From 44d5456d0a36027dcee4237cd695d5ceb248414f Mon Sep 17 00:00:00 2001
From: Israel Figueroa <israel.figueroa@usm.cl>
Date: Tue, 10 Dec 2024 01:05:03 -0300
Subject: [PATCH] middle

---
 trainer.py | 35 ++++++++++++++++++++++++++++++-----
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/trainer.py b/trainer.py
index c2989f3..4d80380 100644
--- a/trainer.py
+++ b/trainer.py
@@ -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)