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"""
Modu艂 do ekstrakcji zaawansowanych cech lingwistycznych przy u偶yciu spaCy.
"""
import re
import math
from collections import Counter
from statistics import mean, variance
from typing import Dict, List
import textstat
import spacy
from ..utils import safe_divide
from ..constants import CAMEL_CASE_PATTERN
# --- Funkcje analizuj膮ce podstawowe statystyki z Doc ---
def analyze_pos_stats(doc: spacy.tokens.Doc) -> Dict[str, float]:
"""Oblicza statystyki cz臋艣ci mowy (POS), interpunkcji i stopwords."""
words = [t for t in doc if not t.is_punct and not t.is_space and t.pos_ != 'SYM']
words_count = len(words)
if not words_count:
return {'words': 0, 'nouns': 0, 'verbs': 0, 'adjectives': 0, 'adverbs': 0,
'punctuations': 0, 'symbols': 0, 'stopwords': 0, 'oovs': 0,
'pos_x': 0, 'pos_num': 0, 'noun_ratio': 0.0, 'verb_ratio': 0.0,
'adj_ratio': 0.0}
stats = {
'words': words_count, 'nouns': sum(1 for t in doc if t.pos_ == "NOUN"),
'verbs': sum(1 for t in doc if t.pos_ == "VERB"),
'adjectives': sum(1 for t in doc if t.pos_ == "ADJ"),
'adverbs': sum(1 for t in doc if t.pos_ == "ADV"),
'punctuations': sum(1 for t in doc if t.is_punct),
'symbols': sum(1 for t in doc if t.pos_ == "SYM"),
'stopwords': sum(1 for t in doc if t.is_stop),
'oovs': sum(1 for t in doc if t.is_oov),
'pos_x': sum(1 for t in doc if t.pos_ == "X"),
'pos_num': sum(1 for t in doc if t.pos_ == "NUM"),
}
stats['noun_ratio'] = safe_divide(stats['nouns'], words_count)
stats['verb_ratio'] = safe_divide(stats['verbs'], words_count)
stats['adj_ratio'] = safe_divide(stats['adjectives'], words_count)
return stats
def analyze_doc_level_stats(doc: spacy.tokens.Doc, text: str) -> Dict[str, float]:
"""Analizuje cechy na poziomie ca艂ego dokumentu."""
words = [t for t in doc if not t.is_punct and not t.is_space and t.pos_ != 'SYM']
words_count = len(words)
sentences_count = len(list(doc.sents))
return {
'sentences': sentences_count,
'avg_word_length': safe_divide(sum(len(t.text) for t in words), words_count),
'avg_sentence_length': safe_divide(words_count, sentences_count),
'lexical_density': safe_divide(len({t.lemma_ for t in words}), words_count),
'gunning_fog': textstat.gunning_fog(text) if text.strip() else 0.0,
'camel_case': sum(1 for t in words if CAMEL_CASE_PATTERN.match(t.text)),
'capitalized_words': sum(1 for t in words if t.text.isupper()),
}
# --- Funkcje analizuj膮ce zaawansowane cechy lingwistyczne ---
def analyze_named_entities(doc: spacy.tokens.Doc) -> Dict[str, float]:
"""Analizuje rozpoznane jednostki nazwane (NER)."""
alpha_words = [t for t in doc if t.is_alpha]
if not alpha_words:
return {"ner_count": 0, "ner_person_ratio": 0.0, "ner_org_ratio": 0.0,
"ner_loc_ratio": 0.0, "ner_misc_ratio": 0.0}
ents = doc.ents
return {
"ner_count": len(ents),
"ner_person_ratio": safe_divide(sum(1 for e in ents if e.label_ == "persName"), len(alpha_words)),
"ner_org_ratio": safe_divide(sum(1 for e in ents if e.label_ == "orgName"), len(alpha_words)),
"ner_loc_ratio": safe_divide(sum(1 for e in ents if e.label_ in ["placeName", "locName"]), len(alpha_words)),
"ner_misc_ratio": safe_divide(sum(1 for e in ents if e.label_ not in ["persName", "orgName", "placeName", "locName"]), len(alpha_words)),
}
def analyze_morphology(doc: spacy.tokens.Doc) -> Dict[str, float]:
"""Analizuje r贸偶norodno艣膰 morfologiczn膮."""
alpha_tokens = [t for t in doc if t.is_alpha]
if not alpha_tokens:
return {"case_diversity": 0.0, "tense_diversity": 0.0, "mood_diversity": 0.0}
cases, tenses, moods = [], [], []
for token in alpha_tokens:
if token.morph:
cases.extend(token.morph.get("Case", []))
tenses.extend(token.morph.get("Tense", []))
moods.extend(token.morph.get("Mood", []))
return {"case_diversity": safe_divide(len(set(cases)), len(alpha_tokens)),
"tense_diversity": safe_divide(len(set(tenses)), len(alpha_tokens)),
"mood_diversity": safe_divide(len(set(moods)), len(alpha_tokens))}
def analyze_dependency_complexity(doc: spacy.tokens.Doc) -> Dict[str, float]:
"""Oblicza 艣redni膮 g艂臋boko艣膰 drzewa zale偶no艣ci."""
