Remove uneeded ignore warnings from tests

Author: eddiebergman

test/test_automl/test_automl.py

@@ -1,5 +1,3 @@
-from ..test_pipeline.ignored_warnings import ignore_warnings
-
 # -*- encoding: utf-8 -*-
 import itertools
 import os
@@ -67,17 +65,16 @@ def test_fit(dask_client):
         dask_client=dask_client,
     )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
+    automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
 
-    score = automl.score(X_test, Y_test)
-    assert score > 0.8
-    assert count_succeses(automl.cv_results_) > 0
-    assert includes_train_scores(automl.performance_over_time_.columns) is True
-    assert performance_over_time_is_plausible(automl.performance_over_time_) is True
-    assert automl._task == MULTICLASS_CLASSIFICATION
+score = automl.score(X_test, Y_test)
+assert score > 0.8
+assert count_succeses(automl.cv_results_) > 0
+assert includes_train_scores(automl.performance_over_time_.columns) is True
+assert performance_over_time_is_plausible(automl.performance_over_time_) is True
+assert automl._task == MULTICLASS_CLASSIFICATION
 
-    del automl
+del automl
 
 
 def test_fit_roar(dask_client_single_worker):
@@ -112,8 +109,8 @@ def get_roar_object_callback(
         metric=accuracy,
         dask_client=dask_client_single_worker,
     )
-    with ignore_warnings():
-        automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
+
+    automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
 
     score = automl.score(X_test, Y_test)
     assert score > 0.8
@@ -227,8 +224,7 @@ def test_delete_non_candidate_models(dask_client):
         max_models_on_disc=3,
     )
 
-    with ignore_warnings():
-        automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y)
+    automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION, X_test=X, y_test=Y)
 
     # Assert at least one model file has been deleted and that there were no
     # deletion errors
@@ -275,8 +271,7 @@ def test_binary_score_and_include(dask_client):
         dask_client=dask_client,
     )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION)
+    automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION)
 
     assert automl._task == BINARY_CLASSIFICATION
 
@@ -301,15 +296,14 @@ def test_automl_outputs(dask_client):
         delete_tmp_folder_after_terminate=False,
     )
 
-    with ignore_warnings():
-        auto.fit(
-            X=X_train,
-            y=Y_train,
-            X_test=X_test,
-            y_test=Y_test,
-            dataset_name=name,
-            task=MULTICLASS_CLASSIFICATION,
-        )
+    auto.fit(
+        X=X_train,
+        y=Y_train,
+        X_test=X_test,
+        y_test=Y_test,
+        dataset_name=name,
+        task=MULTICLASS_CLASSIFICATION,
+    )
 
     data_manager_file = os.path.join(
         auto._backend.temporary_directory,
@@ -633,8 +627,7 @@ def test_load_best_individual_model(metric, dask_client):
     # We cannot easily mock a function sent to dask
     # so for this test we create the whole set of models/ensembles
     # but prevent it to be loaded
-    with ignore_warnings():
-        automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
+    automl.fit(X_train, Y_train, task=MULTICLASS_CLASSIFICATION)
 
     automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None)
 

test/test_automl/test_estimators.py

@@ -41,8 +41,6 @@
 from autosklearn.experimental.askl2 import AutoSklearn2Classifier
 from autosklearn.smbo import get_smac_object
 
-from ..test_pipeline.ignored_warnings import ignore_warnings
-
 sys.path.append(os.path.dirname(__file__))
 from automl_utils import print_debug_information, count_succeses, includes_train_scores, includes_all_scores, include_single_scores, performance_over_time_is_plausible  # noqa (E402: module level import not at top of file)
 
@@ -82,8 +80,7 @@ def __call__(self, *args, **kwargs):
         max_models_on_disc=None,
     )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train)
+    automl.fit(X_train, Y_train)
 
     # Test that the argument is correctly passed to SMAC
     assert getattr(get_smac_object_wrapper_instance, 'n_jobs') == 2
@@ -277,8 +274,7 @@ def test_performance_over_time_no_ensemble(tmp_dir):
                                 initial_configurations_via_metalearning=0,
                                 ensemble_size=0,)
 
