def testSerializePlots_emptyPlot(self):
slice_key = _make_slice_key('fruit', 'apple')
tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'}
actual_plot = metrics_and_plots_serialization._serialize_plots(
(slice_key, tfma_plots), [])
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 testSerializePlots(self):
slice_key = _make_slice_key('fruit', 'apple')
plot_key = metric_types.PlotKey(name='calibration_plot',
output_name='output_name')
calibration_plot = text_format.Parse(
"""
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
""", metrics_for_slice_pb2.CalibrationHistogramBuckets())
expected_plots_for_slice = text_format.Parse(
"""
slice_key {
single_slice_keys {
column: 'fruit'
bytes_value: 'apple'
}
}
plot_keys_and_values {
key {
output_name: "output_name"
}
value {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
}
}
}
""", metrics_for_slice_pb2.PlotsForSlice())
got = metrics_and_plots_serialization._serialize_plots(
(slice_key, {
plot_key: calibration_plot
}), None)
self.assertProtoEquals(
expected_plots_for_slice,
metrics_for_slice_pb2.PlotsForSlice.FromString(got))
def testSerializePlotsLegacyStringKeys(self):
slice_key = _make_slice_key('fruit', 'apple')
tfma_plots = {
metric_keys.CALIBRATION_PLOT_MATRICES:
np.array([
[0.0, 0.0, 0.0],
[0.3, 1.0, 1.0],
[0.7, 0.0, 1.0],
[0.0, 0.0, 0.0],
]),
metric_keys.CALIBRATION_PLOT_BOUNDARIES:
np.array([0.0, 0.5, 1.0]),
}
expected_plot_data = """
slice_key {
single_slice_keys {
column: 'fruit'
bytes_value: 'apple'
}
}
plots {
key: "post_export_metrics"
value {
calibration_histogram_buckets {
buckets {
lower_threshold_inclusive: -inf
upper_threshold_exclusive: 0.0
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
buckets {
lower_threshold_inclusive: 0.0
upper_threshold_exclusive: 0.5
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 1.0 }
total_weighted_refined_prediction { value: 0.3 }
}
buckets {
lower_threshold_inclusive: 0.5
upper_threshold_exclusive: 1.0
num_weighted_examples { value: 1.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.7 }
}
buckets {
lower_threshold_inclusive: 1.0
upper_threshold_exclusive: inf
num_weighted_examples { value: 0.0 }
total_weighted_label { value: 0.0 }
total_weighted_refined_prediction { value: 0.0 }
}
}
}
}
"""
calibration_plot = (
post_export_metrics.calibration_plot_and_prediction_histogram())
serialized = metrics_and_plots_serialization._serialize_plots(
(slice_key, tfma_plots), [calibration_plot])
self.assertProtoEquals(
expected_plot_data,
metrics_for_slice_pb2.PlotsForSlice.FromString(serialized))
