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utils.py
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2398 lines (1954 loc) · 117 KB
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import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
import pickle
import random
import math
from torch.utils.data import Dataset
from torch.distributions import Normal
from collections import defaultdict
from prettytable import PrettyTable
## Data----------------------------------------------------------------------------------------
class Tabledata(Dataset):
def __init__(self, args, data, scale='minmax', binary_t=False):
self.use_treatment = args.use_treatment
# padding tensors
self.diff_tensor = torch.zeros([124,1])
if args.use_treatment:
if not args.single_treatment:
self.cont_tensor = torch.zeros([124,3])
else:
self.cont_tensor = torch.zeros([124,4])
else:
self.cont_tensor = torch.zeros([124,5])
self.cat_tensor = torch.zeros([124,7])
yd=[]
for _, group in data.groupby('cluster'):
yd.append(group[['y', 'd']].tail(1))
yd = pd.concat(yd)
for c in ["age", "dis", "danger", "CT_R", "CT_E"]:
# dis : 0~6
# danger : 3~11
minmax_col(data, c) if scale == 'minmax' else meanvar_col(data, c)
if scale == 'minmax':
self.a_y, self.b_y = minmax_col(yd,"y")
self.a_d, self.b_d = minmax_col(yd,"d")
elif scale =='meanvar':
self.a_y, self.b_y = meanvar_col(yd, "y")
self.a_d, self.b_d = meanvar_col(yd, "d")
self.cluster = data.iloc[:,0].values.astype('float32')
if not binary_t:
self.treatment = data[['dis', 'danger']].values.astype('float32') if not args.single_treatment else data['danger'].values.astype('float32')
else:
raise('do not use binary t')
print("use binary t")
self.treatment = (data['dis'].values >= 0.5).astype('float32')
if args.use_treatment:
drop_col = ['dis'] if args.single_treatment else ['dis', 'danger']
self.cont_X = data.iloc[:, 1:6].drop(columns=drop_col).values.astype('float32')
else:
self.cont_X = data.iloc[:, 1:6].values.astype('float32')
self.cat_X = data.iloc[:, 6:13].astype('category')
self.diff_days = data.iloc[:, 13].values.astype('float32')
# y label tukey transformation
# self.y = yd.values.astype('float32')
y = torch.tensor(yd['y'].values.astype('float32'))
d = torch.tensor(yd['d'].values.astype('float32'))
if args.tukey:
y = tukey_transformation(y, args)
d = tukey_transformation(d, args)
self.yd = torch.stack([y, d], dim=1)
self.cat_cols = self.cat_X.columns
self.cat_map = {col: {cat: i for i, cat in enumerate(self.cat_X[col].cat.categories)} for col in self.cat_cols}
self.cat_X = self.cat_X.apply(lambda x: x.cat.codes)
self.cat_X = torch.from_numpy(self.cat_X.to_numpy()).long()
def __len__(self):
return len(np.unique(self.cluster))
def __getitem__(self, index):
'''
[batch x padding x embedding].
cont_tensor_p: padded patient-related continuous data
cont_tensor_c: padded cluster-related continuous data
cat_tensor_p: patient-related discrete data with padding
cat_tensor_c: Padded cluster related discrete data
data_len : Return data of the number of valid patients by cluster
y : Correct answer label
diff_tensor: cluster-specific effective date return data
'''
diff_days = torch.from_numpy(self.diff_days[self.cluster == index]).unsqueeze(1)
diff_tensor = self.diff_tensor.clone()
diff_tensor[:diff_days.shape[0]] = diff_days
cont_X = torch.from_numpy(self.cont_X[self.cluster == index])
data_len = cont_X.shape[0]
cont_tensor = self.cont_tensor.clone()
cont_tensor[:cont_X.shape[0],] = cont_X
cat_X = self.cat_X[self.cluster == index]
cat_tensor = self.cat_tensor.clone()
cat_tensor[:cat_X.shape[0],] = cat_X
cat_tensor_p = cat_tensor[:, :5]
cat_tensor_c = cat_tensor[:, 5:]
cont_tensor_p = cont_tensor[:, :3]
cont_tensor_c = cont_tensor[:, 3:]
yd = self.yd[index]
treatment = torch.mean(torch.tensor(self.treatment[self.cluster == index]), dim=0) # t1: dis|t2: danger
return cont_tensor_p, cont_tensor_c, cat_tensor_p, cat_tensor_c, data_len, yd, diff_tensor, treatment
import torch
from torch.utils.data import Dataset
import pandas as pd
class MunicipalTabledata(Dataset):
def __init__(self, args, data, scale='minmax', binary_t=False):
self.use_treatment = args.use_treatment
# Determine the number of categorical and continuous columns dynamically
self.cat_feature_count = len([
'gender',
'birthyear_category_1930-1959',
'birthyear_category_1960-1989',
'birthyear_category_1990s+',
'birthyear_category_Before 1930'
])
self.cont_feature_count = len(['test_R', 'test_E', 'symptom', 'distance', 'danger'])
MAX_CLUSTER_SIZE = args.municipal_max_cluster
