4aecd469a1
- Add Prometheus, Grafana, and Loki monitoring stack - Include pre-configured dashboards for ML metrics and logs - Add Podman container support with security policies - Implement ML runtime environments for multiple frameworks - Add containerized ML project templates (PyTorch, TensorFlow, etc.) - Include secure runner with isolation and resource limits - Add comprehensive log aggregation and alerting
75 lines
2 KiB
Python
Executable file
75 lines
2 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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import pandas as pd
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import statsmodels.api as sm
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def main():
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parser = argparse.ArgumentParser()
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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("Training statsmodels linear regression...")
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# Generate synthetic data
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np.random.seed(42)
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n_samples = 1000
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n_features = 5
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X = np.random.randn(n_samples, n_features)
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# True coefficients
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true_coef = np.array([1.5, -2.0, 0.5, 3.0, -1.0])
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noise = np.random.randn(n_samples) * 0.1
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y = X @ true_coef + noise
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# Create DataFrame
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feature_names = [f"feature_{i}" for i in range(n_features)]
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X_df = pd.DataFrame(X, columns=feature_names)
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y_series = pd.Series(y, name="target")
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# Add constant for intercept
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X_with_const = sm.add_constant(X_df)
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# Fit model
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model = sm.OLS(y_series, X_with_const).fit()
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logger.info(f"Model fitted successfully. R-squared: {model.rsquared:.4f}")
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# Save results
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results = {
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"model_type": "LinearRegression",
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"n_samples": n_samples,
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"n_features": n_features,
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"r_squared": float(model.rsquared),
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"adj_r_squared": float(model.rsquared_adj),
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"f_statistic": float(model.fvalue),
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"f_pvalue": float(model.f_pvalue),
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"coefficients": model.params.to_dict(),
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"standard_errors": model.bse.to_dict(),
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"p_values": model.pvalues.to_dict(),
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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 summary
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with open(output_dir / "model_summary.txt", "w") as f:
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f.write(str(model.summary()))
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logger.info("Results and model summary saved successfully!")
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if __name__ == "__main__":
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main()
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