GaussianNB Deployment on Terrain Data

class_vis.py

In [1]:
#!/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 base64
import json
import subprocess

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
    %matplotlib inline
    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")
    
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

prep_terrain_data.py

In [2]:
#!/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

ClassifyNB.py

In [3]:
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
    
        
    ### your code goes here!
    from sklearn.naive_bayes import GaussianNB
    clf = GaussianNB()
    clf.fit(features_train, labels_train)
    return clf

studentMain.py

In [4]:
#!/usr/bin/python

""" Complete the code in ClassifyNB.py with the sklearn
    Naive 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 """


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]



clf = classify(features_train, labels_train)



### 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())

Calculating NB Accuracy

classify.py

In [5]:
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
    from sklearn.metrics import accuracy_score

    ### 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
    # method 1
    accuracy = clf.score(features_test, labels_test)
    
    # method 2
    pred = clf.predict(features_test)
    accuracy = accuracy_score(pred, labels_test)
    
    return accuracy

studentCode.py

In [6]:
features_train, labels_train, features_test, labels_test = makeTerrainData()

def submitAccuracy():
    accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test)
    return accuracy

print submitAccuracy()

0.884

Author ID Accuracy

nb_author_id.py

In [7]:
#!/usr/bin/python

""" 
    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 os
os.chdir('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/naive_bayes')

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.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# the classifier
clf = GaussianNB()

# train
t0 = time()
clf.fit(features_train, labels_train)
print "\ntraining time:", round(time()-t0, 3), "s"

# predict
t0 = time()
pred = clf.predict(features_test)
print "predicting time:", round(time()-t0, 3), "s"

accuracy = accuracy_score(pred, labels_test)

print '\naccuracy = {0}'.format(accuracy)

no. of Chris training emails: 7936
no. of Sara training emails: 7884

training time: 1.33 s
predicting time: 0.203 s

accuracy = 0.973265073948