# Padding tensors
self.diff_tensor = torch.zeros([MAX_CLUSTER_SIZE, 1])
# if args.use_treatment:
# if not args.single_treatment:
# self.cont_tensor = torch.zeros([MAX_CLUSTER_SIZE, self.cont_feature_count - 2])
# else:
# self.cont_tensor = torch.zeros([MAX_CLUSTER_SIZE, self.cont_feature_count - 1])
# else:
# self.cont_tensor = torch.zeros([MAX_CLUSTER_SIZE, self.cont_feature_count])
self.cont_tensor = torch.zeros([MAX_CLUSTER_SIZE, self.cont_feature_count])
self.cat_tensor = torch.zeros([MAX_CLUSTER_SIZE, self.cat_feature_count])
yd = []
for _, group in data.groupby('transmission_cluster'):
yd.append(group[['y', 'd']].tail(1))
yd = pd.concat(yd)
# Data preprocessing
# Normalize continuous data
for c in ["test_R", "test_E", "distance", "danger"]:
minmax_col(data, c) if scale == 'minmax' else meanvar_col(data, c)
# Store values for inverse transformation
if scale == 'minmax':
self.a_y, self.b_y = minmax_col(yd, "y")
self.a_d, self.b_d = minmax_col(yd, "d")
elif scale == 'meanvar':
self.a_y, self.b_y = meanvar_col(yd, "y")
self.a_d, self.b_d = meanvar_col(yd, "d")
# Feature classification and storage
self.cluster = data['transmission_cluster'].values.astype('float32')
if not binary_t:
self.treatment = data[['distance', 'danger']].values.astype('float32') if not args.single_treatment else data['danger'].values.astype('float32')
else:
raise ValueError('Binary treatment is not supported')
# if args.use_treatment:
# if not args.single_treatment:
# self.cont_X = data[['test_R', 'test_E', 'symptom']].values.astype('float32')
# else:
# self.cont_X = data[['test_R', 'test_E', 'symptom', 'distance']].values.astype('float32')
# else:
# self.cont_X = data[['test_R', 'test_E', 'symptom', 'distance', 'danger']].values.astype('float32')
self.cont_X = data[['test_R', 'test_E', 'symptom', 'distance', 'danger']].values.astype('float32')
self.cat_X = data[[
'gender',
'birthyear_category_1930-1959',
'birthyear_category_1960-1989',
'birthyear_category_1990s+',
'birthyear_category_Before 1930'
]].astype('category')
self.diff_days = data['diff_days'].values.astype('float32')
# Tukey transformation for labels
y = torch.tensor(yd['y'].values.astype('float32'))
d = torch.tensor(yd['d'].values.astype('float32'))
if args.tukey:
y = tukey_transformation(y, args)
d = tukey_transformation(d, args)
self.yd = torch.stack([y, d], dim=1)
# Categorize and encode categorical data
self.cat_cols = self.cat_X.columns
self.cat_map = {col: {cat: i for i, cat in enumerate(self.cat_X[col].cat.categories)} for col in self.cat_cols}
self.cat_X = self.cat_X.apply(lambda x: x.cat.codes)
self.cat_X = torch.from_numpy(self.cat_X.to_numpy()).long()
def __len__(self):
return len(np.unique(self.cluster))
def __getitem__(self, index):
"""
Args:
index: Cluster index
Returns:
cont_tensor_p: Padded continuous patient data
cont_tensor_c: Padded continuous cluster data
cat_tensor_p: Padded categorical patient data
cat_tensor_c: Padded categorical cluster data
data_len: Number of valid patients in the cluster
y: Label
diff_tensor: Valid dates for the cluster
treatment: Mean treatment values for the cluster
"""
diff_days = torch.from_numpy(self.diff_days[self.cluster == index]).unsqueeze(1)
diff_tensor = self.diff_tensor.clone()
diff_tensor[:diff_days.shape[0]] = diff_days
cont_X = torch.from_numpy(self.cont_X[self.cluster == index])
data_len = cont_X.shape[0]
cont_tensor = self.cont_tensor.clone()
cont_tensor[:cont_X.shape[0]] = cont_X
cat_X = self.cat_X[self.cluster == index]
cat_tensor = self.cat_tensor.clone()
cat_tensor[:cat_X.shape[0]] = cat_X
# Dynamically adjust slicing ranges
cat_tensor_p = cat_tensor[:, :self.cat_feature_count]
cat_tensor_c = torch.zeros([cat_tensor.shape[0], 0]) # Placeholder for consistency
cont_tensor_p = cont_tensor[:, :3]
cont_tensor_c = cont_tensor[:, 3:]
yd = self.yd[index]
treatment = torch.mean(torch.tensor(self.treatment[self.cluster == index]), dim=0)
return cont_tensor_p, cont_tensor_c, cat_tensor_p, cat_tensor_c, data_len, yd, diff_tensor, treatment
class SyntheticDataset(Dataset):
def __init__(self, args, data_frames):
self.use_treatment = args.use_treatment
self.single_treatment = args.single_treatment
self.X1 = torch.tensor(data_frames['X1'].values, dtype=torch.float32)
self.X2 = torch.tensor(data_frames['X2'].values, dtype=torch.float32)
self.X3 = torch.tensor(data_frames['X3'].values, dtype=torch.float32)
self.X4 = torch.tensor(data_frames['X4'].values, dtype=torch.float32)
self.T1 = torch.tensor(data_frames['T1'].values, dtype=torch.float32)
self.T2 = torch.tensor(data_frames['T2'].values, dtype=torch.float32)
