get feature importance function for decision tree

scripts/decision_tree.py

@@ -59,4 +59,49 @@ def train_model(self, folds=1):
             loss_arr.append(loss)
 
 
-        return self.clf, accuracy_arr, loss_arr
+        return self.clf, accuracy_arr, loss_arr
+
+    def test_model(self):
+
+        y_pred = self.clf.predict(self.X_test)
+
+        accuracy = self.calculate_score(y_pred, self.y_test)
+        self.__printAccuracy(accuracy, label="Test")
+
+        report = self.report(y_pred, self.y_test)
+        matrix = self.confusion_matrix(y_pred, self.y_test)
+
+        loss = calculate_loss_function(self.y_test, y_pred)
+
+        return accuracy, loss,  report, matrix
+
+    def __printLoss(self, loss, step=1, label=""):
+        print(f"step {step}: {label} Loss of DecisionTreesModel is: {loss:.3f}")
+
+    def calculate_score(self, pred, actual):
+        return metrics.accuracy_score(actual, pred)
+
+    def __printAccuracy(self, acc, step=1, label=""):
+        print(f"step {step}: {label} Accuracy of DecisionTreesModel is: {acc:.3f}")
+
+    def report_outcome(self, pred, actual):
+        print("Test Metrics")
+        print("================")
+        print(metrics.classification_report(pred, actual))
+        return metrics.classification_report(pred, actual)
+
+    def get_feature_importance(self):
+        importance = self.clf.feature_importances_
+        featureimportance_df = pd.DataFrame()
+
+        featureimportance_df['feature'] = self.X_train.columns.to_list()
+        featureimportance_df['feature_importances'] = importance
+
+        return featureimportance_df
+
+    def confusion_matrix(self, pred, actual):
+        ax=sns.heatmap(pd.DataFrame(metrics.confusion_matrix(pred, actual)))
+        plt.title('Confusion Matrix')
+        plt.ylabel('Actual')
+        plt.xlabel('Predicted')
+        return metrics.confusion_matrix(pred, actual)

scripts/logistic_regression.py

@@ -83,7 +83,7 @@ def __printAccuracy(self, acc, step=1, label=""):
         print(f"step {step}: {label} Accuracy of LogesticRegression: {acc:.3f}")
 
 
-    def report_output(self, pred, actual):
+    def report_outcome(self, pred, actual):
         print("Test Metrics")
         print("================")
         print(metrics.classification_report(pred, actual))