def evaluate(self, x=None, y=None, input_fn=None, feed_fn=None,
batch_size=None, steps=None, metrics=None, name=None):
"""See evaluable.Evaluable."""
if not metrics:
metrics = {}
metrics["accuracy"] = metric_spec.MetricSpec(
metric_fn=metrics_lib.streaming_accuracy,
prediction_key=linear._CLASSES)
additional_metrics = (
target_column.get_default_binary_metrics_for_eval([0.5]))
additional_metrics = {
name: metric_spec.MetricSpec(metric_fn=metric,
prediction_key=linear._LOGISTIC)
for name, metric in additional_metrics.items()
}
metrics.update(additional_metrics)
# TODO(b/31229024): Remove this loop
for metric_name, metric in metrics.items():
if isinstance(metric, metric_spec.MetricSpec):
continue
if isinstance(metric_name, tuple):
if len(metric_name) != 2:
raise ValueError("Ignoring metric %s. It returned a tuple with len "
"%s, expected 2." % (metric_name, len(metric_name)))
valid_keys = {linear._CLASSES, linear._LOGISTIC, linear._PROBABILITIES}
if metric_name[1] not in valid_keys:
raise ValueError("Ignoring metric %s. The 2nd element of its name "
"should be in %s" % (metric_name, valid_keys))
metrics[metric_name] = linear._wrap_metric(metric)
return self._estimator.evaluate(x=x, y=y, input_fn=input_fn,
feed_fn=feed_fn, batch_size=batch_size,
steps=steps, metrics=metrics, name=name)
def evaluate(self, x=None, y=None, input_fn=None, feed_fn=None,
batch_size=None, steps=None, metrics=None, name=None):
"""See evaluable.Evaluable."""
if not metrics:
metrics = {}
metrics[("accuracy", _CLASSES)] = metrics_lib.streaming_accuracy
if self._n_classes == 2:
additional_metrics = (
target_column.get_default_binary_metrics_for_eval([0.5]))
additional_metrics = {(name, _LOGISTIC): metric
for name, metric in additional_metrics.items()}
metrics.update(additional_metrics)
for metric_name, metric in metrics.items():
if isinstance(metric_name, tuple):
if len(metric_name) != 2:
raise ValueError("Ignoring metric %s. It returned a tuple with len "
"%s, expected 2." % (metric_name, len(metric_name)))
valid_keys = {_CLASSES, _LOGISTIC, _PROBABILITIES}
if metric_name[1] not in valid_keys:
raise ValueError("Ignoring metric %s. The 2nd element of its name "
"should be in %s" % (metric_name, valid_keys))
elif isinstance(metric_name, str):
metrics.pop(metric_name)
metric_name = (metric_name, _CLASSES)
else:
raise ValueError("Ignoring metric %s. Its name is not in the correct "
"form." % metric_name)
metrics[metric_name] = _wrap_metric(metric)
return self._estimator.evaluate(x=x, y=y, input_fn=input_fn,
feed_fn=feed_fn, batch_size=batch_size,
steps=steps, metrics=metrics, name=name)
def evaluate(self,
x=None,
y=None,
input_fn=None,
feed_fn=None,
batch_size=None,
steps=None,
metrics=None,
name=None):
"""See evaluable.Evaluable."""
if not metrics:
metrics = {}
metrics[("accuracy", _CLASSES)] = metrics_lib.streaming_accuracy
if self._n_classes == 2:
additional_metrics = (
target_column.get_default_binary_metrics_for_eval([0.5]))
additional_metrics = {
(name, _LOGISTIC): metric
for name, metric in additional_metrics.items()
}
metrics.update(additional_metrics)
for metric_name, metric in metrics.items():
if isinstance(metric_name, tuple):
if len(metric_name) != 2:
raise ValueError(
"Ignoring metric %s. It returned a tuple with len "
"%s, expected 2." % (metric_name, len(metric_name)))
valid_keys = {_CLASSES, _LOGISTIC, _PROBABILITIES}
if metric_name[1] not in valid_keys:
raise ValueError(
"Ignoring metric %s. The 2nd element of its name "
"should be in %s" % (metric_name, valid_keys))
elif isinstance(metric_name, str):
metrics.pop(metric_name)
metric_name = (metric_name, _CLASSES)
else:
raise ValueError(
"Ignoring metric %s. Its name is not in the correct "
"form." % metric_name)
metrics[metric_name] = _wrap_metric(metric)
return self._estimator.evaluate(x=x,
y=y,
input_fn=input_fn,
feed_fn=feed_fn,
batch_size=batch_size,
steps=steps,
metrics=metrics,
name=name)
