def default_multi_class_classification_specs(
model_names: Optional[List[Text]] = None,
output_names: Optional[List[Text]] = None,
output_weights: Optional[Dict[Text, float]] = None,
binarize: Optional[config_pb2.BinarizationOptions] = None,
aggregate: Optional[config_pb2.AggregationOptions] = None,
sparse: bool = True) -> List[config_pb2.MetricsSpec]:
"""Returns default metric specs for multi-class classification problems.
Args:
model_names: Optional model names if multi-model evaluation.
output_names: Optional list of output names (if multi-output model).
output_weights: Optional output weights for creating overall metric
aggregated across outputs (if multi-output model). If a weight is not
provided for an output, it's weight defaults to 0.0 (i.e. output ignored).
binarize: Optional settings for binarizing multi-class/multi-label metrics.
aggregate: Optional settings for aggregating multi-class/multi-label
metrics.
sparse: True if the labels are sparse.
"""
if sparse:
metrics = [
tf.keras.metrics.SparseCategoricalCrossentropy(name='loss'),
tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy')
]
else:
metrics = [
tf.keras.metrics.CategoricalCrossentropy(name='loss'),
tf.keras.metrics.CategoricalAccuracy(name='accuracy')
]
metrics.append(
multi_class_confusion_matrix_plot.MultiClassConfusionMatrixPlot())
if binarize is not None:
for top_k in binarize.top_k_list.values:
metrics.extend([
tf.keras.metrics.Precision(name='precision', top_k=top_k),
tf.keras.metrics.Recall(name='recall', top_k=top_k)
])
binarize_without_top_k = config_pb2.BinarizationOptions()
binarize_without_top_k.CopyFrom(binarize)
binarize_without_top_k.ClearField('top_k_list')
binarize = binarize_without_top_k
multi_class_metrics = specs_from_metrics(
metrics,
model_names=model_names,
output_names=output_names,
output_weights=output_weights)
if aggregate is None:
aggregate = config_pb2.AggregationOptions(micro_average=True)
multi_class_metrics.extend(
default_binary_classification_specs(
model_names=model_names,
output_names=output_names,
output_weights=output_weights,
binarize=binarize,
aggregate=aggregate))
return multi_class_metrics
def testToComputationsWithMixedAggregationAndNonAggregationMetrics(self):
computations = metric_specs.to_computations([
config_pb2.MetricsSpec(metrics=[
config_pb2.MetricConfig(class_name='CategoricalAccuracy')
]),
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='BinaryCrossentropy')
],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [1]}),
aggregate=config_pb2.AggregationOptions(micro_average=True))
], config_pb2.EvalConfig())
# 3 separate computations should be used (one for aggregated metrics, one
# for non-aggregated metrics, and one for metrics associated with class 1)
self.assertLen(computations, 3)
def _aggregation_options(
self, me_aggregation: me_proto.Aggregation
) -> Optional[config_pb2.AggregationOptions]:
"""Convert ME Aggregation into TFMA AggregationOptions.
Args:
me_aggregation: Input ME Aggregation.
Returns:
TFMA AggregationOptions.
"""
if not me_aggregation:
return None
tfma_aggregation = config_pb2.AggregationOptions()
if me_aggregation.micro_average:
tfma_aggregation.micro_average = True
if me_aggregation.macro_average:
tfma_aggregation.macro_average = True
if me_aggregation.class_weights:
tfma_aggregation.class_weights.update(me_aggregation.class_weights)
return tfma_aggregation
def testToComputations(self):
computations = metric_specs.to_computations(
metric_specs.specs_from_metrics(
[
tf.keras.metrics.MeanSquaredError('mse'),
# Add a loss exactly same as metric
# (https://github.com/tensorflow/tfx/issues/1550)
tf.keras.losses.MeanSquaredError(name='loss'),
calibration.MeanLabel('mean_label')
],
model_names=['model_name'],
output_names=['output_1', 'output_2'],
output_weights={
'output_1': 1.0,
'output_2': 1.0
},
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True,
class_weights={
0: 1.0,
1: 1.0
})),
config_pb2.EvalConfig())
keys = []
for m in computations:
for k in m.keys:
if not k.name.startswith('_'):
keys.append(k)
self.assertLen(keys, 31)
self.assertIn(
metric_types.MetricKey(name='example_count',
model_name='model_name'), keys)
for output_name in ('output_1', 'output_2', ''):
self.assertIn(
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name',
output_name=output_name,
example_weighted=True), keys)
self.assertIn(
metric_types.MetricKey(
name='mse',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=0)), keys)
self.assertIn(
metric_types.MetricKey(
name='mse',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=1)), keys)
aggregation_type = metric_types.AggregationType(
macro_average=True) if output_name else None
self.assertIn(
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name=output_name,
aggregation_type=aggregation_type),
keys)
self.assertIn(
metric_types.MetricKey(
name='loss',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=0)), keys)
self.assertIn(
metric_types.MetricKey(
name='loss',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=1)), keys)
aggregation_type = metric_types.AggregationType(
macro_average=True) if output_name else None
self.assertIn(
metric_types.MetricKey(name='loss',
model_name='model_name',
