def Do(self, input_dict,
output_dict,
exec_properties):
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data.
Returns:
None
"""
if 'model_exports' not in input_dict:
raise ValueError('\'model_exports\' is missing in input dict.')
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'output' not in output_dict:
raise ValueError('\'output\' is missing in output dict.')
self._log_startup(input_dict, output_dict, exec_properties)
# Extract input artifacts
model_exports_uri = types.get_single_uri(input_dict['model_exports'])
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
output_uri = types.get_single_uri(output_dict['output'])
eval_model_path = path_utils.eval_model_path(model_exports_uri)
tf.logging.info('Using {} for model eval.'.format(eval_model_path))
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_path)
tf.logging.info('Evaluating model.')
with beam.Pipeline(argv=self._get_beam_pipeline_args()) as pipeline:
# pylint: disable=expression-not-assigned
(pipeline
| 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(
types.get_split_uri(input_dict['examples'], 'eval')))
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=output_uri))
tf.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
def benchmarkAggregateCombineManualActuation(self):
"""Benchmark the aggregate combine stage "manually".
Runs _AggregateCombineFn "manually" outside a Beam pipeline. Records the
wall time taken.
"""
# Run InputsToExtracts manually.
records = []
for x in self._dataset.read_raw_dataset(deserialize=False,
limit=MAX_NUM_EXAMPLES):
records.append({tfma.constants.INPUT_KEY: x})
fn = tfma.extractors.predict_extractor._TFMAPredictionDoFn( # pylint: disable=protected-access
eval_shared_models={"": tfma.default_eval_shared_model(
eval_saved_model_path=self._dataset.tfma_saved_model_path())},
eval_config=None)
fn.setup()
# Predict
predict_batch_size = 1000
predict_result = []
for batch in benchmark_utils.batched_iterator(records,
predict_batch_size):
predict_result.extend(fn.process(batch))
# AggregateCombineFn
#
# We simulate accumulating records into multiple different accumulators,
# each with inputs_per_accumulator records, and then merging the resulting
# accumulators together at one go.
# Number of elements to feed into a single accumulator.
# (This means we will have len(records) / inputs_per_accumulator
# accumulators to merge).
inputs_per_accumulator = 1000
combiner = tfma.evaluators.aggregate._AggregateCombineFn( # pylint: disable=protected-access
eval_shared_model=tfma.default_eval_shared_model(
eval_saved_model_path=self._dataset.tfma_saved_model_path()))
accumulators = []
start = time.time()
for batch in benchmark_utils.batched_iterator(predict_result,
inputs_per_accumulator):
accumulator = combiner.create_accumulator()
for elem in batch:
combiner.add_input(accumulator, elem)
accumulators.append(accumulator)
final_accumulator = combiner.merge_accumulators(accumulators)
final_output = combiner.extract_output(final_accumulator)
end = time.time()
delta = end - start
# Extract output to sanity check example count. This is not timed.
extract_fn = tfma.evaluators.aggregate._ExtractOutputDoFn( # pylint: disable=protected-access
eval_shared_model=tfma.default_eval_shared_model(
eval_saved_model_path=self._dataset.tfma_saved_model_path()))
extract_fn.setup()
interpreted_output = list(extract_fn.process(((), final_output)))
if len(interpreted_output) != 1:
raise ValueError("expecting exactly 1 interpreted output, got %d" %
(len(interpreted_output)))
got_example_count = interpreted_output[0][1].get(
"post_export_metrics/example_count")
if got_example_count != self._dataset.num_examples(
limit=MAX_NUM_EXAMPLES):
raise ValueError(
"example count mismatch: expecting %d got %d" %
(self._dataset.num_examples(limit=MAX_NUM_EXAMPLES),
got_example_count))
self.report_benchmark(
iters=1,
wall_time=delta,
extras={
"inputs_per_accumulator": inputs_per_accumulator,
"num_examples":
self._dataset.num_examples(limit=MAX_NUM_EXAMPLES)
})
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data.
Returns:
None
"""
if 'model_exports' not in input_dict:
raise ValueError('\'model_exports\' is missing in input dict.')
