File size: 2,477 Bytes
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Train baseline 4-Tier Document Sensitivity Classifier for SecureFlow AI.
Classifies documents into:
- Highly Confidential
- Confidential
- Internal
- Public
"""
import os
from pathlib import Path
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.pipeline import Pipeline
import joblib
DATA_DIR = Path(__file__).resolve().parent.parent
PROCESSED_DIR = DATA_DIR / "processed" / "document_classification"
MODEL_OUTPUT_DIR = Path(__file__).resolve().parent.parent.parent / "models"
def main():
train_path = PROCESSED_DIR / "train.csv"
val_path = PROCESSED_DIR / "validation.csv"
test_path = PROCESSED_DIR / "test.csv"
if not train_path.exists():
print(f"[ERROR] Processed data not found at {train_path}. Please run organize_datasets.py first.")
return
print("=" * 60)
print(" SecureFlow AI — 4-Tier Document Classifier Training")
print("=" * 60)
# 1. Load data
print(f"Loading data from {PROCESSED_DIR}...")
train_df = pd.read_csv(train_path)
val_df = pd.read_csv(val_path)
test_df = pd.read_csv(test_path)
print(f"Train samples: {len(train_df)} | Val samples: {len(val_df)} | Test samples: {len(test_df)}")
# 2. Build Pipeline
pipeline = Pipeline([
("tfidf", TfidfVectorizer(max_features=10000, ngram_range=(1, 2), stop_words="english")),
("clf", LogisticRegression(max_iter=1000, class_weight="balanced", random_state=42)),
])
# 3. Fit
print("Training classifier...")
pipeline.fit(train_df["cleaned_text"].fillna(train_df["text"]), train_df["label"])
# 4. Evaluate on Validation Set
print("\n--- Validation Set Performance ---")
val_preds = pipeline.predict(val_df["cleaned_text"].fillna(val_df["text"]))
print(classification_report(val_df["label"], val_preds))
# 5. Evaluate on Test Set
print("\n--- Test Set Performance ---")
test_preds = pipeline.predict(test_df["cleaned_text"].fillna(test_df["text"]))
print(classification_report(test_df["label"], test_preds))
# 6. Save Model
MODEL_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
model_file = MODEL_OUTPUT_DIR / "document_classifier_baseline.joblib"
joblib.dump(pipeline, model_file)
print(f"\n[SUCCESS] Model saved to {model_file}")
if __name__ == "__main__":
main()
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