def _assert_test(self,
num_buckets,
baseline_examples,
comparison_examples,
lift_metric_value,
ignore_out_of_bound_examples=False):
eval_config = config.EvalConfig(
cross_slicing_specs=[config.CrossSlicingSpec()])
computations = lift.Lift(
num_buckets=num_buckets,
ignore_out_of_bound_examples=ignore_out_of_bound_examples
).computations(eval_config=eval_config)
histogram = computations[0]
lift_metrics = computations[1]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
baseline_result = (
pipeline
| 'CreateB' >> beam.Create(baseline_examples)
| 'ProcessB' >> beam.Map(metric_util.to_standard_metric_inputs)
| 'AddSliceB' >> beam.Map(lambda x: ((), x))
| 'ComputeHistogramB' >> beam.CombinePerKey(histogram.combiner)
) # pyformat: ignore
comparison_result = (
pipeline
| 'CreateC' >> beam.Create(comparison_examples)
| 'ProcessC' >> beam.Map(metric_util.to_standard_metric_inputs)
| 'AddSliceC' >> beam.Map(lambda x: (('slice'), x))
| 'ComputeHistogramC' >> beam.CombinePerKey(histogram.combiner)
) # pyformat: ignore
# pylint: enable=no-value-for-parameter
merged_result = ((baseline_result, comparison_result)
| 'MergePCollections' >> beam.Flatten())
def check_result(got):
try:
self.assertLen(got, 2)
slice_1, metric_1 = got[0]
slice_2, metric_2 = got[1]
lift_value = None
if not slice_1:
lift_value = lift_metrics.cross_slice_comparison(
metric_1, metric_2)
else:
lift_value = lift_metrics.cross_slice_comparison(
metric_2, metric_1)
self.assertDictElementsAlmostEqual(
lift_value, {
metric_types.MetricKey(name=f'lift@{num_buckets}'):
lift_metric_value,
})
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(merged_result, check_result, label='result')
config.SlicingSpec(feature_values={'feature1': 'value1'}),
config.SlicingSpec(feature_values={'feature2': 'value2'})
], (('feature1', 'value1'), ))
_UNMATCHED_SINGLE_SLICE_TEST = ('single_slice',
[config.SlicingSpec(feature_keys='feature1')],
(('unmatched_feature', 'unmatched_value'), ))
_UNMATCHED_MULTIPLE_SLICES_TEST = ('multiple_slices', [
config.SlicingSpec(feature_values={'feature1': 'value1'}),
config.SlicingSpec(feature_values={'feature2': 'value2'})
], (('unmatched_feature', 'unmatched_value'), ))
# Cross slice tests: (<test_name>, <cross_slice_config>, <cross_slice_key>)
_CROSS_SLICE_GLOBAL_TEST = ('global_slice', [
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(),
slicing_specs=[
config.SlicingSpec(feature_values={'feature2': 'value2'})
])
], ((()), (('feature2', 'value2'), )))
_SINGLE_CROSS_SLICE_TEST = ('single_slice', [
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_keys=['feature1']),
slicing_specs=[
config.SlicingSpec(feature_values={'feature2': 'value2'})
])
], ((('feature1', 'value1'), ), (('feature2', 'value2'), )))
_MULTIPLE_CROSS_SLICE_TEST = ('multiple_slice', [
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_keys=['feature1']),
slicing_specs=[
config.SlicingSpec(feature_values={'feature2': 'value2'})
def testIsCrossSliceApplicable(self):
test_cases = [
(True, 'overall pass', ((), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(True, 'value pass', ((('a', 1), ), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(True, 'baseline key pass', ((('a', 1), ), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_keys=['a']),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(True, 'comparison key pass', ((('a', 1), ), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[config.SlicingSpec(feature_keys=['b'])])),
(True, 'comparison multiple key pass', ((('a', 1), ), (('c',
3), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[
config.SlicingSpec(feature_keys=['b']),
config.SlicingSpec(feature_keys=['c'])
])),
(False, 'overall fail', ((('a', 1), ), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(False, 'value fail', ((('a', 1), ), (('b', 3), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(False, 'baseline key fail', ((('c', 1), ), (('b', 2), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_keys=['a']),
slicing_specs=[config.SlicingSpec(feature_values={'b': '2'})
])),
(False, 'comparison key fail', ((('a', 1), ), (('c', 3), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[config.SlicingSpec(feature_keys=['b'])])),
(False, 'comparison multiple key fail', ((('a', 1), ), (('d',
3), )),
config.CrossSlicingSpec(
baseline_spec=config.SlicingSpec(feature_values={'a': '1'}),
slicing_specs=[
config.SlicingSpec(feature_keys=['b']),
config.SlicingSpec(feature_keys=['c'])
])),
] # pyformat: disable
for (expected_result, name, sliced_key, slicing_spec) in test_cases:
self.assertEqual(expected_result,
slicer.is_cross_slice_applicable(
cross_slice_key=sliced_key,
cross_slicing_spec=slicing_spec),
msg=name)
def testMetricThresholdsFromMetricsSpecs(self):
slice_specs = [
config.SlicingSpec(feature_keys=['feature1']),
config.SlicingSpec(feature_values={'feature2': 'value1'})
]
