#!/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

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

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

t1 = time()
#
pred = clf.predict(features_test)
from sklearn.metrics import accuracy_score
acc = accuracy_score(pred, labels_test)
#
print "predicting time:", round(time()-t1, 3), "s"

print acc

import collections, numpy
print collections.Counter(pred)

print pred[10]
print pred[26]
print pred[50]

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



'''
#!/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

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

c_values = [10.0, 100.0, 1000.0, 10000.0]
curr_max = 0
curr_c = 0

for val in c_values:
	t0 = time()
	#
	clf = SVC(C=val, kernel="rbf")
	clf.fit(features_train, labels_train)
	#
	print "training time:", round(time()-t0, 3), "s"

	t1 = time()
	#
	pred = clf.predict(features_test)
	from sklearn.metrics import accuracy_score
	acc = accuracy_score(pred, labels_test)
	if acc >= curr_max:
		curr_max = acc
		curr_c = val
	#
	print "predicting time:", round(time()-t1, 3), "s"

	print acc

print curr_c

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






'''












