def netHO(trial):
pp = {
'LATENT' : trial.suggest_categorical('LATENT', [40,70,100]),
'nC' : trial.suggest_int('nC', 1,3),
'nD' : trial.suggest_int('nD', 1,3),
'gruf' : trial.suggest_categorical('gruf', [64,128,256,501]),
'beta' : trial.suggest_categorical('beta', [0.6,1.0,2.0]),
'EPOCH' : trial.suggest_categorical('EPOCH', [30]),
'ngpu' : trial.suggest_categorical('ngpu', [4]),
'opt' : trial.suggest_categorical('opt', ['adam'])
}
fn = f'{pfix}{trial.number}'
sgv = smilesGVAE(**pp)
sgv.doFitG(genr,vgenr,fn)
sc = sgv.evaluate(XTE)
print(f'Score: {sc}')
sc = 1.0 - sgv.jaccScore(XTE)
#print(f'JaccLoss: {sc}')
return sc
'nD': 3,
'beta': 1.0,
'gruf': 501,
'ngpu': gpus,
'opt': 'SGD',
'wFile': None
}
EPO = 2
lr_finder = LRFinder(min_lr=2.0e-4,
max_lr=1.0e-2,
steps_per_epoch=np.ceil(k / batch),
epochs=EPO)
sgv = smilesGVAE(**params)
print('Training autoencoder.')
if sgv.mgm is None:
sgv.aen.fit_generator(generator=genr,
validation_data=vgenr,
use_multiprocessing=False,
callbacks=[lr_finder],
epochs=EPO)
else:
sgv.mgm.fit_generator(generator=genr,
validation_data=vgenr,
use_multiprocessing=False,
callbacks=[lr_finder],
epochs=EPO)
study.optimize(netHO,n_trials=1)
df = study.trials_dataframe()
df2 = df['params'].copy()
df2['bxe'] = df['value']
df2.to_csv(f'data/{pfix}.csv')
bp = study.best_params
bt = study.best_trial.number
bcn = smilesGVAE(**bp)
fn = f'data/{pfix}{bt}.hdf5'
bcn.loadWeights(fn)
xp = bcn.aen.predict(XTE)
bcn.evaluate(XTE)
logdir = f'logs/{pfix}{bt}/'
fn = getFileList(logdir)
fn = fn[0]