-    with ignore_warnings():
-        cls.fit(X_train, Y_train, X_test, Y_test)
+    cls.fit(X_train, Y_train, X_test, Y_test)
 
     performance_over_time = cls.performance_over_time_
     assert include_single_scores(performance_over_time.columns) is True
@@ -302,8 +298,7 @@ def test_cv_results(tmp_dir):
     params = cls.get_params()
     original_params = copy.deepcopy(params)
 
-    with ignore_warnings():
-        cls.fit(X_train, Y_train)
+    cls.fit(X_train, Y_train)
 
     cv_results = cls.cv_results_
     assert isinstance(cv_results, dict), type(cv_results)
@@ -391,8 +386,7 @@ def test_leaderboard(
         seed=1
     )
 
-    with ignore_warnings():
-        model.fit(X_train, Y_train)
+    model.fit(X_train, Y_train)
 
     for params in params_generator:
         # Convert from iterator to solid list
@@ -551,8 +545,7 @@ def test_can_pickle_classifier(tmp_dir, dask_client):
                                    dask_client=dask_client,
                                    )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train)
+    automl.fit(X_train, Y_train)
 
     initial_predictions = automl.predict(X_test)
     initial_accuracy = sklearn.metrics.accuracy_score(Y_test,
@@ -601,8 +594,7 @@ def test_multilabel(tmp_dir, dask_client):
                                    dask_client=dask_client,
                                    )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train)
+    automl.fit(X_train, Y_train)
 
     predictions = automl.predict(X_test)
     assert predictions.shape == (50, 3), print_debug_information(automl)
@@ -628,9 +620,8 @@ def test_binary(tmp_dir, dask_client):
                                    dask_client=dask_client,
                                    )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test,
-                   dataset_name='binary_test_dataset')
+    automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test,
+               dataset_name='binary_test_dataset')
 
     predictions = automl.predict(X_test)
     assert predictions.shape == (50, ), print_debug_information(automl)
@@ -665,15 +656,13 @@ def test_classification_pandas_support(tmp_dir, dask_client):
         tmp_folder=tmp_dir,
     )
 
-    with ignore_warnings():
-        automl.fit(X, y)
+    automl.fit(X, y)
 
     # Make sure that at least better than random.
     # We use same X_train==X_test to test code quality
     assert automl.score(X, y) > 0.555, print_debug_information(automl)
 
-    with ignore_warnings():
-        automl.refit(X, y)
+    automl.refit(X, y)
 
     # Make sure that at least better than random.
     # accuracy in sklearn needs valid data
@@ -694,8 +683,7 @@ def test_regression(tmp_dir, dask_client):
                                   dask_client=dask_client,
                                   )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train)
+    automl.fit(X_train, Y_train)
 
     predictions = automl.predict(X_test)
     assert predictions.shape == (356,)
@@ -724,8 +712,7 @@ def test_cv_regression(tmp_dir, dask_client):
                                   dask_client=dask_client,
                                   )
 
-    with ignore_warnings():
-        automl.fit(X_train, Y_train)
+    automl.fit(X_train, Y_train)
 
     predictions = automl.predict(X_test)
     assert predictions.shape == (206,)
@@ -754,15 +741,13 @@ def test_regression_pandas_support(tmp_dir, dask_client):
     )
 
     # Make sure we error out because y is not encoded
-    with ignore_warnings():
-        automl.fit(X, y)
+    automl.fit(X, y)
 
     # Make sure that at least better than random.
     # We use same X_train==X_test to test code quality
     assert automl.score(X, y) >= 0.5, print_debug_information(automl)
 
-    with ignore_warnings():
-        automl.refit(X, y)
+    automl.refit(X, y)
 
     # Make sure that at least better than random.
     assert r2(y, automl.predict(X)) > 0.5, print_debug_information(automl)
@@ -784,16 +769,14 @@ def test_autosklearn_classification_methods_returns_self(dask_client):
                                    dask_client=dask_client,
                                    exclude={'feature_preprocessor': ['fast_ica']})
 
-    with ignore_warnings():
-        automl_fitted = automl.fit(X_train, y_train)
+    automl_fitted = automl.fit(X_train, y_train)
 
     assert automl is automl_fitted
 
     automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
     assert automl is automl_ensemble_fitted
 
