def testMetricComputedBeamCounter(self):
with beam.Pipeline() as pipeline:
auc = post_export_metrics.auc()
_ = pipeline | counter_util.IncrementMetricsCallbacksCounters([auc])
result = pipeline.run()
metric_filter = beam.metrics.metric.MetricsFilter().with_namespace(
constants.METRICS_NAMESPACE).with_name('metric_computed_auc_v1')
actual_metrics_count = result.metrics().query(
filter=metric_filter)['counters'][0].committed
self.assertEqual(actual_metrics_count, 1)
def ComputeMetricsAndPlots( # pylint: disable=invalid-name
extracts: beam.pvalue.PCollection,
eval_shared_model: types.EvalSharedModel,
desired_batch_size: Optional[int] = None,
compute_confidence_intervals: Optional[bool] = False,
random_seed_for_testing: Optional[int] = None
) -> Tuple[beam.pvalue.DoOutputsTuple, beam.pvalue.PCollection]:
"""Computes metrics and plots using the EvalSavedModel.
Args:
extracts: PCollection of Extracts. The extracts MUST contain a
FeaturesPredictionsLabels extract keyed by
tfma.FEATURE_PREDICTIONS_LABELS_KEY and a list of SliceKeyType extracts
keyed by tfma.SLICE_KEY_TYPES_KEY. Typically these will be added by
calling the default_extractors function.
eval_shared_model: Shared model parameters for EvalSavedModel including any
additional metrics (see EvalSharedModel for more information on how to
configure additional metrics).
desired_batch_size: Optional batch size for batching in Aggregate.
compute_confidence_intervals: Set to True to run metrics analysis over
multiple bootstrap samples and compute uncertainty intervals.
random_seed_for_testing: Provide for deterministic tests only.
Returns:
Tuple of Tuple[PCollection of (slice key, metrics),
PCollection of (slice key, plot metrics)] and
PCollection of (slice_key and its example count).
"""
# pylint: disable=no-value-for-parameter
slices = (
extracts
# Downstream computation only cares about FPLs, so we prune before fanout.
# Note that fanout itself will prune the slice keys.
# TODO(b/130032676, b/111353165): Prune FPLs to contain only the necessary
# set for the calculation of post_export_metrics if possible.
| 'PruneExtracts' >> extractor.Filter(include=[
constants.FEATURES_PREDICTIONS_LABELS_KEY,
constants.SLICE_KEY_TYPES_KEY,
constants.INPUT_KEY,
])
# Input: one example at a time, with slice keys in extracts.
# Output: one fpl example per slice key (notice that the example turns
# into n logical examples, references to which are replicated once
# per applicable slice key).
| 'FanoutSlices' >> slicer.FanoutSlices())
slices_count = (slices
| 'ExtractSliceKeys' >> beam.Keys()
| 'CountPerSliceKey' >> beam.combiners.Count.PerElement())
_ = (extracts.pipeline
| 'IncrementMetricsCallbacksCounters' >>
counter_util.IncrementMetricsCallbacksCounters(
eval_shared_model.add_metrics_callbacks), slices_count
| 'IncreamentSliceSpecCounters' >>
counter_util.IncrementSliceSpecCounters())
aggregated_metrics = (
slices
# Metrics are computed per slice key.
# Output: Multi-outputs, a dict of slice key to computed metrics, and
# plots if applicable.
| 'ComputePerSliceMetrics' >>
poisson_bootstrap.ComputeWithConfidenceIntervals(
aggregate.ComputePerSliceMetrics,
num_bootstrap_samples=(
poisson_bootstrap.DEFAULT_NUM_BOOTSTRAP_SAMPLES
if compute_confidence_intervals else 1),
random_seed_for_testing=random_seed_for_testing,
eval_shared_model=eval_shared_model,
desired_batch_size=desired_batch_size)
| 'SeparateMetricsAndPlots' >> beam.ParDo(
_SeparateMetricsAndPlotsFn()).with_outputs(
_SeparateMetricsAndPlotsFn.OUTPUT_TAG_PLOTS,
main=_SeparateMetricsAndPlotsFn.OUTPUT_TAG_METRICS))
return (aggregated_metrics, slices_count)