def makeForests(
self, size: int, class_label: str, split_ratio: float,
attribute_choice_fn) -> Tuple[List[RandomForest], List[float]]:
forests = []
tests = []
for (train_pack, test_pack) in self.packs:
forest = RandomForest(size, train_pack, class_label, split_ratio,
attribute_choice_fn)
forests.append(forest)
tests.append(forest.test(test_pack))
return (forests, tests)
def test10Fold():
global allWords
splits = tenFoldCrossValidation()
count = 0
total = 0
print("Naive Bayes")
for split in splits:
nb = naiveBayes()
trainFeatures = []
trainClasses = []
testFeatures = []
testClasses = []
for example in split.train:
trainFeatures.append(example.features)
trainClasses.append(example.klass)
for example in split.test:
testFeatures.append(example.features)
testClasses.append(example.klass)
nb.train(trainFeatures, trainClasses)
nb.test(testFeatures, testClasses)
accuracy = nb.getCorrectCount() / len(testClasses)
total = total + accuracy
print("[INFO]\tFold ", str(count), " Accuracy:", str(accuracy))
count = count + 1
print("[INFO]\tAccuracy:", str(total / 10))
count = 0
total = 0
print("Random Forest")
for split in splits:
nb = RandomForest(100)
trainFeatures = []
trainClasses = []
testFeatures = []
testClasses = []
for example in split.train:
trainFeatures.append(example.features)
trainClasses.append(example.klass)
for example in split.test:
testFeatures.append(example.features)
testClasses.append(example.klass)
nb.train(trainFeatures, trainClasses)
nb.test(testFeatures, testClasses)
accuracy = nb.getCorrectCount() / len(testClasses)
total = total + accuracy
print("[INFO]\tFold ", str(count), " Accuracy:", str(accuracy))
count = count + 1
print("[INFO]\tAccuracy:", str(total / 10))
count = 0
total = 0
print("Neural 5")
for split in splits:
nb = neuralNetwork((5, ), 1000)
trainFeatures = []
trainClasses = []
testFeatures = []
testClasses = []
for example in split.train:
trainFeatures.append(example.features)
trainClasses.append(example.klass)
for example in split.test:
testFeatures.append(example.features)
testClasses.append(example.klass)
nb.train(trainFeatures, trainClasses)
nb.test(testFeatures, testClasses)
accuracy = nb.getCorrectCount() / len(testClasses)
total = total + accuracy
print("[INFO]\tFold ", str(count), " Accuracy:", str(accuracy))
count = count + 1
print("[INFO]\tAccuracy:", str(total / 10))
count = 0
total = 0
print("Neural 3")
for split in splits:
nb = neuralNetwork((3, ), 1000)
trainFeatures = []
trainClasses = []
testFeatures = []
testClasses = []
for example in split.train:
trainFeatures.append(example.features)
trainClasses.append(example.klass)
for example in split.test:
testFeatures.append(example.features)
testClasses.append(example.klass)
nb.train(trainFeatures, trainClasses)
nb.test(testFeatures, testClasses)
accuracy = nb.getCorrectCount() / len(testClasses)
total = total + accuracy
print("[INFO]\tFold ", str(count), " Accuracy:", str(accuracy))
count = count + 1
print("[INFO]\tAccuracy:", str(total / 10))
count = 0
total = 0
print("SVM")
for split in splits:
nb = svm()
trainFeatures = []
trainClasses = []
testFeatures = []
testClasses = []
for example in split.train:
trainFeatures.append(example.features)
trainClasses.append(example.klass)
for example in split.test:
testFeatures.append(example.features)
testClasses.append(example.klass)
nb.train(trainFeatures, trainClasses)
nb.test(testFeatures, testClasses)
accuracy = nb.getCorrectCount() / len(testClasses)
total = total + accuracy
print("[INFO]\tFold ", str(count), " Accuracy:", str(accuracy))
count = count + 1
print("[INFO]\tAccuracy:", str(total / 10))
