squared_error, + fit_time=self.fit_time, + predict_time=self.predict_time, + benchmark_time=self.benchmark_time, + memory_usage=self.memory_usage, + train_estimate_method=self.train_estimate_method, + train_estimate_time=self.train_estimate_time, + fit_and_estimate_time=self.fit_and_estimate_time, + ) + + def load_from_file(self, file_path, calculate_stats=True, verify_values=True): + """Load results from a specified file. + + Parameters + ---------- + file_path : str + The path to the file where the results will be loaded from. + """ + rr = storage.load_regressor_results(file_path) + self.__dict__.update(rr.__dict__) + return self + + def calculate_statistics(self, overwrite=False): + """Calculate statistics from the results. + + This method should handle any necessary calculations to produce statistics + from the results data held within the object. + """ + self.infer_size(overwrite=overwrite) + + if self.mean_squared_error is None or overwrite: + self.mean_squared_error = mean_squared_error( + self.target_labels, self.predictions + ) + if self.root_mean_squared_error is None or overwrite: + self.root_mean_squared_error = mean_squared_error( + self.target_labels, self.predictions, squared=False + ) + if self.mean_absolute_error is None or overwrite: + self.mean_absolute_error = mean_absolute_error( + self.target_labels, self.predictions + ) + if self.r2_score is None or overwrite: + self.r2_score = r2_score(self.target_labels, self.predictions) + if self.mean_absolute_percentage_error is None or overwrite: + self.mean_absolute_percentage_error = mean_absolute_percentage_error( + self.target_labels, self.predictions + ) + + def infer_size(self, overwrite=False): + if self.n_cases is None or overwrite: + self.n_cases = len(self.target_labels) + + +def load_regressor_results(file_path, calculate_stats=True, verify_values=True): + with open(file_path, "r") as file: + lines = file.readlines() + + line1 = lines[0].split(",") + line3 = lines[2].split(",") + mse = float(line3[0]) + n_cases = len(lines) - 3 + + line_size = len(lines[3].split(",")) + + target_labels = np.zeros(n_cases) + predictions = np.zeros(n_cases) + + if line_size > 3: + pred_times = np.zeros(n_cases) + else: + pred_times = None + + if line_size > 5: + pred_descriptions = [] + else: + pred_descriptions = None + + for i in range(0, n_cases): + line = lines[i + 3].split(",") + target_labels[i] = float(line[0]) + predictions[i] = float(line[1]) + + if pred_times is not None: + pred_times[i] = float(line[4]) + + if pred_descriptions is not None: + pred_descriptions.append(",".join(line[6]).strip()) + + rr = RegressorResults( + dataset_name=line1[0], + regressor_name=line1[1], + split=line1[2], + resample_id=None if line1[3] == "None" else int(line1[3]), + time_unit=line1[4].lower(), + description=",".join(line1[5:]).strip(), + parameters=lines[1].strip(), + fit_time=float(line3[1]), + predict_time=float(line3[2]), + benchmark_time=float(line3[3]), + memory_usage=float(line3[4]), + error_estimate_method=line3[5], + error_estimate_time=float(line3[6]), + build_plus_estimate_time=float(line3[7]), + target_labels=target_labels, + predictions=predictions, + pred_times=pred_times, + pred_descriptions=pred_descriptions, + ) + + if calculate_stats: + rr.calculate_statistics() + + if verify_values: + rr.infer_size(overwrite=True) + assert rr.n_cases == n_cases + + if calculate_stats: + assert rr.mean_squared_error == mse + + return rr diff --git a/tsml_eval/evaluation/storage/tests/__init__.py b/tsml_eval/evaluation/storage/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tsml_eval/evaluation/storage/tests/test_io.py b/tsml_eval/evaluation/storage/tests/test_io.py new file mode 100644 index 00000000..81eaaade --- /dev/null +++ b/tsml_eval/evaluation/storage/tests/test_io.py @@ -0,0 +1,68 @@ +import os + +from tsml_eval.evaluation.storage.classifier_results import ClassifierResults +from tsml_eval.evaluation.storage.clusterer_results import ClustererResults +from tsml_eval.evaluation.storage.forecaster_results import ForecasterResults +from tsml_eval.evaluation.storage.regressor_results import RegressorResults +from tsml_eval.testing.test_utils import _TEST_OUTPUT_PATH, _TEST_RESULTS_PATH +from tsml_eval.utils.validation import validate_results_file + + +def test_classifier_results(): + """Test ClassifierResults loading and saving.""" + cr = ClassifierResults().load_from_file( + _TEST_RESULTS_PATH + + "/classification/ROCKET/Predictions/MinimalChinatown/testResample0.csv" + ) + cr.save_to_file(_TEST_OUTPUT_PATH + "/classification/results_io/") + + assert validate_results_file( + _TEST_OUTPUT_PATH + "/classification/results_io/testResample0.csv" + ) + + os.remove(_TEST_OUTPUT_PATH + "/classification/results_io/testResample0.csv") + + +def test_clusterer_results(): + """Test ClustererResults loading and saving.""" + cr = ClustererResults().load_from_file( + _TEST_RESULTS_PATH + + "/clustering/KMeans/Predictions/MinimalChinatown/trainResample0.csv" + ) + cr.save_to_file(_TEST_OUTPUT_PATH + "/clustering/results_io/") + + assert validate_results_file( + _TEST_OUTPUT_PATH + "/clustering/results_io/trainResample0.csv" + ) + + os.remove(_TEST_OUTPUT_PATH + "/clustering/results_io/trainResample0.csv") + + +def test_regressor_results(): + """Test RegressorResults loading and saving.""" + cr = RegressorResults().load_from_file( + _TEST_RESULTS_PATH + + "/regression/ROCKET/Predictions/MinimalGasPrices/testResample0.csv" + ) + cr.save_to_file(_TEST_OUTPUT_PATH + "/regression/results_io/") + + assert validate_results_file( + _TEST_OUTPUT_PATH + "/regression/results_io/testResample0.csv" + ) + + os.remove(_TEST_OUTPUT_PATH + "/regression/results_io/testResample0.csv") + + +def test_forecaster_results(): + """Test ForecasterResults loading and saving.""" + cr = ForecasterResults().load_from_file( + _TEST_RESULTS_PATH + + "/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv" + ) + cr.save_to_file(_TEST_OUTPUT_PATH + "/forecasting/results_io/") + + assert validate_results_file( + _TEST_OUTPUT_PATH + "/forecasting/results_io/testResample0.csv" + ) + + os.remove(_TEST_OUTPUT_PATH + "/forecasting/results_io/testResample0.csv") diff --git a/tsml_eval/evaluation/tests/test_metrics.py b/tsml_eval/evaluation/tests/test_metrics.py index 31fa6918..3f82e628 100644 --- a/tsml_eval/evaluation/tests/test_metrics.py +++ b/tsml_eval/evaluation/tests/test_metrics.py @@ -4,14 +4,14 @@ import numpy as np -from tsml_eval.evaluation.metrics import clustering_accuracy +from tsml_eval.evaluation.metrics import clustering_accuracy_score def test_clustering_accuracy(): """Test clustering accuracy with random labels and clusters.""" labels = np.random.randint(0, 3, 10) clusters = np.random.randint(0, 3, 10) - cl_acc = clustering_accuracy(labels, clusters) + cl_acc = clustering_accuracy_score(labels, clusters) assert isinstance(cl_acc, float) assert 0 <= cl_acc <= 1 diff --git a/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py b/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py new file mode 100644 index 00000000..59d3e54c --- /dev/null +++ b/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py @@ -0,0 +1,67 @@ +from tsml_eval.evaluation.multiple_estimator_evaluation import ( + evaluate_classifiers_by_problem, + evaluate_clusterers_by_problem, + evaluate_forecasters_by_problem, + evaluate_regressors_by_problem, +) +from tsml_eval.testing.test_utils import _TEST_OUTPUT_PATH, _TEST_RESULTS_PATH + + +def test_evaluate_classifiers_by_problem(): + classifiers = ["ROCKET", "TSF", "1NN-DTW"] + datasets = ["Chinatown", "ItalyPowerDemand", "Trace"] + resamples = 3 + + evaluate_classifiers_by_problem( + _TEST_RESULTS_PATH + "/classification/", + classifiers, + datasets, + _TEST_OUTPUT_PATH + "/eval/classification/", + resamples=resamples, + eval_name="test0", + ) + + +def test_evaluate_clusterers_by_problem(): + classifiers = ["ROCKET", "TSF", "1NN-DTW"] + datasets = ["Chinatown", "ItalyPowerDemand", "Trace"] + resamples = 3 + + evaluate_clusterers_by_problem( + _TEST_RESULTS_PATH + "/clustering/", + classifiers, + datasets, + _TEST_OUTPUT_PATH + "/eval/clustering/", + resamples=resamples, + eval_name="test0", + ) + + +def test_evaluate_regressors_by_problem(): + classifiers = ["ROCKET", "TSF", "1NN-DTW"] + datasets = ["Chinatown", "ItalyPowerDemand", "Trace"] + resamples = 3 + + evaluate_regressors_by_problem( + _TEST_RESULTS_PATH + "/regression/", + classifiers, + datasets, + _TEST_OUTPUT_PATH + "/eval/regression/", + resamples=resamples, + eval_name="test0", + ) + + +def test_evaluate_forecasters_by_problem(): + classifiers = ["ROCKET", "TSF"] + datasets = ["Chinatown", "ItalyPowerDemand"] + resamples = 1 + + evaluate_forecasters_by_problem( + _TEST_RESULTS_PATH + "/forecasting/", + classifiers, + datasets, + _TEST_OUTPUT_PATH + "/eval/forecasting/", + resamples=resamples, + eval_name="test0", + ) diff --git a/tsml_eval/experiments/classification_experiments.py b/tsml_eval/experiments/classification_experiments.py index 3fa2e2a2..95b58999 100644 --- a/tsml_eval/experiments/classification_experiments.py +++ b/tsml_eval/experiments/classification_experiments.py @@ -12,6 +12,8 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" import numba from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -19,8 +21,9 @@ from tsml_eval.experiments import load_and_run_classification_experiment from tsml_eval.experiments.set_classifier import set_classifier from tsml_eval.experiments.tests import _CLASSIFIER_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, assign_gpu, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present, assign_gpu def run_experiment(args): diff --git a/tsml_eval/experiments/clustering_experiments.py b/tsml_eval/experiments/clustering_experiments.py index 42b7eca3..0ec6ab4b 100644 --- a/tsml_eval/experiments/clustering_experiments.py +++ b/tsml_eval/experiments/clustering_experiments.py @@ -12,6 +12,8 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" import numba from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -19,8 +21,9 @@ from tsml_eval.experiments import load_and_run_clustering_experiment from tsml_eval.experiments.set_clusterer import set_clusterer from tsml_eval.experiments.tests import _CLUSTERER_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, assign_gpu, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present, assign_gpu def run_experiment(args): diff --git a/tsml_eval/experiments/experiments.py b/tsml_eval/experiments/experiments.py index 7b1ed501..3dd48298 100644 --- a/tsml_eval/experiments/experiments.py +++ b/tsml_eval/experiments/experiments.py @@ -35,7 +35,7 @@ SklearnToTsmlRegressor, ) from tsml_eval.estimators.transformations.scaler import TimeSeriesScaler -from tsml_eval.evaluation.metrics import clustering_accuracy +from tsml_eval.evaluation.metrics import clustering_accuracy_score from tsml_eval.utils.experiments import ( resample_data, stratified_resample_data, @@ -44,6 +44,12 @@ write_forecasting_results, write_regression_results, ) +from tsml_eval.utils.memory_recorder import record_max_memory + +if os.getenv("MEMRECORD_INTERVAL") is not None: + MEMRECORD_INTERVAL = float(os.getenv("MEMRECORD_INTERVAL")) +else: + MEMRECORD_INTERVAL = 5.0 def run_classification_experiment( @@ -140,6 +146,7 @@ def run_classification_experiment( getattr(classifier, "_get_train_probs", None) ) fit_time = -1 + mem_usage = -1 first_comment = ( "PREDICTIONS,Generated by run_classification_experiment on " @@ -150,14 +157,22 @@ def run_classification_experiment( second = str(classifier.get_params()).replace("\n", " ").replace("\r", " ") if build_test_file or classifier_train_probs: - start = int(round(time.time() * 1000)) - classifier.fit(X_train, y_train) - fit_time = int(round(time.time() * 1000)) - start + mem_usage, fit_time = record_max_memory( + classifier.fit, + args=(X_train, y_train), + interval=MEMRECORD_INTERVAL, + return_func_time=True, + ) + fit_time += int(round(getattr(classifier, "_fit_time_milli", 0))) if build_test_file: start = int(round(time.time() * 1000)) test_probs = classifier.predict_proba(X_test) - test_time = int(round(time.time() * 1000)) - start + test_time = ( + int(round(time.time() * 1000)) + - start + + int(round(getattr(classifier, "_predict_time_milli", 0))) + ) test_preds = classifier.classes_[np.argmax(test_probs, axis=1)] test_acc = accuracy_score(y_test, test_preds) @@ -172,12 +187,13 @@ def run_classification_experiment( full_path=False, split="TEST", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, accuracy=test_acc, fit_time=fit_time, predict_time=test_time, + memory_usage=mem_usage, n_classes=n_classes, ) @@ -210,7 +226,7 @@ def run_classification_experiment( full_path=False, split="TRAIN", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, accuracy=train_acc, @@ -397,6 +413,7 @@ def run_regression_experiment( getattr(regressor, "_get_train_preds", None) ) fit_time = -1 + mem_usage = -1 first_comment = ( "Generated by run_regression_experiment on " @@ -406,17 +423,19 @@ def run_regression_experiment( second = str(regressor.get_params()).replace("\n", " ").replace("\r", " ") if build_test_file or regressor_train_preds: - start = int(round(time.time() * 1000)) - regressor.fit(X_train, y_train) - fit_time = (int(round(time.time() * 1000)) - start) + int( - round(getattr(regressor, "_fit_time", 0) * 1000) + mem_usage, fit_time = record_max_memory( + regressor.fit, + args=(X_train, y_train), + interval=MEMRECORD_INTERVAL, + return_func_time=True, ) + fit_time += int(round(getattr(regressor, "_fit_time_milli", 0))) if build_test_file: start = int(round(time.time() * 1000)) test_preds = regressor.predict(X_test) test_time = (int(round(time.time() * 1000)) - start) + int( - round(getattr(regressor, "_test_time", 0) * 1000) + round(getattr(regressor, "_predict_time_milli", 0)) ) test_mse = mean_squared_error(y_test, test_preds) @@ -430,12 +449,13 @@ def run_regression_experiment( full_path=False, split="TEST", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, mse=test_mse, fit_time=fit_time, predict_time=test_time, + memory_usage=mem_usage, ) if build_train_file: @@ -458,7 +478,7 @@ def run_regression_experiment( full_path=False, split="TRAIN", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, mse=train_mse, @@ -680,9 +700,13 @@ def run_clustering_experiment( elif n_clusters is not None: raise ValueError("n_clusters must be an int or None.") - start = int(round(time.time() * 1000)) - clusterer.fit(X_train) - fit_time = int(round(time.time() * 1000)) - start + mem_usage, fit_time = record_max_memory( + clusterer.fit, + args=(X_train,), + interval=MEMRECORD_INTERVAL, + return_func_time=True, + ) + fit_time += int(round(getattr(clusterer, "_fit_time_milli", 0))) first_comment = ( "Generated by run_clustering_experiment on " @@ -703,11 +727,11 @@ def run_clustering_experiment( else clusterer.predict(X_train) ) train_probs = np.zeros((len(train_preds), len(np.unique(train_preds)))) - train_probs[:, train_preds] = 1 + train_probs[np.arange(len(train_preds)), train_preds] = 1 train_time = int(round(time.time() * 1000)) - start if build_train_file: - train_acc = clustering_accuracy(y_train, train_preds) + train_acc = clustering_accuracy_score(y_train, train_preds) write_clustering_results( train_preds, @@ -719,12 +743,13 @@ def run_clustering_experiment( full_path=False, split="TRAIN", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, clustering_accuracy=train_acc, fit_time=fit_time, predict_time=train_time, + memory_usage=mem_usage, n_classes=n_classes, n_clusters=len(train_probs[0]), ) @@ -737,10 +762,14 @@ def run_clustering_experiment( else: test_preds = clusterer.predict(X_test) test_probs = np.zeros((len(test_preds), len(np.unique(train_preds)))) - test_probs[:, test_preds] = 1 - test_time = int(round(time.time() * 1000)) - start + test_probs[np.arange(len(test_preds)), test_preds] = 1 + test_time = ( + int(round(time.time() * 1000)) + - start + + int(round(getattr(clusterer, "_predict_time_milli", 0))) + ) - test_acc = clustering_accuracy(y_test, test_preds) + test_acc = clustering_accuracy_score(y_test, test_preds) write_clustering_results( test_preds, @@ -752,12 +781,13 @@ def run_clustering_experiment( full_path=False, split="TEST", resample_id=resample_id, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, clustering_accuracy=test_acc, fit_time=fit_time, predict_time=test_time, + memory_usage=mem_usage, n_classes=n_classes, n_clusters=len(test_probs[0]), ) @@ -904,13 +934,21 @@ def run_forecasting_experiment( second = str(forecaster.get_params()).replace("\n", " ").replace("\r", " ") - start = int(round(time.time() * 1000)) - forecaster.fit(train) - fit_time = int(round(time.time() * 1000)) - start + mem_usage, fit_time = record_max_memory( + forecaster.fit, + args=(train,), + interval=MEMRECORD_INTERVAL, + return_func_time=True, + ) + fit_time += int(round(getattr(forecaster, "_fit_time_milli", 0))) start = int(round(time.time() * 1000)) test_preds = forecaster.predict(np.arange(1, len(test) + 1)) - test_time = int(round(time.time() * 1000)) - start + test_time = ( + int(round(time.time() * 1000)) + - start + + int(round(getattr(forecaster, "_predict_time_milli", 0))) + ) test_preds = test_preds.flatten() test_mape = mean_absolute_percentage_error(test, test_preds) @@ -924,12 +962,13 @@ def run_forecasting_experiment( full_path=False, split="TEST", random_seed=random_seed, - timing_type="MILLISECONDS", + time_unit="MILLISECONDS", first_line_comment=first_comment, parameter_info=second, mape=test_mape, fit_time=fit_time, predict_time=test_time, + memory_usage=mem_usage, ) diff --git a/tsml_eval/experiments/forecasting_experiments.py b/tsml_eval/experiments/forecasting_experiments.py index 1a950b57..ce31f971 100644 --- a/tsml_eval/experiments/forecasting_experiments.py +++ b/tsml_eval/experiments/forecasting_experiments.py @@ -12,7 +12,8 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! - +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" import numba from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -20,8 +21,9 @@ from tsml_eval.experiments import load_and_run_forecasting_experiment from tsml_eval.experiments.set_forecaster import set_forecaster from tsml_eval.experiments.tests import _FORECASTER_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, assign_gpu, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present, assign_gpu def run_experiment(args, overwrite=False): diff --git a/tsml_eval/experiments/regression_experiments.py b/tsml_eval/experiments/regression_experiments.py index abb3eb0e..6eaf2847 100644 --- a/tsml_eval/experiments/regression_experiments.py +++ b/tsml_eval/experiments/regression_experiments.py @@ -12,6 +12,8 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" import numba from aeon.utils.validation._dependencies import _check_soft_dependencies @@ -19,8 +21,9 @@ from tsml_eval.experiments import load_and_run_regression_experiment from tsml_eval.experiments.set_regressor import set_regressor from tsml_eval.experiments.tests import _REGRESSOR_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, assign_gpu, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present, assign_gpu def run_experiment(args): diff --git a/tsml_eval/experiments/set_clusterer.py b/tsml_eval/experiments/set_clusterer.py index 80fd26a3..9e12a437 100644 --- a/tsml_eval/experiments/set_clusterer.py +++ b/tsml_eval/experiments/set_clusterer.py @@ -93,43 +93,45 @@ def _set_clusterer_distance_based( if c == "timeserieskmeans" or c == "kmeans-dtw" or c == "k-means-dtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="dtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="dtw", random_state=random_state, **kwargs) elif c == "kmeans-ddtw" or c == "k-means-ddtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="ddtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="ddtw", random_state=random_state, **kwargs) elif c == "kmeans-ed" or c == "k-means-ed": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="euclidean", random_state=random_state, **kwargs) + return TimeSeriesKMeans( + distance="euclidean", random_state=random_state, **kwargs + ) elif c == "kmeans-edr" or c == "k-means-edr": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="edr", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="edr", random_state=random_state, **kwargs) elif c == "kmeans-erp" or c == "k-means-erp": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="erp", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="erp", random_state=random_state, **kwargs) elif c == "kmeans-lcss" or c == "k-means-lcss": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="lcss", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="lcss", random_state=random_state, **kwargs) elif c == "kmeans-msm" or c == "k-means-msm": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="msm", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="msm", random_state=random_state, **kwargs) elif c == "kmeans-twe" or c == "k-means-twe": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="twe", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="twe", random_state=random_state, **kwargs) elif c == "kmeans-wdtw" or c == "k-means-wdtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="wdtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="wdtw", random_state=random_state, **kwargs) elif c == "kmeans-wddtw" or c == "k-means-wddtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="wddtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="wddtw", random_state=random_state, **kwargs) elif c == "timeserieskmedoids" or c == "kmedoids-dtw" or c == "k-medoids-dtw": from aeon.clustering.k_medoids import TimeSeriesKMedoids @@ -188,7 +190,7 @@ def _set_clusterer_other(c, random_state, n_jobs, fit_contract, checkpoint, kwar n_clusters=1, n_init=1, init_algorithm="random", - metric="euclidean", + distance="euclidean", max_iter=1, random_state=random_state, **kwargs, diff --git a/tsml_eval/experiments/tests/__init__.py b/tsml_eval/experiments/tests/__init__.py index 616016dc..f066e799 100644 --- a/tsml_eval/experiments/tests/__init__.py +++ b/tsml_eval/experiments/tests/__init__.py @@ -7,22 +7,12 @@ "_REGRESSOR_RESULTS_PATH", ] -import os -from pathlib import Path +from tsml_eval.testing.test_utils import _TEST_OUTPUT_PATH -_CLASSIFIER_RESULTS_PATH = ( - os.path.dirname(Path(__file__).parent.parent.parent) - + "/test_output/classification/" -) +_CLASSIFIER_RESULTS_PATH = _TEST_OUTPUT_PATH + "/classification/" -_CLUSTERER_RESULTS_PATH = ( - os.path.dirname(Path(__file__).parent.parent.parent) + "/test_output/clustering/" -) +_CLUSTERER_RESULTS_PATH = _TEST_OUTPUT_PATH + "/clustering/" -_FORECASTER_RESULTS_PATH = ( - os.path.dirname(Path(__file__).parent.parent.parent) + "/test_output/forecasting/" -) +_FORECASTER_RESULTS_PATH = _TEST_OUTPUT_PATH + "/forecasting/" -_REGRESSOR_RESULTS_PATH = ( - os.path.dirname(Path(__file__).parent.parent.parent) + "/test_output/regression/" -) +_REGRESSOR_RESULTS_PATH = _TEST_OUTPUT_PATH + "/regression/" diff --git a/tsml_eval/experiments/tests/test_classification.py b/tsml_eval/experiments/tests/test_classification.py index 8966fddc..c0a515e2 100644 --- a/tsml_eval/experiments/tests/test_classification.py +++ b/tsml_eval/experiments/tests/test_classification.py @@ -15,7 +15,7 @@ threaded_classification_experiments, ) from tsml_eval.experiments.tests import _CLASSIFIER_RESULTS_PATH -from tsml_eval.utils.test_utils import ( +from tsml_eval.testing.test_utils import ( _TEST_DATA_PATH, _check_set_method, _check_set_method_results, @@ -86,10 +86,7 @@ def test_run_classification_experiment_main(): assert os.path.exists(test_file) _check_classification_file_format(test_file) - os.remove( - f"{_CLASSIFIER_RESULTS_PATH}{classifier}/Predictions/{dataset}/" - "testResample0.csv" - ) + os.remove(test_file) def test_run_threaded_classification_experiment(): diff --git a/tsml_eval/experiments/tests/test_clustering.py b/tsml_eval/experiments/tests/test_clustering.py index dd91a721..4e0ee55e 100644 --- a/tsml_eval/experiments/tests/test_clustering.py +++ b/tsml_eval/experiments/tests/test_clustering.py @@ -15,7 +15,7 @@ threaded_clustering_experiments, ) from tsml_eval.experiments.tests import _CLUSTERER_RESULTS_PATH -from tsml_eval.utils.test_utils import ( +from tsml_eval.testing.test_utils import ( _TEST_DATA_PATH, _check_set_method, _check_set_method_results, @@ -86,9 +86,7 @@ def test_run_clustering_experiment_main(): assert os.path.exists(train_file) _check_clustering_file_format(train_file) - os.remove( - f"{_CLUSTERER_RESULTS_PATH}{clusterer}/Predictions/{dataset}/trainResample0.csv" - ) + os.remove(train_file) def test_run_threaded_clustering_experiment(): @@ -114,8 +112,14 @@ def test_run_threaded_clustering_experiment(): train_file = ( f"{_CLUSTERER_RESULTS_PATH}{clusterer}/Predictions/{dataset}/trainResample1.csv" ) - assert os.path.exists(train_file) + test_file = ( + f"{_CLUSTERER_RESULTS_PATH}{clusterer}/Predictions/{dataset}/testResample1.csv" + ) + + assert os.path.exists(train_file) and os.path.exists(test_file) + _check_clustering_file_format(train_file) + _check_clustering_file_format(test_file) # test present results checking threaded_clustering_experiments.run_experiment(args) @@ -129,6 +133,7 @@ def test_run_threaded_clustering_experiment(): ) os.remove(train_file) + os.remove(test_file) def test_run_clustering_experiment_invalid_build_settings(): diff --git a/tsml_eval/experiments/tests/test_experiments.py b/tsml_eval/experiments/tests/test_experiments.py index bfdb9022..485c9cb7 100644 --- a/tsml_eval/experiments/tests/test_experiments.py +++ b/tsml_eval/experiments/tests/test_experiments.py @@ -7,7 +7,7 @@ load_and_run_classification_experiment, ) from tsml_eval.experiments.tests import _CLASSIFIER_RESULTS_PATH -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH, _TEST_OUTPUT_PATH from tsml_eval.utils.tests.test_results_writing import _check_classification_file_format @@ -16,19 +16,10 @@ def test_kwargs(): dataset = "MinimalChinatown" classifier = "LogisticRegression" - data_path = ( - "./tsml_eval/datasets/" - if os.getcwd().split("\\")[-1] != "tests" - else "../../datasets/" - ) - result_path = ( - "./test_output/kwargs/" - if os.getcwd().split("\\")[-1] != "tests" - else "../../../test_output/kwargs/" - ) + result_path = _TEST_OUTPUT_PATH + "/kwargs/" args = [ - data_path, + _TEST_DATA_PATH, result_path, classifier, dataset, diff --git a/tsml_eval/experiments/tests/test_forecasting.py b/tsml_eval/experiments/tests/test_forecasting.py index afa7de6b..1a81ea9b 100644 --- a/tsml_eval/experiments/tests/test_forecasting.py +++ b/tsml_eval/experiments/tests/test_forecasting.py @@ -11,7 +11,7 @@ threaded_forecasting_experiments, ) from tsml_eval.experiments.tests import _FORECASTER_RESULTS_PATH -from tsml_eval.utils.test_utils import ( +from tsml_eval.testing.test_utils import ( _TEST_DATA_PATH, _check_set_method, _check_set_method_results, @@ -68,10 +68,7 @@ def test_run_forecasting_experiment_main(): assert os.path.exists(test_file) _check_forecasting_file_format(test_file) - os.remove( - f"{_FORECASTER_RESULTS_PATH}{forecaster}/Predictions/{dataset}/" - "testResample0.csv" - ) + os.remove(test_file) def test_run_threaded_forecasting_experiment(): diff --git a/tsml_eval/experiments/tests/test_regression.py b/tsml_eval/experiments/tests/test_regression.py index 1ee1104a..5efebf41 100644 --- a/tsml_eval/experiments/tests/test_regression.py +++ b/tsml_eval/experiments/tests/test_regression.py @@ -15,7 +15,7 @@ threaded_regression_experiments, ) from tsml_eval.experiments.tests import _REGRESSOR_RESULTS_PATH -from tsml_eval.utils.test_utils import ( +from tsml_eval.testing.test_utils import ( _TEST_DATA_PATH, _check_set_method, _check_set_method_results, @@ -86,9 +86,7 @@ def test_run_regression_experiment_main(): assert os.path.exists(test_file) _check_regression_file_format(test_file) - os.remove( - f"{_REGRESSOR_RESULTS_PATH}{regressor}/Predictions/{dataset}/testResample0.csv" - ) + os.remove(test_file) def test_run_threaded_regression_experiment(): diff --git a/tsml_eval/experiments/threaded_classification_experiments.py b/tsml_eval/experiments/threaded_classification_experiments.py index ace01d94..c77da827 100644 --- a/tsml_eval/experiments/threaded_classification_experiments.py +++ b/tsml_eval/experiments/threaded_classification_experiments.py @@ -11,8 +11,9 @@ from tsml_eval.experiments import load_and_run_classification_experiment from tsml_eval.experiments.set_classifier import set_classifier from tsml_eval.experiments.tests import _CLASSIFIER_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present def run_experiment(args): diff --git a/tsml_eval/experiments/threaded_clustering_experiments.py b/tsml_eval/experiments/threaded_clustering_experiments.py index 48b73f18..46e588e9 100644 --- a/tsml_eval/experiments/threaded_clustering_experiments.py +++ b/tsml_eval/experiments/threaded_clustering_experiments.py @@ -10,11 +10,11 @@ import sys from tsml_eval.experiments import load_and_run_clustering_experiment -from tsml_eval.experiments.classification_experiments import _results_present from tsml_eval.experiments.set_clusterer import set_clusterer from tsml_eval.experiments.tests import _CLUSTERER_RESULTS_PATH -from tsml_eval.utils.experiments import parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present def run_experiment(args): diff --git a/tsml_eval/experiments/threaded_forecasting_experiments.py b/tsml_eval/experiments/threaded_forecasting_experiments.py index 3be9fc6c..06e8a2c2 100644 --- a/tsml_eval/experiments/threaded_forecasting_experiments.py +++ b/tsml_eval/experiments/threaded_forecasting_experiments.py @@ -11,8 +11,9 @@ from tsml_eval.experiments import load_and_run_forecasting_experiment from tsml_eval.experiments.set_forecaster import set_forecaster from tsml_eval.experiments.tests import _FORECASTER_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present def run_experiment(args, overwrite=False): diff --git a/tsml_eval/experiments/threaded_regression_experiments.py b/tsml_eval/experiments/threaded_regression_experiments.py index 4f462fcb..9c507963 100644 --- a/tsml_eval/experiments/threaded_regression_experiments.py +++ b/tsml_eval/experiments/threaded_regression_experiments.py @@ -12,8 +12,9 @@ from tsml_eval.experiments import load_and_run_regression_experiment from tsml_eval.experiments.set_regressor import set_regressor from tsml_eval.experiments.tests import _REGRESSOR_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present def run_experiment(args): diff --git a/tsml_eval/publications/y2023/distance_based_clustering/run_distance_experiments.py b/tsml_eval/publications/y2023/distance_based_clustering/run_distance_experiments.py index 74187cd8..dce5e498 100644 --- a/tsml_eval/publications/y2023/distance_based_clustering/run_distance_experiments.py +++ b/tsml_eval/publications/y2023/distance_based_clustering/run_distance_experiments.py @@ -16,8 +16,9 @@ from tsml_eval.publications.y2023.distance_based_clustering.tests import ( _DISTANCE_TEST_RESULTS_PATH, ) -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present classifiers = [ "KMeans-dtw", diff --git a/tsml_eval/publications/y2023/distance_based_clustering/set_distance_clusterer.py b/tsml_eval/publications/y2023/distance_based_clustering/set_distance_clusterer.py index 538989da..0581f7dc 100644 --- a/tsml_eval/publications/y2023/distance_based_clustering/set_distance_clusterer.py +++ b/tsml_eval/publications/y2023/distance_based_clustering/set_distance_clusterer.py @@ -41,43 +41,45 @@ def _set_distance_clusterer( if c == "timeserieskmeans" or c == "kmeans-dtw" or c == "k-means-dtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="dtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="dtw", random_state=random_state, **kwargs) elif c == "kmeans-ddtw" or c == "k-means-ddtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="ddtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="ddtw", random_state=random_state, **kwargs) elif c == "kmeans-ed" or c == "k-means-ed": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="euclidean", random_state=random_state, **kwargs) + return TimeSeriesKMeans( + distance="euclidean", random_state=random_state, **kwargs + ) elif c == "kmeans-edr" or c == "k-means-edr": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="edr", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="edr", random_state=random_state, **kwargs) elif c == "kmeans-erp" or c == "k-means-erp": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="erp", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="erp", random_state=random_state, **kwargs) elif c == "kmeans-lcss" or c == "k-means-lcss": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="lcss", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="lcss", random_state=random_state, **kwargs) elif c == "kmeans-msm" or c == "k-means-msm": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="msm", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="msm", random_state=random_state, **kwargs) elif c == "kmeans-twe" or c == "k-means-twe": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="twe", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="twe", random_state=random_state, **kwargs) elif c == "kmeans-wdtw" or c == "k-means-wdtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="wdtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="wdtw", random_state=random_state, **kwargs) elif c == "kmeans-wddtw" or c == "k-means-wddtw": from aeon.clustering.k_means import TimeSeriesKMeans - return TimeSeriesKMeans(metric="wddtw", random_state=random_state, **kwargs) + return TimeSeriesKMeans(distance="wddtw", random_state=random_state, **kwargs) elif c == "timeserieskmedoids" or c == "kmedoids-dtw" or c == "k-medoids-dtw": from aeon.clustering.k_medoids import TimeSeriesKMedoids diff --git a/tsml_eval/publications/y2023/distance_based_clustering/tests/test_run_experiments.py b/tsml_eval/publications/y2023/distance_based_clustering/tests/test_run_experiments.py index 4c48dd31..51e75cdb 100644 --- a/tsml_eval/publications/y2023/distance_based_clustering/tests/test_run_experiments.py +++ b/tsml_eval/publications/y2023/distance_based_clustering/tests/test_run_experiments.py @@ -7,7 +7,7 @@ from tsml_eval.publications.y2023.distance_based_clustering.tests import ( _DISTANCE_TEST_RESULTS_PATH, ) -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH from tsml_eval.utils.tests.test_results_writing import _check_clustering_file_format diff --git a/tsml_eval/publications/y2023/distance_based_clustering/tests/test_set_distance_clusterer.py b/tsml_eval/publications/y2023/distance_based_clustering/tests/test_set_distance_clusterer.py index 8011e6af..9b8f1876 100644 --- a/tsml_eval/publications/y2023/distance_based_clustering/tests/test_set_distance_clusterer.py +++ b/tsml_eval/publications/y2023/distance_based_clustering/tests/test_set_distance_clusterer.py @@ -6,7 +6,7 @@ _set_distance_clusterer, distance_based_clusterers, ) -from tsml_eval.utils.test_utils import _check_set_method, _check_set_method_results +from tsml_eval.testing.test_utils import _check_set_method, _check_set_method_results def test_set_distance_clusterer(): diff --git a/tsml_eval/publications/y2023/rist_pipeline/run_classification_experiments.py b/tsml_eval/publications/y2023/rist_pipeline/run_classification_experiments.py index fc843e59..80206219 100644 --- a/tsml_eval/publications/y2023/rist_pipeline/run_classification_experiments.py +++ b/tsml_eval/publications/y2023/rist_pipeline/run_classification_experiments.py @@ -14,8 +14,9 @@ _set_rist_classifier, ) from tsml_eval.publications.y2023.rist_pipeline.tests import _RIST_TEST_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present classifiers = [ "FreshPRINCE", diff --git a/tsml_eval/publications/y2023/rist_pipeline/run_regression_experiments.py b/tsml_eval/publications/y2023/rist_pipeline/run_regression_experiments.py index 8ca4cc22..57e02473 100644 --- a/tsml_eval/publications/y2023/rist_pipeline/run_regression_experiments.py +++ b/tsml_eval/publications/y2023/rist_pipeline/run_regression_experiments.py @@ -8,14 +8,17 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" from tsml_eval.experiments import load_and_run_regression_experiment from tsml_eval.publications.y2023.rist_pipeline.set_rist_regressor import ( _set_rist_regressor, ) from tsml_eval.publications.y2023.rist_pipeline.tests import _RIST_TEST_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present regressors = [ "InceptionTime", diff --git a/tsml_eval/publications/y2023/rist_pipeline/tests/test_run_experiments.py b/tsml_eval/publications/y2023/rist_pipeline/tests/test_run_experiments.py index 434f6bed..b03f3038 100644 --- a/tsml_eval/publications/y2023/rist_pipeline/tests/test_run_experiments.py +++ b/tsml_eval/publications/y2023/rist_pipeline/tests/test_run_experiments.py @@ -8,7 +8,7 @@ _run_regression_experiment, ) from tsml_eval.publications.y2023.rist_pipeline.tests import _RIST_TEST_RESULTS_PATH -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH from tsml_eval.utils.tests.test_results_writing import ( _check_classification_file_format, _check_regression_file_format, diff --git a/tsml_eval/publications/y2023/rist_pipeline/tests/test_set_estimator.py b/tsml_eval/publications/y2023/rist_pipeline/tests/test_set_estimator.py index 0eabf9eb..aeaa03be 100644 --- a/tsml_eval/publications/y2023/rist_pipeline/tests/test_set_estimator.py +++ b/tsml_eval/publications/y2023/rist_pipeline/tests/test_set_estimator.py @@ -8,7 +8,7 @@ rist_classifiers, rist_regressors, ) -from tsml_eval.utils.test_utils import _check_set_method, _check_set_method_results +from tsml_eval.testing.test_utils import _check_set_method, _check_set_method_results def test_set_rist_classifier(): diff --git a/tsml_eval/publications/y2023/tsc_bakeoff/run_experiments.py b/tsml_eval/publications/y2023/tsc_bakeoff/run_experiments.py index 22f324c1..9f8c1056 100644 --- a/tsml_eval/publications/y2023/tsc_bakeoff/run_experiments.py +++ b/tsml_eval/publications/y2023/tsc_bakeoff/run_experiments.py @@ -8,14 +8,17 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" from tsml_eval.experiments import load_and_run_classification_experiment from tsml_eval.publications.y2023.tsc_bakeoff.set_bakeoff_classifier import ( _set_bakeoff_classifier, ) from tsml_eval.publications.y2023.tsc_bakeoff.tests import _BAKEOFF_TEST_RESULTS_PATH -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present # all classifiers ran without duplicates distance_based = ["1NN-DTW", "ShapeDTW"] diff --git a/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_run_experiments.py b/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_run_experiments.py index 6725386f..02cb0ca2 100644 --- a/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_run_experiments.py +++ b/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_run_experiments.py @@ -5,7 +5,7 @@ from tsml_eval.publications.y2023.tsc_bakeoff.run_experiments import _run_experiment from tsml_eval.publications.y2023.tsc_bakeoff.tests import _BAKEOFF_TEST_RESULTS_PATH -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH from tsml_eval.utils.tests.test_results_writing import _check_classification_file_format diff --git a/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_set_classifier.py b/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_set_classifier.py index e7b6f1f3..4058794f 100644 --- a/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_set_classifier.py +++ b/tsml_eval/publications/y2023/tsc_bakeoff/tests/test_set_classifier.py @@ -6,7 +6,7 @@ _set_bakeoff_classifier, bakeoff_classifiers, ) -from tsml_eval.utils.test_utils import _check_set_method, _check_set_method_results +from tsml_eval.testing.test_utils import _check_set_method, _check_set_method_results def test_set_bakeoff_classifier(): diff --git a/tsml_eval/publications/y2023/tser_archive_expansion/run_experiments.py b/tsml_eval/publications/y2023/tser_archive_expansion/run_experiments.py index 12fea75c..0b23af43 100644 --- a/tsml_eval/publications/y2023/tser_archive_expansion/run_experiments.py +++ b/tsml_eval/publications/y2023/tser_archive_expansion/run_experiments.py @@ -8,6 +8,8 @@ os.environ["MKL_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["NUMEXPR_NUM_THREADS"] = "1" # must be done before numpy import!! os.environ["OMP_NUM_THREADS"] = "1" # must be done before numpy import!! +os.environ["TF_NUM_INTEROP_THREADS"] = "1" +os.environ["TF_NUM_INTRAOP_THREADS"] = "1" from tsml_eval.experiments import load_and_run_regression_experiment from tsml_eval.publications.y2023.tser_archive_expansion.set_tser_exp_regressor import ( @@ -16,8 +18,9 @@ from tsml_eval.publications.y2023.tser_archive_expansion.tests import ( _TSER_ARCHIVE_TEST_RESULTS_PATH, ) -from tsml_eval.utils.experiments import _results_present, parse_args -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH +from tsml_eval.utils.arguments import parse_args +from tsml_eval.utils.experiments import _results_present # all regressors ran without duplicates regressors_5A2 = [ diff --git a/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_run_experiments.py b/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_run_experiments.py index d8763874..71a1d300 100644 --- a/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_run_experiments.py +++ b/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_run_experiments.py @@ -7,7 +7,7 @@ from tsml_eval.publications.y2023.tser_archive_expansion.tests import ( _TSER_ARCHIVE_TEST_RESULTS_PATH, ) -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_DATA_PATH from tsml_eval.utils.tests.test_results_writing import _check_regression_file_format diff --git a/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_set_regressor.py b/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_set_regressor.py index 723dca07..5cd890ac 100644 --- a/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_set_regressor.py +++ b/tsml_eval/publications/y2023/tser_archive_expansion/tests/test_set_regressor.py @@ -6,7 +6,7 @@ _set_tser_exp_regressor, expansion_regressors, ) -from tsml_eval.utils.test_utils import _check_set_method, _check_set_method_results +from tsml_eval.testing.test_utils import _check_set_method, _check_set_method_results def test_set_expansion_regressor(): diff --git a/tsml_eval/testing/__init__.py b/tsml_eval/testing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tsml_eval/utils/tests/test_files/brokenClassificationResultsFile.csv b/tsml_eval/testing/_test_result_files/broken/brokenClassificationResultsFile.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/brokenClassificationResultsFile.csv rename to tsml_eval/testing/_test_result_files/broken/brokenClassificationResultsFile.csv diff --git a/tsml_eval/utils/tests/test_files/brokenRegressionResultsFile.csv b/tsml_eval/testing/_test_result_files/broken/brokenRegressionResultsFile.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/brokenRegressionResultsFile.csv rename to tsml_eval/testing/_test_result_files/broken/brokenRegressionResultsFile.csv diff --git a/tsml_eval/utils/tests/test_files/brokenResultsFile.csv b/tsml_eval/testing/_test_result_files/broken/brokenResultsFile.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/brokenResultsFile.csv rename to tsml_eval/testing/_test_result_files/broken/brokenResultsFile.csv diff --git a/tsml_eval/utils/tests/test_files/brokenResultsFileLine3.csv b/tsml_eval/testing/_test_result_files/broken/brokenResultsFileLine3.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/brokenResultsFileLine3.csv rename to tsml_eval/testing/_test_result_files/broken/brokenResultsFileLine3.csv diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..025f57ab --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:32. 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+1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv new file mode 100644 index 00000000..b734091f --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv @@ -0,0 +1,346 @@ +Chinatown,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:04. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.9154518950437318,1,244,-1,-1,2,,-1,-1 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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+1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..9c847d7b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:32. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.95,0,-1,-1,-1,2,,9,9 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..6020d7f8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:28. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,1,-1,-1,-1,2,,11,12 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..1e3ab97e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:04. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.85,1,-1,-1,-1,2,,10,11 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv new file mode 100644 index 00000000..b229d2bf --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:41. 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b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:44. 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b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:46. 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b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:41. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.9552238805970149,0,-1,-1,-1,2,,24,24 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..8d374478 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:44. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.8507462686567164,1,-1,-1,-1,2,,23,24 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..e36eae41 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:46. Encoder dictionary: {1.0: 0, 2.0: 1} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +0.9701492537313433,0,-1,-1,-1,2,,22,22 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv new file mode 100644 index 00000000..dd8cf40e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:17. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,0,3698,-1,-1,4,,-1,-1 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..cbe8ce9a --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,1,3638,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..2058ed93 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:21. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,1,3642,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..3ed0a314 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +0.9795918367346939,330,780,-1,-1,2,,-1,-1 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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+1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv new file mode 100644 index 00000000..af347917 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv @@ -0,0 +1,346 @@ +Chinatown,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +0.9620991253644315,329,805,-1,-1,2,,-1,-1 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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+1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv new file mode 100644 index 00000000..63fb2c49 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv @@ -0,0 +1,346 @@ +Chinatown,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} +0.967930029154519,329,785,-1,-1,2,,-1,-1 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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+1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..3c99a3cf --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +0.95,330,-1,-1,-1,2,,696,1026 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..fd271e49 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +1.0,329,-1,-1,-1,2,,705,1034 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..8174fc70 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} +0.95,329,-1,-1,-1,2,,696,1025 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv new file mode 100644 index 00000000..6a305dd8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:13. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +0.9698736637512148,220,1846,-1,-1,2,,-1,-1 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 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00000000..37c9254e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:08. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +0.9689018464528668,161,1927,-1,-1,2,,-1,-1 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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00000000..4cf173f8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:06. 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00000000..0fe7955e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:13. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +0.9701492537313433,220,-1,-1,-1,2,,1676,1896 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..ff9a33a3 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:08. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +0.9552238805970149,161,-1,-1,-1,2,,1730,1891 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..a7914290 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:06. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} +0.9850746268656716,258,-1,-1,-1,2,,1688,1946 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/MinimalChinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/MinimalChinatown/testResample0.csv new file mode 100644 index 00000000..27bf5118 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/MinimalChinatown/testResample0.csv @@ -0,0 +1,23 @@ +MinimalChinatown,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/07/2023, 00:45:48. Encoder dictionary: {1.0: 0, 2.0: 1} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +0.9,526,35,-1,-1,2,,-1,-1 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv new file mode 100644 index 00000000..8d6f78e5 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +1.0,1987,1880,-1,-1,4,,-1,-1 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..ce87a13f --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:29. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +1.0,1941,1995,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..f7867250 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:32. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} +1.0,2060,2094,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..78936eb5 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:32. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} +0.9766763848396501,759,608,-1,-1,2,,-1,-1 +0,0,,0.81,0.19 +0,0,,0.775,0.225 +0,0,,0.93,0.07 +0,0,,0.82,0.18 +0,0,,0.86,0.14 +0,0,,0.965,0.035 +0,0,,0.85,0.15 +0,0,,0.845,0.155 +0,0,,0.965,0.035 +0,0,,0.92,0.08 +0,0,,0.89,0.11 +0,0,,0.89,0.11 +0,0,,0.95,0.05 +0,0,,0.905,0.095 +0,0,,0.925,0.075 +0,0,,0.89,0.11 +0,0,,0.825,0.175 +0,0,,0.8,0.2 +0,0,,0.88,0.12 +0,0,,0.76,0.24 +0,0,,0.805,0.195 +0,0,,0.885,0.115 +0,0,,0.765,0.235 +0,0,,0.935,0.065 +0,0,,0.945,0.055 +0,0,,0.905,0.095 +0,0,,0.905,0.095 +0,0,,0.945,0.055 +0,0,,0.84,0.16 +0,0,,0.8,0.2 +0,0,,0.93,0.07 +0,0,,0.895,0.105 +0,0,,0.925,0.075 +0,0,,0.94,0.06 +0,0,,0.88,0.12 +0,0,,0.89,0.11 +0,0,,0.89,0.11 +0,0,,0.87,0.13 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+1,1,,0.0,1.0 +1,1,,0.11,0.89 +1,1,,0.225,0.775 +1,1,,0.08,0.92 +1,1,,0.04,0.96 +1,1,,0.24,0.76 +1,0,,0.555,0.445 +1,1,,0.06,0.94 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv new file mode 100644 index 00000000..759c97ae --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv @@ -0,0 +1,346 @@ +Chinatown,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:36. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} +0.967930029154519,706,537,-1,-1,2,,-1,-1 +0,0,,0.97,0.03 +0,0,,0.975,0.025 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,0.96,0.04 +0,0,,0.96,0.04 +0,0,,0.78,0.22 +0,0,,0.96,0.04 +0,0,,0.81,0.19 +0,0,,0.945,0.055 +0,1,,0.415,0.585 +0,0,,0.92,0.08 +0,0,,0.975,0.025 +0,0,,0.965,0.035 +0,0,,0.96,0.04 +0,0,,1.0,0.0 +0,0,,0.92,0.08 +0,0,,0.845,0.155 +0,0,,0.995,0.005 +0,0,,0.91,0.09 +0,0,,0.99,0.01 +0,0,,0.695,0.305 +0,0,,0.9,0.1 +0,0,,0.925,0.075 +0,0,,0.8,0.2 +0,0,,0.99,0.01 +0,0,,0.955,0.045 +0,0,,0.985,0.015 +0,0,,0.975,0.025 +0,0,,0.99,0.01 +0,0,,0.985,0.015 +0,0,,0.975,0.025 +0,0,,0.94,0.06 +0,0,,0.985,0.015 +0,0,,0.96,0.04 +0,0,,0.85,0.15 +0,0,,0.745,0.255 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b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:53. