def pr_curve_raw(tag,
tp,
fp,
tn,
fn,
precision,
recall,
num_thresholds=127,
weights=None):
if num_thresholds > 127: # weird, value > 127 breaks protobuf
num_thresholds = 127
data = np.stack((tp, fp, tn, fn, precision, recall))
pr_curve_plugin_data = PrCurvePluginData(
version=0, num_thresholds=num_thresholds).SerializeToString()
plugin_data = SummaryMetadata.PluginData(plugin_name="pr_curves",
content=pr_curve_plugin_data)
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(
dtype="DT_FLOAT",
float_val=data.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(dim=[
TensorShapeProto.Dim(size=data.shape[0]),
TensorShapeProto.Dim(size=data.shape[1]),
]),
)
return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
def pr_curve(tag, labels, predictions, num_thresholds=127, weights=None):
# weird, value > 127 breaks protobuf
num_thresholds = min(num_thresholds, 127)
data = compute_curve(labels, predictions,
num_thresholds=num_thresholds, weights=weights)
pr_curve_plugin_data = PrCurvePluginData(
version=0, num_thresholds=num_thresholds).SerializeToString()
plugin_data = SummaryMetadata.PluginData(
plugin_name='pr_curves', content=pr_curve_plugin_data)
smd = SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(dtype='DT_FLOAT',
float_val=data.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=data.shape[0]), TensorShapeProto.Dim(size=data.shape[1])]))
return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
