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276import NeuralNetUtil
import NeuralNet
import traceback
import sys
import getopt
from os import listdir
import cPickle
def sameList(list1,list2,floatDif):
if len(list1)!=len(list2):
return False
for index in xrange(len(list1)):
if isinstance(list1[index],float):
if abs(list1[index]-list2[index])>floatDif:
return False
elif list1[index]!=list2[index]:
return False
return True
def copyWeights(nnet1, nnet2):
for l in xrange(len(nnet1.layers)):
for n in xrange(len(nnet1.layers[l])):
nnet1.layers[l][n].weights = nnet2.layers[l][n].weights
nnet1.layers[l][n].inSize = nnet2.layers[l][n].inSize
def q1Test(testFile):
if 'sigmoid' in testFile.name and 'Activation' not in testFile.name:
value = float(testFile.readline().strip())
sPercep = NeuralNet.Perceptron()
solution = sPercep.sigmoid(value)
else:
testFuncName = testFile.readline().strip()
getData = getattr(NeuralNetUtil, testFuncName)
examples, tests = getData()
testRangeStart = testFile.readline().strip()
testRangeEnd = testFile.readline().strip()
testRangeStart = 0 if testRangeStart=='None' else int(testRangeStart)
testRangeEnd = len(examples) if testRangeEnd=='None' else int(testRangeEnd)
examples = examples[testRangeStart:testRangeEnd]
if 'feedforward' in testFile.name:
sNet = NeuralNet.NeuralNet([16,24,10])
file = open('test_cases/nnet')
net = cPickle.load(file)
copyWeights(sNet,net)
solution = []
for example in examples:
solution.append(sNet.feedForward(example[0]))
elif 'Activation' in testFile.name:
file = open('test_cases/percep')
percep = cPickle.load(file)
sPercep = NeuralNet.Perceptron(inSize = percep.inSize, weights = percep.weights)
solution = []
for example in examples:
solution.append(sPercep.sigmoidActivation(example[0]))
return solution
def q2Test(testFile):
if 'sigmoidDeriv' in testFile.name:
solution = []
percep = NeuralNet.Perceptron()
for line in testFile:
val = float(line)
solution = percep.sigmoidDeriv(val)
else:
testFuncName = testFile.readline().strip()
getData = getattr(NeuralNetUtil, testFuncName)
examples, tests = getData()
testRangeStart = testFile.readline().strip()
testRangeEnd = testFile.readline().strip()
testRangeStart = 0 if testRangeStart=='None' else int(testRangeStart)
testRangeEnd = len(examples) if testRangeEnd=='None' else int(testRangeEnd)
examples = examples[testRangeStart:testRangeEnd]
file = open('test_cases/percep')
percep = cPickle.load(file)
sPercep = NeuralNet.Perceptron(inSize = percep.inSize-1, weights = percep.weights)
if 'update' in testFile.name:
solution = []
for example in examples:
solution.append(sPercep.updateWeights(example[0],0.1,0.67))
elif 'sigmoid' in testFile.name:
solution = []
for example in examples:
solution.append(sPercep.sigmoidActivationDeriv(example[0]))
return solution
def q3Test(testFile,module=NeuralNet):
testFuncName = testFile.readline().strip()
getData = getattr(NeuralNetUtil, testFuncName)
examples, tests = getData()
testRangeStart = testFile.readline().strip()
testRangeEnd = testFile.readline().strip()
testRangeStart = 0 if testRangeStart=='None' else int(testRangeStart)
testRangeEnd = len(examples) if testRangeEnd=='None' else int(testRangeEnd)
examples = examples[testRangeStart:testRangeEnd]
sNet = NeuralNet.NeuralNet([16,24,10])
file = open('test_cases/nnet')
net = cPickle.load(file)
copyWeights(sNet,net)
solution = sNet.backPropLearning(examples,0.1)
return solution[1]
def q4Test(testFile,module=NeuralNet):
testFuncName = testFile.readline().strip()
getData = getattr(NeuralNetUtil, testFuncName)
examples = getData()
testRangeStart = testFile.readline().strip()
testRangeEnd = testFile.readline().strip()
testRangeStart = 0 if testRangeStart=='None' else int(testRangeStart)
testRangeEnd = len(examples) if testRangeEnd=='None' else int(testRangeEnd)
examples = (examples[0][testRangeStart:testRangeEnd],examples[1][testRangeStart:testRangeEnd])
alph = float(testFile.readline())
weight = float(testFile.readline())
file = open('test_cases/nnet')
net = cPickle.load(file)
sNet = NeuralNet.NeuralNet([16,24,10])
copyWeights(sNet,net)
solution = NeuralNet.buildNeuralNet(examples, alpha=alph, weightChangeThreshold = weight,startNNet = sNet)
return solution[1]
def getFile(name, q):
return open('test_cases/'+q+'/'+name)
def runTests(q,points=2):
total = 0
possible = 0
testFunc = globals()[q+'Test']
correct=False
print 'Running %s tests\n'%q
for fileName in [name for name in listdir('test_cases/'+q) if 'test' in name]:
possible+=1
print 'Running test %s'%fileName
try:
solFile = open('test_cases/'+q+'/'+fileName.replace('.test','.solution'))
solution = solFile.read().split(';')
except Exception,e:
print 'No solution file found'
continue
try:
result = testFunc(getFile(fileName,q))
exec(solution[0].strip())
except Exception,e:
print 'You broke something:'
print traceback.format_exc()
continue
if correct:
total+=1
print 'Correct answer!'
