def default_regression_specs(
model_names: Optional[List[Text]] = None,
output_names: Optional[List[Text]] = None,
loss_functions: Optional[List[Union[tf.keras.metrics.Metric,
tf.keras.losses.Loss]]] = None,
min_value: Optional[float] = None,
max_value: Optional[float] = None) -> List[config.MetricsSpec]:
"""Returns default metric specs for for regression problems.
Args:
model_names: Optional model names (if multi-model evaluation).
output_names: Optional list of output names (if multi-output model).
loss_functions: Loss functions to use (if None MSE is used).
min_value: Min value for calibration plot (if None no plot will be created).
max_value: Max value for calibration plot (if None no plot will be created).
"""
if loss_functions is None:
loss_functions = [tf.keras.metrics.MeanSquaredError(name='mse')]
metrics = [
tf.keras.metrics.Accuracy(name='accuracy'),
calibration.MeanLabel(name='mean_label'),
calibration.MeanPrediction(name='mean_prediction'),
calibration.Calibration(name='calibration'),
]
for fn in loss_functions:
metrics.append(fn)
if min_value is not None and max_value is not None:
metrics.append(
calibration_plot.CalibrationPlot(
name='calibration_plot', left=min_value, right=max_value))
return specs_from_metrics(
metrics, model_names=model_names, output_names=output_names)
def testCalibrationPlotWithSchema(self, eval_config, schema, model_names,
output_names, expected_left,
expected_range):
computations = calibration_plot.CalibrationPlot(
num_buckets=10).computations(eval_config=eval_config,
schema=schema,
model_names=model_names,
output_names=output_names)
histogram = computations[0]
self.assertEqual(expected_left, histogram.combiner._left)
self.assertEqual(expected_range, histogram.combiner._range)
def default_binary_classification_specs(
model_names: Optional[List[Text]] = None,
output_names: Optional[List[Text]] = None,
output_weights: Optional[Dict[Text, float]] = None,
binarize: Optional[config.BinarizationOptions] = None,
aggregate: Optional[config.AggregationOptions] = None,
include_loss: bool = True) -> List[config.MetricsSpec]:
"""Returns default metric specs for binary 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.
include_loss: True to include loss.
"""
metrics = [
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.AUC(
name='auc',
num_thresholds=binary_confusion_matrices.DEFAULT_NUM_THRESHOLDS),
tf.keras.metrics.AUC(
name=
'auc_precison_recall', # Matches default name used by estimator.
curve='PR',
num_thresholds=binary_confusion_matrices.DEFAULT_NUM_THRESHOLDS),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
calibration.MeanLabel(name='mean_label'),
calibration.MeanPrediction(name='mean_prediction'),
calibration.Calibration(name='calibration'),
confusion_matrix_plot.ConfusionMatrixPlot(
name='confusion_matrix_plot'),
calibration_plot.CalibrationPlot(name='calibration_plot')
]
if include_loss:
metrics.append(tf.keras.metrics.BinaryCrossentropy(name='loss'))
return specs_from_metrics(metrics,
model_names=model_names,
output_names=output_names,
output_weights=output_weights,
binarize=binarize,
aggregate=aggregate)
def default_binary_classification_specs(
model_names: Optional[List[Text]] = None,
output_names: Optional[List[Text]] = None,
class_ids: Optional[List[int]] = None,
k_list: Optional[List[int]] = None,
top_k_list: Optional[List[int]] = None,
include_loss: bool = True) -> List[config.MetricsSpec]:
"""Returns default metric specs for binary classification problems.
Args:
model_names: Optional model names (if multi-model evaluation).
output_names: Optional list of output names (if multi-output model).
class_ids: Optional class IDs to compute metrics for particular classes in a
multi-class model. If output_names are provided, all outputs are assumed
to use the same class IDs.
k_list: Optional list of k values to compute metrics for the kth predicted
values of a multi-class model prediction. If output_names are provided,
all outputs are assumed to use the same k value.
top_k_list: Optional list of top_k values to compute metrics for the top k
predicted values in a multi-class model prediction. If output_names are
provided, all outputs are assumed to use the same top_k value. Metrics and
plots will be based on treating each predicted value in the top_k as
though they were separate predictions.
include_loss: True to include loss.
