# -*- coding: utf-8 -*-
# Author: Zhi Qiao <mingshan_ai@163.com>
# License: BSD 2 clause
import os
import torch
import torch.nn as nn
import pickle
import warnings
from ._loss import callLoss
from ._dlbase import BaseControler
warnings.filterwarnings('ignore')
[docs]class callPredictor(nn.Module):
def __init__(self,
input_size = None,
layer_hidden_sizes = [10,20,15],
num_layers = 3,
bias = True,
dropout = 0.5,
bidirectional = True,
batch_first = True,
label_size = 1):
super(callPredictor, self).__init__()
assert input_size != None and isinstance(input_size, int), 'fill in correct input_size'
self.num_layers = num_layers
self.rnn_models = nn.ModuleList([])
if bidirectional:
layer_input_sizes = [input_size] + [2 * chs for chs in layer_hidden_sizes]
else:
layer_input_sizes = [input_size] + layer_hidden_sizes
for i in range(num_layers):
self.rnn_models.append(nn.LSTM(input_size = layer_input_sizes[i],
hidden_size = layer_hidden_sizes[i],
num_layers = num_layers,
bias = bias,
dropout = dropout,
bidirectional = bidirectional,
batch_first = batch_first))
self.label_size = label_size
self.output_size = layer_input_sizes[-1]
self.output_func = nn.Linear(self.output_size, self.label_size)
[docs] def forward(self, input_data):
"""
Parameters
----------
input_data = {
'X': shape (batchsize, n_timestep, n_featdim)
'M': shape (batchsize, n_timestep)
'cur_M': shape (batchsize, n_timestep)
'T': shape (batchsize, n_timestep)
}
Return
----------
all_output, shape (batchsize, n_timestep, n_labels)
predict output of each time step
cur_output, shape (batchsize, n_labels)
predict output of last time step
"""
X = input_data['X']
M = input_data['M']
cur_M = input_data['cur_M']
_data = X
for temp_rnn_model in self.rnn_models:
_data, _ = temp_rnn_model(_data)
outputs = _data
all_output = outputs * M.unsqueeze(-1)
n_batchsize, n_timestep, n_featdim = all_output.shape
all_output = self.output_func(outputs.reshape(n_batchsize*n_timestep, n_featdim)).reshape(n_batchsize, n_timestep, self.label_size)
cur_output = (all_output * cur_M.unsqueeze(-1)).sum(dim=1)
return all_output, cur_output
[docs]class LSTM(BaseControler):
def __init__(self,
expmodel_id = 'test.new',
n_epoch = 100,
n_batchsize = 5,
learn_ratio = 1e-4,
weight_decay = 1e-4,
n_epoch_saved = 1,
layer_hidden_sizes = [10,20,15],
bias = True,
dropout = 0.5,
bidirectional = True,
batch_first = True,
loss_name = 'L1LossSigmoid',
target_repl = False,
target_repl_coef = 0.,
aggregate = 'sum',
optimizer_name = 'adam',
use_gpu = False,
gpu_ids = '0'
):
"""
Applies a multi-layer long short-term memory (LSTM) RNN to an healthcare data sequence.
Parameters
----------
exp_id : str, optional (default='init.test')
name of current experiment
n_epoch : int, optional (default = 100)
number of epochs with the initial learning rate
n_batchsize : int, optional (default = 5)
batch size for model training
learn_ratio : float, optional (default = 1e-4)
initial learning rate for adam
weight_decay : float, optional (default = 1e-4)
weight decay (L2 penalty)
n_epoch_saved : int, optional (default = 1)
frequency of saving checkpoints at the end of epochs
layer_hidden_sizes : list, optional (default = [10,20,15])
The number of features of the hidden state h of each layer
num_layers : int, optional (default = 1)
Number of recurrent layers. E.g., setting num_layers=2 would
mean stacking two LSTMs together to form a stacked LSTM, with
the second LSTM taking in outputs of the first LSTM and computing
the final results.
bias : bool, optional (default = True)
If False, then the layer does not use bias weights b_ih and b_hh.
dropout : float, optional (default = 0.5)
If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer,
with dropout probability equal to dropout.
bidirectional : bool, optional (default = True)
If True, becomes a bidirectional LSTM.
batch_first : bool, optional (default = False)
If True, then the input and output tensors are provided as (batch, seq, feature).
loss_name : str, optional (default='SigmoidCELoss')
Name or objective function.
