def testDoValidation(self, exec_properties, blessed, has_baseline): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. examples = standard_artifacts.Examples() examples.uri = os.path.join(source_data_dir, 'csv_example_gen') examples.split_names = artifact_utils.encode_split_names( ['train', 'eval']) model = standard_artifacts.Model() baseline_model = standard_artifacts.Model() model.uri = os.path.join(source_data_dir, 'trainer/current') baseline_model.uri = os.path.join(source_data_dir, 'trainer/previous/') blessing_output = standard_artifacts.ModelBlessing() blessing_output.uri = os.path.join(output_data_dir, 'blessing_output') schema = standard_artifacts.Schema() schema.uri = os.path.join(source_data_dir, 'schema_gen') input_dict = { EXAMPLES_KEY: [examples], MODEL_KEY: [model], SCHEMA_KEY: [schema], } if has_baseline: input_dict[BASELINE_MODEL_KEY] = [baseline_model] # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') blessing_output = standard_artifacts.ModelBlessing() blessing_output.uri = os.path.join(output_data_dir, 'blessing_output') output_dict = { EVALUATION_KEY: [eval_output], BLESSING_KEY: [blessing_output], } # List needs to be serialized before being passed into Do function. exec_properties[EXAMPLE_SPLITS_KEY] = json_utils.dumps(None) # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( fileio.exists(os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots'))) self.assertTrue( fileio.exists(os.path.join(eval_output.uri, 'validations'))) if blessed: self.assertTrue( fileio.exists(os.path.join(blessing_output.uri, 'BLESSED'))) else: self.assertTrue( fileio.exists(os.path.join(blessing_output.uri, 'NOT_BLESSED')))
def testDoLegacySingleEvalSavedModelWFairness(self, exec_properties): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. examples = standard_artifacts.Examples() examples.uri = os.path.join(source_data_dir, 'csv_example_gen') examples.split_names = artifact_utils.encode_split_names( ['train', 'eval']) model = standard_artifacts.Model() model.uri = os.path.join(source_data_dir, 'trainer/current') input_dict = { EXAMPLES_KEY: [examples], MODEL_KEY: [model], } # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') blessing_output = standard_artifacts.ModelBlessing() blessing_output.uri = os.path.join(output_data_dir, 'blessing_output') output_dict = { EVALUATION_KEY: [eval_output], BLESSING_KEY: [blessing_output], } try: # Need to import the following module so that the fairness indicator # post-export metric is registered. This may raise an ImportError if the # currently-installed version of TFMA does not support fairness # indicators. import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable exec_properties['fairness_indicator_thresholds'] = [ 0.1, 0.3, 0.5, 0.7, 0.9 ] except ImportError: logging.warning( 'Not testing fairness indicators because a compatible TFMA version ' 'is not installed.') # List needs to be serialized before being passed into Do function. exec_properties[EXAMPLE_SPLITS_KEY] = json_utils.dumps(None) # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( fileio.exists(os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots'))) self.assertFalse( fileio.exists(os.path.join(blessing_output.uri, 'BLESSED')))
def testEvalution(self, exec_properties, model_agnostic=False): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. examples = standard_artifacts.Examples() examples.uri = os.path.join(source_data_dir, 'csv_example_gen') examples.split_names = artifact_utils.encode_split_names( ['train', 'eval']) baseline_model = standard_artifacts.Model() baseline_model.uri = os.path.join(source_data_dir, 'trainer/previous/') schema = standard_artifacts.Schema() schema.uri = os.path.join(source_data_dir, 'schema_gen') input_dict = { EXAMPLES_KEY: [examples], SCHEMA_KEY: [schema], } if not model_agnostic: model = standard_artifacts.Model() model.uri = os.path.join(source_data_dir, 'trainer/current') input_dict[MODEL_KEY] = [model] # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') blessing_output = standard_artifacts.ModelBlessing() blessing_output.uri = os.path.join(output_data_dir, 'blessing_output') output_dict = { EVALUATION_KEY: [eval_output], BLESSING_KEY: [blessing_output], } # Test multiple splits. exec_properties[EXAMPLE_SPLITS_KEY] = json_utils.dumps( ['train', 'eval']) if MODULE_FILE_KEY in exec_properties: exec_properties[MODULE_FILE_KEY] = os.path.join( source_data_dir, 'module_file', 'evaluator_module.py') # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( fileio.exists(os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue(fileio.exists(os.path.join(eval_output.uri, 'plots'))) self.assertFalse( fileio.exists(os.path.join(blessing_output.uri, 'BLESSED')))