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} +1.0,2035,689,-1,-1,4,,-1,-1 +2,2,,0.0,0.0,0.86,0.14 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.965,0.035 +1,1,,0.0,1.0,0.0,0.0 +0,0,,0.995,0.005,0.0,0.0 +2,2,,0.0,0.0,0.99,0.01 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +3,3,,0.0,0.0,0.02,0.98 +2,2,,0.005,0.0,0.98,0.015 +1,1,,0.0,0.995,0.005,0.0 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.065,0.935,0.0,0.0 +2,2,,0.0,0.0,0.985,0.015 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.98,0.02 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,0.985,0.015,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.025,0.01,0.94,0.025 +2,2,,0.0,0.0,0.995,0.005 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,0.995,0.005 +3,3,,0.0,0.0,0.005,0.995 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.205,0.795,0.0,0.0 +1,1,,0.02,0.98,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.02,0.01,0.82,0.15 +0,0,,0.995,0.005,0.0,0.0 +1,1,,0.33,0.67,0.0,0.0 +3,3,,0.0,0.0,0.005,0.995 +2,2,,0.0,0.0,0.995,0.005 +3,3,,0.045,0.03,0.115,0.81 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.015,0.985 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.02,0.015,0.06,0.905 +2,2,,0.0,0.0,0.99,0.01 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.04,0.015,0.06,0.885 +1,1,,0.395,0.605,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.835,0.165 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.995,0.005 +0,0,,0.995,0.005,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.005,0.0,0.985,0.01 +2,2,,0.025,0.025,0.715,0.235 +0,0,,0.995,0.005,0.0,0.0 +2,2,,0.0,0.005,0.995,0.0 +1,1,,0.125,0.875,0.0,0.0 +3,3,,0.0,0.0,0.01,0.99 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,0.985,0.015 +3,3,,0.0,0.0,0.035,0.965 +0,0,,0.995,0.0,0.005,0.0 +1,1,,0.36,0.64,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.005,0.995 +2,2,,0.0,0.0,0.985,0.015 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.015,0.985 +0,0,,0.995,0.005,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.13,0.87,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,0.925,0.075,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.995,0.005,0.0,0.0 +2,2,,0.0,0.0,0.985,0.015 +0,0,,0.995,0.005,0.0,0.0 +0,0,,0.89,0.11,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,0.97,0.03 +3,3,,0.0,0.0,0.015,0.985 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..09bdf5ee --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:42. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} +1.0,2160,704,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.99,0.01,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.99,0.01,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.995,0.005,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.915,0.085,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.985,0.015,0.0,0.0 +0,0,,0.995,0.005,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.995,0.005,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.27,0.725,0.005,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.2,0.8,0.0,0.0 +1,1,,0.03,0.97,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.005,0.99,0.005,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.02,0.98,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.02,0.98,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.0,0.0,0.965,0.035 +2,2,,0.0,0.0,0.955,0.045 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.005,0.0,0.96,0.035 +2,2,,0.0,0.0,0.965,0.035 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.005,0.0,0.94,0.055 +2,2,,0.005,0.0,0.97,0.025 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.985,0.015 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.985,0.015 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.0,0.0,0.97,0.03 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.97,0.03 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.18,0.82 +3,3,,0.0,0.0,0.03,0.97 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.01,0.99 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.02,0.98 +3,3,,0.0,0.0,0.175,0.825 +3,3,,0.0,0.0,0.02,0.98 +3,3,,0.0,0.0,0.015,0.985 +3,3,,0.0,0.0,0.015,0.985 +3,3,,0.0,0.0,0.075,0.925 +3,3,,0.0,0.0,0.015,0.985 +3,3,,0.0,0.0,0.2,0.8 +3,3,,0.0,0.0,0.015,0.985 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.21,0.79 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..8f688235 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:39. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} +1.0,2051,649,-1,-1,4,,-1,-1 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.945,0.025,0.03,0.0 +0,0,,0.995,0.005,0.0,0.0 +0,0,,0.935,0.015,0.05,0.0 +0,0,,0.995,0.005,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.92,0.025,0.055,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.895,0.02,0.065,0.02 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.99,0.01,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.885,0.115,0.0,0.0 +0,0,,0.99,0.01,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.99,0.01,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.99,0.005,0.005,0.0 +0,0,,0.86,0.14,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.115,0.885,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.085,0.915,0.0,0.0 +1,1,,0.005,0.995,0.0,0.0 +1,1,,0.045,0.955,0.0,0.0 +1,1,,0.01,0.99,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.015,0.985,0.0,0.0 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.0,0.87,0.13 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.005,0.0,0.99,0.005 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.985,0.015 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.005,0.99,0.005 +2,2,,0.0,0.0,0.995,0.005 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.005,0.97,0.025 +2,2,,0.0,0.005,0.945,0.05 +2,2,,0.0,0.0,0.92,0.08 +2,2,,0.005,0.005,0.94,0.05 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.0,0.0,0.98,0.02 +2,2,,0.0,0.0,0.975,0.025 +2,2,,0.0,0.0,0.985,0.015 +2,2,,0.0,0.0,0.99,0.01 +2,2,,0.0,0.005,0.95,0.045 +3,3,,0.025,0.03,0.225,0.72 +3,3,,0.0,0.0,0.01,0.99 +3,3,,0.0,0.0,0.02,0.98 +3,3,,0.0,0.0,0.045,0.955 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.01,0.99 +3,3,,0.0,0.0,0.01,0.99 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.015,0.985 +3,3,,0.0,0.0,0.07,0.93 +3,3,,0.0,0.0,0.005,0.995 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.06,0.94 +3,3,,0.0,0.0,0.02,0.98 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.02,0.005,0.03,0.945 +3,3,,0.0,0.0,0.085,0.915 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/utils/tests/test_files/classificationResultsFile1.csv b/tsml_eval/testing/_test_result_files/classification/classificationResultsFile1.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/classificationResultsFile1.csv rename to tsml_eval/testing/_test_result_files/classification/classificationResultsFile1.csv diff --git a/tsml_eval/utils/tests/test_files/classificationResultsFile2.csv b/tsml_eval/testing/_test_result_files/classification/classificationResultsFile2.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/classificationResultsFile2.csv rename to tsml_eval/testing/_test_result_files/classification/classificationResultsFile2.csv diff --git a/tsml_eval/utils/tests/test_files/classificationResultsFile3.csv b/tsml_eval/testing/_test_result_files/classification/classificationResultsFile3.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/classificationResultsFile3.csv rename to tsml_eval/testing/_test_result_files/classification/classificationResultsFile3.csv diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/MinimalChinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/MinimalChinatown/trainResample0.csv new file mode 100644 index 00000000..f1cd19d2 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/MinimalChinatown/trainResample0.csv @@ -0,0 +1,23 @@ +MinimalChinatown,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/08/2023, 11:51:40. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 2, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(n_clusters=2, random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.65,442,0,-1,-1,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv new file mode 100644 index 00000000..4087352c --- /dev/null +++ b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv @@ -0,0 +1,15 @@ +ShampooSales,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/09/2023, 00:04:56 +{'sp': 1, 'strategy': 'last', 'window_length': None} +0.2603808539887312,2,124,-1,-1 +339.7,342.3 +440.4,342.3 +315.9,342.3 +439.3,342.3 +401.3,342.3 +437.4,342.3 +575.5,342.3 +407.6,342.3 +682.0,342.3 +475.3,342.3 +581.3,342.3 +646.9,342.3 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/MinimalGasPrices/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/MinimalGasPrices/testResample0.csv new file mode 100644 index 00000000..b7a0924d --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/MinimalGasPrices/testResample0.csv @@ -0,0 +1,23 @@ +MinimalGasPrices,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/08/2023, 14:27:00 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.010243031151472574,105,44,-1,-1,,-1,-1 +-0.3745973154329336,-0.38960027365847794 +-0.27649220292280596,-0.31629007271453524 +-0.3359852990851952,-0.2742327322615531 +-0.1588933546382647,-0.30198812439850553 +-0.21406789399110351,-0.38853285715940317 +-0.3459467332277681,-0.34653488608001776 +-0.23691177769349164,-0.35698460358484424 +-0.31200024393888615,-0.3482196012148857 +-0.3456104081983749,-0.3654146913823181 +-0.3716620670488247,-0.37699084160709095 +-0.38911418863213976,-0.30012151008330346 +-0.3566989751389393,-0.36169107297551983 +-0.384325153266008,-0.3641645791372737 +-0.12073821121683478,-0.2937557031205802 +-0.28387368441774286,-0.3582794151892187 +-0.2905381690424222,-0.38368351259443756 +-0.421656129681147,-0.319876211104088 +-0.26659133841211974,-0.3337420635785012 +-0.12289009959651871,-0.3679339187015041 +-0.4409556758518402,-0.3719582908247117 diff --git a/tsml_eval/utils/tests/test_files/regressionResultsFile.csv b/tsml_eval/testing/_test_result_files/regression/regressionResultsFile.csv similarity index 100% rename from tsml_eval/utils/tests/test_files/regressionResultsFile.csv rename to tsml_eval/testing/_test_result_files/regression/regressionResultsFile.csv diff --git a/tsml_eval/utils/test_utils.py b/tsml_eval/testing/test_utils.py similarity index 92% rename from tsml_eval/utils/test_utils.py rename to tsml_eval/testing/test_utils.py index c88f7247..0d132097 100644 --- a/tsml_eval/utils/test_utils.py +++ b/tsml_eval/testing/test_utils.py @@ -6,6 +6,13 @@ _TEST_DATA_PATH = os.path.dirname(Path(__file__).parent.parent) + "/tsml_eval/datasets/" +_TEST_RESULTS_PATH = ( + os.path.dirname(Path(__file__).parent.parent) + + "/tsml_eval/testing/_test_result_files/" +) + +_TEST_OUTPUT_PATH = os.path.dirname(Path(__file__).parent.parent) + "/test_output/" + def _check_set_method( set_method, estimator_sub_list, estimator_dict, all_estimator_names diff --git a/tsml_eval/testing/tests/__init__.py b/tsml_eval/testing/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tsml_eval/utils/tests/test_test_utils.py b/tsml_eval/testing/tests/test_test_utils.py similarity index 81% rename from tsml_eval/utils/tests/test_test_utils.py rename to tsml_eval/testing/tests/test_test_utils.py index 94d159de..be90fa6a 100644 --- a/tsml_eval/utils/tests/test_test_utils.py +++ b/tsml_eval/testing/tests/test_test_utils.py @@ -2,7 +2,7 @@ import pytest -from tsml_eval.utils.test_utils import _check_set_method_results +from tsml_eval.testing.test_utils import _check_set_method_results def test_check_set_method_results_fail(): diff --git a/tsml_eval/utils/arguments.py b/tsml_eval/utils/arguments.py new file mode 100644 index 00000000..88da4c81 --- /dev/null +++ b/tsml_eval/utils/arguments.py @@ -0,0 +1,223 @@ +"""tsml-eval command line argument parser.""" + +__author__ = ["MatthewMiddlehurst"] + +__all__ = [ + "parse_args", +] + +import argparse + +import tsml_eval + + +def parse_args(args): + """Parse the command line arguments for tsml_eval. + + The following is the --help output for tsml_eval: + + usage: tsml_eval [-h] [--version] [-ow] [-pr] [-rs RANDOM_SEED] [-nj N_JOBS] + [-tr] [-te] [-fc FIT_CONTRACT] [-ch] [-rn] [-nc N_CLUSTERS] + [-kw KEY VALUE TYPE] + data_path results_path estimator_name dataset_name + resample_id + + positional arguments: + data_path the path to the directory storing dataset files. + results_path the path to the directory where results files are + written to. + estimator_name the name of the estimator to run. See the + set_{task}.py file for each task learning task for + available options. + dataset_name the name of the dataset to load. + {data_dir}/{dataset_name}/{dataset_name}_TRAIN.ts and + {data_dir}/{dataset_name}/{dataset_name}_TEST.ts will + be loaded. + resample_id the resample ID to use when randomly resampling the + data, as a random seed for estimators and the suffix + when writing results files. An ID of 0 will use the + default TRAIN/TEST split. + + options: + -h, --help show this help message and exit + --version show program's version number and exit + -ow, --overwrite overwrite existing results files. If False, existing + results files will be skipped (default: False). + -pr, --predefined_resample + load a dataset file with a predefined resample. The + dataset file must follow the naming format + '{dataset_name}_{resample_id}.ts' (default: False). + -rs RANDOM_SEED, --random_seed RANDOM_SEED + use a different random seed than the resample ID. If + None use the {resample_id} (default: None). + -nj N_JOBS, --n_jobs N_JOBS + the number of jobs to run in parallel. Only used if + the experiments file and selected estimator allows + threading (default: 1). + -tr, --train_fold write a results file for the training data in the + classification and regression task (default: False). + -te, --test_fold write a results file for the test data in the + clustering task (default: False). + -fc FIT_CONTRACT, --fit_contract FIT_CONTRACT + a time limit for estimator fit in minutes. Only used + if the estimator can contract fit (default: 0). + -ch, --checkpoint save the estimator fit to file periodically while + building. Only used if the estimator can checkpoint + (default: False). + -rn, --row_normalise normalise the data rows prior to fitting and + predicting. (default: False). + -nc N_CLUSTERS, --n_clusters N_CLUSTERS + the number of clusters to find for clusterers which + have an {n_clusters} parameter. If {-1}, use the + number of classes in the dataset (default: None). + -kw KEY VALUE TYPE, --kwargs KEY VALUE TYPE, --kwarg KEY VALUE TYPE + additional keyword arguments to pass to the estimator. + Should contain the parameter to set, the parameter + value, and the type of the value i.e. {--kwargs + n_estimators 200 int} to change the size of an + ensemble. Valid types are {int, float, bool, str}. Any + other type will be passed as a str. Can be used + multiple times (default: None). + + Parameters + ---------- + args : list + List of command line arguments to parse. + + Returns + ------- + same_resample : argparse.Namespace + The parsed command line arguments. + """ + parser = argparse.ArgumentParser(prog="tsml_eval") + parser.add_argument( + "--version", action="version", version=f"%(prog)s {tsml_eval.__version__}" + ) + parser.add_argument( + "data_path", help="the path to the directory storing dataset files." + ) + parser.add_argument( + "results_path", + help="the path to the directory where results files are written to.", + ) + parser.add_argument( + "estimator_name", + help="the name of the estimator to run. See the set_{task}.py file for each " + "task learning task for available options.", + ) + parser.add_argument( + "dataset_name", + help="the name of the dataset to load. " + "{data_dir}/{dataset_name}/{dataset_name}_TRAIN.ts and " + "{data_dir}/{dataset_name}/{dataset_name}_TEST.ts will be loaded.", + ) + parser.add_argument( + "resample_id", + type=int, + help="the resample ID to use when randomly resampling the data, as a random " + "seed for estimators and the suffix when writing results files. An ID of " + "0 will use the default TRAIN/TEST split.", + ) + parser.add_argument( + "-ow", + "--overwrite", + action="store_true", + help="overwrite existing results files. If False, existing results files " + "will be skipped (default: %(default)s).", + ) + parser.add_argument( + "-pr", + "--predefined_resample", + action="store_true", + help="load a dataset file with a predefined resample. The dataset file must " + "follow the naming format '{dataset_name}{resample_id}.ts' " + "(default: %(default)s).", + ) + parser.add_argument( + "-rs", + "--random_seed", + type=int, + help="use a different random seed than the resample ID. If None use the " + "{resample_id} (default: %(default)s).", + ) + parser.add_argument( + "-nj", + "--n_jobs", + type=int, + default=1, + help="the number of jobs to run in parallel. Only used if the experiments file " + "and selected estimator allows threading (default: %(default)s).", + ) + parser.add_argument( + "-tr", + "--train_fold", + action="store_true", + help="write a results file for the training data in the classification and " + "regression task (default: %(default)s).", + ) + parser.add_argument( + "-te", + "--test_fold", + action="store_true", + help="write a results file for the test data in the clustering task " + "(default: %(default)s).", + ) + parser.add_argument( + "-fc", + "--fit_contract", + type=int, + default=0, + help="a time limit for estimator fit in minutes. Only used if the estimator " + "can contract fit (default: %(default)s).", + ) + parser.add_argument( + "-ch", + "--checkpoint", + action="store_true", + help="save the estimator fit to file periodically while building. Only used if " + "the estimator can checkpoint (default: %(default)s).", + ) + parser.add_argument( + "-rn", + "--row_normalise", + action="store_true", + help="normalise the data rows prior to fitting and predicting. " + "(default: %(default)s).", + ) + parser.add_argument( + "-nc", + "--n_clusters", + type=int, + help="the number of clusters to find for clusterers which have an {n_clusters} " + "parameter. If {-1}, use the number of classes in the dataset " + "(default: %(default)s).", + ) + parser.add_argument( + "-kw", + "--kwargs", + "--kwarg", + action="append", + nargs=3, + metavar=("KEY", "VALUE", "TYPE"), + help="additional keyword arguments to pass to the estimator. Should contain " + "the parameter to set, the parameter value, and the type of the value i.e. " + "{--kwargs n_estimators 200 int} to change the size of an ensemble. Valid " + "types are {int, float, bool, str}. Any other type will be passed as a str. " + "Can be used multiple times (default: %(default)s).", + ) + args = parser.parse_args(args=args) + + kwargs = {} + if args.kwargs is not None: + for kwarg in args.kwargs: + if kwarg[2] == "int": + kwargs[kwarg[0]] = int(kwarg[1]) + elif kwarg[2] == "float": + kwargs[kwarg[0]] = float(kwarg[1]) + elif kwarg[2] == "bool": + kwargs[kwarg[0]] = kwarg[1].lower() == "true" or kwarg[1] == "1" + else: + kwargs[kwarg[0]] = kwarg[1] + args.kwargs = kwargs + + return args diff --git a/tsml_eval/utils/experiments.py b/tsml_eval/utils/experiments.py index 57b99ec2..8f825794 100644 --- a/tsml_eval/utils/experiments.py +++ b/tsml_eval/utils/experiments.py @@ -10,21 +10,22 @@ "write_clustering_results", "write_forecasting_results", "write_results_to_tsml_format", - "validate_results_file", "fix_broken_second_line", "compare_result_file_resample", "assign_gpu", - "parse_args", ] -import argparse import os import gpustat import numpy as np from sklearn.utils import check_random_state -import tsml_eval +from tsml_eval.utils.validation import ( + _check_classification_third_line, + _check_clustering_third_line, + _check_regression_third_line, +) def resample_data(X_train, y_train, X_test, y_test, random_state=None): @@ -208,11 +209,11 @@ def write_classification_results( class_labels, classifier_name, dataset_name, - output_path, + file_path, full_path=True, split=None, resample_id=None, - timing_type="N/A", + time_unit="N/A", first_line_comment=None, parameter_info="No Parameter Info", accuracy=-1, @@ -241,7 +242,7 @@ def write_classification_results( determine file structure if full_path is False. dataset_name : str Name of the problem the classifier was built on. - output_path : str + file_path : str Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True @@ -254,7 +255,7 @@ def write_classification_results( resample_id : int or None, default=None Indicates what random seed was used to resample the data or used as a random_state for the classifier. - timing_type : str, default="N/A" + time_unit : str, default="N/A" The format used for timings in the file, i.e. 'Seconds', 'Milliseconds', 'Nanoseconds' first_line_comment : str or None, default=None @@ -317,12 +318,12 @@ def write_classification_results( class_labels, classifier_name, dataset_name, - output_path, + file_path, predicted_probabilities=probabilities, full_path=full_path, split=split, resample_id=resample_id, - timing_type=timing_type, + time_unit=time_unit, first_line_comment=first_line_comment, second_line=parameter_info, third_line=third_line, @@ -334,11 +335,11 @@ def write_regression_results( labels, regressor_name, dataset_name, - output_path, + file_path, full_path=True, split=None, resample_id=None, - timing_type="N/A", + time_unit="N/A", first_line_comment=None, parameter_info="No Parameter Info", mse=-1, @@ -363,7 +364,7 @@ def write_regression_results( determine file structure if full_path is False. dataset_name : str Name of the problem the regressor was built on. - output_path : str + file_path : str Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True @@ -376,7 +377,7 @@ def write_regression_results( resample_id : int or None, default=None Indicates what random seed was used to resample the data or used as a random_state for the regressor. - timing_type : str, default="N/A" + time_unit : str, default="N/A" The format used for timings in the file, i.e. 'Seconds', 'Milliseconds', 'Nanoseconds' first_line_comment : str or None, default=None @@ -424,11 +425,11 @@ def write_regression_results( labels, regressor_name, dataset_name, - output_path, + file_path, full_path=full_path, split=split, resample_id=resample_id, - timing_type=timing_type, + time_unit=time_unit, first_line_comment=first_line_comment, second_line=parameter_info, third_line=third_line, @@ -441,11 +442,11 @@ def write_clustering_results( class_labels, clusterer_name, dataset_name, - output_path, + file_path, full_path=True, split=None, resample_id=None, - timing_type="N/A", + time_unit="N/A", first_line_comment=None, parameter_info="No Parameter Info", clustering_accuracy=-1, @@ -473,7 +474,7 @@ def write_clustering_results( determine file structure if full_path is False. dataset_name : str Name of the problem the clusterer was built on. - output_path : str + file_path : str Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True @@ -486,7 +487,7 @@ def write_clustering_results( resample_id : int or None, default=None Indicates what random seed was used to resample the data or used as a random_state for the clusterer. - timing_type : str, default="N/A" + time_unit : str, default="N/A" The format used for timings in the file, i.e. 'Seconds', 'Milliseconds', 'Nanoseconds' first_line_comment : str or None, default=None @@ -537,12 +538,12 @@ def write_clustering_results( class_labels, clusterer_name, dataset_name, - output_path, + file_path, predicted_probabilities=cluster_probabilities, full_path=full_path, split=split, resample_id=resample_id, - timing_type=timing_type, + time_unit=time_unit, first_line_comment=first_line_comment, second_line=parameter_info, third_line=third_line, @@ -554,11 +555,11 @@ def write_forecasting_results( labels, forecaster_name, dataset_name, - output_path, + file_path, full_path=True, split=None, random_seed=None, - timing_type="N/A", + time_unit="N/A", first_line_comment=None, parameter_info="No Parameter Info", mape=-1, @@ -580,7 +581,7 @@ def write_forecasting_results( determine file structure if full_path is False. dataset_name : str Name of the problem the forecaster was built on. - output_path : str + file_path : str Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True @@ -592,7 +593,7 @@ def write_forecasting_results( of the file. random_seed : int or None, default=None Indicates what random seed was used as a random_state for the forecaster. - timing_type : str, default="N/A" + time_unit : str, default="N/A" The format used for timings in the file, i.e. 'Seconds', 'Milliseconds', 'Nanoseconds' first_line_comment : str or None, default=None @@ -625,11 +626,11 @@ def write_forecasting_results( labels, forecaster_name, dataset_name, - output_path, + file_path, full_path=full_path, split=split, resample_id=random_seed, - timing_type=timing_type, + time_unit=time_unit, first_line_comment=first_line_comment, second_line=parameter_info, third_line=third_line, @@ -641,12 +642,12 @@ def write_results_to_tsml_format( labels, estimator_name, dataset_name, - output_path, + file_path, predicted_probabilities=None, full_path=True, split=None, resample_id=None, - timing_type="N/A", + time_unit="N/A", first_line_comment=None, second_line="No Parameter Info", third_line="N/A", @@ -665,7 +666,7 @@ def write_results_to_tsml_format( determine file structure if full_path is False. dataset_name : str Name of the problem the estimator was built on. - output_path : str + file_path : str Path to write the results file to or the directory to build the default file structure if full_path is False. predicted_probabilities : np.ndarray, default=None @@ -681,7 +682,7 @@ def write_results_to_tsml_format( resample_id : int or None, default=None Indicates what random seed was used to resample the data or used as a random_state for the estimator. - timing_type : str, default="N/A" + time_unit : str, default="N/A" The format used for timings in the file, i.e. 'Seconds', 'Milliseconds', 'Nanoseconds' first_line_comment : str or None, default=None @@ -701,20 +702,16 @@ def write_results_to_tsml_format( # If the full directory path is not passed, make the standard structure if not full_path: - output_path = f"{output_path}/{estimator_name}/Predictions/{dataset_name}/" + file_path = f"{file_path}/{estimator_name}/Predictions/{dataset_name}/" try: - os.makedirs(output_path) + os.makedirs(file_path) except os.error: pass # raises os.error if path already exists, so just ignore this if split is None: split = "" - elif split.lower() == "train": - split = "TRAIN" - elif split.lower() == "test": - split = "TEST" - else: + elif split.lower() != "train" and split.lower() != "test": raise ValueError("Unknown 'split' value - should be 'TRAIN', 'TEST' or None") fname = ( @@ -724,52 +721,51 @@ def write_results_to_tsml_format( ) fname = fname.lower() if split == "" else fname - file = open(f"{output_path}/{fname}.csv", "w") - - # the first line of the output file is in the form of: - first_line = ( - f"{dataset_name}," - f"{estimator_name}," - f"{'No split' if split == '' else split}," - f"{'None' if resample_id is None else resample_id}," - f"{timing_type}," - f"{'' if first_line_comment is None else first_line_comment}" - ) - file.write(first_line + "\n") - - # the second line of the output is free form and estimator-specific; usually this - # will record info such as paramater options used, any constituent model - # names for ensembles, etc. - file.write(str(second_line) + "\n") - - # the third line of the file depends on the task i.e. classification or regression - file.write(str(third_line) + "\n") - - # from line 4 onwards each line should include the actual and predicted class - # labels (comma-separated). If present, for each case, the probabilities of - # predicting every class value for this case should also be appended to the line ( - # a space is also included between the predicted value and the predict_proba). E.g.: - # - # if predict_proba data IS provided for case i: - # labels[i], preds[i],,prob_class_0[i], - # prob_class_1[i],...,prob_class_c[i] - # - # if predict_proba data IS NOT provided for case i: - # labels[i], predd[i] - # - # If labels[i] is NaN (if clustering), labels[i] is replaced with ? to indicate - # missing - for i in range(0, len(predictions)): - label = "?" if np.isnan(labels[i]) else labels[i] - file.write(f"{label},{predictions[i]}") - - if predicted_probabilities is not None: - file.write(",") - for j in predicted_probabilities[i]: - file.write(f",{j}") - file.write("\n") - - file.close() + with open(f"{file_path}/{fname}.csv", "w") as file: + # the first line of the output file is in the form of: + first_line = ( + f"{dataset_name}," + f"{estimator_name}," + f"{'No split' if split == '' else split.upper()}," + f"{'None' if resample_id is None else resample_id}," + f"{time_unit.upper()}," + f"{'' if first_line_comment is None else first_line_comment}" + ) + file.write(first_line + "\n") + + # the second line of the output is free form and estimator-specific; usually + # this will record info such as paramater options used, any constituent model + # names for ensembles, etc. + file.write(str(second_line) + "\n") + + # the third line of the file depends on the task i.e. classification or + # regression + file.write(str(third_line) + "\n") + + # from line 4 onwards each line should include the actual and predicted class + # labels (comma-separated). If present, for each case, the probabilities of + # predicting every class value for this case should also be appended to the + # line (a space is also included between the predicted value and the + # predict_proba). E.g.: + # + # if predict_proba data IS provided for case i: + # labels[i], preds[i],,prob_class_0[i], + # prob_class_1[i],...,prob_class_c[i] + # + # if predict_proba data IS NOT provided for case i: + # labels[i], preds[i] + # + # If labels[i] is NaN (if clustering), labels[i] is replaced with ? to indicate + # missing + for i in range(0, len(predictions)): + label = "?" if np.isnan(labels[i]) else labels[i] + file.write(f"{label},{predictions[i]}") + + if predicted_probabilities is not None: + file.write(",") + for j in predicted_probabilities[i]: + file.write(f",{j}") + file.write("\n") def _results_present(path, estimator, dataset, resample_id=None, split="TEST"): @@ -799,47 +795,6 @@ def _results_present(path, estimator, dataset, resample_id=None, split="TEST"): return False -def validate_results_file(file_path): - """Validate that a results file is in the correct format. - - Validates that the first, second, third and results lines follow the expected - format. This does not verify that the actual contents of the results file make - sense. - - Works for classification, regression and clustering results files. - - Parameters - ---------- - file_path : str - Path to the results file to be validated, including the file itself. - - Returns - ------- - valid_file : bool - True if the results file is valid, False otherwise. - """ - with open(file_path, "r") as f: - lines = f.readlines() - - if not _check_first_line(lines[0]) or not _check_second_line(lines[1]): - return False - - if _check_classification_third_line(lines[2]) or _check_clustering_third_line( - lines[2] - ): - probabilities = True - elif _check_regression_third_line(lines[2]): - probabilities = False - else: - return False - - for i in range(3, len(lines)): - if not _check_results_line(lines[i], probabilities=probabilities): - return False - - return True - - def fix_broken_second_line(file_path, save_path=None): """Fix a results while where the written second line has line breaks. @@ -887,81 +842,6 @@ def fix_broken_second_line(file_path, save_path=None): f.writelines(lines) -def _check_first_line(line): - line = line.split(",") - return len(line) >= 5 - - -def _check_second_line(line): - line = line.split(",") - return len(line) >= 1 - - -def _check_classification_third_line(line): - line = line.split(",") - floats = [0, 1, 2, 3, 4, 5, 7, 8] - return _check_line_length_and_floats(line, 9, floats) - - -def _check_regression_third_line(line): - line = line.split(",") - floats = [0, 1, 2, 3, 4, 6, 7] - return _check_line_length_and_floats(line, 8, floats) - - -def _check_clustering_third_line(line): - line = line.split(",") - floats = [0, 1, 2, 3, 4, 5, 6] - return _check_line_length_and_floats(line, 7, floats) - - -def _check_forecasting_third_line(line): - line = line.split(",") - floats = [0, 1, 2, 3, 4] - return _check_line_length_and_floats(line, 5, floats) - - -def _check_line_length_and_floats(line, length, floats): - if len(line) != length: - return False - - for i in floats: - try: - float(line[i]) - except ValueError: - return False - - return True - - -def _check_results_line(line, probabilities=True, n_probas=1): - line = line.split(",") - - if len(line) < 2: - return False - - try: - float(line[0]) - float(line[1]) - except ValueError: - return False - - if probabilities: - if len(line) < 3 + n_probas or line[2] != "": - return False - - try: - for i in range(n_probas): - float(line[3 + i]) - except ValueError: - return False - else: - if len(line) != 2: - return False - - return True - - def compare_result_file_resample(file_path1, file_path2): """Validate that a two results files use the same data resample. @@ -1028,215 +908,3 @@ def assign_gpu(set_environ=False): # pragma: no cover os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu) return gpu - - -def parse_args(args): - """Parse the command line arguments for tsml_eval. - - The following is the --help output for tsml_eval: - - usage: tsml_eval [-h] [--version] [-ow] [-pr] [-rs RANDOM_SEED] [-nj N_JOBS] - [-tr] [-te] [-fc FIT_CONTRACT] [-ch] [-rn] [-nc N_CLUSTERS] - [-kw KEY VALUE TYPE] - data_path results_path estimator_name dataset_name - resample_id - - positional arguments: - data_path the path to the directory storing dataset files. - results_path the path to the directory where results files are - written to. - estimator_name the name of the estimator to run. See the - set_{task}.py file for each task learning task for - available options. - dataset_name the name of the dataset to load. - {data_dir}/{dataset_name}/{dataset_name}_TRAIN.ts and - {data_dir}/{dataset_name}/{dataset_name}_TEST.ts will - be loaded. - resample_id the resample ID to use when randomly resampling the - data, as a random seed for estimators and the suffix - when writing results files. An ID of 0 will use the - default TRAIN/TEST split. - - options: - -h, --help show this help message and exit - --version show program's version number and exit - -ow, --overwrite overwrite existing results files. If False, existing - results files will be skipped (default: False). - -pr, --predefined_resample - load a dataset file with a predefined resample. The - dataset file must follow the naming format - '{dataset_name}_{resample_id}.ts' (default: False). - -rs RANDOM_SEED, --random_seed RANDOM_SEED - use a different random seed than the resample ID. If - None use the {resample_id} (default: None). - -nj N_JOBS, --n_jobs N_JOBS - the number of jobs to run in parallel. Only used if - the experiments file and selected estimator allows - threading (default: 1). - -tr, --train_fold write a results file for the training data in the - classification and regression task (default: False). - -te, --test_fold write a results file for the test data in the - clustering task (default: False). - -fc FIT_CONTRACT, --fit_contract FIT_CONTRACT - a time limit for estimator fit in minutes. Only used - if the estimator can contract fit (default: 0). - -ch, --checkpoint save the estimator fit to file periodically while - building. Only used if the estimator can checkpoint - (default: False). - -rn, --row_normalise normalise the data rows prior to fitting and - predicting. (default: False). - -nc N_CLUSTERS, --n_clusters N_CLUSTERS - the number of clusters to find for clusterers which - have an {n_clusters} parameter. If {-1}, use the - number of classes in the dataset (default: None). - -kw KEY VALUE TYPE, --kwargs KEY VALUE TYPE, --kwarg KEY VALUE TYPE - additional keyword arguments to pass to the estimator. - Should contain the parameter to set, the parameter - value, and the type of the value i.e. {--kwargs - n_estimators 200 int} to change the size of an - ensemble. Valid types are {int, float, bool, str}. Any - other type will be passed as a str. Can be used - multiple times (default: None). - - Parameters - ---------- - args : list - List of command line arguments to parse. - - Returns - ------- - same_resample : argparse.Namespace - The parsed command line arguments. - """ - parser = argparse.ArgumentParser(prog="tsml_eval") - parser.add_argument( - "--version", action="version", version=f"%(prog)s {tsml_eval.__version__}" - ) - parser.add_argument( - "data_path", help="the path to the directory storing dataset files." - ) - parser.add_argument( - "results_path", - help="the path to the directory where results files are written to.", - ) - parser.add_argument( - "estimator_name", - help="the name of the estimator to run. See the set_{task}.py file for each " - "task learning task for available options.", - ) - parser.add_argument( - "dataset_name", - help="the name of the dataset to load. " - "{data_dir}/{dataset_name}/{dataset_name}_TRAIN.ts and " - "{data_dir}/{dataset_name}/{dataset_name}_TEST.ts will be loaded.", - ) - parser.add_argument( - "resample_id", - type=int, - help="the resample ID to use when randomly resampling the data, as a random " - "seed for estimators and the suffix when writing results files. An ID of " - "0 will use the default TRAIN/TEST split.", - ) - parser.add_argument( - "-ow", - "--overwrite", - action="store_true", - help="overwrite existing results files. If False, existing results files " - "will be skipped (default: %(default)s).", - ) - parser.add_argument( - "-pr", - "--predefined_resample", - action="store_true", - help="load a dataset file with a predefined resample. The dataset file must " - "follow the naming format '{dataset_name}{resample_id}.ts' " - "(default: %(default)s).", - ) - parser.add_argument( - "-rs", - "--random_seed", - type=int, - help="use a different random seed than the resample ID. If None use the " - "{resample_id} (default: %(default)s).", - ) - parser.add_argument( - "-nj", - "--n_jobs", - type=int, - default=1, - help="the number of jobs to run in parallel. Only used if the experiments file " - "and selected estimator allows threading (default: %(default)s).", - ) - parser.add_argument( - "-tr", - "--train_fold", - action="store_true", - help="write a results file for the training data in the classification and " - "regression task (default: %(default)s).", - ) - parser.add_argument( - "-te", - "--test_fold", - action="store_true", - help="write a results file for the test data in the clustering task " - "(default: %(default)s).", - ) - parser.add_argument( - "-fc", - "--fit_contract", - type=int, - default=0, - help="a time limit for estimator fit in minutes. Only used if the estimator " - "can contract fit (default: %(default)s).", - ) - parser.add_argument( - "-ch", - "--checkpoint", - action="store_true", - help="save the estimator fit to file periodically while building. Only used if " - "the estimator can checkpoint (default: %(default)s).", - ) - parser.add_argument( - "-rn", - "--row_normalise", - action="store_true", - help="normalise the data rows prior to fitting and predicting. " - "(default: %(default)s).", - ) - parser.add_argument( - "-nc", - "--n_clusters", - type=int, - help="the number of clusters to find for clusterers which have an {n_clusters} " - "parameter. If {-1}, use the number of classes in the dataset " - "(default: %(default)s).", - ) - parser.add_argument( - "-kw", - "--kwargs", - "--kwarg", - action="append", - nargs=3, - metavar=("KEY", "VALUE", "TYPE"), - help="additional keyword arguments to pass to the estimator. Should contain " - "the parameter to set, the parameter value, and the type of the value i.e. " - "{--kwargs n_estimators 200 int} to change the size of an ensemble. Valid " - "types are {int, float, bool, str}. Any other type will be passed as a str. " - "Can be used multiple times (default: %(default)s).", - ) - args = parser.parse_args(args=args) - - kwargs = {} - if args.kwargs is not None: - for kwarg in args.kwargs: - if kwarg[2] == "int": - kwargs[kwarg[0]] = int(kwarg[1]) - elif kwarg[2] == "float": - kwargs[kwarg[0]] = float(kwarg[1]) - elif kwarg[2] == "bool": - kwargs[kwarg[0]] = kwarg[1].lower() == "true" or kwarg[1] == "1" - else: - kwargs[kwarg[0]] = kwarg[1] - args.kwargs = kwargs - - return args diff --git a/tsml_eval/utils/functions.py b/tsml_eval/utils/functions.py index d367d4cf..837b2e7c 100644 --- a/tsml_eval/utils/functions.py +++ b/tsml_eval/utils/functions.py @@ -3,8 +3,12 @@ __all__ = [ "str_in_nested_list", "pair_list_to_dict", + "time_to_milliseconds", + "rank_array", ] +import numpy as np + def str_in_nested_list(nested_list, item): """Find an item in a nested list.""" @@ -25,3 +29,73 @@ def pair_list_to_dict(pl): If ls is None, returns an empty dict. """ return {} if pl is None else {k: v for k, v in pl} + + +def time_to_milliseconds(time_value, time_unit): + """Convert a time value from the given time unit to milliseconds. + + Parameters + ---------- + time_value : float + The time value to convert. + time_unit : str {"nanoseconds", "microseconds", "milliseconds", "seconds", + "minutes", "hours", "days"} + The current time unit of the value. + + Returns + ------- + float + The time in milliseconds. + """ + time_units = { + "nanoseconds": 1e-6, + "microseconds": 1e-3, + "milliseconds": 1, + "seconds": 1e3, + "minutes": 60e3, + "hours": 3600e3, + "days": 86400e3, + } + + if time_unit not in time_units: + raise ValueError(f"Unknown time unit: {time_unit}") + + # Convert the time value to milliseconds + return time_value * time_units[time_unit] + + +def rank_array(arr, higher_better=True): + """ + Assign a rank to each value in a 1D numpy array. + + A lower rank number is assumed to be better. Lower values can receive better ranks + or vice versa based on the `higher_better` parameter. Equal values receive the + average of the ranks they would cover. + + Parameters + ---------- + arr : numpy.ndarray + The input 1D array containing values to be ranked. + higher_better : bool, default=True + If True, lower values receive better ranks. + If False (default), higher values receive better ranks. + + Returns + ------- + ranks : numpy.ndarray + Array of ranks, same shape as `arr`. + """ + # argsort returns indices that would sort the array + sorter = np.argsort(arr) + ranks = np.zeros(len(arr), dtype=float) + ranks[sorter] = np.arange(1, len(arr) + 1) + + # Handle ties: find unique values and their corresponding indices + unique_vals, inv_sorter = np.unique(arr, return_inverse=True) + for i in np.unique(inv_sorter): + ranks[inv_sorter == i] = np.mean(ranks[inv_sorter == i]) + + if higher_better: + ranks = len(arr) + 1 - ranks + + return ranks diff --git a/tsml_eval/utils/memory_recorder.py b/tsml_eval/utils/memory_recorder.py new file mode 100644 index 00000000..b07b21af --- /dev/null +++ b/tsml_eval/utils/memory_recorder.py @@ -0,0 +1,49 @@ +import time +from threading import Thread + +import psutil + + +def record_max_memory( + function, args=None, kwargs=None, interval=0.1, return_func_time=False +): + process = psutil.Process() + start_memory = process.memory_info().rss + + thread = FunctionThread(function, args, kwargs) + thread.start() + + max_memory = process.memory_info().rss + + while True: + time.sleep(interval) + + mem = process.memory_info().rss + if mem > max_memory: + max_memory = mem + + if thread.has_shutdown: + if return_func_time: + return max_memory - start_memory, thread.function_time + else: + return max_memory - start_memory + + +class FunctionThread(Thread): + def __init__(self, function, args=None, kwargs=None): + self.function = function + self.args = args if args is not None else [] + self.kwargs = kwargs if kwargs is not None else {} + + self.function_time = -1 + self.has_shutdown = False + + super(FunctionThread, self).__init__(daemon=True) + + def run(self): + """Overloads the threading.Thread.run.""" + start = int(round(time.time() * 1000)) + self.function(*self.args, **self.kwargs) + end = int(round(time.time() * 1000)) - start + self.function_time = end - start + self.has_shutdown = True diff --git a/tsml_eval/utils/tests/test_args.py b/tsml_eval/utils/tests/test_args.py index 09b152d6..839d7b76 100644 --- a/tsml_eval/utils/tests/test_args.py +++ b/tsml_eval/utils/tests/test_args.py @@ -1,7 +1,7 @@ import pytest -from tsml_eval.utils.experiments import parse_args -from tsml_eval.utils.test_utils import suppress_output +from tsml_eval.testing.test_utils import suppress_output +from tsml_eval.utils.arguments import parse_args def test_positional_args(): diff --git a/tsml_eval/utils/tests/test_functions.py b/tsml_eval/utils/tests/test_functions.py index b11b2e9b..b246bdf3 100644 --- a/tsml_eval/utils/tests/test_functions.py +++ b/tsml_eval/utils/tests/test_functions.py @@ -1,9 +1,80 @@ """Test utility functions.""" +import numpy as np -from tsml_eval.utils.functions import pair_list_to_dict +from tsml_eval.utils.functions import pair_list_to_dict, rank_array def test_pair_list_to_dict(): """Test pair_list_to_dict function.""" assert pair_list_to_dict([("a", 1), ("b", 2)]) == {"a": 1, "b": 2} assert pair_list_to_dict(None) == {} + + +def test_rank_array(): + arr1 = [ + 0.611111111, + 0.638888889, + 0.666666667, + 0.666666667, + 0.611111111, + 0.666666667, + 0.611111111, + 0.638888889, + 0.666666667, + 0.666666667, + 0.666666667, + ] + arr2 = [ + 0.683333333, + 0.7, + 0.716666667, + 0.666666667, + 0.783333333, + 0.516666667, + 0.4, + 0.583333333, + 0.633333333, + 0.533333333, + 0.583333333, + ] + arr3 = [0.584, 0.6, 0.604, 0.548, 0.616, 0.504, 0.584, 0.588, 0.544, 0.572, 0.516] + arr4 = [ + 0.342541436, + 0.370165746, + 0.364640884, + 0.375690608, + 0.46961326, + 0.337016575, + 0.359116022, + 0.453038674, + 0.419889503, + 0.303867403, + 0.29281768, + ] + ranks1 = [10, 7.5, 3.5, 3.5, 10, 3.5, 10, 7.5, 3.5, 3.5, 3.5] + ranks2 = [4, 3, 2, 5, 1, 10, 11, 7.5, 6, 9, 7.5] + ranks3 = [5.5, 3, 2, 8, 1, 11, 5.5, 4, 9, 7, 10] + ranks4 = [8, 5, 6, 4, 1, 9, 7, 2, 3, 10, 11] + + assert (rank_array(np.array(arr1)) == np.array(ranks1)).all() + assert (rank_array(np.array(arr2)) == np.array(ranks2)).all() + assert (rank_array(np.array(arr3)) == np.array(ranks3)).all() + assert (rank_array(np.array(arr4)) == np.array(ranks4)).all() + + inverse_ranks1 = [2, 4.5, 8.5, 8.5, 2, 8.5, 2, 4.5, 8.5, 8.5, 8.5] + inverse_ranks2 = [8, 9, 10, 7, 11, 2, 1, 4.5, 6, 3, 4.5] + inverse_ranks3 = [6.5, 9, 10, 4, 11, 1, 6.5, 8, 3, 5, 2] + inverse_ranks4 = [4, 7, 6, 8, 11, 3, 5, 10, 9, 2, 1] + + assert ( + rank_array(np.array(arr1), higher_better=False) == np.array(inverse_ranks1) + ).all() + assert ( + rank_array(np.array(arr2), higher_better=False) == np.array(inverse_ranks2) + ).all() + assert ( + rank_array(np.array(arr3), higher_better=False) == np.array(inverse_ranks3) + ).all() + assert ( + rank_array(np.array(arr4), higher_better=False) == np.array(inverse_ranks4) + ).all() diff --git a/tsml_eval/utils/tests/test_misc_experiments.py b/tsml_eval/utils/tests/test_misc_experiments.py index 86d3e809..d3d82cc1 100644 --- a/tsml_eval/utils/tests/test_misc_experiments.py +++ b/tsml_eval/utils/tests/test_misc_experiments.py @@ -8,7 +8,6 @@ @pytest.mark.parametrize("split", ["BOTH", "TRAIN", "TEST", None, "invalid"]) def test_results_present_split_inputs(split): """Test _results_present function with valid and invalid split inputs.""" - if split == "invalid": with pytest.raises(ValueError, match="Unknown split value"): _results_present( diff --git a/tsml_eval/utils/tests/test_resampling.py b/tsml_eval/utils/tests/test_resampling.py index 74d7122a..d561d63f 100644 --- a/tsml_eval/utils/tests/test_resampling.py +++ b/tsml_eval/utils/tests/test_resampling.py @@ -2,7 +2,6 @@ __author__ = ["TonyBagnall", "MatthewMiddlehurst"] -import os import numpy as np import pandas as pd @@ -13,6 +12,7 @@ load_unequal_minimal_chinatown, ) +from tsml_eval.testing.test_utils import _TEST_RESULTS_PATH from tsml_eval.utils.experiments import ( compare_result_file_resample, resample_data, @@ -148,38 +148,26 @@ def test_stratified_resample_data_invalid(): "paths", [ [ - "test_files/classificationResultsFile1.csv", - "test_files/classificationResultsFile1.csv", + _TEST_RESULTS_PATH + "/classification/classificationResultsFile1.csv", + _TEST_RESULTS_PATH + "/classification/classificationResultsFile1.csv", True, ], [ - "test_files/classificationResultsFile1.csv", - "test_files/classificationResultsFile2.csv", + _TEST_RESULTS_PATH + "/classification/classificationResultsFile1.csv", + _TEST_RESULTS_PATH + "/classification/classificationResultsFile2.csv", False, ], ], ) def test_compare_result_file_resample(paths): """Test compare result file resample function.""" - if os.getcwd().split("\\")[-1] != "tests": - paths[0] = f"tsml_eval/utils/tests/{paths[0]}" - paths[1] = f"tsml_eval/utils/tests/{paths[1]}" - assert compare_result_file_resample(paths[0], paths[1]) == paths[2] def test_compare_result_file_resample_invalid(): """Test compare result file resample function with invalid input.""" - p1 = ( - "tsml_eval/utils/tests/test_files/classificationResultsFile1.csv" - if os.getcwd().split("\\")[-1] != "tests" - else "test_files/classificationResultsFile1.csv" - ) - p3 = ( - "tsml_eval/utils/tests/test_files/classificationResultsFile3.csv" - if os.getcwd().split("\\")[-1] != "tests" - else "test_files/classificationResultsFile3.csv" - ) + p1 = _TEST_RESULTS_PATH + "/classification/classificationResultsFile1.csv" + p3 = _TEST_RESULTS_PATH + "/classification/classificationResultsFile3.csv" with pytest.raises(ValueError, match="Input results file have different"): compare_result_file_resample(p1, p3) diff --git a/tsml_eval/utils/tests/test_results_validation_and_repair.py b/tsml_eval/utils/tests/test_results_validation_and_repair.py index 871e721b..396c511b 100644 --- a/tsml_eval/utils/tests/test_results_validation_and_repair.py +++ b/tsml_eval/utils/tests/test_results_validation_and_repair.py @@ -4,81 +4,60 @@ import pytest -from tsml_eval.utils.experiments import fix_broken_second_line, validate_results_file -from tsml_eval.utils.test_utils import _TEST_DATA_PATH +from tsml_eval.testing.test_utils import _TEST_OUTPUT_PATH, _TEST_RESULTS_PATH +from tsml_eval.utils.experiments import fix_broken_second_line +from tsml_eval.utils.validation import validate_results_file @pytest.mark.parametrize( "path", [ - "test_files/regressionResultsFile.csv", - "test_files/classificationResultsFile1.csv", + _TEST_RESULTS_PATH + "/regression/regressionResultsFile.csv", + _TEST_RESULTS_PATH + "/classification/classificationResultsFile1.csv", ], ) def test_validate_results_file(path): """Test results file validation with valid files.""" - path = ( - f"tsml_eval/utils/tests/{path}" - if os.getcwd().split("\\")[-1] != "tests" - else path - ) - assert validate_results_file(path) @pytest.mark.parametrize( "path", [ - "test_files/brokenRegressionResultsFile.csv", - "test_files/brokenClassificationResultsFile.csv", - "test_files/brokenResultsFile.csv", + _TEST_RESULTS_PATH + "/broken/brokenRegressionResultsFile.csv", + _TEST_RESULTS_PATH + "/broken/brokenClassificationResultsFile.csv", + _TEST_RESULTS_PATH + "/broken/brokenResultsFile.csv", ], ) def test_validate_broken_results_file(path): """Test results file validation with broken files.""" - path = ( - f"tsml_eval/utils/tests/{path}" - if os.getcwd().split("\\")[-1] != "tests" - else path - ) - assert not validate_results_file(path) @pytest.mark.parametrize( "path", [ - ["test_files/regressionResultsFile.csv", 1], - ["test_files/brokenRegressionResultsFile.csv", 2], + [_TEST_RESULTS_PATH + "/regression/regressionResultsFile.csv", 1], + [_TEST_RESULTS_PATH + "/broken/brokenRegressionResultsFile.csv", 2], ], ) def test_fix_broken_second_line(path): """Test that the second line of a broken results file is fixed.""" - path[0] = ( - f"tsml_eval/utils/tests/{path[0]}" - if os.getcwd().split("\\")[-1] != "tests" - else path[0] - ) - - fix_broken_second_line(path[0], f"{_TEST_DATA_PATH}/secondLineTest{path[1]}.csv") + fix_broken_second_line(path[0], f"{_TEST_OUTPUT_PATH}/secondLineTest{path[1]}.csv") - assert validate_results_file(f"{_TEST_DATA_PATH}/secondLineTest{path[1]}.csv") + assert validate_results_file(f"{_TEST_OUTPUT_PATH}/secondLineTest{path[1]}.csv") # validate again while overwriting the original file - fix_broken_second_line(f"{_TEST_DATA_PATH}/secondLineTest{path[1]}.csv") + fix_broken_second_line(f"{_TEST_OUTPUT_PATH}/secondLineTest{path[1]}.csv") - assert validate_results_file(f"{_TEST_DATA_PATH}/secondLineTest{path[1]}.csv") + assert validate_results_file(f"{_TEST_OUTPUT_PATH}/secondLineTest{path[1]}.csv") - os.remove(f"{_TEST_DATA_PATH}/secondLineTest{path[1]}.csv") + os.remove(f"{_TEST_OUTPUT_PATH}/secondLineTest{path[1]}.csv") def test_fix_broken_second_line_invalid_third_line(): """Test that an error is raised if the third line is broken.""" - path = ( - "tsml_eval/utils/tests/test_files/brokenResultsFileLine3.csv" - if os.getcwd().split("\\")[-1] != "tests" - else "test_files/brokenResultsFileLine3.csv" - ) + path = _TEST_RESULTS_PATH + "/broken/brokenResultsFileLine3.csv" with pytest.raises(ValueError, match="No valid third line"): - fix_broken_second_line(path, f"{_TEST_DATA_PATH}/secondLineTest3.csv") + fix_broken_second_line(path, f"{_TEST_OUTPUT_PATH}/secondLineTest3.csv") diff --git a/tsml_eval/utils/tests/test_results_writing.py b/tsml_eval/utils/tests/test_results_writing.py index 7a3b86f0..27467992 100644 --- a/tsml_eval/utils/tests/test_results_writing.py +++ b/tsml_eval/utils/tests/test_results_writing.py @@ -14,6 +14,13 @@ _REGRESSOR_RESULTS_PATH, ) from tsml_eval.utils.experiments import ( + write_classification_results, + write_clustering_results, + write_forecasting_results, + write_regression_results, + write_results_to_tsml_format, +) +from tsml_eval.utils.validation import ( _check_classification_third_line, _check_clustering_third_line, _check_first_line, @@ -21,11 +28,6 @@ _check_regression_third_line, _check_results_line, _check_second_line, - write_classification_results, - write_clustering_results, - write_forecasting_results, - write_regression_results, - write_results_to_tsml_format, ) @@ -42,6 +44,7 @@ def test_write_classification_results(): _CLASSIFIER_RESULTS_PATH, full_path=False, first_line_comment="test_write_classification_results", + n_classes=3, ) _check_classification_file_format( @@ -58,9 +61,10 @@ def _check_classification_file_format(file_path): assert _check_first_line(lines[0]) assert _check_second_line(lines[1]) assert _check_classification_third_line(lines[2]) + n_classes = int(lines[2].split(",")[5]) for i in range(3, 6): - assert _check_results_line(lines[i]) + assert _check_results_line(lines[i], n_probas=n_classes) def test_write_classification_results_invalid(): @@ -170,6 +174,7 @@ def test_write_clustering_results(): _CLUSTERER_RESULTS_PATH, full_path=False, first_line_comment="test_write_clustering_results", + n_clusters=3, ) _check_clustering_file_format( @@ -186,9 +191,10 @@ def _check_clustering_file_format(file_path): assert _check_first_line(lines[0]) assert _check_second_line(lines[1]) assert _check_clustering_third_line(lines[2]) + n_probas = int(lines[2].split(",")[6]) for i in range(3, 6): - assert _check_results_line(lines[i]) + assert _check_results_line(lines[i], n_probas=n_probas) def test_write_clustering_results_invalid(): diff --git a/tsml_eval/utils/validation.py b/tsml_eval/utils/validation.py index 5b28dc2a..a5e806fd 100644 --- a/tsml_eval/utils/validation.py +++ b/tsml_eval/utils/validation.py @@ -1,4 +1,12 @@ -"""Utilities for validating estimators.""" +"""Utilities for validating results and estimators.""" + +__all__ = [ + "is_sklearn_estimator", + "is_sklearn_classifier", + "is_sklearn_regressor", + "is_sklearn_clusterer", + "validate_results_file", +] from aeon.base import BaseEstimator as AeonBaseEstimator from sklearn.base import BaseEstimator as SklearnBaseEstimator @@ -29,3 +37,141 @@ def is_sklearn_regressor(regressor): def is_sklearn_clusterer(clusterer): """Check if estimator is a scikit-learn clusterer.""" return is_sklearn_estimator(clusterer) and is_clusterer(clusterer) + + +def validate_results_file(file_path): + """Validate that a results file is in the correct format. + + Validates that the first, second, third and results lines follow the expected + format. This does not verify that the actual contents of the results file make + sense. + + Works for classification, regression and clustering results files. + + Parameters + ---------- + file_path : str + Path to the results file to be validated, including the file itself. + + Returns + ------- + valid_file : bool + True if the results file is valid, False otherwise. + """ + with open(file_path, "r") as f: + lines = f.readlines() + + if not _check_first_line(lines[0]) or not _check_second_line(lines[1]): + return False + + if _check_classification_third_line(lines[2]): + n_probas = int(lines[2].split(",")[5]) + probabilities = True + elif _check_clustering_third_line(lines[2]): + n_probas = int(lines[2].split(",")[6]) + probabilities = True + elif _check_regression_third_line(lines[2]) or _check_forecasting_third_line( + lines[2] + ): + n_probas = 0 + probabilities = False + else: + return False + + for i in range(3, len(lines)): + if not _check_results_line( + lines[i], probabilities=probabilities, n_probas=n_probas + ): + return False + + return True + + +def _check_first_line(line): + line = line.split(",") + return len(line) >= 5 + + +def _check_second_line(line): + line = line.split(",") + return len(line) >= 1 + + +def _check_classification_third_line(line): + line = line.split(",") + floats = [0, 1, 2, 3, 4, 5, 7, 8] + return _check_line_length_and_floats(line, 9, floats) + + +def _check_regression_third_line(line): + line = line.split(",") + floats = [0, 1, 2, 3, 4, 6, 7] + return _check_line_length_and_floats(line, 8, floats) + + +def _check_clustering_third_line(line): + line = line.split(",") + floats = [0, 1, 2, 3, 4, 5, 6] + return _check_line_length_and_floats(line, 7, floats) + + +def _check_forecasting_third_line(line): + line = line.split(",") + floats = [0, 1, 2, 3, 4] + return _check_line_length_and_floats(line, 5, floats) + + +def _check_line_length_and_floats(line, length, floats): + if len(line) != length: + return False + + for i in floats: + try: + float(line[i]) + except ValueError: + return False + + return True + + +def _check_results_line(line, probabilities=True, n_probas=2): + line = line.split(",") + + if len(line) < 2: + return False + + try: + float(line[0]) + float(line[1]) + except ValueError: + return False + + if probabilities: + if len(line) < 3 + n_probas or line[2] != "": + return False + + psum = 0 + try: + for i in range(n_probas): + psum += float(line[3 + i]) + except ValueError: + return False + + if psum < 0.999 or psum > 1.001: + return False + else: + n_probas = 0 + + if len(line) > 4 + n_probas: + if line[4 + n_probas] != "": + return False + + try: + float(line[5 + n_probas]) + except ValueError: + return False + + if len(line) > 5 + n_probas and line[5 + n_probas] != "": + return False + + return True From a02fb0e838c032ff73cc4d4ab13305b474175c9c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci-lite[bot]" <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com> Date: Tue, 14 Nov 2023 12:48:02 +0000 Subject: [PATCH 02/19] [pre-commit.ci lite] apply automatic fixes --- tsml_eval/evaluation/storage/classifier_results.