self.Y = torch.tensor(data_frames['Y'].values, dtype=torch.float32)
if args.scaling == 'minmax':
self.Y, self.a_y, self.b_y = minmax_tensor(self.Y)
self.T1, self.a_t1, self.b_t1 = minmax_tensor(self.T1)
self.T2, self.a_t2, self.b_t2 = minmax_tensor(self.T2)
self.X1, _, _ = minmax_tensor(self.X1)
self.X2, _, _ = minmax_tensor(self.X2)
self.X3, _, _ = minmax_tensor(self.X3)
self.X4, _, _ = minmax_tensor(self.X4)
self.a_d, self.b_d = None, None
if not args.use_treatment:
self.X1 = torch.stack([self.X1, self.X2], dim=-1)
self.X2 = torch.stack([self.T1, self.T2], dim=-1)
def __len__(self):
return len(self.Y)
def __getitem__(self, idx):
x1 = self.X1[idx]
x2 = self.X2[idx]
x3 = self.X3[idx]
x4 = self.X4[idx]
y = self.Y[idx]
# y = torch.sum(self.P[idx])
t1 = self.T1[idx]
t2 = self.T2[idx]
t = torch.cat((t1.unsqueeze(0), t2.unsqueeze(0)), dim=0) if not self.single_treatment else t2
index_tensor = torch.tensor([0, 0, 0, 0, 0], dtype=torch.float).cuda()
if self.use_treatment:
x1 = x1.unsqueeze(-1)
x2 = x2.unsqueeze(-1)
x3 = x3.unsqueeze(-1)
x4 = x4.unsqueeze(-1)
x1_t = torch.zeros(5, x1.shape[0])
x1_t[0, :] = x1
x2_t = torch.zeros(5 ,x2.shape[0])
x2_t[0, :] = x2
x3_t = torch.zeros(5, 1)
x3_t[0, :] = x3
x4_t = torch.zeros(5, 1)
x4_t[0, :] = x4
return x1_t, x2_t, x3_t, x4_t, x1.shape[0], torch.stack([y,torch.zeros_like(y)]).cuda(), index_tensor, t
## MinMax Scaling Functions ------------------------------------
def minmax_col(data, name):
minval, maxval = data[name].min(), data[name].max()
if maxval == minval:
data[name] = 0
else:
data[name] = (data[name] - minval) / (maxval - minval)
return minval, maxval
def minmax_tensor(tensor):
minvals = tensor.min()
maxvals = tensor.max()
normalized = (tensor - minvals) / (maxvals - minvals)
return normalized, minvals, maxvals
def restore_minmax(data, minv, maxv):
minv=0 if minv==None else minv
maxv=0 if maxv==None else maxv
data = (data * (maxv - minv)) + minv
return data
class SyntheticTimeSeriesDataset(Dataset):
def __init__(self, args, data_frames):
self.use_treatment = args.use_treatment
self.X1 = torch.tensor(np.array([df['X1'].values for df in data_frames]), dtype=torch.float32)
self.X2 = torch.tensor(np.array([df['X2'].values for df in data_frames]), dtype=torch.float32)
self.X3 = torch.tensor(np.array([df['X3'].values for df in data_frames]), dtype=torch.float32)
self.X4 = torch.tensor(np.array([df['X4'].values for df in data_frames]), dtype=torch.float32)
self.T1 = torch.tensor(np.array([df['T1'].values for df in data_frames]), dtype=torch.float32)
self.T2 = torch.tensor(np.array([df['T2'].values for df in data_frames]), dtype=torch.float32)
# self.P = torch.tensor(np.array([df['P'].values for df in data_frames]), dtype=torch.float32)
self.Y = torch.tensor(np.array([df['Y'].values for df in data_frames]), dtype=torch.float32)[:,0]
if args.scaling == 'minmax':
self.Y, self.a_y, self.b_y = minmax_tensor(self.Y)
self.a_d, self.b_d = None, None
if not args.use_treatment:
self.X1 = torch.stack([self.X1, self.X2], dim=-1)
self.X2 = torch.stack([self.T1, self.T2], dim=-1)
def __len__(self):
return len(self.Y)
def __getitem__(self, idx):
x1 = self.X1[idx]
x2 = self.X2[idx]
x3 = self.X3[idx]
x4 = self.X4[idx]
y = self.Y[idx]
# y = torch.sum(self.P[idx])
t1 = self.T1[idx]
t2 = self.T2[idx]
t = torch.cat((t1.unsqueeze(0), t2.unsqueeze(0)), dim=0)
index_tensor = torch.tensor([0, 1, 2, 3, 4], dtype=torch.float).cuda()
x1_length = x1.shape[0]
if self.use_treatment:
x1 = x1.unsqueeze(-1)
x2 = x2.unsqueeze(-1)
x3 = x3.unsqueeze(-1)
x4 = x4.unsqueeze(-1)
return x1, x2, x3, x4, x1_length, torch.stack([y,torch.zeros_like(y)]).cuda(), index_tensor, torch.mean(t, dim=-1)
## MinMax Scaling Functions ------------------------------------
def minmax_col(data, name):
minval , maxval = data[name].min(), data[name].max()
data[name]=(data[name]-data[name].min())/(data[name].max()-data[name].min())
return minval, maxval
def minmax_tensor(tensor):
minvals = tensor.min()
maxvals = tensor.max()
normalized = (tensor - minvals) / (maxvals - minvals)
return normalized, minvals, maxvals
def restore_minmax(data, minv, maxv):
minv=0 if minv==None else minv
maxv=0 if maxv==None else maxv
data = (data * (maxv - minv)) + minv
return data
# ---------------------------------------------------------------
## Normalization Scaling Functions ---------------------------------
def meanvar_col(data, name):
mean_val = data[name].mean()
std_val = data[name].var()
data[name]=(data[name]-data[name].mean())/data[name].var()
return mean_val, std_val
