Исправлен .gitignore: пакет stylometry/features добавлен в репозиторий

lab1/stylometry/features/base.py

@@ -0,0 +1,99 @@
+"""Вспомогательные функции для вычисления признаков."""
+
+from __future__ import annotations
+
+import math
+import re
+from collections import Counter
+from typing import Iterable, Sequence
+
+import numpy as np
+import pandas as pd
+
+
+def safe_div(numerator: float, denominator: float, default: float = 0.0) -> float:
+    """Деление без исключения при нулевом знаменателе."""
+    if denominator == 0:
+        return default
+    return float(numerator) / float(denominator)
+
+
+def finite(value: float, default: float = 0.0) -> float:
+    """Заменяет NaN и бесконечности на допустимое число."""
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return default
+    return result if math.isfinite(result) else default
+
+
+def as_list(row: pd.Series, column: str) -> list:
+    value = row.get(column, [])
+    if isinstance(value, list):
+        return value
+    if isinstance(value, tuple):
+        return list(value)
+    if value is None or (isinstance(value, float) and math.isnan(value)):
+        return []
+    return list(value)
+
+
+def word_tokens(tokens: Sequence[str]) -> list[str]:
+    """Токены, содержащие хотя бы одну букву или цифру."""
+    return [token for token in tokens if re.search(r"[A-Za-z0-9]", token)]
+
+
+def alphabetic_tokens(tokens: Sequence[str]) -> list[str]:
+    return [token for token in tokens if re.search(r"[A-Za-z]", token)]
+
+
+def lower_tokens(tokens: Sequence[str]) -> list[str]:
+    return [token.lower() for token in tokens]
+
+
+def sentence_word_counts(row: pd.Series) -> list[int]:
+    sentences = as_list(row, "sentences")
+    counts = [len(word_tokens(sentence)) for sentence in sentences]
+    counts = [count for count in counts if count > 0]
+    return counts or [len(word_tokens(as_list(row, "tokens")))]
+
+
+def count_set(tokens: Iterable[str], vocabulary: set[str]) -> int:
+    return sum(1 for token in tokens if token.lower() in vocabulary)
+
+
+def rate_set(tokens: Sequence[str], vocabulary: set[str]) -> float:
+    return safe_div(count_set(tokens, vocabulary), len(tokens))
+
+
+def counter(tokens: Iterable[str]) -> Counter:
+    return Counter(token.lower() for token in tokens)
+
+
+def mean(values: Sequence[float]) -> float:
+    return float(np.mean(values)) if values else 0.0
+
+
+def std(values: Sequence[float]) -> float:
+    return float(np.std(values, ddof=0)) if values else 0.0
+
+
+def median(values: Sequence[float]) -> float:
+    return float(np.median(values)) if values else 0.0
+
+
+def percentile(values: Sequence[float], q: float) -> float:
+    return float(np.percentile(values, q)) if values else 0.0
+
+
+def word_lengths(tokens: Sequence[str]) -> list[int]:
+    lengths: list[int] = []
+    for token in tokens:
+        letters = re.sub(r"[^A-Za-z]", "", token)
+        if letters:
+            lengths.append(len(letters))
+    return lengths
+
+
+def strip_punctuation(token: str) -> str:
+    return re.sub(r"^[^A-Za-z0-9]+|[^A-Za-z0-9]+$", "", token)