(y_true, y_score, pos_label=None,
                           sample_weight=None)
return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]
Compute precision-recall pairs for different probability thresholds

    Parameters
    ----------
    y_true : array, shape = [n_samples]
        True targets of binary classification in range {-1, 1} or {0, 1}.

    y_score : array, shape = [n_samples]
        Estimated probabilities or decision function.

    pos_label : int or str, default=None
        The label of the positive class

    sample_weight : array-like of shape = [n_samples], optional
        Sample weights.

    Returns
    -------
    precision : array, shape = [n_thresholds + 1]
        Precision values such that element i is the precision of
        predictions with score >= thresholds[i] and the last element is 1.

    recall : array, shape = [n_thresholds + 1]
        Decreasing recall values such that element i is the recall of
        predictions with score >= thresholds[i] and the last element is 0.

    thresholds : array, shape = [n_thresholds <= len(np.unique(probas_pred))]
        Increasing thresholds on the decision function used to compute
        precision and recall.
Compute precision-recall pairs for different probability thresholds
Compute precision - recall pairs for different probability thresholds
def precision_recall_curve(y_true, y_score, pos_label=None,
                           sample_weight=None):
    '''
    Compute precision-recall pairs for different probability thresholds

    Parameters
    ----------
    y_true : array, shape = [n_samples]
        True targets of binary classification in range {-1, 1} or {0, 1}.

    y_score : array, shape = [n_samples]
        Estimated probabilities or decision function.

    pos_label : int or str, default=None
        The label of the positive class

    sample_weight : array-like of shape = [n_samples], optional
        Sample weights.

    Returns
    -------
    precision : array, shape = [n_thresholds + 1]
        Precision values such that element i is the precision of
        predictions with score >= thresholds[i] and the last element is 1.

    recall : array, shape = [n_thresholds + 1]
        Decreasing recall values such that element i is the recall of
        predictions with score >= thresholds[i] and the last element is 0.

    thresholds : array, shape = [n_thresholds <= len(np.unique(probas_pred))]
        Increasing thresholds on the decision function used to compute
        precision and recall.
    '''
    fps, tps, thresholds = get_fps_tps_thresholds(y_true, y_score, pos_label=pos_label)

    precision = tps / (tps + fps)
    precision[np.isnan(precision)] = 0
    recall = tps / tps[-1]
    last_ind = tps.searchsorted(tps[-1])
    sl = slice(last_ind, None, -1)
    return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]
def precision_recall_curve ( y_true , y_score , pos_label = None , sample_weight = None ) : fps , tps , thresholds = get_fps_tps_thresholds ( y_true , y_score , pos_label = pos_label ) precision = tps / ( tps + fps ) precision [ np . isnan ( precision ) ] = 0 recall = tps / tps [ - 1 ] last_ind = tps . searchsorted ( tps [ - 1 ] ) sl = slice ( last_ind , None , - 1 ) return np . r_ [ precision [ sl ] , 1 ] , np . r_ [ recall [ sl ] , 0 ] , thresholds [ sl ]