dataClassifier.py (original)
# This file contains feature extraction methods and harness
# code for data classification
import mostFrequent
import naiveBayes
import perceptron
import mira
import minicontest
import samples
import sys
import util
TEST_SET_SIZE = 100
DIGIT_DATUM_WIDTH=28
DIGIT_DATUM_HEIGHT=28
FACE_DATUM_WIDTH=60
FACE_DATUM_HEIGHT=70
def basicFeatureExtractorDigit(datum):
"""
Returns a set of pixel features indicating whether
each pixel in the provided datum is white (0) or gray/black (1)
"""
a = datum.getPixels()
features = util.Counter()
for x in range(DIGIT_DATUM_WIDTH):
for y in range(DIGIT_DATUM_HEIGHT):
if datum.getPixel(x, y) > 0:
features[(x,y)] = 1
else:
features[(x,y)] = 0
return features
def basicFeatureExtractorFace(datum):
"""
Returns a set of pixel features indicating whether
each pixel in the provided datum is an edge (1) or no edge (0)
"""
a = datum.getPixels()
features = util.Counter()
for x in range(FACE_DATUM_WIDTH):
for y in range(FACE_DATUM_HEIGHT):
if datum.getPixel(x, y) > 0:
features[(x,y)] = 1
else:
features[(x,y)] = 0
return features
def enhancedFeatureExtractorDigit(datum):
"""
Your feature extraction playground.
You should return a util.counter() of features
for this datum (datum is of type samples.Datum).
## DESCRIBE YOUR ENHANCED FEATURES HERE...
##
"""
features = basicFeatureExtractorDigit(datum)
"*** YOUR CODE HERE ***"
return features
def contestFeatureExtractorDigit(datum):
"""
Specify features to use for the minicontest
"""
features = basicFeatureExtractorDigit(datum)
return features
def enhancedFeatureExtractorFace(datum):
"""
Your feature extraction playground for faces.
It is your choice to modify this.
"""
features = basicFeatureExtractorFace(datum)
return features
def analysis(classifier, guesses, testLabels, testData, rawTestData, printImage):
"""
This function is called after learning.
Include any code that you want here to help you analyze your results.
Use the printImage(<list of pixels>) function to visualize features.
An example of use has been given to you.
- classifier is the trained classifier
- guesses is the list of labels predicted by your classifier on the test set
- testLabels is the list of true labels
- testData is the list of training datapoints (as util.Counter of features)
- rawTestData is the list of training datapoints (as samples.Datum)
- printImage is a method to visualize the features
(see its use in the odds ratio part in runClassifier method)
This code won't be evaluated. It is for your own optional use
(and you can modify the signature if you want).
"""
# Put any code here...
# Example of use:
for i in range(len(guesses)):
prediction = guesses[i]
truth = testLabels[i]
if (prediction != truth):
print "==================================="
print "Mistake on example %d" % i
print "Predicted %d; truth is %d" % (prediction, truth)
print "Image: "
print rawTestData[i]
break
## =====================
## You don't have to modify any code below.
## =====================
class ImagePrinter:
def __init__(self, width, height):
self.width = width
self.height = height
def printImage(self, pixels):
"""
Prints a Datum object that contains all pixels in the
provided list of pixels. This will serve as a helper function
to the analysis function you write.
Pixels should take the form
[(2,2), (2, 3), ...]
where each tuple represents a pixel.
"""
image = samples.Datum(None,self.width,self.height)
for pix in pixels:
try:
# This is so that new features that you could define which
# which are not of the form of (x,y) will not break
# this image printer...
x,y = pix
image.pixels[x][y] = 2
except:
print "new features:", pix
continue
print image
def default(str):
return str + ' [Default: %default]'
def readCommand( argv ):
"Processes the command used to run from the command line."
