# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
import numpy
from matplotlib import pyplot as plt

import credible.bayesian.metrics
import credible.curves
import credible.plot

# We simulate scores for 2 hypothethical systems.
# Scores are beta distributed for a "perfect" example.

nb_samples1 = 200  # Sample size, higher makes it more precise (thinner CI)
a1 = 6
b1 = 10

nb_samples2 = 100  # Sample size, higher makes it more precise (thinner CI)
a2 = 7
b2 = 10
rng = numpy.random.default_rng(42)

y_true1 = numpy.concatenate(
    (
        numpy.zeros((nb_samples1,), dtype=int),
        numpy.ones((nb_samples1,), dtype=int),
    )
)
y_score1 = numpy.concatenate(
    (
        rng.beta(a=a1, b=b1, size=nb_samples1),
        rng.beta(a=b1, b=a1, size=nb_samples1),
    )
)

y_true2 = numpy.concatenate(
    (
        numpy.zeros((nb_samples2,), dtype=int),
        numpy.ones((nb_samples2,), dtype=int),
    )
)
y_score2 = numpy.concatenate(
    (
        rng.beta(a=a2, b=b2, size=nb_samples2),
        rng.beta(a=b2, b=a2, size=nb_samples2),
    )
)

# Plotting scores (example):
# negatives1 = y_score1[y_true1 == 0]
# ph = plt.hist(negatives1, bins=100, alpha=0.3, label="Negatives")
# plt.hist(negatives1, bins=100, color=h1[2][0].get_facecolor()[:3], histtype="step")
# positives1 = y_score1[y_true1 == 1]
# nh = plt.hist(positives1, bins=100, alpha=0.3, label="Positives")
# plt.hist(positives1, bins=100, color=h1[2][0].get_facecolor()[:3], histtype="step")
# plt.title("Scores (i.i.d. samples)")

# We now compute the ROC curve with the confidence interval
with credible.plot.tight_layout(
    axis_labels=("False Positive Rate", "True Positive Rate")
):
    roc1 = credible.bayesian.metrics.roc_curve(y_true1, y_score1)
    lower1, upper1 = credible.curves.curve_ci_hull(
        roc1, extrapolate_from_origin=False
    )
    auc1 = credible.bayesian.metrics.roc_auc_score(y_true1, y_score1)
    label1 = (
        f"System 1 (AUC: {auc1[0]:.2f} - 95% CI: {auc1[1]:.2f}-{auc1[2]:.2f})"
    )
    objs1 = credible.plot.curve_ci(roc1[:2], lower1, upper1)

    roc2 = credible.bayesian.metrics.roc_curve(y_true2, y_score2)
    lower2, upper2 = credible.curves.curve_ci_hull(
        roc2, extrapolate_from_origin=False
    )
    auc2 = credible.bayesian.metrics.roc_auc_score(y_true2, y_score2)
    label2 = (
        f"System 2 (AUC: {auc2[0]:.2f} - 95% CI: {auc2[1]:.2f}-{auc2[2]:.2f})"
    )
    objs2 = credible.plot.curve_ci(roc2[:2], lower2, upper2)

    plt.legend(
        (objs1, objs2),
        (label1, label2),
        loc="best",
        fancybox=True,
        framealpha=0.7,
    )

plt.show()