"""=====================================
Visualising OOD detection results
=====================================

Out-of-distribution (OOD) detection asks whether a test sample comes from the
same distribution as the training data.  The :mod:`probly.plot` module provides
three standard diagnostic plots:

- **Histogram** -- overlapping score distributions for in-distribution (ID) and
  OOD samples.
- **ROC curve** -- receiver operating characteristic with AUROC and optional
  FPR\@95 annotation.
- **Precision-Recall curve** -- with AUPR summary.

All three accept an optional :class:`~probly.plot.PlotConfig` for consistent
styling across your project.
"""

from __future__ import annotations

import matplotlib.pyplot as plt
import numpy as np
from probly.metrics import auc, precision_recall_curve, roc_curve

from probly.plot import PlotConfig, plot_histogram, plot_pr_curve, plot_roc_curve

# %%
# Synthetic scores
# ----------------
# Simulate confidence scores: ID samples cluster near 1, OOD samples are more
# spread out.

rng = np.random.default_rng(42)
id_scores = rng.beta(a=5, b=1, size=500)
ood_scores = rng.beta(a=2, b=2, size=500)

# %%
# Score histogram
# ---------------
# A quick visual check of how well the two distributions separate.

plot_histogram(id_scores, ood_scores)
plt.show()

# %%
# ROC curve
# ---------
# Compute FPR/TPR from the scores and plot with AUROC annotation.

anomaly_id = 1.0 - id_scores
anomaly_ood = 1.0 - ood_scores
labels = np.concatenate([np.zeros(len(anomaly_id)), np.ones(len(anomaly_ood))])
preds = np.concatenate([anomaly_id, anomaly_ood])

fpr, tpr, _ = roc_curve(labels, preds)
auroc = auc(fpr, tpr)
idx_95 = np.where(tpr >= 0.95)[0]
fpr95 = float(fpr[idx_95[0]]) if len(idx_95) > 0 else None

plot_roc_curve(fpr, tpr, auroc=auroc, fpr95=fpr95)
plt.show()

# %%
# Precision-Recall curve
# ----------------------

precision, recall, _ = precision_recall_curve(labels, preds)
aupr = auc(recall, precision)

plot_pr_curve(recall, precision, aupr=aupr)
plt.show()