File size: 6,087 Bytes
7d235bd 5e95de5 7d235bd 5e95de5 7d235bd 5e95de5 7d235bd 5e95de5 7d235bd | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | import re
import logging
import yake
import numpy as np
from functools import lru_cache
from pathlib import Path
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
logger = logging.getLogger(__name__)
MODEL_NAME = "all-mpnet-base-v2"
@lru_cache(maxsize=1)
def _get_embed_model():
logger.info(f"Loading embed model: {MODEL_NAME}")
return SentenceTransformer(MODEL_NAME)
MIN_WORDS = 8
MAX_WORDS = 4000
def normalize_text(text):
if pd.isna(text):
return ""
text = str(text).lower().strip()
text = re.sub(r"http\S+|www\S+|\S+@\S+", " ", text)
text = re.sub(r"[^a-z0-9\+\#\./\- ]", " ", text)
text = re.sub(r"\s+", " ", text)
return text.strip()
def substring_deduplicate(features):
features = sorted(features, key=len, reverse=True)
kept = []
for feat in features:
is_substring = False
for longer_feat in kept:
if feat in longer_feat:
is_substring = True
break
if not is_substring:
kept.append(feat)
return kept
def semantic_deduplicate(features, model, threshold=0.85):
if len(features) <= 1:
return features
embeddings = model.encode(
features,
convert_to_numpy=True,
normalize_embeddings=True
)
kept = []
for i, feat in enumerate(features):
redundant = False
for existing in kept:
sim = cosine_similarity(
embeddings[i].reshape(1, -1),
embeddings[existing].reshape(1, -1)
)[0][0]
if sim >= threshold:
redundant = True
break
if not redundant:
kept.append(i)
return [features[i] for i in kept]
@lru_cache(maxsize=1)
def _get_yake_extractor():
logger.info("Initializing YAKE NLP feature extractor")
return yake.KeywordExtractor(lan="en", n=3, dedupLim=0.9, top=20, features=None)
import json
_feature_db_frequencies = None
def load_feature_frequencies_cache():
global _feature_db_frequencies
if _feature_db_frequencies is None:
try:
from src.similarity_model.semantic_search import load_metadata
df = load_metadata()
from collections import Counter
counter = Counter()
total_docs = len(df)
if total_docs > 0:
for feats in df["features"]:
if isinstance(feats, str):
try:
feats = json.loads(feats)
except:
feats = []
if isinstance(feats, list):
seen = set(str(f).strip().lower() for f in feats)
for f in seen:
if f:
counter[f] += 1
_feature_db_frequencies = {k: v / total_docs for k, v in counter.items()}
else:
_feature_db_frequencies = {}
except Exception:
_feature_db_frequencies = {}
return _feature_db_frequencies
def extract_features(text: str) -> list:
"""
Extracts detailed, multi-word phrases generated purely by YAKE.
Filters out highly generic features appearing in > 15% of indexed projects.
"""
matched = []
try:
kw_extractor = _get_yake_extractor()
yake_results = kw_extractor.extract_keywords(text)
freq_cache = load_feature_frequencies_cache()
max_df_threshold = 0.15 # Filter if keyword appears in > 15% of database
for kw, score in yake_results:
kw_clean = str(kw).strip().lower()
if len(kw_clean.split()) > 1 and kw_clean not in matched:
# Apply IDF filter check
doc_freq = freq_cache.get(kw_clean, 0.0)
if doc_freq <= max_df_threshold:
matched.append(kw_clean)
except Exception as e:
logger.error(f"YAKE extraction failed: {e}")
if not matched:
return []
matched = substring_deduplicate(matched)
return semantic_deduplicate(matched, _get_embed_model(), threshold=0.85)
def preprocess_dataset(df):
logger.info("Starting preprocessing...")
df = df.copy()
df.columns = df.columns.str.strip().str.lower().str.replace(r"\W+", "_", regex=True)
column_mapping = {
"title": "project_title",
"ai_summary": "ai_summary",
"technologies": "technologies",
"keywords": "keywords",
"abstract": "abstract",
"description": "description",
"problem_statement": "problem_statement",
"proposed_solution": "proposed_solution",
"objectives": "objectives",
"category": "category"
}
df = df.rename(columns=column_mapping)
for col in ["project_title", "abstract", "description"]:
if col not in df.columns:
df[col] = ""
df[col] = df[col].fillna("").astype(str)
df["full_content"] = df["project_title"] + ". " + df["abstract"] + ". " + df["description"]
df["clean_text"] = df["full_content"].apply(normalize_text)
before = len(df)
df = df.drop_duplicates(subset=["project_title", "clean_text"]).copy()
logger.info(f"Removed duplicates: {before-len(df)}")
df["word_count"] = df["clean_text"].str.split().str.len()
df = df[df["word_count"].between(MIN_WORDS, MAX_WORDS)].copy()
df.reset_index(drop=True, inplace=True)
logger.info("Extracting features...")
df["features"] = df["clean_text"].apply(extract_features)
df = df[df["features"].apply(len) > 0].copy()
df.reset_index(drop=True, inplace=True)
logger.info(f"Final rows: {len(df)}")
return df
|