Setting time series regression default data checks to regression data checks.

Author: freddyaboulton

evalml/data_checks/default_data_checks.py

@@ -32,7 +32,7 @@ def __init__(self, problem_type):
         Arguments:
             problem_type (str): The problem type that is being validated. Can be regression, binary, or multiclass.
         """
-        if handle_problem_types(problem_type) == ProblemTypes.REGRESSION:
+        if handle_problem_types(problem_type) in [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION]:
             super().__init__(self._DEFAULT_DATA_CHECK_CLASSES,
                              data_check_params={"InvalidTargetDataCheck": {"problem_type": problem_type}})
         else:

evalml/tests/automl_tests/test_automl.py

@@ -1828,7 +1828,7 @@ class Pipeline2(TimeSeriesRegressionPipeline):
 
     automl = AutoMLSearch(problem_type="time series regression", problem_configuration=configuration,
                           allowed_pipelines=[Pipeline1, Pipeline2], max_iterations=4)
-    automl.search(X, y, data_checks='disabled')
+    automl.search(X, y)
     assert isinstance(automl.data_split, TimeSeriesSplit)
     for result in automl.results['pipeline_results'].values():
         if result["id"] == 0:

evalml/tests/data_checks_tests/test_data_checks.py

@@ -105,6 +105,12 @@ def test_default_data_checks_regression():
     assert data_checks.validate(X, y) == {"warnings": messages[:3], "errors": messages[3:]}
 
 
+def test_default_data_checks_time_series_regression():
+    regression_data_check_classes = [check.__class__ for check in DefaultDataChecks("regression").data_checks]
+    ts_regression_data_check_classes = [check.__class__ for check in DefaultDataChecks("time series regression").data_checks]
+    assert regression_data_check_classes == ts_regression_data_check_classes
+
+
 def test_data_checks_init_from_classes():
     def make_mock_data_check(check_name):
         class MockCheck(DataCheck):