Naive Bayes is one of the machine learning algorithm. Here we first want to take the definition of machine learning.
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from IPython.display import Image
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Image('nb-ud/Screen Shot 2014-11-19 at 11.22.14 AM.jpg')
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Sebastian Thrun is the head of the project of google automatic driving car. He uses supervised classification to train the car. Supervised mean that we're giving lot of correct examples, and then give it as material lesson to the system as a student. That's what the learner do, observing a teacher, for e.g. we saw parents learn to drive to correctly, then it's our turn to drive it. That's what machine learning does. To win DARPA Grand Challenge 2006, Stanley, the car project he did at Standford, observe thousand miles how man drive in the dessert.
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Image('nb-ud/Screen Shot 2014-11-19 at 11.45.46 AM.jpg')
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Image('nb-ud/Screen Shot 2014-11-19 at 11.52.49 AM.jpg')
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Katie loves to listen to Let It Go. What sort of features in the song that makes Katie likes the song? Then based on the song,Katie makes the decision. Is she labels the song,like or dislike?
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Image('nb-ud/Screen Shot 2014-11-19 at 12.02.38 PM.jpg')
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Image('nb-ud/Screen Shot 2014-11-19 at 12.03.19 PM.jpg')
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If we plot two features, and it seems like we can make line separable, it may easier to draw conclusion based on the next point. But if it the case that the we plot like in the second, it could be unclear to us to make a conclusion. It's better to add new samples, or manipulate the features (use polynomial,log10..etc)
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Image('nb-ud/Screen Shot 2014-11-19 at 12.11.55 PM.jpg')
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Suppose Stanley's speed decided by two factor. It's bumpiness and slope. We can take these into a classification.
The machine learning system always draw some decision surface(boundary) that make it easier to the system if the next sample drop plot in which side of the boundary
Okay, we have enough description of the machine learning. Let's dig deeper about Naive Bayes. Bayes is actually a religious man trying to prove the existing of God, the algorithm that he makes that makes it naive.
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Image('nb-ud/Screen Shot 2014-11-19 at 12.35.35 PM.jpg')
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Naive Bayes itself later will make decision boundary as the one in the picture. So the the incoming sample will be known its label by plotting in this graph.
Now we want to create a picture as above using the scikit learn library
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import numpy as np
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%pylab inline
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from sklearn.naive_bayes import GaussianNB
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clf = GaussianNB()
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X = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]] )
Y = np.array([1,1,1,2,2,2])
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clf.fit(X,Y)
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print clf.predict([[-0.8,-1]])
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%%writefile prep_terrain_data.py
#!/usr/bin/python
import random
def makeTerrainData(n_points=1000):
###############################################################################
### make the toy dataset
random.seed(42)
grade = [random.random() for ii in range(0,n_points)]
bumpy = [random.random() for ii in range(0,n_points)]
error = [random.random() for ii in range(0,n_points)]
y = [round(grade[ii]*bumpy[ii]+0.3+0.1*error[ii]) for ii in range(0,n_points)]
for ii in range(0, len(y)):
if grade[ii]>0.8 or bumpy[ii]>0.8:
y[ii] = 1.0
### split into train/test sets
X = [[gg, ss] for gg, ss in zip(grade, bumpy)]
split = int(0.75*n_points)
X_train = X[0:split]
X_test = X[split:]
y_train = y[0:split]
y_test = y[split:]
grade_sig = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==0]
bumpy_sig = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==0]
grade_bkg = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==1]
bumpy_bkg = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==1]
# training_data = {"fast":{"grade":grade_sig, "bumpiness":bumpy_sig}
# , "slow":{"grade":grade_bkg, "bumpiness":bumpy_bkg}}
grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]
bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]
grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]
bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]
test_data = {"fast":{"grade":grade_sig, "bumpiness":bumpy_sig}
, "slow":{"grade":grade_bkg, "bumpiness":bumpy_bkg}}
return X_train, y_train, X_test, y_test
# return training_data, test_data
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%%writefile class_vis.py
#!/usr/bin/python
#from udacityplots import *
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import pylab as pl
import numpy as np
#import numpy as np
#import matplotlib.pyplot as plt
#plt.ioff()
def prettyPicture(clf, X_test, y_test):
