####################### SET MODEL TRAINING #############################
sampleSize = '0' # choose between 0 to 2. 0 correspondes to 50D, 1 to 100D, 2 to 200D. 3 to 5 is also available but for generation based scaling
loss='WCategoricalCrossentropy' # loss function chosen. choose between "WCategoricalCrossentropy" or "categorical_crossentropy". WCategoricalCrossentropy is the expected loss described in the thesis.
######################THESE PARAMETERS ARE FIXED #########################
esconfig = [0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1]
#performance = Performance()
timeSteps = 2 # the number of time steps for the LSTM network
precision_value = 'ert-2'
#set to precision value to negative 2
result = Result(ert_column=precision_value)
#load the performance and ELA files generated from data gathering
performance = pd.read_csv("./perf/DataGathering_performance.csv")
ela = pd.read_csv("./perf/DataGathering_elaFeatures.csv")
result.addPerformance(performance)
result.addELA(ela)
#this could sometimes fail if the training sample does not contain at least two time steps. This could happen if the CMA-ES finds the optimal value before the 2nd checkpoint
Xtrain, Ytrain = result.createTrainSet(dataset=sampleSize, algorithm=None, reset=False, interface=None, RNN=timeSteps)
model = Models(Xtrain,Ytrain,_shuffle=False)
model.trainLSTM(stepSize=timeSteps, size=sampleSize, loss=loss, precision_value=precision_value)
localSearch=None)
suite.runDataGathering()
performance.saveToCSVPerformance('DataGathering')
performance.saveToCSVELA('DataGathering')
####################### Data Preprocessing #############################
sampleSize = '2' # choose between 0 to 2. 0 correspondes to 50D, 1 to 100D, 2 to 200D. 3 to 5 is also available but for generation based scaling
timeSteps = 2 # the number of time steps for the LSTM network
result = Result()
#load the performance and ELA files generated from data gathering
performance = pd.read_csv("./perf/DataGathering_performance.csv")
ela = pd.read_csv("./perf/DataGathering_elaFeatures.csv")
result.addPerformance(performance)
result.addELA(ela)
#this could sometimes fail if the training sample does not contain at least two time steps. This could happen if the CMA-ES finds the optimal value before the 2nd checkpoint
Xtrain, Ytrain = result.createTrainSet(dataset=sampleSize,
algorithm=None,
reset=False,
interface=None,
RNN=timeSteps)
####################### Model Training #############################
loss = 'WCategoricalCrossentropy' # loss function chosen. choose between "WCategoricalCrossentropy" or "categorical_crossentropy". WCategoricalCrossentropy is the expected loss described in the thesis.
model = Models(Xtrain, Ytrain, _shuffle=False)
model.trainLSTM(stepSize=timeSteps, size=sampleSize, loss=loss)