def _serialize_plots(
plots: Tuple[slicer.SliceKeyType, Dict[Text, Any]],
post_export_metrics: List[types.AddMetricsCallbackType]) -> bytes:
"""Converts the given slice plots into serialized proto PlotsForSlice..
Args:
plots: The slice plots.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto PlotsForSlice.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
if metric_keys.ERROR_METRIC in slice_plots:
tf.compat.v1.logging.warning(
'Error for slice: %s with error message: %s ', slice_key,
slice_plots[metric_keys.ERROR_METRIC])
metrics = metrics_for_slice_pb2.PlotsForSlice()
metrics.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
metrics.plots[metric_keys.ERROR_METRIC].debug_message = slice_plots[
metric_keys.ERROR_METRIC]
return metrics.SerializeToString()
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice plots.
_convert_slice_plots(slice_plots, post_export_metrics, result.plots) # pytype: disable=wrong-arg-types
return result.SerializeToString()
def _serialize_metrics(metrics: Tuple[slicer.SliceKeyType, Dict[Text, Any]],
post_export_metrics: List[types.AddMetricsCallbackType]
) -> bytes:
"""Converts the given slice metrics into serialized proto MetricsForSlice.
Args:
metrics: The slice metrics.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto MetricsForSlice.
Raises:
TypeError: If the type of the feature value in slice key cannot be
recognized.
"""
result = metrics_for_slice_pb2.MetricsForSlice()
slice_key, slice_metrics = metrics
if metric_keys.ERROR_METRIC in slice_metrics:
tf.logging.warning('Error for slice: %s with error message: %s ', slice_key,
slice_metrics[metric_keys.ERROR_METRIC])
metrics = metrics_for_slice_pb2.MetricsForSlice()
metrics.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
metrics.metrics[metric_keys.ERROR_METRIC].debug_message = slice_metrics[
metric_keys.ERROR_METRIC]
return metrics.SerializeToString()
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice metrics.
convert_slice_metrics(slice_metrics, post_export_metrics, result)
return result.SerializeToString()
def _serialize_metrics(
metrics,
post_export_metrics):
"""Converts the given slice metrics into serialized proto MetricsForSlice.
Args:
metrics: The slice metrics.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto MetricsForSlice.
Raises:
TypeError: If the type of the feature value in slice key cannot be
recognized.
"""
result = metrics_for_slice_pb2.MetricsForSlice()
slice_key, slice_metrics = metrics
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice metrics.
_convert_slice_metrics(slice_metrics, post_export_metrics, result)
return result.SerializeToString()
def testSerializePlots_emptyPlot(self):
slice_key = _make_slice_key('fruit', 'apple')
tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'}
calibration_plot = (
post_export_metrics.calibration_plot_and_prediction_histogram())
actual_plot = metrics_and_plots_serialization._serialize_plots(
(slice_key, tfma_plots), [calibration_plot])
expected_plot = metrics_for_slice_pb2.PlotsForSlice()
expected_plot.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
expected_plot.plots[
metric_keys.ERROR_METRIC].debug_message = 'error_message'
self.assertProtoEquals(
expected_plot,
metrics_for_slice_pb2.PlotsForSlice.FromString(actual_plot))
def testSerializeMetrics_emptyMetrics(self):
slice_key = _make_slice_key('age', 5, 'language', 'english', 'price',
0.3)
slice_metrics = {metric_keys.ERROR_METRIC: 'error_message'}
actual_metrics = metrics_and_plots_serialization._serialize_metrics(
(slice_key, slice_metrics),
[post_export_metrics.auc(),
post_export_metrics.auc(curve='PR')])
expected_metrics = metrics_for_slice_pb2.MetricsForSlice()
expected_metrics.slice_key.CopyFrom(
slicer.serialize_slice_key(slice_key))
expected_metrics.metrics[
metric_keys.ERROR_METRIC].debug_message = 'error_message'
self.assertProtoEquals(
expected_metrics,
metrics_for_slice_pb2.MetricsForSlice.FromString(actual_metrics))
def _serialize_plots(plots, post_export_metrics):
"""Converts the given slice plots into serialized proto PlotsForSlice..
Args:
plots: The slice plots.
post_export_metrics: A list of metric callbacks. This should be the same
list as the one passed to tfma.Evaluate().
Returns:
The serialized proto PlotsForSlice.
"""
result = metrics_for_slice_pb2.PlotsForSlice()
slice_key, slice_plots = plots
# Convert the slice key.
result.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key))
# Convert the slice plots.
_convert_slice_plots(slice_plots, post_export_metrics, result.plot_data) # pytype: disable=wrong-arg-types
return result.SerializeToString()