output_name=output_name,
aggregation_type=aggregation_type),
keys)
self.assertIn(
metric_types.MetricKey(
name='mean_label',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=0)), keys)
self.assertIn(
metric_types.MetricKey(
name='mean_label',
model_name='model_name',
output_name=output_name,
sub_key=metric_types.SubKey(class_id=1)), keys)
aggregation_type = metric_types.AggregationType(
macro_average=True) if output_name else None
self.assertIn(
metric_types.MetricKey(name='mean_label',
model_name='model_name',
output_name=output_name,
aggregation_type=aggregation_type),
keys)
def testSpecsFromMetrics(self):
metrics_specs = metric_specs.specs_from_metrics(
{
'output_name1': [
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.MeanSquaredError('mse'),
tf.keras.losses.MeanAbsoluteError(name='mae'),
],
'output_name2': [
confusion_matrix_metrics.Precision(name='precision'),
tf.keras.losses.MeanAbsolutePercentageError(name='mape'),
calibration.MeanPrediction('mean_prediction')
]
},
unweighted_metrics={
'output_name1': [calibration.MeanLabel('mean_label')],
'output_name2':
[tf.keras.metrics.RootMeanSquaredError('rmse')]
},
model_names=['model_name1', 'model_name2'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True))
self.assertLen(metrics_specs, 7)
self.assertProtoEquals(
metrics_specs[0],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='ExampleCount',
config=json.dumps(
{'name': 'example_count'})),
],
model_names=['model_name1', 'model_name2'],
example_weights=config_pb2.ExampleWeightOptions(
unweighted=True)))
self.assertProtoEquals(
metrics_specs[1],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(
class_name='WeightedExampleCount',
config=json.dumps({'name': 'weighted_example_count'})),
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name1'],
example_weights=config_pb2.ExampleWeightOptions(
weighted=True)))
self.assertProtoEquals(
metrics_specs[2],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='Precision',
config=json.dumps(
{
'name': 'precision',
'class_id': None,
'thresholds': None,
'top_k': None
},
sort_keys=True)),
config_pb2.MetricConfig(class_name='MeanSquaredError',
config=json.dumps(
{
'name': 'mse',
'dtype': 'float32',
},
sort_keys=True)),
config_pb2.MetricConfig(
class_name='MeanAbsoluteError',
module=metric_specs._TF_LOSSES_MODULE,
config=json.dumps({
'reduction': 'auto',
'name': 'mae'
},
sort_keys=True))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name1'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True)))
self.assertProtoEquals(
metrics_specs[3],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='MeanLabel',
config=json.dumps(
{'name': 'mean_label'}))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name1'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True),
example_weights=config_pb2.ExampleWeightOptions(
unweighted=True)))
self.assertProtoEquals(
metrics_specs[4],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(
class_name='WeightedExampleCount',
config=json.dumps({'name': 'weighted_example_count'})),
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name2'],
example_weights=config_pb2.ExampleWeightOptions(
weighted=True)))
self.assertProtoEquals(
metrics_specs[5],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='Precision',
config=json.dumps(
{
'name': 'precision',
},
sort_keys=True)),
config_pb2.MetricConfig(
class_name='MeanAbsolutePercentageError',
module=metric_specs._TF_LOSSES_MODULE,
config=json.dumps({
'reduction': 'auto',
'name': 'mape'
},
sort_keys=True)),
config_pb2.MetricConfig(class_name='MeanPrediction',
config=json.dumps(
{'name': 'mean_prediction'}))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name2'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True)))
self.assertProtoEquals(
metrics_specs[6],
config_pb2.MetricsSpec(
metrics=[
config_pb2.MetricConfig(class_name='RootMeanSquaredError',
config=json.dumps(
{
'name': 'rmse',
'dtype': 'float32'
},
sort_keys=True))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name2'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(macro_average=True),
example_weights=config_pb2.ExampleWeightOptions(
unweighted=True)))
def testMetricThresholdsFromMetricsSpecs(self):
slice_specs = [
config_pb2.SlicingSpec(feature_keys=['feature1']),
config_pb2.SlicingSpec(feature_values={'feature2': 'value1'})
]
# For cross slice tests.
baseline_slice_spec = config_pb2.SlicingSpec(feature_keys=['feature3'])
metrics_specs = [
config_pb2.MetricsSpec(
thresholds={
'auc':
config_pb2.MetricThreshold(
value_threshold=config_pb2.GenericValueThreshold()),
'mean/label':
config_pb2.MetricThreshold(
value_threshold=config_pb2.GenericValueThreshold(),
change_threshold=config_pb2.GenericChangeThreshold()),
'mse':
config_pb2.MetricThreshold(
change_threshold=config_pb2.GenericChangeThreshold())
},
per_slice_thresholds={
'auc':
config_pb2.PerSliceMetricThresholds(thresholds=[
config_pb2.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.
GenericValueThreshold()))
]),
'mean/label':
config_pb2.PerSliceMetricThresholds(thresholds=[
config_pb2.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.
GenericValueThreshold(),
change_threshold=config_pb2.