def test_do(self): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. train_examples = types.TfxArtifact(type_name='ExamplesPath', split='train') eval_examples = types.TfxArtifact(type_name='ExamplesPath', split='eval') eval_examples.uri = os.path.join(source_data_dir, 'csv_example_gen/eval/') model_exports = types.TfxArtifact(type_name='ModelExportPath') model_exports.uri = os.path.join(source_data_dir, 'trainer/current/') input_dict = { 'examples': [train_examples, eval_examples], 'model_exports': [model_exports], } # Create output dict. eval_output = types.TfxArtifact('ModelEvalPath') eval_output.uri = os.path.join(output_data_dir, 'eval_output') output_dict = {'output': [eval_output]} # Create exec proterties. exec_properties = { 'feature_slicing_spec': json_format.MessageToJson( evaluator_pb2.FeatureSlicingSpec(specs=[ evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_hour']), evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_day', 'trip_miles']), ])) } # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( tf.gfile.Exists(os.path.join(eval_output.uri, 'eval_config'))) self.assertTrue( tf.gfile.Exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue(tf.gfile.Exists(os.path.join(eval_output.uri, 'plots')))
def testEvalution(self, exec_properties): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. examples = standard_artifacts.Examples() examples.uri = os.path.join(source_data_dir, 'csv_example_gen') examples.split_names = artifact_utils.encode_split_names( ['train', 'eval']) model = standard_artifacts.Model() baseline_model = standard_artifacts.Model() model.uri = os.path.join(source_data_dir, 'trainer/current') baseline_model.uri = os.path.join(source_data_dir, 'trainer/previous/') schema = standard_artifacts.Schema() schema.uri = os.path.join(source_data_dir, 'schema_gen') input_dict = { constants.EXAMPLES_KEY: [examples], constants.MODEL_KEY: [model], constants.SCHEMA_KEY: [schema], } # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') blessing_output = standard_artifacts.ModelBlessing() blessing_output.uri = os.path.join(output_data_dir, 'blessing_output') output_dict = { constants.EVALUATION_KEY: [eval_output], constants.BLESSING_KEY: [blessing_output], } # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( tf.io.gfile.exists( os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue( tf.io.gfile.exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue( tf.io.gfile.exists(os.path.join(eval_output.uri, 'plots'))) self.assertFalse( tf.io.gfile.exists(os.path.join(blessing_output.uri, 'BLESSED')))
def testDo(self): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. train_examples = standard_artifacts.Examples(split='train') eval_examples = standard_artifacts.Examples(split='eval') eval_examples.uri = os.path.join(source_data_dir, 'csv_example_gen/eval/') model_exports = standard_artifacts.Model() model_exports.uri = os.path.join(source_data_dir, 'trainer/current/') input_dict = { 'examples': [train_examples, eval_examples], 'model_exports': [model_exports], } # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') output_dict = {'output': [eval_output]} # Create exec proterties. exec_properties = { 'feature_slicing_spec': json_format.MessageToJson( evaluator_pb2.FeatureSlicingSpec(specs=[ evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_hour']), evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_day', 'trip_miles']), ])) } # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( # TODO(b/141490237): Update to only check eval_config.json after TFMA # released with corresponding change. tf.gfile.Exists(os.path.join(eval_output.uri, 'eval_config')) or tf.gfile.Exists(os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue(tf.gfile.Exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue(tf.gfile.Exists(os.path.join(eval_output.uri, 'plots')))
def testDo(self): source_data_dir = os.path.join( os.path.dirname(os.path.dirname(__file__)), 'testdata') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) # Create input dict. train_examples = standard_artifacts.Examples(split='train') eval_examples = standard_artifacts.Examples(split='eval') eval_examples.uri = os.path.join(source_data_dir, 'csv_example_gen/eval/') model_exports = standard_artifacts.Model() model_exports.uri = os.path.join(source_data_dir, 'trainer/current/') input_dict = { 'examples': [train_examples, eval_examples], 'model_exports': [model_exports], } # Create output dict. eval_output = standard_artifacts.ModelEvaluation() eval_output.uri = os.path.join(output_data_dir, 'eval_output') output_dict = {'output': [eval_output]} # Create exec proterties. exec_properties = { 'feature_slicing_spec': json_format.MessageToJson( evaluator_pb2.FeatureSlicingSpec(specs=[ evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_hour']), evaluator_pb2.SingleSlicingSpec( column_for_slicing=['trip_start_day', 'trip_miles']), ])) } try: # Need to import the following module so that the fairness indicator # post-export metric is registered. This may raise an ImportError if the # currently-installed version of TFMA does not support fairness # indicators. import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable exec_properties['fairness_indicator_thresholds'] = [ 0.1, 0.3, 0.5, 0.7, 0.9 ] except ImportError: absl.logging.warning( 'Not testing fairness indicators because a compatible TFMA version ' 'is not installed.') # Run executor. evaluator = executor.Executor() evaluator.Do(input_dict, output_dict, exec_properties) # Check evaluator outputs. self.assertTrue( # TODO(b/141490237): Update to only check eval_config.json after TFMA # released with corresponding change. tf.io.gfile.exists(os.path.join(eval_output.uri, 'eval_config')) or tf.io.gfile.exists( os.path.join(eval_output.uri, 'eval_config.json'))) self.assertTrue( tf.io.gfile.exists(os.path.join(eval_output.uri, 'metrics'))) self.assertTrue( tf.io.gfile.exists(os.path.join(eval_output.uri, 'plots')))