accuracy_score

pai4sk.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

Accuracy classification score.

In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

For SnapML solver this supports both local and distributed(MPI) method of execution.

Read more in the User Guide.

Parameters:
  • y_true (1d array-like, or label indicator array / sparse matrix) – Ground truth (correct) labels. It also accepts SnapML data partition, which includes the correct labels.
  • y_pred (1d array-like, or label indicator array / sparse matrix) – Predicted labels, as returned by a classifier.
  • normalize (bool, optional (default=True)) – If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.
  • sample_weight (array-like of shape = [n_samples], optional) – Sample weights.
Returns:

score – If normalize == True, return the fraction of correctly classified samples (float), else returns the number of correctly classified samples (int).

The best performance is 1 with normalize == True and the number of samples with normalize == False.
Return type:

float

See also

jaccard_similarity_score(), hamming_loss(), zero_one_loss()

Notes

In binary and multiclass classification, this function is equal to the jaccard_similarity_score function.

Examples

>>> import numpy as np
>>> from pai4sk.metrics import accuracy_score
>>> y_pred = [0, 2, 1, 3]
>>> y_true = [0, 1, 2, 3]
>>> accuracy_score(y_true, y_pred)
0.5
>>> accuracy_score(y_true, y_pred, normalize=False)
2

In the multilabel case with binary label indicators:

>>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5