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index 9415007e..eae7e22e 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -67,7 +67,6 @@ class ClassifierResults(EstimatorResults): """ - def __init__( self, dataset_name="N/A", From 93a3a8ac5dedec42942f98a68b9a1d00355734a5 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Wed, 15 Nov 2023 23:27:10 +0000 Subject: [PATCH 03/19] more result loading and evaluation --- .../multiple_estimator_evaluation.py | 288 ++++- tsml_eval/evaluation/storage/__init__.py | 2 +- .../evaluation/storage/classifier_results.py | 205 ++-- .../evaluation/storage/clusterer_results.py | 175 ++- .../evaluation/storage/estimator_results.py | 106 +- .../evaluation/storage/forecaster_results.py | 144 ++- .../evaluation/storage/regressor_results.py | 172 ++- .../evaluation/storage/tests/__init__.py | 1 + .../test_multiple_estimator_evaluation.py | 9 +- .../experiments/classification_experiments.py | 1 + .../experiments/clustering_experiments.py | 1 + tsml_eval/experiments/experiments.py | 76 +- .../experiments/forecasting_experiments.py | 1 + .../experiments/regression_experiments.py | 1 + tsml_eval/experiments/set_regressor.py | 17 +- .../experiments/tests/test_classification.py | 3 +- .../experiments/tests/test_clustering.py | 3 +- .../experiments/tests/test_forecasting.py | 3 +- .../experiments/tests/test_regression.py | 3 +- .../threaded_classification_experiments.py | 1 + .../threaded_clustering_experiments.py | 1 + .../threaded_forecasting_experiments.py | 1 + .../threaded_regression_experiments.py | 1 + tsml_eval/testing/__init__.py | 1 + .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/trainResample0.csv | 70 ++ .../ItalyPowerDemand/trainResample1.csv | 70 ++ .../ItalyPowerDemand/trainResample2.csv | 70 ++ .../Predictions/Trace/trainResample0.csv | 103 ++ .../Predictions/Trace/trainResample1.csv | 103 ++ .../Predictions/Trace/trainResample2.csv | 103 ++ .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/trainResample0.csv | 70 ++ .../ItalyPowerDemand/trainResample1.csv | 70 ++ .../ItalyPowerDemand/trainResample2.csv | 70 ++ .../Predictions/Trace/trainResample0.csv | 103 ++ .../Predictions/Trace/trainResample1.csv | 103 ++ .../Predictions/Trace/trainResample2.csv | 103 ++ .../Predictions/Chinatown/testResample0.csv | 346 ++++++ .../Predictions/Chinatown/testResample1.csv | 346 ++++++ .../Predictions/Chinatown/testResample2.csv | 346 ++++++ .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample1.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/testResample2.csv | 1032 +++++++++++++++++ .../ItalyPowerDemand/trainResample0.csv | 70 ++ .../ItalyPowerDemand/trainResample1.csv | 70 ++ .../ItalyPowerDemand/trainResample2.csv | 70 ++ .../Predictions/Trace/trainResample0.csv | 103 ++ .../Predictions/Trace/trainResample1.csv | 103 ++ .../Predictions/Trace/trainResample2.csv | 103 ++ .../Predictions/Airline/testResample0.csv | 39 + .../ShampooSales/testResample0.csv | 15 + .../Predictions/Airline/testResample0.csv | 39 + .../Predictions/Airline/testResample0.csv | 39 + .../ShampooSales/testResample0.csv | 15 + .../Predictions/Covid3Month/testResample0.csv | 64 + .../Predictions/Covid3Month/testResample1.csv | 64 + .../Predictions/Covid3Month/testResample2.csv | 64 + .../Covid3Month/trainResample0.csv | 143 +++ .../Covid3Month/trainResample1.csv | 143 +++ .../Covid3Month/trainResample2.csv | 143 +++ .../FloodModeling1/testResample0.csv | 205 ++++ .../FloodModeling1/testResample1.csv | 205 ++++ .../FloodModeling1/testResample2.csv | 205 ++++ .../testResample0.csv | 31 + .../testResample1.csv | 31 + .../testResample2.csv | 31 + .../trainResample0.csv | 68 ++ .../trainResample1.csv | 68 ++ .../trainResample2.csv | 68 ++ .../Predictions/Covid3Month/testResample0.csv | 64 + .../Predictions/Covid3Month/testResample1.csv | 64 + .../Predictions/Covid3Month/testResample2.csv | 64 + .../Covid3Month/trainResample0.csv | 143 +++ .../Covid3Month/trainResample1.csv | 143 +++ .../Covid3Month/trainResample2.csv | 143 +++ .../FloodModeling1/testResample0.csv | 205 ++++ .../FloodModeling1/testResample1.csv | 205 ++++ .../FloodModeling1/testResample2.csv | 205 ++++ .../testResample0.csv | 31 + .../testResample1.csv | 31 + .../testResample2.csv | 31 + .../trainResample0.csv | 68 ++ .../trainResample1.csv | 68 ++ .../trainResample2.csv | 68 ++ .../Predictions/Covid3Month/testResample0.csv | 64 + .../Predictions/Covid3Month/testResample1.csv | 64 + .../Predictions/Covid3Month/testResample2.csv | 64 + .../Covid3Month/trainResample0.csv | 143 +++ .../Covid3Month/trainResample1.csv | 143 +++ .../Covid3Month/trainResample2.csv | 143 +++ .../FloodModeling1/testResample0.csv | 205 ++++ .../FloodModeling1/testResample1.csv | 205 ++++ .../FloodModeling1/testResample2.csv | 205 ++++ .../testResample0.csv | 31 + .../testResample1.csv | 31 + .../testResample2.csv | 31 + .../trainResample0.csv | 68 ++ .../trainResample1.csv | 68 ++ .../trainResample2.csv | 68 ++ tsml_eval/testing/tests/__init__.py | 1 + tsml_eval/utils/arguments.py | 10 + tsml_eval/utils/experiments.py | 53 +- tsml_eval/utils/memory_recorder.py | 39 +- 123 files changed, 20017 insertions(+), 214 deletions(-) create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample1.csv create mode 100644 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create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample2.csv diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 117c17b6..247926ab 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -1,3 +1,5 @@ +"""Functions for evaluating multiple estimators on multiple datasets.""" + import os from datetime import datetime @@ -10,12 +12,30 @@ ForecasterResults, RegressorResults, ) -from tsml_eval.utils.functions import rank_array +from tsml_eval.utils.functions import rank_array, time_to_milliseconds def evaluate_classifiers( classifier_results, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple classifiers on multiple datasets. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each classifier. + + Parameters + ---------- + classifier_results : list of ClassifierResults + The results to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ _evaluate_estimators( classifier_results, ClassifierResults.statistics, @@ -28,6 +48,24 @@ def evaluate_classifiers( def evaluate_classifiers_from_file( load_paths, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple classifiers on multiple datasets from file. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each classifier. + + Parameters + ---------- + load_paths : list of str + The paths to the classifier result files to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ classifier_results = [] for load_path in load_paths: classifier_results.append(ClassifierResults().load_from_file(load_path)) @@ -50,6 +88,36 @@ def evaluate_classifiers_by_problem( error_on_missing=True, eval_name=None, ): + """ + Evaluate multiple classifiers on multiple datasets from file using standard paths. + + Finds files using classifier, dataset and resample names. It is expected the + common tsml-eval file structure of + {classifier}/Predictions/{dataset}/{split}Resample{resample}.csv is followed. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each classifier. + + Parameters + ---------- + load_path : list of str + The path to the collection of classifier result files to evaluate. + classifier_names : list of str + The names of the classifiers to evaluate. + dataset_names : list of str + The names of the datasets to evaluate. + save_path : str + The path to save the evaluation results to. + resamples : int or list of int, default=None + The resamples to evaluate. If int, evaluates resamples 0 to resamples-1. + load_train_results : bool, default=False + Whether to load train results as well as test results. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ if resamples is None: resamples = [""] elif isinstance(resamples, int): @@ -85,6 +153,24 @@ def evaluate_classifiers_by_problem( def evaluate_clusterers( clusterer_results, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple clusterers on multiple datasets. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each clusterer. + + Parameters + ---------- + clusterer_results : list of ClustererResults + The results to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ _evaluate_estimators( clusterer_results, ClustererResults.statistics, @@ -97,11 +183,29 @@ def evaluate_clusterers( def evaluate_clusterers_from_file( load_paths, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple clusterers on multiple datasets from file. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each clusterer. + + Parameters + ---------- + load_paths : list of str + The paths to the clusterer result files to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ clusterer_results = [] for load_path in load_paths: clusterer_results.append(ClustererResults().load_from_file(load_path)) - evaluate_classifiers( + evaluate_clusterers( clusterer_results, save_path, error_on_missing=error_on_missing, @@ -119,6 +223,36 @@ def evaluate_clusterers_by_problem( error_on_missing=True, eval_name=None, ): + """ + Evaluate multiple clusterers on multiple datasets from file using standard paths. + + Finds files using clusterer, dataset and resample names. It is expected the + common tsml-eval file structure of + {clusterer}/Predictions/{dataset}/{split}Resample{resample}.csv is followed. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each clusterer. + + Parameters + ---------- + load_path : list of str + The path to the collection of clusterer result files to evaluate. + clusterer_names : list of str + The names of the clusterers to evaluate. + dataset_names : list of str + The names of the datasets to evaluate. + save_path : str + The path to save the evaluation results to. + resamples : int or list of int, default=None + The resamples to evaluate. If int, evaluates resamples 0 to resamples-1. + load_test_results : bool, default=True + Whether to load test results as well as train results. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ if resamples is None: resamples = [""] elif isinstance(resamples, int): @@ -154,6 +288,24 @@ def evaluate_clusterers_by_problem( def evaluate_regressors( regressor_results, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple regressors on multiple datasets. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each regressor. + + Parameters + ---------- + regressor_results : list of RegressorResults + The results to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ _evaluate_estimators( regressor_results, RegressorResults.statistics, @@ -166,11 +318,29 @@ def evaluate_regressors( def evaluate_regressors_from_file( load_paths, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple regressors on multiple datasets from file. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each regressor. + + Parameters + ---------- + load_paths : list of str + The paths to the regressor result files to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ regressor_results = [] for load_path in load_paths: regressor_results.append(RegressorResults().load_from_file(load_path)) - evaluate_classifiers( + evaluate_regressors( regressor_results, save_path, error_on_missing=error_on_missing, @@ -188,6 +358,36 @@ def evaluate_regressors_by_problem( error_on_missing=True, eval_name=None, ): + """ + Evaluate multiple regressors on multiple datasets from file using standard paths. + + Finds files using regressor, dataset and resample names. It is expected the + common tsml-eval file structure of + {regressor}/Predictions/{dataset}/{split}Resample{resample}.csv is followed. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each regressor. + + Parameters + ---------- + load_path : list of str + The path to the collection of regressor result files to evaluate. + regressor_names : list of str + The names of the regressors to evaluate. + dataset_names : list of str + The names of the datasets to evaluate. + save_path : str + The path to save the evaluation results to. + resamples : int or list of int, default=None + The resamples to evaluate. If int, evaluates resamples 0 to resamples-1. + load_train_results : bool, default=False + Whether to load train results as well as test results. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ if resamples is None: resamples = [""] elif isinstance(resamples, int): @@ -223,6 +423,24 @@ def evaluate_regressors_by_problem( def evaluate_forecasters( forecaster_results, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple forecasters on multiple datasets. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each forecaster. + + Parameters + ---------- + forecaster_results : list of ForecasterResults + The results to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ _evaluate_estimators( forecaster_results, ForecasterResults.statistics, @@ -235,11 +453,29 @@ def evaluate_forecasters( def evaluate_forecasters_from_file( load_paths, save_path, error_on_missing=True, eval_name=None ): + """ + Evaluate multiple forecasters on multiple datasets from file. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each forecaster. + + Parameters + ---------- + load_paths : list of str + The paths to the forecaster result files to evaluate. + save_path : str + The path to save the evaluation results to. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ forecaster_results = [] for load_path in load_paths: forecaster_results.append(ForecasterResults().load_from_file(load_path)) - evaluate_classifiers( + evaluate_forecasters( forecaster_results, save_path, error_on_missing=error_on_missing, @@ -256,6 +492,34 @@ def evaluate_forecasters_by_problem( error_on_missing=True, eval_name=None, ): + """ + Evaluate multiple forecasters on multiple datasets from file using standard paths. + + Finds files using forecaster, dataset and resample names. It is expected the + common tsml-eval file structure of + {forecaster}/Predictions/{dataset}/{split}Resample{resample}.csv is followed. + + Writes multiple csv files and figures to save_path, one for each statistic + evaluated. Provides a summary csv file with the average statistic and + average rank for each forecaster. + + Parameters + ---------- + load_path : list of str + The path to the collection of clusterer result files to evaluate. + forecaster_names : list of str + The names of the clusterers to evaluate. + dataset_names : list of str + The names of the datasets to evaluate. + save_path : str + The path to save the evaluation results to. + resamples : int or list of int, default=None + The resamples to evaluate. If int, evaluates resamples 0 to resamples-1. + error_on_missing : bool, default=True + Whether to raise an error if results are missing. + eval_name : str, default=None + The name of the evaluation, used in save_path. + """ if resamples is None: resamples = [""] elif isinstance(resamples, int): @@ -270,7 +534,7 @@ def evaluate_forecasters_by_problem( forecaster_results.append( ForecasterResults().load_from_file( f"{load_path}/{forecaster_name}/Predictions/{dataset_name}" - f"/resample{resample}.csv" + f"/testResample{resample}.csv" ) ) @@ -395,14 +659,14 @@ def _evaluate_estimators( print(msg) # noqa: T201 else: msg += "All results present, continuing evaluation.\n" - print(msg) + print(msg) # noqa: T201 print(f"Estimators: {estimators}\n") # noqa: T201 print(f"Datasets: {datasets}\n") # noqa: T201 print(f"Resamples: {resamples}\n") # noqa: T201 stats = [] - for var, (stat, ascending) in statistics.items(): + for var, (stat, ascending, time) in statistics.items(): for split in splits: average, rank = _create_directory_for_statistic( estimators, @@ -412,6 +676,7 @@ def _evaluate_estimators( results_dict, stat, ascending, + time, var, save_path, ) @@ -462,6 +727,7 @@ def _create_directory_for_statistic( results_dict, statistic_name, higher_better, + is_timing, variable_name, save_path, ): @@ -476,7 +742,13 @@ def _create_directory_for_statistic( for j, resample in enumerate(resamples): er = results_dict[estimator_name][dataset_name][split][resample] er.calculate_statistics() - est_stats[n, j] = er.__dict__[variable_name] + est_stats[n, j] = ( + er.__dict__[variable_name] + if not is_timing + else ( + time_to_milliseconds(er.__dict__[variable_name], er.time_unit) + ) + ) average_stats[n, i] = np.mean(est_stats[n, :]) diff --git a/tsml_eval/evaluation/storage/__init__.py b/tsml_eval/evaluation/storage/__init__.py index c05a0a0a..3637fd83 100644 --- a/tsml_eval/evaluation/storage/__init__.py +++ b/tsml_eval/evaluation/storage/__init__.py @@ -1,4 +1,4 @@ -"""Storage for estimator results and result i/o""" +"""Storage for estimator results and result i/o.""" __all__ = [ "ClassifierResults", diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index eae7e22e..bde761ca 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -9,7 +9,6 @@ roc_auc_score, ) -import tsml_eval.evaluation.storage as storage from tsml_eval.evaluation.storage.estimator_results import EstimatorResults from tsml_eval.utils.experiments import write_classification_results @@ -24,47 +23,79 @@ class ClassifierResults(EstimatorResults): Parameters ---------- - dataset_name : str, optional - Name of the dataset used, by default "N/A". - classifier_name : str, optional - Name of the classifier used, by default "N/A". - split : str, optional - Type of data split used, by default "N/A". - resample_id : int or None, optional - Identifier for the resampling method, by default None. - time_unit : str, optional - Unit of time measurement, by default "nanoseconds". - description : str, optional - Description of the classification experiment, by default "". - parameters : str, optional - Information about parameters used, by default "No parameter info". - fit_time : float, optional - Time taken for fitting the model, by default -1.0. - predict_time : float, optional - Time taken for making predictions, by default -1.0. - benchmark_time : float, optional - Time taken for benchmarking, by default -1.0. - memory_usage : float, optional - Memory usage during the experiment, by default -1.0. - n_classes : int or None, optional - Number of classes in the classification task, by default None. - error_estimate_method : str, optional - Method used for error estimation, by default "N/A". - error_estimate_time : float, optional - Time taken for error estimation, by default -1.0. - build_plus_estimate_time : float, optional - Total time for building and estimating, by default -1.0. - class_labels : array-like or None, optional - Actual class labels, by default None. - predictions : array-like or None, optional - Predicted class labels, by default None. - probabilities : array-like or None, optional - Predicted class probabilities, by default None. - pred_times : array-like or None, optional - Prediction times for each instance, by default None. - pred_descriptions : list of str or None, optional - Descriptions for each prediction, by default None. - + dataset_name : str, default="N/A" + Name of the dataset used. + classifier_name : str, default="N/A" + Name of the classifier used. + split : str, default="N/A" + Type of data split used, i.e. "train" or "test". + resample_id : int or None, default=None + Random seed used for the data resample, with 0 usually being the original data. + time_unit : str, default="nanoseconds" + Time measurement used for other fields. + description : str, default="" + Additional description of the classification experiment. Appended to the end + of the first line of the results file. + parameters : str, default="No parameter info" + Information about parameters used in the classifier and other build information. + Written to the second line of the results file. + fit_time : float, default=-1.0 + Time taken fitting the model. + predict_time : float, default=-1.0 + Time taken making predictions. + benchmark_time : float, default=-1.0 + Time taken to run a simple benchmark function. In tsml-eval experiments, this + is the time spent to sort 1,000 (seeded) random numpy arrays of size 20,000. + memory_usage : float, default=-1.0 + Memory usage during the experiment. In tsml-eval experiments, this is the peak + memory usage during the fit method. + n_classes : int or None, default=None + Number of classes in the dataset. + error_estimate_method : str, default="N/A" + Method used for train error/accuracy estimation (i.e. 10-fold CV, OOB error). + error_estimate_time : float, default=-1.0 + Time taken for train error/accuracy estimation. + build_plus_estimate_time : float, default=-1.0 + Total time for building the classifier and estimating error/accuracy on the + train set. For certain methods this can be different from the sum of fit_time + and error_estimate_time. + class_labels : array-like or None, default=None + Actual class labels. + predictions : array-like or None, default=None + Predicted class labels. + probabilities : array-like or None, default=None + Predicted class probabilities. + pred_times : array-like or None, default=None + Prediction times for each case. + pred_descriptions : list of str or None, default=None + Descriptions for each prediction. + + Attributes + ---------- + n_cases : int or None + Number of cases in the dataset. + accuracy : float or None + Accuracy of the classifier. + balanced_accuracy : float or None + Balanced accuracy of the classifier. + f1_score : float or None + F1 score of the classifier. + negative_log_likelihood : float or None + Negative log likelihood of the classifier. + mean_auroc : float or None + Mean area under the ROC curve of the classifier. + + Examples + -------- + >>> from tsml_eval.evaluation.storage import ClassifierResults + >>> from tsml_eval.testing.test_utils import _TEST_RESULTS_PATH + >>> cr = ClassifierResults().load_from_file( + ... _TEST_RESULTS_PATH + + ... "/classification/ROCKET/Predictions/Chinatown/testResample0.csv" + ... ) + >>> cr.calculate_statistics() + >>> cr.accuracy + 0.9795918367346939 """ def __init__( @@ -90,6 +121,9 @@ def __init__( pred_times=None, pred_descriptions=None, ): + # Line 1 + self.classifier_name = classifier_name + # Line 3 self.n_classes = n_classes self.train_estimate_method = error_estimate_method @@ -125,27 +159,29 @@ def __init__( memory_usage=memory_usage, ) - # var_name: (display_name, higher is better) + # var_name: (display_name, higher is better, is timing) statistics = { - "accuracy": ("Accuracy", True), - "balanced_accuracy": ("BalAcc", True), - "f1_score": ("F1", True), - "negative_log_likelihood": ("NLL", False), - "mean_auroc": ("AUROC", True), + "accuracy": ("Accuracy", True, False), + "balanced_accuracy": ("BalAcc", True, False), + "f1_score": ("F1", True, False), + "negative_log_likelihood": ("NLL", False, False), + "mean_auroc": ("AUROC", True, False), **EstimatorResults.statistics, } def save_to_file(self, file_path, full_path=True): """ - Save the classifier results to a specified file. - - This method serializes the results of the classifier and saves them to a file - in a chosen format. + Write the classifier results into a file format used by tsml. Parameters ---------- file_path : str - The path to the file where the results should be saved. + Path to write the results file to or the directory to build the default file + structure if full_path is False. + full_path : boolean, default=True + If True, results are written directly to the directory passed in file_path. + If False, then a standard file structure using the classifier and dataset + names is created and used to write the results file. """ self.infer_size() @@ -180,35 +216,36 @@ def load_from_file(self, file_path): """ Load classifier results from a specified file. - This method deserializes classifier results from a given file, allowing for the - analysis and comparison of previously computed results. + This method reads a file containing classifier results and reconstructs the + ClassifierResults object. It calculates performance statistics and + verifies values based on the loaded data. Parameters ---------- file_path : str - The path to the file from which the results should be loaded. + The path to the file from which classifier results should be loaded. The + file should be a tsml formatted classifier results file. Returns ------- - self: ClassifierResults - The classifier results object loaded from the file. + self : ClassifierResults + The same ClassifierResults object with loaded results. """ - cr = storage.load_classifier_results(file_path) + cr = load_classifier_results(file_path) self.__dict__.update(cr.__dict__) return self def calculate_statistics(self, overwrite=False): """ - Calculate and return various statistics based on the classifier results. + Calculate various performance statistics based on the classifier results. This method computes various performance metrics, such as accuracy, F1 score, - and others, based on the classifier's output. + and others, based on the classifiers output. - Returns - ------- - dict - A dictionary containing the calculated statistics. Keys are the names of the - metrics, and values are their computed values. + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ self.infer_size(overwrite=overwrite) @@ -235,18 +272,15 @@ def calculate_statistics(self, overwrite=False): def infer_size(self, overwrite=False): """ - Infer and return the size of the dataset used in the classifier. - - This method estimates the size of the dataset that was used for the classifier, based on the results data. + Infer and return the size of the dataset used in the results. - Returns - ------- - int - The inferred size of the dataset. + This method estimates the size of the dataset that was used for the estimator, + based on the results data. - Notes - ----- - The accuracy of the inferred size may vary and should be validated with actual dataset parameters when possible. + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ if self.n_cases is None or overwrite: self.n_cases = len(self.class_labels) @@ -258,22 +292,23 @@ def load_classifier_results(file_path, calculate_stats=True, verify_values=True) """ Load and return classifier results from a specified file. - This function reads a file containing serialized classifier results and - deserializes it to reconstruct the classifier results object. It optionally - calculates statistics and verifies values based on the loaded data. + This function reads a file containing classifier results and reconstructs the + ClassifierResults object. It optionally calculates performance statistics and + verifies values based on the loaded data. Parameters ---------- file_path : str - The path to the file from which classifier results should be loaded. The file should be in a format compatible with the serialization method used. - calculate_stats : bool, optional - A flag to indicate whether to calculate statistics from the loaded results. Default is True. - verify_values : bool, optional - A flag to determine if the function should perform verification of the loaded values. Default is True. + The path to the file from which classifier results should be loaded. The file + should be a tsml formatted classifier results file. + calculate_stats : bool, default=True + Whether to calculate performance statistics from the loaded results. + verify_values : bool, default=True + If the function should perform verification of the loaded values. Returns ------- - ClassifierResults + cr : ClassifierResults A ClassifierResults object containing the results loaded from the file. """ with open(file_path, "r") as file: diff --git a/tsml_eval/evaluation/storage/clusterer_results.py b/tsml_eval/evaluation/storage/clusterer_results.py index edea259f..8f4e4f6a 100644 --- a/tsml_eval/evaluation/storage/clusterer_results.py +++ b/tsml_eval/evaluation/storage/clusterer_results.py @@ -9,13 +9,92 @@ rand_score, ) -import tsml_eval.evaluation.storage as storage from tsml_eval.evaluation.metrics import clustering_accuracy_score from tsml_eval.evaluation.storage.estimator_results import EstimatorResults from tsml_eval.utils.experiments import write_clustering_results class ClustererResults(EstimatorResults): + """ + A class for storing and managing results from clustering experiments. + + This class provides functionalities for storing clustering results, + including cluster labels, probabilities, and various performance metrics. + It extends the `EstimatorResults` class, inheriting its base functionalities. + + Parameters + ---------- + dataset_name : str, default="N/A" + Name of the dataset used. + clusterer_name : str, default="N/A" + Name of the clusterer used. + split : str, default="N/A" + Type of data split used, i.e. "train" or "test". + resample_id : int or None, default=None + Random seed used for the data resample, with 0 usually being the original data. + time_unit : str, default="nanoseconds" + Time measurement used for other fields. + description : str, default="" + Additional description of the clustering experiment. Appended to the end + of the first line of the results file. + parameters : str, default="No parameter info" + Information about parameters used in the clusterer and other build information. + Written to the second line of the results file. + fit_time : float, default=-1.0 + Time taken fitting the model. + predict_time : float, default=-1.0 + Time taken making predictions. + benchmark_time : float, default=-1.0 + Time taken to run a simple benchmark function. In tsml-eval experiments, this + is the time spent to sort 1,000 (seeded) random numpy arrays of size 20,000. + memory_usage : float, default=-1.0 + Memory usage during the experiment. In tsml-eval experiments, this is the peak + memory usage during the fit method. + n_classes : int or None, default=None + Number of classes in the dataset. + n_clusters : int or None, default=None + Number of clusters generated. + class_labels : array-like or None, default=None + Actual class labels. + predictions : array-like or None, default=None + Predicted cluster labels. + probabilities : array-like or None, default=None + Predicted cluster probabilities. + pred_times : array-like or None, default=None + Prediction times for each case. + pred_descriptions : list of str or None, default=None + Descriptions for each prediction. + + Attributes + ---------- + n_cases : int or None + Number of cases in the dataset. + clustering_accuracy : float or None + Clustering accuracy score. + rand_index : float or None + Rand score. + adjusted_rand_index : float or None + Adjusted Rand score. + mutual_information : float or None + Mutual information score. + adjusted_mutual_information : float or None + Adjusted mutual information score. + normalised_mutual_information : float or None + Normalised mutual information score. + + Examples + -------- + >>> from tsml_eval.evaluation.storage import ClustererResults + >>> from tsml_eval.testing.test_utils import _TEST_RESULTS_PATH + >>> cr = ClustererResults().load_from_file( + ... _TEST_RESULTS_PATH + + ... "/clustering/KMeans/Predictions/Trace/trainResample0.csv" + ... ) + >>> cr.calculate_statistics() + >>> cr.clustering_accuracy + 0.57 + """ + def __init__( self, dataset_name="N/A", @@ -37,6 +116,9 @@ def __init__( pred_times=None, pred_descriptions=None, ): + # Line 1 + self.clusterer_name = clusterer_name + # Line 3 self.n_classes = n_classes self.n_clusters = n_clusters @@ -71,29 +153,30 @@ def __init__( memory_usage=memory_usage, ) - # var_name: (display_name, higher is better) + # var_name: (display_name, higher is better, is timing) statistics = { - "clustering_accuracy": ("CLAcc", True), - "rand_index": ("RI", True), - "adjusted_rand_index": ("ARI", True), - "mutual_information": ("MI", True), - "adjusted_mutual_information": ("AMI", True), - "normalised_mutual_information": ("NMI", True), + "clustering_accuracy": ("CLAcc", True, False), + "rand_index": ("RI", True, False), + "adjusted_rand_index": ("ARI", True, False), + "mutual_information": ("MI", True, False), + "adjusted_mutual_information": ("AMI", True, False), + "normalised_mutual_information": ("NMI", True, False), **EstimatorResults.statistics, } def save_to_file(self, file_path, full_path=True): """ - Writes the full results to a file. + Write the clusterer results into a file format used by tsml. Parameters ---------- file_path : str - The path of the file to write the results to. + Path to write the results file to or the directory to build the default file + structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. - If False, then a standard file structure using the classifier and dataset names - is created and used to write the results file. + If True, results are written directly to the directory passed in file_path. + If False, then a standard file structure using the clusterer and dataset + names is created and used to write the results file. """ self.infer_size() @@ -125,22 +208,39 @@ def save_to_file(self, file_path, full_path=True): ) def load_from_file(self, file_path): - """Load results from a specified file. + """ + Load clusterer results from a specified file. + + This method reads a file containing clusterer results and reconstructs the + ClustererResults object. It calculates performance statistics and + verifies values based on the loaded data. Parameters ---------- file_path : str - The path to the file where the results will be loaded from. + The path to the file from which clusterer results should be loaded. The + file should be a tsml formatted clusterer results file. + + Returns + ------- + self : ClustererResults + The same ClustererResults object with loaded results. """ - cr = storage.load_clusterer_results(file_path) + cr = load_clusterer_results(file_path) self.__dict__.update(cr.__dict__) return self def calculate_statistics(self, overwrite=False): - """Calculate statistics from the results. + """ + Calculate various performance statistics based on the clusterer results. + + This method computes various performance metrics, such as clustering accuracy, + Rand score, and others, based on the clusterers output. - This method should handle any necessary calculations to produce statistics - from the results data held within the object. + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ self.infer_size(overwrite=overwrite) @@ -168,6 +268,19 @@ def calculate_statistics(self, overwrite=False): ) def infer_size(self, overwrite=False): + """ + Infer and return the size of the dataset used in the results. + + This method estimates the size of the dataset that was used for the estimator, + based on the results data. + + Also infers the number of clusters generated. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. + """ if self.n_cases is None or overwrite: self.n_cases = len(self.class_labels) if self.n_clusters is None or overwrite: @@ -175,8 +288,28 @@ def infer_size(self, overwrite=False): def load_clusterer_results(file_path, calculate_stats=True, verify_values=True): - """Load clusterer results from a file.""" - + """ + Load and return clusterer results from a specified file. + + This function reads a file containing clusterer results and reconstructs the + ClustererResults object. It optionally calculates performance statistics and + verifies values based on the loaded data. + + Parameters + ---------- + file_path : str + The path to the file from which clusterer results should be loaded. The file + should be a tsml formatted clusterer results file. + calculate_stats : bool, default=True + Whether to calculate performance statistics from the loaded results. + verify_values : bool, default=True + If the function should perform verification of the loaded values. + + Returns + ------- + cr : ClustererResults + A ClustererResults object containing the results loaded from the file. + """ with open(file_path, "r") as file: lines = file.readlines() diff --git a/tsml_eval/evaluation/storage/estimator_results.py b/tsml_eval/evaluation/storage/estimator_results.py index 24bce6e1..c764c6a9 100644 --- a/tsml_eval/evaluation/storage/estimator_results.py +++ b/tsml_eval/evaluation/storage/estimator_results.py @@ -5,33 +5,36 @@ class EstimatorResults(ABC): """ - Abstract base class for storing estimator results. + Abstract class for storing and loading results from an experiment. Parameters ---------- - dataset_name : str, optional - Name of the dataset. - estimator_name : str, optional - Name of the estimator. - split : str, optional - Dataset split (e.g., 'train' or 'test'). - resample_id : int, optional - Identifier for the data fold. - time_unit : str, optional - Unit of time measurement, default is "nanoseconds". - description : str, optional - A human-friendly description of the estimator results. - parameters : str, optional - Estimator parameters and other related information as a string. - fit_time : float, optional - Time taken to build the estimator. - predict_time : float, optional - Time taken to test the estimator. - benchmark_time : float, optional - Time taken to benchmark the estimator. - memory_usage : float, optional - Memory usage of the estimator. - + dataset_name : str, default="N/A" + Name of the dataset used. + estimator_name : str, default="N/A" + Name of the estimator used. + split : str, default="N/A" + Type of data split used, i.e. "train" or "test". + resample_id : int or None, default=None + Random seed used for the data resample, with 0 usually being the original data. + time_unit : str, default="nanoseconds" + Time measurement used for other fields. + description : str, default="" + Additional description of the experiment. Appended to the end + of the first line of the results file. + parameters : str, default="No parameter info" + Information about parameters used in the estimator and other build information. + Written to the second line of the results file. + fit_time : float, default=-1.0 + Time taken fitting the model. + predict_time : float, default=-1.0 + Time taken making predictions. + benchmark_time : float, default=-1.0 + Time taken to run a simple benchmark function. In tsml-eval experiments, this + is the time spent to sort 1,000 (seeded) random numpy arrays of size 20,000. + memory_usage : float, default=-1.0 + Memory usage during the experiment. In tsml-eval experiments, this is the peak + memory usage during the fit method. """ def __init__( @@ -65,43 +68,68 @@ def __init__( self.benchmark_time = benchmark_time self.memory_usage = memory_usage - self.build_time_milli_ = None - self.median_pred_time_milli_ = None - - # var_name: (display_name, higher is better) + # var_name: (display_name, higher is better, is timing) statistics = { - "fit_time": ("FitTime", False), - "predict_time": ("PredictTime", False), - "memory_usage": ("MemoryUsage", False), + "fit_time": ("FitTime", False, True), + "predict_time": ("PredictTime", False, True), + "memory_usage": ("MemoryUsage", False, False), } @abstractmethod - def save_to_file(self, file_path): - """Save results to a specified file. + def save_to_file(self, file_path, full_path=True): + """ + Write the estimator results into a file format used by tsml. + + Abstract, must be implemented by subclasses. Parameters ---------- file_path : str - The path to the file where the results will be saved. + Path to write the results file to or the directory to build the default file + structure if full_path is False. + full_path : boolean, default=True + If True, results are written directly to the directory passed in file_path. + If False, then a standard file structure using the estimator and dataset + names is created and used to write the results file. """ pass @abstractmethod def load_from_file(self, file_path): - """Load results from a specified file. + """ + Load estimator results from a specified file. + + This method reads a file containing estimator results and reconstructs the + EstimatorResults object. It calculates performance statistics and + verifies values based on the loaded data. + + Abstract, must be implemented by subclasses. Parameters ---------- file_path : str - The path to the file where the results will be loaded from. + The path to the file from which estimator results should be loaded. The + file should be a tsml formatted estimator results file. + + Returns + ------- + self : EstimatorResults + The same EstimatorResults object with loaded results. """ pass @abstractmethod def calculate_statistics(self, overwrite=False): - """Calculate statistics from the results. + """ + Calculate various performance statistics based on the estimator results. + + This method computes various performance metrics based on the estimators output. - This method should handle any necessary calculations to produce statistics - from the results data held within the object. + Abstract, must be implemented by subclasses. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ pass diff --git a/tsml_eval/evaluation/storage/forecaster_results.py b/tsml_eval/evaluation/storage/forecaster_results.py index ade227d0..09a712f7 100644 --- a/tsml_eval/evaluation/storage/forecaster_results.py +++ b/tsml_eval/evaluation/storage/forecaster_results.py @@ -3,12 +3,73 @@ import numpy as np from sklearn.metrics import mean_absolute_percentage_error -import tsml_eval.evaluation.storage as storage from tsml_eval.evaluation.storage.estimator_results import EstimatorResults from tsml_eval.utils.experiments import write_forecasting_results class ForecasterResults(EstimatorResults): + """ + A class for storing and managing results from forecasting experiments. + + This class provides functionalities for storing forecaster results, + including predictions, probabilities, and various performance metrics. + It extends the `EstimatorResults` class, inheriting its base functionalities. + + Parameters + ---------- + dataset_name : str, default="N/A" + Name of the dataset used. + forecaster_name : str, default="N/A" + Name of the forecaster used. + split : str, default="N/A" + Type of data split used, i.e. "train" or "test". + random_seed : int or None, default=None + Random seed used. + time_unit : str, default="nanoseconds" + Time measurement used for other fields. + description : str, default="" + Additional description of the forecasting experiment. Appended to the end + of the first line of the results file. + parameters : str, default="No parameter info" + Information about parameters used in the forecaster and other build information. + Written to the second line of the results file. + fit_time : float, default=-1.0 + Time taken fitting the model. + predict_time : float, default=-1.0 + Time taken making predictions. + benchmark_time : float, default=-1.0 + Time taken to run a simple benchmark function. In tsml-eval experiments, this + is the time spent to sort 1,000 (seeded) random numpy arrays of size 20,000. + memory_usage : float, default=-1.0 + Memory usage during the experiment. In tsml-eval experiments, this is the peak + memory usage during the fit method. + target_labels : array-like or None, default=None + Actual target labels. + predictions : array-like or None, default=None + Predicted target labels. + pred_times : array-like or None, default=None + Prediction times for each case. + pred_descriptions : list of str or None, default=None + Descriptions for each prediction. + + Attributes + ---------- + mean_absolute_percentage_error : float or None + Mean absolute percentage error of the predictions. + + Examples + -------- + >>> from tsml_eval.evaluation.storage import ForecasterResults + >>> from tsml_eval.testing.test_utils import _TEST_RESULTS_PATH + >>> fr = ForecasterResults().load_from_file( + ... _TEST_RESULTS_PATH + + ... "/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv" + ... ) + >>> fr.calculate_statistics() + >>> fr.mean_absolute_percentage_error + 0.19886711926999853 + """ + def __init__( self, dataset_name="N/A", @@ -27,6 +88,10 @@ def __init__( pred_times=None, pred_descriptions=None, ): + # Line 1 + self.forecaster_name = forecaster_name + self.random_seed = random_seed + # Results self.target_labels = target_labels self.predictions = predictions @@ -51,24 +116,25 @@ def __init__( memory_usage=memory_usage, ) - # var_name: (display_name, higher is better) + # var_name: (display_name, higher is better, is timing) statistics = { - "mean_absolute_percentage_error": ("MAPE", False), + "mean_absolute_percentage_error": ("MAPE", False, False), **EstimatorResults.statistics, } def save_to_file(self, file_path, full_path=True): """ - Writes the full results to a file. + Write the forecaster results into a file format used by tsml. Parameters ---------- file_path : str - The path of the file to write the results to. + Path to write the results file to or the directory to build the default file + structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. - If False, then a standard file structure using the classifier and dataset names - is created and used to write the results file. + If True, results are written directly to the directory passed in file_path. + If False, then a standard file structure using the forecaster and dataset + names is created and used to write the results file. """ self.infer_size() @@ -97,22 +163,39 @@ def save_to_file(self, file_path, full_path=True): ) def load_from_file(self, file_path): - """Load results from a specified file. + """ + Load forecaster results from a specified file. + + This method reads a file containing forecaster results and reconstructs the + ForecasterResults object. It calculates performance statistics and + verifies values based on the loaded data. Parameters ---------- file_path : str - The path to the file where the results will be loaded from. + The path to the file from which forecaster results should be loaded. The + file should be a tsml formatted forecaster results file. + + Returns + ------- + self : ForecasterResults + The same ForecasterResults object with loaded results. """ - fr = storage.load_forecaster_results(file_path) + fr = load_forecaster_results(file_path) self.