def testPrepareMultipleLabelsAndPredictionsMultiDimension(self):
labels = [[0], [1]]
preds = {'probabilities': [[0.2, 0.8], [0.3, 0.7]]}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([[0], [1]]))
self.assertAllClose(got_preds, np.array([[0.2, 0.8], [0.3, 0.7]]))
def testPrepareLabelsAndPredictionsEmptySparseTensorValue(self):
labels = tf.compat.v1.SparseTensorValue(
values=np.array([]), indices=np.array([]), dense_shape=(0, 2))
preds = {'probabilities': [0.2, 0.7, 0.1], 'all_classes': ['a', 'b', 'c']}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([0, 0, 0]))
self.assertAllClose(got_preds, np.array([0.2, 0.7, 0.1]))
def testPrepareLabelsAndPredictionsMixedBatching(self):
labels = np.array([1])
preds = {
'predictions': np.array([[0.8]]),
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1]))
self.assertAllClose(got_preds, np.array([[0.8]]))
def testPrepareLabelsAndPredictions(self):
labels = [0]
preds = {
'logistic': np.array([0.8]),
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([0]))
self.assertAllClose(got_preds, np.array([0.8]))
def testPrepareMultipleLabelsAndPredictionsPythonList(self):
labels = ['b', 'c', 'a']
preds = {
'probabilities': [0.2, 0.7, 0.1],
'all_classes': ['a', 'b', 'c']
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1, 2, 0]))
self.assertAllClose(got_preds, np.array([0.2, 0.7, 0.1]))
def testPrepareMultipleLabelsAndPredictions(self):
labels = np.array(['b', 'c', 'a'])
preds = {
'scores': np.array([0.2, 0.7, 0.1]),
'classes': np.array(['a', 'b', 'c'])
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1, 2, 0]))
self.assertAllClose(got_preds, np.array([0.2, 0.7, 0.1]))
def testPrepareLabelsAndPredictionsBatched(self):
labels = [['b']]
preds = {
'logistic': np.array([[0.8]]),
'all_classes': np.array([['a', 'b', 'c']])
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([[1]]))
self.assertAllClose(got_preds, np.array([[0.8]]))
def testPrepareLabelsAndPredictionsUsingBinaryScoresUnbatched(self):
labels = np.array([1])
preds = {
'scores': np.array([0.3, 0.7]),
'classes': np.array(['a', 'b'])
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1]))
self.assertAllClose(got_preds, np.array([0.3, 0.7]))
def testPrepareLabelsAndPredictionsUsingBinaryScoresSparse(self):
labels = np.array([1, 0])
preds = {
'scores': np.array([[0.9, 0.2], [0.3, 0.7]]),
'classes': np.array([['a', 'b'], ['a', 'b']])
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1, 0]))
self.assertAllClose(got_preds, np.array([[0.9, 0.2], [0.3, 0.7]]))
def testPrepareLabelsAndPredictionsSparseTensorValueWithBatching(self):
labels = tf.compat.v1.SparseTensorValue(
indices=np.array([1, 2]), values=np.array([1, 1]), dense_shape=(1, 2))
preds = {
'probabilities': [[0.2, 0.7, 0.1]],
'all_classes': [['a', 'b', 'c']]
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([[0, 1, 1]]))
self.assertAllClose(got_preds, np.array([[0.2, 0.7, 0.1]]))
def testPrepareLabelsAndPredictionsSparseTensorValueAndVocab(self):
labels = types.SparseTensorValue(indices=np.array([0, 2]),
values=np.array(['c', 'a']),
dense_shape=np.array([1, 2]))
preds = {
'probabilities': [0.2, 0.7, 0.1],
'all_classes': ['a', 'b', 'c']
}
got_labels, got_preds = metric_util.prepare_labels_and_predictions(
labels, preds)
self.assertAllClose(got_labels, np.array([1, 0, 1]))
self.assertAllClose(got_preds, np.array([0.2, 0.7, 0.1]))
