import matplotlib.pyplot as plt
from time import time

from sklearn.calibration import CalibratedClassifierCV, calibration_curve
from sklearn.metrics import (brier_score_loss, precision_score, recall_score,
                             f1_score, log_loss)

from sklearn.linear_model import LogisticRegression

def plot_calibration_curve(est, name, X_train, X_test, y_train, y_test, plot=True):
    """Plot calibration curve for est w/o and with calibration. """
    # Calibrated with isotonic calibration
    isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic')

    # Calibrated with sigmoid calibration
    sigmoid = CalibratedClassifierCV(est, cv=2, method='sigmoid')

    # Logistic regression with no calibration as baseline
    lr = LogisticRegression(C=1., solver='lbfgs')

    if plot:
        fig = plt.figure(1, figsize=(10, 10))
        ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
        ax2 = plt.subplot2grid((3, 1), (2, 0))
        ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
    best_logloss = 100

    for clf, name in [(lr, 'Logistic'),
                      (est, name),
                      (isotonic, name + ' + Isotonic'),
                      (sigmoid, name + ' + Sigmoid')]:
        t0 = time()
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        t = time()
        if hasattr(clf, "predict_proba"):
            prob_pos = clf.predict_proba(X_test)[:, 1]
        else:  # use decision function
            prob_pos = clf.decision_function(X_test)
            prob_pos = \
                (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())

        clf_score = brier_score_loss(y_test, prob_pos, pos_label=y.max())
        print("%s:" % name)
        print("\tPrecision: %1.3f" % precision_score(y_test, y_pred))
        print("\tRecall: %1.3f" % recall_score(y_test, y_pred))
        logloss = log_loss(y_test, prob_pos)
        print("\tLog_loss: %1.3f" % logloss)
        print("\tTime: %1.2f\n" % (t-t0))

        if logloss < best_logloss:
            best_logloss = logloss
            best_clf = name
            best_time = t-t0

        fraction_of_positives, mean_predicted_value = \
            calibration_curve(y_test, prob_pos, n_bins=10)

        if plot:
            ax1.plot(mean_predicted_value, fraction_of_positives, "s-",
                     label="%s (%1.3f)" % (name, clf_score))

            ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,
                     histtype="step", lw=2)

    if plot:
        ax1.set_ylabel("Fraction of positives")
        ax1.set_ylim([-0.05, 1.05])
        ax1.legend(loc="lower right")
        ax1.set_title('Calibration plots  (reliability curve)')

        ax2.set_xlabel("Mean predicted value")
        ax2.set_ylabel("Count")
        ax2.legend(loc="upper center", ncol=2)

        plt.tight_layout()

    return best_clf, best_logloss, t-t0