def test_get_skip_evaluation_pipeline_and_parameters(self): _, parameter_values = utils.get_skip_evaluation_pipeline_and_parameters( 'project', 'us-central1', 'gs://foo', 'target', 'classification', 'maximize-au-prc', {'auto': { 'column_name': 'feature_1' }}, { 'fraction_split': { 'training_fraction': 0.8, 'validation_fraction': 0.2, 'test_fraction': 0.0 } }, {'csv_data_source': { 'csv_filenames': ['gs://foo/bar.csv'] }}, 1000) expected_parameter_values = { 'project': 'project', 'location': 'us-central1', 'root_dir': 'gs://foo', 'target_column_name': 'target', 'prediction_type': 'classification', 'optimization_objective': 'maximize-au-prc', 'transformations': '{\\"auto\\": {\\"column_name\\": \\"feature_1\\"}}', 'split_spec': '{\\"fraction_split\\": {\\"training_fraction\\": 0.8, ' '\\"validation_fraction\\": 0.2, \\"test_fraction\\": 0.0}}', 'data_source': '{\\"csv_data_source\\": {\\"csv_filenames\\": ' '[\\"gs://foo/bar.csv\\"]}}', 'stage_1_deadline_hours': 0.7708333333333334, 'stage_1_num_parallel_trials': 35, 'stage_1_num_selected_trials': 7, 'stage_1_single_run_max_secs': 634, 'reduce_search_space_mode': 'minimal', 'stage_2_deadline_hours': 0.22916666666666663, 'stage_2_num_parallel_trials': 35, 'stage_2_num_selected_trials': 5, 'stage_2_single_run_max_secs': 634, 'weight_column_name': '', 'optimization_objective_recall_value': -1, 'optimization_objective_precision_value': -1, 'study_spec_override': '', 'stage_1_tuner_worker_pool_specs_override': '', 'cv_trainer_worker_pool_specs_override': '', 'export_additional_model_without_custom_ops': False, 'stats_and_example_gen_dataflow_machine_type': 'n1-standard-16', 'stats_and_example_gen_dataflow_max_num_workers': 25, 'stats_and_example_gen_dataflow_disk_size_gb': 40, 'transform_dataflow_machine_type': 'n1-standard-16', 'transform_dataflow_max_num_workers': 25, 'transform_dataflow_disk_size_gb': 40, 'encryption_spec_key_name': '', 'dataflow_subnetwork': '', 'dataflow_use_public_ips': True, } self.assertEqual(parameter_values, expected_parameter_values)
def test_get_skip_evaluation_pipeline_and_parameters(self): _, parameter_values = utils.get_skip_evaluation_pipeline_and_parameters( 'project', 'us-central1', 'gs://foo', 'target', 'classification', 'maximize-au-prc', {'auto': { 'column_name': 'feature_1' }}, { 'fraction_split': { 'training_fraction': 0.8, 'validation_fraction': 0.2, 'test_fraction': 0.0 } }, {'csv_data_source': { 'csv_filenames': ['gs://foo/bar.csv'] }}, 1000) self.assertEqual(parameter_values, self.parameter_values)