#!/usr/bin/python

""" 
    This is the code to accompany the Lesson 3 (decision tree) mini-project.

    Use a Decision Tree to identify emails from the Enron corpus by author:    
    Sara has label 0
    Chris has label 1
"""
    
import sys
from time import time
sys.path.append("../tools/")
# noinspection PyUnresolvedReferences
from email_preprocess import preprocess


### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()




#########################################################
### your code goes here ###
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
print len(features_test[0])
clf = DecisionTreeClassifier(min_samples_split=40)
t0 = time()
clf.fit(features_train, labels_train)
print "Training Time:", round(time()-t0, 3), "s"
t1 = time()
predictions = clf.predict(features_test)
print "Prediction Time:", round(time()-t1, 3), "s"
accuracy = accuracy_score(predictions, labels_test)
print "Accuracy:", accuracy
total = len(predictions)
chris = 0
for prediction in predictions:
    if prediction == 1:
        chris += 1
sara = total - chris
print "Out of %d emails, Chris' emails are %d and Sara's emails are %d" % (total, chris, sara)
#########################################################