def restore_meanvar(data, mean, var):
data = data * var + mean
return data
# ----------------------------------------------------------------
## Loss ----------------------------------------------------------------------------------------
class RMSELoss(nn.Module):
def __init__(self, reduction):
super(RMSELoss,self).__init__()
self.mse = nn.MSELoss(reduction=reduction)
self.eps = 1e-12
def forward(self, target, pred):
x = torch.sqrt(self.mse(target, pred) + self.eps)
return x
# ---------------------------------------------------------------------------------------------
## Train --------------------------------------------------------------------------------------
def train(args, data, model, optimizer, criterion, epoch, warmup_iter=0, lamb=0.0, aux_criterion=None, use_treatment=False, eval_criterion = None, scaling="minmax",a_y=None, b_y=None, a_d=None, b_d=None, pred_model="enc", binary_t=False, lambdas=[1,1,1]):
eval_loss_y = None; eval_loss_d=None
model.train()
optimizer.zero_grad()
batch_num, cont_p, cont_c, cat_p, cat_c, len, y, diff_days, *t = data_load(data)
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
eval_loss_t1 = None; eval_loss_t2 = None; pred_loss=0; kl_loss=0; recon_loss=0
if use_treatment:
gt_t = t[0] # t1: dis|t2: danger
x, x_reconstructed, (enc_yd_pred, enc_t_pred), (dec_yd_pred, dec_t_pred), (z_mu, z_logvar) = out
if args.model == 'cevt':
loss, *ind_losses = cevtransformer_loss(x_reconstructed, x, enc_t_pred, enc_yd_pred[:, 0], enc_yd_pred[:, 1], dec_t_pred, dec_yd_pred[:, 0], dec_yd_pred[:, 1], z_mu, z_logvar, gt_t, y[:,0] , y[:,1], criterion, lambdas, val_len=len)
elif args.model == 'cevae':
loss, *ind_losses = cevae_loss(x_reconstructed, x, enc_t_pred, enc_yd_pred[:, 0], enc_yd_pred[:, 1], dec_t_pred, dec_yd_pred[:, 0], dec_yd_pred[:, 1], z_mu, z_logvar, gt_t, y[:,0] , y[:,1], criterion, lambdas, val_len=len)
(enc_loss_y, enc_loss_d), (dec_loss_y, dec_loss_d), (enc_loss_t, dec_loss_t), (pred_loss, kl_loss, recon_loss) = ind_losses
else:
loss_d = criterion(out[:,0], y[:,0])
loss_y = criterion(out[:,1], y[:,1])
loss = loss_d + loss_y
if eval_criterion != None:
if use_treatment:
if args.model=='cevt' or 'cevae':
# enc loss
enc_pred_y, enc_pred_d, gt_y, gt_d = reverse_scaling(scaling, enc_yd_pred, y, a_y, b_y, a_d, b_d)
enc_eval_loss_y = eval_criterion(enc_pred_y, gt_y)
enc_eval_loss_d = eval_criterion(enc_pred_d, gt_d)
if args.single_treatment:
enc_eval_loss_t2 = eval_criterion(enc_t_pred[:,0].squeeze(), gt_t)
enc_eval_loss_t1 = torch.zeros_like(enc_eval_loss_t2)
else:
enc_eval_loss_t1 = eval_criterion(enc_t_pred[:,0].squeeze(), gt_t[:,0])
enc_eval_loss_t2 = eval_criterion(enc_t_pred[:,1].squeeze(), gt_t[:,1])
# dec loss
dec_pred_y, dec_pred_d, gt_y, gt_d = reverse_scaling(scaling, dec_yd_pred, y, a_y, b_y, a_d, b_d)
dec_eval_loss_y = eval_criterion(dec_pred_y, gt_y)
dec_eval_loss_d = eval_criterion(dec_pred_d, gt_d)
if args.single_treatment:
dec_eval_loss_t2 = eval_criterion(dec_t_pred[:,0].squeeze(), gt_t)
dec_eval_loss_t1 = torch.zeros_like(dec_eval_loss_t2)
else:
dec_eval_loss_t1 = eval_criterion(dec_t_pred[:,0].squeeze(), gt_t[:,0])
dec_eval_loss_t2 = eval_criterion(dec_t_pred[:,1].squeeze(), gt_t[:,1])
if enc_eval_loss_y + enc_eval_loss_d > dec_eval_loss_y + dec_eval_loss_d:
eval_loss_y, eval_loss_d = dec_eval_loss_y, dec_eval_loss_d
out = dec_yd_pred
loss_y = dec_loss_y
loss_d = dec_loss_d
eval_loss_t1 = dec_eval_loss_t1
eval_loss_t2 = dec_eval_loss_t2
eval_model = "Decoder"
else:
eval_loss_y, eval_loss_d = enc_eval_loss_y, enc_eval_loss_d
out = enc_yd_pred
loss_y = enc_loss_y
loss_d = enc_loss_d
eval_loss_t1 = enc_eval_loss_t1
eval_loss_t2 = enc_eval_loss_t2
eval_model = "Encoder"
elif args.model == 'iTransformer':
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, out, y, a_y, b_y, a_d, b_d)
eval_loss_y = eval_criterion(pred_y, gt_y)
eval_loss_d = eval_criterion(pred_d, gt_d)
eval_model = "nan"
else:
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, out, y, a_y, b_y, a_d, b_d)
eval_loss_y = eval_criterion(pred_y, gt_y)
eval_loss_d = eval_criterion(pred_d, gt_d)
eval_model = "nan"
# Add Penalty term for ridge regression
if lamb != 0.0:
loss += lamb * torch.norm(model.linear1.weight, p=2)
if not torch.isnan(loss):
loss.backward()
optimizer.step()
return loss_d.item(), loss_y.item(), batch_num, out, y, eval_loss_y, eval_loss_d, eval_model, (eval_loss_t1, eval_loss_t2), (pred_loss, kl_loss, recon_loss)
else:
# return 0, batch_num, out, y
import pdb; pdb.set_trace()
raise ValueError("Loss raised nan.")
