def main():
# Check if the number of command line arguments is correct
if len(sys.argv) < 6:
print(
"-- python --|-- main.py --|-- L --|-- K --|-- training_set.csv --|-- validation_set.csv --|-- test_set.csv --|"
)
sys.exit(1)
# The program takes two integer L and K as input to prune the decision tree
L = int(sys.argv[1])
K = int(sys.argv[2])
# Get the file path of the training data, validation data and test data
dataDir = './data1/'
training_set = dataDir + sys.argv[3]
validation_set = dataDir + sys.argv[4]
test_set = dataDir + sys.argv[5]
# Build a decision tree on training data
decisionTree = DecisionTree(training_set)
#############################################
# decisionTree.exportTree('tree.txt')
# print decisionTree
#############################################
# Create a validator using test data to calculate the prediction accuracy of a given decision tree
validator = Validator(test_set)
# Calculate the prediction accuracy of the original decision tree on test data
validator.calculateAccuracy(decisionTree.root)
# Display the prediction accuracy before pruning
print("\nA decision tree is fully grown to fit the training data.")
validator.displayAccuracy()
# Post pruning the decision tree
print("\nPost prunning", '.' * 30)
print("L =", L, ", K =", K, ", the pruned decision tree is:\n")
# Prune the original decision tree using L, K and validation data as inputs
decisionTree.pruneTree(L, K, validation_set)
##############################################
# decisionTree.exportTree('pruned_tree.txt')
##############################################
# print the decision tree to standard output
print(decisionTree) # Override the __str__ method in DecisionTree class
# Calculate the prediction accuracy of the pruned decision tree on test data
validator.calculateAccuracy(decisionTree.root)
# Display the prediction accuracy after pruning
validator.displayAccuracy()
