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models.py
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from networkx import dijkstra_predecessor_and_distance
from numpy import argmin
import torch
import torch.nn as nn
from copy import deepcopy
import torch.nn.functional as F
class SLearner(nn.Module):
"""
Single learner with treatment as covariates
"""
def __init__(self, input_dim, hparams):
super(SLearner, self).__init__()
out_backbone = hparams.get('dim_backbone', '100,100').split(',')
out_task = hparams.get('dim_task', '50').split(',')
in_backbone = [input_dim + 1] + list(map(int, out_backbone))
in_task = [in_backbone[-1]] + list(map(int, out_task))
self.backbone = torch.nn.Sequential()
for i in range(1, len(in_backbone)):
self.backbone.add_module(f"backbone_dense{i}", torch.nn.Linear(in_backbone[i-1], in_backbone[i]))
self.backbone.add_module(f"backbone_relu{i}", torch.nn.LeakyReLU())
self.backbone.add_module(f"backbone_drop{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
self.tower = torch.nn.Sequential()
for i in range(1, len(in_task)):
self.tower.add_module(f"tower_dense{i}", torch.nn.Linear(in_task[i-1], in_task[i]))
self.tower.add_module(f"tower_relu{i}", torch.nn.LeakyReLU())
self.tower.add_module(f"tower_dropout{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
self.output = torch.nn.Sequential()
self.output.add_module("output_dense", torch.nn.Linear(in_task[-1], 1))
self.rep_1 = None
self.rep_0 = None
def forward(self, x):
covariates = x[:, :-1]
t = x[:, -1]
covariates = torch.cat([covariates, t.reshape([-1, 1])], dim=-1)
rep = self.backbone(covariates)
self.rep_1 = rep[t == 1]
self.rep_0 = rep[t == 0]
out = self.tower(rep)
out = self.output(out)
return out
class TLearner(nn.Module):
"""
Two learner with covariates in different groups modeled isolatedly.
"""
def __init__(self, input_dim, hparams):
super(TLearner, self).__init__()
out_backbone= hparams.get('dim_backbone', '32,16').split(',')
out_task = hparams.get('dim_task', '16').split(',')
in_backbone = [input_dim] + list(map(int, out_backbone))
in_task = [in_backbone[-1]] + list(map(int, out_task))
self.backbone_1 = torch.nn.Sequential()
for i in range(1, len(in_backbone)):
self.backbone_1.add_module(f"backbone_dense{i}", torch.nn.Linear(in_backbone[i-1], in_backbone[i]))
self.backbone_1.add_module(f"backbone_relu{i}", torch.nn.LeakyReLU())
self.backbone_1.add_module(f"backbone_dropout{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
self.tower_1 = torch.nn.Sequential()
for i in range(1, len(in_task)):
self.tower_1.add_module(f"tower_dense{i}", torch.nn.Linear(in_task[i-1], in_task[i]))
self.tower_1.add_module(f"tower_relu{i}", torch.nn.LeakyReLU())
self.tower_1.add_module(f"tower_dropout{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
self.output_1 = torch.nn.Sequential()
self.output_1.add_module("output_dense", torch.nn.Linear(in_task[-1], 1))
self.backbone_0 = deepcopy(self.backbone_1)
self.tower_0 = deepcopy(self.tower_1)
self.output_0 = deepcopy(self.output_1)
self.rep_1 = None
self.rep_0 = None
def forward(self, x):
covariates = x[:, :-1]
t = x[:, -1] # shape: (-1)
rep_1 = self.backbone_1(covariates)
rep_0 = self.backbone_0(covariates)
out_1 = self.tower_1(rep_1)
out_0 = self.tower_0(rep_0)
out_1 = self.output_1(out_1)
out_0 = self.output_0(out_0)
self.rep_1 = rep_1[t == 1]
self.rep_0 = rep_0[t == 0]
t = t.reshape(-1, 1)
output_f = t * out_1 + (1 - t) * out_0
return output_f
class GMCFR(nn.Module):
"""
Our proposed GMCFR model.
"""
def __init__(self, input_dim, hparams):
super(GMCFR, self).__init__()
out_backbone = hparams.get('dim_backbone', '32,16').split(',')
out_task = hparams.get('dim_task', '16').split(',')
self.treat_embed = hparams.get('treat_embed', True)
in_backbone = [input_dim] + list(map(int, out_backbone))
print('in_backbone is ' + str(input_dim))
self.backbone = torch.nn.Sequential()
for i in range(1, len(in_backbone)):
self.backbone.add_module(f"backbone_dense{i}", torch.nn.Linear(in_backbone[i-1], in_backbone[i]))
self.backbone.add_module(f"backbone_relu{i}", torch.nn.ELU())
self.backbone.add_module(f"backbone_dropout{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
in_task = [in_backbone[-1]] + list(map(int, out_task))
if self.treat_embed is True:
in_task[0] += 2
self.tower_1 = torch.nn.Sequential()
for i in range(1, len(in_task)):
self.tower_1.add_module(f"tower_dense{i}", torch.nn.Linear(in_task[i-1], in_task[i]))
self.tower_1.add_module(f"tower_relu{i}", torch.nn.ELU())
self.tower_1.add_module(f"tower_dropout{i}", torch.nn.Dropout(p=hparams.get('dropout', 0.1)))
self.output_1 = torch.nn.Sequential()
self.output_1.add_module("output_dense", torch.nn.Linear(in_task[-1], 1))
self.tower_0 = deepcopy(self.tower_1)
self.output_0 = deepcopy(self.output_1)
self.rep_1, self.rep_0 = None, None
self.out_1, self.out_0 = None, None
self.embedding = nn.Embedding(2, 2)
def forward(self, x):
covariates = x[:, :-1]
t = x[:, -1]
rep = self.backbone(covariates)
if self.treat_embed is True:
t_embed = self.embedding(t.int())
rep_t = torch.cat([rep, t_embed], dim=-1)
else:
rep_t = rep
self.rep_1 = rep[t == 1]
self.rep_0 = rep[t == 0]
self.out_1 = self.output_1(self.tower_1(rep_t))
self.out_0 = self.output_0(self.tower_0(rep_t))
t = t.reshape(-1, 1)
output_f = t * self.out_1 + (1 - t) * self.out_0
return output_f