From 5f878dac6620273f34399235989257e0d7bc5d89 Mon Sep 17 00:00:00 2001 From: chenyangkang Date: Fri, 25 Oct 2024 10:42:04 -0500 Subject: [PATCH] fix tests --- tests/make_models.py | 48 +++++++++++++++----------------------------- tests/test_model.py | 24 +++++++++++----------- 2 files changed, 28 insertions(+), 44 deletions(-) diff --git a/tests/make_models.py b/tests/make_models.py index 4bcec3b..cfb2fa5 100644 --- a/tests/make_models.py +++ b/tests/make_models.py @@ -23,7 +23,7 @@ min_req = 1 -def make_STEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""): +def make_STEMClassifier(fold_=2, min_req=1): model = STEMClassifier( base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), save_gridding_plot=True, @@ -41,16 +41,14 @@ def make_STEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, en temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model def make_parallel_STEMClassifier( - fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir="" + fold_=2, min_req=1 ): model = STEMClassifier( base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), @@ -69,15 +67,13 @@ def make_parallel_STEMClassifier( temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=2, + n_job=2, ) return model -def make_STEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""): +def make_STEMRegressor(fold_=2, min_req=1): model = STEMRegressor( base_model=Hurdle( classifier=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), @@ -98,15 +94,13 @@ def make_STEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ens temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model -def make_AdaSTEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""): +def make_AdaSTEMClassifier(fold_=2, min_req=1): model = AdaSTEMClassifier( base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), save_gridding_plot=True, @@ -125,14 +119,12 @@ def make_AdaSTEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model -def make_AdaSTEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""): +def make_AdaSTEMRegressor(fold_=2, min_req=1): model = AdaSTEMRegressor( base_model=Hurdle( classifier=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), @@ -154,15 +146,13 @@ def make_AdaSTEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model def make_SphereAdaSTEMRegressor( - fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir="" + fold_=2, min_req=1 ): model = SphereAdaSTEMRegressor( base_model=Hurdle( @@ -185,14 +175,12 @@ def make_SphereAdaSTEMRegressor( temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model -def make_SphereAdaClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""): +def make_SphereAdaClassifier(fold_=2, min_req=1): model = SphereAdaSTEMClassifier( base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), save_gridding_plot=True, @@ -211,15 +199,13 @@ def make_SphereAdaClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=Fals temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=1, + n_job=1, ) return model def make_parallel_SphereAdaClassifier( - fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir="" + fold_=2, min_req=1 ): model = SphereAdaSTEMClassifier( base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1), @@ -239,8 +225,6 @@ def make_parallel_SphereAdaClassifier( temporal_bin_start_jitter="adaptive", spatio_bin_jitter_magnitude="adaptive", use_temporal_to_train=True, - ensemble_models_disk_saver=ensemble_models_disk_saver, - ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir, - njobs=2, + n_job=2, ) return model diff --git a/tests/test_model.py b/tests/test_model.py index e1c86cf..2c65e85 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -26,7 +26,7 @@ def test_STEMClassifier(): model = make_STEMClassifier() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -47,7 +47,7 @@ def test_STEMClassifier(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3 @@ -86,7 +86,7 @@ def test_STEMRegressor(): model = make_STEMRegressor() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -107,7 +107,7 @@ def test_STEMRegressor(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3 @@ -116,7 +116,7 @@ def test_AdaSTEMClassifier(): model = make_AdaSTEMClassifier() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -137,7 +137,7 @@ def test_AdaSTEMClassifier(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3 @@ -146,7 +146,7 @@ def test_AdaSTEMRegressor(): model = make_AdaSTEMRegressor() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -167,7 +167,7 @@ def test_AdaSTEMRegressor(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3 @@ -176,7 +176,7 @@ def test_SphereAdaClassifier(): model = make_SphereAdaClassifier() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -197,7 +197,7 @@ def test_SphereAdaClassifier(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3 @@ -236,7 +236,7 @@ def test_SphereAdaSTEMRegressor(): model = make_SphereAdaSTEMRegressor() model = model.fit(X_train, np.where(y_train > 0, 1, 0)) - pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1) + pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1) assert np.sum(~np.isnan(pred_mean)) > 0 assert np.sum(~np.isnan(pred_std)) > 0 @@ -257,6 +257,6 @@ def test_SphereAdaSTEMRegressor(): model.calculate_feature_importances() assert model.feature_importances_.shape[0] > 0 - importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1) + importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1) assert importances_by_points.shape[0] > 0 assert importances_by_points.shape[1] == len(x_names) + 3