update tests to cover more

tests/test_model.py

@@ -151,6 +151,13 @@ def test_AdaSTEMRegressor():
     assert np.sum(~np.isnan(pred_mean)) > 0
     assert np.sum(~np.isnan(pred_std)) > 0
 
+    pred_ = model.predict(X_test, aggregation='median')
+    assert len(pred_) == len(X_test)
+    assert np.sum(np.isnan(pred_)) / len(pred_) <= 0.3
+
+    pred_return_by_separate_ensembles = model.predict(X_test, return_by_separate_ensembles=True)
+    assert pred_return_by_separate_ensembles.shape[1]==model.ensemble_fold
+
     pred = model.predict(X_test)
     assert len(pred) == len(X_test)
     assert np.sum(np.isnan(pred)) / len(pred) <= 0.3
@@ -171,6 +178,20 @@ def test_AdaSTEMRegressor():
     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
+
+    #
+    eval = AdaSTEM.eval_STEM_res("AAA", pred_df.y_true, pred_df.y_pred) # this should not work
+    all_nan = True
+    for eval_metric in eval:
+        if not np.isnan(eval[eval_metric]):
+            all_nan = False
+            break
+    assert all_nan
+
+    # score
+    score_df = model.score(X_test, y_test)
+
+
 
 
 def test_SphereAdaClassifier():
@@ -202,10 +223,14 @@ def test_SphereAdaClassifier():
     assert importances_by_points.shape[0] > 0
     assert importances_by_points.shape[1] == len(x_names) + 3
 
-
+    importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=2, aggregation='median')
+    assert importances_by_points.shape[0] > 0
+    assert importances_by_points.shape[1] == len(x_names) + 3
+
 def test_parallel_SphereAdaClassifier():
     model = make_parallel_SphereAdaClassifier()
     model = model.fit(X_train, np.where(y_train > 0, 1, 0))
+    model = model.set_params(save_gridding_plot=True)
 
     pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1)
     assert np.sum(~np.isnan(pred_mean)) > 0
@@ -261,8 +286,9 @@ def test_SphereAdaSTEMRegressor():
     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
+
 
-def test_AdaSTEMRegressor():
+def test_AdaSTEMRegressor_Hurdle_for_AdaSTEM():
     model = make_AdaSTEMRegressor_Hurdle_for_AdaSTEM()
     model = model.fit(X_train, np.where(y_train > 0, 1, 0))
 
@@ -286,3 +312,33 @@ def test_AdaSTEMRegressor():
 
 
 
+# def test_AdaSTEMRegressor_median():
+#     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, n_jobs=1)
+#     assert np.sum(~np.isnan(pred_mean)) > 0
+#     assert np.sum(~np.isnan(pred_std)) > 0
+
+#     pred = model.predict(X_test)
+#     assert len(pred) == len(X_test)
+#     assert np.sum(np.isnan(pred)) / len(pred) <= 0.3
+
+#     pred_df = pd.DataFrame(
+#         {"y_true": y_test.flatten(), "y_pred": np.where(pred.flatten() < 0, 0, pred.flatten())}
+#     ).dropna()
+#     assert len(pred_df) > 0
+
+#     eval = AdaSTEM.eval_STEM_res("hurdle", pred_df.y_true, pred_df.y_pred)
+#     assert eval["AUC"] >= 0.5
+#     assert eval["kappa"] >= 0.2
+#     assert eval["Spearman_r"] >= 0.2
+
+#     model.calculate_feature_importances()
+#     assert model.feature_importances_.shape[0] > 0
+
+#     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
+
+