CODEWITHSUNDEEP
pythonscikit-learn
tanmald
tanmald

Reputation: 121

Scikit's Average Precision Score bad input shape

I'm trying to plot a precision/recall score curve. This is the code I have:

    lbl_enc = preprocessing.LabelEncoder()
    labels = lbl_enc.fit_transform(test_tags)

    y_score = clf.predict_proba(test_set)

    average_precision = average_precision_score(labels, y_score)
    print('Average precision-recall score: {0:0.2f}'.format(average_precision))

    precision, recall, _ = precision_recall_curve(labels, y_score)

    plt.step(recall, precision, color='b', alpha=0.2,
             where='post')
    plt.fill_between(recall, precision, step='post', alpha=0.2,
                     color='b')

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title('2-class Precision-Recall curve: Average P-R = {0:0.2f}'.format(
        average_precision))

At the point I'm calculating the average_precision_score, I get this "ValueError: bad input shape (119, 2)" that is caused by the "y_score" variable.

y_score is in this format:

array([[0.45953712, 0.54046288],
   [0.78289908, 0.21710092],
   [0.13488789, 0.86511211],
   [0.56162583, 0.43837417],
   (...)
   [0.4595595 , 0.5404405 ]])

while labels is in this:

array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 1, 1])

How can I make this work for calculating avg precision score? Thanks in advance.

Upvotes: 0

Views: 586

Answers (1)

Stev
Stev

Reputation: 1140

In the documentation, it says:

y_score : array, shape = [n_samples] or [n_samples, n_classes]

Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

Therefore I believe you just need to do:

average_precision  = average_precision_score(labels, y_score[:,1])

Upvotes: 2

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