Tensorflow model support #37
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i did changess in feature importance and genrate visuaalization ,also add tensorflow
download scikeras library for using Keras Classifier and keras Regressor
SHAP Calculation for TensorFlow Models: Modified calculate_shap_values to use shap.DeepExplainer for TensorFlow models. Handling SHAP Output: Ensured that the output from shap.DeepExplainer is correctly wrapped in a shap.Explanation object for consistency.
Handling Predictions from TensorFlow Models: TensorFlow models return predictions differently. For regression, they output continuous values. For classification, they might output probabilities or logits. We adjust y_pred accordingly, converting probabilities to class labels when necessary. Model Type Parameter: Added model_type parameter to indicate whether the model is a TensorFlow or scikit-learn model. Default is 'sklearn' for backward compatibility. Classification Handling: For binary classification, we threshold probabilities at 0.5. For multi-class classification, we use np.argmax to get class labels.
please install scikeras libarary
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There are some merge conflicts arised, please do git pull and modify the codes |
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Hey @Sakeebhasan123456 any update? |
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hii @ombhojane thank you very much for your patience, please check I think now all problems are resolved |
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The modifications made to core.py, model_interpretability.py, and utils.py successfully extend support for TensorFlow models within the codebase. By carefully adjusting the handling of model fitting, evaluation, interpretability, and utility functions, we ensure that both scikit-learn and TensorFlow models can be integrated seamlessly.