c980167041
- Add end-to-end tests for complete workflow validation - Include integration tests for API and database interactions - Add unit tests for all major components and utilities - Include performance tests for payload handling - Add CLI API integration tests - Include Podman container integration tests - Add WebSocket and queue execution tests - Include shell script tests for setup validation Provides comprehensive test coverage ensuring platform reliability and functionality across all components and interactions.
84 lines
2.3 KiB
Python
Executable file
84 lines
2.3 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import json
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import logging
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from pathlib import Path
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import time
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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import xgboost as xgb
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--n_estimators", type=int, default=100)
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parser.add_argument("--max_depth", type=int, default=6)
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parser.add_argument("--learning_rate", type=float, default=0.1)
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parser.add_argument("--output_dir", type=str, required=True)
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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logger.info(
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f"Training XGBoost with {args.n_estimators} estimators, depth {args.max_depth}..."
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)
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# Generate synthetic data
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X, y = make_classification(
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n_samples=1000, n_features=20, n_classes=2, random_state=42
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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# Convert to DMatrix (XGBoost format)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test, label=y_test)
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# Train model
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params = {
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"max_depth": args.max_depth,
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"eta": args.learning_rate,
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"objective": "binary:logistic",
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"eval_metric": "logloss",
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"seed": 42,
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}
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model = xgb.train(params, dtrain, args.n_estimators)
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# Evaluate
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y_pred_prob = model.predict(dtest)
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y_pred = (y_pred_prob > 0.5).astype(int)
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accuracy = accuracy_score(y_test, y_pred)
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logger.info(f"Training completed. Accuracy: {accuracy:.4f}")
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# Save results
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results = {
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"model_type": "XGBoost",
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"n_estimators": args.n_estimators,
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"max_depth": args.max_depth,
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"learning_rate": args.learning_rate,
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"accuracy": accuracy,
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"n_samples": len(X),
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"n_features": X.shape[1],
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}
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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with open(output_dir / "results.json", "w") as f:
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json.dump(results, f, indent=2)
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# Save model
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model.save_model(str(output_dir / "xgboost_model.json"))
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logger.info("Results and model saved successfully!")
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if __name__ == "__main__":
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main()
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