depths = []
for sent in doc.sents:
if not list(sent): continue
max_depth = 0
for token in sent:
dist = 0
curr = token
while curr.head != curr and dist < 100:
curr = curr.head
dist += 1
max_depth = max(max_depth, dist)
depths.append(max_depth)
return {"avg_dependency_tree_depth": mean(depths) if depths else 0.0}
def analyze_pos_frequencies(doc: spacy.tokens.Doc, top_k=10) -> Dict[str, float]:
"""Analizuje cz臋stotliwo艣膰 POS dla najcz臋stszych s艂贸w."""
tokens = [t for t in doc if t.is_alpha]
if not tokens:
return {"top_words_total_count": 0, "top_words_noun_ratio": 0.0, "top_words_verb_ratio": 0.0,
"top_words_adj_ratio": 0.0, "top_words_other_ratio": 0.0, "top_words_noun_prop_of_all_nouns": 0.0,
"top_words_verb_prop_of_all_verbs": 0.0, "top_words_adj_prop_of_all_adjs": 0.0,
"top_words_other_prop_of_all_others": 0.0}
word_counts = Counter(t.text.lower() for t in tokens)
top_words_list = [w for w, _ in word_counts.most_common(top_k)]
top_tokens = [t for t in tokens if t.text.lower() in top_words_list]
total_top_count = len(top_tokens)
top_noun = sum(1 for t in top_tokens if t.pos_ == 'NOUN')
top_verb = sum(1 for t in top_tokens if t.pos_ == 'VERB')
top_adj = sum(1 for t in top_tokens if t.pos_ == 'ADJ')
top_other = total_top_count - (top_noun + top_verb + top_adj)
total_nouns = sum(1 for t in tokens if t.pos_ == "NOUN")
total_verbs = sum(1 for t in tokens if t.pos_ == "VERB")
total_adjs = sum(1 for t in tokens if t.pos_ == "ADJ")
total_others = len(tokens) - (total_nouns + total_verbs + total_adjs)
return {
"top_words_total_count": total_top_count,
"top_words_noun_ratio": safe_divide(top_noun, total_top_count),
"top_words_verb_ratio": safe_divide(top_verb, total_top_count),
"top_words_adj_ratio": safe_divide(top_adj, total_top_count),
"top_words_other_ratio": safe_divide(top_other, total_top_count),
"top_words_noun_prop_of_all_nouns": safe_divide(top_noun, total_nouns),
"top_words_verb_prop_of_all_verbs": safe_divide(top_verb, total_verbs),
"top_words_adj_prop_of_all_adjs": safe_divide(top_adj, total_adjs),
"top_words_other_prop_of_all_others": safe_divide(top_other, total_others),
}
# --- Funkcje wymagaj膮ce tylko tekstu ---
def compute_readability_indices(text: str, sentences: List[str]) -> Dict[str, float]:
"""Oblicza wska藕niki czytelno艣ci LIX i RIX."""
if not text.strip(): return {"lix": 0.0, "rix": 0.0}
words = re.findall(r'\w+', text)
num_words = len(words)
num_sentences = len(sentences)
long_words = sum(1 for w in words if len(w) > 6)
lix = safe_divide(num_words, num_sentences) + safe_divide(long_words * 100, num_words)
rix = safe_divide(long_words, num_words) * 100
return {"lix": lix, "rix": rix}
def analyze_polish_diacritics_distribution(text: str) -> Dict[str, float]:
"""Analizuje rozk艂ad polskich znak贸w diakrytycznych."""
polish_diacritics = '膮膰臋艂艅贸艣藕偶膭膯臉艁艃脫艢殴呕'
total = len(text)
if total == 0: return {"diacritics_std_dev": 0.0}
counts = Counter(text)
diac_counts = [counts[ch] for ch in polish_diacritics if ch in counts]
if not diac_counts: return {"diacritics_std_dev": 0.0}
diac_freqs = [c / total for c in diac_counts]
mean_freq = mean(diac_freqs)
variance_val = sum((x - mean_freq) ** 2 for x in diac_freqs) / len(diac_freqs)
return {"diacritics_std_dev": math.sqrt(variance_val)}
def analyze_question_sentences(sentences: List[str]) -> Dict[str, float]:
"""Oblicza stosunek zda艅 pytaj膮cych do wszystkich."""
if not sentences: return {"question_sentence_ratio": 0.0}
questions = sum(1 for s in sentences if s.strip().endswith('?'))
return {"question_sentence_ratio": safe_divide(questions, len(sentences))}
# --- G艂贸wna funkcja agreguj膮ca ---
def calculate_all_spacy_features(doc: spacy.tokens.Doc, text: str, sentences: List[str]) -> Dict[str, float]:
"""Agreguje wszystkie zaawansowane cechy lingwistyczne."""
features = {}
features.update(analyze_pos_stats(doc))
features.update(analyze_doc_level_stats(doc, text))
features.update(analyze_named_entities(doc))
features.update(analyze_morphology(doc))
features.update(analyze_dependency_complexity(doc))
features.update(analyze_pos_frequencies(doc))
features.update(compute_readability_indices(text, sentences))
features.update(analyze_polish_diacritics_distribution(text))
features.update(analyze_question_sentences(sentences))
return features |