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ifiguero 2024-12-09 17:11:25 -03:00
parent d004fc495c
commit 6d13cc2a37
1 changed files with 4 additions and 2 deletions
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6
trainer.py
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@ -645,14 +645,15 @@ class BinaryTuner:
# expected_value = expected_value[1]
# shap_values = explainer.shap_values(X_test)[1]
self.logger.info("Columns: {}".format(Xbase.columns))
eng_columns = ['sex', 'family hist', 'age diag', 'BMI', 'base glu', 'glu 120','HbA1c']
# eng_columns = ['sex', 'family hist', 'age diag', 'BMI', 'base glu', 'glu 120','HbA1c']
esp_columns = ['sexo', 'hist fam', 'edad diag', 'IMC', 'glu ayu', 'glu 120','A1c']
explainer = shap.Explainer(model.predict, X_train, seed=seed)
shap_values = explainer(X_model)
exp = shap.Explanation(shap_values,
data=X_model,
feature_names=eng_columns)
feature_names=esp_columns)
#
# shap.plots.initjs()
@ -661,6 +662,7 @@ class BinaryTuner:
# shap.plots.force(exp.base_values[0], exp.values[0, :], feature_names=Xbase.columns, matplotlib=True, show=False)
# shap.plots.force(expected_values[0], shap_values.values, Xbase.columns , show=False)
plt.title(r"{0}".format(modelname))
plt.ylabel("Respuesta del Modelo: 0 Negativo, 1 Positivo")
plt.savefig("{}/shap_{}_{}_{}.png".format(self.name, modelname, dataset, seed),dpi=150, bbox_inches='tight')
plt.close()