else:
print 'Your answer is incorrect'
print 'Your answer: %s'%str(result)
print 'Correct answer: %s\n'%str(solution[1].strip())
print '--------------------------------------------------------------------'
if total==possible:
print 'All tests passed - score %d/%d'%(points,points)
print 'Done\n____________________________________________________________________\n'
return (points,points)
else:
print 'Not all tests passed - score 0/%d'%points
print 'Done\n____________________________________________________________________\n'
return (0,points)
def makeSolutionsFiles(q, floatDif = 0.000001):
testFunc = globals()[q+'Test']
for fileName in [name for name in listdir('test_cases/'+q) if 'test' in name]:
print 'Creating solution file for test %s'%fileName
solution = testFunc(getFile(fileName,q))
solFile = open('test_cases/'+q+'/'+fileName.replace('.test','.solution'),'w')
if isinstance(solution,float):
solFile.write('correct=abs(result-%f)<%f;\n%f'%(solution,floatDif,solution))
elif isinstance(solution,list):
solFile.write('correct=sameList(result,%s,%f);\n%s'%(str(solution),floatDif,str(solution)))
else:
solFile.write('correct=str(result)=="""%s""";\n%s'%(str(solution),str(solution)))
print 'Solution is \n%s\n'%str(solution)
def runTest(test,questions):
try:
solFile = open(test.replace('.test','.solution'))
solution = solFile.read().split(';')
q = None
for question in questions:
if question in test:
q = question
testFunc = globals()[q+'Test']
result = testFunc(open(test))
correct = False
exec(solution[0].strip())
if correct:
print 'Correct answer!'
else:
print 'Your answer is incorrect'
print 'Your answer: %s'%str(result)
print 'Correct answer: %s\n'%str(solution[1].strip())
except Exception,e:
print 'No solution file found for test %s, or you broke something'%test
def q1Tests():
return runTests('q1',2)
def q2Tests():
return runTests('q2',2)
def q3Tests():
return runTests('q3',4)
def q4Tests():
return runTests('q4',4)
def makePickledObjects():
data = NeuralNetUtil.buildExamplesFromPenData()
net = NeuralNet.buildNeuralNet(data, weightChangeThreshold=0.00075,hiddenLayerList = [24])[0]
print net
f = open('test_cases/nnet','w')
cPickle.dump(net,f)
f.close()
f = open('test_cases/percep','w')
cPickle.dump(net.layers[0][1],f)
f.close()
help_string = 'Usage: autograder.py [options]\n\
\
Run public tests on student code\n\
\
Options:\n\
-h, --help show this help message and exit\n\
-t RUNTEST, --test=RUNTEST\n\
Run one particular test. Relative to test root.\n\
-q GRADEQUESTION, --question=GRADEQUESTION\n\
Grade one particular question.'
def main():
args = sys.argv
questions = ['q1','q2','q3','q4']
if len(args)==1:
sumTotal= 0
sumPossible = 0
for q in questions:
func = globals()[q+'Tests']
total, possible = func()
sumTotal+=total
sumPossible+=possible
print '\nAutograder finished. Final score %d/%d'%(sumTotal,sumPossible)
else:
opts, args = getopt.getopt(args[1:],"q:t:h",["q=","test=","help"])
for opt, arg in opts:
if opt=='-q' or opt=='--q':
if arg in questions:
func = globals()[arg+'Tests']
func()
elif opt=='-t' or opt=='--test':
runTest(arg,questions)
elif opt=='-h' or opt=='--telp':
print help_string
break
if __name__=='__main__':
#questions = ['q1','q2','q3','q4']
#for question in questions:
# makeSolutionsFiles(question)
main()