"""Тесты кросс-валидации на синтетических данных."""

from __future__ import annotations

import numpy as np
import pandas as pd

from stylometry.cv import run_repeated_cv


def test_repeated_cv_runs_and_has_all_predictions() -> None:
    rng = np.random.default_rng(0)
    n_samples = 60
    n_classes = 3
    X = pd.DataFrame(
        {
            "feature_1": rng.normal(size=n_samples),
            "feature_2": rng.normal(size=n_samples),
        }
    )
    y = np.repeat([f"c{index}" for index in range(n_classes)], 20)
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    pipeline = Pipeline(
        [("scale", StandardScaler()), ("model", LogisticRegression(max_iter=1000))]
    )
    result = run_repeated_cv(
        pipeline, X, y, n_splits=3, n_repeats=2, seed=42, verbose=False
    )
    assert len(result.oof) == n_samples * 2
    assert set(result.oof["y_true"]) == set(y)
    assert result.fold_metrics["macro_f1"].between(0, 1).all()
    assert result.labels.tolist() == ["c0", "c1", "c2"]