use_gpu : bool, optional (default=False)
If yes, use GPU recources; else use CPU recources
gpu_ids : str, optional (default='')
If yes, assign concrete used gpu ids such as '0,2,6'; else use '0'
"""
super(LSTM, self).__init__(expmodel_id)
self.n_batchsize = n_batchsize
self.n_epoch = n_epoch
self.learn_ratio = learn_ratio
self.weight_decay = weight_decay
self.n_epoch_saved = n_epoch_saved
self.layer_hidden_sizes = layer_hidden_sizes
self.num_layers = len(layer_hidden_sizes)
self.bias = bias
self.dropout = dropout
self.bidirectional = bidirectional
self.batch_first = batch_first
self.loss_name = loss_name
self.target_repl = target_repl
self.target_repl_coef = target_repl_coef
self.aggregate = aggregate
self.optimizer_name = optimizer_name
self.use_gpu = use_gpu
self.gpu_ids = gpu_ids
self._args_check()
def _build_model(self):
"""
Build the crucial components for model training
"""
if self.is_loadmodel is False:
_config = {
'input_size': self.input_size,
'layer_hidden_sizes': self.layer_hidden_sizes,
'num_layers': self.num_layers,
'bias': self.bias,
'dropout': self.dropout,
'bidirectional': self.bidirectional,
'batch_first': self.batch_first,
'label_size': self.label_size
}
self.predictor = callPredictor(**_config).to(self.device)
self._save_predictor_config(_config)
if self.dataparallal:
self.predictor= torch.nn.DataParallel(self.predictor)
self.criterion = callLoss(task = self.task_type,
loss_name = self.loss_name,
target_repl = self.target_repl,
target_repl_coef = self.target_repl_coef,
aggregate = self.aggregate)
self.optimizer = self._get_optimizer(self.optimizer_name)
[docs] def fit(self, train_data, valid_data, assign_task_type = None):
"""
Parameters
----------
train_data : {
'x':list[episode_file_path],
'y':list[label],
'l':list[seq_len],
'feat_n': n of feature space,
'label_n': n of label space
}
The input train samples dict.
valid_data : {
'x':list[episode_file_path],
'y':list[label],
'l':list[seq_len],
'feat_n': n of feature space,
'label_n': n of label space
}
The input valid samples dict.
assign_task_type: str (default = None)
predifine task type to model mapping <feature, label>
current support ['binary','multiclass','multilabel','regression']
Returns
-------
self : object
Fitted estimator.
"""
self.task_type = assign_task_type
self._data_check([train_data, valid_data])
self._build_model()
train_reader = self._get_reader(train_data, 'train')
valid_reader = self._get_reader(valid_data, 'valid')
self._fit_model(train_reader, valid_reader)
[docs] def load_model(self,
loaded_epoch = '',
config_file_path = '',
model_file_path = ''):
"""
Parameters
----------
loaded_epoch : str, loaded model name
we save the model by <epoch_count>.epoch, latest.epoch, best.epoch
Returns
-------
self : object
loaded estimator.
"""
predictor_config = self._load_predictor_config(config_file_path)
self.predictor = callPredictor(**predictor_config).to(self.device)
self._load_model(loaded_epoch, model_file_path)
def _args_check(self):
"""
Check args whether valid/not and give tips
"""
assert isinstance(self.n_batchsize,int) and self.n_batchsize>0, \
'fill in correct n_batchsize (int, >0)'
assert isinstance(self.n_epoch,int) and self.n_epoch>0, \
'fill in correct n_epoch (int, >0)'
assert isinstance(self.learn_ratio,float) and self.learn_ratio>0., \
'fill in correct learn_ratio (float, >0.)'
assert isinstance(self.weight_decay,float) and self.weight_decay>=0., \
'fill in correct weight_decay (float, >=0.)'
assert isinstance(self.n_epoch_saved,int) and self.n_epoch_saved>0 and self.n_epoch_saved < self.n_epoch, \
'fill in correct n_epoch (int, >0 and <{0}).format(self.n_epoch)'
assert isinstance(self.layer_hidden_sizes,list) and len(self.layer_hidden_sizes)>0, \
'fill in correct layer_hidden_sizes (list, such as [10,20,15])'
assert isinstance(self.num_layers,int) and self.num_layers>0, \
'fill in correct num_layers (int, >0)'
assert isinstance(self.bias,bool), \
'fill in correct bias (bool)'
assert isinstance(self.dropout,float) and self.dropout>0. and self.dropout<1., \
'fill in correct learn_ratio (float, >0 and <1.)'
assert isinstance(self.bidirectional,bool), \
'fill in correct bidirectional (bool)'
assert isinstance(self.batch_first,bool), \
'fill in correct batch_first (bool)'
assert isinstance(self.target_repl,bool), \
'fill in correct target_repl (bool)'
assert isinstance(self.target_repl_coef,float) and self.target_repl_coef>=0. and self.target_repl_coef<=1., \
'fill in correct target_repl_coef (float, >=0 and <=1.)'
assert isinstance(self.aggregate,str) and self.aggregate in ['sum','avg'], \
'fill in correct aggregate (str, [\'sum\',\'avg\'])'
assert isinstance(self.optimizer_name,str) and self.optimizer_name in ['adam'], \
'fill in correct optimizer_name (str, [\'adam\'])'
assert isinstance(self.loss_name,str), \
'fill in correct optimizer_name (str)'
assert isinstance(self.use_gpu,bool), \
'fill in correct use_gpu (bool)'
assert isinstance(self.gpu_ids,str), \
'fill in correct use_gpu (str, \'0,2,7\')'
self.device = self._get_device()