def cevtransformer_lagging_loss(
model,
cont_p, cont_c, cat_p, cat_c, val_len, diff_days,
t, y, d,
criterion,
lambdas,
optimizer_enc,
optimizer_dec,
lagging_num=1,
t_loss=True
):
sum_enc_y, sum_enc_d = 0.0, 0.0
for _ in range(lagging_num):
# a) zero grad φ
optimizer_enc.zero_grad()
# b) forward
x, x_recon, (enc_yd, enc_t), (dec_yd, dec_t), (z_mu, z_logvar) = \
model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
enc_y_pred, enc_d_pred = enc_yd[:,0], enc_yd[:,1]
dec_y_pred, dec_d_pred = dec_yd[:,0], dec_yd[:,1]
# c) ELBO
total_i, (e_y, e_d), _, _, _ = cevtransformer_loss(
x_recon, x,
enc_t, enc_y_pred, enc_d_pred,
dec_t, dec_y_pred, dec_d_pred,
z_mu, z_logvar,
t, y, d,
criterion,
lambdas
)
# d) backward & φ step
total_i.backward()
optimizer_enc.step()
# e) logging
sum_enc_y += e_y.item()
sum_enc_d += e_d.item()
avg_enc_losses = (sum_enc_y / lagging_num,
sum_enc_d / lagging_num)
# --- 2) outer loop: θ 1 ---
optimizer_dec.zero_grad()
# a) fresh forward
x, x_recon, (enc_yd, enc_t), (dec_yd, dec_t), (z_mu, z_logvar) = \
model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
enc_y_pred, enc_d_pred = enc_yd[:,0], enc_yd[:,1]
dec_y_pred, dec_d_pred = dec_yd[:,0], dec_yd[:,1]
# b) ELBO
total, _, dec_losses, t_losses, misc_losses = cevtransformer_loss(
x_recon, x,
enc_t, enc_y_pred, enc_d_pred,
dec_t, dec_y_pred, dec_d_pred,
z_mu, z_logvar,
t, y, d,
criterion,
lambdas
)
# c) backward & θ step
total.backward()
optimizer_dec.step()
return total, avg_enc_losses, dec_losses, t_losses, misc_losses
def train_with_lagging(args, data, model, criterion,
epoch, warmup_iter=0, lamb=0.0, aux_criterion=None,
use_treatment=False, eval_criterion=None, scaling="minmax",
a_y=None, b_y=None, a_d=None, b_d=None,
pred_model="enc", binary_t=False, lambdas=[1,1,1],
optimizer_enc=None, optimizer_dec=None, lagging_num=1):
"""
Train function with lagging support, preserving original return signature.
Returns:
loss_d, loss_y, batch_num, out, y,
eval_loss_y, eval_loss_d, eval_model,
(eval_loss_t1, eval_loss_t2), (pred_loss, kl_loss, recon_loss)
"""
model.train()
# Unpack and forward
batch_num, cont_p, cont_c, cat_p, cat_c, val_len, y, diff_days, *t = data_load(data)
x, x_recon, (enc_yd_pred, enc_t_pred), (dec_yd_pred, dec_t_pred), (z_mu, z_logvar) = \
model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
if use_treatment and args.model=='cevt':
# apply lagging via cevtransformer_loss
total, enc_losses, dec_losses, t_losses, misc_losses = cevtransformer_lagging_loss(
model,
cont_p, cont_c, cat_p, cat_c, val_len, diff_days,
t[0], y[:,0], y[:,1],
criterion, lambdas,
optimizer_enc, optimizer_dec, lagging_num,
t_loss=True
)
# unpack losses for logging and return
pred_loss, kl_loss, recon_loss = misc_losses
# for training return, use dec_losses and enc_losses for metrics
loss_y, loss_d = dec_losses[0], dec_losses[1]
else:
# fallback to original logic
if use_treatment:
# cevae or other models
total, enc_losses, dec_losses, t_losses, misc_losses = cevtransformer_lagging_loss(
model,
cont_p, cont_c, cat_p, cat_c, val_len, diff_days,
t[0], y[:,0], y[:,1],
criterion, lambdas,
optimizer_enc, optimizer_dec, lagging_num,
t_loss=True
)
pred_loss, kl_loss, recon_loss = misc_losses
loss_y, loss_d = dec_losses[0], dec_losses[1]
else:
loss_d = criterion(x[:,0], y[:,0])
loss_y = criterion(x[:,1], y[:,1])
total = loss_d+loss_y
pred_loss=kl_loss=recon_loss=None
enc_pred_y, enc_pred_d, gt_y, gt_d = reverse_scaling(scaling, enc_yd_pred, y, a_y, b_y, a_d, b_d)
enc_eval_loss_y = eval_criterion(enc_pred_y, gt_y)
enc_eval_loss_d = eval_criterion(enc_pred_d, gt_d)
dec_pred_y, dec_pred_d, gt_y, gt_d = reverse_scaling(scaling, dec_yd_pred, y, a_y, b_y, a_d, b_d)
dec_eval_loss_y = eval_criterion(dec_pred_y, gt_y)
dec_eval_loss_d = eval_criterion(dec_pred_d, gt_d)
if enc_eval_loss_y + enc_eval_loss_d > dec_eval_loss_y + dec_eval_loss_d:
eval_loss_y, eval_loss_d = dec_eval_loss_y, dec_eval_loss_d
out = dec_yd_pred
loss_y = dec_losses[0]
loss_d = dec_losses[1]
dec_eval_loss_t1 = eval_criterion(dec_t_pred[:,0].squeeze(), t[0][:,0])
dec_eval_loss_t2 = eval_criterion(dec_t_pred[:,1].squeeze(), t[0][:,1])
eval_loss_t1 = dec_eval_loss_t1
eval_loss_t2 = dec_eval_loss_t2
eval_model = "Decoder"
else:
eval_loss_y, eval_loss_d = enc_eval_loss_y, enc_eval_loss_d
out = enc_yd_pred
loss_y = enc_losses[0]
loss_d = enc_losses[1]
enc_eval_loss_t1 = eval_criterion(enc_t_pred[:,0].squeeze(), t[0][:,0])
enc_eval_loss_t2 = eval_criterion(enc_t_pred[:,1].squeeze(), t[0][:,1])
eval_loss_t1 = enc_eval_loss_t1
eval_loss_t2 = enc_eval_loss_t2
eval_model = "Encoder"
# optimizer step if not already inside cevtransformer_loss
if not (optimizer_enc and optimizer_dec):
if lamb!=0.0:
total += lamb * torch.norm(model.linear1.weight, p=2)
total.backward()
# return original signature
return loss_d, loss_y, batch_num, out, y, eval_loss_y, eval_loss_d, eval_model, (eval_loss_t1, eval_loss_t2), (pred_loss, kl_loss, recon_loss)
## Validation --------------------------------------------------------------------------------
@torch.no_grad()
def valid(args, data, model, eval_criterion, scaling, a_y, b_y, a_d, b_d, use_treatment=False, MC_sample=1):
model.eval()
batch_num, cont_p, cont_c, cat_p, cat_c, len, y, diff_days, *rest = data_load(data)
accumulated_outputs = [0] * 6 # (x, x_reconstructed, enc_yd_pred, enc_t_pred, dec_yd_pred, dec_t_pred)
if use_treatment:
if args.model =='cevt' or 'cevae':
gt_t = rest[0]
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days, is_MAP=True)
x, x_reconstructed, (enc_yd_pred, enc_t_pred), (dec_yd_pred, dec_t_pred), (z_mu, z_logvar) = out
# enc loss
enc_pred_y, enc_pred_d, gt_y, gt_d = reverse_scaling(scaling, enc_yd_pred, y, a_y, b_y, a_d, b_d)
enc_loss_y = eval_criterion(enc_pred_y, gt_y)
enc_loss_d = eval_criterion(enc_pred_d, gt_d)
if args.single_treatment:
enc_loss_t2 = eval_criterion(enc_t_pred[:,0].squeeze(), gt_t)
enc_loss_t1 = torch.zeros_like(enc_loss_t2)
else:
enc_loss_t1 = eval_criterion(enc_t_pred[:,0].squeeze(), gt_t[:,0])
enc_loss_t2 = eval_criterion(enc_t_pred[:,1].squeeze(), gt_t[:,1])
# dec loss
dec_pred_y, dec_pred_d, gt_y, gt_d = reverse_scaling(scaling, dec_yd_pred, y, a_y, b_y, a_d, b_d)
dec_loss_y = eval_criterion(dec_pred_y, gt_y)
dec_loss_d = eval_criterion(dec_pred_d, gt_d)
# dec_loss_t = eval_criterion(dec_t_pred.squeeze(), gt_t)
if args.single_treatment:
dec_loss_t2 = eval_criterion(dec_t_pred[:,0].squeeze(), gt_t)
dec_loss_t1 = torch.zeros_like(dec_loss_t2)
else:
dec_loss_t1 = eval_criterion(dec_t_pred[:,0].squeeze(), gt_t[:,0])
dec_loss_t2 = eval_criterion(dec_t_pred[:,1].squeeze(), gt_t[:,1])
if enc_loss_y + enc_loss_d > dec_loss_y + dec_loss_d:
loss_y, loss_d, loss_t1, loss_t2 = dec_loss_y, dec_loss_d, dec_loss_t1, dec_loss_t2
out = dec_yd_pred
eval_model = "Decoder"
else:
loss_y, loss_d, loss_t1, loss_t2 = enc_loss_y, enc_loss_d, enc_loss_t1, enc_loss_t2
out = enc_yd_pred
eval_model = "Encoder"
elif args.model=='iTransformer':
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, out, y, a_y, b_y, a_d, b_d)
loss_y = eval_criterion(pred_y, gt_y)
loss_d = eval_criterion(pred_d, gt_d)
loss = loss_y + loss_d
if not torch.isnan(loss):
return loss_d.item(), loss_y.item(), batch_num, out, y
else:
return 0, batch_num, out, y
else:
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, out, y, a_y, b_y, a_d, b_d)
loss_y = eval_criterion(pred_y, gt_y)
loss_d = eval_criterion(pred_d, gt_d)
eval_model = "nan"
loss = loss_y + loss_d
if not torch.isnan(loss):
if use_treatment:
return loss_d.item(), loss_y.item(), batch_num, out, y, eval_model, loss_t1, loss_t2
else:
return loss_d.item(), loss_y.item(), batch_num, out, y, eval_model
else:
return 0, batch_num, out, y
## Test ----------------------------------------------------------------------------------------
@torch.no_grad()
