Update init_expainer #87
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WilliamsRizzi
requested changes
Jan 26, 2021
| @@ -21,7 +37,7 @@ def explain(shap_exp: Explanation, training_df, test_df, explanation_target, pre | |||
| model = model[0] | |||
| prefix_int = int(prefix_target.strip('/').split('_')[1])-1 | |||
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| explainer = _init_explainer(model) | |||
| explainer = _init_explainer(model, training_df) | |||
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The explanation object contains a reference to the predictive_model, please pass to the function you customised the 'prediction_method' string contained in the PredictiveModel, otherwise your code will never be executed.
| if model_type in [ClassificationMethods.PERCEPTRON.value, | ||
| ClassificationMethods.NN.value]: | ||
| return shap.DeepExplainer(model, df) | ||
| return shap.KernelExplainer(model) |
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Are you sure the shap.KernelExplainer function takes only 'model' as parameter?
| Initialises the explainer according to the model type | ||
| :param model: model to explain | ||
| :param df: model training data | ||
| :param model_type: model type |
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rewrite in:
:param model_type: one of ClassificationMethods enumerator
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Extend init_explainer to work with different models
Use TreeExplainer for tree-based models, DeepExpainer for deep elarning models, and KernelExplainer for the rest