__dict__.update(fr.__dict__) return self def calculate_statistics(self, overwrite=False): - """Calculate statistics from the results. + """ + Calculate various performance statistics based on the forecaster results. - This method should handle any necessary calculations to produce statistics - from the results data held within the object. + This method computes various performance metrics, such as MAPE based on the + forecasters output. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ self.infer_size(overwrite=overwrite) @@ -122,11 +205,44 @@ def calculate_statistics(self, overwrite=False): ) def infer_size(self, overwrite=False): + """ + Infer and return the size of the dataset used in the results. + + This method estimates the size of the dataset that was used for the estimator, + based on the results data. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. + """ if self.forecasting_horizon is None or overwrite: self.forecasting_horizon = len(self.target_labels) def load_forecaster_results(file_path, calculate_stats=True, verify_values=True): + """ + Load and return forecaster results from a specified file. + + This function reads a file containing forecaster results and reconstructs the + ForecasterResults object. It optionally calculates performance statistics and + verifies values based on the loaded data. + + Parameters + ---------- + file_path : str + The path to the file from which forecaster results should be loaded. The file + should be a tsml formatted forecaster results file. + calculate_stats : bool, default=True + Whether to calculate performance statistics from the loaded results. + verify_values : bool, default=True + If the function should perform verification of the loaded values. + + Returns + ------- + fr : ForecasterResults + A ForecasterResults object containing the results loaded from the file. + """ with open(file_path, "r") as file: lines = file.readlines() diff --git a/tsml_eval/evaluation/storage/regressor_results.py b/tsml_eval/evaluation/storage/regressor_results.py index 382eeb1e..873b4e25 100644 --- a/tsml_eval/evaluation/storage/regressor_results.py +++ b/tsml_eval/evaluation/storage/regressor_results.py @@ -8,12 +8,92 @@ r2_score, ) -import tsml_eval.evaluation.storage as storage from tsml_eval.evaluation.storage.estimator_results import EstimatorResults from tsml_eval.utils.experiments import write_regression_results class RegressorResults(EstimatorResults): + """ + A class for storing and managing results from regression experiments. + + This class provides functionalities for storing regressor results, + including predictions, probabilities, and various performance metrics. + It extends the `EstimatorResults` class, inheriting its base functionalities. + + Parameters + ---------- + dataset_name : str, default="N/A" + Name of the dataset used. + regressor_name : str, default="N/A" + Name of the regressor used. + split : str, default="N/A" + Type of data split used, i.e. "train" or "test". + resample_id : int or None, default=None + Random seed used for the data resample, with 0 usually being the original data. + time_unit : str, default="nanoseconds" + Time measurement used for other fields. + description : str, default="" + Additional description of the regression experiment. Appended to the end + of the first line of the results file. + parameters : str, default="No parameter info" + Information about parameters used in the regressor and other build information. + Written to the second line of the results file. + fit_time : float, default=-1.0 + Time taken fitting the model. + predict_time : float, default=-1.0 + Time taken making predictions. + benchmark_time : float, default=-1.0 + Time taken to run a simple benchmark function. In tsml-eval experiments, this + is the time spent to sort 1,000 (seeded) random numpy arrays of size 20,000. + memory_usage : float, default=-1.0 + Memory usage during the experiment. In tsml-eval experiments, this is the peak + memory usage during the fit method. + error_estimate_method : str, default="N/A" + Method used for train error/accuracy estimation (i.e. 10-fold CV, OOB error). + error_estimate_time : float, default=-1.0 + Time taken for train error/accuracy estimation. + build_plus_estimate_time : float, default=-1.0 + Total time for building the regressor and estimating error/accuracy on the + train set. For certain methods this can be different from the sum of fit_time + and error_estimate_time. + target_labels : array-like or None, default=None + Actual target labels. + predictions : array-like or None, default=None + Predicted class labels. + pred_times : array-like or None, default=None + Prediction times for each case. + pred_descriptions : list of str or None, default=None + Descriptions for each prediction. + + Attributes + ---------- + n_cases : int or None + Number of cases in the dataset. + mean_squared_error : float or None + Mean squared error of the predictions. + root_mean_squared_error : float or None + Root mean squared error of the predictions. + mean_absolute_error : float or None + Mean absolute error of the predictions. + r2_score : float or None + R2 score of the predictions. + mean_absolute_percentage_error : float or None + Mean absolute percentage error of the predictions. + + Examples + -------- + >>> from tsml_eval.evaluation.storage import RegressorResults + >>> from tsml_eval.testing.test_utils import _TEST_RESULTS_PATH + >>> rr = RegressorResults().load_from_file( + ... _TEST_RESULTS_PATH + + ... "/regression/ROCKET/Predictions/Covid3Month/testResample0.csv" + ... ) + >>> rr.calculate_statistics() + >>> rr.mean_squared_error + 0.0015126663111567206 + + """ + def __init__( self, dataset_name="N/A", @@ -35,6 +115,9 @@ def __init__( pred_times=None, pred_descriptions=None, ): + # Line 1 + self.regressor_name = regressor_name + # Line 3 self.train_estimate_method = error_estimate_method self.train_estimate_time = error_estimate_time @@ -68,28 +151,29 @@ def __init__( memory_usage=memory_usage, ) - # var_name: (display_name, higher is better) + # var_name: (display_name, higher is better, is timing) statistics = { - "mean_squared_error": ("MSE", False), - "root_mean_squared_error": ("RMSE", False), - "mean_absolute_error": ("MAE", False), - "r2_score": ("R2", True), - "mean_absolute_percentage_error": ("MAPE", False), + "mean_squared_error": ("MSE", False, False), + "root_mean_squared_error": ("RMSE", False, False), + "mean_absolute_error": ("MAE", False, False), + "r2_score": ("R2", True, False), + "mean_absolute_percentage_error": ("MAPE", False, False), **EstimatorResults.statistics, } def save_to_file(self, file_path, full_path=True): """ - Writes the full results to a file. + Write the regressor results into a file format used by tsml. Parameters ---------- file_path : str - The path of the file to write the results to. + Path to write the results file to or the directory to build the default file + structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. - If False, then a standard file structure using the classifier and dataset names - is created and used to write the results file. + If True, results are written directly to the directory passed in file_path. + If False, then a standard file structure using the regressor and dataset + names is created and used to write the results file. """ self.infer_size() @@ -120,23 +204,40 @@ def save_to_file(self, file_path, full_path=True): fit_and_estimate_time=self.fit_and_estimate_time, ) - def load_from_file(self, file_path, calculate_stats=True, verify_values=True): - """Load results from a specified file. + def load_from_file(self, file_path): + """ + Load regressor results from a specified file. + + This method reads a file containing regressor results and reconstructs the + RegressorResults object. It calculates performance statistics and + verifies values based on the loaded data. Parameters ---------- file_path : str - The path to the file where the results will be loaded from. + The path to the file from which regressor results should be loaded. The + file should be a tsml formatted regressor results file. + + Returns + ------- + self : RegressorResults + The same RegressorResults object with loaded results. """ - rr = storage.load_regressor_results(file_path) + rr = load_regressor_results(file_path) self.__dict__.update(rr.__dict__) return self def calculate_statistics(self, overwrite=False): - """Calculate statistics from the results. + """ + Calculate various performance statistics based on the regressor results. - This method should handle any necessary calculations to produce statistics - from the results data held within the object. + This method computes various performance metrics, such as MSE, MAPE, + and others, based on the regressors output. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. """ self.infer_size(overwrite=overwrite) @@ -160,11 +261,44 @@ def calculate_statistics(self, overwrite=False): ) def infer_size(self, overwrite=False): + """ + Infer and return the size of the dataset used in the results. + + This method estimates the size of the dataset that was used for the estimator, + based on the results data. + + Parameters + ---------- + overwrite : bool, default=False + If the function should overwrite the current values when they are not None. + """ if self.n_cases is None or overwrite: self.n_cases = len(self.target_labels) def load_regressor_results(file_path, calculate_stats=True, verify_values=True): + """ + Load and return regressor results from a specified file. + + This function reads a file containing regressor results and reconstructs the + RegressorResults object. It optionally calculates performance statistics and + verifies values based on the loaded data. + + Parameters + ---------- + file_path : str + The path to the file from which regressor results should be loaded. The file + should be a tsml formatted regressor results file. + calculate_stats : bool, default=True + Whether to calculate performance statistics from the loaded results. + verify_values : bool, default=True + If the function should perform verification of the loaded values. + + Returns + ------- + rr : RegressorResults + A RegressorResults object containing the results loaded from the file. + """ with open(file_path, "r") as file: lines = file.readlines() diff --git a/tsml_eval/evaluation/storage/tests/__init__.py b/tsml_eval/evaluation/storage/tests/__init__.py index e69de29b..09516d2b 100644 --- a/tsml_eval/evaluation/storage/tests/__init__.py +++ b/tsml_eval/evaluation/storage/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for evaluation storage functions and classes.""" diff --git a/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py b/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py index 59d3e54c..2073be2e 100644 --- a/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/tests/test_multiple_estimator_evaluation.py @@ -23,7 +23,7 @@ def test_evaluate_classifiers_by_problem(): def test_evaluate_clusterers_by_problem(): - classifiers = ["ROCKET", "TSF", "1NN-DTW"] + classifiers = ["KMeans", "KMeans-dtw", "KMeans-msm"] datasets = ["Chinatown", "ItalyPowerDemand", "Trace"] resamples = 3 @@ -33,13 +33,14 @@ def test_evaluate_clusterers_by_problem(): datasets, _TEST_OUTPUT_PATH + "/eval/clustering/", resamples=resamples, + load_test_results=False, eval_name="test0", ) def test_evaluate_regressors_by_problem(): classifiers = ["ROCKET", "TSF", "1NN-DTW"] - datasets = ["Chinatown", "ItalyPowerDemand", "Trace"] + datasets = ["Covid3Month", "NaturalGasPricesSentiment", "FloodModeling1"] resamples = 3 evaluate_regressors_by_problem( @@ -53,8 +54,8 @@ def test_evaluate_regressors_by_problem(): def test_evaluate_forecasters_by_problem(): - classifiers = ["ROCKET", "TSF"] - datasets = ["Chinatown", "ItalyPowerDemand"] + classifiers = ["NaiveForecaster", "RandomForest", "LinearRegression"] + datasets = ["Airline", "ShampooSales"] resamples = 1 evaluate_forecasters_by_problem( diff --git a/tsml_eval/experiments/classification_experiments.py b/tsml_eval/experiments/classification_experiments.py index 95b58999..ae65f40f 100644 --- a/tsml_eval/experiments/classification_experiments.py +++ b/tsml_eval/experiments/classification_experiments.py @@ -84,6 +84,7 @@ def run_experiment(args): classifier_name=args.estimator_name, resample_id=args.resample_id, build_train_file=args.train_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/experiments/clustering_experiments.py b/tsml_eval/experiments/clustering_experiments.py index 0ec6ab4b..49a3d453 100644 --- a/tsml_eval/experiments/clustering_experiments.py +++ b/tsml_eval/experiments/clustering_experiments.py @@ -84,6 +84,7 @@ def run_experiment(args): clusterer_name=args.estimator_name, resample_id=args.resample_id, build_test_file=args.test_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/experiments/experiments.py b/tsml_eval/experiments/experiments.py index 3dd48298..e532ef2d 100644 --- a/tsml_eval/experiments/experiments.py +++ b/tsml_eval/experiments/experiments.py @@ -39,6 +39,7 @@ from tsml_eval.utils.experiments import ( resample_data, stratified_resample_data, + timing_benchmark, write_classification_results, write_clustering_results, write_forecasting_results, @@ -65,6 +66,7 @@ def run_classification_experiment( resample_id=None, build_test_file=True, build_train_file=False, + benchmark_time=True, ): """Run a classification experiment and save the results to file. @@ -104,6 +106,9 @@ def run_classification_experiment( Whether to generate train files or not. If true, it performs a 10-fold cross-validation on the train data and saves. If the classifier can produce its own estimates, those are used instead. + benchmark_time : bool, 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. """ if not build_test_file and not build_train_file: raise ValueError( @@ -147,6 +152,10 @@ def run_classification_experiment( ) fit_time = -1 mem_usage = -1 + benchmark = -1 + + if benchmark_time: + benchmark = timing_benchmark(random_state=resample_id) first_comment = ( "PREDICTIONS,Generated by run_classification_experiment on " @@ -193,6 +202,7 @@ def run_classification_experiment( accuracy=test_acc, fit_time=fit_time, predict_time=test_time, + benchmark_time=benchmark, memory_usage=mem_usage, n_classes=n_classes, ) @@ -231,6 +241,7 @@ def run_classification_experiment( parameter_info=second, accuracy=train_acc, fit_time=fit_time, + benchmark_time=benchmark, n_classes=n_classes, train_estimate_time=train_time, fit_and_estimate_time=fit_time + train_time, @@ -246,6 +257,7 @@ def load_and_run_classification_experiment( classifier_name=None, resample_id=0, build_train_file=False, + benchmark_time=True, overwrite=False, predefined_resample=False, ): @@ -279,6 +291,9 @@ def load_and_run_classification_experiment( Whether to generate train files or not. If true, it performs a 10-fold cross-validation on the train data and saves. If the classifier can produce its own estimates, those are used instead. + benchmark_time : bool, 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. overwrite : bool, default=False If set to False, this will only build results if there is not a result file already present. If True, it will overwrite anything already there. @@ -323,6 +338,7 @@ def load_and_run_classification_experiment( resample_id=resample_id, build_test_file=build_test_file, build_train_file=build_train_file, + benchmark_time=benchmark_time, ) @@ -339,6 +355,7 @@ def run_regression_experiment( resample_id=None, build_test_file=True, build_train_file=False, + benchmark_time=True, ): """Run a regression experiment and save the results to file. @@ -378,6 +395,9 @@ def run_regression_experiment( 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. + benchmark_time : bool, 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. """ if not build_test_file and not build_train_file: raise ValueError( @@ -414,6 +434,10 @@ def run_regression_experiment( ) fit_time = -1 mem_usage = -1 + benchmark = -1 + + if benchmark_time: + benchmark = timing_benchmark(random_state=resample_id) first_comment = ( "Generated by run_regression_experiment on " @@ -455,6 +479,7 @@ def run_regression_experiment( mse=test_mse, fit_time=fit_time, predict_time=test_time, + benchmark_time=benchmark, memory_usage=mem_usage, ) @@ -483,6 +508,7 @@ def run_regression_experiment( parameter_info=second, mse=train_mse, fit_time=fit_time, + benchmark_time=benchmark, train_estimate_time=train_time, fit_and_estimate_time=fit_time + train_time, ) @@ -497,6 +523,7 @@ def load_and_run_regression_experiment( regressor_name=None, resample_id=0, build_train_file=False, + benchmark_time=True, overwrite=False, predefined_resample=False, ): @@ -530,6 +557,9 @@ def load_and_run_regression_experiment( 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. + benchmark_time : bool, 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. overwrite : bool, default=False If set to False, this will only build results if there is not a result file already present. If True, it will overwrite anything already there. @@ -578,6 +608,7 @@ def load_and_run_regression_experiment( resample_id=resample_id, build_test_file=build_test_file, build_train_file=build_train_file, + benchmark_time=benchmark_time, ) @@ -595,6 +626,7 @@ def run_clustering_experiment( resample_id=None, build_test_file=False, build_train_file=True, + benchmark_time=True, ): """Run a clustering experiment and save the results to file. @@ -639,6 +671,9 @@ def run_clustering_experiment( build_train_file : bool, default=True Whether to generate train files or not. The clusterer is fit using train data regardless of input. + benchmark_time : bool, 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. """ if not build_test_file and not build_train_file: raise ValueError( @@ -682,6 +717,18 @@ def run_clustering_experiment( encoder_dict = {label: i for i, label in enumerate(le.classes_)} n_classes = len(np.unique(y_train)) + benchmark = -1 + if benchmark_time: + benchmark = timing_benchmark(random_state=resample_id) + + first_comment = ( + "Generated by run_clustering_experiment on " + f"{datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}. " + f"Encoder dictionary: {str(encoder_dict)}" + ) + + second = str(clusterer.get_params()).replace("\n", " ").replace("\r", " ") + if isinstance(n_clusters, int): try: if n_clusters == -1: @@ -708,14 +755,6 @@ def run_clustering_experiment( ) fit_time += int(round(getattr(clusterer, "_fit_time_milli", 0))) - first_comment = ( - "Generated by run_clustering_experiment on " - f"{datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}. " - f"Encoder dictionary: {str(encoder_dict)}" - ) - - second = str(clusterer.get_params()).replace("\n", " ").replace("\r", " ") - start = int(round(time.time() * 1000)) if callable(getattr(clusterer, "predict_proba", None)): train_probs = clusterer.predict_proba(X_train) @@ -749,6 +788,7 @@ def run_clustering_experiment( clustering_accuracy=train_acc, fit_time=fit_time, predict_time=train_time, + benchmark_time=benchmark, memory_usage=mem_usage, n_classes=n_classes, n_clusters=len(train_probs[0]), @@ -787,6 +827,7 @@ def run_clustering_experiment( clustering_accuracy=test_acc, fit_time=fit_time, predict_time=test_time, + benchmark_time=benchmark, memory_usage=mem_usage, n_classes=n_classes, n_clusters=len(test_probs[0]), @@ -803,6 +844,7 @@ def load_and_run_clustering_experiment( clusterer_name=None, resample_id=0, build_test_file=False, + benchmark_time=True, overwrite=False, predefined_resample=False, ): @@ -839,6 +881,9 @@ def load_and_run_clustering_experiment( build_test_file : bool, default=False Whether to generate test files or not. If true, the clusterer will assign clusters to the loaded test data. + benchmark_time : bool, 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. overwrite : bool, default=False If set to False, this will only build results if there is not a result file already present. If True, it will overwrite anything already there. @@ -884,6 +929,7 @@ def load_and_run_clustering_experiment( resample_id=resample_id, build_train_file=build_train_file, build_test_file=build_test_file, + benchmark_time=benchmark_time, ) @@ -895,6 +941,7 @@ def run_forecasting_experiment( forecaster_name=None, dataset_name="N/A", random_seed=None, + benchmark_time=True, ): """Run a forecasting experiment and save the results to file. @@ -920,6 +967,9 @@ def run_forecasting_experiment( random_seed : int or None, default=None Indicates what random seed was used as a random_state for the forecaster. Only used for the results file name. + benchmark_time : bool, 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. """ if not isinstance(forecaster, BaseForecaster): raise TypeError("forecaster must be an aeon forecaster.") @@ -927,6 +977,10 @@ def run_forecasting_experiment( if forecaster_name is None: forecaster_name = type(forecaster).__name__ + benchmark = -1 + if benchmark_time: + benchmark = timing_benchmark(random_state=random_seed) + first_comment = ( "Generated by run_forecasting_experiment on " f"{datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}" @@ -968,6 +1022,7 @@ def run_forecasting_experiment( mape=test_mape, fit_time=fit_time, predict_time=test_time, + benchmark_time=benchmark, memory_usage=mem_usage, ) @@ -979,6 +1034,7 @@ def load_and_run_forecasting_experiment( forecaster, forecaster_name=None, random_seed=None, + benchmark_time=True, overwrite=False, ): """Load a dataset and run a regression experiment. @@ -1004,6 +1060,9 @@ def load_and_run_forecasting_experiment( random_seed : int or None, default=None Indicates what random seed was used as a random_state for the forecaster. Only used for the results file name. + benchmark_time : bool, 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. overwrite : bool, default=False If set to False, this will only build results if there is not a result file already present. If True, it will overwrite anything already there. @@ -1039,6 +1098,7 @@ def load_and_run_forecasting_experiment( forecaster_name=forecaster_name, dataset_name=dataset, random_seed=random_seed, + benchmark_time=benchmark_time, ) diff --git a/tsml_eval/experiments/forecasting_experiments.py b/tsml_eval/experiments/forecasting_experiments.py index ce31f971..21c5cf4d 100644 --- a/tsml_eval/experiments/forecasting_experiments.py +++ b/tsml_eval/experiments/forecasting_experiments.py @@ -77,6 +77,7 @@ def run_experiment(args, overwrite=False): random_seed=args.resample_id if args.random_seed is None else args.random_seed, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, ) # local run (no args) diff --git a/tsml_eval/experiments/regression_experiments.py b/tsml_eval/experiments/regression_experiments.py index 6eaf2847..03da48fd 100644 --- a/tsml_eval/experiments/regression_experiments.py +++ b/tsml_eval/experiments/regression_experiments.py @@ -84,6 +84,7 @@ def run_experiment(args): regressor_name=args.estimator_name, resample_id=args.resample_id, build_train_file=args.train_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/experiments/set_regressor.py b/tsml_eval/experiments/set_regressor.py index 39028d96..d691b437 100644 --- a/tsml_eval/experiments/set_regressor.py +++ b/tsml_eval/experiments/set_regressor.py @@ -345,7 +345,10 @@ def _set_regressor_interval_based( from aeon.regression.interval_based import TimeSeriesForestRegressor return TimeSeriesForestRegressor( - random_state=random_state, n_jobs=n_jobs, **kwargs + random_state=random_state, + n_jobs=n_jobs, + save_transformed_data=build_train_file, + **kwargs, ) elif r == "tsf-i": from aeon.regression.interval_based import TimeSeriesForestRegressor @@ -357,7 +360,11 @@ def _set_regressor_interval_based( estimators = [ ( "tsf", - TimeSeriesForestRegressor(random_state=random_state, n_jobs=n_jobs), + TimeSeriesForestRegressor( + random_state=random_state, + n_jobs=n_jobs, + save_transformed_data=build_train_file, + ), None, ) ] @@ -367,7 +374,11 @@ def _set_regressor_interval_based( from aeon.regression.interval_based import TimeSeriesForestRegressor return TimeSeriesForestRegressor( - n_estimators=500, random_state=random_state, n_jobs=n_jobs, **kwargs + n_estimators=500, + random_state=random_state, + n_jobs=n_jobs, + save_transformed_data=build_train_file, + **kwargs, ) elif r == "drcif" or r == "drcifregressor": from aeon.regression.interval_based import DrCIFRegressor diff --git a/tsml_eval/experiments/tests/test_classification.py b/tsml_eval/experiments/tests/test_classification.py index c0a515e2..3f63f63f 100644 --- a/tsml_eval/experiments/tests/test_classification.py +++ b/tsml_eval/experiments/tests/test_classification.py @@ -102,8 +102,9 @@ def test_run_threaded_classification_experiment(): "1", "-nj", "2", - # also test normalisation here + # also test normalisation and benchmark time here "--row_normalise", + "--benchmark_time", ] threaded_classification_experiments.run_experiment(args) diff --git a/tsml_eval/experiments/tests/test_clustering.py b/tsml_eval/experiments/tests/test_clustering.py index 4e0ee55e..971bdfd3 100644 --- a/tsml_eval/experiments/tests/test_clustering.py +++ b/tsml_eval/experiments/tests/test_clustering.py @@ -102,8 +102,9 @@ def test_run_threaded_clustering_experiment(): "1", "-nj", "2", - # also test normalisation here + # also test normalisation and benchmark time here "--row_normalise", + "--benchmark_time", "-te", ] diff --git a/tsml_eval/experiments/tests/test_forecasting.py b/tsml_eval/experiments/tests/test_forecasting.py index 1a81ea9b..ffafe51d 100644 --- a/tsml_eval/experiments/tests/test_forecasting.py +++ b/tsml_eval/experiments/tests/test_forecasting.py @@ -84,8 +84,9 @@ def test_run_threaded_forecasting_experiment(): "1", "-nj", "2", - # also test normalisation here + # also test normalisation and benchmark time here "--row_normalise", + "--benchmark_time", ] threaded_forecasting_experiments.run_experiment(args) diff --git a/tsml_eval/experiments/tests/test_regression.py b/tsml_eval/experiments/tests/test_regression.py index 5efebf41..85ffdc4b 100644 --- a/tsml_eval/experiments/tests/test_regression.py +++ b/tsml_eval/experiments/tests/test_regression.py @@ -102,8 +102,9 @@ def test_run_threaded_regression_experiment(): "1", "-nj", "2", - # also test normalisation here + # also test normalisation and benchmark time here "--row_normalise", + "--benchmark_time", ] threaded_regression_experiments.run_experiment(args) diff --git a/tsml_eval/experiments/threaded_classification_experiments.py b/tsml_eval/experiments/threaded_classification_experiments.py index c77da827..8d92d072 100644 --- a/tsml_eval/experiments/threaded_classification_experiments.py +++ b/tsml_eval/experiments/threaded_classification_experiments.py @@ -60,6 +60,7 @@ def run_experiment(args): classifier_name=args.estimator_name, resample_id=args.resample_id, build_train_file=args.train_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/experiments/threaded_clustering_experiments.py b/tsml_eval/experiments/threaded_clustering_experiments.py index 46e588e9..dd94578e 100644 --- a/tsml_eval/experiments/threaded_clustering_experiments.py +++ b/tsml_eval/experiments/threaded_clustering_experiments.py @@ -61,6 +61,7 @@ def run_experiment(args): clusterer_name=args.estimator_name, resample_id=args.resample_id, build_test_file=args.test_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/experiments/threaded_forecasting_experiments.py b/tsml_eval/experiments/threaded_forecasting_experiments.py index 06e8a2c2..a904b3c1 100644 --- a/tsml_eval/experiments/threaded_forecasting_experiments.py +++ b/tsml_eval/experiments/threaded_forecasting_experiments.py @@ -52,6 +52,7 @@ def run_experiment(args, overwrite=False): random_seed=args.resample_id if args.random_seed is None else args.random_seed, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, ) # local run (no args) diff --git a/tsml_eval/experiments/threaded_regression_experiments.py b/tsml_eval/experiments/threaded_regression_experiments.py index 9c507963..73f3e8a3 100644 --- a/tsml_eval/experiments/threaded_regression_experiments.py +++ b/tsml_eval/experiments/threaded_regression_experiments.py @@ -61,6 +61,7 @@ def run_experiment(args): regressor_name=args.estimator_name, resample_id=args.resample_id, build_train_file=args.train_fold, + benchmark_time=args.benchmark_time, overwrite=args.overwrite, predefined_resample=args.predefined_resample, ) diff --git a/tsml_eval/testing/__init__.py b/tsml_eval/testing/__init__.py index e69de29b..aafb1fa9 100644 --- a/tsml_eval/testing/__init__.py +++ b/tsml_eval/testing/__init__.py @@ -0,0 +1 @@ +"""Classes and functions for unit testing in tsml-eval.""" diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..9024e681 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:34. 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.7230320699708455,-1700006610010,2,858,31801344,2,2 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.7988338192419825,-1700006607550,2,775,31412224,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 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+1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..fc67bfeb --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:34. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.65,-1700006613954,1,842,31289344,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..7cdb8450 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:30. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.6,-1700006610010,0,858,31801344,2,2 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..75f961cd --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:27. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.85,-1700006607550,0,775,31412224,2,2 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv new file mode 100644 index 00000000..029afbc4 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:12. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.5276967930029155,-1700006588326,5,856,45379584,2,2 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv new file mode 100644 index 00000000..e0c2a67e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:15. 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv new file mode 100644 index 00000000..07bf0c48 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:17. 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv new file mode 100644 index 00000000..4f9c6dfe --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:12. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.5373134328358209,-1700006588326,0,856,45379584,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..0fdbdee7 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:15. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.5074626865671642,-1700006593298,0,781,44728320,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..05ed1585 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:17. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.582089552238806,-1700006596769,0,759,31227904,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..bd40e926 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:50. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.79,-1700006589602,144,823,31612928,4,4 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..50d03b87 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:54. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.77,-1700006579984,126,912,31813632,4,4 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..8f707e38 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:57. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.74,-1700006583515,133,770,30945280,4,4 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..c584cade --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:08. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.7813411078717201,-1700006767950,4,1253,35344384,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.7288629737609329,-1700006771548,8,1056,36016128,2,2 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.8950437317784257,-1700006773773,2,1275,35799040,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 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+1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..2bda1000 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:08. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.7,-1700006767950,2,1253,35344384,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..9290c870 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:12. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.55,-1700006771548,1,1056,36016128,2,2 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..e1c1bdb3 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.75,-1700006773773,0,1275,35799040,2,2 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv new file mode 100644 index 00000000..895e0f46 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:31. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.5170068027210885,-1700006790414,14,928,35729408,2,2 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv new file mode 100644 index 00000000..e05e0384 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:30. 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv new file mode 100644 index 00000000..a9b942c0 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:27. 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b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv new file mode 100644 index 00000000..71a6f96b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:31. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.5223880597014925,-1700006790414,2,928,35729408,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..2981a60a --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:30. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.5223880597014925,-1700006789322,0,909,35213312,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..bfaf623f --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:27. Encoder dictionary: {1.0: 0, 2.0: 1} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.9850746268656716,-1700006786391,1,1280,35540992,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..f24e6cc1 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:49. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.57,-1700006689679,155,822,38416384,4,4 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..fd8d1e7a --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:46. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.53,-1700006674978,131,921,56549376,4,4 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..3c7bf431 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:41. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.59,-1700006665628,127,855,72638464,4,4 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv new file mode 100644 index 00000000..13b9a944 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv @@ -0,0 +1,346 @@ +Chinatown,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:43. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.7230320699708455,-1700006443520,18,798,1474560,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} +0.7142857142857143,-1700006445933,1,754,1687552,2,2 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 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Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} +0.7376093294460642,-1700006448251,1,769,1945600,2,2 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 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+1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..6d588832 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:43. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.65,-1700006443520,0,798,1474560,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..a04d8f1d --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:46. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} +0.65,-1700006445933,0,754,1687552,2,2 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..00333a0b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:48. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} +0.65,-1700006448251,0,769,1945600,2,2 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv new file mode 100644 index 00000000..7180084e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:06. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.5208940719144801,-1700006466919,2,838,1421312,2,2 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 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a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv new file mode 100644 index 00000000..b26e0872 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:02. 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a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv new file mode 100644 index 00000000..e196089c --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv @@ -0,0 +1,1032 @@ +ItalyPowerDemand,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:00. 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a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv new file mode 100644 index 00000000..3f85dd82 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:06. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.5522388059701493,-1700006466919,0,838,1421312,2,2 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +0,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..e9ecb33b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:02. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} +0.5223880597014925,-1700006462919,0,767,1175552,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,0,,1.0,0.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..ed557573 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:00. Encoder dictionary: {1.0: 0, 2.0: 1} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} +0.9850746268656716,-1700006460133,0,768,1212416,2,2 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +0,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,1,,0.0,1.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 +1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..4c724e32 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:00. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.57,-1700006339683,0,780,1634304,4,4 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..50d598e9 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:17. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} +0.52,-1700006356942,0,818,1478656,4,4 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..5a1e1eb7 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} +0.57,-1700006359724,0,778,1433600,4,4 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 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b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/Airline/testResample0.csv @@ -0,0 +1,39 @@ +Airline,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:58 +{'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__copy_X': True, 'RecursiveTabularRegressionForecaster__estimator__fit_intercept': True, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__positive': False, 'RecursiveTabularRegressionForecaster__estimator': LinearRegression(n_jobs=1), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} +0.04931900082960808,-1700009578949,7,752,786432 +340.0,343.51466438602256 +318.0,324.0668832781254 +362.0,377.78979016255755 +348.0,371.5544719955919 +363.0,381.4169732290495 +435.0,456.92889498114357 +491.0,505.59122153685763 +505.0,504.75656498304284 +404.0,437.0482522925041 +359.0,364.63563974493337 +310.0,320.9034785064191 +337.0,350.6066106913254 +360.0,357.3904094853399 +342.0,335.6264610858123 +406.0,388.1504026799085 +396.0,387.31104524717426 +420.0,401.92210310535575 +472.0,489.79154123404646 +548.0,545.5366699170966 +559.0,543.9433262145312 +463.0,470.67827637032127 +407.0,383.4717981780984 +362.0,336.85425865567527 +405.0,366.6666541763234 +417.0,371.87206880544466 +391.0,347.9944775742125 +419.0,399.14205569737817 +461.0,404.4940002023249 +472.0,426.00799550638305 +535.0,527.8682064575188 +622.0,592.2058534258638 +606.0,589.9965388618455 +508.0,510.0964299230877 +461.0,407.00234994554063 +390.0,356.0835641697555 +432.0,386.02362910127977 diff --git a/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv new file mode 100644 index 00000000..b4c2b39d --- /dev/null +++ b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv @@ -0,0 +1,15 @@ +ShampooSales,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:29 +{'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__copy_X': True, 'RecursiveTabularRegressionForecaster__estimator__fit_intercept': True, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__positive': False, 'RecursiveTabularRegressionForecaster__estimator': LinearRegression(n_jobs=1), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} +0.2952136312226724,-1700009549777,5,752,798720 +339.7,360.34405087149526 +440.4,375.6301808613457 +315.9,330.9948497742497 +439.3,251.34440202033534 +401.3,368.94921838173354 +437.4,248.93164771071037 +575.5,335.1713599482991 +407.6,265.1384656364762 +682.0,361.73784679707137 +475.3,258.05583208424434 +581.3,402.61921141293715 +646.9,422.79242277277547 diff --git a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv new file mode 100644 index 00000000..5af98ff6 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv @@ -0,0 +1,39 @@ +Airline,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:51:54 +{'sp': 1, 'strategy': 'last', 'window_length': None} +0.19886711926999853,-1700009514187,2,762,102400 +340.0,336.0 +318.0,336.0 +362.0,336.0 +348.0,336.0 +363.0,336.0 +435.0,336.0 +491.0,336.0 +505.0,336.0 +404.0,336.0 +359.0,336.0 +310.0,336.0 +337.0,336.0 +360.0,336.0 +342.0,336.0 +406.0,336.0 +396.0,336.0 +420.0,336.0 +472.0,336.0 +548.0,336.0 +559.0,336.0 +463.0,336.0 +407.0,336.0 +362.0,336.0 +405.0,336.0 +417.0,336.0 +391.0,336.0 +419.0,336.0 +461.0,336.0 +472.0,336.0 +535.0,336.0 +622.0,336.0 +606.0,336.0 +508.0,336.0 +461.0,336.0 +390.0,336.0 +432.0,336.0 diff --git a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv new file mode 100644 index 00000000..0f239c15 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv @@ -0,0 +1,39 @@ +Airline,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:12 +{'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__bootstrap': True, 'RecursiveTabularRegressionForecaster__estimator__ccp_alpha': 0.0, 'RecursiveTabularRegressionForecaster__estimator__criterion': 'squared_error', 'RecursiveTabularRegressionForecaster__estimator__max_depth': None, 'RecursiveTabularRegressionForecaster__estimator__max_features': 1.0, 'RecursiveTabularRegressionForecaster__estimator__max_leaf_nodes': None, 'RecursiveTabularRegressionForecaster__estimator__max_samples': None, 'RecursiveTabularRegressionForecaster__estimator__min_impurity_decrease': 0.0, 'RecursiveTabularRegressionForecaster__estimator__min_samples_leaf': 1, 'RecursiveTabularRegressionForecaster__estimator__min_samples_split': 2, 'RecursiveTabularRegressionForecaster__estimator__min_weight_fraction_leaf': 0.0, 'RecursiveTabularRegressionForecaster__estimator__n_estimators': 200, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__oob_score': False, 'RecursiveTabularRegressionForecaster__estimator__random_state': 0, 'RecursiveTabularRegressionForecaster__estimator__verbose': 0, 'RecursiveTabularRegressionForecaster__estimator__warm_start': False, 'RecursiveTabularRegressionForecaster__estimator': RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} +0.051909914023904186,-1700009532020,158,773,2195456 +340.0,335.44736767269706 +318.0,325.90729807939636 +362.0,376.6901395438566 +348.0,375.1648744605447 +363.0,369.3092826316842 +435.0,459.36298086541495 +491.0,479.6219578296036 +505.0,482.6168748035096 +404.0,471.8567382605961 +359.0,384.39221798279453 +310.0,337.01400933150416 +337.0,360.5268615136186 +360.0,368.1957796021606 +342.0,361.232911415016 +406.0,406.17354347080504 +396.0,407.05568864500265 +420.0,408.2558225442281 +472.0,493.875533167567 +548.0,509.47902540798543 +559.0,515.318720097745 +463.0,514.8040307668124 +407.0,414.49560854840087 +362.0,368.73243469376087 +405.0,387.16855683214715 +417.0,396.48402019206026 +391.0,391.85779902254984 +419.0,433.94571551058897 +461.0,436.8618885221069 +472.0,438.7877347880762 +535.0,525.2212322444198 +622.0,539.5895022006916 +606.0,545.4785353968389 +508.0,545.362911415016 +461.0,444.21086841578773 +390.0,408.2292264711766 +432.0,416.8078874074708 diff --git a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv new file mode 100644 index 00000000..d0941db0 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv @@ -0,0 +1,15 @@ +ShampooSales,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:20 +{'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__bootstrap': True, 'RecursiveTabularRegressionForecaster__estimator__ccp_alpha': 0.0, 'RecursiveTabularRegressionForecaster__estimator__criterion': 'squared_error', 'RecursiveTabularRegressionForecaster__estimator__max_depth': None, 'RecursiveTabularRegressionForecaster__estimator__max_features': 1.0, 'RecursiveTabularRegressionForecaster__estimator__max_leaf_nodes': None, 'RecursiveTabularRegressionForecaster__estimator__max_samples': None, 'RecursiveTabularRegressionForecaster__estimator__min_impurity_decrease': 0.0, 'RecursiveTabularRegressionForecaster__estimator__min_samples_leaf': 1, 'RecursiveTabularRegressionForecaster__estimator__min_samples_split': 2, 'RecursiveTabularRegressionForecaster__estimator__min_weight_fraction_leaf': 0.0, 'RecursiveTabularRegressionForecaster__estimator__n_estimators': 200, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__oob_score': False, 'RecursiveTabularRegressionForecaster__estimator__random_state': 0, 'RecursiveTabularRegressionForecaster__estimator__verbose': 0, 'RecursiveTabularRegressionForecaster__estimator__warm_start': False, 'RecursiveTabularRegressionForecaster__estimator': RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} +0.24900813645841494,-1700009539952,56,747,1052672 +339.7,302.8515104347826 +440.4,336.4869356521739 +315.9,311.8008417391304 +439.3,315.6485304347826 +401.3,359.6416486956521 +437.4,319.3666252173913 +575.5,367.24838086956515 +407.6,330.572144347826 +682.0,394.7812913043477 +475.3,366.88587565217387 +581.3,390.19383043478257 +646.9,357.9128973913043 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv new file mode 100644 index 00000000..0a75bf59 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv @@ -0,0 +1,64 @@ +Covid3Month,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:41 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.0027199984296669712,-1700007701301,293,838,94208,,-1,-1 +0.011883802816901408,0.04218472468916518 +0.003795066413662239,0.014598540145985401 +0.08298755186721991,0.0 +0.04510921177587844,0.016446804865513105 +0.12783074753173485,0.02122884895739488 +0.0,0.1111111111111111 +0.04842105263157895,0.014598540145985401 +0.10526315789473684,0.0 +0.023722627737226276,0.075 +0.0125,0.027522935779816515 +0.008395522388059701,0.06855277475516866 +0.07488584474885844,0.0367504835589942 +0.002214022140221402,0.004920049200492005 +0.06350568547521991,0.025588235294117648 +0.0078125,0.0 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run_regression_experiment on 11/15/2023, 00:21:38 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.004409923607335828,-1700007698314,398,903,90112,,-1,-1 +0.0,0.10526315789473684 +0.002214022140221402,0.0 +0.006968641114982578,0.010135135135135136 +0.046153846153846156,0.015151515151515152 +0.0,0.18181818181818182 +0.0,0.18181818181818182 +0.0,0.0 +0.08394227256119839,0.023601254332398087 +0.12829434037964843,0.103182375985274 +0.08695652173913043,0.0 +0.03211009174311927,0.007352941176470588 +0.049723756906077346,0.021220159151193633 +0.045454545454545456,0.0 +0.025668306955712195,0.03926512968299712 +0.016446804865513105,0.03533314104413037 +0.006872852233676976,0.04326923076923077 +0.0,0.0 +0.013474494706448507,0.08068783068783068 +0.014134275618374558,0.07079646017699115 +0.0125,0.0 +0.04081632653061224,0.056338028169014086 +0.0,0.0 +0.029062870699881376,0.06855277475516866 +0.06297229219143577,0.029154518950437316 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--git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv new file mode 100644 index 00000000..05a79638 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv @@ -0,0 +1,143 @@ +Covid3Month,1NN-DTW,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:41 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.003077656676598191,-1700007701301,-1,838,-1,,222,-1700007701079 +0.0,0.06060606060606061 +0.07758620689655173,0.0 +0.0,0.0 +0.0,0.017543859649122806 +0.1540030911901082,0.06254256981449577 +0.05555555555555555,0.04054054054054054 +0.004675628287551139,0.015151515151515152 +0.02857142857142857,0.017543859649122806 +0.008056108425741636,0.055888853804862644 +0.04794210764360018,0.03533314104413037 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+0.04794210764360018,0.09485430874147552 +0.039473684210526314,0.05555555555555555 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv new file mode 100644 index 00000000..057c56cf --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv @@ -0,0 +1,143 @@ +Covid3Month,1NN-DTW,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:36 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.002920234195645965,-1700007696127,-1,889,-1,,263,-1700007695864 +0.038461538461538464,0.0 +0.03926512968299712,0.025588235294117648 +0.08068783068783068,0.009584664536741214 +0.008771929824561403,0.005509641873278237 +0.03533314104413037,0.04794210764360018 +0.12759488705090066,0.10632367895747806 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b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample0.csv @@ -0,0 +1,205 @@ +FloodModeling1,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:23:58 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.00013119306930693054,-1700007838950,12088,811,110592,,-1,-1 +0.45,0.444 +0.447,0.436 +0.42700000000000005,0.431 +0.455,0.465 +0.445,0.434 +0.435,0.446 +0.44799999999999995,0.47100000000000003 +0.434,0.431 +0.424,0.42200000000000004 +0.462,0.43700000000000006 +0.42100000000000004,0.42200000000000004 +0.425,0.42200000000000004 +0.441,0.45 +0.485,0.46799999999999997 +0.426,0.423 +0.431,0.43799999999999994 +0.426,0.42 +0.429,0.41600000000000004 +0.447,0.44 +0.42700000000000005,0.43 +0.45799999999999996,0.46 +0.445,0.45899999999999996 +0.414,0.41200000000000003 +0.419,0.42200000000000004 +0.43799999999999994,0.428 +0.451,0.442 +0.44,0.44299999999999995 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b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv new file mode 100644 index 00000000..c8b50be0 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv @@ -0,0 +1,68 @@ +NaturalGasPricesSentiment,1NN-DTW,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:34 +{'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} +0.006514222945537523,-1700007394445,-1,827,-1,,14,-1700007394431 +-0.28843834113616207,-0.5199578351126266 +-0.3716620670488247,-0.3456104081983749 +-0.07298391111768207,-0.12289009959651871 +-0.34286396841590216,-0.24187951964827675 +-0.2904184248011846,-0.3034813138154836 +-0.384325153266008,-0.5199578351126266 +-0.2083443989547399,-0.22273496475357274 +-0.2818573163105891,-0.21406789399110351 +-0.15584264414814797,-0.12363863375324469 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv new file mode 100644 index 00000000..18566ee0 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv @@ -0,0 +1,64 @@ +Covid3Month,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:41 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.0015126663111567206,-1700007639291,356,1102,4612096,,-1,-1 +0.011883802816901408,0.01891871685908819 +0.003795066413662239,0.01080200649655172 +0.08298755186721991,0.023297205297694773 +0.04510921177587844,0.036759823938724595 +0.12783074753173485,0.0734349028905487 +0.0,0.022006874996059606 +0.04842105263157895,0.02556255771763205 +0.10526315789473684,0.05430307661946471 +0.023722627737226276,0.05358839755162756 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a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv new file mode 100644 index 00000000..1c4758d8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv @@ -0,0 +1,64 @@ +Covid3Month,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:44 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.0015289716434647286,-1700007643003,365,783,4562944,,-1,-1 +0.0,0.07113338403983059 +0.002214022140221402,0.03218489211584037 +0.006968641114982578,0.042411265387673244 +0.046153846153846156,0.04763483146042975 +0.0,0.026408680740635793 +0.0,0.026408680740635793 +0.0,0.01650499080080714 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'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.00186857456998448,-1700007643003,-1,783,-1,,17327,-1700007625676 +0.0367504835589942,0.009250952031640523 +0.03205128205128205,0.03927891429888249 +0.021220159151193633,0.023865034510376484 +0.039673396867585614,0.04521532914265966 +0.022429906542056073,0.016041498451210976 +0.10526315789473684,0.0640713836725339 +0.0256797583081571,0.035899246813585806 +0.0,0.019983005611702864 +0.1540030911901082,0.06936283961326822 +0.04054054054054054,0.042442587144886454 +0.005042016806722689,0.03178916443514292 +0.11764705882352941,0.048905583669662495 +0.1578416540595058,0.018869373191720388 +0.025588235294117648,0.03764009428321966 +0.0,-0.00015262321730259015 +0.0,0.0888605847605482 +0.04218472468916518,0.018737333137380102 +0.0,0.049255350937664505 +0.007633587786259542,0.03143989322169083 +0.0,0.02530582227384666 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-0,0 +1,143 @@ +Covid3Month,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:46 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.002276961255842811,-1700007644659,-1,792,-1,,16987,-1700007627672 +0.038461538461538464,0.07437688666617187 +0.03926512968299712,0.049128492870533 +0.08068783068783068,0.032070069569347284 +0.008771929824561403,0.012161547515718837 +0.03533314104413037,0.06267809382246517 +0.12759488705090066,0.06924966913952608 +0.04218472468916518,0.012771156865575672 +0.07079646017699115,-0.021158729815522775 +0.019255874673629242,0.0487362689425342 +0.0445468509984639,0.025905227502413045 +0.04326923076923077,0.06287304309404587 +0.0,0.08980921358439495 +0.0,0.008886547502732128 +0.0,0.06500707831034544 +0.004675628287551139,0.0513117271085649 +0.0,-0.02319808434173247 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv new file mode 100644 index 00000000..6235370e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv @@ -0,0 +1,205 @@ +FloodModeling1,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:18 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.00019258705659831615,-1700007838085,4103,901,50520064,,-1,-1 +0.45,0.4428951017017777 +0.447,0.44024766072092425 +0.42700000000000005,0.43673071020646126 +0.455,0.4794350667870188 +0.445,0.42846186594435975 +0.435,0.4303760832248291 +0.44799999999999995,0.43151527623126373 +0.434,0.4363650172362134 +0.424,0.4353981211392389 +0.462,0.4460592998507211 +0.42100000000000004,0.41898082283146576 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv new file mode 100644 index 00000000..967a4c65 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv @@ -0,0 +1,205 @@ +FloodModeling1,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:14 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.0004281903653824091,-1700007833524,3975,1046,47988736,,-1,-1 +0.434,0.44352830001859506 +0.425,0.4398790255172182 +0.494,0.5101060389775232 +0.431,0.4408229750077518 +0.442,0.43364913966547247 +0.42200000000000004,0.4450764655340528 +0.495,0.45963793801214 +0.48100000000000004,0.46931651525283347 +0.466,0.4561916943868981 +0.469,0.44287038668824036 +0.446,0.4414809935420202 +0.445,0.4305085953409749 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample2.csv @@ -0,0 +1,205 @@ +FloodModeling1,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:11 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.0002265774966634495,-1700007829191,3880,923,55861248,,-1,-1 +0.451,0.47181819601803116 +0.446,0.4357226693640668 +0.42700000000000005,0.44952848255382694 +0.43799999999999994,0.4498567018342505 +0.44299999999999995,0.45965133563023414 +0.431,0.41173297661167557 +0.419,0.41925616151134926 +0.43200000000000005,0.43849665788486075 +0.419,0.42309141941483885 +0.42700000000000005,0.4321356719629631 +0.418,0.433370064181858 +0.509,0.4990734353484845 +0.42700000000000005,0.4306736481469638 +0.447,0.43925286676279796 +0.42,0.4278687784763779 +0.447,0.4324614507803087 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample0.csv @@ -0,0 +1,31 @@ +NaturalGasPricesSentiment,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:03 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.008392555993079687,-1700007362253,83,796,4575232,,-1,-1 +-0.3745973154329336,-0.315432353273626 +-0.27649220292280596,-0.20552725396703111 +-0.3359852990851952,-0.30337277066180235 +-0.1588933546382647,-0.2024891330174071 +-0.21406789399110351,-0.3444056834333251 +-0.3459467332277681,-0.27807125874470723 +-0.23691177769349164,-0.32478388764380095 +-0.31200024393888615,-0.2783213862769476 +-0.3456104081983749,-0.27042110886710874 +-0.3716620670488247,-0.3000850663924516 +-0.38911418863213976,-0.35793009228928124 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/dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample1.csv @@ -0,0 +1,31 @@ +NaturalGasPricesSentiment,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:10 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.008943107571809389,-1700007370343,106,787,4485120,,-1,-1 +-0.06869880430973492,-0.22998553482152959 +-0.3310506358794816,-0.25952912217459845 +-0.328501794888423,-0.31401904012991955 +-0.34666764587163923,-0.2893041451535802 +-0.3625439577950881,-0.3401300255634961 +-0.1588933546382647,-0.2563439549217178 +-0.12073821121683478,-0.2618298852378693 +-0.19291852431801648,-0.373758533318602 +-0.16597553503054835,-0.2617393155450437 +-0.3215639365407137,-0.26422336905935406 +-0.39712400132646924,-0.2905024253623084 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/dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample2.csv @@ -0,0 +1,31 @@ +NaturalGasPricesSentiment,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:13 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.006343102045309365,-1700007373507,76,854,4317184,,-1,-1 +-0.12363863375324469,-0.2824037699946651 +-0.28387368441774286,-0.3181336904710754 +-0.3627388186179675,-0.31517423643317344 +-0.3266325629101349,-0.3421820353413992 +-0.2783406943082809,-0.31748171862640573 +-0.2786594656797555,-0.29865885111572804 +-0.39814494183430305,-0.2890296207613182 +-0.2542594075776063,-0.3725505459298193 +-0.352045882321321,-0.30566675820969824 +-0.3879279331519053,-0.31725258288619856 +-0.3359852990851952,-0.2723721066325765 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/dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample0.csv @@ -0,0 +1,68 @@ +NaturalGasPricesSentiment,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:03 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.00870341203373147,-1700007362253,-1,796,-1,,3060,-1700007359193 +-0.4272497972616782,-0.26846957662551674 +-0.3330772297886702,-0.32824666151417725 +-0.27904755897246875,-0.3605367777519264 +-0.41383876536901176,-0.3130741271228392 +-0.3879279331519053,-0.35866749639632933 +-0.2238585824576708,-0.32945169274500896 +-0.34554364217015404,-0.2974900618841865 +-0.328501794888423,-0.3005442311003172 +-0.3218208770339306,-0.24523938884270394 +-0.30665818378329274,-0.29622191829637146 +-0.32848593586912517,-0.3462888532995607 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'rocket', 'use_multivariate': 'auto'} +0.009778602090463495,-1700007370343,-1,787,-1,,3853,-1700007366490 +-0.28843834113616207,-0.3709752003975283 +-0.3716620670488247,-0.3225483754475697 +-0.07298391111768207,-0.3775412052579303 +-0.34286396841590216,-0.24709640135786654 +-0.2904184248011846,-0.1758102808569817 +-0.384325153266008,-0.3764489991913439 +-0.2083443989547399,-0.354811013603788 +-0.2818573163105891,-0.30544806328380986 +-0.15584264414814797,-0.3189612693165138 +-0.3012756023842555,-0.2940385254230822 +-0.29108327604257134,-0.3338568483345705 +-0.26583937985392714,-0.2564731786288316 +-0.11726184109082587,-0.3252763081002872 +-0.29261542988511224,-0.3039146748731249 +-0.39814494183430305,-0.27236970519654724 +-0.14818325655964704,-0.2842023496981461 +-0.32848593586912517,-0.3252909012931655 +-0.27904755897246875,-0.33623409959439016 +-0.4053674006691346,-0.342528346078669 +-0.3456104081983749,-0.2746302411354465 +-0.33957513731259564,-0.30672122775281074 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b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv new file mode 100644 index 00000000..6eea155a --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv @@ -0,0 +1,68 @@ +NaturalGasPricesSentiment,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:13 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.011333943564240391,-1700007373507,-1,854,-1,,3285,-1700007370222 +-0.5199578351126266,-0.28152186125908024 +-0.3215639365407137,-0.23219737296818604 +-0.15584264414814797,-0.27337520335728216 +-0.33414440074792273,-0.30332898695183175 +-0.5024348328319881,-0.2680798840635601 +-0.384325153266008,-0.3514696692738935 +-0.27904755897246875,-0.3256825682705754 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+{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0016445301992041238,-1700007628363,270,929,33886208,,-1,-1 +0.011883802816901408,0.026445338330364613 +0.003795066413662239,0.027436010797557166 +0.08298755186721991,0.02533176698674866 +0.04510921177587844,0.04835731634221221 +0.12783074753173485,0.06801609271225578 +0.0,0.06719803627598536 +0.04842105263157895,0.029869238860907567 +0.10526315789473684,0.05294135117758967 +0.023722627737226276,0.05213486552338442 +0.0125,0.031706363939596784 +0.008395522388059701,0.039255368218065044 +0.07488584474885844,0.035081346098815684 +0.002214022140221402,0.026844417250171223 +0.06350568547521991,0.03708779355151529 +0.0078125,0.018399903007643697 +0.013596572918606817,0.040512733601081076 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv @@ -0,0 +1,64 @@ +Covid3Month,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:28 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0013619652197387026,-1700007625407,343,805,33710080,,-1,-1 +0.0,0.07596352816580666 +0.002214022140221402,0.031158938188908546 +0.006968641114982578,0.025077431128716104 +0.046153846153846156,0.026839811658630017 +0.0,0.01214928139761021 +0.0,0.01214928139761021 +0.0,0.006304010397540454 +0.08394227256119839,0.03367326708560431 +0.12829434037964843,0.08030267988649623 +0.08695652173913043,0.038122828780652825 +0.03211009174311927,0.011625177474447655 +0.049723756906077346,0.029906591238137818 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a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv new file mode 100644 index 00000000..fb3e7101 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv @@ -0,0 +1,64 @@ +Covid3Month,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:25 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0011462550188405171,-1700007622481,290,791,34246656,,-1,-1 +0.04003775568724909,0.03912589097540546 +0.025668306955712195,0.03248295224260966 +0.0078125,0.01699045214013797 +0.045454545454545456,0.0376462021138724 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv @@ -0,0 +1,143 @@ +Covid3Month,TSF,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:25 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0018743701647611062,-1700007622481,-1,791,-1,,1367,-1700007621114 +0.038461538461538464,0.08654048064200939 +0.03926512968299712,0.03355508040915349 +0.08068783068783068,0.036774778389509294 +0.008771929824561403,0.04983175274350275 +0.03533314104413037,0.03157843186141803 +0.12759488705090066,0.04970515256490117 +0.04218472468916518,0.03613123943467761 +0.07079646017699115,0.02825310679936053 +0.019255874673629242,0.032986543548042445 +0.0445468509984639,0.02346883306278825 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a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv new file mode 100644 index 00000000..9118121b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv @@ -0,0 +1,205 @@ +FloodModeling1,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:27 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} +5.020268895420901e-05,-1700007847335,1936,909,35610624,,-1,-1 +0.45,0.4539149999999995 +0.447,0.4442049999999999 +0.42700000000000005,0.4269699999999998 +0.455,0.4484549999999995 +0.445,0.44433999999999996 +0.435,0.44013499999999967 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a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv new file mode 100644 index 00000000..bebda4a9 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv @@ -0,0 +1,205 @@ +FloodModeling1,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:29 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} +0.00019749039628712877,-1700007848870,2114,918,33239040,,-1,-1 +0.434,0.4365050000000003 +0.425,0.42416499999999985 +0.494,0.47634 +0.431,0.4505249999999994 +0.442,0.4492349999999998 +0.42200000000000004,0.42235999999999996 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/dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv @@ -0,0 +1,205 @@ +FloodModeling1,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:30 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} +5.410471896658498e-05,-1700007850615,1947,887,34709504,,-1,-1 +0.451,0.4460799999999994 +0.446,0.4464950000000002 +0.42700000000000005,0.42839000000000005 +0.43799999999999994,0.4458099999999998 +0.44299999999999995,0.44224999999999975 +0.431,0.43115000000000053 +0.419,0.42416000000000037 +0.43200000000000005,0.44240750000000006 +0.419,0.4262850000000002 +0.42700000000000005,0.42562000000000055 +0.418,0.4207600000000006 +0.509,0.521295 +0.42700000000000005,0.42931499999999984 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-0,0 +1,31 @@ +NaturalGasPricesSentiment,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:27 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0028600017079850088,-1700007565322,138,840,27795456,,-1,-1 +-0.3745973154329336,-0.3170056165599792 +-0.27649220292280596,-0.3075135142470262 +-0.3359852990851952,-0.41088529794465245 +-0.1588933546382647,-0.15692760707609926 +-0.21406789399110351,-0.29564334074725646 +-0.3459467332277681,-0.31661995504749646 +-0.23691177769349164,-0.32838085540384104 +-0.31200024393888615,-0.32664156494977414 +-0.3456104081983749,-0.3288243677115684 +-0.3716620670488247,-0.3416620992952244 +-0.38911418863213976,-0.3428953297938503 +-0.3566989751389393,-0.3218012279182726 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv @@ -0,0 +1,31 @@ +NaturalGasPricesSentiment,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:31 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.00206232858333723,-1700007570322,120,893,27361280,,-1,-1 +-0.06869880430973492,-0.12565121136949606 +-0.3310506358794816,-0.3015908183865471 +-0.328501794888423,-0.30169368873646774 +-0.34666764587163923,-0.37099663200422184 +-0.3625439577950881,-0.32459321850844125 +-0.1588933546382647,-0.16288211214026582 +-0.12073821121683478,-0.13966429000319194 +-0.19291852431801648,-0.13818764549923634 +-0.16597553503054835,-0.21936692119705017 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv new file mode 100644 index 00000000..1df38c97 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv @@ -0,0 +1,31 @@ +NaturalGasPricesSentiment,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:33 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.003674450842994001,-1700007572840,124,773,27308032,,-1,-1 +-0.12363863375324469,-0.15626487639861644 +-0.28387368441774286,-0.32852148022221306 +-0.3627388186179675,-0.2977592638731001 +-0.3266325629101349,-0.35174381775953995 +-0.2783406943082809,-0.30159674868459185 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a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv new file mode 100644 index 00000000..05213a2b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv @@ -0,0 +1,68 @@ +NaturalGasPricesSentiment,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:27 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.003790880115008313,-1700007565322,-1,840,-1,,448,-1700007564874 +-0.4272497972616782,-0.33260701375286467 +-0.3330772297886702,-0.3227860910507832 +-0.27904755897246875,-0.3431144077016081 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv @@ -0,0 +1,68 @@ +NaturalGasPricesSentiment,TSF,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:31 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.0035720382659537673,-1700007570322,-1,893,-1,,400,-1700007569922 +-0.28843834113616207,-0.3692448847335458 +-0.3716620670488247,-0.3321425630759839 +-0.07298391111768207,-0.14438751290787705 +-0.34286396841590216,-0.3268399943337783 +-0.2904184248011846,-0.3476921568267589 +-0.384325153266008,-0.3665817741408461 +-0.2083443989547399,-0.21601362817298017 +-0.2818573163105891,-0.25994249770742067 +-0.15584264414814797,-0.17436674762065765 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+-0.3605343759059906,-0.3231452109374645 +-0.3716620670488247,-0.34357908712959795 +-0.32095611238708854,-0.35024012579219743 diff --git a/tsml_eval/testing/tests/__init__.py b/tsml_eval/testing/tests/__init__.py index e69de29b..b225e3e8 100644 --- a/tsml_eval/testing/tests/__init__.py +++ b/tsml_eval/testing/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for testing functions and classes.""" diff --git a/tsml_eval/utils/arguments.py b/tsml_eval/utils/arguments.py index 88da4c81..1b71bc61 100644 --- a/tsml_eval/utils/arguments.py +++ b/tsml_eval/utils/arguments.py @@ -70,6 +70,9 @@ def parse_args(args): the number of clusters to find for clusterers which have an {n_clusters} parameter. If {-1}, use the number of classes in the dataset (default: None). + -bt, --benchmark_time + run a benchmark function and save the time spent in the + results file (default: %(default)s). -kw KEY VALUE TYPE, --kwargs KEY VALUE TYPE, --kwarg KEY VALUE TYPE additional keyword arguments to pass to the estimator. Should contain the parameter to set, the parameter @@ -192,6 +195,13 @@ def parse_args(args): "parameter. If {-1}, use the number of classes in the dataset " "(default: %(default)s).", ) + parser.add_argument( + "-bt", + "--benchmark_time", + action="store_true", + help="run a benchmark function and save the time spent in the results file " + "(default: %(default)s).", + ) parser.add_argument( "-kw", "--kwargs", diff --git a/tsml_eval/utils/experiments.py b/tsml_eval/utils/experiments.py index 8f825794..aa5dc3d2 100644 --- a/tsml_eval/utils/experiments.py +++ b/tsml_eval/utils/experiments.py @@ -13,9 +13,11 @@ "fix_broken_second_line", "compare_result_file_resample", "assign_gpu", + "timing_benchmark", ] import os +import time import gpustat import numpy as np @@ -246,7 +248,7 @@ def write_classification_results( Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. + If True, results are written directly to the directory passed in file_path. If False, then a standard file structure using the classifier and dataset names is created and used to write the results file. split : str or None, default=None @@ -368,7 +370,7 @@ def write_regression_results( Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. + If True, results are written directly to the directory passed in file_path. If False, then a standard file structure using the regressor and dataset names is created and used to write the results file. split : str or None, default=None @@ -478,7 +480,7 @@ def write_clustering_results( Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. + If True, results are written directly to the directory passed in file_path. If False, then a standard file structure using the clusterer and dataset names is created and used to write the results file. split : str or None, default=None @@ -585,7 +587,7 @@ def write_forecasting_results( Path to write the results file to or the directory to build the default file structure if full_path is False. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. + If True, results are written directly to the directory passed in file_path. If False, then a standard file structure using the forecaster and dataset names is created and used to write the results file. split : str or None, default=None @@ -673,7 +675,7 @@ def write_results_to_tsml_format( Estimated label probabilities. If passed, these are written after the predicted values for each case. full_path : boolean, default=True - If True, results are written directly to the directory passed in output_path. + If True, results are written directly to the directory passed in file_path. If False, then a standard file structure using the estimator and dataset names is created and used to write the results file. split : str or None, default=None @@ -908,3 +910,44 @@ def assign_gpu(set_environ=False): # pragma: no cover os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu) return gpu + + +def timing_benchmark(num_arrays=1000, array_size=20000, random_state=None): + """ + Measures the time taken to sort a given number of numpy arrays of a specified size. + + Returns the time taken in milliseconds. + + Parameters + ---------- + num_arrays: int, default=1000 + Number of arrays to generate and sort. + array_size: int, default=20000 + Size of each numpy array to be sorted. + random_state: int, RandomState instance or None, default=None + If `int`, random_state is the seed used by the random number generator; + If `RandomState` instance, random_state is the random number generator; + If `None`, the random number generator is the `RandomState` instance used + by `np.random`. + + Returns + ------- + time_taken: int + Time taken to sort the arrays in milliseconds. + """ + if random_state is None: + random_state = check_random_state(0) + elif isinstance(random_state, (int, np.random.RandomState)): + random_state = check_random_state(random_state) + else: + raise ValueError("random_state must be an int, RandomState instance or None") + + total_time = 0 + for _ in range(num_arrays): + array = random_state.rand(array_size) + start_time = time.time() + np.sort(array) + end_time = time.time() + total_time += end_time - start_time + + return int(round(total_time * 1000)) diff --git a/tsml_eval/utils/memory_recorder.py b/tsml_eval/utils/memory_recorder.py index b07b21af..c11b0007 100644 --- a/tsml_eval/utils/memory_recorder.py +++ b/tsml_eval/utils/memory_recorder.py @@ -1,3 +1,5 @@ +"""Utility for recording the maximum memory usage of a function.""" + import time from threading import Thread @@ -7,10 +9,39 @@ def record_max_memory( function, args=None, kwargs=None, interval=0.1, return_func_time=False ): + """ + Record the maximum memory usage of a function. + + Parameters + ---------- + function : function + The function to run. + args : list, default=None + The arguments to pass to the function. + kwargs : dict, default=None + The keyword arguments to pass to the function. + interval : float, default=0.1 + The interval (in seconds) to check the memory usage. + return_func_time : bool, default=False + Whether to return the function's runtime. + + Returns + ------- + max_memory : int + The maximum memory usage (in bytes). + runtime : int, optional + The function's runtime (in milliseconds). + + Examples + -------- + >>> def f(n): + ... return [i for i in range(n)] + >>> max_mem = record_max_memory(f, args=[10000]) + """ process = psutil.Process() start_memory = process.memory_info().rss - thread = FunctionThread(function, args, kwargs) + thread = _FunctionThread(function, args, kwargs) thread.start() max_memory = process.memory_info().rss @@ -29,7 +60,9 @@ def record_max_memory( return max_memory - start_memory -class FunctionThread(Thread): +class _FunctionThread(Thread): + """Thread that runs a function with arguments.""" + def __init__(self, function, args=None, kwargs=None): self.function = function self.args = args if args is not None else [] @@ -38,7 +71,7 @@ def __init__(self, function, args=None, kwargs=None): self.function_time = -1 self.has_shutdown = False - super(FunctionThread, self).__init__(daemon=True) + super(_FunctionThread, self).__init__(daemon=True) def run(self): """Overloads the threading.Thread.run.""" From 1fe7c9e8738db5e41ac3b7184242614e4e17636a Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 16 Nov 2023 11:26:29 +0000 Subject: [PATCH 04/19] bound --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 82924e0c..059cb8ce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,6 +59,8 @@ unstable_extras = [ deep_learning = [ "aeon[dl]", "torch>=1.13.1", + # temp + "pycatch22<=0.4.3", ] dev = [ "pre-commit", From 6d510c3d6abb373608072576af2a81f71097c0f4 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Thu, 16 Nov 2023 11:43:01 +0000 Subject: [PATCH 05/19] fix --- .../distance_based_clustering.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tsml_eval/publications/y2023/distance_based_clustering/distance_based_clustering.ipynb b/tsml_eval/publications/y2023/distance_based_clustering/distance_based_clustering.ipynb index e4efce04..52d6bf0f 100644 --- a/tsml_eval/publications/y2023/distance_based_clustering/distance_based_clustering.ipynb +++ b/tsml_eval/publications/y2023/distance_based_clustering/distance_based_clustering.ipynb @@ -74,7 +74,7 @@ "from aeon.clustering.k_means import TimeSeriesKMeans\n", "from tsml.datasets import load_minimal_chinatown\n", "\n", - "from tsml_eval.evaluation.metrics import clustering_accuracy\n", + "from tsml_eval.evaluation.metrics import clustering_accuracy_score\n", "from tsml_eval.publications.y2023.distance_based_clustering import (\n", " _set_distance_clusterer,\n", ")" @@ -166,7 +166,7 @@ ], "source": [ "# build a TSF classifier and make predictions\n", - "km = TimeSeriesKMeans(metric=\"dtw\", n_clusters=2, random_state=0)\n", + "km = TimeSeriesKMeans(distance=\"dtw\", n_clusters=2, random_state=0)\n", "km.fit(X_train)\n", "km.predict(X_test)" ], @@ -250,8 +250,8 @@ " clusterer.fit(X_train)\n", " test_cl = clusterer.predict(X_test)\n", "\n", - " cl_acc_train.append(clustering_accuracy(y_train, clusterer.labels_))\n", - " cl_acc_test.append(clustering_accuracy(y_test, test_cl))\n", + " cl_acc_train.append(clustering_accuracy_score(y_train, clusterer.labels_))\n", + " cl_acc_test.append(clustering_accuracy_score(y_test, test_cl))\n", "\n", "print(cl_acc_train)\n", "print(cl_acc_test)" From 92e75acbd4c6f8e5473530ceb41e86a86e547ff5 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 00:14:13 +0000 Subject: [PATCH 06/19] cd dias --- .../evaluation/multiple_estimator_evaluation.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 247926ab..a8f6111b 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -1,6 +1,7 @@ """Functions for evaluating multiple estimators on multiple datasets.""" import os +import pickle from datetime import datetime import numpy as np @@ -793,7 +794,17 @@ def _figures_for_statistic( cd = plot_critical_difference(scores, estimators, errors=not higher_better) cd.savefig( - f"{save_path}/{statistic_name}/figures/{statistic_name}_critical_difference.png" + f"{save_path}/{statistic_name}/figures/" + f"{statistic_name}_critical_difference.png", + bbox_inches="tight", + ) + pickle.dump( + cd, + open( + f"{save_path}/{statistic_name}/figures/" + f"{statistic_name}_critical_difference.pickle", + "wb", + ), ) From 188cd3350d4818210f0e30da6cad0bdacfe0c1ab Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 10:58:40 +0000 Subject: [PATCH 07/19] time fix and resamples --- examples/images/cd_diagram.png | Bin 0 -> 10050 bytes .../evaluation/multiple_estimator_evaluation.py | 4 ++-- tsml_eval/utils/memory_recorder.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 examples/images/cd_diagram.png diff --git 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variable_name, save_path, ): - os.makedirs(f"{save_path}/{statistic_name}/all_folds/", exist_ok=True) + os.makedirs(f"{save_path}/{statistic_name}/all_resamples/", exist_ok=True) average_stats = np.zeros((len(datasets), len(estimators))) @@ -754,7 +754,7 @@ def _create_directory_for_statistic( average_stats[n, i] = np.mean(est_stats[n, :]) with open( - f"{save_path}/{statistic_name}/all_folds/{estimator_name}_" + f"{save_path}/{statistic_name}/all_resamples/{estimator_name}_" f"{statistic_name}.csv", "w", ) as file: diff --git a/tsml_eval/utils/memory_recorder.py b/tsml_eval/utils/memory_recorder.py index c11b0007..194ed9ce 100644 --- a/tsml_eval/utils/memory_recorder.py +++ b/tsml_eval/utils/memory_recorder.py @@ -77,6 +77,6 @@ def run(self): """Overloads the threading.Thread.run.""" start = int(round(time.time() * 1000)) self.function(*self.args, **self.kwargs) - end = int(round(time.time() * 1000)) - start + end = int(round(time.time() * 1000)) self.function_time = end - start self.has_shutdown = True From 384bb3be315696f363b03fa80bf2108fdc77ac76 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 11:53:17 +0000 Subject: [PATCH 08/19] remade test results --- tsml_eval/experiments/set_classifier.py | 44 +- .../Predictions/Chinatown/testResample0.csv | 4 +- .../Predictions/Chinatown/testResample1.csv | 4 +- .../Predictions/Chinatown/testResample2.csv | 4 +- .../Predictions/Chinatown/trainResample0.csv | 4 +- .../Predictions/Chinatown/trainResample1.csv | 4 +- .../Predictions/Chinatown/trainResample2.csv | 4 +- .../ItalyPowerDemand/testResample0.csv | 4 +- .../ItalyPowerDemand/testResample1.csv | 4 +- .../ItalyPowerDemand/testResample2.csv | 4 +- .../ItalyPowerDemand/trainResample0.csv | 4 +- .../ItalyPowerDemand/trainResample1.csv | 4 +- .../ItalyPowerDemand/trainResample2.csv | 4 +- .../Predictions/Trace/testResample0.csv | 4 +- .../Predictions/Trace/testResample1.csv | 4 +- .../Predictions/Trace/testResample2.csv | 4 +- .../Predictions/Trace/trainResample0.csv | 103 ++++ .../Predictions/Trace/trainResample1.csv | 103 ++++ .../Predictions/Trace/trainResample2.csv | 103 ++++ .../Predictions/Chinatown/testResample0.csv | 4 +- .../Predictions/Chinatown/testResample1.csv | 4 +- .../Predictions/Chinatown/testResample2.csv | 4 +- .../Predictions/Chinatown/trainResample0.csv | 4 +- .../Predictions/Chinatown/trainResample1.csv | 4 +- .../Predictions/Chinatown/trainResample2.csv | 4 +- .../ItalyPowerDemand/testResample0.csv | 4 +- .../ItalyPowerDemand/testResample1.csv | 4 +- .../ItalyPowerDemand/testResample2.csv | 4 +- .../ItalyPowerDemand/trainResample0.csv | 4 +- .../ItalyPowerDemand/trainResample1.csv | 4 +- .../ItalyPowerDemand/trainResample2.csv | 4 +- .../Predictions/Trace/testResample0.csv | 4 +- .../Predictions/Trace/testResample1.csv | 4 +- .../Predictions/Trace/testResample2.csv | 4 +- .../Predictions/Trace/trainResample0.csv | 103 ++++ .../Predictions/Trace/trainResample1.csv | 103 ++++ .../Predictions/Trace/trainResample2.csv | 103 ++++ .../Predictions/Chinatown/testResample0.csv | 6 +- .../Predictions/Chinatown/testResample1.csv | 6 +- .../Predictions/Chinatown/testResample2.csv | 6 +- .../Predictions/Chinatown/trainResample0.csv | 23 + .../Predictions/Chinatown/trainResample1.csv | 23 + .../Predictions/Chinatown/trainResample2.csv | 23 + .../ItalyPowerDemand/testResample0.csv | 6 +- .../ItalyPowerDemand/testResample1.csv | 6 +- .../ItalyPowerDemand/testResample2.csv | 6 +- .../ItalyPowerDemand/trainResample0.csv | 70 +++ .../ItalyPowerDemand/trainResample1.csv | 70 +++ .../ItalyPowerDemand/trainResample2.csv | 70 +++ .../TSF/Predictions/Trace/testResample0.csv | 6 +- .../TSF/Predictions/Trace/testResample1.csv | 6 +- .../TSF/Predictions/Trace/testResample2.csv | 6 +- .../TSF/Predictions/Trace/trainResample0.csv | 103 ++++ .../TSF/Predictions/Trace/trainResample1.csv | 103 ++++ .../TSF/Predictions/Trace/trainResample2.csv | 103 ++++ .../Predictions/Chinatown/testResample0.csv | 4 +- .../Predictions/Chinatown/testResample1.csv | 4 +- .../Predictions/Chinatown/testResample2.csv | 4 +- .../Predictions/Chinatown/trainResample0.csv | 4 +- .../Predictions/Chinatown/trainResample1.csv | 4 +- .../Predictions/Chinatown/trainResample2.csv | 4 +- .../ItalyPowerDemand/testResample0.csv | 4 +- .../ItalyPowerDemand/testResample1.csv | 4 +- .../ItalyPowerDemand/testResample2.csv | 4 +- .../ItalyPowerDemand/trainResample0.csv | 4 +- .../ItalyPowerDemand/trainResample1.csv | 4 +- .../ItalyPowerDemand/trainResample2.csv | 4 +- .../Predictions/Trace/testResample0.csv | 103 ++++ .../Predictions/Trace/testResample1.csv | 103 ++++ .../Predictions/Trace/testResample2.csv | 103 ++++ .../Predictions/Trace/trainResample0.csv | 4 +- .../Predictions/Trace/trainResample1.csv | 4 +- .../Predictions/Trace/trainResample2.csv | 4 +- .../Predictions/Chinatown/testResample0.csv | 4 +- .../Predictions/Chinatown/testResample1.csv | 4 +- .../Predictions/Chinatown/testResample2.csv | 4 +- .../Predictions/Chinatown/trainResample0.csv | 4 +- .../Predictions/Chinatown/trainResample1.csv | 4 +- .../Predictions/Chinatown/trainResample2.csv | 4 +- .../ItalyPowerDemand/testResample0.csv | 4 +- .../ItalyPowerDemand/testResample1.csv | 4 +- .../ItalyPowerDemand/testResample2.csv | 4 +- .../ItalyPowerDemand/trainResample0.csv | 4 +- .../ItalyPowerDemand/trainResample1.csv | 4 +- .../ItalyPowerDemand/trainResample2.csv | 4 +- .../Predictions/Trace/testResample0.csv | 103 ++++ .../Predictions/Trace/testResample1.csv | 103 ++++ .../Predictions/Trace/testResample2.csv | 103 ++++ .../Predictions/Trace/trainResample0.csv | 4 +- .../Predictions/Trace/trainResample1.csv | 4 +- .../Predictions/Trace/trainResample2.csv | 4 +- .../Predictions/Chinatown/testResample0.csv | 4 +- .../Predictions/Chinatown/testResample1.csv | 4 +- .../Predictions/Chinatown/testResample2.csv | 4 +- .../Predictions/Chinatown/trainResample0.csv | 4 +- .../Predictions/Chinatown/trainResample1.csv | 4 +- .../Predictions/Chinatown/trainResample2.csv | 4 +- .../ItalyPowerDemand/testResample0.csv | 4 +- .../ItalyPowerDemand/testResample1.csv | 4 +- .../ItalyPowerDemand/testResample2.csv | 4 +- .../ItalyPowerDemand/trainResample0.csv | 4 +- .../ItalyPowerDemand/trainResample1.csv | 4 +- .../ItalyPowerDemand/trainResample2.csv | 4 +- .../Predictions/Trace/testResample0.csv | 103 ++++ .../Predictions/Trace/testResample1.csv | 103 ++++ .../Predictions/Trace/testResample2.csv | 103 ++++ .../Predictions/Trace/trainResample0.csv | 4 +- .../Predictions/Trace/trainResample1.csv | 4 +- .../Predictions/Trace/trainResample2.csv | 4 +- .../Predictions/Airline/testResample0.csv | 4 +- .../ShampooSales/testResample0.csv | 4 +- .../Predictions/Airline/testResample0.csv | 4 +- .../ShampooSales/testResample0.csv | 4 +- .../Predictions/Airline/testResample0.csv | 4 +- .../ShampooSales/testResample0.csv | 4 +- .../Predictions/Covid3Month/testResample0.csv | 4 +- .../Predictions/Covid3Month/testResample1.csv | 4 +- .../Predictions/Covid3Month/testResample2.csv | 4 +- .../Covid3Month/trainResample0.csv | 4 +- .../Covid3Month/trainResample1.csv | 4 +- .../Covid3Month/trainResample2.csv | 4 +- .../FloodModeling1/testResample0.csv | 4 +- .../FloodModeling1/testResample1.csv | 4 +- .../FloodModeling1/testResample2.csv | 4 +- .../FloodModeling1/trainResample0.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample1.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample2.csv | 474 ++++++++++++++++++ .../testResample0.csv | 4 +- .../testResample1.csv | 4 +- .../testResample2.csv | 4 +- .../trainResample0.csv | 4 +- .../trainResample1.csv | 4 +- .../trainResample2.csv | 4 +- .../Predictions/Covid3Month/testResample0.csv | 4 +- .../Predictions/Covid3Month/testResample1.csv | 4 +- .../Predictions/Covid3Month/testResample2.csv | 4 +- .../Covid3Month/trainResample0.csv | 4 +- .../Covid3Month/trainResample1.csv | 4 +- .../Covid3Month/trainResample2.csv | 4 +- .../FloodModeling1/testResample0.csv | 4 +- .../FloodModeling1/testResample1.csv | 4 +- .../FloodModeling1/testResample2.csv | 4 +- .../FloodModeling1/trainResample0.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample1.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample2.csv | 474 ++++++++++++++++++ .../testResample0.csv | 4 +- .../testResample1.csv | 4 +- .../testResample2.csv | 4 +- .../trainResample0.csv | 4 +- .../trainResample1.csv | 4 +- .../trainResample2.csv | 4 +- .../Predictions/Covid3Month/testResample0.csv | 4 +- .../Predictions/Covid3Month/testResample1.csv | 4 +- .../Predictions/Covid3Month/testResample2.csv | 4 +- .../Covid3Month/trainResample0.csv | 4 +- .../Covid3Month/trainResample1.csv | 4 +- .../Covid3Month/trainResample2.csv | 4 +- .../FloodModeling1/testResample0.csv | 6 +- .../FloodModeling1/testResample1.csv | 6 +- .../FloodModeling1/testResample2.csv | 6 +- .../FloodModeling1/trainResample0.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample1.csv | 474 ++++++++++++++++++ .../FloodModeling1/trainResample2.csv | 474 ++++++++++++++++++ .../testResample0.csv | 4 +- .../testResample1.csv | 4 +- .../testResample2.csv | 4 +- .../trainResample0.csv | 4 +- .../trainResample1.csv | 4 +- .../trainResample2.csv | 4 +- 169 files changed, 6717 insertions(+), 290 deletions(-) create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample2.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample0.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample1.csv create mode 100644 tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample2.csv diff --git a/tsml_eval/experiments/set_classifier.py b/tsml_eval/experiments/set_classifier.py index b4d1cd20..ef0ffb47 100644 --- a/tsml_eval/experiments/set_classifier.py +++ b/tsml_eval/experiments/set_classifier.py @@ -517,7 +517,11 @@ def _set_classifier_interval_based( ) return RandomIntervalSpectralEnsembleClassifier( - n_estimators=500, random_state=random_state, n_jobs=n_jobs, **kwargs + n_estimators=500, + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "randomintervalspectralensembleclassifier" or c == "rise": from aeon.classification.interval_based import ( @@ -525,43 +529,67 @@ def _set_classifier_interval_based( ) return RandomIntervalSpectralEnsembleClassifier( - random_state=random_state, n_jobs=n_jobs, **kwargs + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "tsf-500": from aeon.classification.interval_based import TimeSeriesForestClassifier return TimeSeriesForestClassifier( - n_estimators=500, random_state=random_state, n_jobs=n_jobs, **kwargs + n_estimators=500, + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "timeseriesforestclassifier" or c == "tsf": from aeon.classification.interval_based import TimeSeriesForestClassifier return TimeSeriesForestClassifier( - random_state=random_state, n_jobs=n_jobs, **kwargs + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "cif-500": from aeon.classification.interval_based import CanonicalIntervalForestClassifier return CanonicalIntervalForestClassifier( - n_estimators=500, random_state=random_state, n_jobs=n_jobs, **kwargs + n_estimators=500, + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "canonicalintervalforestclassifier" or c == "cif": from aeon.classification.interval_based import CanonicalIntervalForestClassifier return CanonicalIntervalForestClassifier( - random_state=random_state, n_jobs=n_jobs, **kwargs + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "stsf-500": from aeon.classification.interval_based import SupervisedTimeSeriesForest return SupervisedTimeSeriesForest( - n_estimators=500, random_state=random_state, n_jobs=n_jobs, **kwargs + n_estimators=500, + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "supervisedtimeseriesforest" or c == "stsf": from aeon.classification.interval_based import SupervisedTimeSeriesForest return SupervisedTimeSeriesForest( - random_state=random_state, n_jobs=n_jobs, **kwargs + random_state=random_state, + save_transformed_data=build_train_file, + n_jobs=n_jobs, + **kwargs, ) elif c == "drcif-500": from aeon.classification.interval_based import DrCIFClassifier diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv index 025f57ab..b13c07a4 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:32. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9737609329446064,0,205,-1,-1,2,,-1,-1 +0.9737609329446064,0,342,1098,45056,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample1.csv index 201579a7..49e4f2a9 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:28. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9358600583090378,1,256,-1,-1,2,,-1,-1 +0.9358600583090378,1,565,1296,49152,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv index b734091f..96088682 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:04. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9154518950437318,1,244,-1,-1,2,,-1,-1 +0.9154518950437318,1,592,1185,49152,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv index 9c847d7b..9fabc5f5 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample0.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:32. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.95,0,-1,-1,-1,2,,9,9 +0.95,0,-1,1098,-1,2,,23,23 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv index 6020d7f8..384979a3 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample1.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:28. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -1.0,1,-1,-1,-1,2,,11,12 +1.0,1,-1,1296,-1,2,,12,13 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv index 1e3ab97e..085c8260 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Chinatown/trainResample2.csv @@ -1,6 +1,6 @@ -Chinatown,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:04. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.85,1,-1,-1,-1,2,,10,11 +0.85,1,-1,1185,-1,2,,12,13 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv index b229d2bf..86c35fc5 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:41. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:10:41. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9504373177842566,0,534,-1,-1,2,,-1,-1 +0.9504373177842566,0,486,777,40960,2,,-1,-1 1,1,,0.0,1.0 1,1,,0.0,1.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv index 6ff1b291..5fd57db5 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:44. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9329446064139941,1,440,-1,-1,2,,-1,-1 +0.9329446064139941,1,797,1272,86016,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv index 786282fa..de657b85 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:46. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9378036929057337,0,462,-1,-1,2,,-1,-1 +0.9378036929057337,0,769,1054,45056,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv index de7a536f..5c0aedea 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:41. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:10:41. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9552238805970149,0,-1,-1,-1,2,,24,24 +0.9552238805970149,0,-1,777,-1,2,,22,22 0,0,,1.0,0.0 0,0,,1.0,0.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv index 8d374478..10064e5a 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:44. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.8507462686567164,1,-1,-1,-1,2,,23,24 +0.8507462686567164,1,-1,1272,-1,2,,41,42 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv index e36eae41..b29bb259 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/ItalyPowerDemand/trainResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:06:46. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -0.9701492537313433,0,-1,-1,-1,2,,22,22 +0.9701492537313433,0,-1,1054,-1,2,,28,28 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv index dd8cf40e..0b18e352 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample0.csv @@ -1,6 +1,6 @@ -Trace,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:17. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,1NN-DTW,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:23. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -1.0,0,3698,-1,-1,4,,-1,-1 +1.0,1,3582,982,40960,4,,-1,-1 2,2,,0.0,0.0,1.0,0.0 0,0,,1.0,0.0,0.0,0.0 2,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv index cbe8ce9a..32c27979 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample1.csv @@ -1,6 +1,6 @@ -Trace,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,1NN-DTW,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:25. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -1.0,1,3638,-1,-1,4,,-1,-1 +1.0,2,3904,984,57344,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv index 2058ed93..0ddd5bf4 100644 --- a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/testResample2.csv @@ -1,6 +1,6 @@ -Trace,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:19:21. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,1NN-DTW,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} -1.0,1,3642,-1,-1,4,,-1,-1 +1.0,1,3711,936,73728,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..920941c8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:23. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,1,-1,982,-1,4,,3187,3188 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..496c7c67 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:25. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,2,-1,984,-1,4,,2909,2911 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..1a5d0fec --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/1NN-DTW/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,1NN-DTW,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'distance': 'dtw', 'distance_params': None, 'n_jobs': 1, 'n_neighbors': 1, 'weights': 'uniform'} +1.0,1,-1,936,-1,4,,3005,3006 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv index 3ed0a314..dd0492b8 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} -0.9795918367346939,330,780,-1,-1,2,,-1,-1 +0.9795918367346939,3863,612,889,3674112,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv index af347917..ae0a4643 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} -0.9620991253644315,329,805,-1,-1,2,,-1,-1 +0.9620991253644315,3867,611,896,3584000,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv index 63fb2c49..ebf90c50 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} -0.967930029154519,329,785,-1,-1,2,,-1,-1 +0.967930029154519,3587,617,1188,3665920,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv index 3c99a3cf..05fd086c 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample0.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} -0.95,330,-1,-1,-1,2,,696,1026 +0.95,3863,-1,889,-1,2,,589,4452 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv index fd271e49..7a9f164c 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample1.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} -1.0,329,-1,-1,-1,2,,705,1034 +1.0,3867,-1,896,-1,2,,606,4473 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv index 8174fc70..0b9448b3 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Chinatown/trainResample2.csv @@ -1,6 +1,6 @@ -Chinatown,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:02. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} -0.95,329,-1,-1,-1,2,,696,1025 +0.95,3587,-1,1188,-1,2,,572,4159 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv index 6a305dd8..7fe3749d 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:13. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} -0.9698736637512148,220,1846,-1,-1,2,,-1,-1 +0.9698736637512148,221,2501,844,3584000,2,,-1,-1 1,1,,0.0,1.0 1,1,,0.0,1.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv index 37c9254e..790c7b86 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:08. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} -0.9689018464528668,161,1927,-1,-1,2,,-1,-1 +0.9689018464528668,226,2593,861,3198976,2,,-1,-1 0,1,,0.0,1.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv index 4cf173f8..b667db2b 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:06. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} -0.9484936831875608,258,1910,-1,-1,2,,-1,-1 +0.9484936831875608,230,2620,874,3469312,2,,-1,-1 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv index 0fe7955e..435abd7c 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:13. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} -0.9701492537313433,220,-1,-1,-1,2,,1676,1896 +0.9701492537313433,221,-1,844,-1,2,,1898,2119 0,0,,1.0,0.0 0,0,,1.0,0.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv index ff9a33a3..5b24fcb5 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:08. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} -0.9552238805970149,161,-1,-1,-1,2,,1730,1891 +0.9552238805970149,226,-1,861,-1,2,,1871,2097 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv index a7914290..bc26e9c8 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/ItalyPowerDemand/trainResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/11/2023, 00:05:06. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} -0.9850746268656716,258,-1,-1,-1,2,,1688,1946 +0.9850746268656716,230,-1,874,-1,2,,1862,2092 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv index 8d6f78e5..2bcbb9e6 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample0.csv @@ -1,6 +1,6 @@ -Trace,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,ROCKET,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} -1.0,1987,1880,-1,-1,4,,-1,-1 +1.0,3276,1839,1200,7909376,4,,-1,-1 2,2,,0.0,0.0,1.0,0.0 0,0,,1.0,0.0,0.0,0.0 2,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv index ce87a13f..4148cf5e 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample1.csv @@ -1,6 +1,6 @@ -Trace,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:29. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,ROCKET,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} -1.0,1941,1995,-1,-1,4,,-1,-1 +1.0,3355,1918,1192,6717440,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv index f7867250..835c3428 100644 --- a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/testResample2.csv @@ -1,6 +1,6 @@ -Trace,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:32. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,ROCKET,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} -1.0,2060,2094,-1,-1,4,,-1,-1 +1.0,3287,1850,1181,6819840,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..da725087 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket'} +1.0,3276,-1,1200,-1,4,,18084,21360 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..c7459be8 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket'} +1.0,3355,-1,1192,-1,4,,17850,21205 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..3a610754 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/ROCKET/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,ROCKET,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:05:31. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket'} +1.0,3287,-1,1181,-1,4,,17793,21080 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv index 78936eb5..99c8a91e 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:32. