e6b43082db
This commit introduces the foundational structure for the Deal Intel project, including: - Environment configuration file (.env.example) for managing secrets and API keys. - Scripts for building a ChromaDB vector store (build_vector_store.py) and training machine learning models (train_rf.py, train_ensemble.py). - Health check functionality (health_check.py) to ensure system readiness. - A launcher script (launcher.py) for executing various commands, including UI launch and health checks. - Logging utilities (logging_utils.py) for consistent logging across the application. - A README file providing an overview and setup instructions for the project. These additions establish a comprehensive framework for an agentic deal-hunting AI system, integrating various components for data processing, model training, and user interaction.
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""
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Train a RandomForestRegressor on embeddings from ChromaDB, save to random_forest_model.pkl.
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Logs simple holdout metrics.
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"""
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import argparse
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import joblib
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import numpy as np
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import chromadb
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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from logging_utils import init_logger
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import config as cfg
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logger = init_logger("DealIntel.TrainRF")
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def main():
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parser = argparse.ArgumentParser(description="Train Random Forest pricer")
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parser.add_argument("--max-datapoints", type=int, default=cfg.RF_MAX_DATAPOINTS)
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args = parser.parse_args()
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logger.info(f"Loading embeddings from {cfg.DB_PATH}/{cfg.COLLECTION_NAME} (limit={args.max_datapoints})")
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client = chromadb.PersistentClient(path=cfg.DB_PATH)
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collection = client.get_or_create_collection(cfg.COLLECTION_NAME)
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result = collection.get(include=['embeddings', 'metadatas'], limit=args.max_datapoints)
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if not result.get("embeddings"):
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raise RuntimeError("No embeddings found — build the vector store first.")
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X = np.array(result["embeddings"])
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y = np.array([md["price"] for md in result["metadatas"]])
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logger.info(f"Training RF on {X.shape[0]} samples, {X.shape[1]} features")
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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rf = RandomForestRegressor(n_estimators=300, random_state=42, n_jobs=-1)
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rf.fit(X_train, y_train)
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preds = rf.predict(X_test)
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rmse = mean_squared_error(y_test, preds, squared=False)
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r2 = r2_score(y_test, preds)
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logger.info(f"Holdout RMSE={rmse:.2f}, R2={r2:.3f}")
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joblib.dump(rf, "random_forest_model.pkl")
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logger.info("Saved model to random_forest_model.pkl")
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if __name__ == "__main__":
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main() |