def _serialize_plots(
plots: Tuple[slicer.SliceKeyType, Dict[Text, Any]],
post_export_metrics: List[types.AddMetricsCallbackType]) -> bytes:
"""Converts the given slice plots into serialized proto PlotsForSlice..
Args:
plots: The slice plots.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto PlotsForSlice.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
if metric_keys.ERROR_METRIC in slice_plots:
tf.compat.v1.logging.warning(
'Error for slice: %s with error message: %s ', slice_key,
slice_plots[metric_keys.ERROR_METRIC])
metrics = metrics_for_slice_pb2.PlotsForSlice()
metrics.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
metrics.plots[metric_keys.ERROR_METRIC].debug_message = slice_plots[
metric_keys.ERROR_METRIC]
return metrics.SerializeToString()
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice plots.
_convert_slice_plots(slice_plots, post_export_metrics, result.plots) # pytype: disable=wrong-arg-types
return result.SerializeToString()
def testConvertSlicePlotsToProtoEmptyPlot(self):
slice_key = _make_slice_key('fruit', 'apple')
tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'}
actual_plot = metrics_plots_and_validations_writer.convert_slice_plots_to_proto(
(slice_key, tfma_plots), [])
expected_plot = metrics_for_slice_pb2.PlotsForSlice()
expected_plot.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
expected_plot.plots[
metric_keys.ERROR_METRIC].debug_message = 'error_message'
self.assertProtoEquals(expected_plot, actual_plot)
def testSerializePlots_emptyPlot(self):
slice_key = _make_slice_key('fruit', 'apple')
tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'}
actual_plot = metrics_and_plots_serialization._serialize_plots(
(slice_key, tfma_plots), [])
expected_plot = metrics_for_slice_pb2.PlotsForSlice()
expected_plot.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
expected_plot.plots[
metric_keys.ERROR_METRIC].debug_message = 'error_message'
self.assertProtoEquals(
expected_plot,
metrics_for_slice_pb2.PlotsForSlice.FromString(actual_plot))
def _serialize_plots(plots, post_export_metrics):
"""Converts the given slice plots into serialized proto PlotsForSlice..
Args:
plots: The slice plots.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto PlotsForSlice.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
# Convert the slice key.
result.slice_key.CopyFrom(_convert_slice_key(slice_key))
# Convert the slice plots.
_convert_slice_plots(slice_plots, post_export_metrics, result.plot_data) # pytype: disable=wrong-arg-types
return result.SerializeToString()
def _serialize_plots(
plots: Tuple[slicer.SliceKeyType, Dict[Text, Any]],
post_export_metrics: List[types.AddMetricsCallbackType]) -> bytes:
"""Converts the given slice plots into serialized proto PlotsForSlice..
Args:
plots: The slice plots.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto PlotsForSlice.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice plots.
_convert_slice_plots(slice_plots, post_export_metrics, result.plots) # pytype: disable=wrong-arg-types
return result.SerializeToString()
def convert_slice_plots_to_proto(
plots: Tuple[slicer.SliceKeyType, Dict[Any, Any]],
add_metrics_callbacks: List[types.AddMetricsCallbackType]
) -> metrics_for_slice_pb2.PlotsForSlice:
"""Converts the given slice plots into PlotsForSlice proto.
Args:
plots: The slice plots.
add_metrics_callbacks: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The PlotsForSlice proto.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
slice_plots = slice_plots.copy()
if metric_keys.ERROR_METRIC in slice_plots:
logging.warning('Error for slice: %s with error message: %s ',
slice_key, slice_plots[metric_keys.ERROR_METRIC])
error_metric = slice_plots.pop(metric_keys.ERROR_METRIC)
result.plots[metric_keys.ERROR_METRIC].debug_message = error_metric
return result
if add_metrics_callbacks and (not any(
isinstance(k, metric_types.MetricKey)
for k in slice_plots.keys())):
for add_metrics_callback in add_metrics_callbacks:
if hasattr(add_metrics_callback, 'populate_plots_and_pop'):
add_metrics_callback.populate_plots_and_pop(
slice_plots, result.plots)
plots_by_key = {}
for key in sorted(slice_plots.keys()):
value = slice_plots[key]
