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ifiguero 2024-12-10 01:05:03 -03:00
parent 041f602b81
commit 44d5456d0a
1 changed files with 30 additions and 5 deletions
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trainer.py
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@ -655,17 +655,42 @@ class BinaryTuner:
data=X_model,
feature_names=label_columns)
#
# shap.plots.initjs()
shap.plots.decision(exp.base_values[0], exp.values, features=label_columns, show=False)
# shap.plots.force(exp.base_values, exp.values, feature_names=Xbase.columns, show=False)
# 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()
y_pred = model.predict(X_model)
# make a numpy array from y_pred where all the values > 0.5 become 1 and all remaining values are 0
if type_of_target(y_pred) == "continuous":
y_pred = np.where(y_pred > 0.5, 1, 0)
X_pos = X_model[y_pred == 1]
shap_values = explainer(X_pos)
exp = shap.Explanation(shap_values,
data=X_pos,
feature_names=label_columns)
shap.plots.decision(exp.base_values[0], exp.values, features=label_columns, show=False)
plt.title(r"{0}".format(modelname))
plt.ylabel("Respuesta del Modelo Positivas")
plt.savefig("{}/shap_pos_{}_{}_{}.png".format(self.name, modelname, dataset, seed),dpi=150, bbox_inches='tight')
plt.close()
X_pos = X_model[y_pred == 0]
shap_values = explainer(X_pos)
exp = shap.Explanation(shap_values,
data=X_pos,
feature_names=label_columns)
shap.plots.decision(exp.base_values[0], exp.values, features=label_columns, show=False)
plt.title(r"{0}".format(modelname))
plt.ylabel("Respuesta del Modelo Negativas")
plt.savefig("{}/shap_pos_{}_{}_{}.png".format(self.name, modelname, dataset, seed),dpi=150, bbox_inches='tight')
plt.close()
shap_values = explainer(X_explain)