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import numpy as np
import joblib
import os
from sklearn.linear_model import LogisticRegression
# Output path
MODEL_PATH = os.path.join(os.path.dirname(__file__), "..", "models", "final_classifier.pkl")
os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)
print(">>> Generating Synthetic Logic-Aligned Training Data...")
# Features:
# 0: sim_pro_india
# 1: sim_anti_india
# 2: sim_pro_govt
# 3: sim_anti_govt
# 4: sim_neutral
# 5: neg
# 6: neu
# 7: pos
# 8: sarcasm
# 9: context_pol_crit (Anti-Govt)
# 10: context_nat_crit (Anti-India)
# 11: context_pol_praise (Pro-Govt)
# 12: context_nat_praise (Pro-India)
def generate_sample(label_idx):
# Base noise for 13 features
feats = np.random.uniform(0.0, 0.3, 13)
# 0: Pro-India
if label_idx == 0:
feats[0] = np.random.uniform(0.6, 1.0) # High Pro-India Sim
feats[7] = np.random.uniform(0.5, 1.0) # High Positive
feats[5] = np.random.uniform(0.0, 0.2) # Low Negative
feats[8] = np.random.uniform(0.0, 0.2) # IGNORE SARCASM
# LLM Context
feats[12] = np.random.uniform(0.7, 1.0) # High National Praise
feats[9] = np.random.uniform(0.0, 0.2) # Low Pol Crit
# 1: Anti-India
elif label_idx == 1:
feats[1] = np.random.uniform(0.6, 1.0) # High Anti-India Sim
feats[5] = np.random.uniform(0.5, 1.0) # High Negative
feats[7] = np.random.uniform(0.0, 0.2) # Low Positive
feats[8] = np.random.uniform(0.0, 0.2) # IGNORE SARCASM
# LLM Context
feats[10] = np.random.uniform(0.7, 1.0) # High National Criticism
feats[9] = np.random.uniform(0.0, 0.3) # Low/Med Pol Crit
# 2: Pro-Government
elif label_idx == 2:
feats[2] = np.random.uniform(0.6, 1.0) # High Pro-Govt Sim
feats[7] = np.random.uniform(0.5, 1.0) # High Positive
feats[5] = np.random.uniform(0.0, 0.2) # Low Negative
feats[8] = np.random.uniform(0.0, 0.2) # IGNORE SARCASM
# LLM Context
feats[11] = np.random.uniform(0.7, 1.0) # High Political Praise
feats[10] = np.random.uniform(0.0, 0.2) # Low Nat Crit
# 3: Anti-Government
elif label_idx == 3:
feats[3] = np.random.uniform(0.6, 1.0) # High Anti-Govt Sim
feats[5] = np.random.uniform(0.5, 1.0) # High Negative
feats[7] = np.random.uniform(0.0, 0.2) # Low Positive
feats[8] = np.random.uniform(0.0, 0.2) # IGNORE SARCASM
# LLM Context
feats[9] = np.random.uniform(0.7, 1.0) # High Political Criticism!
feats[10] = np.random.uniform(0.0, 0.4) # Low/Med Nat Crit
# 4: Neutral
elif label_idx == 4:
feats[4] = np.random.uniform(0.5, 1.0) # High Neutral Sim
feats[6] = np.random.uniform(0.5, 1.0) # High Neutral Sentiment
feats[5] = np.random.uniform(0.0, 0.2)
feats[7] = np.random.uniform(0.0, 0.2)
feats[8] = np.random.uniform(0.0, 0.1)
# LLM Context -> All low or balanced
feats[9] = np.random.uniform(0.0, 0.3)
feats[10] = np.random.uniform(0.0, 0.3)
return feats
# Generate data
X = []
y = []
SAMPLES_PER_CLASS = 500
for label in range(5):
for _ in range(SAMPLES_PER_CLASS):
X.append(generate_sample(label))
y.append(label)
X = np.array(X)
y = np.array(y)
print(f"Training Logistic Regression on {len(X)} synthetic samples (13 features)...")
clf = LogisticRegression(max_iter=1000, multi_class='multinomial', solver='lbfgs')
clf.fit(X, y)
print(f"Accuracy on Training Set: {clf.score(X, y):.4f}")
print(f"Saving model to {MODEL_PATH}...")
joblib.dump(clf, MODEL_PATH)
print("DONE.")
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