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89e8242 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import spacy
import numpy as np
from collections import Counter
import math
class StylometryEngine:
"""
Advanced Stylometric Analysis Engine (2026 Standard)
Uses spaCy for deep linguistic feature extraction.
"""
def __init__(self):
try:
self.nlp = spacy.load("en_core_web_sm", disable=["ner", "lemmatizer"])
print("[Stylometry] Engine initialized with en_core_web_sm.")
except Exception as e:
print(f"[Stylometry] Failed to load spaCy model: {e}")
self.nlp = None
def analyze(self, text: str) -> dict:
if not self.nlp:
return {"stylometry_score": 0.5, "signals": {}}
doc = self.nlp(text)
sentences = list(doc.sents)
if not sentences:
return {"stylometry_score": 0.5, "signals": {}}
# 1. POS Entropy (Measure of syntactic variety)
pos_counts = Counter([token.pos_ for token in doc])
total_pos = sum(pos_counts.values())
pos_entropy = -sum((count/total_pos) * math.log2(count/total_pos) for count in pos_counts.values())
# 2. Dependency Depth (Measure of structural complexity)
# Higher depth often indicates human nuance
def get_depth(token):
if not list(token.children):
return 1
return 1 + max(get_depth(child) for child in token.children)
depths = [get_depth(sent.root) for sent in sentences]
avg_depth = np.mean(depths)
depth_var = np.std(depths)
# 3. Burstiness (Advanced)
sent_lengths = [len(sent) for sent in sentences]
burstiness = np.std(sent_lengths) / (np.mean(sent_lengths) + 1e-9)
# 4. Lexical Density (Content words / Total words)
content_pos = {"NOUN", "VERB", "ADJ", "ADV"}
content_words = sum(1 for token in doc if token.pos_ in content_pos)
lexical_density = content_words / (len(doc) + 1e-9)
# SCORING LOGIC (Research-grounded 2026)
# AI characteristics: Low POS Entropy (< 2.8), Low Depth Var (< 1.5), Low Burstiness (< 0.3)
ai_pos_sig = 1.0 - np.clip((pos_entropy - 2.2) / 1.0, 0, 1)
ai_burst_sig = 1.0 - np.clip((burstiness - 0.2) / 0.6, 0, 1)
ai_depth_sig = 1.0 - np.clip((avg_depth - 3.0) / 4.0, 0, 1)
# Combine signals
stylometry_score = (ai_pos_sig * 0.4) + (ai_burst_sig * 0.4) + (ai_depth_sig * 0.2)
return {
"stylometry_score": float(np.clip(stylometry_score, 0, 1)),
"pos_entropy": float(pos_entropy),
"avg_depth": float(avg_depth),
"burstiness": float(burstiness),
"lexical_density": float(lexical_density),
"signals": {
"repetitive_syntax": ai_pos_sig > 0.7,
"monotonous_rhythm": ai_burst_sig > 0.7,
"shallow_structure": ai_depth_sig > 0.7
}
}
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