def main(args):
# judge input arguments length
if len(args) != 6:
print('Should Have Six Input Arguments')
exit(0)
# input parameters
L = int(args[0])
K = int(args[1])
training_set_file_name = args[2]
validation_set_file_name = args[3]
test_set_file_name = args[4]
to_print = True if args[5].lower() == 'yes' else False
path = './' + DATA_DIRECTORY + '/'
# read data from training set, test set, and validation set
rd = ReadData()
labels, training_set = rd.createDataSet(path + training_set_file_name)
labels, validation_set = rd.createDataSet(path + validation_set_file_name)
labels, test_set = rd.createDataSet(path + test_set_file_name)
# build tree
dt = DecisionTree()
info_gain_tree_root = dt.buildDT(training_set, labels.copy(),
'information_gain')
pruned_info_gain_tree_root = dt.pruneTree(info_gain_tree_root, L, K,
validation_set, labels)
variance_impurity_tree_root = dt.buildDT(training_set, labels.copy(),
'variance_impurity')
pruned_variance_impurity_tree_root = dt.pruneTree(
variance_impurity_tree_root, L, K, validation_set, labels)
print()
info_accuracy = dt.calAccuracy(test_set, info_gain_tree_root, labels)
print('Accuracy of decision tree constructed using information gain: %s' %
info_accuracy)
variance_accuracy = dt.calAccuracy(test_set, variance_impurity_tree_root,
labels)
print('Accuracy of decision tree constructed using variance impurity: %s' %
variance_accuracy)
prune_info_accuracy = dt.calAccuracy(test_set, pruned_info_gain_tree_root,
labels)
print(
'Accuracy of pruned decision tree constructed using information gain: %s'
% prune_info_accuracy)
pruned_variance_accuracy = dt.calAccuracy(
test_set, pruned_variance_impurity_tree_root, labels)
print(
'Accuracy of pruned decision tree constructed using variance impurity: %s'
% pruned_variance_accuracy)
if (to_print):
print()
print('Build Decision Tree By Using Information Gain')
info_gain_tree_root.printTree()
print()
print()
print('Build Decision Tree By Using Variance Impurity')
variance_impurity_tree_root.printTree()
print()