x_min = 0.0; x_max = 1.0
y_min = 0.0; y_max = 1.0
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
h = .01 # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)
# Plot also the test points
grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]
bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]
grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]
bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]
plt.scatter(grade_sig, bumpy_sig, color = "b", label="fast")
plt.scatter(grade_bkg, bumpy_bkg, color = "r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.savefig("test.png")
import base64
import json
import subprocess
def output_image(name, format, bytes):
image_start = "BEGIN_IMAGE_f9825uweof8jw9fj4r8"
image_end = "END_IMAGE_0238jfw08fjsiufhw8frs"
data = {}
data['name'] = name
data['format'] = format
data['bytes'] = base64.encodestring(bytes)
print image_start+json.dumps(data)+image_end
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%%writefile ClassifyNB.py
from sklearn.naive_bayes import GaussianNB
def classify(features_train, labels_train):
### import the sklearn module for GaussianNB
### create classifier
### fit the classifier on the training features and labels
### return the fit classifier
clf = GaussianNB()
clf.fit(features_train,labels_train)
return clf
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# %%writefile GaussianNB_Deployment_on_Terrain_Data.py
#!/usr/bin/python
""" Complete the code below with the sklearn Naaive Bayes
classifier to classify the terrain data
The objective of this exercise is to recreate the decision
boundary found in the lesson video, and make a plot that
visually shows the decision boundary """
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture, output_image
from ClassifyNB import classify
import numpy as np
import pylab as pl
from ggplot import *
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow" points mixed
### in together--separate them so we can give them different colors in the scatterplot,
### and visually identify them
grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
### draw the decision boundary with the text points overlaid
prettyPicture(clf, features_test, labels_test)
#output_image("test.png", "png", open("test.png", "rb").read())
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%%writefile classify.py
from sklearn.naive_bayes import GaussianNB
def NBAccuracy(features_train, labels_train, features_test, labels_test):
""" compute the accuracy of your Naive Bayes classifier """
### import the sklearn module for GaussianNB
from sklearn.naive_bayes import GaussianNB
### create classifier
clf = GaussianNB()
### fit the classifier on the training features and labels
clf.fit(features_train,labels_train)
### use the trained classifier to predict labels for the test features
pred = clf.predict(features_test)
### calculate and return the accuracy on the test data
### this is slightly different than the example,
### where we just print the accuracy
### you might need to import an sklearn module
accuracy = clf.score(features_test,labels_test)
return accuracy
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# %%writefile submitAccuracy.py
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
from classify import NBAccuracy
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
features_train, labels_train, features_test, labels_test = makeTerrainData()
def submitAccuracy():
accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test)
return accuracy
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print submitAccuracy()
That does in fact we have close to 90% accuracy in predicting our data. It always important to split your dataset, (this course recommend 90:10, others 80:20) for the test set so that we know is whether our learning is overfitting.
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Image('nb-ud/Screen Shot 2014-11-19 at 2.12.38 PM.jpg')
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Accoding to Sebastian Thrun, here i quote "Bayes Rule is perhaps the Holy Grail of probabilistic inference". It found by Rev Thomas Bayes, who's trying to infer the existing of a God. What he didn't know back then, is he open endless possiblity for Artificial Inteligence background that we know today.
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Image('nb-ud/Screen Shot 2014-11-19 at 2.19.16 PM.jpg')
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Here Bayes should infer the possibility given the condition. Now let's take it into a quiz.
This question is tricky, especially since both specificity and sensitivity are 90%. Intuitively, given the test result is positive, we know we are in the shaded region (blue and red). The true positive is depicted by red. As an estimate, which answer best describes the ratio of the red shaded region to the total (red + blue) shaded region?
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Image('nb-ud/Screen Shot 2014-11-19 at 2.25.56 PM.jpg')
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If we can see at the graph, we actually observe the probability of the cancer inside positive test probability. We Ignore the whole population for now. And of that 90% test positive, we're calculating person that actually have disease. For this to happen, we have to know the other side of test positive, which is the person who doesn't have cancer but tested posititive. We do this to have the probability of the test positive, independent whether the person have the disease or not.
