#!/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(C=10000,kernel="rbf")
# features_train = features_train[:len(features_train)/100]
# labels_train = labels_train[:len(labels_train)/100]
t0 = time()
clf.fit(features_train, labels_train)
print "training time:", round(time()-t0, 3), "s"

t0 = time()
pred=clf.predict(features_test)
print "predicting time:", round(time()-t0, 3), "s"
print pred[10], pred[26], pred[50]

from sklearn.metrics import accuracy_score

def submitAccuracy(features_test, labels_test):
    acc = clf.score(features_test, labels_test)
    return acc
print submitAccuracy(features_test,labels_test)
import numpy as np
print np.count_nonzero(pred ==1)