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.9766763848396501,759,608,-1,-1,2,,-1,-1 +Chinatown,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:10. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9766763848396501,747,468,785,24551424,2,,-1,-1 0,0,,0.81,0.19 0,0,,0.775,0.225 0,0,,0.93,0.07 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv index 759c97ae..b90d1608 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:36. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.967930029154519,706,537,-1,-1,2,,-1,-1 +Chinatown,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:07. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.967930029154519,696,520,773,25460736,2,,-1,-1 0,0,,0.97,0.03 0,0,,0.975,0.025 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample2.csv index f8e46488..7e579b4c 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:39. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.9766763848396501,924,566,-1,-1,2,,-1,-1 +Chinatown,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:04. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9766763848396501,780,471,760,25210880,2,,-1,-1 0,0,,0.995,0.005 0,0,,0.93,0.07 0,0,,0.705,0.295 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample0.csv new file mode 100644 index 00000000..6e2fc33e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample0.csv @@ -0,0 +1,23 @@ +Chinatown,TSF,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:10. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.95,747,-1,785,-1,2,,209,956 +0,0,,0.7397260273972602,0.2602739726027397 +0,0,,0.7792207792207793,0.22077922077922077 +0,0,,0.8235294117647058,0.17647058823529413 +0,0,,0.8227848101265823,0.17721518987341772 +0,0,,0.625,0.375 +0,0,,0.7638888888888888,0.2361111111111111 +0,0,,0.8235294117647058,0.17647058823529413 +0,0,,0.746268656716418,0.2537313432835821 +0,0,,0.782051282051282,0.21794871794871795 +0,0,,0.8028169014084507,0.19718309859154928 +1,1,,0.25316455696202533,0.7468354430379747 +1,1,,0.05454545454545454,0.9454545454545454 +1,1,,0.11904761904761904,0.8809523809523809 +1,1,,0.05,0.95 +1,1,,0.12345679012345678,0.8765432098765432 +1,0,,0.582089552238806,0.417910447761194 +1,1,,0.1095890410958904,0.8904109589041096 +1,1,,0.0958904109589041,0.9041095890410958 +1,1,,0.14285714285714285,0.8571428571428571 +1,1,,0.10810810810810811,0.8918918918918919 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample1.csv new file mode 100644 index 00000000..9e87b094 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample1.csv @@ -0,0 +1,23 @@ +Chinatown,TSF,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:07. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.95,696,-1,773,-1,2,,198,894 +0,0,,0.9166666666666666,0.08333333333333333 +0,0,,0.9833333333333333,0.016666666666666666 +0,0,,0.7916666666666666,0.20833333333333334 +0,0,,0.9705882352941176,0.029411764705882353 +0,0,,0.55,0.45 +0,0,,0.6617647058823529,0.3382352941176471 +0,0,,0.9117647058823529,0.08823529411764706 +0,0,,0.8554216867469879,0.14457831325301204 +0,0,,0.9295774647887324,0.07042253521126761 +0,0,,0.859375,0.140625 +1,0,,0.5070422535211268,0.49295774647887325 +1,1,,0.04,0.96 +1,1,,0.045454545454545456,0.9545454545454546 +1,1,,0.06349206349206349,0.9365079365079365 +1,1,,0.08974358974358974,0.9102564102564102 +1,1,,0.0945945945945946,0.9054054054054054 +1,1,,0.030303030303030304,0.9696969696969697 +1,1,,0.0967741935483871,0.9032258064516129 +1,1,,0.012195121951219513,0.9878048780487805 +1,1,,0.13846153846153847,0.8615384615384616 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample2.csv new file mode 100644 index 00000000..b711f8b7 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Chinatown/trainResample2.csv @@ -0,0 +1,23 @@ +Chinatown,TSF,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:04. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.95,780,-1,760,-1,2,,189,969 +0,0,,0.8918918918918919,0.10810810810810811 +0,0,,0.8169014084507042,0.18309859154929578 +0,0,,0.9239130434782609,0.07608695652173914 +0,0,,0.9833333333333333,0.016666666666666666 +0,0,,0.835820895522388,0.16417910447761194 +0,0,,0.8658536585365854,0.13414634146341464 +0,0,,0.95,0.05 +0,0,,0.6428571428571429,0.35714285714285715 +0,0,,0.8571428571428571,0.14285714285714285 +0,0,,0.863013698630137,0.136986301369863 +1,1,,0.09722222222222222,0.9027777777777778 +1,1,,0.03488372093023256,0.9651162790697675 +1,1,,0.027777777777777776,0.9722222222222222 +1,1,,0.2,0.8 +1,1,,0.05357142857142857,0.9464285714285714 +1,1,,0.3,0.7 +1,1,,0.0,1.0 +1,0,,0.7375,0.2625 +1,1,,0.07692307692307693,0.9230769230769231 +1,1,,0.05555555555555555,0.9444444444444444 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample0.csv index 7b217660..751c8f0b 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:17. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.9620991253644315,941,2136,-1,-1,2,,-1,-1 +ItalyPowerDemand,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:16. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9620991253644315,869,1891,793,26046464,2,,-1,-1 1,1,,0.105,0.895 1,1,,0.125,0.875 1,1,,0.05,0.95 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample1.csv index 1e408cc6..a7682e2d 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:13. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.9582118561710399,943,2630,-1,-1,2,,-1,-1 +ItalyPowerDemand,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:18. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9582118561710399,822,2364,800,26648576,2,,-1,-1 0,1,,0.135,0.865 0,0,,0.96,0.04 0,0,,0.965,0.035 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample2.csv index 6eeb8ef6..bc1ec6c2 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:17:11. Encoder dictionary: {1.0: 0, 2.0: 1} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.9358600583090378,966,2490,-1,-1,2,,-1,-1 +ItalyPowerDemand,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:20. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9358600583090378,786,2353,772,26279936,2,,-1,-1 0,0,,0.975,0.025 0,0,,0.985,0.015 0,0,,0.66,0.34 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample0.csv new file mode 100644 index 00000000..c9d9c8e1 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample0.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,TSF,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:16. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9402985074626866,869,-1,793,-1,2,,335,1204 +0,0,,0.9344262295081968,0.06557377049180328 +0,0,,0.9452054794520548,0.0547945205479452 +1,1,,0.21666666666666667,0.7833333333333333 +1,1,,0.028985507246376812,0.9710144927536232 +0,0,,1.0,0.0 +0,1,,0.1506849315068493,0.8493150684931506 +1,1,,0.0,1.0 +0,0,,0.9733333333333334,0.02666666666666667 +0,0,,0.9634146341463414,0.036585365853658534 +1,1,,0.0375,0.9625 +1,1,,0.012048192771084338,0.9879518072289156 +0,0,,0.9402985074626866,0.05970149253731343 +0,0,,1.0,0.0 +1,1,,0.21518987341772153,0.7848101265822784 +0,0,,1.0,0.0 +1,1,,0.3048780487804878,0.6951219512195121 +0,0,,0.9324324324324325,0.06756756756756757 +0,0,,0.9154929577464789,0.08450704225352113 +1,1,,0.0,1.0 +0,0,,1.0,0.0 +0,0,,0.9879518072289156,0.012048192771084338 +1,1,,0.029850746268656716,0.9701492537313433 +0,0,,0.9871794871794872,0.01282051282051282 +0,0,,0.9428571428571428,0.05714285714285714 +0,0,,1.0,0.0 +0,0,,0.8533333333333334,0.14666666666666667 +0,0,,0.961038961038961,0.03896103896103896 +1,1,,0.0,1.0 +1,1,,0.06349206349206349,0.9365079365079365 +0,0,,0.9294117647058824,0.07058823529411765 +0,0,,0.9863013698630136,0.0136986301369863 +1,0,,0.7027027027027027,0.2972972972972973 +1,1,,0.014705882352941176,0.9852941176470589 +0,0,,0.9404761904761905,0.05952380952380952 +1,1,,0.015873015873015872,0.9841269841269841 +1,1,,0.0,1.0 +0,0,,0.9859154929577465,0.014084507042253521 +1,1,,0.01282051282051282,0.9871794871794872 +0,0,,0.9571428571428572,0.04285714285714286 +1,1,,0.12698412698412698,0.873015873015873 +0,0,,0.9367088607594937,0.06329113924050633 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.028985507246376812,0.9710144927536232 +0,0,,1.0,0.0 +1,1,,0.0,1.0 +1,1,,0.325,0.675 +1,1,,0.0,1.0 +1,0,,0.5064935064935064,0.4935064935064935 +0,0,,0.782608695652174,0.21739130434782608 +0,0,,1.0,0.0 +1,1,,0.04054054054054054,0.9594594594594594 +1,1,,0.14035087719298245,0.8596491228070176 +1,1,,0.012345679012345678,0.9876543209876543 +0,0,,0.9512195121951219,0.04878048780487805 +1,1,,0.325,0.675 +1,1,,0.056338028169014086,0.9436619718309859 +0,0,,1.0,0.0 +0,0,,0.881578947368421,0.11842105263157894 +1,1,,0.1774193548387097,0.8225806451612904 +1,1,,0.013888888888888888,0.9861111111111112 +0,0,,1.0,0.0 +0,0,,0.8356164383561644,0.1643835616438356 +1,1,,0.09230769230769231,0.9076923076923077 +0,1,,0.48717948717948717,0.5128205128205128 +1,1,,0.013513513513513514,0.9864864864864865 +1,1,,0.1625,0.8375 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample1.csv new file mode 100644 index 00000000..5eeb0ed0 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample1.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,TSF,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:18. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9104477611940298,822,-1,800,-1,2,,268,1090 +0,0,,1.0,0.0 +0,0,,0.8823529411764706,0.11764705882352941 +0,0,,0.958904109589041,0.0410958904109589 +0,0,,0.6911764705882353,0.3088235294117647 +0,0,,0.9411764705882353,0.058823529411764705 +0,0,,0.9710144927536232,0.028985507246376812 +0,0,,0.9538461538461539,0.046153846153846156 +0,0,,1.0,0.0 +0,0,,0.8939393939393939,0.10606060606060606 +0,0,,0.703125,0.296875 +0,0,,0.9473684210526315,0.05263157894736842 +0,0,,0.974025974025974,0.025974025974025976 +0,0,,0.639344262295082,0.36065573770491804 +0,0,,0.9868421052631579,0.013157894736842105 +0,1,,0.18309859154929578,0.8169014084507042 +0,0,,0.9761904761904762,0.023809523809523808 +0,0,,0.961038961038961,0.03896103896103896 +0,0,,0.8405797101449275,0.15942028985507245 +0,0,,0.9444444444444444,0.05555555555555555 +0,1,,0.4126984126984127,0.5873015873015873 +0,0,,0.5753424657534246,0.4246575342465753 +0,0,,0.7971014492753623,0.2028985507246377 +0,0,,0.6666666666666666,0.3333333333333333 +0,0,,0.8571428571428571,0.14285714285714285 +0,0,,0.717948717948718,0.28205128205128205 +0,0,,0.875,0.125 +0,0,,0.8382352941176471,0.16176470588235295 +0,0,,0.9682539682539683,0.031746031746031744 +0,0,,0.9365079365079365,0.06349206349206349 +0,0,,0.7654320987654321,0.2345679012345679 +0,0,,0.7638888888888888,0.2361111111111111 +0,0,,0.975,0.025 +0,0,,0.9230769230769231,0.07692307692307693 +0,0,,0.9538461538461539,0.046153846153846156 +1,1,,0.0,1.0 +1,1,,0.028169014084507043,0.971830985915493 +1,1,,0.08064516129032258,0.9193548387096774 +1,1,,0.027777777777777776,0.9722222222222222 +1,1,,0.04225352112676056,0.9577464788732394 +1,1,,0.05,0.95 +1,1,,0.16049382716049382,0.8395061728395061 +1,1,,0.0,1.0 +1,0,,0.7972972972972973,0.20270270270270271 +1,1,,0.012658227848101266,0.9873417721518988 +1,0,,0.6621621621621622,0.33783783783783783 +1,0,,0.684931506849315,0.3150684931506849 +1,1,,0.07042253521126761,0.9295774647887324 +1,1,,0.2054794520547945,0.7945205479452054 +1,1,,0.2222222222222222,0.7777777777777778 +1,1,,0.1388888888888889,0.8611111111111112 +1,1,,0.02531645569620253,0.9746835443037974 +1,1,,0.14814814814814814,0.8518518518518519 +1,1,,0.2112676056338028,0.7887323943661971 +1,0,,0.8082191780821918,0.1917808219178082 +1,1,,0.06666666666666667,0.9333333333333333 +1,1,,0.13333333333333333,0.8666666666666667 +1,1,,0.24324324324324326,0.7567567567567568 +1,1,,0.060240963855421686,0.9397590361445783 +1,1,,0.03488372093023256,0.9651162790697675 +1,1,,0.075,0.925 +1,1,,0.011904761904761904,0.9880952380952381 +1,1,,0.07058823529411765,0.9294117647058824 +1,1,,0.22666666666666666,0.7733333333333333 +1,1,,0.0,1.0 +1,1,,0.3157894736842105,0.6842105263157895 +1,1,,0.0,1.0 +1,1,,0.25,0.75 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample2.csv new file mode 100644 index 00000000..1173d7de --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/ItalyPowerDemand/trainResample2.csv @@ -0,0 +1,70 @@ +ItalyPowerDemand,TSF,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:20. Encoder dictionary: {1.0: 0, 2.0: 1} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.9850746268656716,786,-1,772,-1,2,,270,1056 +0,0,,0.963855421686747,0.03614457831325301 +0,0,,0.6756756756756757,0.32432432432432434 +0,0,,0.8985507246376812,0.10144927536231885 +0,0,,0.9873417721518988,0.012658227848101266 +0,0,,0.9866666666666667,0.013333333333333334 +0,0,,0.9770114942528736,0.022988505747126436 +0,0,,1.0,0.0 +0,0,,0.96,0.04 +0,0,,0.8311688311688312,0.16883116883116883 +0,0,,0.9710144927536232,0.028985507246376812 +0,0,,0.96,0.04 +0,0,,0.9285714285714286,0.07142857142857142 +0,0,,0.9431818181818182,0.056818181818181816 +0,0,,1.0,0.0 +0,0,,0.8253968253968254,0.1746031746031746 +0,0,,1.0,0.0 +0,0,,0.9583333333333334,0.041666666666666664 +0,0,,0.9428571428571428,0.05714285714285714 +0,0,,0.9,0.1 +0,0,,0.5657894736842105,0.4342105263157895 +0,0,,1.0,0.0 +0,0,,0.9482758620689655,0.05172413793103448 +0,0,,0.9444444444444444,0.05555555555555555 +0,0,,0.9861111111111112,0.013888888888888888 +0,0,,1.0,0.0 +0,0,,0.9859154929577465,0.014084507042253521 +0,0,,0.9864864864864865,0.013513513513513514 +0,0,,0.9506172839506173,0.04938271604938271 +0,0,,0.9736842105263158,0.02631578947368421 +0,0,,1.0,0.0 +0,0,,0.987012987012987,0.012987012987012988 +0,0,,1.0,0.0 +0,0,,0.9264705882352942,0.07352941176470588 +0,0,,0.9855072463768116,0.014492753623188406 +1,1,,0.3142857142857143,0.6857142857142857 +1,1,,0.04477611940298507,0.9552238805970149 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,1,,0.12121212121212122,0.8787878787878788 +1,1,,0.027777777777777776,0.9722222222222222 +1,1,,0.012987012987012988,0.987012987012987 +1,1,,0.0,1.0 +1,1,,0.0,1.0 +1,0,,0.68,0.32 +1,1,,0.24,0.76 +1,1,,0.038461538461538464,0.9615384615384616 +1,1,,0.0136986301369863,0.9863013698630136 +1,1,,0.025974025974025976,0.974025974025974 +1,1,,0.07792207792207792,0.922077922077922 +1,1,,0.0273972602739726,0.9726027397260274 +1,1,,0.03278688524590164,0.9672131147540983 +1,1,,0.11688311688311688,0.8831168831168831 +1,1,,0.07246376811594203,0.927536231884058 +1,1,,0.2222222222222222,0.7777777777777778 +1,1,,0.027777777777777776,0.9722222222222222 +1,1,,0.014925373134328358,0.9850746268656716 +1,1,,0.13636363636363635,0.8636363636363636 +1,1,,0.05970149253731343,0.9402985074626866 +1,1,,0.0125,0.9875 +1,1,,0.04411764705882353,0.9558823529411765 +1,1,,0.014285714285714285,0.9857142857142858 +1,1,,0.0,1.0 +1,1,,0.08571428571428572,0.9142857142857143 +1,1,,0.057971014492753624,0.9420289855072463 +1,1,,0.0,1.0 +1,1,,0.015151515151515152,0.9848484848484849 +1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv index 5ee9b511..f458018d 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample0.csv @@ -1,6 +1,6 @@ -Trace,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:53. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} -1.0,2035,689,-1,-1,4,,-1,-1 +Trace,TSF,TEST,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:32. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,1682,593,755,33943552,4,,-1,-1 2,2,,0.0,0.0,0.86,0.14 0,0,,1.0,0.0,0.0,0.0 2,2,,0.0,0.0,0.965,0.035 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv index 09bdf5ee..816b7776 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample1.csv @@ -1,6 +1,6 @@ -Trace,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:42. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} -1.0,2160,704,-1,-1,4,,-1,-1 +Trace,TSF,TEST,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:29. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,1772,652,857,33386496,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 0,0,,0.99,0.01,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv index 8f688235..dc2af1a1 100644 --- a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/testResample2.csv @@ -1,6 +1,6 @@ -Trace,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/10/2023, 14:18:39. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} -1.0,2051,649,-1,-1,4,,-1,-1 +Trace,TSF,TEST,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,2043,608,793,32518144,4,,-1,-1 0,0,,1.0,0.0,0.0,0.0 0,0,,0.945,0.025,0.03,0.0 0,0,,0.995,0.005,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample0.csv new file mode 100644 index 00000000..fa3544cf --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample0.csv @@ -0,0 +1,103 @@ +Trace,TSF,TRAIN,0,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:32. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,1682,-1,755,-1,4,,383,2065 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.04,0.96,0.0,0.0 +3,3,,0.0,0.0,0.05,0.95 +2,2,,0.0,0.0,0.9857142857142858,0.014285714285714285 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.013888888888888888,0.0,0.9861111111111112 +3,3,,0.0,0.0,0.014492753623188406,0.9855072463768116 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.8641975308641975,0.0,0.12345679012345678,0.012345679012345678 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.023809523809523808,0.9761904761904762 +3,3,,0.0,0.0,0.013888888888888888,0.9861111111111112 +3,3,,0.0,0.0,0.0125,0.9875 +2,2,,0.0,0.0,0.9876543209876543,0.012345679012345678 +3,3,,0.0,0.0,0.029411764705882353,0.9705882352941176 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.013157894736842105,0.9868421052631579 +3,3,,0.0,0.0,0.013157894736842105,0.9868421052631579 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.04938271604938271,0.0,0.6172839506172839,0.3333333333333333 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.07692307692307693,0.9230769230769231,0.0,0.0 +3,3,,0.0,0.0,0.013513513513513514,0.9864864864864865 +3,3,,0.0,0.014925373134328358,0.0,0.9850746268656716 +1,1,,0.015151515151515152,0.9848484848484849,0.0,0.0 +3,3,,0.0,0.0,0.011904761904761904,0.9880952380952381 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.08571428571428572,0.9142857142857143 +3,3,,0.0,0.024390243902439025,0.024390243902439025,0.9512195121951219 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.01282051282051282,0.9871794871794872,0.0,0.0 +0,0,,0.9577464788732394,0.0,0.04225352112676056,0.0 +0,0,,0.90625,0.09375,0.0,0.0 +2,2,,0.012658227848101266,0.012658227848101266,0.7974683544303798,0.17721518987341772 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9506172839506173,0.04938271604938271 +2,2,,0.013888888888888888,0.0,0.9722222222222222,0.013888888888888888 +2,2,,0.011235955056179775,0.0,0.9887640449438202,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.01639344262295082,0.9836065573770492 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,0.9866666666666667,0.013333333333333334 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.12987012987012986,0.8701298701298701,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,0.9836065573770492,0.01639344262295082,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.9583333333333334,0.041666666666666664 +1,1,,0.0821917808219178,0.9178082191780822,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,2,,0.015384615384615385,0.0,0.9846153846153847,0.0 +2,2,,0.0,0.0,0.9876543209876543,0.012345679012345678 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,0,,0.9846153846153847,0.015384615384615385,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.02564102564102564,0.9743589743589743 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,0.984375,0.015625 +1,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.013157894736842105,0.9342105263157895,0.05263157894736842 +3,3,,0.0,0.0,0.015625,0.984375 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,0.927710843373494,0.07228915662650602 +2,2,,0.0,0.0,0.9411764705882353,0.058823529411764705 +0,0,,0.9333333333333333,0.02666666666666667,0.02666666666666667,0.013333333333333334 +2,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,2,,0.013513513513513514,0.0,0.9864864864864865,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9710144927536232,0.028985507246376812 +3,3,,0.0,0.0,0.028985507246376812,0.9710144927536232 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.014084507042253521,0.9859154929577465,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,0.835820895522388,0.14925373134328357,0.0,0.014925373134328358 +2,2,,0.025,0.0125,0.8625,0.1 +0,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.014492753623188406,0.043478260869565216,0.028985507246376812,0.9130434782608695 +0,0,,0.6470588235294118,0.35294117647058826,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample1.csv new file mode 100644 index 00000000..bbd0ff69 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample1.csv @@ -0,0 +1,103 @@ +Trace,TSF,TRAIN,1,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:29. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,1772,-1,857,-1,4,,385,2157 +0,0,,0.927710843373494,0.07228915662650602,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9855072463768116,0.0,0.014492753623188406,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9696969696969697,0.015151515151515152,0.015151515151515152,0.0 +0,0,,0.8194444444444444,0.027777777777777776,0.1527777777777778,0.0 +0,0,,0.9850746268656716,0.014925373134328358,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.984375,0.015625,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.8873239436619719,0.056338028169014086,0.04225352112676056,0.014084507042253521 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+2,2,,0.0,0.0,0.8142857142857143,0.18571428571428572 +2,2,,0.0,0.0,0.9722222222222222,0.027777777777777776 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.012345679012345678,0.0,0.8271604938271605,0.16049382716049382 +2,2,,0.037037037037037035,0.0,0.9135802469135802,0.04938271604938271 +3,3,,0.0,0.0,0.014285714285714285,0.9857142857142858 +3,3,,0.0,0.0,0.025,0.975 +3,3,,0.0,0.0,0.04938271604938271,0.9506172839506173 +3,3,,0.0,0.0,0.013333333333333334,0.9866666666666667 +3,3,,0.0,0.0,0.014705882352941176,0.9852941176470589 +3,3,,0.013888888888888888,0.027777777777777776,0.1111111111111111,0.8472222222222222 +3,3,,0.0,0.0,0.08108108108108109,0.918918918918919 +3,3,,0.0,0.0,0.02631578947368421,0.9736842105263158 +3,3,,0.0,0.0,0.012987012987012988,0.987012987012987 +3,3,,0.0,0.0,0.03614457831325301,0.963855421686747 +3,3,,0.0,0.0,0.03125,0.96875 +3,3,,0.0,0.0,0.037037037037037035,0.9629629629629629 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.03614457831325301,0.963855421686747 +3,3,,0.0,0.0,0.04054054054054054,0.9594594594594594 +3,3,,0.0,0.0,0.014705882352941176,0.9852941176470589 +3,3,,0.0,0.0,0.04,0.96 +3,3,,0.0,0.0,0.012987012987012988,0.987012987012987 +3,3,,0.0,0.0,0.02857142857142857,0.9714285714285714 +3,3,,0.0,0.0,0.014925373134328358,0.9850746268656716 +3,3,,0.0,0.0,0.05263157894736842,0.9473684210526315 +3,3,,0.0,0.0,0.15384615384615385,0.8461538461538461 +3,3,,0.0,0.0,0.05084745762711865,0.9491525423728814 +3,3,,0.0,0.0,0.014925373134328358,0.9850746268656716 +3,3,,0.0,0.0,0.02564102564102564,0.9743589743589743 +3,3,,0.0,0.0,0.028169014084507043,0.971830985915493 +3,3,,0.0,0.0,0.029850746268656716,0.9701492537313433 +3,3,,0.0,0.0,0.1044776119402985,0.8955223880597015 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0375,0.9625 +3,3,,0.0,0.0,0.04411764705882353,0.9558823529411765 diff --git a/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample2.csv new file mode 100644 index 00000000..3089dba6 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/classification/TSF/Predictions/Trace/trainResample2.csv @@ -0,0 +1,103 @@ +Trace,TSF,TRAIN,2,MILLISECONDS,PREDICTIONS,Generated by run_classification_experiment on 11/17/2023, 11:14:27. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +1.0,2043,-1,793,-1,4,,388,2431 +0,0,,0.9878048780487805,0.012195121951219513,0.0,0.0 +0,0,,0.9864864864864865,0.013513513513513514,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9880952380952381,0.0,0.011904761904761904,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9871794871794872,0.01282051282051282,0.0,0.0 +0,0,,0.8676470588235294,0.1323529411764706,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.961038961038961,0.025974025974025976,0.012987012987012988,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9710144927536232,0.028985507246376812,0.0,0.0 +0,0,,0.7428571428571429,0.2571428571428571,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9836065573770492,0.01639344262295082,0.0,0.0 +0,0,,0.9824561403508771,0.017543859649122806,0.0,0.0 +0,0,,0.9722222222222222,0.027777777777777776,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,0.9850746268656716,0.014925373134328358,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.2,0.8,0.0,0.0 +1,1,,0.013333333333333334,0.9733333333333334,0.013333333333333334,0.0 +1,1,,0.0125,0.9625,0.025,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.014925373134328358,0.9850746268656716,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.15714285714285714,0.8428571428571429,0.0,0.0 +1,1,,0.014705882352941176,0.9411764705882353,0.04411764705882353,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.3375,0.6375,0.025,0.0 +1,1,,0.047619047619047616,0.9523809523809523,0.0,0.0 +1,1,,0.3181818181818182,0.6818181818181818,0.0,0.0 +1,1,,0.05555555555555555,0.9444444444444444,0.0,0.0 +1,1,,0.025,0.975,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.015151515151515152,0.9848484848484849,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.013513513513513514,0.972972972972973,0.013513513513513514,0.0 +1,1,,0.012658227848101266,0.9873417721518988,0.0,0.0 +1,1,,0.013888888888888888,0.9861111111111112,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9743589743589743,0.02564102564102564 +2,2,,0.0,0.0,0.9210526315789473,0.07894736842105263 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9512195121951219,0.04878048780487805 +2,2,,0.014492753623188406,0.0,0.9710144927536232,0.014492753623188406 +2,2,,0.0,0.0,0.9466666666666667,0.05333333333333334 +2,2,,0.014285714285714285,0.0,0.9714285714285714,0.014285714285714285 +2,2,,0.0,0.0,0.8059701492537313,0.19402985074626866 +2,2,,0.0,0.0,0.9545454545454546,0.045454545454545456 +2,2,,0.014705882352941176,0.0,0.9705882352941176,0.014705882352941176 +2,2,,0.0,0.0,0.9625,0.0375 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9857142857142858,0.014285714285714285 +2,2,,0.03076923076923077,0.046153846153846156,0.7076923076923077,0.2153846153846154 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.012658227848101266,0.0,0.9746835443037974,0.012658227848101266 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,0.9864864864864865,0.013513513513513514 +2,2,,0.0,0.0,0.7678571428571429,0.23214285714285715 +2,2,,0.0,0.0,0.984375,0.015625 +3,3,,0.0,0.0,0.013157894736842105,0.9868421052631579 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.03225806451612903,0.967741935483871 +3,3,,0.0,0.0,0.036585365853658534,0.9634146341463414 +3,3,,0.0,0.0,0.0125,0.9875 +3,3,,0.0,0.0,0.023529411764705882,0.9764705882352941 +3,3,,0.013333333333333334,0.02666666666666667,0.36,0.6 +3,3,,0.0,0.0,0.014492753623188406,0.9855072463768116 +3,3,,0.0,0.0,0.05555555555555555,0.9444444444444444 +3,3,,0.0,0.0,0.02857142857142857,0.9714285714285714 +3,3,,0.0,0.0,0.012048192771084338,0.9879518072289156 +3,3,,0.0,0.0,0.11627906976744186,0.8837209302325582 +3,3,,0.0,0.0,0.023255813953488372,0.9767441860465116 +3,3,,0.0,0.0,0.05714285714285714,0.9428571428571428 +3,3,,0.0,0.0,0.05333333333333334,0.9466666666666667 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.028985507246376812,0.9710144927536232 +3,3,,0.0,0.0,0.01282051282051282,0.9871794871794872 +3,3,,0.0,0.0,0.014705882352941176,0.9852941176470589 +3,3,,0.0,0.0,0.012987012987012988,0.987012987012987 +3,3,,0.0,0.0,0.01282051282051282,0.9871794871794872 +3,3,,0.0,0.0,0.013333333333333334,0.9866666666666667 +3,3,,0.0,0.0,0.012987012987012988,0.987012987012987 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.012987012987012988,0.987012987012987 +3,3,,0.0,0.0,0.0136986301369863,0.9863013698630136 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.013157894736842105,0.9868421052631579 +3,3,,0.0,0.0,0.09090909090909091,0.9090909090909091 +3,3,,0.0,0.0,0.036585365853658534,0.9634146341463414 +3,3,,0.0,0.0,0.0136986301369863,0.9863013698630136 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv index 9024e681..35f05651 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:34. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:13. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.7317784256559767,-1700006613954,7,842,31289344,2,2 +0.7317784256559767,404,2,869,32210944,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample1.csv index 9fef734e..7354309d 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:30. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:10. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.7230320699708455,-1700006610010,2,858,31801344,2,2 +0.7230320699708455,329,2,768,32350208,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample2.csv index 04a416f9..406d737f 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:27. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:08. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.7988338192419825,-1700006607550,2,775,31412224,2,2 +0.7988338192419825,573,8,793,32387072,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv index fc67bfeb..1599d208 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:34. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:13. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.65,-1700006613954,1,842,31289344,2,2 +0.65,404,0,869,32210944,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv index 7cdb8450..44085046 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:30. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:10. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.6,-1700006610010,0,858,31801344,2,2 +0.6,329,0,768,32350208,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv index 75f961cd..7dd57ac3 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Chinatown/trainResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:27. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:08. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.85,-1700006607550,0,775,31412224,2,2 +0.85,573,0,793,32387072,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv index 029afbc4..535be6eb 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:12. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:34. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.5276967930029155,-1700006588326,5,856,45379584,2,2 +0.5276967930029155,960,7,794,32481280,2,2 1,0,,1.0,0.0 1,1,,0.0,1.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv index e0c2a67e..28a2d80c 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:15. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.5189504373177842,-1700006593298,6,781,44728320,2,2 +0.5189504373177842,690,9,818,31383552,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv index 07bf0c48..b7b450a2 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:17. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:29. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.5354713313896987,-1700006596769,6,759,31227904,2,2 +0.5354713313896987,977,6,938,31375360,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv index 4f9c6dfe..6b5c352b 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:12. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:34. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.5373134328358209,-1700006588326,0,856,45379584,2,2 +0.5373134328358209,960,0,794,32481280,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv index 0fdbdee7..9c6fbf35 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:15. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:31. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.5074626865671642,-1700006593298,0,781,44728320,2,2 +0.5074626865671642,690,0,818,31383552,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv index 05ed1585..8bf7d673 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/ItalyPowerDemand/trainResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:17. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:29. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.582089552238806,-1700006596769,0,759,31227904,2,2 +0.582089552238806,977,0,938,31375360,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample0.csv new file mode 100644 index 00000000..dac00b1a --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.8,43611,139,795,33312768,4,4 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..5d177919 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:21. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.77,58724,159,771,31068160,4,4 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..f3a392d5 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans-dtw,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:24. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.77,58702,167,791,31375360,4,4 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv index bd40e926..1384e216 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample0.csv @@ -1,6 +1,6 @@ -Trace,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:50. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-dtw,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.79,-1700006589602,144,823,31612928,4,4 +0.79,43611,142,795,33312768,4,4 0,2,,0.0,0.0,1.0,0.0 1,0,,1.0,0.0,0.0,0.0 3,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv index 50d03b87..4b80f23d 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample1.csv @@ -1,6 +1,6 @@ -Trace,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:54. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-dtw,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:21. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.77,-1700006579984,126,912,31813632,4,4 +0.77,58724,194,771,31068160,4,4 0,1,,0.0,1.0,0.0,0.0 0,1,,0.0,1.0,0.0,0.0 0,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv index 8f707e38..52e43d0b 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-dtw/Predictions/Trace/trainResample2.csv @@ -1,6 +1,6 @@ -Trace,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:03:57. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-dtw,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:24. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'dtw', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.74,-1700006583515,133,770,30945280,4,4 +0.74,58702,148,791,31375360,4,4 0,3,,0.0,0.0,0.0,1.0 0,3,,0.0,0.0,0.0,1.0 0,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv index c584cade..e5cbd022 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:08. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.7813411078717201,-1700006767950,4,1253,35344384,2,2 +0.7813411078717201,759,3,1092,36085760,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample1.csv index 7aff1a52..e02e76d8 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:12. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:25. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.7288629737609329,-1700006771548,8,1056,36016128,2,2 +0.7288629737609329,590,2,864,36720640,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample2.csv index c207c3de..c74b0ed0 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:26. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.8950437317784257,-1700006773773,2,1275,35799040,2,2 +0.8950437317784257,667,2,958,37388288,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv index 2bda1000..c9204daa 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:08. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:20. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.7,-1700006767950,2,1253,35344384,2,2 +0.7,759,0,1092,36085760,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv index 9290c870..776779f3 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:12. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:25. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.55,-1700006771548,1,1056,36016128,2,2 +0.55,590,0,864,36720640,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv index e1c1bdb3..6bf65faf 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Chinatown/trainResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:14. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:25:26. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.75,-1700006773773,0,1275,35799040,2,2 +0.75,667,0,958,37388288,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv index 895e0f46..04985aa9 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:31. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:41. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.5170068027210885,-1700006790414,14,928,35729408,2,2 +0.5170068027210885,1073,9,915,36339712,2,2 1,0,,1.0,0.0 1,1,,0.0,1.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv index e05e0384..5dd67ed2 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:30. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:43. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.5199222546161322,-1700006789322,7,909,35213312,2,2 +0.5199222546161322,640,6,856,36519936,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv index a9b942c0..de4e5aae 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:27. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:44. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.9475218658892128,-1700006786391,6,1280,35540992,2,2 +0.9475218658892128,661,7,859,36048896,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv index 71a6f96b..ec47c6ec 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:31. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:41. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.5223880597014925,-1700006790414,2,928,35729408,2,2 +0.5223880597014925,1073,0,915,36339712,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv index 2981a60a..4fe53502 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:30. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:43. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.5223880597014925,-1700006789322,0,909,35213312,2,2 +0.5223880597014925,640,0,856,36519936,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv index bfaf623f..408f0b99 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/ItalyPowerDemand/trainResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:06:27. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:44. Encoder dictionary: {1.0: 0, 2.0: 1} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.9850746268656716,-1700006786391,1,1280,35540992,2,2 +0.9850746268656716,661,0,859,36048896,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample0.csv new file mode 100644 index 00000000..12808c4b --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:55. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} +0.55,39438,113,834,39043072,4,4 +2,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..a9188a84 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:53. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} +0.55,47708,122,863,38297600,4,4 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..aa94f52c --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans-msm,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:51. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} +0.55,48152,116,928,38756352,4,4 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv index f24e6cc1..02c17277 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample0.csv @@ -1,6 +1,6 @@ -Trace,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:49. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-msm,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:55. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 0, 'tol': 1e-06, 'verbose': False} -0.57,-1700006689679,155,822,38416384,4,4 +0.57,39438,129,834,39043072,4,4 0,2,,0.0,0.0,1.0,0.0 1,1,,0.0,1.0,0.0,0.0 3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv index fd8d1e7a..e0e82561 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample1.csv @@ -1,6 +1,6 @@ -Trace,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:46. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-msm,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:53. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0.53,-1700006674978,131,921,56549376,4,4 +0.53,47708,118,863,38297600,4,4 0,0,,1.0,0.0,0.0,0.0 0,1,,0.0,1.0,0.0,0.0 0,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv index 3c7bf431..2e769137 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans-msm/Predictions/Trace/trainResample2.csv @@ -1,6 +1,6 @@ -Trace,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:05:41. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans-msm,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:24:51. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'average_params': None, 'averaging_method': 'ba', 'distance': 'msm', 'distance_params': None, 'init_algorithm': 'random', 'max_iter': 300, 'n_clusters': 8, 'n_init': 10, 'random_state': 2, 'tol': 1e-06, 'verbose': False} -0.59,-1700006665628,127,855,72638464,4,4 +0.59,48152,136,928,38756352,4,4 0,3,,0.0,0.0,0.0,1.0 0,1,,0.0,1.0,0.0,0.0 0,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv index 13b9a944..c64fadb5 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:43. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:46. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} -0.7230320699708455,-1700006443520,18,798,1474560,2,2 +0.7230320699708455,109,6,784,1380352,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample1.csv index 823101a5..dfeba325 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:46. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:48. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} -0.7142857142857143,-1700006445933,1,754,1687552,2,2 +0.7142857142857143,92,5,808,1241088,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample2.csv index de835b3c..2ebe6724 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/testResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:48. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:50. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} -0.7376093294460642,-1700006448251,1,769,1945600,2,2 +0.7376093294460642,87,1,771,2105344,2,2 0,0,,1.0,0.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv index 6d588832..a333622c 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample0.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:43. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:46. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} -0.65,-1700006443520,0,798,1474560,2,2 +0.65,109,0,784,1380352,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv index a04d8f1d..b7f042fe 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample1.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:46. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:48. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} -0.65,-1700006445933,0,754,1687552,2,2 +0.65,92,0,808,1241088,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv index 00333a0b..fdb54225 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Chinatown/trainResample2.csv @@ -1,6 +1,6 @@ -Chinatown,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:00:48. Encoder dictionary: {1.0: 0, 2.0: 1} +Chinatown,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:50. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} -0.65,-1700006448251,0,769,1945600,2,2 +0.65,87,0,771,2105344,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv index 7180084e..746266f9 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:06. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:23. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} -0.5208940719144801,-1700006466919,2,838,1421312,2,2 +0.5208940719144801,93,2,820,1327104,2,2 1,1,,0.0,1.0 1,0,,1.0,0.0 1,0,,1.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv index b26e0872..3d708497 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:02. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:25. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} -0.511175898931001,-1700006462919,2,767,1175552,2,2 +0.511175898931001,87,2,764,1196032,2,2 0,1,,0.0,1.0 0,0,,1.0,0.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv index e196089c..cb5e3258 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/testResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:00. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:27. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} -0.9659863945578231,-1700006460133,1,768,1212416,2,2 +0.9659863945578231,86,6,752,1781760,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv index 3f85dd82..04ce5503 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample0.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:06. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:23. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} -0.5522388059701493,-1700006466919,0,838,1421312,2,2 +0.5522388059701493,93,0,820,1327104,2,2 0,0,,1.0,0.0 0,0,,1.0,0.0 1,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv index e9ecb33b..285556f2 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample1.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:02. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:25. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} -0.5223880597014925,-1700006462919,0,767,1175552,2,2 +0.5223880597014925,87,0,764,1196032,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv index ed557573..faaaa8fa 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/ItalyPowerDemand/trainResample2.csv @@ -1,6 +1,6 @@ -ItalyPowerDemand,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/15/2023, 00:01:00. Encoder dictionary: {1.0: 0, 2.0: 1} +ItalyPowerDemand,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:27. Encoder dictionary: {1.0: 0, 2.0: 1} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} -0.9850746268656716,-1700006460133,0,768,1212416,2,2 +0.9850746268656716,86,0,752,1781760,2,2 0,1,,0.0,1.0 0,1,,0.0,1.0 0,1,,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample0.csv new file mode 100644 index 00000000..2db68261 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample0.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TEST,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:41. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} +0.56,112,1,770,1671168,4,4 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +1,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +3,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample1.csv new file mode 100644 index 00000000..66addc08 --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample1.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TEST,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:37. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} +0.57,100,1,785,1441792,4,4 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,0,,1.0,0.0,0.0,0.0 +0,0,,1.0,0.0,0.0,0.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +0,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +1,3,,0.0,0.0,0.0,1.0 +1,3,,0.0,0.0,0.0,1.0 +1,0,,1.0,0.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,1,,0.0,1.0,0.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +2,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,2,,0.0,0.0,1.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 +3,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample2.csv new file mode 100644 index 00000000..1d8cdf5c --- /dev/null +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/testResample2.csv @@ -0,0 +1,103 @@ +Trace,KMeans,TEST,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:36. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +{'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} +0.54,91,1,1048,1499136,4,4 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,1,,0.0,1.0,0.0,0.0 +0,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,2,,0.0,0.0,1.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +1,1,,0.0,1.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,0,,1.0,0.0,0.0,0.0 +2,0,,1.0,0.0,0.0,0.0 +2,3,,0.0,0.0,0.0,1.0 +2,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,0,,1.0,0.0,0.0,0.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 +3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv index 4c724e32..b474f1f8 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample0.csv @@ -1,6 +1,6 @@ -Trace,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:00. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans,TRAIN,0,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:41. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 0, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=0), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 0} -0.57,-1700006339683,0,780,1634304,4,4 +0.57,112,0,770,1671168,4,4 0,0,,1.0,0.0,0.0,0.0 1,2,,0.0,0.0,1.0,0.0 3,3,,0.0,0.0,0.0,1.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv index 50d598e9..08fe2696 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample1.csv @@ -1,6 +1,6 @@ -Trace,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:17. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans,TRAIN,1,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:37. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 1, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=1), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 1} -0.52,-1700006356942,0,818,1478656,4,4 +0.52,100,0,785,1441792,4,4 0,3,,0.0,0.0,0.0,1.0 0,0,,1.0,0.0,0.0,0.0 0,0,,1.0,0.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv index 5a1e1eb7..2976db55 100644 --- a/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/clustering/KMeans/Predictions/Trace/trainResample2.csv @@ -1,6 +1,6 @@ -Trace,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/14/2023, 23:59:19. