def main():
print('Most common label in training data: ',
dt.returnMostCommon(testData, 'label'), '\n')
print('Entropy of the training data: ', dt.calculateTotalEntropy(testData),
'\n')
decisionTree = dt.id3(trainingData, trainingData.attributes,
testData.get_column('label'))
print('Best feature: ', decisionTree.getAttributeName())
print(' ...and its information gain: ',
decisionTree.getInformationGain(), '\n')
trainingError, trainingDepth = dt.getOverallError(trainingData,
decisionTree)
print('Accuracy on the training set: ', 100 - trainingError, '\n')
testError, testDepth = dt.getOverallError(testData, decisionTree)
print('Accuracy on the test set: ', 100 - testError, '\n')
print('////////// CROSS VALIDATION //////////')
accuracies = crossValidation()
bestDepth = getMaxAccuracyDepth(accuracies)
print('Best depth: ', bestDepth)
depthDecisionTree = dt.id3(trainingData, trainingData.attributes,
trainingData.get_column('label'))
depthLimitTree = dt.limitTreeDepth(depthDecisionTree, bestDepth)
err, depth = dt.getOverallError(testData, depthLimitTree)
print('Accuracy on test set using the best depth: ', 100 - err, '\n')
def train(self, trainingData, trainingLabels):
self.generateRandomData(trainingData, trainingLabels)
for i in range(self.forestSize):
tempData = self.allRandomData[i][0]
tempLabels = self.allRandomData[i][1]
tempFeatures = self.allRandomData[i][2]
self.translateToCsv("forest_data/train" + str(i), tempData, tempLabels, tempFeatures)
tempDataObj = get_data_obj("forest_data/train" + str(i) + ".csv")
tempTree = id3.id3(tempDataObj, tempDataObj.attributes, tempDataObj.get_column('label'))
id3.limitTreeDepth(tempTree, 1)
self.trees.append(tempTree)
def crossValidation():
filenames, crossTrainData, crossTestData = crossValidationSetup()
allAvgAccuracy = []
for max_depth in depths:
accuracies = []
print('Testing Depth Limit: ', max_depth)
for i in range(len(filenames)):
crossTree = dt.id3(crossTrainData[i], crossTrainData[i].attributes,
crossTrainData[i].get_column('label'))
crossTreeLimit = dt.limitTreeDepth(crossTree, max_depth)
error, depth = dt.getOverallError(crossTestData[i], crossTreeLimit)
accuracies.append(100.0 - error)
avgAccuracy = statistics.mean(accuracies)
allAvgAccuracy.append(avgAccuracy)
print("Average accuracy: ", avgAccuracy)
print("Standard deviation: ", statistics.stdev(accuracies), '\n')
return allAvgAccuracy
def main():
print("\nFull Decision Tree: ")
data_obj = get_data_obj('data/data_semeion/hand_data_train.csv')
id3_tree = id3.id3(data_obj, data_obj.attributes,
data_obj.get_column('label'))
print(id3_tree)
print('root node: ', id3_tree.getAttributeName())
error, depth = id3.getOverallError(data_obj, id3_tree)
print("Error on training data: {}%; Depth: {}".format(error, depth))
data_obj_test = get_data_obj('data/data_semeion/hand_data_test.csv')
error, depth = id3.getOverallError(data_obj_test, id3_tree)
print(" Error on test data: {}%; Depth: {}".format(error, depth))
print("\nTree with Max Depth 5")
max_depth = 5
pruned_tree = id3.limitTreeDepth(id3_tree, max_depth)
error, depth = id3.getOverallError(data_obj_test, pruned_tree)
print(" Error on test data: {}%; Depth: {}".format(error, depth))