decision treees

src/experiments/decision_tree/decision_tree.py

@@ -1,8 +1,13 @@
 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"
 
@@ -20,34 +25,49 @@ 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(element)
-        
-        ## iterator
+            data[i].append(float(element))
         i += 1
 
-clf = tree.DecisionTreeClassifier()
-clf = clf.fit(data, labels)
-# tree.plot_tree(clf)
-print(Tree[matrix[test_index][0].upper()])
-result = clf.predict([matrix[test_index][1:]])
-print(Tree(result[0]).name)
+    # Werkt niet met genormaliseerde data
+    normalized = preprocessing.normalize(data, axis=0, norm='max')
+    norm = list(normalized.tolist())
+
+actual = []
+predicted = []
+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)