def perform(self):
for model_description in self.models:
modelname = model_description['modelname']
modeltype = model_description['modeltype']
modelparams = model_description['parameters']
mpath = self.outputfolder+self.experiment_name+'/models/'+modelname+'/'
# create folder for model outputs
if not os.path.exists(mpath):
os.makedirs(mpath)
if not os.path.exists(mpath+'results/'):
os.makedirs(mpath+'results/')
n_classes = self.Y.shape[1]
# load respective model
if modeltype == 'WAVELET':
from models.wavelet import WaveletModel
model = WaveletModel(modelname, n_classes, self.sampling_frequency, mpath, self.input_shape, **modelparams)
elif modeltype == "fastai_model":
from models.fastai_model import fastai_model
model = fastai_model(modelname, n_classes, self.sampling_frequency, mpath, self.input_shape, **modelparams)
elif modeltype == "YOUR_MODEL_TYPE":
# YOUR MODEL GOES HERE!
from models.your_model import YourModel
model = YourModel(modelname, n_classes, self.sampling_frequency, mpath, self.input_shape, **modelparams)
else:
assert(True)
break
# fit model
model.fit(self.X_train, self.y_train, self.X_val, self.y_val)
# predict and dump
model.predict(self.X_train).dump(mpath+'y_train_pred.npy')
model.predict(self.X_val).dump(mpath+'y_val_pred.npy')
model.predict(self.X_test).dump(mpath+'y_test_pred.npy')
modelname = 'ensemble'
# create ensemble predictions via simple mean across model predictions (except naive predictions)
ensemblepath = self.outputfolder+self.experiment_name+'/models/'+modelname+'/'
# create folder for model outputs
if not os.path.exists(ensemblepath):
os.makedirs(ensemblepath)
if not os.path.exists(ensemblepath+'results/'):
os.makedirs(ensemblepath+'results/')
# load all predictions
ensemble_train, ensemble_val, ensemble_test = [],[],[]
for model_description in os.listdir(self.outputfolder+self.experiment_name+'/models/'):
if not model_description in ['ensemble', 'naive']:
mpath = self.outputfolder+self.experiment_name+'/models/'+model_description+'/'
ensemble_train.append(np.load(mpath+'y_train_pred.npy', allow_pickle=True))
ensemble_val.append(np.load(mpath+'y_val_pred.npy', allow_pickle=True))
ensemble_test.append(np.load(mpath+'y_test_pred.npy', allow_pickle=True))
# dump mean predictions
np.array(ensemble_train).mean(axis=0).dump(ensemblepath + 'y_train_pred.npy')
np.array(ensemble_test).mean(axis=0).dump(ensemblepath + 'y_test_pred.npy')
np.array(ensemble_val).mean(axis=0).dump(ensemblepath + 'y_val_pred.npy')
def perform(self):
for model_description in self.models:
modelname = model_description['modelname']
modeltype = model_description['modeltype']
modelparams = model_description['parameters']
mpath = self.outputfolder + self.experiment_name + '/models/' + modelname + '/'
# create folder for model outputs
if not os.path.exists(mpath):
os.makedirs(mpath)
if not os.path.exists(mpath + 'results/'):
os.makedirs(mpath + 'results/')
n_classes = self.Y.shape[1]
# load respective model
if modeltype == 'WAVELET':
from models.wavelet import WaveletModel
model = WaveletModel(modelname, n_classes,
self.sampling_frequency, mpath,
self.input_shape, **modelparams)
elif modeltype == "fastai_model":
from models.fastai_model import fastai_model
model = fastai_model(modelname, n_classes,
self.sampling_frequency, mpath,
self.input_shape, **modelparams)
elif modeltype == "NET1D":
# YOUR MODEL GOES HERE!
from models.your_model import YourModel
model = YourModel(modelname, n_classes,
self.sampling_frequency, mpath,
self.input_shape, **modelparams)
elif modeltype == "NET1D_ICBEB":
# YOUR MODEL GOES HERE!
from models.your_model_icbeb import YourModel_ICBEB
model = YourModel_ICBEB(modelname, n_classes,
self.sampling_frequency, mpath,
self.input_shape, **modelparams)
else:
assert (True)
break
print(self.X_train.shape, self.y_train.shape)
# fit model
model.fit(self.X_train, self.y_train, self.X_val, self.y_val,
self.pid_val)
# predict and dump
# model.predict(self.X_train).dump(mpath+'y_train_pred.npy')
model.predict(self.X_val,
self.pid_val).dump(mpath + 'y_val_pred.npy')
model.predict(self.X_test,
self.pid_test).dump(mpath + 'y_test_pred.npy')