def __init__(
self,
trainingData=None,
validationData=None,
classifierType="perceptron",
agentToClone=None,
numTraining=3,
):
from dataClassifier import (enhancedFeatureExtractorPacman,
runClassifier)
legalLabels = ["Stop", "West", "East", "North", "South"]
if classifierType == "perceptron":
classifier = perceptron_pacman.PerceptronClassifierPacman(
legalLabels, numTraining
)
self.classifier = classifier
self.featureFunction = enhancedFeatureExtractorPacman
args = {
"featureFunction": self.featureFunction,
"classifier": self.classifier,
"printImage": None,
"trainingData": trainingData,
"validationData": validationData,
"agentToClone": agentToClone,
}
options = DummyOptions()
options.classifier = classifierType
runClassifier(args, options)
def __init__(self, trainingData=None, validationData=None, classifierType="perceptron", agentToClone=None, numTraining=3):
self.classifier = classifier
self.featureFunction = enhancedFeatureExtractorPacman
args = {'featureFunction': self.featureFunction,
'classifier':self.classifier,
'printImage':None,
'trainingData':trainingData,
'validationData':validationData,
'agentToClone': agentToClone,
}
from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
legalLabels = ['Stop', 'West', 'East', 'North', 'South']
if(classifierType == "perceptron"):
classifier = perceptron_pacman.PerceptronClassifierPacman(legalLabels,numTraining)
options = DummyOptions()
options.classifier = classifierType
runClassifier(args, options)
def __init__(self,
trainingData=None,
validationData=None,
classifierType="perceptron",
agentToClone=None,
numTraining=3):
from dataClassifier import runClassifier, enhancedFeatureExtractorPacman
legalLabels = ['Stop', 'West', 'East', 'North', 'South']
didLoadFromFile = False
self.classifierType = classifierType
if (classifierType == "perceptron"):
try:
classifier = pickle.load(open("perceptron.pkl", "rb"))
print("loaded perceptron from file")
didLoadFromFile = True
except:
# here's the actual perceptron part
print("perceptron.pkl not found")
classifier = perceptron_pacman.PerceptronClassifierPacman(
legalLabels, numTraining)
self.classifier = classifier
#looks like enhanced Feature extractor is in dataClassifier
self.featureFunction = enhancedFeatureExtractorPacman
args = {
'featureFunction': self.featureFunction,
'classifier': self.classifier,
'printImage': None,
'trainingData': trainingData,
'validationData': validationData,
'agentToClone': agentToClone,
}
options = DummyOptions()
options.classifier = classifierType
runClassifier(args, options, didLoadFromFile=didLoadFromFile)
if not didLoadFromFile:
pickle.dump(classifier, open("perceptron.pkl", "wb"))
print("saving perceptron to file")
def __init__(self,
training_data=None,
validation_data=None,
classifier_type="perceptron",
agent_to_clone=None,
num_training=3):
from data_classifier import run_classifier, enhanced_feature_extractor_pacman
legal_labels = ['Stop', 'West', 'East', 'North', 'South']
if (classifier_type == "perceptron"):
classifier = perceptron_pacman.PerceptronClassifierPacman(
legal_labels, num_training)
self.classifier = classifier
self.feature_function = enhanced_feature_extractor_pacman
args = {
'feature_function': self.feature_function,
'classifier': self.classifier,
'print_image': None,
'training_data': training_data,
'validation_data': validation_data,
'agent_to_clone': agent_to_clone,
}
options = DummyOptions()
options.classifier = classifier_type
run_classifier(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
def read_command(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=[
'most_frequent', 'nb', 'naive_bayes', 'perceptron',
'perceptron_numpy', 'logistic', 'minicontest'
],
default='most_frequent')
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(
'-n',
'--num_weights',
help=default(
"Num Weights to Print (when --weights enabled), default: 100"),
default=100,
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")
parser.add_option('-s',
'--test',
help=default("Amount of test data to use"),
default=TEST_SET_SIZE,
type="int")
parser.add_option('-g',
'--agent_to_clone',
help=default("Pacman agent to copy"),
default=None,
type="str")
parser.add_option(
'-l',
'--learning_rates',
help=default(
"Learning rates to use for gradient descent, can be a comma separated list or single value"
),
default=[0.2],
type="str",
action='callback',
callback=learning_rate_callback)
