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Transpose confusion matrix output to make it consistent with sklearn.

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Change: 144356967
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A. Unique TensorFlower 2017-01-12 12:27:19 -08:00 committed by TensorFlower Gardener
parent 9306b5c083
commit 4d9a24026c
4 changed files with 45 additions and 45 deletions
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6
tensorflow/contrib/metrics/python/ops/metric_ops_test.py
View File

@ -4486,7 +4486,7 @@ class StreamingMeanIOUTest(test.TestCase):
num_classes)
sess.run(variables.local_variables_initializer())
confusion_matrix = update_op.eval()
self.assertAllEqual([[3, 2], [0, 5]], confusion_matrix)
self.assertAllEqual([[3, 0], [2, 5]], confusion_matrix)
desired_miou = np.mean([3. / 5., 5. / 7.])
self.assertAlmostEqual(desired_miou, miou.eval())
@ -4509,7 +4509,7 @@ class StreamingMeanIOUTest(test.TestCase):
miou, update_op = metrics.streaming_mean_iou(predictions, labels,
num_classes)
sess.run(variables.local_variables_initializer())
self.assertAllEqual([[0, 40], [0, 0]], update_op.eval())
self.assertAllEqual([[0, 0], [40, 0]], update_op.eval())
self.assertEqual(0., miou.eval())
def testResultsWithSomeMissing(self):
@ -4540,7 +4540,7 @@ class StreamingMeanIOUTest(test.TestCase):
miou, update_op = metrics.streaming_mean_iou(
predictions, labels, num_classes, weights=weights)
sess.run(variables.local_variables_initializer())
self.assertAllEqual([[2, 2], [0, 4]], update_op.eval())
self.assertAllEqual([[2, 0], [2, 4]], update_op.eval())
desired_miou = np.mean([2. / 4., 4. / 6.])
self.assertAlmostEqual(desired_miou, miou.eval())

57
tensorflow/python/kernel_tests/confusion_matrix_test.py
View File

@ -36,14 +36,14 @@ class ConfusionMatrixTest(test.TestCase):
with self.test_session():
self.assertAllEqual([
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 1]
], confusion_matrix.confusion_matrix(
labels=[1, 2, 4], predictions=[2, 2, 4]).eval())
def _testConfMatrix(self, predictions, labels, truth, weights=None):
def _testConfMatrix(self, labels, predictions, truth, weights=None):
with self.test_session():
dtype = predictions.dtype
ans = confusion_matrix.confusion_matrix(
@ -52,8 +52,8 @@ class ConfusionMatrixTest(test.TestCase):
self.assertEqual(ans.dtype, dtype)
def _testBasic(self, dtype):
predictions = np.arange(5, dtype=dtype)
labels = np.arange(5, dtype=dtype)
predictions = np.arange(5, dtype=dtype)
truth = np.asarray(
[[1, 0, 0, 0, 0],
@ -63,7 +63,7 @@ class ConfusionMatrixTest(test.TestCase):
[0, 0, 0, 0, 1]],
dtype=dtype)
self._testConfMatrix(predictions=predictions, labels=labels, truth=truth)
self._testConfMatrix(labels=labels, predictions=predictions, truth=truth)
def testInt32Basic(self):
self._testBasic(dtype=np.int32)
@ -104,32 +104,32 @@ class ConfusionMatrixTest(test.TestCase):
except NameError: # In Python 3.
range_builder = range
for i in range_builder(len(d)):
truth[d[i], l[i]] += 1
truth[l[i], d[i]] += 1
self.assertEqual(cm_out.dtype, np_dtype)
self.assertAllClose(cm_out, truth, atol=1e-10)
def _testOnTensors_int32(self):
def testOnTensors_int32(self):
self._testConfMatrixOnTensors(dtypes.int32, np.int32)
def testOnTensors_int64(self):
self._testConfMatrixOnTensors(dtypes.int64, np.int64)
def _testDifferentLabelsInPredictionAndTarget(self, dtype):
predictions = np.asarray([1, 2, 3], dtype=dtype)
labels = np.asarray([4, 5, 6], dtype=dtype)
predictions = np.asarray([1, 2, 3], dtype=dtype)
truth = np.asarray(
[[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0]],
dtype=dtype)
self._testConfMatrix(predictions=predictions, labels=labels, truth=truth)
self._testConfMatrix(labels=labels, predictions=predictions, truth=truth)
def testInt32DifferentLabels(self, dtype=np.int32):
self._testDifferentLabelsInPredictionAndTarget(dtype)
@ -138,20 +138,20 @@ class ConfusionMatrixTest(test.TestCase):
self._testDifferentLabelsInPredictionAndTarget(dtype)
def _testMultipleLabels(self, dtype):
predictions = np.asarray([1, 1, 2, 3, 5, 6, 1, 2, 3, 4], dtype=dtype)
labels = np.asarray([1, 1, 2, 3, 5, 1, 3, 6, 3, 1], dtype=dtype)
predictions = np.asarray([1, 1, 2, 3, 5, 6, 1, 2, 3, 4], dtype=dtype)
truth = np.asarray(
[[0, 0, 0, 0, 0, 0, 0],
[0, 2, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 2, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 2, 0, 0, 1, 0, 1],
[0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0]],
[0, 0, 1, 0, 0, 0, 0]],
dtype=dtype)
self._testConfMatrix(predictions=predictions, labels=labels, truth=truth)
self._testConfMatrix(labels=labels, predictions=predictions, truth=truth)
def testInt32MultipleLabels(self, dtype=np.int32):
self._testMultipleLabels(dtype)
@ -160,8 +160,8 @@ class ConfusionMatrixTest(test.TestCase):
self._testMultipleLabels(dtype)
def testWeighted(self):
predictions = np.arange(5, dtype=np.int32)
labels = np.arange(5, dtype=np.int32)
predictions = np.arange(5, dtype=np.int32)
weights = constant_op.constant(np.arange(5, dtype=np.int32))
truth = np.asarray(
@ -173,31 +173,32 @@ class ConfusionMatrixTest(test.TestCase):
dtype=np.int32)
self._testConfMatrix(
predictions=predictions, labels=labels, weights=weights, truth=truth)
labels=labels, predictions=predictions, weights=weights, truth=truth)
def testInvalidRank(self):
predictions = np.asarray([[1, 2, 3]])
def testInvalidRank_predictionsTooBig(self):
labels = np.asarray([1, 2, 3])
predictions = np.asarray([[1, 2, 3]])
self.assertRaisesRegexp(ValueError, "an not squeeze dim",
confusion_matrix.confusion_matrix, predictions,
labels)
predictions = np.asarray([1, 2, 3])
def testInvalidRank_predictionsTooSmall(self):
labels = np.asarray([[1, 2, 3]])
predictions = np.asarray([1, 2, 3])
self.assertRaisesRegexp(ValueError, "an not squeeze dim",
confusion_matrix.confusion_matrix, predictions,
labels)
def testInputDifferentSize(self):
predictions = np.asarray([1, 2, 3])
labels = np.asarray([1, 2])
predictions = np.asarray([1, 2, 3])
self.assertRaisesRegexp(ValueError, "must be equal",
confusion_matrix.confusion_matrix, predictions,
labels)
def testOutputIsInt32(self):
predictions = np.arange(2)
labels = np.arange(2)
predictions = np.arange(2)
with self.test_session():
cm = confusion_matrix.confusion_matrix(
labels, predictions, dtype=dtypes.int32)
@ -205,8 +206,8 @@ class ConfusionMatrixTest(test.TestCase):
self.assertEqual(tf_cm.dtype, np.int32)
def testOutputIsInt64(self):
predictions = np.arange(2)
labels = np.arange(2)
predictions = np.arange(2)
with self.test_session():
cm = confusion_matrix.confusion_matrix(
labels, predictions, dtype=dtypes.int64)

