-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathLesson2-Naive Bayes.py
50 lines (36 loc) · 1.39 KB
/
Lesson2-Naive Bayes.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
#!/usr/bin/python3
"""
This is the code to accompany the Lesson 1 (Naive Bayes) mini-project.
Use a Naive Bayes Classifier to identify emails by their authors
authors and labels:
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.naive_bayes import GaussianNB
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()
##############################################################
# Enter Your Code Here
clf = GaussianNB()
##############################################################
##############################################################
'''
You Will be Required to record time for Training and Predicting
The Code Given on Udacity Website is in Python-2
The Following Code is Python-3 version of the same code
'''
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()-t0, 3), "s")
print("Accuracy:", accuracy_score(labels_test,pred))
##############################################################