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c5e6b77 | 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 | """TF-IDF features of word 1-2-grams and character 3-5-grams (within word boundaries), stored without pickle."""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
def vectorizers():
from sklearn.feature_extraction.text import TfidfVectorizer
return (TfidfVectorizer(analyzer="word", ngram_range=(1, 2), min_df=1, sublinear_tf=True, lowercase=True),
TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), min_df=2, sublinear_tf=True, lowercase=True))
def save(vecs, path: Path) -> None:
path.mkdir(parents=True, exist_ok=True)
for i, v in enumerate(vecs):
(path / f"vocab{i}.json").write_text(json.dumps({k: int(x) for k, x in v.vocabulary_.items()}), encoding="utf-8")
np.save(path / f"idf{i}.npy", v.idf_.astype(np.float32))
def load(path: Path):
vecs = vectorizers()
for i, v in enumerate(vecs):
v.vocabulary = json.loads((path / f"vocab{i}.json").read_text(encoding="utf-8"))
v.fit(["x"]) # sets up the fixed vocabulary; idf replaced below
v.idf_ = np.load(path / f"idf{i}.npy").astype(np.float64)
return vecs
def transform(vecs, texts):
from scipy.sparse import hstack
return hstack([v.transform(texts) for v in vecs]).tocsr()
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