# Remove plot name from key (multiple plots are combined into a single
# proto).
if isinstance(key, metric_types.MetricKey):
parent_key = key._replace(name=None)
else:
continue
if parent_key not in plots_by_key:
key_and_value = result.plot_keys_and_values.add()
key_and_value.key.CopyFrom(parent_key.to_proto())
plots_by_key[parent_key] = key_and_value.value
if isinstance(value,
metrics_for_slice_pb2.CalibrationHistogramBuckets):
plots_by_key[parent_key].calibration_histogram_buckets.CopyFrom(
value)
slice_plots.pop(key)
elif isinstance(value,
metrics_for_slice_pb2.ConfusionMatrixAtThresholds):
plots_by_key[parent_key].confusion_matrix_at_thresholds.CopyFrom(
value)
slice_plots.pop(key)
elif isinstance(
value,
metrics_for_slice_pb2.MultiClassConfusionMatrixAtThresholds):
plots_by_key[
parent_key].multi_class_confusion_matrix_at_thresholds.CopyFrom(
value)
slice_plots.pop(key)
elif isinstance(
value,
metrics_for_slice_pb2.MultiLabelConfusionMatrixAtThresholds):
plots_by_key[
parent_key].multi_label_confusion_matrix_at_thresholds.CopyFrom(
value)
slice_plots.pop(key)
if slice_plots:
if add_metrics_callbacks is None:
add_metrics_callbacks = []
raise NotImplementedError(
'some plots were not converted or popped. keys: %s. '
'add_metrics_callbacks were: %s' % (
slice_plots.keys(),
[
x.name for x in add_metrics_callbacks # pytype: disable=attribute-error
]))
return result
def testWriteMetricsAndPlots(self):
metrics_file = os.path.join(self._getTempDir(), 'metrics')
plots_file = os.path.join(self._getTempDir(), 'plots')
temp_eval_export_dir = os.path.join(self._getTempDir(), 'eval_export_dir')
_, eval_export_dir = (
fixed_prediction_estimator.simple_fixed_prediction_estimator(
None, temp_eval_export_dir))
eval_config = config.EvalConfig(
model_specs=[config.ModelSpec()],
options=config.Options(
disabled_outputs={'values': ['eval_config.json']}))
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=eval_export_dir,
add_metrics_callbacks=[
post_export_metrics.example_count(),
post_export_metrics.calibration_plot_and_prediction_histogram(
num_buckets=2)
])
extractors = [
predict_extractor.PredictExtractor(eval_shared_model),
slice_key_extractor.SliceKeyExtractor()
]
evaluators = [
metrics_and_plots_evaluator.MetricsAndPlotsEvaluator(eval_shared_model)
]
output_paths = {
constants.METRICS_KEY: metrics_file,
constants.PLOTS_KEY: plots_file
}
writers = [
metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter(
output_paths, eval_shared_model.add_metrics_callbacks)
]
with beam.Pipeline() as pipeline:
example1 = self._makeExample(prediction=0.0, label=1.0)
example2 = self._makeExample(prediction=1.0, label=1.0)
# pylint: disable=no-value-for-parameter
_ = (
pipeline
| 'Create' >> beam.Create([
example1.SerializeToString(),
example2.SerializeToString(),
])
| 'ExtractEvaluateAndWriteResults' >>
model_eval_lib.ExtractEvaluateAndWriteResults(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
extractors=extractors,
evaluators=evaluators,
writers=writers))
# pylint: enable=no-value-for-parameter
expected_metrics_for_slice = text_format.Parse(
"""
slice_key {}
metrics {
key: "average_loss"
value {
double_value {
value: 0.5
}
}
}
metrics {
key: "post_export_metrics/example_count"
value {
double_value {
value: 2.0
}
}
}
""", metrics_for_slice_pb2.MetricsForSlice())
metric_records = []