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} +Trace,KMeans,TRAIN,2,MILLISECONDS,Generated by run_clustering_experiment on 11/17/2023, 11:23:36. Encoder dictionary: {1.0: 0, 2.0: 1, 3.0: 2, 4.0: 3} {'clusterer__algorithm': 'lloyd', 'clusterer__copy_x': True, 'clusterer__init': 'k-means++', 'clusterer__max_iter': 300, 'clusterer__n_clusters': 8, 'clusterer__n_init': 'warn', 'clusterer__random_state': 2, 'clusterer__tol': 0.0001, 'clusterer__verbose': 0, 'clusterer': KMeans(random_state=2), 'concatenate_channels': True, 'pad_unequal': True, 'random_state': 2} -0.57,-1700006359724,0,778,1433600,4,4 +0.57,91,0,1048,1499136,4,4 0,1,,0.0,1.0,0.0,0.0 0,2,,0.0,0.0,1.0,0.0 0,1,,0.0,1.0,0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/Airline/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/Airline/testResample0.csv index 2e23aaaf..3095bef5 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/Airline/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/Airline/testResample0.csv @@ -1,6 +1,6 @@ -Airline,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:58 +Airline,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:36:18 {'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__copy_X': True, 'RecursiveTabularRegressionForecaster__estimator__fit_intercept': True, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__positive': False, 'RecursiveTabularRegressionForecaster__estimator': LinearRegression(n_jobs=1), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} -0.04931900082960808,-1700009578949,7,752,786432 +0.04931900082960808,99,35,783,724992 340.0,343.51466438602256 318.0,324.0668832781254 362.0,377.78979016255755 diff --git a/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv index b4c2b39d..e1f48b13 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/LinearRegression/Predictions/ShampooSales/testResample0.csv @@ -1,6 +1,6 @@ -ShampooSales,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:29 +ShampooSales,LinearRegression,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:36:21 {'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=LinearRegression(n_jobs=1), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__copy_X': True, 'RecursiveTabularRegressionForecaster__estimator__fit_intercept': True, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__positive': False, 'RecursiveTabularRegressionForecaster__estimator': LinearRegression(n_jobs=1), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} -0.2952136312226724,-1700009549777,5,752,798720 +0.2952136312226724,12,5,782,765952 339.7,360.34405087149526 440.4,375.6301808613457 315.9,330.9948497742497 diff --git a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv index 5af98ff6..ed527446 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/Airline/testResample0.csv @@ -1,6 +1,6 @@ -Airline,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:51:54 +Airline,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:36:07 {'sp': 1, 'strategy': 'last', 'window_length': None} -0.19886711926999853,-1700009514187,2,762,102400 +0.19886711926999853,1,1,759,77824 340.0,336.0 318.0,336.0 362.0,336.0 diff --git a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv index 4087352c..b8444623 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/NaiveForecaster/Predictions/ShampooSales/testResample0.csv @@ -1,6 +1,6 @@ -ShampooSales,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/09/2023, 00:04:56 +ShampooSales,NaiveForecaster,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:35:55 {'sp': 1, 'strategy': 'last', 'window_length': None} -0.2603808539887312,2,124,-1,-1 +0.2603808539887312,1,115,775,77824 339.7,342.3 440.4,342.3 315.9,342.3 diff --git a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv index 0f239c15..edeaad95 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/Airline/testResample0.csv @@ -1,6 +1,6 @@ -Airline,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:12 +Airline,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:36:36 {'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__bootstrap': True, 'RecursiveTabularRegressionForecaster__estimator__ccp_alpha': 0.0, 'RecursiveTabularRegressionForecaster__estimator__criterion': 'squared_error', 'RecursiveTabularRegressionForecaster__estimator__max_depth': None, 'RecursiveTabularRegressionForecaster__estimator__max_features': 1.0, 'RecursiveTabularRegressionForecaster__estimator__max_leaf_nodes': None, 'RecursiveTabularRegressionForecaster__estimator__max_samples': None, 'RecursiveTabularRegressionForecaster__estimator__min_impurity_decrease': 0.0, 'RecursiveTabularRegressionForecaster__estimator__min_samples_leaf': 1, 'RecursiveTabularRegressionForecaster__estimator__min_samples_split': 2, 'RecursiveTabularRegressionForecaster__estimator__min_weight_fraction_leaf': 0.0, 'RecursiveTabularRegressionForecaster__estimator__n_estimators': 200, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__oob_score': False, 'RecursiveTabularRegressionForecaster__estimator__random_state': 0, 'RecursiveTabularRegressionForecaster__estimator__verbose': 0, 'RecursiveTabularRegressionForecaster__estimator__warm_start': False, 'RecursiveTabularRegressionForecaster__estimator': RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} -0.051909914023904186,-1700009532020,158,773,2195456 +0.051909914023904186,257,174,768,1761280 340.0,335.44736767269706 318.0,325.90729807939636 362.0,376.6901395438566 diff --git a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv index d0941db0..4d8f8c36 100644 --- a/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/forecasting/RandomForest/Predictions/ShampooSales/testResample0.csv @@ -1,6 +1,6 @@ -ShampooSales,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/15/2023, 00:52:20 +ShampooSales,RandomForest,TEST,0,MILLISECONDS,Generated by run_forecasting_experiment on 11/17/2023, 11:36:31 {'steps': [Detrender(forecaster=PolynomialTrendForecaster()), TabularToSeriesAdaptor(transformer=StandardScaler()), RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15)], 'Detrender': Detrender(forecaster=PolynomialTrendForecaster()), 'TabularToSeriesAdaptor': TabularToSeriesAdaptor(transformer=StandardScaler()), 'RecursiveTabularRegressionForecaster': RecursiveTabularRegressionForecaster(estimator=RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), window_length=15), 'Detrender__forecaster__degree': 1, 'Detrender__forecaster__regressor': None, 'Detrender__forecaster__with_intercept': True, 'Detrender__forecaster': PolynomialTrendForecaster(), 'Detrender__model': 'additive', 'TabularToSeriesAdaptor__fit_in_transform': False, 'TabularToSeriesAdaptor__transformer__copy': True, 'TabularToSeriesAdaptor__transformer__with_mean': True, 'TabularToSeriesAdaptor__transformer__with_std': True, 'TabularToSeriesAdaptor__transformer': StandardScaler(), 'RecursiveTabularRegressionForecaster__estimator__bootstrap': True, 'RecursiveTabularRegressionForecaster__estimator__ccp_alpha': 0.0, 'RecursiveTabularRegressionForecaster__estimator__criterion': 'squared_error', 'RecursiveTabularRegressionForecaster__estimator__max_depth': None, 'RecursiveTabularRegressionForecaster__estimator__max_features': 1.0, 'RecursiveTabularRegressionForecaster__estimator__max_leaf_nodes': None, 'RecursiveTabularRegressionForecaster__estimator__max_samples': None, 'RecursiveTabularRegressionForecaster__estimator__min_impurity_decrease': 0.0, 'RecursiveTabularRegressionForecaster__estimator__min_samples_leaf': 1, 'RecursiveTabularRegressionForecaster__estimator__min_samples_split': 2, 'RecursiveTabularRegressionForecaster__estimator__min_weight_fraction_leaf': 0.0, 'RecursiveTabularRegressionForecaster__estimator__n_estimators': 200, 'RecursiveTabularRegressionForecaster__estimator__n_jobs': 1, 'RecursiveTabularRegressionForecaster__estimator__oob_score': False, 'RecursiveTabularRegressionForecaster__estimator__random_state': 0, 'RecursiveTabularRegressionForecaster__estimator__verbose': 0, 'RecursiveTabularRegressionForecaster__estimator__warm_start': False, 'RecursiveTabularRegressionForecaster__estimator': RandomForestRegressor(n_estimators=200, n_jobs=1, random_state=0), 'RecursiveTabularRegressionForecaster__pooling': 'local', 'RecursiveTabularRegressionForecaster__transformers': None, 'RecursiveTabularRegressionForecaster__window_length': 15} -0.24900813645841494,-1700009539952,56,747,1052672 +0.24900813645841494,143,56,773,1044480 339.7,302.8515104347826 440.4,336.4869356521739 315.9,311.8008417391304 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv index 0a75bf59..0c38775c 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:41 +Covid3Month,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:06 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.0027199984296669712,-1700007701301,293,838,94208,,-1,-1 +0.0027199984296669712,0,386,865,45056,,-1,-1 0.011883802816901408,0.04218472468916518 0.003795066413662239,0.014598540145985401 0.08298755186721991,0.0 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample1.csv index fa6291a8..cd6a435e 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:38 +Covid3Month,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:07 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.004409923607335828,-1700007698314,398,903,90112,,-1,-1 +0.004409923607335828,1,308,794,40960,,-1,-1 0.0,0.10526315789473684 0.002214022140221402,0.0 0.006968641114982578,0.010135135135135136 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample2.csv index c172f33a..975b5d36 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/testResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:36 +Covid3Month,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:09 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.0022079619169376894,-1700007696127,378,889,98304,,-1,-1 +0.0022079619169376894,0,312,752,40960,,-1,-1 0.04003775568724909,0.021014767133661492 0.025668306955712195,0.03926512968299712 0.0078125,0.014598540145985401 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv index 05a79638..83b60307 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:41 +Covid3Month,1NN-DTW,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:06 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.003077656676598191,-1700007701301,-1,838,-1,,222,-1700007701079 +0.003077656676598191,0,-1,865,-1,,248,248 0.0,0.06060606060606061 0.07758620689655173,0.0 0.0,0.0 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample1.csv index 81ca46fb..2e4ed101 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:38 +Covid3Month,1NN-DTW,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:07 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.003154833245693705,-1700007698314,-1,903,-1,,241,-1700007698073 +0.003154833245693705,1,-1,794,-1,,217,218 0.0367504835589942,0.028925619834710745 0.03205128205128205,0.075 0.021220159151193633,0.0 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv index 057c56cf..2488a7f8 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/Covid3Month/trainResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,1NN-DTW,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:21:36 +Covid3Month,1NN-DTW,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:09 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.002920234195645965,-1700007696127,-1,889,-1,,263,-1700007695864 +0.002920234195645965,0,-1,752,-1,,221,221 0.038461538461538464,0.0 0.03926512968299712,0.025588235294117648 0.08068783068783068,0.009584664536741214 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample0.csv index 15549813..26d0c461 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample0.csv @@ -1,6 +1,6 @@ -FloodModeling1,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:23:58 +FloodModeling1,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:46 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.00013119306930693054,-1700007838950,12088,811,110592,,-1,-1 +0.00013119306930693054,0,11822,765,53248,,-1,-1 0.45,0.444 0.447,0.436 0.42700000000000005,0.431 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample1.csv index fe64feb7..07cb8d2e 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample1.csv @@ -1,6 +1,6 @@ -FloodModeling1,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:01 +FloodModeling1,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:44 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.00024490594059405947,-1700007841710,12473,880,102400,,-1,-1 +0.00024490594059405947,0,11308,768,53248,,-1,-1 0.434,0.433 0.425,0.42700000000000005 0.494,0.509 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample2.csv index 6704ee41..5b507ce6 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/FloodModeling1/testResample2.csv @@ -1,6 +1,6 @@ -FloodModeling1,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:05 +FloodModeling1,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:42 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} 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+0.42100000000000004,0.42700000000000005 +0.455,0.462 +0.424,0.42100000000000004 +0.42700000000000005,0.431 +0.46799999999999997,0.467 +0.436,0.42100000000000004 +0.455,0.445 +0.42700000000000005,0.444 +0.418,0.42 +0.425,0.424 +0.436,0.436 +0.452,0.431 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample0.csv index 4fcd8118..ea3ef7f2 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:31 +NaturalGasPricesSentiment,1NN-DTW,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:05 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.006393664041499493,-1700007391521,591,916,102400,,-1,-1 +0.006393664041499493,0,209,984,53248,,-1,-1 -0.3745973154329336,-0.3215639365407137 -0.27649220292280596,-0.29108327604257134 -0.3359852990851952,-0.5024348328319881 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample1.csv index abc72931..8e7b022a 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:34 +NaturalGasPricesSentiment,1NN-DTW,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:08 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.005644255649515693,-1700007394445,216,827,122880,,-1,-1 +0.005644255649515693,1,207,1065,53248,,-1,-1 -0.06869880430973492,-0.07298391111768207 -0.3310506358794816,-0.32933103711559225 -0.328501794888423,-0.2905381690424222 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample2.csv index 021c81f3..2b1fa582 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/testResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:37 +NaturalGasPricesSentiment,1NN-DTW,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:10 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.005336313430765712,-1700007397977,189,872,106496,,-1,-1 +0.005336313430765712,1,253,799,49152,,-1,-1 -0.12363863375324469,-0.1588933546382647 -0.28387368441774286,-0.2904184248011846 -0.3627388186179675,-0.32848593586912517 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample0.csv index 8423f9d1..41c81912 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:31 +NaturalGasPricesSentiment,1NN-DTW,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:05 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.005633071762109407,-1700007391521,-1,916,-1,,16,-1700007391505 +0.005633071762109407,0,-1,984,-1,,13,13 -0.4272497972616782,-0.5024348328319881 -0.3330772297886702,-0.29108327604257134 -0.27904755897246875,-0.3943423883846172 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv index c8b50be0..21703dad 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:34 +NaturalGasPricesSentiment,1NN-DTW,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:08 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.006514222945537523,-1700007394445,-1,827,-1,,14,-1700007394431 +0.006514222945537523,1,-1,1065,-1,,12,13 -0.28843834113616207,-0.5199578351126266 -0.3716620670488247,-0.3456104081983749 -0.07298391111768207,-0.12289009959651871 diff --git a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample2.csv index 7bd6a76c..a1ee0b40 100644 --- a/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/1NN-DTW/Predictions/NaturalGasPricesSentiment/trainResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,1NN-DTW,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:37 +NaturalGasPricesSentiment,1NN-DTW,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:10 {'distance': 'dtw', 'distance_params': {'window': 0.1}, 'n_neighbors': 1, 'weights': 'uniform'} -0.005423783562511139,-1700007397977,-1,872,-1,,13,-1700007397964 +0.005423783562511139,1,-1,799,-1,,18,19 -0.5199578351126266,-0.41383876536901176 -0.3215639365407137,-0.3745973154329336 -0.15584264414814797,-0.11726184109082587 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv index 18566ee0..fb116497 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:41 +Covid3Month,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:42 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.0015126663111567206,-1700007639291,356,1102,4612096,,-1,-1 +0.0015126663111567206,2045,349,812,4300800,,-1,-1 0.011883802816901408,0.01891871685908819 0.003795066413662239,0.01080200649655172 0.08298755186721991,0.023297205297694773 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv index 1c4758d8..cffda16f 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:44 +Covid3Month,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:46 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.0015289716434647286,-1700007643003,365,783,4562944,,-1,-1 +0.0015289716434647286,1588,358,894,4366336,,-1,-1 0.0,0.07113338403983059 0.002214022140221402,0.03218489211584037 0.006968641114982578,0.042411265387673244 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample2.csv index b383e260..2b595756 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/testResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:46 +Covid3Month,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:48 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.0018301454993524258,-1700007644659,446,792,4452352,,-1,-1 +0.0018301454993524258,1642,374,786,4141056,,-1,-1 0.04003775568724909,-0.151447248167403 0.025668306955712195,0.027886829785527673 0.0078125,0.03031311275744844 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample0.csv index d20d968f..f0c74dff 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:41 +Covid3Month,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:42 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.002813211851892337,-1700007639291,-1,1102,-1,,17148,-1700007622143 +0.002813211851892337,2045,-1,812,-1,,16917,18962 0.0,0.0296618508764441 0.07758620689655173,0.0754565941795033 0.0,-0.006636387191769344 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample1.csv index 15252ee8..e4ca0496 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:44 +Covid3Month,ROCKET,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:46 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.00186857456998448,-1700007643003,-1,783,-1,,17327,-1700007625676 +0.00186857456998448,1588,-1,894,-1,,17159,18747 0.0367504835589942,0.009250952031640523 0.03205128205128205,0.03927891429888249 0.021220159151193633,0.023865034510376484 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample2.csv index a0ca9cdb..b2b43c40 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/Covid3Month/trainResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:46 +Covid3Month,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:48 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.002276961255842811,-1700007644659,-1,792,-1,,16987,-1700007627672 +0.002276961255842811,1642,-1,786,-1,,16993,18635 0.038461538461538464,0.07437688666617187 0.03926512968299712,0.049128492870533 0.08068783068783068,0.032070069569347284 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv index 6235370e..a5a01cfd 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample0.csv @@ -1,6 +1,6 @@ -FloodModeling1,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:18 +FloodModeling1,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:18 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.00019258705659831615,-1700007838085,4103,901,50520064,,-1,-1 +0.00019258705659831615,19973,4298,892,50180096,,-1,-1 0.45,0.4428951017017777 0.447,0.44024766072092425 0.42700000000000005,0.43673071020646126 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv index 967a4c65..57cf645b 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample1.csv @@ -1,6 +1,6 @@ -FloodModeling1,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:14 +FloodModeling1,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:12 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.0004281903653824091,-1700007833524,3975,1046,47988736,,-1,-1 +0.0004281903653824091,18724,3918,883,49283072,,-1,-1 0.434,0.44352830001859506 0.425,0.4398790255172182 0.494,0.5101060389775232 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample2.csv index 4bfcedad..74d6214b 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/testResample2.csv @@ -1,6 +1,6 @@ -FloodModeling1,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:11 +FloodModeling1,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:07 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.0002265774966634495,-1700007829191,3880,923,55861248,,-1,-1 +0.0002265774966634495,20466,3861,1107,127455232,,-1,-1 0.451,0.47181819601803116 0.446,0.4357226693640668 0.42700000000000005,0.44952848255382694 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample0.csv new file mode 100644 index 00000000..2ffa1a9e --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/FloodModeling1/trainResample0.csv @@ -0,0 +1,474 @@ +FloodModeling1,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:18 +{'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} +0.0003531647484937418,19973,-1,892,-1,,154814,174787 +0.426,0.4335351095020865 +0.424,0.43153710878986185 +0.42200000000000004,0.42981529248059047 +0.451,0.4441880216469016 +0.428,0.43820154077376205 +0.442,0.4298608442167553 +0.483,0.4655562927082353 +0.435,0.42693958808622345 +0.435,0.4550378679221102 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a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample0.csv index ea4f0bf1..cabfba16 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:03 +NaturalGasPricesSentiment,ROCKET,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:42 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.008392555993079687,-1700007362253,83,796,4575232,,-1,-1 +0.008392555993079687,310,59,911,3915776,,-1,-1 -0.3745973154329336,-0.315432353273626 -0.27649220292280596,-0.20552725396703111 -0.3359852990851952,-0.30337277066180235 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample1.csv index e3e569ea..ce5047cf 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:10 +NaturalGasPricesSentiment,ROCKET,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:44 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.008943107571809389,-1700007370343,106,787,4485120,,-1,-1 +0.008943107571809389,281,62,837,4112384,,-1,-1 -0.06869880430973492,-0.22998553482152959 -0.3310506358794816,-0.25952912217459845 -0.328501794888423,-0.31401904012991955 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample2.csv index 260e6a90..68104a52 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/testResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:13 +NaturalGasPricesSentiment,ROCKET,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:46 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.006343102045309365,-1700007373507,76,854,4317184,,-1,-1 +0.006343102045309365,272,72,803,3973120,,-1,-1 -0.12363863375324469,-0.2824037699946651 -0.28387368441774286,-0.3181336904710754 -0.3627388186179675,-0.31517423643317344 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample0.csv index 773209dd..9e928a9f 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:03 +NaturalGasPricesSentiment,ROCKET,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:42 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 0, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.00870341203373147,-1700007362253,-1,796,-1,,3060,-1700007359193 +0.00870341203373147,310,-1,911,-1,,2791,3101 -0.4272497972616782,-0.26846957662551674 -0.3330772297886702,-0.32824666151417725 -0.27904755897246875,-0.3605367777519264 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample1.csv index ec6e7450..bf85703b 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:10 +NaturalGasPricesSentiment,ROCKET,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:44 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 1, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.009778602090463495,-1700007370343,-1,787,-1,,3853,-1700007366490 +0.009778602090463495,281,-1,837,-1,,3123,3404 -0.28843834113616207,-0.3709752003975283 -0.3716620670488247,-0.3225483754475697 -0.07298391111768207,-0.3775412052579303 diff --git a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv index 6eea155a..b5ee68de 100644 --- a/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/ROCKET/Predictions/NaturalGasPricesSentiment/trainResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:16:13 +NaturalGasPricesSentiment,ROCKET,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:46 {'estimator': None, 'max_dilations_per_kernel': 32, 'n_features_per_kernel': 4, 'n_jobs': 1, 'num_kernels': 10000, 'random_state': 2, 'rocket_transform': 'rocket', 'use_multivariate': 'auto'} -0.011333943564240391,-1700007373507,-1,854,-1,,3285,-1700007370222 +0.011333943564240391,272,-1,803,-1,,3210,3482 -0.5199578351126266,-0.28152186125908024 -0.3215639365407137,-0.23219737296818604 -0.15584264414814797,-0.27337520335728216 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample0.csv index c5686dfa..67697c0f 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:31 +Covid3Month,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:56 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0016445301992041238,-1700007628363,270,929,33886208,,-1,-1 +0.0016445301992041238,2514,287,868,34398208,,-1,-1 0.011883802816901408,0.026445338330364613 0.003795066413662239,0.027436010797557166 0.08298755186721991,0.02533176698674866 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv index 65ef047a..951d8bfb 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:28 +Covid3Month,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:54 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0013619652197387026,-1700007625407,343,805,33710080,,-1,-1 +0.0013619652197387026,2513,257,922,35065856,,-1,-1 0.0,0.07596352816580666 0.002214022140221402,0.031158938188908546 0.006968641114982578,0.025077431128716104 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv index fb3e7101..48c4b325 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/testResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:25 +Covid3Month,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:52 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0011462550188405171,-1700007622481,290,791,34246656,,-1,-1 +0.0011462550188405171,2421,230,810,34828288,,-1,-1 0.04003775568724909,0.03912589097540546 0.025668306955712195,0.03248295224260966 0.0078125,0.01699045214013797 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample0.csv index 8fa6ea5d..0107b112 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample0.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:31 +Covid3Month,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:56 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.001566027774057378,-1700007628363,-1,929,-1,,1493,-1700007626870 +0.001566027774057378,2514,-1,868,-1,,1375,3889 0.0,0.03712074183596225 0.07758620689655173,0.029990022376458834 0.0,0.024851177200936186 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample1.csv index 95bb130b..0e9ef1cc 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample1.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:28 +Covid3Month,TSF,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:54 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0018055441404281145,-1700007625407,-1,805,-1,,1213,-1700007624194 +0.0018055441404281145,2513,-1,922,-1,,1417,3930 0.0367504835589942,0.031358900200363284 0.03205128205128205,0.02517444332933096 0.021220159151193633,0.027927201631437828 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv index b0be5b02..0e2e0c56 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/Covid3Month/trainResample2.csv @@ -1,6 +1,6 @@ -Covid3Month,TSF,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:20:25 +Covid3Month,TSF,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:27:52 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0018743701647611062,-1700007622481,-1,791,-1,,1367,-1700007621114 +0.0018743701647611062,2421,-1,810,-1,,1207,3628 0.038461538461538464,0.08654048064200939 0.03926512968299712,0.03355508040915349 0.08068783068783068,0.036774778389509294 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv index 9118121b..c034dba2 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample0.csv @@ -1,6 +1,6 @@ -FloodModeling1,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:27 -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': False, 'time_limit_in_minutes': None} -5.020268895420901e-05,-1700007847335,1936,909,35610624,,-1,-1 +FloodModeling1,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:53 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +5.020268895420901e-05,21423,1781,846,72503296,,-1,-1 0.45,0.4539149999999995 0.447,0.4442049999999999 0.42700000000000005,0.4269699999999998 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv index bebda4a9..ac65266d 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample1.csv @@ -1,6 +1,6 @@ -FloodModeling1,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:29 -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': False, 'time_limit_in_minutes': None} -0.00019749039628712877,-1700007848870,2114,918,33239040,,-1,-1 +FloodModeling1,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:55 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.00019749039628712877,21448,1996,871,71933952,,-1,-1 0.434,0.4365050000000003 0.425,0.42416499999999985 0.494,0.47634 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv index e768dce7..3163596a 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/testResample2.csv @@ -1,6 +1,6 @@ -FloodModeling1,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:24:30 -{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': False, 'time_limit_in_minutes': None} -5.410471896658498e-05,-1700007850615,1947,887,34709504,,-1,-1 +FloodModeling1,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:57 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} +5.410471896658498e-05,21496,2206,892,70725632,,-1,-1 0.451,0.4460799999999994 0.446,0.4464950000000002 0.42700000000000005,0.42839000000000005 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample0.csv new file mode 100644 index 00000000..ed2f40fe --- /dev/null +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/FloodModeling1/trainResample0.csv @@ -0,0 +1,474 @@ +FloodModeling1,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:28:53 +{'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} +0.00012786886780162882,21423,-1,846,-1,,14831,36254 +0.426,0.43231343283582097 +0.424,0.4289493670886074 +0.42200000000000004,0.4299411764705882 +0.451,0.4565797101449275 +0.428,0.4327887323943662 +0.442,0.44425000000000014 +0.483,0.4753466666666668 +0.435,0.4376623376623376 +0.435,0.43042028985507236 +0.435,0.4289264705882353 +0.428,0.43479268292682915 +0.428,0.4312923076923076 +0.439,0.44143589743589745 +0.456,0.45284810126582287 +0.431,0.44089655172413794 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b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:27 +NaturalGasPricesSentiment,TSF,TEST,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:00 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0028600017079850088,-1700007565322,138,840,27795456,,-1,-1 +0.0028600017079850088,1181,159,864,28442624,,-1,-1 -0.3745973154329336,-0.3170056165599792 -0.27649220292280596,-0.3075135142470262 -0.3359852990851952,-0.41088529794465245 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv index 5a6520a8..09ba54db 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:31 +NaturalGasPricesSentiment,TSF,TEST,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:58 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.00206232858333723,-1700007570322,120,893,27361280,,-1,-1 +0.00206232858333723,1167,170,902,28495872,,-1,-1 -0.06869880430973492,-0.12565121136949606 -0.3310506358794816,-0.3015908183865471 -0.328501794888423,-0.30169368873646774 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv index 1df38c97..bb1e5a24 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/testResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:33 +NaturalGasPricesSentiment,TSF,TEST,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:56 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.003674450842994001,-1700007572840,124,773,27308032,,-1,-1 +0.003674450842994001,1312,130,929,28758016,,-1,-1 -0.12363863375324469,-0.15626487639861644 -0.28387368441774286,-0.32852148022221306 -0.3627388186179675,-0.2977592638731001 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv index 05213a2b..bd047200 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample0.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:27 +NaturalGasPricesSentiment,TSF,TRAIN,0,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:30:00 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 0, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.003790880115008313,-1700007565322,-1,840,-1,,448,-1700007564874 +0.003790880115008313,1181,-1,864,-1,,514,1695 -0.4272497972616782,-0.33260701375286467 -0.3330772297886702,-0.3227860910507832 -0.27904755897246875,-0.3431144077016081 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv index 0af3611e..be9d9784 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample1.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:31 +NaturalGasPricesSentiment,TSF,TRAIN,1,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:58 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 1, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.0035720382659537673,-1700007570322,-1,893,-1,,400,-1700007569922 +0.0035720382659537673,1167,-1,902,-1,,554,1721 -0.28843834113616207,-0.3692448847335458 -0.3716620670488247,-0.3321425630759839 -0.07298391111768207,-0.14438751290787705 diff --git a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample2.csv b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample2.csv index 131cd21e..a75de6f1 100644 --- a/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample2.csv +++ b/tsml_eval/testing/_test_result_files/regression/TSF/Predictions/NaturalGasPricesSentiment/trainResample2.csv @@ -1,6 +1,6 @@ -NaturalGasPricesSentiment,TSF,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/15/2023, 00:19:33 +NaturalGasPricesSentiment,TSF,TRAIN,2,MILLISECONDS,Generated by run_regression_experiment on 11/17/2023, 11:29:56 {'base_estimator': None, 'contract_max_n_estimators': 500, 'max_interval_length': inf, 'min_interval_length': 3, 'n_estimators': 200, 'n_intervals': 'sqrt', 'n_jobs': 1, 'parallel_backend': None, 'random_state': 2, 'save_transformed_data': True, 'time_limit_in_minutes': None} -0.003109564444753066,-1700007572840,-1,773,-1,,439,-1700007572401 +0.003109564444753066,1312,-1,929,-1,,481,1793 -0.5199578351126266,-0.391464037400132 -0.3215639365407137,-0.37162470323941044 -0.15584264414814797,-0.16985194652093155 From 2b82b3901972a2d434904e7c74df4997c403098f Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 15:37:51 +0000 Subject: [PATCH 09/19] sorting an verify parameter --- .../multiple_estimator_evaluation.py | 100 ++++++++++++++---- .../evaluation/storage/classifier_results.py | 23 ++-- .../evaluation/storage/clusterer_results.py | 6 +- .../evaluation/storage/estimator_results.py | 5 +- .../evaluation/storage/forecaster_results.py | 6 +- .../evaluation/storage/regressor_results.py | 6 +- 6 files changed, 111 insertions(+), 35 deletions(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index f8cad8a6..280063bb 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -47,7 +47,11 @@ def evaluate_classifiers( def evaluate_classifiers_from_file( - load_paths, save_path, error_on_missing=True, eval_name=None + load_paths, + save_path, + error_on_missing=True, + eval_name=None, + verify_results=True, ): """ Evaluate multiple classifiers on multiple datasets from file. @@ -66,10 +70,14 @@ def evaluate_classifiers_from_file( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ classifier_results = [] for load_path in load_paths: - classifier_results.append(ClassifierResults().load_from_file(load_path)) + classifier_results.append( + ClassifierResults().load_from_file(load_path, verify_values=verify_results) + ) evaluate_classifiers( classifier_results, @@ -88,6 +96,7 @@ def evaluate_classifiers_by_problem( load_train_results=False, error_on_missing=True, eval_name=None, + verify_results=True, ): """ Evaluate multiple classifiers on multiple datasets from file using standard paths. @@ -118,6 +127,8 @@ def evaluate_classifiers_by_problem( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ if resamples is None: resamples = [""] @@ -139,7 +150,8 @@ def evaluate_classifiers_by_problem( classifier_results.append( ClassifierResults().load_from_file( f"{load_path}/{classifier_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv" + f"/{split}Resample{resample}.csv", + verify_values=verify_results, ) ) @@ -182,7 +194,11 @@ def evaluate_clusterers( def evaluate_clusterers_from_file( - load_paths, save_path, error_on_missing=True, eval_name=None + load_paths, + save_path, + error_on_missing=True, + eval_name=None, + verify_results=True, ): """ Evaluate multiple clusterers on multiple datasets from file. @@ -201,10 +217,14 @@ def evaluate_clusterers_from_file( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ clusterer_results = [] for load_path in load_paths: - clusterer_results.append(ClustererResults().load_from_file(load_path)) + clusterer_results.append( + ClustererResults().load_from_file(load_path, verify_values=verify_results) + ) evaluate_clusterers( clusterer_results, @@ -223,6 +243,7 @@ def evaluate_clusterers_by_problem( load_test_results=True, error_on_missing=True, eval_name=None, + verify_results=True, ): """ Evaluate multiple clusterers on multiple datasets from file using standard paths. @@ -253,6 +274,8 @@ def evaluate_clusterers_by_problem( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ if resamples is None: resamples = [""] @@ -274,7 +297,8 @@ def evaluate_clusterers_by_problem( clusterer_results.append( ClustererResults().load_from_file( f"{load_path}/{clusterer_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv" + f"/{split}Resample{resample}.csv", + verify_values=verify_results, ) ) @@ -317,7 +341,11 @@ def evaluate_regressors( def evaluate_regressors_from_file( - load_paths, save_path, error_on_missing=True, eval_name=None + load_paths, + save_path, + error_on_missing=True, + eval_name=None, + verify_results=True, ): """ Evaluate multiple regressors on multiple datasets from file. @@ -336,10 +364,14 @@ def evaluate_regressors_from_file( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ regressor_results = [] for load_path in load_paths: - regressor_results.append(RegressorResults().load_from_file(load_path)) + regressor_results.append( + RegressorResults().load_from_file(load_path, verify_values=verify_results) + ) evaluate_regressors( regressor_results, @@ -358,6 +390,7 @@ def evaluate_regressors_by_problem( load_train_results=False, error_on_missing=True, eval_name=None, + verify_results=True, ): """ Evaluate multiple regressors on multiple datasets from file using standard paths. @@ -388,6 +421,8 @@ def evaluate_regressors_by_problem( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ if resamples is None: resamples = [""] @@ -409,7 +444,8 @@ def evaluate_regressors_by_problem( regressor_results.append( RegressorResults().load_from_file( f"{load_path}/{regressor_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv" + f"/{split}Resample{resample}.csv", + verify_values=verify_results, ) ) @@ -452,7 +488,11 @@ def evaluate_forecasters( def evaluate_forecasters_from_file( - load_paths, save_path, error_on_missing=True, eval_name=None + load_paths, + save_path, + error_on_missing=True, + eval_name=None, + verify_results=True, ): """ Evaluate multiple forecasters on multiple datasets from file. @@ -471,10 +511,14 @@ def evaluate_forecasters_from_file( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ forecaster_results = [] for load_path in load_paths: - forecaster_results.append(ForecasterResults().load_from_file(load_path)) + forecaster_results.append( + ForecasterResults().load_from_file(load_path, verify_values=verify_results) + ) evaluate_forecasters( forecaster_results, @@ -492,6 +536,7 @@ def evaluate_forecasters_by_problem( resamples=None, error_on_missing=True, eval_name=None, + verify_results=True, ): """ Evaluate multiple forecasters on multiple datasets from file using standard paths. @@ -520,6 +565,8 @@ def evaluate_forecasters_by_problem( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + verify_results : bool, default=True + If the verification should be performed on the loaded results values. """ if resamples is None: resamples = [""] @@ -535,7 +582,8 @@ def evaluate_forecasters_by_problem( forecaster_results.append( ForecasterResults().load_from_file( f"{load_path}/{forecaster_name}/Predictions/{dataset_name}" - f"/testResample{resample}.csv" + f"/testResample{resample}.csv", + verify_values=verify_results, ) ) @@ -681,7 +729,7 @@ def _evaluate_estimators( var, save_path, ) - stats.append((average, rank, stat, split)) + stats.append((average, rank, stat, ascending, split)) _summary_evaluation(stats, estimators, save_path, eval_name) @@ -811,12 +859,28 @@ def _figures_for_statistic( def _summary_evaluation(stats, estimators, save_path, eval_name): with open(f"{save_path}/{eval_name}_summary.csv", "w") as file: for stat in stats: - file.write(f"{stat[3]}{stat[2]},{','.join(estimators)}\n") + avg_stat = np.mean(stat[0], axis=0) + avg_rank = np.mean(stat[1], axis=0) + sorted_indices = [ + i + for i in sorted( + range(len(avg_rank)), + key=lambda x: ( + avg_rank[x], + -avg_stat[x] if stat[3] else avg_stat[x], + ), + ) + ] + + file.write( + f"{stat[4]}{stat[2]}," + f"{','.join([estimators[i] for i in sorted_indices])}\n" + ) file.write( - f"{stat[3]}{stat[2]}Mean," - f"{','.join([str(n) for n in np.mean(stat[0], axis=0)])}\n" + f"{stat[4]}{stat[2]}Mean," + f"{','.join([str(n) for n in avg_stat[sorted_indices]])}\n" ) file.write( - f"{stat[3]}{stat[2]}AvgRank," - f"{','.join([str(n) for n in np.mean(stat[1], axis=0)])}\n\n" + f"{stat[4]}{stat[2]}AvgRank," + f"{','.join([str(n) for n in avg_rank[sorted_indices]])}\n\n" ) diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index bde761ca..48d36ff8 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -141,9 +141,9 @@ def __init__( self.accuracy = None self.balanced_accuracy = None - self.f1_score = None - self.negative_log_likelihood = None self.mean_auroc = None + self.negative_log_likelihood = None + self.f1_score = None super(ClassifierResults, self).__init__( dataset_name=dataset_name, @@ -163,9 +163,9 @@ def __init__( statistics = { "accuracy": ("Accuracy", True, False), "balanced_accuracy": ("BalAcc", True, False), - "f1_score": ("F1", True, False), - "negative_log_likelihood": ("NLL", False, False), "mean_auroc": ("AUROC", True, False), + "negative_log_likelihood": ("NLL", False, False), + "f1_score": ("F1", True, False), **EstimatorResults.statistics, } @@ -212,7 +212,7 @@ def save_to_file(self, file_path, full_path=True): fit_and_estimate_time=self.fit_and_estimate_time, ) - def load_from_file(self, file_path): + def load_from_file(self, file_path, verify_values=True): """ Load classifier results from a specified file. @@ -225,13 +225,15 @@ def load_from_file(self, file_path): file_path : str The path to the file from which classifier results should be loaded. The file should be a tsml formatted classifier results file. + verify_values : bool, default=True + If the method should perform verification of the loaded values. Returns ------- self : ClassifierResults The same ClassifierResults object with loaded results. """ - cr = load_classifier_results(file_path) + cr = load_classifier_results(file_path, verify_values=verify_values) self.__dict__.update(cr.__dict__) return self @@ -255,10 +257,6 @@ def calculate_statistics(self, overwrite=False): self.balanced_accuracy = balanced_accuracy_score( self.class_labels, self.predictions ) - if self.f1_score is None or overwrite: - self.f1_score = f1_score( - self.class_labels, self.predictions, average="macro" - ) if self.negative_log_likelihood is None or overwrite: self.negative_log_likelihood = log_loss( self.class_labels, self.probabilities @@ -267,8 +265,13 @@ def calculate_statistics(self, overwrite=False): self.mean_auroc = roc_auc_score( self.class_labels, self.predictions if self.n_classes == 2 else self.probabilities, + average="micro", multi_class="ovr", ) + if self.f1_score is None or overwrite: + self.f1_score = f1_score( + self.class_labels, self.predictions, average="micro" + ) def infer_size(self, overwrite=False): """ diff --git a/tsml_eval/evaluation/storage/clusterer_results.py b/tsml_eval/evaluation/storage/clusterer_results.py index 8f4e4f6a..c653645d 100644 --- a/tsml_eval/evaluation/storage/clusterer_results.py +++ b/tsml_eval/evaluation/storage/clusterer_results.py @@ -207,7 +207,7 @@ def save_to_file(self, file_path, full_path=True): n_clusters=self.n_clusters, ) - def load_from_file(self, file_path): + def load_from_file(self, file_path, verify_values=True): """ Load clusterer results from a specified file. @@ -220,13 +220,15 @@ def load_from_file(self, file_path): file_path : str The path to the file from which clusterer results should be loaded. The file should be a tsml formatted clusterer results file. + verify_values : bool, default=True + If the method should perform verification of the loaded values. Returns ------- self : ClustererResults The same ClustererResults object with loaded results. """ - cr = load_clusterer_results(file_path) + cr = load_clusterer_results(file_path, verify_values=verify_values) self.__dict__.update(cr.__dict__) return self diff --git a/tsml_eval/evaluation/storage/estimator_results.py b/tsml_eval/evaluation/storage/estimator_results.py index c764c6a9..a372a4d9 100644 --- a/tsml_eval/evaluation/storage/estimator_results.py +++ b/tsml_eval/evaluation/storage/estimator_results.py @@ -95,7 +95,7 @@ def save_to_file(self, file_path, full_path=True): pass @abstractmethod - def load_from_file(self, file_path): + def load_from_file(self, file_path, verify_values=True): """ Load estimator results from a specified file. @@ -110,6 +110,9 @@ def load_from_file(self, file_path): file_path : str The path to the file from which estimator results should be loaded. The file should be a tsml formatted estimator results file. + verify_values : bool, default=True + If the method should perform verification of the loaded values. + Returns ------- diff --git a/tsml_eval/evaluation/storage/forecaster_results.py b/tsml_eval/evaluation/storage/forecaster_results.py index 09a712f7..81665275 100644 --- a/tsml_eval/evaluation/storage/forecaster_results.py +++ b/tsml_eval/evaluation/storage/forecaster_results.py @@ -162,7 +162,7 @@ def save_to_file(self, file_path, full_path=True): memory_usage=self.memory_usage, ) - def load_from_file(self, file_path): + def load_from_file(self, file_path, verify_values=True): """ Load forecaster results from a specified file. @@ -175,13 +175,15 @@ def load_from_file(self, file_path): file_path : str The path to the file from which forecaster results should be loaded. The file should be a tsml formatted forecaster results file. + verify_values : bool, default=True + If the method should perform verification of the loaded values. Returns ------- self : ForecasterResults The same ForecasterResults object with loaded results. """ - fr = load_forecaster_results(file_path) + fr = load_forecaster_results(file_path, verify_values=verify_values) self.__dict__.update(fr.__dict__) return self diff --git a/tsml_eval/evaluation/storage/regressor_results.py b/tsml_eval/evaluation/storage/regressor_results.py index 873b4e25..11120268 100644 --- a/tsml_eval/evaluation/storage/regressor_results.py +++ b/tsml_eval/evaluation/storage/regressor_results.py @@ -204,7 +204,7 @@ def save_to_file(self, file_path, full_path=True): fit_and_estimate_time=self.fit_and_estimate_time, ) - def load_from_file(self, file_path): + def load_from_file(self, file_path, verify_values=True): """ Load regressor results from a specified file. @@ -217,13 +217,15 @@ def load_from_file(self, file_path): file_path : str The path to the file from which regressor results should be loaded. The file should be a tsml formatted regressor results file. + verify_values : bool, default=True + If the method should perform verification of the loaded values. Returns ------- self : RegressorResults The same RegressorResults object with loaded results. """ - rr = load_regressor_results(file_path) + rr = load_regressor_results(file_path, verify_values=verify_values) self.__dict__.update(rr.__dict__) return self From 6991cc6c2a80ed195ab44137af99e55bece734a2 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 16:25:22 +0000 Subject: [PATCH 10/19] sorting an verify parameter --- tsml_eval/evaluation/storage/classifier_results.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index 48d36ff8..9cacd2c0 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -265,12 +265,12 @@ def calculate_statistics(self, overwrite=False): self.mean_auroc = roc_auc_score( self.class_labels, self.predictions if self.n_classes == 2 else self.probabilities, - average="micro", + average="weighted", multi_class="ovr", ) if self.f1_score is None or overwrite: self.f1_score = f1_score( - self.class_labels, self.predictions, average="micro" + self.class_labels, self.predictions, average="macro" ) def infer_size(self, overwrite=False): From b89da119dab365c6812bc40bdd9149b92cd0a1a9 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 17:54:00 +0000 Subject: [PATCH 11/19] close enough --- .../evaluation/storage/classifier_results.