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"):
print_image = ImagePrinter(DIGIT_DATUM_WIDTH,
DIGIT_DATUM_HEIGHT).print_image
if (options.features):
feature_function = enhanced_feature_extractor_digit
else:
feature_function = basic_feature_extractor_digit
if (options.classifier == 'minicontest'):
feature_function = contest_feature_extractor_digit
elif (options.data == "faces"):
print_image = ImagePrinter(FACE_DATUM_WIDTH,
FACE_DATUM_HEIGHT).print_image
if (options.features):
feature_function = enhanced_feature_extractor_face
else:
feature_function = basic_feature_extractor_face
elif (options.data == "pacman"):
print_image = None
if (options.features):
feature_function = enhanced_feature_extractor_pacman
else:
feature_function = basic_feature_extractor_pacman
else:
print("Unknown dataset", options.data)
print(USAGE_STRING)
sys.exit(2)
if (options.data == "digits"):
legal_labels = list(range(10))
else:
legal_labels = ['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 legal_labels or options.label2 not in legal_labels:
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 == "most_frequent"):
classifier = most_frequent.MostFrequentClassifier(legal_labels)
elif (options.classifier == "naive_bayes" or options.classifier == "nb"):
classifier = naive_bayes.NaiveBayesClassifier(legal_labels)
classifier.set_smoothing(options.smoothing)
if (options.autotune):
print("using automatic tuning for naivebayes")
classifier.automatic_tuning = True
else:
print("using smoothing parameter k=%f for naivebayes" %
options.smoothing)
elif (options.classifier == "perceptron"):
if options.data != 'pacman':
classifier = perceptron.PerceptronClassifier(
legal_labels, options.iterations)
else:
classifier = perceptron_pacman.PerceptronClassifierPacman(
legal_labels, options.iterations)
elif (options.classifier == "perceptron_numpy"):
if options.data != 'pacman':
classifier = perceptron_numpy.OptimizedPerceptronClassifier(
legal_labels, options.iterations)
elif (options.classifier == "logistic"):
if options.data != 'pacman':
classifier = logistic.SoftmaxClassifier(legal_labels,
options.iterations)
classifier.learning_rates = options.learning_rates
elif (options.classifier == 'minicontest'):
import minicontest
classifier = minicontest.contest_classifier(legal_labels)
else:
print("Unknown classifier:", options.classifier)
print(USAGE_STRING)
sys.exit(2)
args['agent_to_clone'] = options.agent_to_clone
args['classifier'] = classifier
args['feature_function'] = feature_function
args['print_image'] = print_image
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=['perceptron'],
default='perceptron')
parser.add_option('-d',
'--data',
help=default('Dataset to use'),
choices=['digits', 'pacman'],
default='digits')
parser.add_option('-t',
'--training',
help=default('The size of the training set'),
default=100,
type="int")
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
print "training set size:\t" + str(options.training)
if (options.data == "digits"):
printImage = ImagePrinter(DIGIT_DATUM_WIDTH,
DIGIT_DATUM_HEIGHT).printImage
featureFunction = basicFeatureExtractorDigit
if (options.classifier == 'minicontest'):
featureFunction = contestFeatureExtractorDigit
elif (options.data == "pacman"):
printImage = None
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.classifier == "perceptron"):
if options.data != 'pacman':
classifier = perceptron.PerceptronClassifier(
legalLabels, options.iterations)
else:
classifier = perceptron_pacman.PerceptronClassifierPacman(
legalLabels, options.iterations)
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",
"perceptron",
],
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)
print("using enhanced features?:\t" + str(options.features))
print("training set size:\t" + str(options.training))
if options.data == "digits":
printImage = ImagePrinter(
DIGIT_DATUM_WIDTH, DIGIT_DATUM_HEIGHT
).printImage
featureFunction = basicFeatureExtractorDigit
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 = list(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 == "perceptron":
if options.data != "pacman":
classifier = perceptron.PerceptronClassifier(
legalLabels, options.iterations
)
else:
classifier = perceptron_pacman.PerceptronClassifierPacman(
legalLabels, options.iterations
)
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
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
