def __init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=1,
dropout_r=0.1,
cell_type='TLSTM',
bii=False,
time=True,
preTrainEmb=''):
EHREmbeddings.__init__(self, input_size, embed_dim, hidden_size,
n_layers, dropout_r, cell_type, time,
preTrainEmb)
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=n_layers,
dropout_r=dropout_r,
cell_type=cell_type,
bii=False,
time=True,
preTrainEmb=preTrainEmb,
packPadMode=False)
if self.cell_type != 'TLSTM' or self.bi != 1:
print(
"TLSTM only supports Time aware LSTM cell type and 1 direction. Implementing corrected parameters instead"
)
self.cell_type = 'TLSTM'
self.bi = 1 #enforcing 1 directional
self.packPadMode = False
def __init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=1,
dropout_r=0.1,
cell_type='QRNN',
bii=False,
time=False,
preTrainEmb='',
packPadMode=False):
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=n_layers,
dropout_r=dropout_r,
cell_type=cell_type,
bii=bii,
time=time,
preTrainEmb=preTrainEmb,
packPadMode=packPadMode)
#super(EHR_QRNN, self).__init__()
#basically, we dont allow cell_type and bii choices
#let's enfroce these:
if (self.cell_type != 'QRNN' or self.bi != 1):
print(
'QRNN only supports 1-direction & QRNN cell_type implementation. Implementing corrected parameters instead'
)
self.cell_type = 'QRNN'
self.bi = 1 #enforcing 1 directional
self.packPadMode = False #enforcing correct packpaddedmode
def __init__(self,
input_size,
embed_dim,
time=False,
cell_type='LR',
preTrainEmb=''):
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size=embed_dim)
def __init__(self,
input_size,
embed_dim,
hidden_size,
n_layers,
dropout_r=0.1,
cell_type='GRU',
bii=False,
time=False,
preTrainEmb='',
packPadMode=False):
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=n_layers,
dropout_r=dropout_r,
cell_type=cell_type,
bii=False,
time=time,
preTrainEmb=preTrainEmb,
packPadMode=False)
#super(DRNN, self).__init__()
#The additional parameters that norma RNNs don't have
self.dilations = [2**i for i in range(n_layers)]
self.layers = nn.ModuleList([])
if self.bi == 2:
print(
'DRNN only supports 1-direction, implementing 1-direction instead'
)
self.bi = 1 #Enforcing 1-directional
self.packPadMode = False #Enforcing no packpadded indicator
for i in range(n_layers):
if i == 0:
c = self.cell(self.in_size,
self.hidden_size,
dropout=self.dropout_r)
else:
c = self.cell(self.hidden_size,
self.hidden_size,
dropout=self.dropout_r)
self.layers.append(c)
self.cells = nn.Sequential(*self.layers)
def __init__(self, input_size, embed_dim, hidden_size, n_layers):
EHREmbeddings.__init__(self,
input_size=input_size,
embed_dim=embed_dim,
hidden_size=hidden_size)
self.embed_dim = embed_dim
self.RNN1 = nn.RNN(embed_dim,
hidden_size,
1,
batch_first=True,
bidirectional=True)
self.RNN2 = nn.RNN(embed_dim,
hidden_size,
1,
batch_first=True,
bidirectional=True)
self.wa = nn.Linear(hidden_size * 2, 1, bias=False)
self.Wb = nn.Linear(hidden_size * 2, hidden_size, bias=False)
self.W_out = nn.Linear(hidden_size, n_layers, bias=False)
self.sigmoid = nn.Sigmoid()
def __init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=1,
dropout_r=0.1,
cell_type='GRU',
bii=False,
time=False,
preTrainEmb='',
packPadMode=True):
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=n_layers,
dropout_r=dropout_r,
cell_type=cell_type,
bii=bii,
time=time,
preTrainEmb=preTrainEmb,
packPadMode=packPadMode)
def __init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=1,
dropout_r=0.1,
cell_type='TLSTM',
bii=False,
time=True,
preTrainEmb=''):
EHREmbeddings.__init__(self, input_size, embed_dim, hidden_size,
n_layers, dropout_r, cell_type, time,
preTrainEmb)
EHREmbeddings.__init__(self,
input_size,
embed_dim,
hidden_size,
n_layers=n_layers,
dropout_r=dropout_r,
cell_type=cell_type,
bii=False,
time=True,
preTrainEmb=preTrainEmb,
packPadMode=False)
#test on EHR_TLSTM() parameters please
#modify something here to make sure everything runs correctly
'''ask laila if i Implemented the right model parameters regarding, time, bii, and pretrained,
'''
if self.cell_type != 'TLSTM' or self.bi != 1:
print(
"TLSTM only supports Time aware LSTM cell type and 1 direction. Implementing corrected parameters instead"
)
self.cell_type = 'TLSTM'
self.bi = 1 #enforcing 1 directional
self.packPadMode = False
def EmbedPatient_MB(self, input, mtd):
return EHREmbeddings.EmbedPatients_MB(self, input, mtd)
def EmbedPatient_SMB(self, input):
return EHREmbeddings.EmbedPatients_SMB(self, input)