#!/usr/bin/python

""" 
    This is the code to accompany the Lesson 2 (SVM) mini-project.

    Use a SVM to identify emails from the Enron corpus by their authors:    
    Sara has label 0
    Chris has label 1
"""
    
import sys
from time import time
sys.path.append("../tools/")
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.svm import SVC
clf = SVC(kernel="rbf", C=10000.0)

t0 = time()
#features_train = features_train[:len(features_train)/100] 
#labels_train = labels_train[:len(labels_train)/100]
clf.fit(features_train,labels_train)
print "training time:", round(time()-t0, 3), "s"
t1 = time()
pred=clf.predict(features_test)
count=0
for i in pred:
	if i==1:
		count+=1
print count
print "classifying time:", round(time()-t1, 3), "s"
#### now your job is to fit the classifier
#### using the training features/labels, and to
#### make a set of predictions on the test data
#features_train = features_train[:len(features_train)/100] 
#labels_train = labels_train[:len(labels_train)/100] 

print clf.score(features_test, labels_test)

#########################################################


