"""
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""

from __future__ import annotations

import numpy as np
from sklearn.metrics import accuracy_score, f1_score

from sentence_transformers import (
    evaluation,
)


def test_BinaryClassificationEvaluator_find_best_f1_and_threshold() -> None:
    """Tests that the F1 score for the computed threshold is correct"""
    y_true = np.random.randint(0, 2, 1000)
    y_pred_cosine = np.random.randn(1000)
    (
        best_f1,
        best_precision,
        best_recall,
        threshold,
    ) = evaluation.BinaryClassificationEvaluator.find_best_f1_and_threshold(
        y_pred_cosine, y_true, high_score_more_similar=True
    )
    y_pred_labels = [1 if pred >= threshold else 0 for pred in y_pred_cosine]
    sklearn_f1score = f1_score(y_true, y_pred_labels)
    assert np.abs(best_f1 - sklearn_f1score) < 1e-6


def test_BinaryClassificationEvaluator_find_best_accuracy_and_threshold() -> None:
    """Tests that the Acc score for the computed threshold is correct"""
    y_true = np.random.randint(0, 2, 1000)
    y_pred_cosine = np.random.randn(1000)
    (
        max_acc,
        threshold,
    ) = evaluation.BinaryClassificationEvaluator.find_best_acc_and_threshold(
        y_pred_cosine, y_true, high_score_more_similar=True
    )
    y_pred_labels = [1 if pred >= threshold else 0 for pred in y_pred_cosine]
    sklearn_acc = accuracy_score(y_true, y_pred_labels)
    assert np.abs(max_acc - sklearn_acc) < 1e-6