from optparse import OptionParser
parser = OptionParser(USAGE_STRING)
parser.add_option('-c', '--classifier', help=default('The type of classifier'), choices=['mostFrequent', 'nb', 'naiveBayes', 'perceptron', 'mira', 'minicontest'], default='mostFrequent')
parser.add_option('-d', '--data', help=default('Dataset to use'), choices=['digits', 'faces'], default='digits')
parser.add_option('-t', '--training', help=default('The size of the training set'), default=100, type="int")
parser.add_option('-f', '--features', help=default('Whether to use enhanced features'), default=False, action="store_true")
parser.add_option('-o', '--odds', help=default('Whether to compute odds ratios'), default=False, action="store_true")
parser.add_option('-1', '--label1', help=default("First label in an odds ratio comparison"), default=0, type="int")
parser.add_option('-2', '--label2', help=default("Second label in an odds ratio comparison"), default=1, type="int")
parser.add_option('-k', '--smoothing', help=default("Smoothing parameter (ignored when using --autotune)"), type="float", default=2.0)
parser.add_option('-a', '--autotune', help=default("Whether to automatically tune hyperparameters"), default=False, action="store_true")
parser.add_option('-i', '--iterations', help=default("Maximum iterations to run training"), default=3, type="int")
options, otherjunk = parser.parse_args(argv)
if len(otherjunk) != 0: raise Exception('Command line input not understood: ' + str(otherjunk))
args = {}
# Set up variables according to the command line input.
print "Doing classification"
print "--------------------"
print "data:\t\t" + options.data
print "classifier:\t\t" + options.classifier
if not options.classifier == 'minicontest':
print "using enhanced features?:\t" + str(options.features)
else:
print "using minicontest feature extractor"
print "training set size:\t" + str(options.training)
if(options.data=="digits"):
printImage = ImagePrinter(DIGIT_DATUM_WIDTH, DIGIT_DATUM_HEIGHT).printImage
if (options.features):
featureFunction = enhancedFeatureExtractorDigit
else:
featureFunction = basicFeatureExtractorDigit
if (options.classifier == 'minicontest'):
featureFunction = contestFeatureExtractorDigit
elif(options.data=="faces"):
printImage = ImagePrinter(FACE_DATUM_WIDTH, FACE_DATUM_HEIGHT).printImage
if (options.features):
featureFunction = enhancedFeatureExtractorFace
else:
featureFunction = basicFeatureExtractorFace
else:
print "Unknown dataset", options.data
print USAGE_STRING
sys.exit(2)
if(options.data=="digits"):
legalLabels = range(10)
else:
legalLabels = range(2)
if options.training <= 0:
print "Training set size should be a positive integer (you provided: %d)" % options.training
print USAGE_STRING
sys.exit(2)
if options.smoothing <= 0:
print "Please provide a positive number for smoothing (you provided: %f)" % options.smoothing
print USAGE_STRING
sys.exit(2)
if options.odds:
if options.label1 not in legalLabels or options.label2 not in legalLabels:
print "Didn't provide a legal labels for the odds ratio: (%d,%d)" % (options.label1, options.label2)
print USAGE_STRING
sys.exit(2)
if(options.classifier == "mostFrequent"):
classifier = mostFrequent.MostFrequentClassifier(legalLabels)
elif(options.classifier == "naiveBayes" or options.classifier == "nb"):
classifier = naiveBayes.NaiveBayesClassifier(legalLabels)
classifier.setSmoothing(options.smoothing)
if (options.autotune):
print "using automatic tuning for naivebayes"
classifier.automaticTuning = True
else:
print "using smoothing parameter k=%f for naivebayes" % options.smoothing
elif(options.classifier == "perceptron"):
classifier = perceptron.PerceptronClassifier(legalLabels,options.iterations)
elif(options.classifier == "mira"):
classifier = mira.MiraClassifier(legalLabels, options.iterations)
if (options.autotune):
print "using automatic tuning for MIRA"
classifier.automaticTuning = True
else:
print "using default C=0.001 for MIRA"
elif(options.classifier == 'minicontest'):
