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execLoops.py
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execLoops.py
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from random import sample, shuffle
from utils import *
#import matplotlib.pyplot as plt
from logisticRegression import *
#from logisticRegressionWEKA import *
from svm import *
from dt import *
from nn import *
from rf import *
from perceptron import *
import sys
from copy import deepcopy
from samplingMethods import *
import pickle
##########################################################################
# uses an active learning strategy to learn a classifier
##########################################################################
def learn(numRelabels, state, dataGenerator,
samplingStrategy,
gamma, budget,
classifier,
outputfileAcc,
outputfileFscore,
statfile,
precisionfile,
recallfile,
interval,
numClasses,
bayesOptimal = False, smartBudgeting = False,
budgetInterval = None):
if budgetInterval == None:
budgetInterval = budget
accuracy = (1.0 / 2.0)*(1.0+((1.0 - 0.5) ** gamma))
outputStringAcc = ""
outputStringFscore = ""
statOutputString = ""
precisionOutputString = ""
recallOutputString = ""
activeTasks = []
activeTaskIndices = []
accuracies = []
#activeTaskIndices = range(len(trainingTasks))
#shuffle(activeTaskIndices)
#while state[-1] > 0:
numExamples = 0.0
totalSpentSoFar = 0.0
goldLabels = []
sampledIndices = []
#a hack for the first point when the budgetinterval is 1
firstPointManaged = False
#for idx in range(len(deepcopy(activeTaskIndices))):
idx = -1
while True:
#index = samplingStrategy(activeTaskIndices)
#index = passive(activeTaskIndices)
#Active Learning
print "CURRENT INDEX"
print idx
print len(dataGenerator.trainingTasks)
#print "Amount of training data"
#print len(dataGenerator.trainingTasks)
idx += 1
num_pretrain_examples = 61
#num_pretrain_examples = 0
if idx < num_pretrain_examples:
nextTask = passive(dataGenerator, state,
classifier, accuracy)
else:
nextTask = samplingStrategy.sample(dataGenerator,
state, classifier, accuracy)
#print nextTask
if nextTask not in state:
#print "REMOVING"
sampledIndices.append('a')
#print len(dataGenerator.trainingTasks)
#dataGenerator.trainingTasks.remove(nextTask)
#print len(dataGenerator.trainingTasks)
state[nextTask] = [0,0]
else:
#print "NOT REMOVING"
sampledIndices.append('r')
if nextTask in dataGenerator.trainingTasks:
dataGenerator.trainingTasks = filter(
lambda a: a != nextTask, dataGenerator.trainingTasks)
#dataGenerator.trainingTasks.remove(nextTask)
nextClass = dataGenerator.trainingTaskClasses[nextTask]
dataGenerator.replenish()
#if index in activeTaskIndices:
# activeTaskIndices.remove(index)
#print len(activeTaskIndices)
#print numExamples
#print state[-1]
#print numRelabels
#print state
if state[-1] <= 0:
break
numExamples += 1
#index = sample(activeTaskIndices, 1)[0]
#activeTaskIndices.remove(index)
"""
if sum(state[index]) > 0:
numRelabels = 2
else:
numRelabels = 1
"""
#(trues, falses) = state[index]
for r in range(numRelabels):
#(trues, falses) = state[index]
if isinstance(nextClass, list):
print nextClass
nextClass = sample(nextClass, 1)[0]
workerLabel = nextClass
else:
incorrectLabels = [i for i in range(numClasses)]
incorrectLabels.remove(nextClass)
workerLabel = simLabel(0.5, gamma, nextClass, incorrectLabels)
state[nextTask][workerLabel] += 1
state[-1] -= 1
#print state[-1]
totalSpentSoFar += 1
if totalSpentSoFar % budgetInterval == 0:
