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run_sample.py
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run_sample.py
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import os
import edfx_database
import sleeploader
import keras
import models
import pickle
import tools
import keras_utils
from scipy.stats.mstats import zscore
import numpy as np
#%% ## download edfx database and prepare it
if __name__ == '__main__':
datadir = 'edfx'
# prepare dataset if it does not exist
if not os.path.isfile(os.path.join(datadir, 'sleepdata.pkl')):
edfx_database.download_edfx(datadir)
edfx_database.convert_hypnograms(datadir)
channels = {'EEG':'EEG FPZ-CZ', 'EMG':'EMG SUBMENTAL', 'EOG':'EOG HORIZONTAL'} # set channels that are used
references = {'RefEEG':False, 'RefEMG':False, 'RefEOG':False} # we do not set a reference, because the data is already referenced
sleep = sleeploader.SleepDataset(datadir)
# use float16 is you have problems with memory or a small hard disk. Should be around 2.6 GB for float32.
sleep.load( channels = channels, references = references, verbose=0, dtype=np.float32)
edfx_database.truncate_eeg(sleep)
# if the pickle file already exist, just load that one.
else:
sleep = sleeploader.SleepDataset(datadir)
sleep.load_object() # load the prepared files. Should be around 2.6 GB for float32
# load data
data, target, groups = sleep.get_all_data(groups=True)
data = zscore(data,1)
data = tools.normalize(data)
target[target==4] = 3 # Set S4 to S3
target[target==5] = 4 # Set REM to now empty class 4
target = keras.utils.to_categorical(target)
#%%
batch_size = 256
epochs = 256
###
rnn = {'model':models.bi_lstm, 'layers': ['fc1'], 'seqlen':6,
'epochs': 250, 'batch_size': 512, 'stop_after':15, 'balanced':False}
print(rnn)
model = models.cnn3adam_filter_morel2
results = keras_utils.cv (data, target, groups, model, rnn=rnn, name='edfx-sample',
epochs=epochs, folds=5, batch_size=batch_size, counter=0,
plot=True, stop_after=15, balanced=False, cropsize=2800)
with open('results_dataset_edfx-sample.pkl', 'wb') as f:
pickle.dump(results, f)