Generate Precision Recall Curve

pr_curve.py

from sklearn.metrics import precision_recall_curve, auc, average_precision_score, roc_auc_score
import matplotlib.pyplot as plt
from inspect import signature


def generate_curve(y_true, y_pred):
    precision, recall, _ = precision_recall_curve(y_true, y_pred)
    average_precision = average_precision_score(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_pred)
    
    # In matplotlib < 1.5, plt.fill_between does not have a 'step' argument
    step_kwargs = ({'step': 'post'}
                   if 'step' in signature(plt.fill_between).parameters
                   else {})
    plt.step(recall, precision, color='b', alpha=0.2,
             where='post')
    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title('Precision-Recall Curve: AP={0:0.2f}; AUC={0:0.2f}'.format(average_precision, auc))
    plt.clf()