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'output' not in output_dict:
raise ValueError('\'output\' is missing in output dict.')
self._log_startup(input_dict, output_dict, exec_properties)
# Extract input artifacts
model_exports_uri = artifact_utils.get_single_uri(
input_dict['model_exports'])
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
output_uri = artifact_utils.get_single_uri(output_dict['output'])
eval_model_path = path_utils.eval_model_path(model_exports_uri)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
# Need to import the following module so that the fairness indicator
# post-export metric is registered.
import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
absl.logging.info('Using {} for model eval.'.format(eval_model_path))
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_path,
add_metrics_callbacks=add_metrics_callbacks)
absl.logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
# pylint: disable=expression-not-assigned
(pipeline
| 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict['examples'], 'eval')))
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=output_uri))
absl.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
def process_tfma(eval_result_dir,
schema_file,
input_csv=None,
big_query_table=None,
eval_model_dir=None,
max_eval_rows=None,
pipeline_args=None):
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
eval_result_dir: A directory where the evaluation result should be written
to.
schema_file: A file containing a text-serialized Schema that describes the
eval data.
input_csv: A path to a csv file which should be the input for evaluation.
This can only be set if big_query_table is None.
big_query_table: A BigQuery table name specified as DATASET.TABLE which
should be the input for evaluation. This can only be set if input_csv is
None.
eval_model_dir: A directory where the eval model is located.
max_eval_rows: Number of rows to query from BigQuery.
pipeline_args: additional DataflowRunner or DirectRunner args passed to the
beam pipeline.
Raises:
ValueError: if input_csv and big_query_table are not specified correctly.
"""
if input_csv == big_query_table and input_csv is None:
raise ValueError(
'one of --input_csv or --big_query_table should be provided.')
slice_spec = [
tfma.slicer.slicer.SingleSliceSpec(),
tfma.slicer.slicer.SingleSliceSpec(columns=['trip_start_hour'])
]
schema = taxi.read_schema(schema_file)
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_dir,
add_metrics_callbacks=[
tfma.post_export_metrics.calibration_plot_and_prediction_histogram(
),
tfma.post_export_metrics.auc_plots()
])
with beam.Pipeline(argv=pipeline_args) as pipeline:
if input_csv:
csv_coder = taxi.make_csv_coder(schema, input_csv)
raw_data = (pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_csv, skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
else:
assert big_query_table
query = taxi.make_sql(big_query_table,
max_eval_rows,
for_eval=True)
raw_feature_spec = taxi.get_raw_feature_spec(schema)
raw_data = (
pipeline
| 'ReadBigQuery' >> beam.io.Read(
beam.io.BigQuerySource(query=query, use_standard_sql=True))
| 'CleanData' >> beam.Map(
lambda x: (taxi.clean_raw_data_dict(x, raw_feature_spec))))
# Examples must be in clean tf-example format.
coder = taxi.make_proto_coder(schema)
_ = (raw_data
| 'ToSerializedTFExample' >> beam.Map(coder.encode)
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=eval_result_dir))
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- eval_config: JSON string of tfma.EvalConfig.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data. Deprecated, use
eval_config.slicing_specs instead.
Returns:
None
"""
if constants.EXAMPLES_KEY not in input_dict:
raise ValueError('EXAMPLES_KEY is missing from input dict.')
if constants.MODEL_KEY not in input_dict:
raise ValueError('MODEL_KEY is missing from input dict.')
if constants.EVALUATION_KEY not in output_dict:
raise ValueError('EVALUATION_KEY is missing from output dict.')
if len(input_dict[constants.MODEL_KEY]) > 1:
raise ValueError(
'There can be only one candidate model, there are {}.'.format(
len(input_dict[constants.MODEL_KEY])))
if constants.BASELINE_MODEL_KEY in input_dict and len(
input_dict[constants.BASELINE_MODEL_KEY]) > 1:
raise ValueError(
'There can be only one baseline model, there are {}.'.format(
len(input_dict[constants.BASELINE_MODEL_KEY])))
self._log_startup(input_dict, output_dict, exec_properties)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
# Need to import the following module so that the fairness indicator
# post-export metric is registered.
import tensorflow_model_analysis.addons.fairness.post_export_metrics.fairness_indicators # pylint: disable=g-import-not-at-top, unused-variable
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
output_uri = artifact_utils.get_single_uri(
output_dict[constants.EVALUATION_KEY])
run_validation = False
models = []
if 'eval_config' in exec_properties and exec_properties['eval_config']:
slice_spec = None
has_baseline = bool(input_dict.get(constants.BASELINE_MODEL_KEY))
eval_config = tfma.EvalConfig()
json_format.Parse(exec_properties['eval_config'], eval_config)
eval_config = tfma.update_eval_config_with_defaults(
eval_config,
maybe_add_baseline=has_baseline,
maybe_remove_baseline=not has_baseline)
tfma.verify_eval_config(eval_config)
