def confusion_matrix(predictions, labels, num_classes=None, name=None):
"""Computes the confusion matrix from predictions and labels.
Calculate the Confusion Matrix for a pair of prediction and
label 1-D int arrays.
Considering a prediction array such as: `[1, 2, 3]`
And a label array such as: `[2, 2, 3]`
The confusion matrix returned would be the following one:
[[0, 0, 0]
[0, 1, 0]
[0, 1, 0]
[0, 0, 1]]
Where 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.
Args:
predictions: A 1-D array represeting the predictions for a given
classification.
labels: A 1-D represeting the real labels for the classification task.
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.
name: Scope name.
Returns:
A l X l matrix represeting the confusion matrix, where l in the number of
possible labels in the classification task.
Raises:
ValueError: If both predictions and labels are not 1-D vectors and do not
have the same size.
"""
with ops.op_scope([predictions, labels, num_classes], name,
'confusion_matrix') as name:
predictions, labels = metric_ops.remove_squeezable_dimensions(
ops.convert_to_tensor(
predictions, name='predictions', dtype=dtypes.int64),
ops.convert_to_tensor(labels, name='labels', dtype=dtypes.int64))
if num_classes is None:
num_classes = math_ops.maximum(math_ops.reduce_max(predictions),
math_ops.reduce_max(labels)) + 1
shape = array_ops.pack([num_classes, num_classes])
indices = array_ops.transpose(array_ops.pack([predictions, labels]))
values = array_ops.ones_like(predictions, dtype=dtypes.int32)
cm_sparse = ops.SparseTensor(
indices=indices, values=values, shape=shape)
zero_matrix = array_ops.zeros(math_ops.to_int32(shape), dtypes.int32)
return sparse_ops.sparse_add(zero_matrix, cm_sparse)
def auc_using_histogram(boolean_labels,
scores,
score_range,
nbins=100,
collections=None,
check_shape=True,
name=None):
"""AUC computed by maintaining histograms.
Rather than computing AUC directly, this Op maintains Variables containing
histograms of the scores associated with `True` and `False` labels. By
comparing these the AUC is generated, with some discretization error.
See: "Efficient AUC Learning Curve Calculation" by Bouckaert.
This AUC Op updates in `O(batch_size + nbins)` time and works well even with
large class imbalance. The accuracy is limited by discretization error due
to finite number of bins. If scores are concentrated in a fewer bins,
accuracy is lower. If this is a concern, we recommend trying different
numbers of bins and comparing results.
Args:
boolean_labels: 1-D boolean `Tensor`. Entry is `True` if the corresponding
record is in class.
scores: 1-D numeric `Tensor`, same shape as boolean_labels.
score_range: `Tensor` of shape `[2]`, same dtype as `scores`. The min/max
values of score that we expect. Scores outside range will be clipped.
nbins: Integer number of bins to use. Accuracy strictly increases as the
number of bins increases.
collections: List of graph collections keys. Internal histogram Variables
are added to these collections. Defaults to `[GraphKeys.LOCAL_VARIABLES]`.
check_shape: Boolean. If `True`, do a runtime shape check on the scores
and labels.
name: A name for this Op. Defaults to "auc_using_histogram".
Returns:
auc: `float32` scalar `Tensor`. Fetching this converts internal histograms
to auc value.
update_op: `Op`, when run, updates internal histograms.
"""
if collections is None:
collections = [ops.GraphKeys.LOCAL_VARIABLES]
with variable_scope.variable_op_scope(
[boolean_labels, scores, score_range], name, 'auc_using_histogram'):
scores, boolean_labels = metric_ops.remove_squeezable_dimensions(
scores, boolean_labels)
score_range = ops.convert_to_tensor(score_range, name='score_range')
boolean_labels, scores = _check_labels_and_scores(
boolean_labels, scores, check_shape)
hist_true, hist_false = _make_auc_histograms(boolean_labels, scores,
score_range, nbins)
hist_true_acc, hist_false_acc, update_op = _auc_hist_accumulate(hist_true,
hist_false,
nbins,
collections)
auc = _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins)
return auc, update_op
def auc_using_histogram(boolean_labels,
scores,
score_range,
nbins=100,
collections=None,
check_shape=True,
name=None):
"""AUC computed by maintaining histograms.
Rather than computing AUC directly, this Op maintains Variables containing
histograms of the scores associated with `True` and `False` labels. By
comparing these the AUC is generated, with some discretization error.
See: "Efficient AUC Learning Curve Calculation" by Bouckaert.
This AUC Op updates in `O(batch_size + nbins)` time and works well even with
large class imbalance. The accuracy is limited by discretization error due
to finite number of bins. If scores are concentrated in a fewer bins,
accuracy is lower. If this is a concern, we recommend trying different
numbers of bins and comparing results.
Args:
boolean_labels: 1-D boolean `Tensor`. Entry is `True` if the corresponding
record is in class.
scores: 1-D numeric `Tensor`, same shape as boolean_labels.
score_range: `Tensor` of shape `[2]`, same dtype as `scores`. The min/max
values of score that we expect. Scores outside range will be clipped.
nbins: Integer number of bins to use. Accuracy strictly increases as the
number of bins increases.
collections: List of graph collections keys. Internal histogram Variables
are added to these collections. Defaults to `[GraphKeys.LOCAL_VARIABLES]`.
check_shape: Boolean. If `True`, do a runtime shape check on the scores
and labels.
name: A name for this Op. Defaults to "auc_using_histogram".
Returns:
auc: `float32` scalar `Tensor`. Fetching this converts internal histograms
to auc value.
update_op: `Op`, when run, updates internal histograms.
"""
if collections is None:
collections = [ops.GraphKeys.LOCAL_VARIABLES]
with variable_scope.variable_op_scope(
[boolean_labels, scores, score_range], name, 'auc_using_histogram'):
scores, boolean_labels = metric_ops.remove_squeezable_dimensions(
scores, boolean_labels)
score_range = ops.convert_to_tensor(score_range, name='score_range')
boolean_labels, scores = _check_labels_and_scores(
boolean_labels, scores, check_shape)
hist_true, hist_false = _make_auc_histograms(boolean_labels, scores,
score_range, nbins)
hist_true_acc, hist_false_acc, update_op = _auc_hist_accumulate(
hist_true, hist_false, nbins, collections)
auc = _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins)
return auc, update_op
