#!/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 import svm

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

clf = svm.SVC(kernel='rbf',C=10000.0)
t0 = time()
clf.fit(features_train, labels_train)
print "training time:", round(time()-t0, 3), "s"

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

print "prediction is ", pred

#print "prediction for 10th element is ", pred[10]
#print "prediction for 26th element is ", pred[26]
#print "prediction for 50th element is ", pred[50]

count1 = 0
for p in pred:
    if p == 1:
        count1 = count1 + 1

print "Number of test events in Chris' class [1] is", count1
    

#from sklearn.metrics import accuracy_score

#accuracy = accuracy_score(pred, labels_test)

#print "accuracy is ", accuracy
#########################################################


