diff --git a/aeon/testing/expected_results/expected_regressor_outputs.py b/aeon/testing/expected_results/expected_regressor_outputs.py index c9fb44203a..cef5d95109 100644 --- a/aeon/testing/expected_results/expected_regressor_outputs.py +++ b/aeon/testing/expected_results/expected_regressor_outputs.py @@ -24,6 +24,217 @@ ] ) +covid_3month_preds["Catch22Regressor"] = np.array( + [ + 0.0302, + 0.0354, + 0.0352, + 0.0345, + 0.0259, + 0.0484, + 0.0369, + 0.0827, + 0.0737, + 0.0526, + ] +) + +covid_3month_preds["RandomForestRegressor"] = np.array( + [ + 0.0319, + 0.0505, + 0.0082, + 0.0291, + 0.028, + 0.0266, + 0.0239, + 0.0946, + 0.0946, + 0.0251, + ] +) + +covid_3month_preds["TSFreshRegressor"] = np.array( + [ + 0.0106, + 0.0587, + 0.0082, + 0.0291, + 0.0453, + 0.0185, + 0.036, + 0.0946, + 0.0946, + 0.0251, + ] +) + +covid_3month_preds["HydraRegressor"] = np.array( + [ + -0.0073, + 0.0516, + 0.0378, + 0.0439, + 0.0247, + 0.0426, + 0.0272, + 0.054, + 0.0484, + 0.044, + ] +) + + +covid_3month_preds["MultiRocketHydraRegressor"] = np.array( + [ + -0.0751, + 0.0604, + 0.0315, + 0.0376, + 0.022, + 0.0337, + 0.0249, + 0.0835, + 0.1012, + 0.029, + ] +) + +covid_3month_preds["RocketRegressor"] = np.array( + [ + 0.0381, + 0.0379, + 0.0379, + 0.0368, + 0.04, + 0.0375, + 0.0387, + 0.0419, + 0.0371, + 0.0371, + ] +) + +covid_3month_preds["KNeighborsTimeSeriesRegressor"] = np.array( + [ + 0.0081, + 0.1111, + 0.0408, + 0.0212, + 0.0557, + 0.0408, + 0.0818, + 0.1111, + 0.1111, + 0.0, + ] +) + +covid_3month_preds["CanonicalIntervalForestRegressor"] = np.array( + [ + 0.049, + 0.04, + 0.0299, + 0.0352, + 0.0423, + 0.0315, + 0.0519, + 0.0605, + 0.0647, + 0.037, + ] +) + +covid_3month_preds["DrCIFRegressor"] = np.array( + [ + 0.0302, + 0.0778, + 0.0272, + 0.03, + 0.0405, + 0.0388, + 0.0351, + 0.093, + 0.1041, + 0.0263, + ] +) + +covid_3month_preds["RandomIntervalRegressor"] = np.array( + [ + 0.0405, + 0.062, + 0.0069, + 0.032, + 0.0258, + 0.0478, + 0.032, + 0.062, + 0.062, + 0.0505, + ] +) + +covid_3month_preds["IntervalForestRegressor"] = np.array( + [ + 0.0358, + 0.0511, + 0.024, + 0.023, + 0.0475, + 0.0367, + 0.0308, + 0.0555, + 0.0606, + 0.026, + ] +) + +covid_3month_preds["RandomIntervalSpectralEnsembleRegressor"] = np.array( + [ + 0.0432, + 0.0516, + 0.0291, + 0.0423, + 0.0259, + 0.0247, + 0.0397, + 0.0536, + 0.0406, + 0.0468, + ] +) + +covid_3month_preds["TimeSeriesForestRegressor"] = np.array( + [ + 0.0319, + 0.0556, + 0.0249, + 0.0212, + 0.0385, + 0.0249, + 0.0105, + 0.0556, + 0.076, + 0.0143, + ] +) + +covid_3month_preds["RDSTRegressor"] = np.array( + [ + 0.0368, + 0.0368, + 0.0368, + 0.0369, + 0.0369, + 0.0368, + 0.0368, + 0.0369, + 0.0369, + 0.0368, + ] +) + cardano_sentiment_preds["FreshPRINCERegressor"] = np.array( [ 0.3484, @@ -38,3 +249,213 @@ 0.409, ] ) + +cardano_sentiment_preds["Catch22Regressor"] = np.array( + [ + 0.2174, + 0.1394, + 0.3623, + 0.1496, + 0.3502, + 0.2719, + 0.1378, + 0.076, + 0.0587, + 0.3773, + ] +) + +cardano_sentiment_preds["SummaryRegressor"] = np.array( + [ + 0.3172, + 0.5002, + 0.3072, + 0.4486, + 0.1765, + 0.4664, + 0.0828, + 0.251, + 0.0402, + 0.2641, + ] +) + +cardano_sentiment_preds["TSFreshRegressor"] = np.array( + [ + 0.2997, + 0.2633, + 0.3408, + 0.1045, + 0.2517, + 0.2001, + 0.1546, + 0.1751, + 0.0936, + 0.2973, + ] +) + +cardano_sentiment_preds["HydraRegressor"] = np.array( + [ + 0.5925, + 0.2068, + 0.5268, + 0.2383, + 0.4586, + 0.1701, + 0.2336, + 0.1333, + 0.0025, + 0.4788, + ] +) + +cardano_sentiment_preds["RocketRegressor"] = np.array( + [ + 0.1841, + 0.1884, + 0.1882, + 0.1879, + 0.1862, + 0.1817, + 0.1858, + 0.1894, + 0.1845, + 0.1844, + ] +) + +cardano_sentiment_preds["KNeighborsTimeSeriesRegressor"] = np.array( + [ + 0.3503, + -0.101, + 0.3847, + 0.0, + 0.3847, + 0.0, + 0.178, + 0.0, + 0.0, + 0.3847, + ] +) + +cardano_sentiment_preds["RISTRegressor"] = np.array( + [ + 0.3002, + 0.3174, + 0.718, + 0.089, + 0.4002, + 0.0825, + 0.5342, + 0.0, + 0.3503, + 0.448, + ] +) + +cardano_sentiment_preds["CanonicalIntervalForestRegressor"] = np.array( + [ + 0.276, + 0.1466, + 0.282, + 0.205, + 0.125, + 0.0111, + 0.3672, + 0.0677, + 0.1773, + 0.2586, + ] +) + +cardano_sentiment_preds["DrCIFRegressor"] = np.array( + [ + 0.2361, + 0.2222, + 0.2046, + 0.1709, + 0.2462, + 0.2369, + 0.1916, + 0.1995, + 0.0407, + 0.1428, + ] +) + +cardano_sentiment_preds["IntervalForestRegressor"] = np.array( + [ + 0.173, + 0.083, + 