""" 
    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 ###

#features_train = features_train[:len(features_train)/100] 
#labels_train = labels_train[:len(labels_train)/100] 


from sklearn import svm
#clf = svm.SVC(kernel = 'linear')
clf = svm.SVC(C = 10000.0,kernel = 'rbf')

t0 = time()
clf.fit(features_train, labels_train)
print "training time:", round(time()-t0, 3), "s"

t1 = time()
pred = clf.predict(features_test)
print "predicting time:", round(time()-t1,3), "s"

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(pred, labels_test)
print accuracy

##Extracting predictions for an SVM
print("pred[10]: ",pred[10])
print("pred[26]: ",pred[26])
print("pred[50]: ",pred[50])

##how many are predicted to be by Chris
print sum(pred)

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