def calculate_metric(self):
return compute_multi_label_classification_metrics(
[
LabelListPrediction(scores, pred, expect)
for scores, pred, expect in zip(
self.all_scores, self.all_preds, self.all_targets)
],
self.label_names,
self.calculate_loss(),
)
def calculate_metric(self):
return compute_multi_label_classification_metrics(
[
LabelListPrediction(scores, pred, expect)
for scores, pred, expect in zip(
self.all_scores, self.all_preds, self.all_targets)
],
self.label_names,
self.calculate_loss(),
average_precisions=(not self.is_memory_efficient),
recall_at_precision_thresholds=self.recall_at_precision_thresholds,
)
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from pytext.metrics import (
LabelListPrediction,
compute_multi_label_classification_metrics,
)
from pytext.metrics.tests.metrics_test_base import MetricsTestBase
LABEL_NAMES = ["label1", "label2", "label3"]
PREDICTIONS = [
LabelListPrediction(scores, predicted, expected)
for scores, predicted, expected in [
([-0.5, -0.7, -0.8], [1, 0, 0], [0]),
([-0.9, -0.2, -0.9], [0, 1, 0], [2]),
([-0.7, -0.4, -0.7], [0, 1, 0], [1]),
([-0.8, -0.9, -0.3], [0, 0, 1], [1]),
]
]
class BasicMetricsTest(MetricsTestBase):
def test_compute_multi_label_classification_metrics(self) -> None:
roc_auc_dict = {"label1": 1.0, "label2": 0.25, "label3": 0.0}
metrics = compute_multi_label_classification_metrics(PREDICTIONS,
LABEL_NAMES,
loss=5.0)
self.assertAlmostEqual(metrics.roc_auc, 1.25 / 3)
for k, v in metrics.per_label_soft_scores.items():