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Calculates Cohen's kappa.
tf.contrib.metrics.cohen_kappa(
labels, predictions_idx, num_classes, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
Cohen's kappa is a statistic that measures inter-annotator agreement.
The cohen_kappa
function calculates the confusion matrix, and creates three
local variables to compute the Cohen's kappa: po
, pe_row
, and pe_col
,
which refer to the diagonal part, rows and columns totals of the confusion
matrix, respectively. This value is ultimately returned as kappa
, an
idempotent operation that is calculated by
pe = (pe_row * pe_col) / N
k = (sum(po) - sum(pe)) / (N - sum(pe))
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
kappa
. update_op
weights each prediction by the corresponding value in
weights
.
Class labels are expected to start at 0. E.g., if num_classes
was three, then the possible labels would be [0, 1, 2].
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args | |
---|---|
labels
|
1-D Tensor of real labels for the classification task. Must be
one of the following types: int16, int32, int64.
|
predictions_idx
|
1-D Tensor of predicted class indices for a given
classification. Must have the same type as labels .
|
num_classes
|
The possible number of labels. |
weights
|
Optional Tensor whose shape matches predictions .
|
metrics_collections
|
An optional list of collections that kappa should be
added to.
|
updates_collections
|
An optional list of collections that update_op should
be added to.
|
name
|
An optional variable_scope name. |
Returns | |
---|---|
kappa
|
Scalar float Tensor representing the current Cohen's kappa.
|
update_op
|
Operation that increments po , pe_row and pe_col
variables appropriately and whose value matches kappa .
|
Raises | |
---|---|
ValueError
|
If num_classes is less than 2, or predictions and labels
have mismatched shapes, or if weights is not None and its shape
doesn't match predictions , or if either metrics_collections or
updates_collections are not a list or tuple.
|
RuntimeError
|
If eager execution is enabled. |