DecisionTree.py

import numpy as np 
import pandas as pd 
from sklearn.metrics import confusion_matrix 
from sklearn.cross_validation import train_test_split 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.metrics import accuracy_score 
from sklearn.metrics import classification_report 

# Function importing Dataset 
def importdata(): 
	balance_data = pd.read_csv( 
'https://archive.ics.uci.edu/ml/machine-learning-'+
'databases/balance-scale/balance-scale.data', 
	sep= ',', header = None) 
	print ("Dataset Length: ", len(balance_data)) 
	print ("Dataset Shape: ", balance_data.shape) 
	
	# Printing the dataset obseravtions 
	print ("Dataset: ",balance_data.head()) 
	return balance_data 

# Function to split the dataset 
def splitdataset(balance_data): 

	# Separating the target variable 
	X = balance_data.values[:, 1:5] 
	Y = balance_data.values[:, 0] 

	# Splitting the dataset into train and test 
	X_train, X_test, y_train, y_test = train_test_split( 
	X, Y, test_size = 0.3, random_state = 100) 
	
	return X, Y, X_train, X_test, y_train, y_test 
	
# Function to perform training with giniIndex. 
def train_using_gini(X_train, X_test, y_train): 

	# Creating the classifier object 
	clf_gini = DecisionTreeClassifier(criterion = "gini", 
			random_state = 100,max_depth=3, min_samples_leaf=5) 

	# Performing training 
	clf_gini.fit(X_train, y_train) 
	return clf_gini 
	
# Function to perform training with entropy. 
def tarin_using_entropy(X_train, X_test, y_train): 

	# Decision tree with entropy 
	clf_entropy = DecisionTreeClassifier( 
			criterion = "entropy", random_state = 100, 
			max_depth = 3, min_samples_leaf = 5) 

	# Performing training 
	clf_entropy.fit(X_train, y_train) 
	return clf_entropy 


# Function to make predictions 
def prediction(X_test, clf_object): 

	# Predicton on test with giniIndex 
	y_pred = clf_object.predict(X_test) 
	print("Predicted values:") 
	print(y_pred) 
	return y_pred 
	
# Function to calculate accuracy 
def cal_accuracy(y_test, y_pred): 
	
	print("Confusion Matrix: ", 
		confusion_matrix(y_test, y_pred)) 
	
	print ("Accuracy : ", 
	accuracy_score(y_test,y_pred)*100) 
	
	print("Report : ", 
	classification_report(y_test, y_pred)) 
def main(): 
	
	# Building Phase 
	data = importdata() 
	X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
	clf_gini = train_using_gini(X_train, X_test, y_train) 
	clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
	
	# Operational Phase 
	print("Results Using Gini Index:") 
	
	# Prediction using gini 
	y_pred_gini = prediction(X_test, clf_gini) 
	cal_accuracy(y_test, y_pred_gini) 
	
	print("Results Using Entropy:") 
	# Prediction using entropy 
	y_pred_entropy = prediction(X_test, clf_entropy) 
	cal_accuracy(y_test, y_pred_entropy) 
	
	

if __name__=="__main__": 
	main()