Первичная версия: стилометрия реплик, LSA и протокол экспериментов

lab1/tests/test_metrics.py

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+"""Тесты метрик и статистических критериев."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from stylometry.evaluation import (
+    bootstrap_ci,
+    classification_metrics,
+    confusion_matrix_frame,
+    per_class_report,
+)
+from stylometry.stats import (
+    bootstrap_difference,
+    holm_bonferroni,
+    mcnemar_exact,
+    wilcoxon_signed_rank,
+)
+
+
+def test_classification_metrics_perfect() -> None:
+    y_true = np.array(["a", "b", "c", "a", "b", "c"])
+    y_pred = y_true.copy()
+    metrics = classification_metrics(y_true, y_pred)
+    assert metrics["accuracy"] == 1.0
+    assert metrics["macro_f1"] == 1.0
+    assert metrics["balanced_accuracy"] == 1.0
+
+
+def test_confusion_matrix_and_report() -> None:
+    y_true = np.array(["a", "a", "b", "b"])
+    y_pred = np.array(["a", "b", "b", "b"])
+    matrix = confusion_matrix_frame(y_true, y_pred, ["a", "b"])
+    assert matrix.loc["a", "a"] == 1
+    report = per_class_report(y_true, y_pred, ["a", "b"])
+    assert {"precision", "recall", "f1", "support"} <= set(report.columns)
+
+
+def test_bootstrap_ci() -> None:
+    y_true = np.array(["a", "b", "a", "b", "a", "b"])
+    y_pred = np.array(["a", "b", "a", "a", "a", "b"])
+    interval = bootstrap_ci(y_true, y_pred, ["a", "b"], n_boot=50, seed=1)
+    assert interval["lower"] <= interval["upper"]
+
+
+def test_mcnemar() -> None:
+    y_true = np.array([1, 1, 0, 0, 1, 0])
+    pred_a = np.array([1, 1, 0, 0, 1, 0])
+    pred_b = np.array([1, 0, 0, 1, 1, 0])
+    result = mcnemar_exact(y_true, pred_a, pred_b)
+    assert result["b"] >= 0 and result["c"] >= 0
+    assert 0 <= result["p_value"] <= 1
+
+
+def test_holm() -> None:
+    adjusted = holm_bonferroni([0.01, 0.02, 0.03])
+    assert len(adjusted) == 3
+    assert all(0 <= value <= 1 for value in adjusted)
+
+
+def test_wilcoxon() -> None:
+    result = wilcoxon_signed_rank([0.5, 0.6, 0.7], [0.4, 0.6, 0.5])
+    assert 0 <= result["p_value"] <= 1
+
+
+def test_bootstrap_difference() -> None:
+    y_true = np.array([0, 1, 2, 0, 1, 2])
+    pred_a = np.array([0, 1, 2, 0, 1, 2])
+    pred_b = np.array([0, 1, 1, 0, 1, 1])
+    result = bootstrap_difference(y_true, pred_a, pred_b, labels=[0, 1, 2], n_boot=50)
+    assert result["lower"] <= result["upper"]