GenericChangeThreshold()))
])
},
cross_slice_thresholds={
'auc':
config_pb2.CrossSliceMetricThresholds(thresholds=[
config_pb2.CrossSliceMetricThreshold(
cross_slicing_specs=[
config_pb2.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.
GenericValueThreshold(),
change_threshold=config_pb2.
GenericChangeThreshold()))
]),
'mse':
config_pb2.CrossSliceMetricThresholds(thresholds=[
config_pb2.CrossSliceMetricThreshold(
cross_slicing_specs=[
config_pb2.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config_pb2.MetricThreshold(
change_threshold=config_pb2.
GenericChangeThreshold())),
# Test for duplicate cross_slicing_spec.
config_pb2.CrossSliceMetricThreshold(
cross_slicing_specs=[
config_pb2.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.
GenericValueThreshold()))
])
},
model_names=['model_name'],
output_names=['output_name']),
config_pb2.MetricsSpec(metrics=[
config_pb2.MetricConfig(
class_name='ExampleCount',
config=json.dumps({'name': 'example_count'}),
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.GenericValueThreshold()))
],
model_names=['model_name1', 'model_name2'],
example_weights=config_pb2.
ExampleWeightOptions(unweighted=True)),
config_pb2.MetricsSpec(metrics=[
config_pb2.MetricConfig(
class_name='WeightedExampleCount',
config=json.dumps({'name': 'weighted_example_count'}),
threshold=config_pb2.MetricThreshold(
value_threshold=config_pb2.GenericValueThreshold()))
],
model_names=['model_name1', 'model_name2'],
output_names=[
'output_name1', 'output_name2'
],
example_weights=config_pb2.
ExampleWeightOptions(weighted=True)),
config_pb2.MetricsSpec(metrics=[
config_pb2.MetricConfig(
class_name='MeanSquaredError',
config=json.dumps({'name': 'mse'}),
threshold=config_pb2.MetricThreshold(
change_threshold=config_pb2.GenericChangeThreshold())),
config_pb2.MetricConfig(
class_name='MeanLabel',
config=json.dumps({'name': 'mean_label'}),
threshold=config_pb2.MetricThreshold(
change_threshold=config_pb2.GenericChangeThreshold()),
per_slice_thresholds=[
config_pb2.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config_pb2.MetricThreshold(
change_threshold=config_pb2.
GenericChangeThreshold())),
],
cross_slice_thresholds=[
config_pb2.CrossSliceMetricThreshold(
cross_slicing_specs=[
config_pb2.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config_pb2.MetricThreshold(
change_threshold=config_pb2.
GenericChangeThreshold()))
]),
],
model_names=['model_name'],
output_names=['output_name'],
binarize=config_pb2.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config_pb2.AggregationOptions(
macro_average=True,
class_weights={
0: 1.0,
1: 1.0
}))
]
thresholds = metric_specs.metric_thresholds_from_metrics_specs(
metrics_specs, eval_config=config_pb2.EvalConfig())
expected_keys_and_threshold_counts = {
metric_types.MetricKey(name='auc',
model_name='model_name',
output_name='output_name',
is_diff=False,
example_weighted=None):
4,
metric_types.MetricKey(name='auc',
model_name='model_name',
output_name='output_name',
is_diff=True,
example_weighted=None):
1,
metric_types.MetricKey(name='mean/label',
model_name='model_name',
output_name='output_name',
is_diff=True,
example_weighted=None):
3,
metric_types.MetricKey(name='mean/label',
model_name='model_name',
output_name='output_name',
is_diff=False,
example_weighted=None):
3,
metric_types.MetricKey(name='example_count',
model_name='model_name1'):
1,
metric_types.MetricKey(name='example_count',
model_name='model_name2'):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name1',
output_name='output_name1',
example_weighted=True):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name1',
output_name='output_name2',
example_weighted=True):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name2',
output_name='output_name1',
example_weighted=True):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name2',
output_name='output_name2',
example_weighted=True):
1,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
sub_key=metric_types.SubKey(class_id=0),
is_diff=True):
1,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
sub_key=metric_types.SubKey(class_id=1),
is_diff=True):
1,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
is_diff=False,
example_weighted=None):
1,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
is_diff=True,
example_weighted=None):
2,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
aggregation_type=metric_types.AggregationType(macro_average=True),
is_diff=True):
1,
metric_types.MetricKey(name='mean_label',
model_name='model_name',
output_name='output_name',
sub_key=metric_types.SubKey(class_id=0),
is_diff=True):
4,
metric_types.MetricKey(name='mean_label',
model_name='model_name',
output_name='output_name',
sub_key=metric_types.SubKey(class_id=1),
is_diff=True):
4,
metric_types.MetricKey(name='mean_label',
model_name='model_name',
output_name='output_name',
aggregation_type=metric_types.AggregationType(macro_average=True),
is_diff=True):
4
}
self.assertLen(thresholds, len(expected_keys_and_threshold_counts))
for key, count in expected_keys_and_threshold_counts.items():
self.assertIn(key, thresholds)
self.assertLen(thresholds[key], count,
'failed for key {}'.format(key))
