#!/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
from sklearn import svm


### 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()


# defining constants
GAMMA = 1000
# can be linear, poly, rbf(default), sigmoid, precomputed, a callable
KERNEL = 'rbf'
C = 100.0
#########################################################
### your code goes here ###

clf = svm.SVC(kernel=KERNEL,gamma=GAMMA,C=C)
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 done in %0.3fs" % (time() - t0))
t0 = time()
print(clf.score(features_test,labels_test))
print("prediction done in %0.3fs" % (time() - t0))
#########################################################



#!/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
from sklearn import svm


### 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()


# defining constants
GAMMA = 1000
# can be linear, poly, rbf(default), sigmoid, precomputed, a callable
KERNEL = 'rbf'
C = 100.0
#########################################################
### your code goes here ###

clf = svm.SVC(kernel=KERNEL,gamma=GAMMA,C=C)
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 done in %0.3fs" % (time() - t0))
t0 = time()
print(clf.score(features_test,labels_test))
print("prediction done in %0.3fs" % (time() - t0))
#########################################################



#!/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 ### 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 # defining constants # can be linear, poly, rbf(default), sigmoid, precomputed, a callable ######################################################### ### your code goes here ### #########################################################