Victor Phan
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
"""
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Inspect model_odc.joblib to see what it actually contains
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"""
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import joblib
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from pathlib import Path
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model_path = Path("model_train/model_odc.joblib")
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if model_path.exists():
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print("Loading model_odc.joblib...")
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model_data = joblib.load(model_path)
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print(f"\nModel type: {type(model_data)}")
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print(f"Model class: {model_data.__class__.__name__}")
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# Check if it's a dict
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if isinstance(model_data, dict):
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print(f"\nModel is a dict with keys: {model_data.keys()}")
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model = model_data.get('model')
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else:
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model = model_data
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print(f"\nActual model type: {type(model)}")
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print(f"Actual model class: {model.__class__.__name__}")
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# Try to get feature info
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if hasattr(model, 'n_features_in_'):
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print(f"\nn_features_in_: {model.n_features_in_}")
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if hasattr(model, 'feature_names_in_'):
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print(f"feature_names_in_: {model.feature_names_in_}")
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# If it's a GridSearchCV
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if hasattr(model, 'best_estimator_'):
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print(f"\nThis is a GridSearchCV!")
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print(f"Best estimator: {model.best_estimator_}")
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best_est = model.best_estimator_
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if hasattr(best_est, 'steps'):
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print(f"\nPipeline steps:")
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for step_name, step in best_est.steps:
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print(f" - {step_name}: {step.__class__.__name__}")
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if hasattr(step, 'n_features_in_'):
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print(f" n_features_in_: {step.n_features_in_}")
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# If it's a Pipeline
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if hasattr(model, 'steps'):
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print(f"\nThis is a Pipeline!")
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print(f"Pipeline steps:")
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for step_name, step in model.steps:
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print(f" - {step_name}: {step.__class__.__name__}")
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if hasattr(step, 'n_features_in_'):
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print(f" n_features_in_: {step.n_features_in_}")
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# Try to get booster for XGBoost
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try:
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if hasattr(model, 'get_booster'):
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print(f"\nXGBoost num_features: {model.get_booster().num_features()}")
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except:
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pass
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else:
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print(f"Model file not found: {model_path}")
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