[experimental] Increase the coverage

Author: nabenabe0928

test/test_api/test_api.py

@@ -206,8 +206,8 @@ def _get_estimator(
     resampling_strategy,
     resampling_strategy_args,
     metric,
-    total_walltime_limit=40,
-    func_eval_time_limit_secs=10,
+    total_walltime_limit=18,
+    func_eval_time_limit_secs=6,
     **kwargs
 ):
 
@@ -253,6 +253,10 @@ def _check_tabular_task(estimator, X_test, y_test, task_type, resampling_strateg
 
     _check_picklable(estimator, X_test)
 
+    representation = estimator.show_models()
+    assert isinstance(representation, str)
+    assert all(word in representation for word in ['Weight', 'Preprocessing', 'Estimator'])
+
 
 # Test
 # ====
@@ -314,10 +318,6 @@ def test_tabular_regression(openml_id, resampling_strategy, backend, resampling_
         n_successful_runs=1
     )
 
-    representation = estimator.show_models()
-    assert isinstance(representation, str)
-    assert all(word in representation for word in ['Weight', 'Preprocessing', 'Estimator'])
-
 
 @pytest.mark.parametrize('openml_id', (
     1590,  # Adult to test NaN in categorical columns
@@ -354,8 +354,8 @@ def test_tabular_input_support(openml_id, backend):
             X_train=X_train, y_train=y_train,
             X_test=X_test, y_test=y_test,
             optimize_metric='accuracy',
-            total_walltime_limit=150,
-            func_eval_time_limit_secs=50,
+            total_walltime_limit=30,
+            func_eval_time_limit_secs=6,
             enable_traditional_pipeline=False,
             load_models=False,
         )

test/test_api/utils.py

@@ -2,12 +2,12 @@
 
 from smac.runhistory.runhistory import DataOrigin, RunHistory, RunKey, RunValue, StatusType
 
-from autoPyTorch.constants import REGRESSION_TASKS
+# from autoPyTorch.constants import REGRESSION_TASKS
 from autoPyTorch.evaluation.abstract_evaluator import fit_pipeline
-from autoPyTorch.evaluation.pipeline_class_collection import (
-    DummyClassificationPipeline,
-    DummyRegressionPipeline
-)
+# from autoPyTorch.evaluation.pipeline_class_collection import (
+#     DummyClassificationPipeline,
+#     DummyRegressionPipeline
+# )
 from autoPyTorch.evaluation.train_evaluator import TrainEvaluator
 from autoPyTorch.pipeline.traditional_tabular_classification import TraditionalTabularClassificationPipeline
 from autoPyTorch.utils.common import subsampler
@@ -29,13 +29,15 @@ def dummy_traditional_classification(self, time_left: int, func_eval_time_limit_
 # Fixtures
 # ========
 class DummyTrainEvaluator(TrainEvaluator):
+    """
     def _get_pipeline(self):
         if self.task_type in REGRESSION_TASKS:
             pipeline = DummyRegressionPipeline(config=1)
         else:
             pipeline = DummyClassificationPipeline(config=1)
 
         return pipeline
+    """
 
     def _fit_and_evaluate_loss(self, pipeline, split_id, train_indices, opt_indices):
         X = dict(train_indices=train_indices, val_indices=opt_indices, split_id=split_id, num_run=self.num_run)

test/test_evaluation/test_pipeline_class_collection.py

@@ -21,6 +21,7 @@
     get_default_pipeline_config,
     get_pipeline_class,
 )
+import autoPyTorch.pipeline.tabular_regression
 
 
 def test_get_default_pipeline_config():
@@ -42,6 +43,17 @@ def test_get_pipeline_class(task_type, config):
         assert 'Classification' in pipeline_cls.__mro__[0].__name__
 
 
+@pytest.mark.parametrize('config,ans', (
+    (1, DummyRegressionPipeline),
+    ('tradition', MyTraditionalTabularRegressionPipeline),
+    (unittest.mock.Mock(spec=Configuration), autoPyTorch.pipeline.tabular_regression.TabularRegressionPipeline)
+))
+def test_get_pipeline_class_check_class(config, ans):
+    task_type = TABULAR_REGRESSION
+    pipeline_cls = get_pipeline_class(config, task_type)
+    assert ans is pipeline_cls
+
+
 def test_get_pipeline_class_errors():
     with pytest.raises(RuntimeError):
         get_pipeline_class(config=1.5, task_type=TABULAR_CLASSIFICATION)