"""
metrics = [
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.AUC(name='auc_pr', curve='PR'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
calibration.MeanLabel(name='mean_label'),
calibration.MeanPrediction(name='mean_prediction'),
calibration.Calibration(name='calibration'),
auc_plot.AUCPlot(name='auc_plot'),
calibration_plot.CalibrationPlot(name='calibration_plot')
]
if include_loss:
metrics.append(tf.keras.metrics.BinaryCrossentropy(name='loss'))
return specs_from_metrics(metrics,
model_names=model_names,
output_names=output_names,
class_ids=class_ids,
k_list=k_list,
top_k_list=top_k_list)
def default_binary_classification_specs(
model_names: Optional[List[Text]] = None,
output_names: Optional[List[Text]] = None,
binarize: Optional[config.BinarizationOptions] = None,
aggregate: Optional[config.AggregationOptions] = None,
include_loss: bool = True) -> List[config.MetricsSpec]:
"""Returns default metric specs for binary classification problems.
Args:
model_names: Optional model names (if multi-model evaluation).
output_names: Optional list of output names (if multi-output model).
binarize: Optional settings for binarizing multi-class/multi-label metrics.
aggregate: Optional settings for aggregating multi-class/multi-label
metrics.
include_loss: True to include loss.
"""
metrics = [
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.AUC(name='auc_pr', curve='PR'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
calibration.MeanLabel(name='mean_label'),
calibration.MeanPrediction(name='mean_prediction'),
calibration.Calibration(name='calibration'),
auc_plot.AUCPlot(name='auc_plot'),
calibration_plot.CalibrationPlot(name='calibration_plot')
]
if include_loss:
metrics.append(tf.keras.metrics.BinaryCrossentropy(name='loss'))
return specs_from_metrics(metrics,
model_names=model_names,
output_names=output_names,
binarize=binarize,
aggregate=aggregate)
def testCalibrationPlot(self):
computations = calibration_plot.CalibrationPlot(
num_buckets=10).computations()
histogram = computations[0]
plot = computations[1]
example1 = {
'labels': np.array([0.0]),
'predictions': np.array([0.2]),
'example_weights': np.array([1.0])
}
example2 = {
'labels': np.array([1.0]),
'predictions': np.array([0.8]),
'example_weights': np.array([2.0])
}
example3 = {
'labels': np.array([0.0]),
'predictions': np.array([0.5]),
'example_weights': np.array([3.0])
}
example4 = {
'labels': np.array([1.0]),
'predictions': np.array([-0.1]),
'example_weights': np.array([4.0])
}
example5 = {
'labels': np.array([1.0]),
'predictions': np.array([0.5]),
'example_weights': np.array([5.0])
}
example6 = {
'labels': np.array([1.0]),
'predictions': np.array([0.8]),
'example_weights': np.array([6.0])
}
example7 = {
'labels': np.array([0.0]),
'predictions': np.array([0.2]),
'example_weights': np.array([7.0])
}
example8 = {
'labels': np.array([1.0]),
'predictions': np.array([1.1]),
'example_weights': np.array([8.0])
}
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create([
example1, example2, example3, example4, example5, example6,
example7, example8
])
| 'Process' >> beam.Map(metric_util.to_standard_metric_inputs)
| 'AddSlice' >> beam.Map(lambda x: ((), x))
| 'ComputeHistogram' >> beam.CombinePerKey(histogram.combiner)
|
'ComputePlot' >> beam.Map(lambda x: (x[0], plot.result(x[1]))))
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_plots = got[0]
self.assertEqual(got_slice_key, ())
self.assertLen(got_plots, 1)
key = metric_types.PlotKey(name='calibration_plot')
self.assertIn(key, got_plots)
got_plot = got_plots[key]
self.assertProtoEquals(
"""
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
total_weighted_label {
value: 4.0
}
total_weighted_refined_prediction {
value: -0.4
}
num_weighted_examples {
value: 4.0
}
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.1
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.1
upper_threshold_exclusive: 0.2
