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train.py
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train.py
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from scipy import diag
import pandas as pd
import numpy as np
from pybrain.datasets import ClassificationDataSet
from pybrain.utilities import percentError
from pybrain.tools.shortcuts import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.structure.modules import SoftmaxLayer
import pickle
from mlCVF.TXLHelper import TXLHelper
import argparse
def read_file_in_line_range(filePath, startLine, endLine):
fileContent = ''
with open(filePath) as f:
fileContent = ''.join(f.readlines()[int(startLine):int(endLine)])
return fileContent
def run(args):
manual_validated_file = args.manual_validated_file # 'JHotDraw54b1_clones.xml.clones2'
save_target_name = args.save_target_name #'newTrainedModel'
print 'Training the Model. Please wait ...'
manual_validation_data = pd.read_csv('manual_validator/input_clone_pairs/'+manual_validated_file)
inputDim = 6
alldata = ClassificationDataSet(inputDim, 1, nb_classes=2)
txlHelper = TXLHelper()
for i in range(0, len(manual_validation_data)):
#print manual_validation_data.iloc[i][3], manual_validation_data.iloc[i][4]
#print manual_validation_data.iloc[i][2]
cloneFragment_1_path, cloneFragment_1_start, cloneFragment_1_end = manual_validation_data.iloc[i][3].split()[0], \
manual_validation_data.iloc[i][3].split()[1], \
manual_validation_data.iloc[i][3].split()[2]
cloneFragment_2_path, cloneFragment_2_start, cloneFragment_2_end = manual_validation_data.iloc[i][4].split()[0], \
manual_validation_data.iloc[i][4].split()[1], \
manual_validation_data.iloc[i][4].split()[2]
cloneFragment_1 = read_file_in_line_range(filePath='manual_validator/input_clone_pairs/'+cloneFragment_1_path, \
startLine=cloneFragment_1_start, endLine=cloneFragment_1_end)
cloneFragment_2 = read_file_in_line_range(filePath='manual_validator/input_clone_pairs/' + cloneFragment_2_path,
startLine=cloneFragment_2_start, endLine=cloneFragment_2_end)
type1sim_by_line, type2sim_by_line, type3sim_by_line = txlHelper.app_code_clone_similaritiesNormalizedByLine(cloneFragment_1,
cloneFragment_2, 'java')
type1sim_by_token, type2sim_by_token, type3sim_by_token = txlHelper.app_code_clone_similaritiesNormalizedByToken(cloneFragment_1,
cloneFragment_2, 'java')
label = manual_validation_data.iloc[i][2]
if label == 'true':
label = 1
else:
label = 0
input = np.array([type1sim_by_token, type2sim_by_line, type3sim_by_line, type1sim_by_token, type2sim_by_token, type3sim_by_token])
alldata.addSample(input, int(label))
# # np.nan_to_num(alldata)
# # alldata = alldata[~np.isnan(alldata)]
# #alldata.fillna(0)
# np.set_printoptions(precision=3)
# print alldata
#
# def load_training_dataSet(fileName):
# data = pd.read_csv(fileName, sep=',', header=None)
# #data.columns = ["state", "outcome"]
# return data
#
# myclones_data = load_training_dataSet('Datasets/new_dataset_with_new_features.csv')
# myclones_data = myclones_data.values
#
#
# inputDim = 6
#
#
# means = [(-1,0),(2,4),(3,1)]
# cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])]
# alldata = ClassificationDataSet(inputDim, 1, nb_classes=2)
#
#
# #input = np.array([ myclones_data[n][16], myclones_data[n][17], myclones_data[n][18], myclones_data[n][15],myclones_data[n][11],myclones_data[n][12], myclones_data[n][26], myclones_data[n][27]] )
#
# for n in xrange(len(myclones_data)):
# #for klass in range(3):
# input = np.array(
# [myclones_data[n][11], myclones_data[n][17], myclones_data[n][12], myclones_data[n][15], myclones_data[n][18],
# myclones_data[n][16]])
# #print (n, "-->", input)
# alldata.addSample(input, int(myclones_data[n][35]))
#
#
tstdata, trndata = alldata.splitWithProportion( 0.25 )
#print(tstdata)
tstdata_new = ClassificationDataSet(inputDim, 1, nb_classes=2)
for n in xrange(0, tstdata.getLength()):
tstdata_new.addSample( tstdata.getSample(n)[0], tstdata.getSample(n)[1] )
trndata_new = ClassificationDataSet(inputDim, 1, nb_classes=2)
for n in xrange(0, trndata.getLength()):
trndata_new.addSample( trndata.getSample(n)[0], trndata.getSample(n)[1])
trndata = trndata_new
tstdata = tstdata_new
#print("Before --> ", trndata)
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
fnn = buildNetwork( trndata.indim, 107, trndata.outdim, outclass=SoftmaxLayer )
trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1,learningrate=0.05 , verbose=True, weightdecay=0.001)
#print "Printing Non-Trained Network..."