def test(args, data, model, scaling, a_y, b_y, a_d, b_d, use_treatment=False, MC_sample=1):
criterion_mae = nn.L1Loss(reduction="sum")
criterion_rmse = nn.MSELoss(reduction="sum")
model.eval()
batch_num, cont_p, cont_c, cat_p, cat_c, len, y, diff_days, *rest = data_load(data)
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
max_unique_tensor = torch.tensor([batch.unique().max() for batch in diff_days], device='cuda:0') + 1
accumulated_outputs = [0] * 6 # (x, x_reconstructed, enc_yd_pred, enc_t_pred, dec_yd_pred, dec_t_pred)
if use_treatment:
gt_t = rest[0]
if args.model=='cevt' or 'cevae':
for i in range(MC_sample):
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
x, x_reconstructed, (enc_yd_pred, enc_t_pred), (dec_yd_pred, dec_t_pred), (z_mu, z_logvar) = out
# accumulate predictions
outputs = [x, x_reconstructed, enc_yd_pred, enc_t_pred, dec_yd_pred, dec_t_pred]
accumulated_outputs = [accumulated + output for accumulated, output in zip(accumulated_outputs, outputs)]
# calculate average
avg_outputs = [accumulated / MC_sample for accumulated in accumulated_outputs]
x, x_reconstructed, enc_yd_pred, enc_t_pred, dec_yd_pred, dec_t_pred = avg_outputs
# enc loss
enc_pred_y, enc_pred_d, gt_y, gt_d = reverse_scaling(scaling, enc_yd_pred, y, a_y, b_y, a_d, b_d)
enc_loss_y = criterion_mae(enc_pred_y, gt_y)
enc_loss_d = criterion_mae(enc_pred_d, gt_d)
if args.single_treatment:
enc_loss_t2 = criterion_mae(enc_t_pred[:,0].squeeze(), gt_t)
enc_loss_t1 = torch.zeros_like(enc_loss_t2)
else:
enc_loss_t1 = criterion_mae(enc_t_pred[:,0].squeeze(), gt_t[:,0])
enc_loss_t2 = criterion_mae(enc_t_pred[:,1].squeeze(), gt_t[:,1])
# dec loss
dec_pred_y, dec_pred_d, gt_y, gt_d = reverse_scaling(scaling, dec_yd_pred, y, a_y, b_y, a_d, b_d)
dec_loss_y = criterion_mae(dec_pred_y, gt_y)
dec_loss_d = criterion_mae(dec_pred_d, gt_d)
# dec_loss_t = criterion_mae(dec_t_pred.squeeze(), gt_t)
if args.single_treatment:
dec_loss_t2 = criterion_mae(dec_t_pred[:,0].squeeze(), gt_t)
dec_loss_t1 = torch.zeros_like(dec_loss_t2)
else:
dec_loss_t1 = criterion_mae(dec_t_pred[:,0].squeeze(), gt_t[:,0])
dec_loss_t2 = criterion_mae(dec_t_pred[:,1].squeeze(), gt_t[:,1])
if enc_loss_y + enc_loss_d > dec_loss_y + dec_loss_d:
mae_y, mae_d, loss_t1, loss_t2 = dec_loss_y, dec_loss_d, dec_loss_t1, dec_loss_t2
rmse_y, rmse_d = criterion_rmse(dec_pred_y, gt_y), criterion_rmse(dec_pred_d, gt_d)
out = dec_yd_pred
eval_model = "Decoder"
else:
mae_y, mae_d, loss_t1, loss_t2 = enc_loss_y, enc_loss_d, enc_loss_t1, enc_loss_t2
rmse_y, rmse_d = criterion_rmse(enc_pred_y, gt_y), criterion_rmse(enc_pred_d, gt_d)
out = enc_yd_pred
eval_model = "Encoder"
mae = mae_y + mae_d
rmse = rmse_y + rmse_d
elif args.model == 'iTransformer':
yd_pred = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, yd_pred, yd_true, a_y, b_y, a_d, b_d)
# MAE
mae_y = criterion_mae(pred_y, gt_y)
mae_d = criterion_mae(pred_d, gt_d)
mae = mae_y + mae_d
# RMSE
rmse_y = criterion_rmse(pred_y, gt_y)
rmse_d = criterion_rmse(pred_d, gt_d)
rmse = rmse_y + rmse_d
if not torch.isnan(mae) and not torch.isnan(rmse):
return mae_d.item(), mae_y.item(), rmse_d.item(), rmse_y.item(), batch_num, yd_pred, y
else:
return 0, batch_num, yd_pred, y
else:
out = model(cont_p, cont_c, cat_p, cat_c, len, diff_days)
if out.shape == torch.Size([2]):
out = out.unsqueeze(0)
pred_y, pred_d, gt_y, gt_d = reverse_scaling(scaling, out, y, a_y, b_y, a_d, b_d)
# MAE
mae_y = criterion_mae(pred_y, gt_y)
mae_d = criterion_mae(pred_d, gt_d)
mae = mae_y + mae_d
# RMSE
rmse_y = criterion_rmse(pred_y, gt_y)
rmse_d = criterion_rmse(pred_d, gt_d)
rmse = rmse_y + rmse_d
eval_model = "nan"
if not torch.isnan(mae) and not torch.isnan(rmse):
if use_treatment:
return mae_d.item(), mae_y.item(), rmse_d.item(), rmse_y.item(), batch_num, out, y, loss_t1, loss_t2
else:
return mae_d.item(), mae_y.item(), rmse_d.item(), rmse_y.item(), batch_num, out, y
else:
return 0, batch_num, out, y
@torch.no_grad()