for record in tf.compat.v1.python_io.tf_record_iterator(metrics_file):
metric_records.append(
metrics_for_slice_pb2.MetricsForSlice.FromString(record))
self.assertEqual(1, len(metric_records), 'metrics: %s' % metric_records)
self.assertProtoEquals(expected_metrics_for_slice, metric_records[0])
expected_plots_for_slice = text_format.Parse(
"""
slice_key {}
plots {
key: "post_export_metrics"
value {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
num_weighted_examples {}
total_weighted_label {}
total_weighted_refined_prediction {}
}
buckets {
upper_threshold_exclusive: 0.5
num_weighted_examples {
value: 1.0
}
total_weighted_label {
value: 1.0
}
total_weighted_refined_prediction {}
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples {
}
total_weighted_label {}
total_weighted_refined_prediction {}
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples {
value: 1.0
}
total_weighted_label {
value: 1.0
}
total_weighted_refined_prediction {
value: 1.0
}
}
}
}
}
""", metrics_for_slice_pb2.PlotsForSlice())
plot_records = []
for record in tf.compat.v1.python_io.tf_record_iterator(plots_file):
plot_records.append(
metrics_for_slice_pb2.PlotsForSlice.FromString(record))
self.assertEqual(1, len(plot_records), 'plots: %s' % plot_records)
self.assertProtoEquals(expected_plots_for_slice, plot_records[0])
def testSerializeDeserializeToFile(self):
metrics_slice_key = _make_slice_key(b'fruit', b'pear', b'animal',
b'duck')
metrics_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: "fruit"
bytes_value: "pear"
}
single_slice_keys {
column: "animal"
bytes_value: "duck"
}
}
metrics {
key: "accuracy"
value {
double_value {
value: 0.8
}
}
}
metrics {
key: "example_weight"
value {
double_value {
value: 10.0
}
}
}
metrics {
key: "auc"
value {
bounded_value {
lower_bound {
value: 0.1
}
upper_bound {
value: 0.3
}
value {
value: 0.2
}
}
}
}
metrics {
key: "auprc"
value {
bounded_value {
lower_bound {
value: 0.05
}
upper_bound {
value: 0.17
}
value {
value: 0.1
}
}
}
}""", metrics_for_slice_pb2.MetricsForSlice())
plots_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: "fruit"
bytes_value: "peach"
}
single_slice_keys {
column: "animal"
bytes_value: "cow"
}
}
plots {
key: ''
value {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
}
}
}""", metrics_for_slice_pb2.PlotsForSlice())
plots_slice_key = _make_slice_key(b'fruit', b'peach', b'animal',
b'cow')
eval_config = model_eval_lib.EvalConfig(
model_location='/path/to/model',
data_location='/path/to/data',
slice_spec=[
slicer.SingleSliceSpec(features=[('age', 5), ('gender', 'f')],
columns=['country']),
slicer.SingleSliceSpec(features=[('age', 6), ('gender', 'm')],
columns=['interest'])
],
example_weight_metric_key='key')
output_path = self._getTempDir()
with beam.Pipeline() as pipeline:
metrics = (pipeline
| 'CreateMetrics' >> beam.Create(
[metrics_for_slice.SerializeToString()]))
plots = (pipeline
| 'CreatePlots' >> beam.Create(
[plots_for_slice.SerializeToString()]))
evaluation = {
constants.METRICS_KEY: metrics,
constants.PLOTS_KEY: plots
}
_ = (evaluation
| 'WriteResults' >> model_eval_lib.WriteResults(
writers=model_eval_lib.default_writers(
output_path=output_path)))
_ = pipeline | model_eval_lib.WriteEvalConfig(
eval_config, output_path)
metrics = metrics_and_plots_evaluator.load_and_deserialize_metrics(
path=os.path.join(output_path,
model_eval_lib._METRICS_OUTPUT_FILE))