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.2
upper_threshold_exclusive: 0.3
total_weighted_label {
}
total_weighted_refined_prediction {
value: 1.6
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.3
upper_threshold_exclusive: 0.4
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.4
upper_threshold_exclusive: 0.5
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 0.6
total_weighted_label {
value: 5.0
}
total_weighted_refined_prediction {
value: 4.0
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.6
upper_threshold_exclusive: 0.7
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.7
upper_threshold_exclusive: 0.8
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 0.8
upper_threshold_exclusive: 0.9
total_weighted_label {
value: 8.0
}
total_weighted_refined_prediction {
value: 6.4
}
num_weighted_examples {
value: 8.0
}
}
buckets {
lower_threshold_inclusive: 0.9
upper_threshold_exclusive: 1.0
total_weighted_label {
}
total_weighted_refined_prediction {
}
num_weighted_examples {
}
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
total_weighted_label {
value: 8.0
}
total_weighted_refined_prediction {
value: 8.8
}
num_weighted_examples {
value: 8.0
}
}
""", got_plot)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testEvaluateWithBinaryClassificationModel(self):
n_classes = 2
temp_export_dir = self._getExportDir()
_, export_dir = dnn_classifier.simple_dnn_classifier(
None, temp_export_dir, n_classes=n_classes)
# Add mean_label, example_count, weighted_example_count, calibration_plot
eval_config = config.EvalConfig(
model_specs=[
config.ModelSpec(location=export_dir,
label_key='label',
example_weight_key='age')
],
slicing_specs=[config.SlicingSpec()],
metrics_specs=metric_specs.specs_from_metrics([
calibration.MeanLabel('mean_label'),
calibration_plot.CalibrationPlot(name='calibration_plot',
num_buckets=10)
]))
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
slice_spec = [
slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs
]
extractors = [
input_extractor.InputExtractor(eval_config=eval_config),
predict_extractor_v2.PredictExtractor(
eval_config=eval_config,
eval_shared_models=[eval_shared_model]),
slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec)
]
evaluators = [
metrics_and_plots_evaluator_v2.MetricsAndPlotsEvaluator(
eval_config=eval_config,
eval_shared_models=[eval_shared_model])
]
examples = [
self._makeExample(age=1.0, language='english', label=0.0),
self._makeExample(age=2.0, language='chinese', label=1.0),
self._makeExample(age=3.0, language='chinese', label=0.0),
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
metrics_and_plots = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples])
| 'InputsToExtracts' >> model_eval_lib.InputsToExtracts()
| 'ExtractAndEvaluate' >> model_eval_lib.ExtractAndEvaluate(
extractors=extractors, evaluators=evaluators))
# pylint: enable=no-value-for-parameter
def check_metrics(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
example_count_key = metric_types.MetricKey(
name='example_count')
weighted_example_count_key = metric_types.MetricKey(
name='weighted_example_count')
label_key = metric_types.MetricKey(name='mean_label')
self.assertDictElementsAlmostEqual(
got_metrics, {
example_count_key:
3,
weighted_example_count_key: (1.0 + 2.0 + 3.0),
label_key:
(0 * 1.0 + 1 * 2.0 + 0 * 3.0) / (1.0 + 2.0 + 3.0),
})
except AssertionError as err:
raise util.BeamAssertException(err)
def check_plots(got):
try:
self.assertLen(got, 1)
got_slice_key, got_plots = got[0]
self.assertEqual(got_slice_key, ())
plot_key = metric_types.PlotKey('calibration_plot')
self.assertIn(plot_key, got_plots)
# 10 buckets + 2 for edge cases
self.assertLen(got_plots[plot_key].buckets, 12)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(metrics_and_plots[constants.METRICS_KEY],
check_metrics,
label='metrics')
util.assert_that(metrics_and_plots[constants.PLOTS_KEY],
check_plots,
label='plots')
