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PhilVerify — TF-IDF + Logistic Regression Baseline Classifier (Layer 1)
Seed dataset of 30 labeled PH news headlines (10 per class).
Replaced by fine-tuned XLM-RoBERTa in Phase 10.
"""
import os
import logging
import pickle
from dataclasses import dataclass, field
from pathlib import Path
logger = logging.getLogger(__name__)
MODEL_PATH = Path(__file__).parent / "models" / "tfidf_model.pkl"
# ── Seed dataset (30 samples — 10 per class) ──────────────────────────────────
# Labels: 0=Credible, 1=Unverified, 2=Fake
SEED_DATA = [
# Credible (0)
("DOH reports 500 new COVID-19 cases as vaccination drive continues in Metro Manila", 0),
("Rappler: Supreme Court upholds Comelec ruling on disqualification case", 0),
("GMA News: PNP arrests 12 suspects in Bulacan drug bust", 0),
("Philippine Star: GDP growth slows to 5.3% in Q3 says BSP", 0),
("Inquirer: Senate passes revised anti-terrorism bill on third reading", 0),
("Manila Bulletin: Typhoon Carina leaves P2B damage in Isabela province", 0),
("ABS-CBN News: Marcos signs executive order on agricultural modernization", 0),
("DOF confirms revenue collection targets met for fiscal year 2025", 0),
("DSWD distributes relief packs to 10,000 families in Cotabato", 0),
("PhilStar: Meralco rate hike of P0.18 per kilowatt-hour approved by ERC", 0),
# Unverified (1)
("SHOCKING: Politician caught taking selfie during Senate hearing", 1),
("VIRAL: Celebrity spotted at secret meeting with government official", 1),
("BREAKING: 'Anonymous source' says president planning cabinet reshuffle", 1),
("Rumor has it: New tax policy to affect OFW remittances starting 2026", 1),
("CLAIM: Government hiding true COVID-19 death count from public", 1),
("Unconfirmed: Military says there are 500 rebels still in Mindanao", 1),
("REPORT: Certain barangay officials accepting bribes according to residents", 1),
("Alleged: Shipment of smuggled goods found in Manila port last week", 1),
("CLAIM: New mandatory vaccine policy for all government employees", 1),
("Source says: Manila Water to increase rates by 20% next month", 1),
# Fake (2)
("GRABE! Namatay daw ang tatlong tao sa bagong sakit na kumakalat sa Pilipinas!", 2),
("TOTOO BA? Marcos nagsabi na libreng kuryente na simula bukas!", 2),
("SHOCKING TRUTH: Bill Gates microchip found in COVID vaccine in Cebu!", 2),
("WATCH: Senator caught stealing money in Senate vault - full video", 2),
("CONFIRMED: Philippines to become 51st state of the United States in 2026!", 2),
("KATOTOHANAN: DOH secretly poisoning water supply to control population", 2),
("EXPOSED: Duterte has secret family in Davao that government is hiding", 2),
("100% TOTOO: Garlic cures COVID-19, doctors don't want you to know this!", 2),
("GALING NG PILIPINAS: Filipino scientist discovers cure for cancer, suppressed by big pharma", 2),
("BREAKING: Entire Luzon to experience 3-day total blackout next week, says NGCP", 2),
]
@dataclass
class Layer1Result:
verdict: str # "Credible" | "Unverified" | "Fake"
confidence: float # 0.0 – 100.0
triggered_features: list[str] = field(default_factory=list)
class TFIDFClassifier:
"""
TF-IDF + Logistic Regression baseline.
Train() fits on the seed dataset and saves to disk.
Predict() loads persisted model first call.
"""
_LABELS = {0: "Credible", 1: "Unverified", 2: "Likely Fake"}
def __init__(self):
self._vectorizer = None
self._clf = None
def train(self) -> None:
"""Fit on seed data. Skips training if persisted model exists."""
if MODEL_PATH.exists():
self._load()
return
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
texts, labels = zip(*SEED_DATA)
self._vectorizer = TfidfVectorizer(
ngram_range=(1, 2),
max_features=1000,
sublinear_tf=True,
)
X = self._vectorizer.fit_transform(texts)
self._clf = LogisticRegression(max_iter=500, C=1.0, random_state=42)
self._clf.fit(X, labels)
MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
with open(MODEL_PATH, "wb") as f:
pickle.dump({"vectorizer": self._vectorizer, "clf": self._clf}, f)
logger.info("TF-IDF model trained and saved to %s", MODEL_PATH)
def _load(self) -> None:
with open(MODEL_PATH, "rb") as f:
data = pickle.load(f)
self._vectorizer = data["vectorizer"]
self._clf = data["clf"]
logger.info("TF-IDF model loaded from %s", MODEL_PATH)
def predict(self, text: str) -> Layer1Result:
if self._vectorizer is None:
self.train()
X = self._vectorizer.transform([text])
pred_label = int(self._clf.predict(X)[0])
proba = self._clf.predict_proba(X)[0]
confidence = round(float(max(proba)) * 100, 1)
verdict = self._LABELS[pred_label]
# Extract top TF-IDF features as human-readable triggers
feature_names = self._vectorizer.get_feature_names_out()
tfidf_scores = X.toarray()[0]
top_indices = tfidf_scores.argsort()[-5:][::-1]
triggered = [feature_names[i] for i in top_indices if tfidf_scores[i] > 0]
return Layer1Result(
verdict=verdict,
confidence=confidence,
triggered_features=triggered,
)
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