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index 9cacd2c0..e7a96711 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -82,7 +82,7 @@ class ClassifierResults(EstimatorResults): F1 score of the classifier. negative_log_likelihood : float or None Negative log likelihood of the classifier. - mean_auroc : float or None + auroc_score : float or None Mean area under the ROC curve of the classifier. Examples @@ -141,7 +141,7 @@ def __init__( self.accuracy = None self.balanced_accuracy = None - self.mean_auroc = None + self.auroc_score = None self.negative_log_likelihood = None self.f1_score = None @@ -163,7 +163,7 @@ def __init__( statistics = { "accuracy": ("Accuracy", True, False), "balanced_accuracy": ("BalAcc", True, False), - "mean_auroc": ("AUROC", True, False), + "auroc_score": ("AUROC", True, False), "negative_log_likelihood": ("NLL", False, False), "f1_score": ("F1", True, False), **EstimatorResults.statistics, @@ -259,18 +259,20 @@ def calculate_statistics(self, overwrite=False): ) if self.negative_log_likelihood is None or overwrite: self.negative_log_likelihood = log_loss( - self.class_labels, self.probabilities + self.class_labels, + self.probabilities, + eps=0.01, ) - if self.mean_auroc is None or overwrite: - self.mean_auroc = roc_auc_score( + if self.auroc_score is None or overwrite: + self.auroc_score = roc_auc_score( self.class_labels, - self.predictions if self.n_classes == 2 else self.probabilities, + self.probabilities[:, 1] if self.n_classes == 2 else self.probabilities, average="weighted", multi_class="ovr", ) if self.f1_score is None or overwrite: self.f1_score = f1_score( - self.class_labels, self.predictions, average="macro" + self.class_labels, self.predictions, average="weighted" ) def infer_size(self, overwrite=False): From a05641e5269ec9e9bc8c87fc01ccc74b96876290 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Fri, 17 Nov 2023 20:54:32 +0000 Subject: [PATCH 12/19] fixes --- .../multiple_estimator_evaluation.py | 148 +++++++++++++----- 1 file changed, 113 insertions(+), 35 deletions(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 280063bb..55889f49 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -17,7 +17,11 @@ def evaluate_classifiers( - classifier_results, save_path, error_on_missing=True, eval_name=None + classifier_results, + save_path, + error_on_missing=True, + eval_name=None, + estimator_names=None, ): """ Evaluate multiple classifiers on multiple datasets. @@ -36,6 +40,9 @@ def evaluate_classifiers( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + estimator_names : list of str, default=None + The names of the estimator for each classifier result. If None, uses + the estimator_name attribute of each classifier result. """ _evaluate_estimators( classifier_results, @@ -43,6 +50,7 @@ def evaluate_classifiers( save_path, error_on_missing, eval_name, + estimator_names, ) @@ -52,6 +60,7 @@ def evaluate_classifiers_from_file( error_on_missing=True, eval_name=None, verify_results=True, + estimator_names=None, ): """ Evaluate multiple classifiers on multiple datasets from file. @@ -72,6 +81,9 @@ def evaluate_classifiers_from_file( The name of the evaluation, used in save_path. verify_results : bool, default=True If the verification should be performed on the loaded results values. + estimator_names : list of str, default=None + The names of the estimator for each classifier result. If None, uses + the estimator_name attribute of each classifier result. """ classifier_results = [] for load_path in load_paths: @@ -84,6 +96,7 @@ def evaluate_classifiers_from_file( save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=estimator_names, ) @@ -143,6 +156,7 @@ def evaluate_classifiers_by_problem( splits = ["test"] classifier_results = [] + names = [] for classifier_name in classifier_names: for dataset_name in dataset_names: for resample in resamples: @@ -154,17 +168,23 @@ def evaluate_classifiers_by_problem( verify_values=verify_results, ) ) + names.append(classifier_name) evaluate_classifiers( classifier_results, save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=names, ) def evaluate_clusterers( - clusterer_results, save_path, error_on_missing=True, eval_name=None + clusterer_results, + save_path, + error_on_missing=True, + eval_name=None, + estimator_names=None, ): """ Evaluate multiple clusterers on multiple datasets. @@ -183,6 +203,9 @@ def evaluate_clusterers( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + estimator_names : list of str, default=None + The names of the estimator for each clusterer result. If None, uses + the estimator_name attribute of each clusterer result. """ _evaluate_estimators( clusterer_results, @@ -190,6 +213,7 @@ def evaluate_clusterers( save_path, error_on_missing, eval_name, + estimator_names, ) @@ -199,6 +223,7 @@ def evaluate_clusterers_from_file( error_on_missing=True, eval_name=None, verify_results=True, + estimator_names=None, ): """ Evaluate multiple clusterers on multiple datasets from file. @@ -219,6 +244,9 @@ def evaluate_clusterers_from_file( The name of the evaluation, used in save_path. verify_results : bool, default=True If the verification should be performed on the loaded results values. + estimator_names : list of str, default=None + The names of the estimator for each clusterer result. If None, uses + the estimator_name attribute of each clusterer result. """ clusterer_results = [] for load_path in load_paths: @@ -231,6 +259,7 @@ def evaluate_clusterers_from_file( save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=estimator_names, ) @@ -290,6 +319,7 @@ def evaluate_clusterers_by_problem( splits = ["train"] clusterer_results = [] + names = [] for clusterer_name in clusterer_names: for dataset_name in dataset_names: for resample in resamples: @@ -301,17 +331,23 @@ def evaluate_clusterers_by_problem( verify_values=verify_results, ) ) + names.append(clusterer_name) evaluate_clusterers( clusterer_results, save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=names, ) def evaluate_regressors( - regressor_results, save_path, error_on_missing=True, eval_name=None + regressor_results, + save_path, + error_on_missing=True, + eval_name=None, + estimator_names=None, ): """ Evaluate multiple regressors on multiple datasets. @@ -330,6 +366,9 @@ def evaluate_regressors( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + estimator_names : list of str, default=None + The names of the estimator for each regressor result. If None, uses + the estimator_name attribute of each regressor result. """ _evaluate_estimators( regressor_results, @@ -337,6 +376,7 @@ def evaluate_regressors( save_path, error_on_missing, eval_name, + estimator_names, ) @@ -346,6 +386,7 @@ def evaluate_regressors_from_file( error_on_missing=True, eval_name=None, verify_results=True, + estimator_names=None, ): """ Evaluate multiple regressors on multiple datasets from file. @@ -366,6 +407,9 @@ def evaluate_regressors_from_file( The name of the evaluation, used in save_path. verify_results : bool, default=True If the verification should be performed on the loaded results values. + estimator_names : list of str, default=None + The names of the estimator for each regressor result. If None, uses + the estimator_name attribute of each regressor result. """ regressor_results = [] for load_path in load_paths: @@ -378,6 +422,7 @@ def evaluate_regressors_from_file( save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=estimator_names, ) @@ -437,6 +482,7 @@ def evaluate_regressors_by_problem( splits = ["test"] regressor_results = [] + names = [] for regressor_name in regressor_names: for dataset_name in dataset_names: for resample in resamples: @@ -448,17 +494,23 @@ def evaluate_regressors_by_problem( verify_values=verify_results, ) ) + names.append(regressor_name) evaluate_regressors( regressor_results, save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=names, ) def evaluate_forecasters( - forecaster_results, save_path, error_on_missing=True, eval_name=None + forecaster_results, + save_path, + error_on_missing=True, + eval_name=None, + estimator_names=None, ): """ Evaluate multiple forecasters on multiple datasets. @@ -477,6 +529,9 @@ def evaluate_forecasters( Whether to raise an error if results are missing. eval_name : str, default=None The name of the evaluation, used in save_path. + estimator_names : list of str, default=None + The names of the estimator for each forecaster result. If None, uses + the estimator_name attribute of each forecaster result. """ _evaluate_estimators( forecaster_results, @@ -484,6 +539,7 @@ def evaluate_forecasters( save_path, error_on_missing, eval_name, + estimator_names, ) @@ -493,6 +549,7 @@ def evaluate_forecasters_from_file( error_on_missing=True, eval_name=None, verify_results=True, + estimator_names=None, ): """ Evaluate multiple forecasters on multiple datasets from file. @@ -513,6 +570,9 @@ def evaluate_forecasters_from_file( The name of the evaluation, used in save_path. verify_results : bool, default=True If the verification should be performed on the loaded results values. + estimator_names : list of str, default=None + The names of the estimator for each forecaster result. If None, uses + the estimator_name attribute of each forecaster result. """ forecaster_results = [] for load_path in load_paths: @@ -525,6 +585,7 @@ def evaluate_forecasters_from_file( save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=estimator_names, ) @@ -576,6 +637,7 @@ def evaluate_forecasters_by_problem( resamples = [str(resample) for resample in resamples] forecaster_results = [] + names = [] for forecaster_name in forecaster_names: for dataset_name in dataset_names: for resample in resamples: @@ -586,17 +648,24 @@ def evaluate_forecasters_by_problem( verify_values=verify_results, ) ) + names.append(forecaster_name) evaluate_forecasters( forecaster_results, save_path, error_on_missing=error_on_missing, eval_name=eval_name, + estimator_names=names, ) def _evaluate_estimators( - estimator_results, statistics, save_path, error_on_missing, eval_name + estimator_results, + statistics, + save_path, + error_on_missing, + eval_name, + estimator_names, ): save_path = save_path + "/" + eval_name + "/" @@ -606,7 +675,7 @@ def _evaluate_estimators( has_test = False has_train = False - results_dict = _create_results_dictionary(estimator_results) + results_dict = _create_results_dictionary(estimator_results, estimator_names) if eval_name is None: dt = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") @@ -695,14 +764,24 @@ def _evaluate_estimators( raise ValueError("Missing results, exiting evaluation.") else: if has_test and has_train: - datasets = datasets[ - has_dataset_train.any(axis=(0, 2)) - & has_dataset_test.any(axis=(0, 2)) - ] + has_both = has_dataset_train.any(axis=(0, 2)) & has_dataset_test.any( + axis=(0, 2) + ) + datasets = [dataset for dataset, has in zip(datasets, has_both) if has] elif has_test: - datasets = datasets[has_dataset_test.any(axis=(0, 2))] + datasets = [ + dataset + for dataset, has in zip(datasets, has_dataset_test.any(axis=(0, 2))) + if has + ] else: - datasets = datasets[has_dataset_train.any(axis=(0, 2))] + datasets = [ + dataset + for dataset, has in zip( + datasets, has_dataset_train.any(axis=(0, 2)) + ) + if has + ] msg += "\nMissing results, continuing evaluation with available datasets.\n" print(msg) # noqa: T201 @@ -710,9 +789,9 @@ def _evaluate_estimators( msg += "All results present, continuing evaluation.\n" print(msg) # noqa: T201 - print(f"Estimators: {estimators}\n") # noqa: T201 - print(f"Datasets: {datasets}\n") # noqa: T201 - print(f"Resamples: {resamples}\n") # noqa: T201 + print(f"Estimators ({len(estimators)}): {estimators}\n") # noqa: T201 + print(f"Datasets ({len(datasets)}): {datasets}\n") # noqa: T201 + print(f"Resamples ({len(resamples)}): {resamples}\n") # noqa: T201 stats = [] for var, (stat, ascending, time) in statistics.items(): @@ -734,34 +813,33 @@ def _evaluate_estimators( _summary_evaluation(stats, estimators, save_path, eval_name) -def _create_results_dictionary(estimator_results): +def _create_results_dictionary(estimator_results, estimator_names): results_dict = {} - for estimator_result in estimator_results: - if results_dict.get(estimator_result.estimator_name) is None: - results_dict[estimator_result.estimator_name] = {} + for i, estimator_result in enumerate(estimator_results): + name = ( + estimator_result.estimator_name + if estimator_names is None + else estimator_names[i] + ) + + if results_dict.get(name) is None: + results_dict[name] = {} + + if results_dict[name].get(estimator_result.dataset_name) is None: + results_dict[name][estimator_result.dataset_name] = {} if ( - results_dict[estimator_result.estimator_name].get( - estimator_result.dataset_name + results_dict[name][estimator_result.dataset_name].get( + estimator_result.split.lower() ) is None ): - results_dict[estimator_result.estimator_name][ - estimator_result.dataset_name + results_dict[name][estimator_result.dataset_name][ + estimator_result.split.lower() ] = {} - if ( - results_dict[estimator_result.estimator_name][ - estimator_result.dataset_name - ].get(estimator_result.split.lower()) - is None - ): - results_dict[estimator_result.estimator_name][ - estimator_result.dataset_name - ][estimator_result.split.lower()] = {} - - results_dict[estimator_result.estimator_name][estimator_result.dataset_name][ + results_dict[name][estimator_result.dataset_name][ estimator_result.split.lower() ][estimator_result.resample_id] = estimator_result @@ -843,7 +921,7 @@ def _figures_for_statistic( cd = plot_critical_difference(scores, estimators, errors=not higher_better) cd.savefig( f"{save_path}/{statistic_name}/figures/" - f"{statistic_name}_critical_difference.png", + f"{statistic_name}_critical_difference.pdf", bbox_inches="tight", ) pickle.dump( From f75eb293aef0c3a5cd160403c789b9c16eaa52a3 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 18 Nov 2023 11:24:25 +0000 Subject: [PATCH 13/19] figure start --- .../multiple_estimator_evaluation.py | 34 ++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 55889f49..020222e9 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -6,6 +6,7 @@ import numpy as np from aeon.benchmarking import plot_critical_difference +from aeon.benchmarking.results_plotting import plot_boxplot_median, plot_scatter from tsml_eval.evaluation.storage import ( ClassifierResults, @@ -916,7 +917,7 @@ def _create_directory_for_statistic( def _figures_for_statistic( scores, estimators, statistic_name, higher_better, save_path ): - os.makedirs(f"{save_path}/{statistic_name}/figures/", exist_ok=True) + os.makedirs(f"{save_path}/{statistic_name}/figures/scatters/", exist_ok=True) cd = plot_critical_difference(scores, estimators, errors=not higher_better) cd.savefig( @@ -933,6 +934,37 @@ def _figures_for_statistic( ), ) + box = plot_boxplot_median(scores, estimators) + box.savefig( + f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pdf", + bbox_inches="tight", + ) + pickle.dump( + box, + open( + f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pickle", + "wb", + ), + ) + + for i, est1 in enumerate(estimators): + for n, est2 in enumerate(estimators): + if i < n: + scatter = plot_scatter(scores[:, i], scores[:, n], est1, est2) + scatter.savefig( + f"{save_path}/{statistic_name}/figures/scatters/" + f"{statistic_name}_scatter_{est1}_{est2}.pdf", + bbox_inches="tight", + ) + pickle.dump( + scatter, + open( + f"{save_path}/{statistic_name}/figures/scatters/" + f"{statistic_name}_scatter_{est1}_{est2}.pickle", + "wb", + ), + ) + def _summary_evaluation(stats, estimators, save_path, eval_name): with open(f"{save_path}/{eval_name}_summary.csv", "w") as file: From 7395a5693a3198f661d13f159e5976aeb0f3e968 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 18 Nov 2023 23:27:39 +0000 Subject: [PATCH 14/19] no boxplot --- pyproject.toml | 4 +- .../multiple_estimator_evaluation.py | 64 ++++++++++--------- 2 files changed, 37 insertions(+), 31 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 059cb8ce..2d753a0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,12 +55,12 @@ all_extras = [ unstable_extras = [ "aeon[unstable_extras]", "tsml[unstable_extras]", + # temp + "pycatch22<=0.4.3", ] deep_learning = [ "aeon[dl]", "torch>=1.13.1", - # temp - "pycatch22<=0.4.3", ] dev = [ "pre-commit", diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 020222e9..f5e5b538 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -6,7 +6,7 @@ import numpy as np from aeon.benchmarking import plot_critical_difference -from aeon.benchmarking.results_plotting import plot_boxplot_median, plot_scatter +from aeon.benchmarking.results_plotting import plot_scatter from tsml_eval.evaluation.storage import ( ClassifierResults, @@ -917,7 +917,7 @@ def _create_directory_for_statistic( def _figures_for_statistic( scores, estimators, statistic_name, higher_better, save_path ): - os.makedirs(f"{save_path}/{statistic_name}/figures/scatters/", exist_ok=True) + os.makedirs(f"{save_path}/{statistic_name}/figures/", exist_ok=True) cd = plot_critical_difference(scores, estimators, errors=not higher_better) cd.savefig( @@ -934,36 +934,42 @@ def _figures_for_statistic( ), ) - box = plot_boxplot_median(scores, estimators) - box.savefig( - f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pdf", - bbox_inches="tight", - ) - pickle.dump( - box, - open( - f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pickle", - "wb", - ), - ) + # crashes when scores are the same? + + # box = plot_boxplot_median(scores.transpose(), estimators) + # box.savefig( + # f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pdf", + # bbox_inches="tight", + # ) + # pickle.dump( + # box, + # open( + # f"{save_path}/{statistic_name}/figures/" + # f"{statistic_name}_boxplot.pickle", + # "wb", + # ), + # ) for i, est1 in enumerate(estimators): for n, est2 in enumerate(estimators): - if i < n: - scatter = plot_scatter(scores[:, i], scores[:, n], est1, est2) - scatter.savefig( - f"{save_path}/{statistic_name}/figures/scatters/" - f"{statistic_name}_scatter_{est1}_{est2}.pdf", - bbox_inches="tight", - ) - pickle.dump( - scatter, - open( - f"{save_path}/{statistic_name}/figures/scatters/" - f"{statistic_name}_scatter_{est1}_{est2}.pickle", - "wb", - ), - ) + os.makedirs( + f"{save_path}/{statistic_name}/figures/scatters/{est1}/", exist_ok=True + ) + + scatter = plot_scatter(scores[:, (i, n)], est1, est2) + scatter.savefig( + f"{save_path}/{statistic_name}/figures/scatters/{est1}/" + f"{statistic_name}_scatter_{est1}_{est2}.pdf", + bbox_inches="tight", + ) + pickle.dump( + scatter, + open( + f"{save_path}/{statistic_name}/figures/scatters/{est1}/" + f"{statistic_name}_scatter_{est1}_{est2}.pickle", + "wb", + ), + ) def _summary_evaluation(stats, estimators, save_path, eval_name): From 6cc106c3585c53ac686a69f3e136685ee2ff05e7 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Sat, 18 Nov 2023 23:28:02 +0000 Subject: [PATCH 15/19] no boxplot --- tsml_eval/evaluation/multiple_estimator_evaluation.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index f5e5b538..7526fc36 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -938,14 +938,13 @@ def _figures_for_statistic( # box = plot_boxplot_median(scores.transpose(), estimators) # box.savefig( - # f"{save_path}/{statistic_name}/figures/" f"{statistic_name}_boxplot.pdf", + # f"{save_path}/{statistic_name}/figures/{statistic_name}_boxplot.pdf", # bbox_inches="tight", # ) # pickle.dump( # box, # open( - # f"{save_path}/{statistic_name}/figures/" - # f"{statistic_name}_boxplot.pickle", + # f"{save_path}/{statistic_name}/figures/{statistic_name}_boxplot.pickle", # "wb", # ), # ) From 451b388d3961e36026433bce8a83c1e56b45043a Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Mon, 20 Nov 2023 09:56:17 +0000 Subject: [PATCH 16/19] fixes --- .../multiple_estimator_evaluation.py | 218 ++++++++++++++---- .../evaluation/storage/classifier_results.py | 16 +- 2 files changed, 184 insertions(+), 50 deletions(-) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 7526fc36..6237cc25 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -7,6 +7,7 @@ import numpy as np from aeon.benchmarking import plot_critical_difference from aeon.benchmarking.results_plotting import plot_scatter +from matplotlib import pyplot as plt from tsml_eval.evaluation.storage import ( ClassifierResults, @@ -88,9 +89,15 @@ def evaluate_classifiers_from_file( """ classifier_results = [] for load_path in load_paths: - classifier_results.append( - ClassifierResults().load_from_file(load_path, verify_values=verify_results) - ) + try: + classifier_results.append( + ClassifierResults().load_from_file( + load_path, verify_values=verify_results + ) + ) + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError(f"Results for {load_path} not found.") evaluate_classifiers( classifier_results, @@ -157,19 +164,43 @@ def evaluate_classifiers_by_problem( splits = ["test"] classifier_results = [] + found_datasets = np.zeros(len(dataset_names), dtype=bool) names = [] for classifier_name in classifier_names: - for dataset_name in dataset_names: + found_estimator = False + for i, dataset_name in enumerate(dataset_names): for resample in resamples: for split in splits: - classifier_results.append( - ClassifierResults().load_from_file( - f"{load_path}/{classifier_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv", - verify_values=verify_results, + try: + classifier_results.append( + ClassifierResults().load_from_file( + f"{load_path}/{classifier_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) ) - ) - names.append(classifier_name) + names.append(classifier_name) + found_estimator = True + found_datasets[i] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {classifier_name} on {dataset_name} " + f"{split} resample {resample} not found." + ) + + if not found_estimator: + print(f"Classifier {classifier_names} not found.") # noqa: T201 + + missing_datasets = [ + dataset for dataset, found in zip(dataset_names, found_datasets) if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_classifiers( classifier_results, @@ -251,9 +282,15 @@ def evaluate_clusterers_from_file( """ clusterer_results = [] for load_path in load_paths: - clusterer_results.append( - ClustererResults().load_from_file(load_path, verify_values=verify_results) - ) + try: + clusterer_results.append( + ClustererResults().load_from_file( + load_path, verify_values=verify_results + ) + ) + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError(f"Results for {load_path} not found.") evaluate_clusterers( clusterer_results, @@ -320,19 +357,43 @@ def evaluate_clusterers_by_problem( splits = ["train"] clusterer_results = [] + found_datasets = np.zeros(len(dataset_names), dtype=bool) names = [] for clusterer_name in clusterer_names: - for dataset_name in dataset_names: + found_estimator = False + for i, dataset_name in enumerate(dataset_names): for resample in resamples: for split in splits: - clusterer_results.append( - ClustererResults().load_from_file( - f"{load_path}/{clusterer_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv", - verify_values=verify_results, + try: + clusterer_results.append( + ClustererResults().load_from_file( + f"{load_path}/{clusterer_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) ) - ) - names.append(clusterer_name) + names.append(clusterer_name) + found_estimator = True + found_datasets[i] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {clusterer_results} on {dataset_name} " + f"{split} resample {resample} not found." + ) + + if not found_estimator: + print(f"Clusterer {clusterer_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset for dataset, found in zip(dataset_names, found_datasets) if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_clusterers( clusterer_results, @@ -414,9 +475,15 @@ def evaluate_regressors_from_file( """ regressor_results = [] for load_path in load_paths: - regressor_results.append( - RegressorResults().load_from_file(load_path, verify_values=verify_results) - ) + try: + regressor_results.append( + RegressorResults().load_from_file( + load_path, verify_values=verify_results + ) + ) + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError(f"Results for {load_path} not found.") evaluate_regressors( regressor_results, @@ -483,19 +550,43 @@ def evaluate_regressors_by_problem( splits = ["test"] regressor_results = [] + found_datasets = np.zeros(len(dataset_names), dtype=bool) names = [] for regressor_name in regressor_names: - for dataset_name in dataset_names: + found_estimator = False + for i, dataset_name in enumerate(dataset_names): for resample in resamples: for split in splits: - regressor_results.append( - RegressorResults().load_from_file( - f"{load_path}/{regressor_name}/Predictions/{dataset_name}" - f"/{split}Resample{resample}.csv", - verify_values=verify_results, + try: + regressor_results.append( + RegressorResults().load_from_file( + f"{load_path}/{regressor_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) ) - ) - names.append(regressor_name) + names.append(regressor_name) + found_estimator = True + found_datasets[i] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {regressor_results} on {dataset_name} " + f"{split} resample {resample} not found." + ) + + if not found_estimator: + print(f"Regressor {regressor_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset for dataset, found in zip(dataset_names, found_datasets) if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_regressors( regressor_results, @@ -577,9 +668,15 @@ def evaluate_forecasters_from_file( """ forecaster_results = [] for load_path in load_paths: - forecaster_results.append( - ForecasterResults().load_from_file(load_path, verify_values=verify_results) - ) + try: + forecaster_results.append( + ForecasterResults().load_from_file( + load_path, verify_values=verify_results + ) + ) + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError(f"Results for {load_path} not found.") evaluate_forecasters( forecaster_results, @@ -638,18 +735,42 @@ def evaluate_forecasters_by_problem( resamples = [str(resample) for resample in resamples] forecaster_results = [] + found_datasets = np.zeros(len(dataset_names), dtype=bool) names = [] for forecaster_name in forecaster_names: - for dataset_name in dataset_names: + found_estimator = False + for i, dataset_name in enumerate(dataset_names): for resample in resamples: - forecaster_results.append( - ForecasterResults().load_from_file( - f"{load_path}/{forecaster_name}/Predictions/{dataset_name}" - f"/testResample{resample}.csv", - verify_values=verify_results, + try: + forecaster_results.append( + ForecasterResults().load_from_file( + f"{load_path}/{forecaster_name}/Predictions/" + f"{dataset_name}/testResample{resample}.csv", + verify_values=verify_results, + ) ) - ) - names.append(forecaster_name) + names.append(forecaster_name) + found_estimator = True + found_datasets[i] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {forecaster_name} on {dataset_name} " + f"resample {resample} not found." + ) + + if not found_estimator: + print(f"Forecaster {forecaster_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset for dataset, found in zip(dataset_names, found_datasets) if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_forecasters( forecaster_results, @@ -765,21 +886,21 @@ def _evaluate_estimators( raise ValueError("Missing results, exiting evaluation.") else: if has_test and has_train: - has_both = has_dataset_train.any(axis=(0, 2)) & has_dataset_test.any( + has_both = has_dataset_train.all(axis=(0, 2)) & has_dataset_test.all( axis=(0, 2) ) datasets = [dataset for dataset, has in zip(datasets, has_both) if has] elif has_test: datasets = [ dataset - for dataset, has in zip(datasets, has_dataset_test.any(axis=(0, 2))) + for dataset, has in zip(datasets, has_dataset_test.all(axis=(0, 2))) if has ] else: datasets = [ dataset for dataset, has in zip( - datasets, has_dataset_train.any(axis=(0, 2)) + datasets, has_dataset_train.all(axis=(0, 2)) ) if has ] @@ -933,6 +1054,7 @@ def _figures_for_statistic( "wb", ), ) + plt.close() # crashes when scores are the same? @@ -948,6 +1070,7 @@ def _figures_for_statistic( # "wb", # ), # ) + # plt.close() for i, est1 in enumerate(estimators): for n, est2 in enumerate(estimators): @@ -969,6 +1092,7 @@ def _figures_for_statistic( "wb", ), ) + plt.close() def _summary_evaluation(stats, estimators, save_path, eval_name): diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index e7a96711..911e5654 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -355,6 +355,16 @@ def load_classifier_results(file_path, calculate_stats=True, verify_values=True) if pred_descriptions is not None: pred_descriptions.append(",".join(line[6 + n_classes :]).strip()) + # compatability with old results files + if len(line3) > 6: + error_estimate_method = line3[6] + error_estimate_time = float(line3[7]) + build_plus_estimate_time = float(line3[8]) + else: + error_estimate_method = "N/A" + error_estimate_time = -1.0 + build_plus_estimate_time = -1.0 + cr = ClassifierResults( dataset_name=line1[0], classifier_name=line1[1], @@ -368,9 +378,9 @@ def load_classifier_results(file_path, calculate_stats=True, verify_values=True) benchmark_time=float(line3[3]), memory_usage=float(line3[4]), n_classes=n_classes, - error_estimate_method=line3[6], - error_estimate_time=float(line3[7]), - build_plus_estimate_time=float(line3[8]), + error_estimate_method=error_estimate_method, + error_estimate_time=error_estimate_time, + build_plus_estimate_time=build_plus_estimate_time, class_labels=class_labels, predictions=predictions, probabilities=probabilities, From 96515ad9ca1df719e43fa451d65af65068deacae Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 21 Nov 2023 16:59:43 +0000 Subject: [PATCH 17/19] fixes --- tsml_eval/experiments/experiments.py | 4 ++-- tsml_eval/utils/arguments.py | 16 +++++++++++++++- tsml_eval/utils/tests/test_results_writing.py | 4 ++-- tsml_eval/utils/validation.py | 15 +++++++++++++++ 4 files changed, 34 insertions(+), 5 deletions(-) diff --git a/tsml_eval/experiments/experiments.py b/tsml_eval/experiments/experiments.py index 5fd73747..fe4524cf 100644 --- a/tsml_eval/experiments/experiments.py +++ b/tsml_eval/experiments/experiments.py @@ -775,7 +775,7 @@ def run_clustering_experiment( n_clusters if n_clusters is not None else len(np.unique(train_preds)), ) ) - train_probs[:, train_preds] = 1 + train_probs[np.arange(len(train_preds)), train_preds] = 1 train_time = int(round(time.time() * 1000)) - start if build_train_file: @@ -818,7 +818,7 @@ def run_clustering_experiment( else len(np.unique(train_preds)), ) ) - test_probs[:, test_preds] = 1 + test_probs[np.arange(len(test_preds)), test_preds] = 1 test_time = ( int(round(time.time() * 1000)) - start diff --git a/tsml_eval/utils/arguments.py b/tsml_eval/utils/arguments.py index 1b71bc61..617edbe3 100644 --- a/tsml_eval/utils/arguments.py +++ b/tsml_eval/utils/arguments.py @@ -69,7 +69,12 @@ def parse_args(args): -nc N_CLUSTERS, --n_clusters N_CLUSTERS the number of clusters to find for clusterers which have an {n_clusters} parameter. If {-1}, use the - number of classes in the dataset (default: None). + number of classes in the dataset (default: -1). + -ctts, --combine_test_train_split + whether to use a train/test split or not. If True, the + train and test sets are combined and used the fit the + estimator. Only available for clustering + (default: False). -bt, --benchmark_time run a benchmark function and save the time spent in the results file (default: %(default)s). @@ -191,10 +196,19 @@ def parse_args(args): "-nc", "--n_clusters", type=int, + default=-1, help="the number of clusters to find for clusterers which have an {n_clusters} " "parameter. If {-1}, use the number of classes in the dataset " "(default: %(default)s).", ) + parser.add_argument( + "-ctts", + "--combine_test_train_split", + action="store_true", + help="whether to use a train/test split or not. If True, the train and test " + "sets are combined and used the fit the estimator. Only available for " + "clustering (default: %(default)s).", + ) parser.add_argument( "-bt", "--benchmark_time", diff --git a/tsml_eval/utils/tests/test_results_writing.py b/tsml_eval/utils/tests/test_results_writing.py index 460b144a..de14f15c 100644 --- a/tsml_eval/utils/tests/test_results_writing.py +++ b/tsml_eval/utils/tests/test_results_writing.py @@ -63,7 +63,7 @@ def _check_classification_file_format(file_path, num_results_lines=None): assert _check_classification_third_line(lines[2]) n_classes = int(lines[2].split(",")[5]) - _check_results_lines(lines, n_probas=n_classes, num_results_lines=num_results_lines) + _check_results_lines(lines, num_results_lines=num_results_lines, n_probas=n_classes) def test_write_classification_results_invalid(): @@ -194,7 +194,7 @@ def _check_clustering_file_format(file_path, num_results_lines=None): assert _check_clustering_third_line(lines[2]) n_probas = int(lines[2].split(",")[6]) - _check_results_lines(lines, n_probas=n_probas, num_results_lines=num_results_lines) + _check_results_lines(lines, num_results_lines=num_results_lines, n_probas=n_probas) def test_write_clustering_results_invalid(): diff --git a/tsml_eval/utils/validation.py b/tsml_eval/utils/validation.py index a5e806fd..57a95810 100644 --- a/tsml_eval/utils/validation.py +++ b/tsml_eval/utils/validation.py @@ -134,6 +134,21 @@ def _check_line_length_and_floats(line, length, floats): return True +def _check_results_lines(lines, num_results_lines=None, probabilities=True, n_probas=2): + if num_results_lines is not None: + assert len(lines) - 3 == num_results_lines + + for i in range(3, num_results_lines): + assert _check_results_line( + lines[i], probabilities=probabilities, n_probas=n_probas + ) + else: + for i in range(3, 6): + assert _check_results_line( + lines[i], probabilities=probabilities, n_probas=n_probas + ) + + def _check_results_line(line, probabilities=True, n_probas=2): line = line.split(",") From 29924bb8923f674f83ea898848a3cfc2acb7455e Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 21 Nov 2023 20:58:08 +0000 Subject: [PATCH 18/19] allow for multiple load paths --- .github/workflows/pr_opened.yml | 4 +- pyproject.toml | 2 +- .../multiple_estimator_evaluation.py | 500 ++++++++++++------ .../evaluation/storage/classifier_results.py | 8 +- 4 files changed, 335 insertions(+), 179 deletions(-) diff --git a/.github/workflows/pr_opened.yml b/.github/workflows/pr_opened.yml index 9ac1386f..6f0344ad 100644 --- a/.github/workflows/pr_opened.yml +++ b/.github/workflows/pr_opened.yml @@ -33,11 +33,11 @@ jobs: - name: Label pull request id: label-pr - run: _python build_tools/pr_labeler.py ${{ steps.app-token.outputs.token }} + run: python _build_tools/pr_labeler.py ${{ steps.app-token.outputs.token }} env: CONTEXT_GITHUB: ${{ toJson(github) }} - name: Write pull request comment - run: _python build_tools/pr_open_commenter.py ${{ steps.app-token.outputs.token }} ${{ steps.label-pr.outputs.title-labels }} ${{ steps.label-pr.outputs.title-labels-new }} ${{ steps.label-pr.outputs.content-labels }} ${{ steps.label-pr.outputs.content-labels-status }} + run: python _build_tools/pr_open_commenter.py ${{ steps.app-token.outputs.token }} ${{ steps.label-pr.outputs.title-labels }} ${{ steps.label-pr.outputs.title-labels-new }} ${{ steps.label-pr.outputs.content-labels }} ${{ steps.label-pr.outputs.content-labels-status }} env: CONTEXT_GITHUB: ${{ toJson(github) }} diff --git a/pyproject.toml b/pyproject.toml index 854e4d1f..27088807 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,7 +53,7 @@ dependencies = [ all_extras = [ "aeon[all_extras,dl]", "tsml[extras]", - "xgboost<=1.7.5", + "xgboost", "torch>=1.13.1", ] unstable_extras = [ diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 6237cc25..50041fec 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -132,12 +132,22 @@ def evaluate_classifiers_by_problem( Parameters ---------- - load_path : list of str + load_path : str or list of str The path to the collection of classifier result files to evaluate. - classifier_names : list of str + If load_path is a list, it will load results from each path in the list. It + is expected that classifier_names and dataset_names are lists of lists with + the same length as load_path. + classifier_names : list of str, list of tuple or list of list The names of the classifiers to evaluate. - dataset_names : list of str + A length 2 tuple containing strings can be used to specify a classifier name to + load from in the first item and a classifier name to use in the evaluation + results in the second. + If load_path is a list, classifier_names must be a list of lists with the same + length as load_path. + dataset_names : list of str or list of list The names of the datasets to evaluate. + If load_path is a list, dataset_names must be a list of lists with the same + length as load_path. save_path : str The path to save the evaluation results to. resamples : int or list of int, default=None @@ -151,12 +161,13 @@ def evaluate_classifiers_by_problem( verify_results : bool, default=True If the verification should be performed on the loaded results values. """ - if resamples is None: - resamples = [""] - elif isinstance(resamples, int): - resamples = [str(i) for i in range(resamples)] - else: - resamples = [str(resample) for resample in resamples] + load_path, classifier_names, dataset_names, resamples = _evaluate_by_problem_init( + "classifier", + load_path, + classifier_names, + dataset_names, + resamples, + ) if load_train_results: splits = ["test", "train"] @@ -164,43 +175,63 @@ def evaluate_classifiers_by_problem( splits = ["test"] classifier_results = [] - found_datasets = np.zeros(len(dataset_names), dtype=bool) + estimator_eval_names = [] names = [] - for classifier_name in classifier_names: - found_estimator = False - for i, dataset_name in enumerate(dataset_names): - for resample in resamples: - for split in splits: - try: - classifier_results.append( - ClassifierResults().load_from_file( - f"{load_path}/{classifier_name}/Predictions/" - f"{dataset_name}/{split}Resample{resample}.csv", - verify_values=verify_results, - ) - ) - names.append(classifier_name) - found_estimator = True - found_datasets[i] = True - except FileNotFoundError: - if error_on_missing: - raise FileNotFoundError( - f"Results for {classifier_name} on {dataset_name} " - f"{split} resample {resample} not found." - ) + for i, path in enumerate(load_path): + found_datasets = np.zeros(len(dataset_names[i]), dtype=bool) - if not found_estimator: - print(f"Classifier {classifier_names} not found.") # noqa: T201 + for classifier_name in classifier_names[i]: + found_estimator = False - missing_datasets = [ - dataset for dataset, found in zip(dataset_names, found_datasets) if not found - ] - if missing_datasets: - msg = f"Files for datasets {missing_datasets} not found." - if error_on_missing: - raise FileNotFoundError(msg) - else: - print("\n\n" + msg) # noqa: T201 + if isinstance(classifier_name, tuple): + classifier_eval_name = classifier_name[1] + classifier_name = classifier_name[0] + else: + classifier_eval_name = classifier_name + + if classifier_eval_name not in estimator_eval_names: + estimator_eval_names.append(classifier_eval_name) + else: + raise ValueError( + f"Duplicate evaluation name {classifier_eval_name} found." + ) + + for n, dataset_name in enumerate(dataset_names[i]): + for resample in resamples: + for split in splits: + try: + classifier_results.append( + ClassifierResults().load_from_file( + f"{path}/{classifier_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) + ) + names.append(classifier_eval_name) + found_estimator = True + found_datasets[n] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {classifier_eval_name} on " + f"{dataset_name} {split} resample {resample} not " + "found." + ) + + if not found_estimator: + print(f"Classifier {classifier_eval_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset + for dataset, found in zip(dataset_names[i], found_datasets) + if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_classifiers( classifier_results, @@ -325,12 +356,22 @@ def evaluate_clusterers_by_problem( Parameters ---------- - load_path : list of str + load_path : str or list of str The path to the collection of clusterer result files to evaluate. - clusterer_names : list of str + If load_path is a list, it will load results from each path in the list. It + is expected that clusterer_names and dataset_names are lists of lists with + the same length as load_path. + clusterer_names : list of str, list of tuple or list of list The names of the clusterers to evaluate. - dataset_names : list of str + A length 2 tuple containing strings can be used to specify a clusterer name to + load from in the first item and a clusterer name to use in the evaluation + results in the second. + If load_path is a list, clusterer_names must be a list of lists with the same + length as load_path. + dataset_names : list of str or list of list The names of the datasets to evaluate. + If load_path is a list, dataset_names must be a list of lists with the same + length as load_path. save_path : str The path to save the evaluation results to. resamples : int or list of int, default=None @@ -344,12 +385,13 @@ def evaluate_clusterers_by_problem( verify_results : bool, default=True If the verification should be performed on the loaded results values. """ - if resamples is None: - resamples = [""] - elif isinstance(resamples, int): - resamples = [str(i) for i in range(resamples)] - else: - resamples = [str(resample) for resample in resamples] + load_path, clusterer_names, dataset_names, resamples = _evaluate_by_problem_init( + "clusterer", + load_path, + clusterer_names, + dataset_names, + resamples, + ) if load_test_results: splits = ["test", "train"] @@ -357,43 +399,63 @@ def evaluate_clusterers_by_problem( splits = ["train"] clusterer_results = [] - found_datasets = np.zeros(len(dataset_names), dtype=bool) + estimator_eval_names = [] names = [] - for clusterer_name in clusterer_names: - found_estimator = False - for i, dataset_name in enumerate(dataset_names): - for resample in resamples: - for split in splits: - try: - clusterer_results.append( - ClustererResults().load_from_file( - f"{load_path}/{clusterer_name}/Predictions/" - f"{dataset_name}/{split}Resample{resample}.csv", - verify_values=verify_results, - ) - ) - names.append(clusterer_name) - found_estimator = True - found_datasets[i] = True - except FileNotFoundError: - if error_on_missing: - raise FileNotFoundError( - f"Results for {clusterer_results} on {dataset_name} " - f"{split} resample {resample} not found." - ) + for i, path in enumerate(load_path): + found_datasets = np.zeros(len(dataset_names[i]), dtype=bool) - if not found_estimator: - print(f"Clusterer {clusterer_name} not found.") # noqa: T201 + for clusterer_name in clusterer_names[i]: + found_estimator = False - missing_datasets = [ - dataset for dataset, found in zip(dataset_names, found_datasets) if not found - ] - if missing_datasets: - msg = f"Files for datasets {missing_datasets} not found." - if error_on_missing: - raise FileNotFoundError(msg) - else: - print("\n\n" + msg) # noqa: T201 + if isinstance(clusterer_name, tuple): + clusterer_eval_name = clusterer_name[1] + clusterer_name = clusterer_name[0] + else: + clusterer_eval_name = clusterer_name + + if clusterer_eval_name not in estimator_eval_names: + estimator_eval_names.append(clusterer_eval_name) + else: + raise ValueError( + f"Duplicate evaluation name {clusterer_eval_name} found." + ) + + for n, dataset_name in enumerate(dataset_names[i]): + for resample in resamples: + for split in splits: + try: + clusterer_results.append( + ClustererResults().load_from_file( + f"{path}/{clusterer_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) + ) + names.append(clusterer_eval_name) + found_estimator = True + found_datasets[n] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {clusterer_eval_name} on " + f"{dataset_name} {split} resample {resample} not " + "found." + ) + + if not found_estimator: + print(f"Clusterer {clusterer_eval_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset + for dataset, found in zip(dataset_names[i], found_datasets) + if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_clusterers( clusterer_results, @@ -518,12 +580,22 @@ def evaluate_regressors_by_problem( Parameters ---------- - load_path : list of str + load_path : str or list of str The path to the collection of regressor result files to evaluate. - regressor_names : list of str + If load_path is a list, it will load results from each path in the list. It + is expected that regressor_names and dataset_names are lists of lists with + the same length as load_path. + regressor_names : list of str, list of tuple or list of list The names of the regressors to evaluate. - dataset_names : list of str + A length 2 tuple containing strings can be used to specify a regressor name to + load from in the first item and a regressor name to use in the evaluation + results in the second. + If load_path is a list, regressor_names must be a list of lists with the same + length as load_path. + dataset_names : list of str or list of list The names of the datasets to evaluate. + If load_path is a list, dataset_names must be a list of lists with the same + length as load_path. save_path : str The path to save the evaluation results to. resamples : int or list of int, default=None @@ -537,12 +609,13 @@ def evaluate_regressors_by_problem( verify_results : bool, default=True If the verification should be performed on the loaded results values. """ - if resamples is None: - resamples = [""] - elif isinstance(resamples, int): - resamples = [str(i) for i in range(resamples)] - else: - resamples = [str(resample) for resample in resamples] + load_path, regressor_names, dataset_names, resamples = _evaluate_by_problem_init( + "regressor", + load_path, + regressor_names, + dataset_names, + resamples, + ) if load_train_results: splits = ["test", "train"] @@ -550,43 +623,63 @@ def evaluate_regressors_by_problem( splits = ["test"] regressor_results = [] - found_datasets = np.zeros(len(dataset_names), dtype=bool) + estimator_eval_names = [] names = [] - for regressor_name in regressor_names: - found_estimator = False - for i, dataset_name in enumerate(dataset_names): - for resample in resamples: - for split in splits: - try: - regressor_results.append( - RegressorResults().load_from_file( - f"{load_path}/{regressor_name}/Predictions/" - f"{dataset_name}/{split}Resample{resample}.csv", - verify_values=verify_results, - ) - ) - names.append(regressor_name) - found_estimator = True - found_datasets[i] = True - except FileNotFoundError: - if error_on_missing: - raise FileNotFoundError( - f"Results for {regressor_results} on {dataset_name} " - f"{split} resample {resample} not found." - ) + for i, path in enumerate(load_path): + found_datasets = np.zeros(len(dataset_names[i]), dtype=bool) - if not found_estimator: - print(f"Regressor {regressor_name} not found.") # noqa: T201 + for regressor_name in regressor_names[i]: + found_estimator = False - missing_datasets = [ - dataset for dataset, found in zip(dataset_names, found_datasets) if not found - ] - if missing_datasets: - msg = f"Files for datasets {missing_datasets} not found." - if error_on_missing: - raise FileNotFoundError(msg) - else: - print("\n\n" + msg) # noqa: T201 + if isinstance(regressor_name, tuple): + regressor_eval_name = regressor_name[1] + regressor_name = regressor_name[0] + else: + regressor_eval_name = regressor_name + + if regressor_eval_name not in estimator_eval_names: + estimator_eval_names.append(regressor_eval_name) + else: + raise ValueError( + f"Duplicate evaluation name {regressor_eval_name} found." + ) + + for n, dataset_name in enumerate(dataset_names[i]): + for resample in resamples: + for split in splits: + try: + regressor_results.append( + RegressorResults().load_from_file( + f"{path}/{regressor_name}/Predictions/" + f"{dataset_name}/{split}Resample{resample}.csv", + verify_values=verify_results, + ) + ) + names.append(regressor_eval_name) + found_estimator = True + found_datasets[n] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {regressor_eval_name} on " + f"{dataset_name} {split} resample {resample} not " + f"found." + ) + + if not found_estimator: + print(f"Regressor {regressor_eval_name} not found.") # noqa: T201 + + missing_datasets = [ + dataset + for dataset, found in zip(dataset_names[i], found_datasets) + if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_regressors( regressor_results, @@ -710,12 +803,22 @@ def evaluate_forecasters_by_problem( Parameters ---------- - load_path : list of str - The path to the collection of clusterer result files to evaluate. - forecaster_names : list of str - The names of the clusterers to evaluate. - dataset_names : list of str + load_path : str or list of str + The path to the collection of forecaster result files to evaluate. + If load_path is a list, it will load results from each path in the list. It + is expected that forecaster_names and dataset_names are lists of lists with + the same length as load_path. + forecaster_names : list of str, list of tuple or list of list + The names of the forecasters to evaluate. + A length 2 tuple containing strings can be used to specify a forecaster name to + load from in the first item and a forecaster name to use in the evaluation + results in the second. + If load_path is a list, regressor_names must be a list of lists with the same + length as load_path. + dataset_names : list of str or list of list The names of the datasets to evaluate. + If load_path is a list, dataset_names must be a list of lists with the same + length as load_path.. save_path : str The path to save the evaluation results to. resamples : int or list of int, default=None @@ -727,50 +830,70 @@ def evaluate_forecasters_by_problem( verify_results : bool, default=True If the verification should be performed on the loaded results values. """ - if resamples is None: - resamples = [""] - elif isinstance(resamples, int): - resamples = [str(i) for i in range(resamples)] - else: - resamples = [str(resample) for resample in resamples] + load_path, forecaster_names, dataset_names, resamples = _evaluate_by_problem_init( + "forecaster", + load_path, + forecaster_names, + dataset_names, + resamples, + ) forecaster_results = [] - found_datasets = np.zeros(len(dataset_names), dtype=bool) + estimator_eval_names = [] names = [] - for forecaster_name in forecaster_names: - found_estimator = False - for i, dataset_name in enumerate(dataset_names): - for resample in resamples: - try: - forecaster_results.append( - ForecasterResults().load_from_file( - f"{load_path}/{forecaster_name}/Predictions/" - f"{dataset_name}/testResample{resample}.csv", - verify_values=verify_results, - ) - ) - names.append(forecaster_name) - found_estimator = True - found_datasets[i] = True - except FileNotFoundError: - if error_on_missing: - raise FileNotFoundError( - f"Results for {forecaster_name} on {dataset_name} " - f"resample {resample} not found." + for i, path in enumerate(load_path): + found_datasets = np.zeros(len(dataset_names[i]), dtype=bool) + + for forecaster_name in forecaster_names[i]: + found_estimator = False + + if isinstance(forecaster_name, tuple): + forecaster_eval_name = forecaster_name[1] + forecaster_name = forecaster_name[0] + else: + forecaster_eval_name = forecaster_name + + if forecaster_eval_name not in estimator_eval_names: + estimator_eval_names.append(forecaster_eval_name) + else: + raise ValueError( + f"Duplicate evaluation name {forecaster_eval_name} found." + ) + + for n, dataset_name in enumerate(dataset_names[i]): + for resample in resamples: + try: + forecaster_results.append( + ForecasterResults().load_from_file( + f"{path}/{forecaster_name}/Predictions/" + f"{dataset_name}/testResample{resample}.csv", + verify_values=verify_results, + ) ) + names.append(forecaster_eval_name) + found_estimator = True + found_datasets[n] = True + except FileNotFoundError: + if error_on_missing: + raise FileNotFoundError( + f"Results for {forecaster_eval_name} on {dataset_name} " + f"resample {resample} not found." + ) - if not found_estimator: - print(f"Forecaster {forecaster_name} not found.") # noqa: T201 + if not found_estimator: + print(f"Forecaster {forecaster_eval_name} not found.") # noqa: T201 - missing_datasets = [ - dataset for dataset, found in zip(dataset_names, found_datasets) if not found - ] - if missing_datasets: - msg = f"Files for datasets {missing_datasets} not found." - if error_on_missing: - raise FileNotFoundError(msg) - else: - print("\n\n" + msg) # noqa: T201 + missing_datasets = [ + dataset + for dataset, found in zip(dataset_names[i], found_datasets) + if not found + ] + if missing_datasets: + msg = f"Files for datasets {missing_datasets} not found." + if error_on_missing: + raise FileNotFoundError(msg) + else: + print("\n\n" + msg) # noqa: T201 evaluate_forecasters( forecaster_results, @@ -781,6 +904,39 @@ def evaluate_forecasters_by_problem( ) +def _evaluate_by_problem_init( + type, load_path, estimator_names, dataset_names, resamples +): + if isinstance(load_path, str): + load_path = [load_path] + elif not isinstance(load_path, list): + raise TypeError("load_path must be a str or list of str.") + + if isinstance(estimator_names[0], (str, tuple)): + estimator_names = [estimator_names] + elif not isinstance(estimator_names[0], list): + raise TypeError(f"{type}_names must be a str, tuple or list of str or tuple.") + + if isinstance(dataset_names[0], str): + dataset_names = [dataset_names] + elif not isinstance(dataset_names[0], list): + raise TypeError("dataset_names must be a str or list of str.") + + if len(load_path) != len(estimator_names) or len(load_path) != len(dataset_names): + raise ValueError( + f"load_path, {type}_names and dataset_names must be the same length." + ) + + if resamples is None: + resamples = [""] + elif isinstance(resamples, int): + resamples = [str(i) for i in range(resamples)] + else: + resamples = [str(resample) for resample in resamples] + + return load_path, estimator_names, dataset_names, resamples + + def _evaluate_estimators( estimator_results, statistics, diff --git a/tsml_eval/evaluation/storage/classifier_results.py b/tsml_eval/evaluation/storage/classifier_results.py index 911e5654..bc0768b4 100644 --- a/tsml_eval/evaluation/storage/classifier_results.py +++ b/tsml_eval/evaluation/storage/classifier_results.py @@ -142,7 +142,7 @@ def __init__( self.accuracy = None self.balanced_accuracy = None self.auroc_score = None - self.negative_log_likelihood = None + self.log_loss = None self.f1_score = None super(ClassifierResults, self).__init__( @@ -164,7 +164,7 @@ def __init__( "accuracy": ("Accuracy", True, False), "balanced_accuracy": ("BalAcc", True, False), "auroc_score": ("AUROC", True, False), - "negative_log_likelihood": ("NLL", False, False), + "log_loss": ("LogLoss", False, False), "f1_score": ("F1", True, False), **EstimatorResults.statistics, } @@ -257,8 +257,8 @@ def calculate_statistics(self, overwrite=False): self.balanced_accuracy = balanced_accuracy_score( self.class_labels, self.predictions ) - if self.negative_log_likelihood is None or overwrite: - self.negative_log_likelihood = log_loss( + if self.log_loss is None or overwrite: + self.log_loss = log_loss( self.class_labels, self.probabilities, eps=0.01, From 115027c848325b75a7e55417d3747a28a7920547 Mon Sep 17 00:00:00 2001 From: Matthew Middlehurst Date: Tue, 21 Nov 2023 21:00:40 +0000 Subject: [PATCH 19/19] email --- pyproject.toml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 37184c03..fa029720 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,12 +7,12 @@ name = "tsml-eval" version = "0.1.1" description = "A package for benchmarking time series machine learning tools." authors = [ - {name = "Matthew Middlehurst", email = "m.middlehurst@uea.ac.uk"}, - {name = "Tony Bagnall", email = "ajb@uea.ac.uk"}, + {name = "Matthew Middlehurst", email = "m.b.middlehurst@soton.ac.uk"}, + {name = "Tony Bagnall", email = "a.j.bagnall@soton.ac.uk"}, ] maintainers = [ - {name = "Matthew Middlehurst", email = "m.middlehurst@uea.ac.uk"}, - {name = "Tony Bagnall", email = "ajb@uea.ac.uk"}, + {name = "Matthew Middlehurst", email = "m.b.middlehurst@soton.ac.uk"}, + {name = "Tony Bagnall", email = "a.j.bagnall@soton.ac.uk"}, ] readme = "README.md" keywords = [