modified decision tree and random forest code to handle separate trai…

…n and test data files

project3/Code/main.py

@@ -94,8 +94,8 @@ def decision_tree(self, kCrossValidation = 10):
             print("Running iteration " + str(i+1) + " of k cross validation .....")
             testData = data.loc[foldSize*i:foldSize*(i+1)-1]
             trainData = data.loc[:foldSize*i-1].append(data.loc[foldSize*(i+1):])
-            # root = dt.decision(trainData, depth=10, minLeafRows=5)
-            root = dt.decision(trainData, depth=15, minLeafRows=5)
+            # root = dt.decision(trainData)
+            root = dt.decision(trainData, depth=10, minLeafRows=3)
             target = testData.iloc[:,-1].values.tolist()
             predicted = dt.predictData(testData.iloc[:, :-1], root)
             models.append(root)
@@ -106,7 +106,28 @@ def decision_tree(self, kCrossValidation = 10):
             precision.append(tmpPrecision)
             recall.append(tmpRecall)
             f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
-        return accuracy, precision, recall, f_score
+
+        print("\nMetrics on train data with k-cross validation")
+        h.calculateMetrics(accuracy, precision, recall, f_score)
+
+        fileName = input("\nEnter test data file name without extension (if no test file, just press enter): ")
+        if fileName != '':
+            # filePath = "../Data/"+fileName+".txt"
+            filePath = "CSE-601/project3/Data/"+fileName+".txt"
+            testData, testLabels = h.readData(filePath)
+            testData = h.oneHotEncoding(testData, testLabels)
+            predLabels = []
+            for _,row in testData.iloc[:,:-1].iterrows():
+                predictedRow = [dt.predictRow(row, root) for root in models]
+                predLabels.append(max(set(predictedRow), key=predictedRow.count))
+            print(predLabels)
+            truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(predLabels, testData.iloc[:,-1].values.tolist())
+            accuracy = [h.findAccuracy(truePositives, trueNegatives, falsePositives, falseNegatives)]
+            precision = h.findPrecision(truePositives, trueNegatives, falsePositives, falseNegatives)
+            recall = h.findRecall(truePositives, trueNegatives, falsePositives, falseNegatives)
+            f_score = [h.findFMeasure(precision, recall)]
+            print("\nMetrics on test data with bagging")
+            h.calculateMetrics(accuracy, [precision], [recall], f_score)
 
     def random_forest(self, kCrossValidation = 10):
         print("\nRunning Random Forest Classifier ....................\n")
@@ -141,7 +162,30 @@ def random_forest(self, kCrossValidation = 10):
             precision.append(tmpPrecision)
             recall.append(tmpRecall)
             f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
-        return accuracy, precision, recall, f_score
+
+        print("\nMetrics on train data with k-cross validation")
+        h.calculateMetrics(accuracy, precision, recall, f_score)
+
+        fileName = input("\nEnter test data file name without extension (if no test file, just press enter): ")
+        if fileName != '':
+            # filePath = "../Data/"+fileName+".txt"
+            filePath = "CSE-601/project3/Data/"+fileName+".txt"
+            testData, testLabels = h.readData(filePath)
+            testData = h.oneHotEncoding(testData, testLabels)
+            predLabels = []
+            for forest in models:
+                predLabels.append(rf.predictForest(testData, forest))
+            predLabels = pd.DataFrame(predLabels)
+            pred = []
+            for _, colData in predLabels.iteritems():
+                pred.append(colData.value_counts().index[0])
+            truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(pred, testData.iloc[:,-1].values.tolist())
+            accuracy = [h.findAccuracy(truePositives, trueNegatives, falsePositives, falseNegatives)]
+            precision = h.findPrecision(truePositives, trueNegatives, falsePositives, falseNegatives)
+            recall = h.findRecall(truePositives, trueNegatives, falsePositives, falseNegatives)
+            f_score = [h.findFMeasure(precision, recall)]
+            print("\nMetrics on test data with bagging")
+            h.calculateMetrics(accuracy, [precision], [recall], f_score)
 
 if __name__ == "__main__":
     m = main()
@@ -159,8 +203,7 @@ def random_forest(self, kCrossValidation = 10):
         accuracy, precision, recall, f_score = m.knn(predictData, trainData)
         h.calculateMetrics(accuracy, precision, recall, f_score)
     elif algorithm == 2:
-        accuracy, precision, recall, f_score = m.decision_tree()
-        h.calculateMetrics(accuracy, precision, recall, f_score)
+        m.decision_tree()
     elif algorithm == 3:
         print("Enter train File name")
         trainData = h.get_file_bayes(h.get_fileName(), kCrossValidation = 10)
@@ -173,7 +216,6 @@ def random_forest(self, kCrossValidation = 10):
         accuracy, precision, recall, f_score = m.bayes_naive(predictData, trainData)
         h.calculateMetrics(accuracy, precision, recall, f_score)
     elif algorithm == 4:
-        accuracy, precision, recall, f_score = m.random_forest()
-        h.calculateMetrics(accuracy, precision, recall, f_score)
+        m.random_forest()
     else:
         print("\nWrong input")