"""
ticks = arange(-3.,6.,0.2)
X, Y = meshgrid(ticks, ticks)
# need column vectors in dataset, not arrays
griddata = ClassificationDataSet(7,1, nb_classes=2)
for i in xrange(X.size):
griddata.addSample([X.ravel()[i],Y.ravel()[i]], [0])
griddata._convertToOneOfMany() # this is still needed to make the fnn feel comfy
"""
#trainer.trainEpochs(1)
#trainer.testOnData(verbose=True)
#print(np.array([fnn.activate(x) for x, _ in tstdata]))
for i in range(1):
trainer.trainEpochs(10)
trnresult = percentError(trainer.testOnClassData(),
trndata['class'])
tstresult = percentError(trainer.testOnClassData(
dataset=tstdata), tstdata['class'])
#print "epoch: %4d" % trainer.totalepochs, \
# " train error: %5.2f%%" % trnresult, \
# " test error: %5.2f%%" % tstresult
#print "Printing Trained Network..."
#print fnn.params
print "Saving the trined Model at : ", 'pybrain/'+save_target_name
#saving the trained network...
import pickle
fileObject = open('pybrain/'+save_target_name, 'w')
pickle.dump(fnn, fileObject)
fileObject.close()
#
# fileObject = open('trainedNetwork79', 'r')
# loaded_fnn = pickle.load(fileObject)
#
#
# print "Printing the result prediction..."
#
# print loaded_fnn.activate([0.2,0.5,0.6,0.1,0.3,0.7])
#
# print fnn.activate([0.2,0.5,0.6,0.1,0.3,0.7])
#
#out = fnn.activateOnDataset(griddata)
#out = out.argmax(axis=1) # the highest output activation gives the class
#out = out.reshape(X.shape)
"""
figure(1)
ioff() # interactive graphics off
clf() # clear the plot
hold(True) # overplot on
for c in [0, 1, 2]:
here, _ = where(tstdata['class'] == c)
plot(tstdata['input'][here, 0], tstdata['input'][here, 1], 'o')
if out.max() != out.min(): # safety check against flat field
contourf(X, Y, out) # plot the contour
ion() # interactive graphics on
draw() # update the plot
ioff()
show()
"""
def main():
parser = argparse.ArgumentParser(description="This is a machine learning based framework for automatic code clone validation.")
parser.add_argument("-in", help="(required) validated input clone file (i.e., output file from manual validation)", dest="manual_validated_file", type=str, required=True, default="JHotDraw54b1_clones.xml.clones2")
parser.add_argument("-out", help="(required) save output name for the newly trained model", dest="save_target_name", type=str, required=True, default="latestTrainedModel")
#parser.add_argument("-t", help="(optional) the threshold for automatic clone validation. Default=0.7", dest="val_threshold", type=float, default=0.7)
parser.set_defaults(func=run)
args = parser.parse_args()
args.func(args)
if __name__ == "__main__":
main()