classifier = minicontest.contestClassifier(legalLabels)
else:
print "Unknown classifier:", options.classifier
print USAGE_STRING
sys.exit(2)
args['classifier'] = classifier
args['featureFunction'] = featureFunction
args['printImage'] = printImage
return args, options
USAGE_STRING = """
USAGE: python dataClassifier.py <options>
EXAMPLES: (1) python dataClassifier.py
- trains the default mostFrequent classifier on the digit dataset
using the default 100 training examples and
then test the classifier on test data
(2) python dataClassifier.py -c naiveBayes -d digits -t 1000 -f -o -1 3 -2 6 -k 2.5
- would run the naive Bayes classifier on 1000 training examples
using the enhancedFeatureExtractorDigits function to get the features
on the faces dataset, would use the smoothing parameter equals to 2.5, would
test the classifier on the test data and performs an odd ratio analysis
with label1=3 vs. label2=6
"""
# Main harness code
def runClassifier(args, options):
featureFunction = args['featureFunction']
classifier = args['classifier']
printImage = args['printImage']
# Load data
numTraining = options.training
if(options.data=="faces"):
rawTrainingData = samples.loadDataFile("facedata/facedatatrain", numTraining,FACE_DATUM_WIDTH,FACE_DATUM_HEIGHT)
trainingLabels = samples.loadLabelsFile("facedata/facedatatrainlabels", numTraining)
rawValidationData = samples.loadDataFile("facedata/facedatatrain", TEST_SET_SIZE,FACE_DATUM_WIDTH,FACE_DATUM_HEIGHT)
validationLabels = samples.loadLabelsFile("facedata/facedatatrainlabels", TEST_SET_SIZE)
rawTestData = samples.loadDataFile("facedata/facedatatest", TEST_SET_SIZE,FACE_DATUM_WIDTH,FACE_DATUM_HEIGHT)
testLabels = samples.loadLabelsFile("facedata/facedatatestlabels", TEST_SET_SIZE)
else:
rawTrainingData = samples.loadDataFile("digitdata/trainingimages", numTraining,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
trainingLabels = samples.loadLabelsFile("digitdata/traininglabels", numTraining)
rawValidationData = samples.loadDataFile("digitdata/validationimages", TEST_SET_SIZE,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
validationLabels = samples.loadLabelsFile("digitdata/validationlabels", TEST_SET_SIZE)
rawTestData = samples.loadDataFile("digitdata/testimages", TEST_SET_SIZE,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
testLabels = samples.loadLabelsFile("digitdata/testlabels", TEST_SET_SIZE)
# Extract features
print "Extracting features..."
trainingData = map(featureFunction, rawTrainingData)
validationData = map(featureFunction, rawValidationData)
testData = map(featureFunction, rawTestData)
# Conduct training and testing
print "Training..."
classifier.train(trainingData, trainingLabels, validationData, validationLabels)
print "Validating..."
guesses = classifier.classify(validationData)
correct = [guesses[i] == validationLabels[i] for i in range(len(validationLabels))].count(True)
print str(correct), ("correct out of " + str(len(validationLabels)) + " (%.1f%%).") % (100.0 * correct / len(validationLabels))
print "Testing..."
guesses = classifier.classify(testData)
correct = [guesses[i] == testLabels[i] for i in range(len(testLabels))].count(True)
print str(correct), ("correct out of " + str(len(testLabels)) + " (%.1f%%).") % (100.0 * correct / len(testLabels))
analysis(classifier, guesses, testLabels, testData, rawTestData, printImage)
# do odds ratio computation if specified at command line
if((options.odds) & (options.classifier != "mostFrequent")):
label1, label2 = options.label1, options.label2
features_odds = classifier.findHighOddsFeatures(label1,label2)
if(options.classifier == "naiveBayes" or options.classifier == "nb"):
string3 = "=== Features with highest odd ratio of label %d over label %d ===" % (label1, label2)
else:
string3 = "=== Features for which weight(label %d)-weight(label %d) is biggest ===" % (label1, label2)
print string3
printImage(features_odds)
if __name__ == '__main__':
# Read input
args, options = readCommand( sys.argv[1:] )
# Run classifier
runClassifier(args, options)