-    with ignore_warnings():
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
 
     assert automl is automl_refitted
 
@@ -808,15 +791,13 @@ def test_autosklearn_regression_methods_returns_self(dask_client):
                                   dask_client=dask_client,
                                   ensemble_size=0)
 
-    with ignore_warnings():
-        automl_fitted = automl.fit(X_train, y_train)
+    automl_fitted = automl.fit(X_train, y_train)
     assert automl is automl_fitted
 
     automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
     assert automl is automl_ensemble_fitted
 
-    with ignore_warnings():
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
     assert automl is automl_refitted
 
 
@@ -826,16 +807,14 @@ def test_autosklearn2_classification_methods_returns_self(dask_client):
                                     delete_tmp_folder_after_terminate=False,
                                     dask_client=dask_client)
 
-    with ignore_warnings():
-        automl_fitted = automl.fit(X_train, y_train)
+    automl_fitted = automl.fit(X_train, y_train)
 
     assert automl is automl_fitted
 
     automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
     assert automl is automl_ensemble_fitted
 
-    with ignore_warnings():
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
 
     assert automl is automl_refitted
 
@@ -853,16 +832,14 @@ def test_autosklearn2_classification_methods_returns_self_sparse(dask_client):
                                     delete_tmp_folder_after_terminate=False,
                                     dask_client=dask_client)
 
-    with ignore_warnings():
-        automl_fitted = automl.fit(X_train, y_train)
+    automl_fitted = automl.fit(X_train, y_train)
 
     assert automl is automl_fitted
 
     automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
     assert automl is automl_ensemble_fitted
 
-    with ignore_warnings():
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
 
     assert automl is automl_refitted
 
@@ -967,16 +944,15 @@ def test_fit_pipeline(dask_client, task_type, resampling_strategy, disable_file_
                                             X_test=X_test, y_test=y_test,
                                             ).get_default_configuration()
 
-    with ignore_warnings():
-        pipeline, run_info, run_value = automl.fit_pipeline(
-            X=X_train,
-            y=y_train,
-            config=config,
-            X_test=X_test,
-            y_test=y_test,
-            disable_file_output=disable_file_output,
-            resampling_strategy=resampling_strategy
-        )
+    pipeline, run_info, run_value = automl.fit_pipeline(
+        X=X_train,
+        y=y_train,
+        config=config,
+        X_test=X_test,
+        y_test=y_test,
+        disable_file_output=disable_file_output,
+        resampling_strategy=resampling_strategy
+    )
 
     assert isinstance(run_info.config, Configuration)
     assert run_info.cutoff == 30
@@ -1076,11 +1052,10 @@ def test_pass_categorical_and_numeric_columns_to_pipeline(
     )
     config = config_space.get_default_configuration()
 
-    with ignore_warnings():
-        pipeline, _, run_value = automl.fit_pipeline(
-            X=X_train, y=y_train, X_test=X_test, y_test=y_test,
-            config=config, feat_type=feat_type,
-        )
+    pipeline, _, run_value = automl.fit_pipeline(
+        X=X_train, y=y_train, X_test=X_test, y_test=y_test,
+        config=config, feat_type=feat_type,
+    )
 
     assert pipeline is not None, "Expected a pipeline from automl.fit_pipeline"
 
@@ -1129,17 +1104,15 @@ def test_autosklearn_anneal(as_frame):
                                    resampling_strategy='holdout-iterative-fit')
 
     if as_frame:
-        with ignore_warnings():
-            # Let autosklearn calculate the feat types
-            automl_fitted = automl.fit(X, y)
+        # Let autosklearn calculate the feat types
+        automl_fitted = automl.fit(X, y)
 
     else:
         X_, y_ = sklearn.datasets.fetch_openml(data_id=2, return_X_y=True, as_frame=True)
         feat_type = ['categorical' if X_[col].dtype.name == 'category' else 'numerical'
                      for col in X_.columns]
 
-        with ignore_warnings():
-            automl_fitted = automl.fit(X, y, feat_type=feat_type)
+        automl_fitted = automl.fit(X, y, feat_type=feat_type)
 
     assert automl is automl_fitted