check the model type classfy or regression

Author: tawabshakeel

explain.py

@@ -17,6 +17,7 @@
 from calculate_shap import *
 from analytics import Analytics
 
+
 class explain():
     def __init__(self):
         super(explain, self).__init__()
@@ -35,7 +36,6 @@ def __init__(self):
     #         if total_unique < 20:
     #             is_classification = True
     #     return is_classification
-    
 
     def random_string_generator(self):
         random_str = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10))
@@ -74,8 +74,7 @@ def ai_h2o_automl(self, df, y_column_name, model, model_name="h2o", mode=None):
             prediction_col = model.predict(df[y_column_name])
         # is classification?
 
-        is_classification = self.is_classification_given_y_array(prediction_col.as_data_frame()[y_column_name].tolist())
-
+        is_classification = True if model.type == 'classifier' else False
         # shap
         c = calculate_shap()
         self.df_final, self.explainer = c.find(model, df, prediction_col, is_classification,
@@ -134,12 +133,11 @@ def ai_h2o_automl(self, df, y_column_name, model, model_name="h2o", mode=None):
 
         return True
 
-
     def ai(self, df, y, model, model_name="xgboost", mode=None):
         y_variable = "y_actual"
         y_variable_predict = "y_prediction"
-        
-        #Code for Analytics
+
+        # Code for Analytics
         instance_id = self.random_string_generator()
         analytics = Analytics()
         analytics['ip'] = analytics.finding_ip()
@@ -172,38 +170,38 @@ def ai(self, df, y, model, model_name="xgboost", mode=None):
             prediction_col = model.predict(df)
 
         # is classification?
-        #is_classification = self.is_classification_given_y_array(prediction_col)
+        # is_classification = self.is_classification_given_y_array(prediction_col)
         ModelType = lambda model: True if is_classifier(model) else False
         is_classification = ModelType(model)
 
         # shap
         c = calculate_shap()
         self.df_final, self.explainer = c.find(model, df, prediction_col, is_classification, model_name=model_name)
 
-        #Append Model Decision & True Labels Columns into the dataset.
+        # Append Model Decision & True Labels Columns into the dataset.
         self.df_final[y_variable_predict] = prediction_col
         self.df_final[y_variable] = y
 
         # additional inputs.
         if is_classification == True:
             # find and add probabilities in the dataset.
-            #prediction_col_prob = model.predict_proba(df)
-            #pd_prediction_col_prob = pd.DataFrame(prediction_col_prob)
+            # prediction_col_prob = model.predict_proba(df)
+            # pd_prediction_col_prob = pd.DataFrame(prediction_col_prob)
 
             probabilities = model.predict_proba(df)
 
             for i in range(len(np.unique(prediction_col))):
-                self.df_final['Probability: {}'.format(np.unique(prediction_col)[i])] = probabilities[:,i]
-            
+                self.df_final['Probability: {}'.format(np.unique(prediction_col)[i])] = probabilities[:, i]
+
             self.param['classes'] = np.unique(prediction_col)
 
-            #for c in pd_prediction_col_prob.columns:
-             #   self.df_final["probability_of_predicting_class_" + str(c)] = list(pd_prediction_col_prob[c])
+            # for c in pd_prediction_col_prob.columns:
+            #   self.df_final["probability_of_predicting_class_" + str(c)] = list(pd_prediction_col_prob[c])
 
-            #classes = []
-            #for c in pd_prediction_col_prob.columns:
-             #   classes.append(str(c))
-            #self.param["classes"] = classes
+            # classes = []
+            # for c in pd_prediction_col_prob.columns:
+            #   classes.append(str(c))
+            # self.param["classes"] = classes
 
             try:
                 expected_values_by_class = self.explainer.expected_value