# Do not validate model when there is no thresholds configured. This is to
# avoid accidentally blessing models when users forget to set thresholds.
run_validation = bool(
tfma.metrics.metric_thresholds_from_metrics_specs(
eval_config.metrics_specs))
if len(eval_config.model_specs) > 2:
raise ValueError(
"""Cannot support more than two models. There are {} models in this
eval_config.""".format(len(eval_config.model_specs)))
# Extract model artifacts.
for model_spec in eval_config.model_specs:
if model_spec.is_baseline:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.BASELINE_MODEL_KEY])
else:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
if tfma.get_model_type(model_spec) == tfma.TF_ESTIMATOR:
model_path = path_utils.eval_model_path(model_uri)
else:
model_path = path_utils.serving_model_path(model_uri)
absl.logging.info('Using {} as {} model.'.format(
model_path, model_spec.name))
models.append(
tfma.default_eval_shared_model(
model_name=model_spec.name,
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks,
eval_config=eval_config))
else:
eval_config = None
assert ('feature_slicing_spec' in exec_properties
and exec_properties['feature_slicing_spec']
), 'both eval_config and feature_slicing_spec are unset.'
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
model_path = path_utils.eval_model_path(model_uri)
absl.logging.info('Using {} for model eval.'.format(model_path))
models.append(
tfma.default_eval_shared_model(
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks))
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict[constants.EXAMPLES_KEY],
'eval'))
eval_shared_model = models[0] if len(models) == 1 else models
schema = None
if constants.SCHEMA_KEY in input_dict:
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(
input_dict[constants.SCHEMA_KEY])))
absl.logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
# pylint: disable=expression-not-assigned
if _USE_TFXIO:
tensor_adapter_config = None
if tfma.is_batched_input(eval_shared_model, eval_config):
tfxio = tf_example_record.TFExampleRecord(
file_pattern=file_pattern,
schema=schema,
raw_record_column_name=tfma.BATCHED_INPUT_KEY)
if schema is not None:
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations(
))
data = pipeline | 'ReadFromTFRecordToArrow' >> tfxio.BeamSource(
)
else:
data = pipeline | 'ReadFromTFRecord' >> beam.io.ReadFromTFRecord(
file_pattern=file_pattern)
(data
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0]
if len(models) == 1 else models,
eval_config=eval_config,
output_path=output_uri,
slice_spec=slice_spec,
tensor_adapter_config=tensor_adapter_config))
else:
data = pipeline | 'ReadFromTFRecord' >> beam.io.ReadFromTFRecord(
file_pattern=file_pattern)
(data
| 'ExtractEvaluateAndWriteResults' >>
tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0]
if len(models) == 1 else models,
eval_config=eval_config,
output_path=output_uri,
slice_spec=slice_spec))
absl.logging.info(
'Evaluation complete. Results written to {}.'.format(output_uri))
if not run_validation:
# TODO(jinhuang): delete the BLESSING_KEY from output_dict when supported.
absl.logging.info(
'No threshold configured, will not validate model.')
return
# Set up blessing artifact
blessing = artifact_utils.get_single_instance(
output_dict[constants.BLESSING_KEY])
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_URI_KEY,
artifact_utils.get_single_uri(input_dict[constants.MODEL_KEY]))
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY,
input_dict[constants.MODEL_KEY][0].id)
if input_dict.get(constants.BASELINE_MODEL_KEY):
baseline_model = input_dict[constants.BASELINE_MODEL_KEY][0]
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_URI_KEY,
baseline_model.uri)
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_ID_KEY,
baseline_model.id)
if 'current_component_id' in exec_properties:
blessing.set_string_custom_property(
'component_id', exec_properties['current_component_id'])
# Check validation result and write BLESSED file accordingly.
absl.logging.info('Checking validation results.')
validation_result = tfma.load_validation_result(output_uri)
if validation_result.validation_ok:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.BLESSED_VALUE)
else:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.NOT_BLESSED_FILE_NAME),
'')
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.NOT_BLESSED_VALUE)
absl.logging.info('Blessing result {} written to {}.'.format(
validation_result.validation_ok, blessing.uri))
def process_tfma(
schema_file,
big_query_table=None,
eval_model_dir=None,
max_eval_rows=None,
pipeline_args=None,
publish_to_bq=False,
project=None,
metrics_table=None,
metrics_dataset=None):
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
schema_file: A file containing a text-serialized Schema that describes the
eval data.
big_query_table: A BigQuery table name specified as DATASET.TABLE which
should be the input for evaluation. This can only be set if input_csv is
None.
eval_model_dir: A directory where the eval model is located.
max_eval_rows: Number of rows to query from BigQuery.
pipeline_args: additional DataflowRunner or DirectRunner args passed to
the beam pipeline.
publish_to_bq:
project:
metrics_dataset:
metrics_table:
Raises:
ValueError: if input_csv and big_query_table are not specified correctly.
