evaluator.py 文件源码

def generate_prec_recall_points(clf, test_examples, test_labels, pk_file):
    # Generate precision-recall points and store in a pickle file.

    precision = dict()
    recall = dict()
    average_precision = dict()
    thresholds = dict()

    n_classes = len(clf.model.classes_)
    y_test = label_binarize(test_labels, clf.model.classes_)

    y_score = clf.predict_raw_prob(test_examples)
    # It only output 1 column of positive probability.
    y_score = y_score[:, 1:]

    for i in range(n_classes - 1):
        precision[i], recall[i], thresholds[i] = precision_recall_curve(
            y_test[:, i],
            y_score[:, i])
        average_precision[i] = average_precision_score(y_test[:, i],
                                                       y_score[:, i])
    # Compute micro-average ROC curve and ROC area
    precision["micro"], recall["micro"], thresholds['micro'] = \
        precision_recall_curve(y_test.ravel(), y_score.ravel())
    average_precision["micro"] = average_precision_score(y_test, y_score,
                                                         average="micro")

    if pk_file is not None:
        with open(pk_file, 'wb') as f:
            pickle.dump((precision, recall, average_precision, thresholds), f)