src/experiments/decision_tree/decision_tree.py

@@ -1,13 +1,8 @@
 from sklearn import tree
-from sklearn import metrics
-from sklearn import preprocessing
-from sklearn.ensemble import RandomForestClassifier
 # from ...helpers.treenum import Tree
 from enum import Enum
 import csv
 import random
-from matplotlib import pyplot as plt
-import numpy as np
 
 SIFT_PATH = "..\\algorithms\\data\\sift.csv"
 
@@ -25,49 +20,34 @@ class Tree(Enum):
 # [tree1_label, tree2_label]
 
 labels = []
+dialect = csv.Dialect
 i = 0
 done = False
+test_index = random.randint(0, 102)
+print(test_index)
 
 with open(SIFT_PATH, 'r') as file:
     reader = csv.reader(file, delimiter= ',')
     matrix = list(reader)
-
     data = [[] for x in range(len(matrix)-1)]
     for row in matrix[1:]:
+        ## Remove test case
+        if i == test_index and done == False:
+            done = True
+            data.pop(i)
+            continue
+
         ## append data to lists
         labels.append(Tree[row[0].upper()].value)
         for element in row[1:]:
-            data[i].append(float(element))
+            data[i].append(element)
+        
+        ## iterator
         i += 1
 
-    # Werkt niet met genormaliseerde data
+clf = tree.DecisionTreeClassifier()
-    normalized = preprocessing.normalize(data, axis=0, norm='max')
+clf = clf.fit(data, labels)
-    norm = list(normalized.tolist())
+# tree.plot_tree(clf)
-
+print(Tree[matrix[test_index][0].upper()])
-actual = []
+result = clf.predict([matrix[test_index][1:]])
-predicted = []
+print(Tree(result[0]).name)
-for i in range(75):
-    test_index = random.randint(1, 101)
-    temp_data = data.pop(test_index)
-    temp_label = labels.pop(test_index)
-
-    # dec_tree = tree.DecisionTreeClassifier(
-    #     criterion='entropy', 
-    #     splitter='best')
-    dec_tree = RandomForestClassifier(max_depth=None)
-    dec_tree = dec_tree.fit(data, labels)
-    result = dec_tree.predict([matrix[test_index][1:]])
-
-    # normalized_list.append(temp_data)
-    data.append(temp_data)
-    labels.append(temp_label)
-
-    actual.append(temp_label)
-    predicted.append(result[0])
-
-c_matrix = metrics.confusion_matrix(actual, predicted)
-cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=c_matrix)
-cm_display.plot()
-plt.show(block=False)
-# print("Testdata: \t" + Tree[matrix[test_index][0].upper()].name)
-# print("Predicted: \t" + Tree(result[0]).name)