kwcoco.metrics.sklearn_alts module

Faster pure-python versions of sklearn functions that avoid expensive checks and label rectifications. It is assumed that all labels are consecutive non-negative integers.

kwcoco.metrics.sklearn_alts.confusion_matrix(y_true, y_pred, n_labels=None, labels=None, sample_weight=None)

faster version of sklearn confusion matrix that avoids the expensive checks and label rectification

Runs in about 0.7ms

Returns:matrix where rows represent real and cols represent pred Return type:ndarray

Example

>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 0,  0, 1])
>>> y_pred = np.array([0, 0, 0, 0, 0, 0, 0, 1,  1, 1])
>>> confusion_matrix(y_true, y_pred, 2)
array([[4, 2],
       [3, 1]])
>>> confusion_matrix(y_true, y_pred, 2).ravel()
array([4, 2, 3, 1])

Benchmarks:

import ubelt as ub y_true = np.random.randint(0, 2, 10000) y_pred = np.random.randint(0, 2, 10000)

n = 1000 for timer in ub.Timerit(n, bestof=10, label=’py-time’):

sample_weight = [1] * len(y_true) confusion_matrix(y_true, y_pred, 2, sample_weight=sample_weight)

for timer in ub.Timerit(n, bestof=10, label=’np-time’):

sample_weight = np.ones(len(y_true), dtype=np.int) confusion_matrix(y_true, y_pred, 2, sample_weight=sample_weight)

kwcoco.metrics.sklearn_alts.global_accuracy_from_confusion(cfsn)

kwcoco.metrics.sklearn_alts.class_accuracy_from_confusion(cfsn)