if not firstPointManaged and budgetInterval == 1:
accuracies.append((0.5, 0.5))
firstPointManaged = True
continue
#classifier = LRWrapper(1.0 * (1.0 * budget / numExamples))
#classifier = LRWrapper(100000000.0)
#classifier = SVMWrapper(1.0 * (1.0 * budget / numExamples))
#classifier = DTWrapper()
#classifier = RFWrapper()
#classifier = NNWrapper()
#classifier = PerceptronWrapper()
classifier.C = 1.0 * (1.0 * budget / numExamples)
#print "RETRAINING"
#print totalSpentSoFar
retrain(state, classifier,
bayesOptimal, accuracy)
#print len(testingTasks)
#print classifier.predict(testingTasks[0:10])
#print testingTaskClasses[0:10]
#print classifier.score(testingTasks, testingTaskClasses)
#print classifier.getParams()
(precision, recall, fscore) = classifier.fscore(
dataGenerator.testingTasks,
dataGenerator.testingTaskClasses)
accuracies.append(
(classifier.score(dataGenerator.testingTasks,
dataGenerator.testingTaskClasses),
(precision, recall, fscore)))
print accuracies
#print accuracies
if smartBudgeting:
if state[nextTask][workerLabel] > int(numRelabels / 2):
break
if state[-1] <= 0:
#print "TERMINATING"
#print state[-1]
break
#print state[-1]
#print budget-state[-1]
if (samplingStrategy.validate() and
(budget-state[-1]) > 50):
retrain(state, classifier, True, accuracy)
samplingStrategy.setValidation(classifier.score(
dataGenerator.validationTasks,
dataGenerator.validationTaskClasses))
if ((budget - state[-1]) % interval == 0 and
(budget-state[-1]) > 50):
#print "HERE"
#print state
retrain(state, classifier, True, accuracy)
#pickle.dump(computeStats(state[0:-1]), statfile)
(precision, recall, fscore) = classifier.fscore(
dataGenerator.testingTasks,
dataGenerator.testingTaskClasses)
outputStringAcc += ("%f\t"% classifier.score(
dataGenerator.testingTasks,
dataGenerator.testingTaskClasses))
outputStringFscore += ("%f\t"% fscore)
precisionOutputString += ("%f\t"% precision)
recallOutputString += ("%f\t"% recall)
statOutputString+= ("%f,%f\t"% computeStats(state)[1:])
outputStringAcc += "\n"
outputfileAcc.write(outputStringAcc)
outputStringFscore += "\n"
outputfileFscore.write(outputStringFscore)
statOutputString += "\n"
statfile.write(statOutputString)
precisionOutputString += "\n"
precisionfile.write(precisionOutputString)
recallOutputString += "\n"
recallfile.write(recallOutputString)
#pickle.dump("END", statfile)
#print trainingTasks
#print state[0:-1]
#print "RETRAINING"
accuracy = (1.0 / 2.0)*(1.0+((1.0 - 0.5) ** gamma))
classifier.C = 1.0 * (1.0 * budget / numExamples)
#classifier = LRWrapper(0.01 * (1.0 * budget / numExamples))
#classifier = LRWrapper(1.0 * (1.0 * budget / numExamples))
#classifier = LRWrapper(100000000.0)
#print budget
#print numExamples
#print 1.0 * (1.0 * budget / numExamples)
#classifier = DTWrapper()
#classifier = SVMWrapper(1.0 * (1.0 * budget / numExamples))
#classifier = RFWrapper()
#classifier = NNWrapper()
#classifier = PerceptronWrapper()
retrain(state, classifier, bayesOptimal, accuracy)
#print classifier.getParams()
#print labelAccuracy(trainingTasks, state[0:-1], trainingTaskClasses)
#return (classifier.score(testingTasks, testingTaskClasses),
# classifier.fscore(testingTasks, testingTaskClasses))
#print "HUH"
#print accuracies
print sampledIndices
(sampledIndices, numExamplesRelabeled,
numTimesRelabeled) = computeStats(state)
#print "HUH"
#print accuracies
print numExamplesRelabeled, numTimesRelabeled
return ((numExamples, numExamplesRelabeled, numTimesRelabeled),
accuracies)