db144750a3
- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training. - Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance. - Adjusted deployment scripts to support new backend options and improved error handling for model file transfers. - Added new entries to the training log for better tracking of model performance metrics over time. - Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.
49 lines
1.0 KiB
JSON
49 lines
1.0 KiB
JSON
[
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{
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"date": "2026-03-01T18:04:50.871434",
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"auc": 0.546,
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"model_path": "models/lgbm_filter.pkl"
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{
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"date": "2026-03-01T18:44:22.163935",
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"backend": "mlx",
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"auc": 0.5054,
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"samples": 1772,
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"train_sec": 0.7,
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"model_path": "models/mlx_filter.weights"
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{
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"date": "2026-03-01T18:52:52.991917",
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"model_path": "models/lgbm_filter.pkl"
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{
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"date": "2026-03-01T18:54:48.200006",
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"auc": 0.5405,
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"samples": 1704,
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"model_path": "models/lgbm_filter.pkl"
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{
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"date": "2026-03-01T19:29:21.454533",
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"auc": 0.5321,
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"samples": 1696,
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"features": 21,
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"model_path": "models/lgbm_filter.pkl"
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{
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"date": "2026-03-01T21:03:56.314547",
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"model_path": "models/lgbm_filter.pkl"
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"date": "2026-03-01T21:12:23.866860",
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"features": 21,
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"model_path": "models/lgbm_filter.pkl"
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}
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] |