"""
if big_query_table is None:
raise ValueError('--big_query_table should be provided.')
slice_spec = [
tfma.slicer.SingleSliceSpec(),
tfma.slicer.SingleSliceSpec(columns=['trip_start_hour'])
]
metrics_namespace = metrics_table
schema = taxi.read_schema(schema_file)
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_dir,
add_metrics_callbacks=[
tfma.post_export_metrics.calibration_plot_and_prediction_histogram(),
tfma.post_export_metrics.auc_plots()
])
metrics_monitor = None
if publish_to_bq:
metrics_monitor = MetricsReader(
publish_to_bq=publish_to_bq,
project_name=project,
bq_table=metrics_table,
bq_dataset=metrics_dataset,
namespace=metrics_namespace,
filters=MetricsFilter().with_namespace(metrics_namespace))
pipeline = beam.Pipeline(argv=pipeline_args)
query = taxi.make_sql(big_query_table, max_eval_rows, for_eval=True)
raw_feature_spec = taxi.get_raw_feature_spec(schema)
raw_data = (
pipeline
| 'ReadBigQuery' >> ReadFromBigQuery(
query=query, project=project, use_standard_sql=True)
| 'Measure time: Start' >> beam.ParDo(MeasureTime(metrics_namespace))
| 'CleanData' >>
beam.Map(lambda x: (taxi.clean_raw_data_dict(x, raw_feature_spec))))
# Examples must be in clean tf-example format.
coder = taxi.make_proto_coder(schema)
# Prepare arguments for Extract, Evaluate and Write steps
extractors = tfma.default_extractors(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
desired_batch_size=None,
materialize=False)
evaluators = tfma.default_evaluators(
eval_shared_model=eval_shared_model,
desired_batch_size=None,
num_bootstrap_samples=1)
_ = (
raw_data
| 'ToSerializedTFExample' >> beam.Map(coder.encode)
| 'Extract Results' >> tfma.InputsToExtracts()
| 'Extract and evaluate' >> tfma.ExtractAndEvaluate(
extractors=extractors, evaluators=evaluators)
| 'Map Evaluations to PCollection' >> MapEvalToPCollection()
| 'Measure time: End' >> beam.ParDo(MeasureTime(metrics_namespace)))
result = pipeline.run()
result.wait_until_finish()
if metrics_monitor:
metrics_monitor.publish_metrics(result)
def process_tfma(eval_result_dir,
schema_file,
input_csv=None,
big_query_table=None,
eval_model_dir=None,
max_eval_rows=None,
pipeline_args=None):
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
eval_result_dir: A directory where the evaluation result should be written
to.
schema_file: A file containing a text-serialized Schema that describes the
eval data.
input_csv: A path to a csv file which should be the input for evaluation.
This can only be set if big_query_table is None.
big_query_table: A BigQuery table name specified as DATASET.TABLE which
should be the input for evaluation. This can only be set if input_csv is
None.
eval_model_dir: A directory where the eval model is located.
max_eval_rows: Number of rows to query from BigQuery.
pipeline_args: additional DataflowRunner or DirectRunner args passed to the
beam pipeline.
Raises:
ValueError: if input_csv and big_query_table are not specified correctly.
"""
if input_csv == big_query_table and input_csv is None:
raise ValueError(
'one of --input_csv or --big_query_table should be provided.')
slice_spec = [
tfma.slicer.SingleSliceSpec(),
tfma.slicer.SingleSliceSpec(columns=['trip_start_hour'])
]
schema = taxi.read_schema(schema_file)
eval_shared_model = tfma.default_eval_shared_model(
eval_saved_model_path=eval_model_dir,
add_metrics_callbacks=[
tfma.post_export_metrics.calibration_plot_and_prediction_histogram(),
tfma.post_export_metrics.auc_plots()
])
with beam.Pipeline(argv=pipeline_args) as pipeline:
if input_csv:
csv_coder = taxi.make_csv_coder(schema)
raw_data = (
pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_csv, skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
else:
assert big_query_table
query = taxi.make_sql(big_query_table, max_eval_rows, for_eval=True)
raw_feature_spec = taxi.get_raw_feature_spec(schema)
raw_data = (
pipeline
| 'ReadBigQuery' >> beam.io.Read(
beam.io.BigQuerySource(query=query, use_standard_sql=True))
| 'CleanData' >>
beam.Map(lambda x: (taxi.clean_raw_data_dict(x, raw_feature_spec))))
