def crossValidationPositions():
'''
Performs 10 fold cross validation on the total
joint position dataset
'''
theData = generateAllPositionTrainingData()
means, stdDevs = theData.normalizeData()
k = 10
#Partition the data into 10 subsets
dataSets = theData.getKSegments(k)
#For each of the 10 subsets leave one out, train on the
# other 9, test on the one left out, print the accuracy.
results = confusionMatrix(labels)
for i in xrange(k):
print i
#testing set
testSet = dataSets[i]
#Build the training set
trainingSet = TrainingData("CrossVal")
trainingList = copy.deepcopy(dataSets)
trainingList.pop(i)
for elem in trainingList:
trainingSet.combineWithNewData(elem)
#train the classifier on the trainingSet
testForest = RandomForest(trainingSet)
testForest.train()
#Evaluate the classifer on the test set
for samp in testSet.getData():
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def crossValidationPositions():
'''
Performs 10 fold cross validation on the total
joint position dataset
'''
theData = generateAllPositionTrainingData()
means, stdDevs = theData.normalizeData()
k = 10
#Partition the data into 10 subsets
dataSets = theData.getKSegments(k)
#For each of the 10 subsets leave one out, train on the
# other 9, test on the one left out, print the accuracy.
results = confusionMatrix(labels)
for i in xrange(k):
print i
#testing set
testSet = dataSets[i]
#Build the training set
trainingSet = TrainingData("CrossVal")
trainingList = copy.deepcopy(dataSets)
trainingList.pop(i)
for elem in trainingList:
trainingSet.combineWithNewData(elem)
#train the classifier on the trainingSet
testForest = RandomForest(trainingSet)
testForest.train()
#Evaluate the classifer on the test set
for samp in testSet.getData():
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def twoVsOneAngles():
'''
Trains a random forest on the data from participants 1 and 2
and tests it on participant 3. The data used here
uses the angle features
'''
theData = generateTwoAngleTrainingData()
testForest = RandomForest(theData)
print "Training"
testForest.train()
print "Done!"
testList = generateOneTestAngleData()
results = confusionMatrix(labels)
for samp in testList:
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def twoVsOneAngles():
'''
Trains a random forest on the data from participants 1 and 2
and tests it on participant 3. The data used here
uses the angle features
'''
theData = generateTwoAngleTrainingData()
testForest = RandomForest(theData)
print "Training"
testForest.train()
print "Done!"
testList = generateOneTestAngleData()
results = confusionMatrix(labels)
for samp in testList:
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def oneVsTwoPositions():
'''
Trains a random forest on the data from participant 1
and tests it on participant 2 and 3. The data used here
uses the position features
'''
theData = generateOneTrainPositionData()
means, stdDevs = theData.normalizeData()
testForest = RandomForest(theData)
print "Training"
testForest.train()
print "Done!"
testList = generateTwoTestPositionData(means, stdDevs)
results = confusionMatrix(labels)
for samp in testList:
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
def oneVsTwoPositions():
'''
Trains a random forest on the data from participant 1
and tests it on participant 2 and 3. The data used here
uses the position features
'''
theData = generateOneTrainPositionData()
means, stdDevs = theData.normalizeData()
testForest = RandomForest(theData)
print "Training"
testForest.train()
print "Done!"
testList = generateTwoTestPositionData(means, stdDevs)
results = confusionMatrix(labels)
for samp in testList:
resultLabel = testForest.classify(samp)
trueLabel = samp.getLabel()
results.update(trueLabel, resultLabel)
results.printMatrix()
from Dataset import Dataset
from RandomForest import RandomForest
import ClassifierStats as stats
dataset_path = 'synthetic.social'
train = Dataset.from_file('../data/{}.train'.format(dataset_path))
test = Dataset.from_file('../data/{}.test'.format(dataset_path))
model = RandomForest(num_trees=100, max_depth=100, bagging_data_fraction=0.4)
model.train(train)
predictions = model.classify(test)
accuracy = stats.accuracy(test.labels, predictions)
print(accuracy)