Here's the total probability for the problem
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Image('nb-ud/Screen Shot 2014-11-19 at 2.40.25 PM.jpg')
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And here's the simpler intuition
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Image('nb-ud/Screen Shot 2014-11-19 at 2.43.11 PM.jpg')
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By adding P(C|Pos) and P(notC|Pos) you have the total probability of 1
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Image('nb-ud/Screen Shot 2014-11-19 at 3.13.58 PM.jpg')
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Now let's do some example for Naive Bayes classification, specifically, Text Learning
Suppose the system only learning from two people, Chris and Sara, stated by their own frequent email, the P(Chris) and P(Sara). The system want to learn, based on the email containing words, who's the one that send it.
Now, the Chris has 3 frequent words (only three words) that contained as described above. Same goes for Sara.
Now the question is:
Now this maybe simple question, but chris won by only small margin
P(C, 'Life Deal') = 0.5 * 0.1 * 0.8 = 0.04
P(S, 'Life Deal') = 0.5 * 0.3 * 0.2 = 0.03
Now if accumulate the probability, we get only the P('Life Deal'). So the posterior probability it
P('Life Deal') = 0.04 + 0.03 = 0.07
P(C|'Life Deal') = 0.04/0.07 = 0.57
P(S|'Life Deal') = 0.03/0.07 = 0.43
If you look at my other blog post,here, the prior actually doesn't matter. Because Chris and Sara is at uniform distribution. So we can safely ignore it. What we have then is
P(C, 'Love Deal') = 0.1 * 0.8 = 0.08
P(S, 'Love Deal') = 0.5 * 0.2 = 0.1
P('Love Deal') = 0.08 + 0.1 = 0.18
P(C|'Love Deal') = 0.08/0.18 = 0.44
P(S|'Love Deal') = 0.1/0.18 = 0.56
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Image('nb-ud/Screen Shot 2014-11-19 at 3.39.09 PM.jpg')
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The reason why it's called Naive Bayes, is,no matter what's the relation of the features in one data samples, it only calculate the accumulate probabilities. In Text Learning, for example, the Word Order is ignored. Although it's dangerous because it's ignore the relationships of the features, it's proven to be quite powerful.
Google have experience of trying to find words "Chicago Bulls", basketball team. But because 'Chicago' and 'Bulls' has each different meaning, ignoring the relatinships make the Naive Bayes break. It can handle well at 20k-200k words.
The importance is you should know when and where you use Naive Bayes. Understand what's the problem are and know which algorithm to use. And you can calso use test set to measure the algorithm.
A couple of years ago, J.K. Rowling (of Harry Potter fame) tried something interesting. She wrote a book, “The Cuckoo’s Calling,” under the name Robert Galbraith. The book received some good reviews, but no one paid much attention to it--until an anonymous tipster on Twitter said it was J.K. Rowling. The London Sunday Times enlisted two experts to compare the linguistic patterns of “Cuckoo” to Rowling’s “The Casual Vacancy,” as well as to books by several other authors. After the results of their analysis pointed strongly toward Rowling as the author, the Times directly asked the publisher if they were the same person, and the publisher confirmed. The book exploded in popularity overnight.
We’ll do something very similar in this project. We have a set of emails, half of which were written by one person and the other half by another person at the same company . Our objective is to classify the emails as written by one person or the other based only on the text of the email. We will start with Naive Bayes in this mini-project, and then expand in later projects to other algorithms.
We will start by giving you a list of strings. Each string is the text of an email, which has undergone some basic preprocessing; we will then provide the code to split the dataset into training and testing sets. (In the next lessons you’ll learn how to do this preprocessing and splitting yourself, but for now we’ll give the code to you).
One particular feature of Naive Bayes is that it’s a good algorithm for working with text classification. When dealing with text, it’s very common to treat each unique word as a feature, and since the typical person’s vocabulary is many thousands of words, this makes for a large number of features. The relative simplicity of the algorithm and the independent features assumption of Naive Bayes make it a strong performer for classifying texts. In this mini-project, you will download and install sklearn on your computer and use Naive Bayes to classify emails by author.
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%load nb_author_id.py
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#!/usr/bin/python
from sklearn.naive_bayes import GaussianNB
"""
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
### 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()
clf = GaussianNB()
t0 = time()
clf.fit(features_train,labels_train)
print 'training time',round(time()-t0,3) ,'s'
t0 = time()
clf.score(features_test,labels_test)
print 'predicting time', round(time()-t0,3),'s'
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