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 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)
class CalibrationMetricsTest(testutil.TensorflowModelAnalysisTest,
parameterized.TestCase):
@parameterized.named_parameters(
('mean_label', calibration.MeanLabel(), 2.0 / 3.0),
('mean_prediction', calibration.MeanPrediction(), (0.3 + 0.9) / 3.0),
('calibration', calibration.Calibration(), (0.3 + 0.9) / 2.0))
def testCalibrationMetricsWithoutWeights(self, metric, expected_value):
computations = metric.computations()
weighted_totals = computations[0]
metric = computations[1]
example1 = {
'labels': np.array([0.0]),
'predictions': np.array([0.0]),
'example_weights': np.array([1.0]),
}
example2 = {
'labels': np.array([1.0]),
'predictions': np.array([0.3]),
'example_weights': np.array([1.0]),
}
example3 = {
'labels': np.array([1.0]),
'predictions': np.array([0.9]),
'example_weights': None, # defaults to 1.0
}
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create([example1, example2, example3])
| 'Process' >> beam.Map(metric_util.to_standard_metric_inputs)
| 'AddSlice' >> beam.Map(lambda x: ((), x))
| 'ComputeWeightedTotals' >> beam.CombinePerKey(
weighted_totals.combiner)
| 'ComputeMetric' >> beam.Map(lambda x:
(x[0], metric.result(x[1]))))
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
key = metric.keys[0]
self.assertDictElementsAlmostEqual(got_metrics,
{key: expected_value},
places=5)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
@parameterized.named_parameters(
('mean_label', calibration.MeanLabel(), 1.0 * 0.7 / 2.1),
('mean_prediction', calibration.MeanPrediction(),
(1.0 * 0.5 + 0.7 * 0.7 + 0.5 * 0.9) / 2.1),
('calibration', calibration.Calibration(),
(1.0 * 0.5 + 0.7 * 0.7 + 0.5 * 0.9) / (1.0 * 0.7)))
def testCalibrationMetricsWithWeights(self, metric, expected_value):
computations = metric.computations()
weighted_totals = computations[0]
metric = computations[1]
example1 = {
'labels': np.array([0.0]),
'predictions': np.array([1.0]),
'example_weights': np.array([0.5]),
}
example2 = {
'labels': np.array([1.0]),
'predictions': np.array([0.7]),
'example_weights': np.array([0.7]),
}
example3 = {
'labels': np.array([0.0]),
'predictions': np.array([0.5]),
'example_weights': np.array([0.9]),
}
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create([example1, example2, example3])
| 'Process' >> beam.Map(metric_util.to_standard_metric_inputs)
| 'AddSlice' >> beam.Map(lambda x: ((), x))
| 'ComputeWeightedTotals' >> beam.CombinePerKey(
weighted_totals.combiner)
| 'ComputeMetric' >> beam.Map(lambda x:
(x[0], metric.result(x[1]))))
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
got_slice_key, got_metrics = got[0]
self.assertEqual(got_slice_key, ())
key = metric.keys[0]
self.assertDictElementsAlmostEqual(got_metrics,
{key: expected_value},
places=5)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