plots = metrics_and_plots_evaluator.load_and_deserialize_plots(
path=os.path.join(output_path, model_eval_lib._PLOTS_OUTPUT_FILE))
self.assertSliceMetricsListEqual(
[(metrics_slice_key, metrics_for_slice.metrics)], metrics)
self.assertSlicePlotsListEqual(
[(plots_slice_key, plots_for_slice.plots)], plots)
got_eval_config = model_eval_lib.load_eval_config(output_path)
self.assertEqual(eval_config, got_eval_config)
def testSerializePlots(self):
slice_key = _make_slice_key('fruit', 'apple')
plot_key = metric_types.PlotKey(name='calibration_plot',
output_name='output_name')
calibration_plot = text_format.Parse(
"""
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
""", metrics_for_slice_pb2.CalibrationHistogramBuckets())
expected_plots_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: 'fruit'
bytes_value: 'apple'
}
}
plot_keys_and_values {
key {
output_name: "output_name"
}
value {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
}
}
}
""", metrics_for_slice_pb2.PlotsForSlice())
got = metrics_and_plots_serialization._serialize_plots(
(slice_key, {
plot_key: calibration_plot
}), None)
self.assertProtoEquals(
expected_plots_for_slice,
metrics_for_slice_pb2.PlotsForSlice.FromString(got))
def testSerializeDeserializeToFile(self):
metrics_slice_key = _make_slice_key('fruit', 'pear', 'animal', 'duck')
metrics_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: "fruit"
bytes_value: "pear"
}
single_slice_keys {
column: "animal"
bytes_value: "duck"
}
}
metrics {
key: "accuracy"
value {
double_value {
value: 0.8
}
}
}
metrics {
key: "example_weight"
value {
double_value {
value: 10.0
}
}
}
metrics {
key: "auc"
value {
bounded_value {
lower_bound {
value: 0.1
}
upper_bound {
value: 0.3
}
value {
value: 0.2
}
}
}
}
metrics {
key: "auprc"
value {
bounded_value {
lower_bound {
value: 0.05
}
upper_bound {
value: 0.17
}
value {
value: 0.1
}
}
}
}""", metrics_for_slice_pb2.MetricsForSlice())
plots_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: "fruit"
bytes_value: "peach"
}
single_slice_keys {
column: "animal"
bytes_value: "cow"
}
}
plot_data {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
}
}""", metrics_for_slice_pb2.PlotsForSlice())
plots_slice_key = _make_slice_key('fruit', 'peach', 'animal', 'cow')
eval_config = api_types.EvalConfig(
model_location='/path/to/model',
data_location='/path/to/data',
slice_spec=[
slicer.SingleSliceSpec(features=[('age', 5), ('gender', 'f')],
columns=['country']),
slicer.SingleSliceSpec(features=[('age', 6), ('gender', 'm')],
columns=['interest'])
],
example_weight_metric_key='key')
output_path = self._getTempDir()
with beam.Pipeline() as pipeline:
metrics = (pipeline
| 'CreateMetrics' >> beam.Create(
[metrics_for_slice.SerializeToString()]))
plots = (pipeline
| 'CreatePlots' >> beam.Create(
[plots_for_slice.SerializeToString()]))
_ = ((metrics, plots)
| 'WriteMetricsPlotsAndConfig' >>
serialization.WriteMetricsPlotsAndConfig(
output_path=output_path, eval_config=eval_config))
metrics, plots = serialization.load_plots_and_metrics(output_path)
self.assertSliceMetricsListEqual(
[(metrics_slice_key, metrics_for_slice.metrics)], metrics)
self.assertSlicePlotsListEqual(
[(plots_slice_key, plots_for_slice.plot_data)], plots)
got_eval_config = serialization.load_eval_config(output_path)
self.assertEqual(eval_config, got_eval_config)