>>> from sklearn.metrics import fbeta_score, make_scorer
    >>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
    >>> ftwo_scorer
    make_scorer(fbeta_score, beta=2)
    >>> from sklearn.model_selection import GridSearchCV
    >>> from sklearn.svm import LinearSVC
    >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
    ...                     scoring=ftwo_scorer)

def pr_auc_score(y_true, y_score):
    """
    Generates the Area Under the Curve for precision and recall.
    """
    precision, recall, thresholds = \
        precision_recall_curve(y_true, y_score[:, 1])
    return auc(recall, precision, reorder=True)

pr_auc_scorer = make_scorer(pr_auc_score, greater_is_better=True,
                            needs_proba=True)