def train(path, weightPath1, weightPath2, hiddenNodes, epochs, learningRate, proceed):
# read sequences and their measured binding affinities
allSequences, allTargets = fileUtils.readHLA(path)
# log transform the data to fit between 0 and 1
allTargets = logTransform.transform(allTargets)
# divide the data into training set, validation set and evaluation set
numOfSequences = len(allSequences)
indexes = np.arange(numOfSequences)
np.random.shuffle(indexes)
numOfTrain = (int) (numOfSequences * 0.7) # 70 % is for training
trainSequence = allSequences[indexes[0:numOfTrain]]
trainTarget = allTargets[indexes[0:numOfTrain]]
numOfVal = (int) (numOfSequences * 0.2) # 20 % is for vaidation
valSequence = allSequences[indexes[numOfTrain:(numOfTrain + numOfVal)]]
valTarget = allTargets[indexes[numOfTrain:(numOfTrain + numOfVal)]]
evalSequence = allSequences[indexes[(numOfTrain + numOfVal):numOfSequences]]
evalTarget = allTargets[indexes[(numOfTrain + numOfVal):numOfSequences]]
evalPrediction = np.zeros(len(evalSequence))
trainError = np.zeros(epochs)
valError = np.zeros(epochs)
# længden af sekvensbiderne og antallet er mulige aminosyrer. Der er 20 normale.
mer = 9
numOfAminoAcids = 20
# create weight matrix with random values or load the files
if(proceed):
weight1 = np.load(weightPath1)
weight2 = np.load(weightPath2)
else:
weight1 = weight(hiddenNodes, numOfAminoAcids * mer + 1) # plus 1 for bias
weight2 = weight(1, hiddenNodes + 1) # plus 1 for bias
bestWeight1 = weight1
bestWeight2 = weight2
bestError = 999 # just a large number so any validation will be better
bestEpoch = 0
print("Starting training and validation.")
for epoch in range(epochs):
# train on training set
# make scrampled order of sequences
indexes = np.arange(numOfTrain)
np.random.shuffle(indexes)
error = np.zeros(numOfTrain)
for index in indexes:
# convert peptide sequence to quasi-binary
inputLayer = sequenceUtils.createInputLayer(trainSequence[index])
# run the forward function
hiddenLayer, outputLayer = forward(inputLayer, weight1, weight2)
# save the error
error[index] = 1/2 * (outputLayer - trainTarget[index])**2
errorDelta = outputLayer - trainTarget[index]
# backpropagation
outputDelta = backpropagation.backward(outputLayer, 1, errorDelta)
weight2 = backpropagation.updateWeight(hiddenLayer, weight2, outputDelta, learningRate)
hiddenDelta = backpropagation.backward(hiddenLayer, weight2, outputDelta)
# bias is not a part of calculating the weights for the input
hiddenDelta = hiddenDelta[0,0:hiddenNodes]
weight1 = backpropagation.updateWeight(inputLayer, weight1, hiddenDelta, learningRate)
trainError[epoch] = error.mean()
# validation
error = np.zeros(numOfVal)
for index in range(numOfVal):
# convert peptide sequence to quasi-binary
inputLayer = sequenceUtils.createInputLayer(valSequence[index])
# run the forward function
hiddenLayer, outputLayer = forward(inputLayer, weight1, weight2)
# save the error
error[index] = 1/2 * (outputLayer - valTarget[index])**2
valError[epoch] = error.mean()
# find the best weight matrices so far
if(valError[epoch] < bestError):
bestWeight1 = weight1
bestWeight2 = weight2
bestError = valError[epoch]
bestEpoch = epoch
if(epoch % 10 == 0):
percent = (int) (epoch/epochs*100)
print("Training error: {:.8f}. Validation error: {:.8f}. {:2}% complete."
.format(trainError[epoch], valError[epoch], percent))
print("Training and validation complete.")
# plot error
pyplot.plot(trainError, label = "Training set")
pyplot.plot(valError, label = "Validation set")
pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)
pyplot.xlabel("epoch")
pyplot.ylabel("error")
pyplot.title("Validation")
pyplot.savefig('validation.png', bbox_inches='tight')
pyplot.show()
# save the best weight matrices
np.save(weightPath1, bestWeight1)
np.save(weightPath2, bestWeight2)
print("The minimum error of the validation set is at epoch {}. The validation error is {}."
.format(bestEpoch, bestError))
#evaluation
print("Predicting on evaluation set.")
for index in range(len(evalSequence)):
# convert peptide sequence to quasi-binary
inputLayer = sequenceUtils.createInputLayer(evalSequence[index])
# run the forward function
hiddenLayer, outputLayer = forward(inputLayer, bestWeight1, bestWeight2)
evalPrediction[index] = outputLayer
# plot comparison of prediction and target for evaluation set
pyplot.plot(evalTarget, evalPrediction, '.')
pyplot.xlabel("target")
pyplot.ylabel("prediction")
pyplot.title("Evaluation")
pyplot.savefig('evaluationLog.png', bbox_inches='tight')
pyplot.show()
# how correlated is it?
corr = np.corrcoef(evalTarget, evalPrediction)[1,0]
print("The Pearson correlation coefficient is {}.".format(corr))
# plot comparison again, now inverse log transfomed back but with a logarithmic scale
evalPrediction = logTransform.invTransform(evalPrediction)
evalTarget = logTransform.invTransform(evalTarget)
pyplot.axes().set_xscale('log')
pyplot.axes().set_yscale('log')
pyplot.plot(evalTarget, evalPrediction, '.')
pyplot.xlabel("target")
pyplot.ylabel("prediction")
pyplot.title("Evaluation")
pyplot.savefig('evaluation.png', bbox_inches='tight')
pyplot.show()
# importer hjælpefunktioner
import fileUtils
import logTransform
import sequenceUtils
from forward import forward
# set seed to be able to reproduce results
np.random.seed(1234)
limit = 500
syfLimit = 21
names = np.array(["gag", "pol", "vif", "vpr", "tat", "rev", "vpu", "env", "nef"])
# mhc epitopes
mhcSequences, mhcAffinities = fileUtils.readHLA("data/mhcSequences.txt")
mhcEpitopes = mhcSequences[mhcAffinities <= limit]
# complete hiv
hivProteins = fileUtils.readFasta("data/hivCodingSequences.txt")
# SMMPMBEC
smm0 = fileUtils.readColumn("data/smmpmbec.csv", 0, True)
smm1 = fileUtils.readColumn("data/smmpmbec.csv", 1, True)
smm2 = fileUtils.readColumn("data/smmpmbec.csv", 2, True)
smm3 = fileUtils.readColumn("data/smmpmbec.csv", 3, True)
index = smm3 <= limit
smm = [smm2[index], np.repeat(0, sum(index)), smm1[index]]
# replace names wih numbers
for name in names: