Mercurial > repos > goeckslab > pycaret_predict

comparison feature_importance.py @ 11:4eca9d109de1 draft

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planemo upload for repository https://github.com/goeckslab/gleam commit 55deacbbc78a00f27d789e11d563ba49dfb9cf9e
author goeckslab
date Fri, 22 Aug 2025 21:13:30 +0000
parents 1aed7d47c5ec
children e674b9e946fb
comparison
equal deleted inserted replaced
10:e2a6fed32d54 11:4eca9d109de1
134 max_bg = min(len(X_data), 100) 134 max_bg = min(len(X_data), 100)
135 bg = X_data.sample(max_bg, random_state=42) 135 bg = X_data.sample(max_bg, random_state=42)
136 136
137 predict_fn = model.predict_proba if hasattr(model, "predict_proba") else model.predict 137 predict_fn = model.predict_proba if hasattr(model, "predict_proba") else model.predict
138 138
139 explainer = shap.Explainer(predict_fn, bg) 139 try:
140 self.shap_model_name = explainer.__class__.__name__ 140 explainer = shap.Explainer(predict_fn, bg)
141 141 self.shap_model_name = explainer.__class__.__name__
142 shap_values = explainer(X_data) 142
143 shap_values = explainer(X_data)
144 except Exception as e:
145 LOG.error(f"SHAP computation failed: {e}")
146 self.shap_model_name = None
147 return
143 148
144 output_names = getattr(shap_values, "output_names", None) 149 output_names = getattr(shap_values, "output_names", None)
145 if output_names is None and hasattr(model, "classes_"): 150 if output_names is None and hasattr(model, "classes_"):
146 output_names = list(model.classes_) 151 output_names = list(model.classes_)
147 if output_names is None: 152 if output_names is None: