[test] Add tests for pipeline repr

Since the modifications in tests removed the coverage on pipeline repr,
I added tests to increase those parts.
Basically, the decrease in the coverage happened due to the usage of
dummy pipelines.

Author: nabenabe0928

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
@@ -29,15 +29,13 @@ 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

@@ -6,6 +6,7 @@
 
 import pytest
 
+import autoPyTorch.pipeline.tabular_regression
 from autoPyTorch.constants import (
     IMAGE_CLASSIFICATION,
     REGRESSION_TASKS,
@@ -21,7 +22,6 @@
     get_default_pipeline_config,
     get_pipeline_class,
 )
-import autoPyTorch.pipeline.tabular_regression
 
 
 def test_get_default_pipeline_config():

test/test_pipeline/test_tabular_classification.py

@@ -491,3 +491,17 @@ def test_train_pipeline_with_runtime(fit_dictionary_tabular_dummy):
 
     # More than 200 epochs would have pass in 5 seconds for this dataset
     assert len(run_summary.performance_tracker['start_time']) > 100
+
+
+def test_get_pipeline_representation():
+    pipeline = TabularClassificationPipeline(
+        dataset_properties={
+            'numerical_columns': None,
+            'categorical_columns': None,
+            'task_type': 'tabular_classification'
+        }
+    )
+    repr = pipeline.get_pipeline_representation()
+    print(repr)
+    assert isinstance(repr, dict)
+    assert all(word in repr for word in ['Preprocessing', 'Estimator'])

test/test_pipeline/test_tabular_regression.py

@@ -317,3 +317,17 @@ def test_pipeline_score(fit_dictionary_tabular_dummy):
     # we should be able to get a decent score on this dummy data
     assert r2_score >= 0.8, f"Pipeline:{pipeline} Config:{config} FitDict: {fit_dictionary_tabular_dummy}, " \
                             f"{pipeline.named_steps['trainer'].run_summary.performance_tracker['train_metrics']}"
+
+
+def test_get_pipeline_representation():
+    pipeline = TabularRegressionPipeline(
+        dataset_properties={
+            'numerical_columns': None,
+            'categorical_columns': None,
+            'task_type': 'tabular_classification'
+        }
+    )
+    repr = pipeline.get_pipeline_representation()
+    print(repr)
+    assert isinstance(repr, dict)
+    assert all(word in repr for word in ['Preprocessing', 'Estimator'])