# Examples must be in clean tf-example format.
coder = taxi.make_proto_coder(schema)
_ = (
raw_data
| 'ToSerializedTFExample' >> beam.Map(coder.encode)
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=eval_shared_model,
slice_spec=slice_spec,
output_path=eval_result_dir))
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs a batch job to evaluate the eval_model against the given input.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- model_exports: exported model.
- examples: examples for eval the model.
output_dict: Output dict from output key to a list of Artifacts.
- output: model evaluation results.
exec_properties: A dict of execution properties.
- eval_config: JSON string of tfma.EvalConfig.
- feature_slicing_spec: JSON string of evaluator_pb2.FeatureSlicingSpec
instance, providing the way to slice the data. Deprecated, use
eval_config.slicing_specs instead.
- example_splits: JSON-serialized list of names of splits on which the
metrics are computed. Default behavior (when example_splits is set to
None) is using the 'eval' split.
Returns:
None
"""
if constants.EXAMPLES_KEY not in input_dict:
raise ValueError('EXAMPLES_KEY is missing from input dict.')
if constants.MODEL_KEY not in input_dict:
raise ValueError('MODEL_KEY is missing from input dict.')
if constants.EVALUATION_KEY not in output_dict:
raise ValueError('EVALUATION_KEY is missing from output dict.')
if len(input_dict[constants.MODEL_KEY]) > 1:
raise ValueError('There can be only one candidate model, there are %d.' %
(len(input_dict[constants.MODEL_KEY])))
if constants.BASELINE_MODEL_KEY in input_dict and len(
input_dict[constants.BASELINE_MODEL_KEY]) > 1:
raise ValueError('There can be only one baseline model, there are %d.' %
(len(input_dict[constants.BASELINE_MODEL_KEY])))
self._log_startup(input_dict, output_dict, exec_properties)
# Add fairness indicator metric callback if necessary.
fairness_indicator_thresholds = exec_properties.get(
'fairness_indicator_thresholds', None)
add_metrics_callbacks = None
if fairness_indicator_thresholds:
add_metrics_callbacks = [
tfma.post_export_metrics.fairness_indicators( # pytype: disable=module-attr
thresholds=fairness_indicator_thresholds),
]
output_uri = artifact_utils.get_single_uri(
output_dict[constants.EVALUATION_KEY])
run_validation = False
models = []
if 'eval_config' in exec_properties and exec_properties['eval_config']:
slice_spec = None
has_baseline = bool(input_dict.get(constants.BASELINE_MODEL_KEY))
eval_config = tfma.EvalConfig()
json_format.Parse(exec_properties['eval_config'], eval_config)
eval_config = tfma.update_eval_config_with_defaults(
eval_config,
maybe_add_baseline=has_baseline,
maybe_remove_baseline=not has_baseline)
tfma.verify_eval_config(eval_config)
# Do not validate model when there is no thresholds configured. This is to
# avoid accidentally blessing models when users forget to set thresholds.
run_validation = bool(tfma.metrics.metric_thresholds_from_metrics_specs(
eval_config.metrics_specs))
if len(eval_config.model_specs) > 2:
raise ValueError(
"""Cannot support more than two models. There are %d models in this
eval_config.""" % (len(eval_config.model_specs)))
# Extract model artifacts.
for model_spec in eval_config.model_specs:
if model_spec.is_baseline:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.BASELINE_MODEL_KEY])
else:
model_uri = artifact_utils.get_single_uri(
input_dict[constants.MODEL_KEY])
if tfma.get_model_type(model_spec) == tfma.TF_ESTIMATOR:
model_path = path_utils.eval_model_path(model_uri)
else:
model_path = path_utils.serving_model_path(model_uri)
logging.info('Using %s as %s model.', model_path, model_spec.name)
models.append(tfma.default_eval_shared_model(
model_name=model_spec.name,
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks,
eval_config=eval_config))
else:
eval_config = None
assert ('feature_slicing_spec' in exec_properties and
exec_properties['feature_slicing_spec']
), 'both eval_config and feature_slicing_spec are unset.'
