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Merge pull request #32 from Techtonique/v0200
do v0.20.0
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| __pycache__/ | ||
| *.py[cod] | ||
| *$py.class | ||
| mlsauce/.DS_Store | ||
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| # Distribution / packaging | ||
| .Python | ||
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| import mlsauce as ms | ||
| import numpy as np | ||
| #from shap import KernelExplainer | ||
| from sklearn.datasets import load_diabetes | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.linear_model import LinearRegression | ||
| from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor | ||
| from time import time | ||
| from sklearn.datasets import load_diabetes | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.linear_model import LinearRegression | ||
| from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor | ||
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| X, y = load_diabetes(as_frame=False, return_X_y=True) | ||
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, | ||
| random_state=42) | ||
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| lin_model = LinearRegression().fit(X_train, y_train); | ||
| #rfr_model = RandomForestRegressor().fit(X_train, y_train); | ||
| #gbt_model = GradientBoostingRegressor().fit(X_train, y_train); | ||
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| from_scratch_explainer = ms.ShapExplainer(lin_model.predict, X_train[:100, :]) | ||
| start = time() | ||
| from_scratch_shap_values = from_scratch_explainer.shap_values(X_train[:100, :]) | ||
| end = time() | ||
| print(f"Time taken: {end - start}") | ||
| print(f"From scratch shap values: {from_scratch_shap_values}") | ||
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| # def compare_methods(model, X_instances, X_background): | ||
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| # #library_explainer = KernelExplainer(model.predict, X_background) | ||
| # #library_shap_values = library_explainer.shap_values(X_instances) | ||
| # #print(f"Library shap values: {library_shap_values}") | ||
| # from_scratch_explainer = ms.ShapExplainer(model.predict, X_background) | ||
| # start = time() | ||
| # from_scratch_shap_values = from_scratch_explainer.shap_values(X_instances) | ||
| # end = time() | ||
| # print(f"Time taken: {end - start}") | ||
| # print(f"From scratch shap values: {from_scratch_shap_values}") | ||
| # #return np.allclose(library_shap_values, from_scratch_shap_values) | ||
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| # print(compare_methods(lin_model, | ||
| # X_background=X_train[:100, :], | ||
| # X_instances=X_test[:5, :])) | ||
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