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', 'pacman'],
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('-w',
'--weights',
help=default('Whether to print weights'),
default=False,
action="store_true")
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")
parser.add_option('-s',
'--test',
help=default("Amount of test data to use"),
default=TEST_SET_SIZE,
type="int")
parser.add_option('-g',
'--agentToClone',
help=default("Pacman agent to copy"),
default=None,
type="str")
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
elif (options.data == "pacman"):
printImage = None
if (options.features):
featureFunction = enhancedFeatureExtractorPacman
else:
featureFunction = basicFeatureExtractorPacman
else:
print "Unknown dataset", options.data
print USAGE_STRING
sys.exit(2)
if (options.data == "digits"):
legalLabels = range(10)
else:
legalLabels = ['Stop', 'West', 'East', 'North', 'South']
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"):
if options.data != 'pacman':
classifier = perceptron.PerceptronClassifier(
legalLabels, options.iterations)
else:
classifier = perceptron_pacman.PerceptronClassifierPacman(
legalLabels, options.iterations)
elif (options.classifier == "mira"):
if options.data != 'pacman':
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'):
import minicontest
classifier = minicontest.contestClassifier(legalLabels)
else:
print "Unknown classifier:", options.classifier
print USAGE_STRING
sys.exit(2)
args['agentToClone'] = options.agentToClone
args['classifier'] = classifier
args['featureFunction'] = featureFunction
args['printImage'] = printImage
return args, options
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', 'pacman'], 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('-w', '--weights', help=default('Whether to print weights'), default=False, action="store_true")
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")
parser.add_option('-s', '--test', help=default("Amount of test data to use"), default=TEST_SET_SIZE, type="int")
parser.add_option('-g', '--agentToClone', help=default("Pacman agent to copy"), default=None, type="str")
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
elif(options.data=="pacman"):
printImage = None
if (options.features):
featureFunction = enhancedFeatureExtractorPacman
else:
featureFunction = basicFeatureExtractorPacman
else:
print "Unknown dataset", options.data
print USAGE_STRING
sys.exit(2)
if(options.data=="digits"):
legalLabels = range(10)
else:
legalLabels = ['Stop', 'West', 'East', 'North', 'South']
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"):
if options.data != 'pacman':
classifier = perceptron.PerceptronClassifier(legalLabels,options.iterations)
else:
classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,options.iterations)
elif(options.classifier == "mira"):
if options.data != 'pacman':
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'):
import minicontest
classifier = minicontest.contestClassifier(legalLabels)
else:
print "Unknown classifier:", options.classifier
print USAGE_STRING
sys.exit(2)
args['agentToClone'] = options.agentToClone
args['classifier'] = classifier
args['featureFunction'] = featureFunction
args['printImage'] = printImage
return args, options
MINICONTEST_PATH = "minicontest_output.pickle"
if __name__ == '__main__':
print "Loading training data"
rawTrainingData = samples.loadDataFile("digitdata/trainingimages", 5000,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
trainingLabels = samples.loadLabelsFile("digitdata/traininglabels", 5000)
rawValidationData = samples.loadDataFile("digitdata/validationimages", 100,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
validationLabels = samples.loadLabelsFile("digitdata/validationlabels", 100)
rawTestData = samples.loadDataFile("digitdata/testimages", TEST_SIZE,DIGIT_DATUM_WIDTH,DIGIT_DATUM_HEIGHT)
featureFunction = contestFeatureExtractorDigit
legalLabels = range(10)
classifier = minicontest.contestClassifier(legalLabels)
print "Extracting features..."
trainingData = map(featureFunction, rawTrainingData)
validationData = map(featureFunction, rawValidationData)
testData = map(featureFunction, rawTestData)
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)
print "Loading training data"
rawTrainingData = samples.loadDataFile("digitdata/trainingimages", 5000,
DIGIT_DATUM_WIDTH,
DIGIT_DATUM_HEIGHT)
trainingLabels = samples.loadLabelsFile("digitdata/traininglabels", 5000)
rawValidationData = samples.loadDataFile("digitdata/validationimages", 100,
DIGIT_DATUM_WIDTH,
DIGIT_DATUM_HEIGHT)
validationLabels = samples.loadLabelsFile("digitdata/validationlabels",
100)
rawTestData = samples.loadDataFile("digitdata/testimages", TEST_SIZE,
DIGIT_DATUM_WIDTH, DIGIT_DATUM_HEIGHT)
featureFunction = contestFeatureExtractorDigit
legalLabels = range(10)
classifier = minicontest.contestClassifier(legalLabels)
print "Extracting features..."
trainingData = map(featureFunction, rawTrainingData)
validationData = map(featureFunction, rawValidationData)
testData = map(featureFunction, rawTestData)
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)) +
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',
'nearestNeighbors', '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('-w',
'--weights',
help=default('Whether to print weights'),
default=False,
action="store_true")
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")
parser.add_option('-s',
'--test',
help=default("Amount of test data to use"),
default=TEST_SET_SIZE,
type="int")
parser.add_option(
'-n',
'--k_number_of_neighbors',
help=default("Number of neighbors to search (For nearestNeighbors)"),
default=3,
type="int")
parser.add_option(
'-x',
'--specialMode',
help=default(
"Trains in increments of random 10% of data and displays results"),
default=False,
action="store_true")
parser.add_option('-y',
'--analysis',
help=default("Shows which data is wrongly predicted"),
default=False,
action="store_true")
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 not options.specialMode:
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.k_number_of_neighbors <= 0:
print(
"Please provide a positive number for neighbors (you provided: %f)"
% options.k_number_of_neighbors)
print(USAGE_STRING)
sys.exit(2)
if options.classifier == "mostFrequent":
classifier = mostFrequent.MostFrequentClassifier(legalLabels)
elif options.classifier == "nearestNeighbors":
classifier = nearestNeighbors.NNClassifier(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':
import 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