# For cross slice tests.
baseline_slice_spec = config.SlicingSpec(feature_keys=['feature3'])
metrics_specs = [
config.MetricsSpec(
thresholds={
'auc':
config.MetricThreshold(
value_threshold=config.GenericValueThreshold()),
'mean/label':
config.MetricThreshold(
value_threshold=config.GenericValueThreshold(),
change_threshold=config.GenericChangeThreshold()),
'mse':
config.MetricThreshold(
change_threshold=config.GenericChangeThreshold())
},
per_slice_thresholds={
'auc':
config.PerSliceMetricThresholds(thresholds=[
config.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(
)))
]),
'mean/label':
config.PerSliceMetricThresholds(thresholds=[
config.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(),
change_threshold=config.GenericChangeThreshold(
)))
])
},
cross_slice_thresholds={
'auc':
config.CrossSliceMetricThresholds(thresholds=[
config.CrossSliceMetricThreshold(
cross_slicing_specs=[
config.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(),
change_threshold=config.GenericChangeThreshold(
)))
]),
'mse':
config.CrossSliceMetricThresholds(thresholds=[
config.CrossSliceMetricThreshold(
cross_slicing_specs=[
config.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold(
))),
# Test for duplicate cross_slicing_spec.
config.CrossSliceMetricThreshold(
cross_slicing_specs=[
config.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold())
)
])
},
model_names=['model_name'],
output_names=['output_name']),
config.MetricsSpec(metrics=[
config.MetricConfig(
class_name='ExampleCount',
config=json.dumps({'name': 'example_count'}),
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold()))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name1', 'output_name2']),
config.MetricsSpec(metrics=[
config.MetricConfig(
class_name='WeightedExampleCount',
config=json.dumps({'name': 'weighted_example_count'}),
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold()))
],
model_names=['model_name1', 'model_name2'],
output_names=['output_name1', 'output_name2']),
config.MetricsSpec(metrics=[
config.MetricConfig(
class_name='MeanSquaredError',
config=json.dumps({'name': 'mse'}),
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold())),
config.MetricConfig(
class_name='MeanLabel',
config=json.dumps({'name': 'mean_label'}),
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold()),
per_slice_thresholds=[
config.PerSliceMetricThreshold(
slicing_specs=slice_specs,
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold(
))),
],
cross_slice_thresholds=[
config.CrossSliceMetricThreshold(
cross_slicing_specs=[
config.CrossSlicingSpec(
baseline_spec=baseline_slice_spec,
slicing_specs=slice_specs)
],
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold(
)))
]),
],
model_names=['model_name'],
output_names=['output_name'],
binarize=config.BinarizationOptions(
class_ids={'values': [0, 1]}),
aggregate=config.AggregationOptions(
macro_average=True,
class_weights={
0: 1.0,
1: 1.0
}))
]
thresholds = metric_specs.metric_thresholds_from_metrics_specs(
metrics_specs)
expected_keys_and_threshold_counts = {
metric_types.MetricKey(name='auc',
model_name='model_name',
output_name='output_name',
is_diff=False):
4,
metric_types.MetricKey(name='auc',
model_name='model_name',
output_name='output_name',
is_diff=True):
1,
metric_types.MetricKey(name='mean/label',
model_name='model_name',
output_name='output_name',
is_diff=True):
3,
metric_types.MetricKey(name='mean/label',
model_name='model_name',
output_name='output_name',
is_diff=False):
3,
metric_types.MetricKey(name='example_count',
model_name='model_name1',
output_name='output_name1'):
1,
metric_types.MetricKey(name='example_count',
model_name='model_name1',
output_name='output_name2'):
1,
metric_types.MetricKey(name='example_count',
model_name='model_name2',
output_name='output_name1'):
1,
metric_types.MetricKey(name='example_count',
model_name='model_name2',
output_name='output_name2'):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name1',
output_name='output_name1'):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name1',
output_name='output_name2'):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name2',
output_name='output_name1'):
1,
metric_types.MetricKey(name='weighted_example_count',
model_name='model_name2',
output_name='output_name2'):
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=True):
2,
metric_types.MetricKey(name='mse',
model_name='model_name',
output_name='output_name',
is_diff=False):
1,
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))