6
tensorflow/python/kernel_tests/metrics_test.py
View File

@ -3296,7 +3296,7 @@ class MeanIOUTest(test.TestCase):
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
sess.run(variables.local_variables_initializer())
confusion_matrix = update_op.eval()
self.assertAllEqual([[3, 2], [0, 5]], confusion_matrix)
self.assertAllEqual([[3, 0], [2, 5]], confusion_matrix)
desired_miou = np.mean([3. / 5., 5. / 7.])
self.assertAlmostEqual(desired_miou, miou.eval())
@ -3317,7 +3317,7 @@ class MeanIOUTest(test.TestCase):
with self.test_session() as sess:
miou, update_op = metrics.mean_iou(labels, predictions, num_classes)
sess.run(variables.local_variables_initializer())
self.assertAllEqual([[0, 40], [0, 0]], update_op.eval())
self.assertAllEqual([[0, 0], [40, 0]], update_op.eval())
self.assertEqual(0., miou.eval())
def testResultsWithSomeMissing(self):
@ -3348,7 +3348,7 @@ class MeanIOUTest(test.TestCase):
miou, update_op = metrics.mean_iou(
labels, predictions, num_classes, weights=weights)
sess.run(variables.local_variables_initializer())
self.assertAllEqual([[2, 2], [0, 4]], update_op.eval())
self.assertAllEqual([[2, 0], [2, 4]], update_op.eval())
desired_miou = np.mean([2. / 4., 4. / 6.])
self.assertAlmostEqual(desired_miou, miou.eval())

21
tensorflow/python/ops/confusion_matrix.py
View File

@ -87,11 +87,11 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
Calculate the Confusion Matrix for a pair of prediction and
label 1-D int arrays.
The matrix rows represent the prediction labels and the columns
represents the real labels. The confusion matrix is always a 2-D array
of shape `[n, n]`, where `n` is the number of valid labels for a given
classification task. Both prediction and labels must be 1-D arrays of
the same shape in order for this function to work.
The matrix columns represent the prediction labels and the rows represent the
real labels. The confusion matrix is always a 2-D array of shape `[n, n]`,
where `n` is the number of valid labels for a given classification task. Both
prediction and labels must be 1-D arrays of the same shape in order for this
function to work.
If `num_classes` is None, then `num_classes` will be set to the one plus
the maximum value in either predictions or labels.
@ -106,8 +106,8 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
```python
tf.contrib.metrics.confusion_matrix([1, 2, 4], [2, 2, 4]) ==>
[[0 0 0 0 0]
[0 0 0 0 0]
[0 1 1 0 0]
[0 0 1 0 0]
[0 0 1 0 0]
[0 0 0 0 0]
[0 0 0 0 1]]
```
@ -116,9 +116,8 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
resulting in a 5x5 confusion matrix.
Args:
labels: A 1-D representing the real labels for the classification task.
predictions: A 1-D array representing the predictions for a given
classification.
labels: 1-D `Tensor` of real labels for the classification task.
predictions: 1-D `Tensor` of predictions for a given classification.
num_classes: The possible number of labels the classification task can
have. If this value is not provided, it will be calculated
using both predictions and labels array.
@ -153,7 +152,7 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
weights = math_ops.cast(weights, dtype)
shape = array_ops.stack([num_classes, num_classes])
indices = array_ops.transpose(array_ops.stack([predictions, labels]))
indices = array_ops.transpose(array_ops.stack([labels, predictions]))
values = (array_ops.ones_like(predictions, dtype)
if weights is None else weights)
cm_sparse = sparse_tensor.SparseTensor(