#!/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.svm import SVC
from sklearn.metrics import accuracy_score


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

#########################################################
### your code goes here ###
# c_list = [10.0, 100, 1000, 10000]
# c_list = [10000]
# for c_item in c_list:
#     print("C is", c_item)
#     clf = SVC(kernel="rbf", C=c_item)
#     t0 = time()
#     labels_fit = clf.fit(features_train, labels_train)
#     print("training time:", round(time() - t0, 3), "s")
#     t1 = time()
#     labels_pred = labels_fit.predict(features_test)
#     print("predict time:", round(time() - t1, 3), "s")
#     print("Accuracy is:", accuracy_score(labels_test, labels_pred))
#     print("-----------------")

clf = SVC(kernel="rbf", C=10000)
labels_fit = clf.fit(features_train, labels_train)
labels_pred = labels_fit.predict(features_test)
print("Accuracy is:", accuracy_score(labels_test, labels_pred))
####################################