feature_slicing_spec = evaluator_pb2.FeatureSlicingSpec()
json_format.Parse(exec_properties['feature_slicing_spec'],
feature_slicing_spec)
slice_spec = self._get_slice_spec_from_feature_slicing_spec(
feature_slicing_spec)
model_uri = artifact_utils.get_single_uri(input_dict[constants.MODEL_KEY])
model_path = path_utils.eval_model_path(model_uri)
logging.info('Using %s for model eval.', model_path)
models.append(tfma.default_eval_shared_model(
eval_saved_model_path=model_path,
add_metrics_callbacks=add_metrics_callbacks))
eval_shared_model = models[0] if len(models) == 1 else models
schema = None
if constants.SCHEMA_KEY in input_dict:
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(input_dict[constants.SCHEMA_KEY])))
# Load and deserialize example splits from execution properties.
example_splits = json_utils.loads(
exec_properties.get(constants.EXAMPLE_SPLITS_KEY, 'null'))
if not example_splits:
example_splits = ['eval']
logging.info("The 'example_splits' parameter is not set, using 'eval' "
'split.')
logging.info('Evaluating model.')
with self._make_beam_pipeline() as pipeline:
examples_list = []
tensor_adapter_config = None
# pylint: disable=expression-not-assigned
if _USE_TFXIO and tfma.is_batched_input(eval_shared_model, eval_config):
tfxio_factory = tfxio_utils.get_tfxio_factory_from_artifact(
examples=[
artifact_utils.get_single_instance(
input_dict[constants.EXAMPLES_KEY])
],
telemetry_descriptors=_TELEMETRY_DESCRIPTORS,
schema=schema,
raw_record_column_name=tfma_constants.ARROW_INPUT_COLUMN)
# TODO(b/161935932): refactor after TFXIO supports multiple patterns.
for split in example_splits:
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict[constants.EXAMPLES_KEY],
split))
tfxio = tfxio_factory(file_pattern)
data = (
pipeline
| 'ReadFromTFRecordToArrow[%s]' % split >> tfxio.BeamSource())
examples_list.append(data)
if schema is not None:
# Use last tfxio as TensorRepresentations and ArrowSchema are fixed.
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfxio.ArrowSchema(),
tensor_representations=tfxio.TensorRepresentations())
else:
for split in example_splits:
file_pattern = io_utils.all_files_pattern(
artifact_utils.get_split_uri(input_dict[constants.EXAMPLES_KEY],
split))
data = (
pipeline
| 'ReadFromTFRecord[%s]' % split >>
beam.io.ReadFromTFRecord(file_pattern=file_pattern))
examples_list.append(data)
(examples_list | 'FlattenExamples' >> beam.Flatten()
|
'ExtractEvaluateAndWriteResults' >> tfma.ExtractEvaluateAndWriteResults(
eval_shared_model=models[0] if len(models) == 1 else models,
eval_config=eval_config,
output_path=output_uri,
slice_spec=slice_spec,
tensor_adapter_config=tensor_adapter_config))
logging.info('Evaluation complete. Results written to %s.', output_uri)
if not run_validation:
# TODO(jinhuang): delete the BLESSING_KEY from output_dict when supported.
logging.info('No threshold configured, will not validate model.')
return
# Set up blessing artifact
blessing = artifact_utils.get_single_instance(
output_dict[constants.BLESSING_KEY])
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_URI_KEY,
artifact_utils.get_single_uri(input_dict[constants.MODEL_KEY]))
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY,
input_dict[constants.MODEL_KEY][0].id)
if input_dict.get(constants.BASELINE_MODEL_KEY):
baseline_model = input_dict[constants.BASELINE_MODEL_KEY][0]
blessing.set_string_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_URI_KEY,
baseline_model.uri)
blessing.set_int_custom_property(
constants.ARTIFACT_PROPERTY_BASELINE_MODEL_ID_KEY, baseline_model.id)
if 'current_component_id' in exec_properties:
blessing.set_string_custom_property(
'component_id', exec_properties['current_component_id'])
# Check validation result and write BLESSED file accordingly.
logging.info('Checking validation results.')
validation_result = tfma.load_validation_result(output_uri)
if validation_result.validation_ok:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.BLESSED_VALUE)
else:
io_utils.write_string_file(
os.path.join(blessing.uri, constants.NOT_BLESSED_FILE_NAME), '')
blessing.set_int_custom_property(constants.ARTIFACT_PROPERTY_BLESSED_KEY,
constants.NOT_BLESSED_VALUE)
logging.info('Blessing result %s written to %s.',
validation_result.validation_ok, blessing.uri)