0.3016, + 0.1179, + 0.1651, + 0.1383, + -0.0245, + 0.0961, + 0.049, + 0.1718, + ] +) + +cardano_sentiment_preds["RandomIntervalRegressor"] = np.array( + [ + 0.2546, + 0.1749, + 0.5911, + 0.1562, + 0.3726, + 0.228, + 0.211, + 0.2102, + 0.176, + 0.3056, + ] +) + +cardano_sentiment_preds["RandomIntervalSpectralEnsembleRegressor"] = np.array( + [ + 0.4, + -0.0056, + 0.2023, + 0.0986, + 0.068, + 0.2063, + 0.1309, + 0.1811, + 0.1295, + 0.2968, + ] +) + +cardano_sentiment_preds["TimeSeriesForestRegressor"] = np.array( + [ + 0.2336, + -0.0505, + 0.5514, + 0.089, + 0.3174, + 0.125, + 0.089, + 0.0, + 0.0412, + 0.1924, + ] +) + +cardano_sentiment_preds["RDSTRegressor"] = np.array( + [ + 0.1816, + 0.1706, + 0.1993, + 0.2251, + 0.1606, + 0.1682, + 0.1815, + 0.1829, + 0.1578, + 0.2048, + ] +) diff --git a/aeon/testing/expected_results/regressor_results_reproduction.py b/aeon/testing/expected_results/regressor_results_reproduction.py index f1f6033311..d60feb2067 100644 --- a/aeon/testing/expected_results/regressor_results_reproduction.py +++ b/aeon/testing/expected_results/regressor_results_reproduction.py @@ -4,8 +4,30 @@ from sklearn.utils._testing import set_random_state from aeon.datasets import load_cardano_sentiment, load_covid_3month -from aeon.regression.feature_based import FreshPRINCERegressor - +from aeon.regression.convolution_based import ( + HydraRegressor, + MultiRocketHydraRegressor, + RocketRegressor +) +from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor +from aeon.regression.feature_based import ( + Catch22Regressor, + FreshPRINCERegressor, + SummaryRegressor, + TSFreshRegressor, +) +from aeon.regression.hybrid import RISTRegressor +from aeon.regression.interval_based import ( + CanonicalIntervalForestRegressor, + DrCIFRegressor, + IntervalForestRegressor, + RandomIntervalRegressor, + RandomIntervalSpectralEnsembleRegressor, + TimeSeriesForestRegressor, +) +from aeon.regression.shapelet_based import ( + RDSTRegressor, +) def _reproduce_regression_covid_3month(estimator): X_train, y_train = load_covid_3month(split="train") @@ -42,9 +64,68 @@ def _print_results_for_regressor(regressor_name, dataset_name): regressor = FreshPRINCERegressor.create_test_instance( parameter_set="results_comparison" ) + elif regressor_name == "Catch22Regressor": + regressor = Catch22Regressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "SummaryRegressor": + regressor = SummaryRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "TSFreshRegressor": + regressor = TSFreshRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "HydraRegressor": + regressor = HydraRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "MultiRocketHydraRegressor": + regressor = MultiRocketHydraRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "RocketRegressor": + regressor = RocketRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "KNeighborsTimeSeriesRegressor": + regressor = KNeighborsTimeSeriesRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "RISTRegressor": + regressor = RISTRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "CanonicalIntervalForestRegressor": + regressor = CanonicalIntervalForestRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "DrCIFRegressor": + regressor = DrCIFRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "IntervalForestRegressor": + regressor = IntervalForestRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "RandomIntervalRegressor": + regressor = RandomIntervalRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "RandomIntervalSpectralEnsembleRegressor": + regressor = RandomIntervalSpectralEnsembleRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "TimeSeriesForestRegressor": + regressor = TimeSeriesForestRegressor.create_test_instance( + parameter_set="results_comparison" + ) + elif regressor_name == "RDSTRegressor": + regressor = RDSTRegressor.create_test_instance( + parameter_set="results_comparison" + ) else: raise ValueError(f"Unknown regressor: {regressor_name}") - if dataset_name == "Covid3Month": data_function = _reproduce_regression_covid_3month elif dataset_name == "CardanoSentiment": @@ -62,4 +143,4 @@ def _print_results_for_regressor(regressor_name, dataset_name): if __name__ == "__main__": # change as required when adding new classifiers, datasets or updating results - _print_results_for_regressor("FreshPRINCERegressor", "Covid3Month") + _print_results_for_regressor("RISTRegressor", "Covid3Month")