def CE(args, model, dataloader, intervene_var):
model.eval()
data_points_y = []; data_points_d=[]
for data in dataloader:
_, cont_p, cont_c, cat_p, cat_c, val_len, y, diff_days, *rest = data_load(data)
gt_t = rest[0]
if args.use_treatment:
if args.model == 'cevt':
if args.is_synthetic:
# x = model.embedding(cont_p, cont_c, cat_p, cat_c, val_len, diff_days).unsqueeze(1)
(x, diff_days, _), _ = model.embedding(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
else:
(x, diff_days, _), _ = model.embedding(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
src_key_padding_mask = ~(torch.arange(x.size(1)).expand(x.size(0), -1).cuda() < val_len.unsqueeze(1)).cuda()
src_mask = model.generate_square_subsequent_mask(x.size(1)).cuda() if model.unidir else None
# use original prediction with x2t_pred
# _, original_t, original_enc_yd = model.transformer_encoder(x, mask=src_mask, src_key_padding_mask=src_key_padding_mask, val_len=val_len)
# use ground truth t instead of x2t_pred
original_t = gt_t[:,0].unsqueeze(1) if intervene_var == 't1' else gt_t[:,1].unsqueeze(1)
_, _, original_enc_yd = model.transformer_encoder(x, mask=src_mask, src_key_padding_mask=src_key_padding_mask, val_len=val_len, intervene_t=(intervene_var,original_t))
saved_original_t = original_t.clone()
saved_original_enc_yd = original_enc_yd.clone()
if args.is_synthetic:
intervene_t_value_range = range(0, 11)
else:
intervene_t_value_range = range(0, 61) if intervene_var == 't1' else range(30, 111)
for intervene_t_value in [x * 0.1 for x in intervene_t_value_range]:
original_t=saved_original_t.clone()
original_enc_yd=saved_original_enc_yd.clone()
if not args.is_synthetic:
if intervene_var == 't1':
intervene_t_value = intervene_t_value / 6 # t1 norm [0, 6]
elif intervene_var == 't2':
intervene_t_value = (intervene_t_value - 3) / 8 # t2 norm [3, 11]
intervene_t = torch.full((x.size(0),), intervene_t_value, dtype=torch.float).unsqueeze(1).cuda()
_, _, intervene_enc_yd = model.transformer_encoder(x, mask=src_mask, src_key_padding_mask=src_key_padding_mask, val_len=val_len, intervene_t=(intervene_var,intervene_t))
delta_y = original_enc_yd - intervene_enc_yd
delta_t = (original_t - intervene_t)
if not args.is_synthetic:
if intervene_var == 't1':
delta_t = delta_t*6 # denormalize
elif intervene_var == 't2':
delta_t = delta_t*8 # +3 denormalize
else:
t_min = dataloader.dataset.dataset.a_t1 if intervene_var=='t1' else dataloader.dataset.dataset.a_t2
t_max = dataloader.dataset.dataset.b_t1 if intervene_var=='t1' else dataloader.dataset.dataset.b_t2
delta_t = delta_t * (t_max - t_min) + t_min
delta_y, delta_d, _, _ = reverse_scaling(args.scaling, delta_y, y, dataloader.dataset.dataset.a_y, dataloader.dataset.dataset.b_y, dataloader.dataset.dataset.a_d, dataloader.dataset.dataset.b_d)
for i in range(delta_y.size(0)):
data_points_y.append((delta_t[i].item(), delta_y[i].item()))
data_points_d.append((delta_t[i].item(), delta_d[i].item()))
if args.model == 'cevae':
x = model.embedding(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
original_t = gt_t
_, _, original_enc_yd, _ = model.encoder(x, t_gt=original_t)
saved_original_t = original_t.clone()
saved_original_enc_yd = original_enc_yd.clone()
if args.is_synthetic:
intervene_t_value_range = range(0, 11)
else:
intervene_t_value_range = range(0, 61) if intervene_var == 't1' else range(30, 111)
for intervene_t_value in [x * 0.1 for x in intervene_t_value_range]:
original_t=saved_original_t.clone()
original_enc_yd=saved_original_enc_yd.clone()
if intervene_var == 't1':
intervene_t_value = intervene_t_value / 6 # t1 norm [0, 6]
elif intervene_var == 't2':
intervene_t_value = (intervene_t_value - 3) / 8 # t2 norm [3, 11]
intervene_t = torch.full((x.size(0),), intervene_t_value, dtype=torch.float).cuda()
_, _, intervene_enc_yd, _ = model.encoder(x, t_gt=intervene_t)
delta_y = original_enc_yd - intervene_enc_yd
if intervene_var == 't1':
delta_t = (original_t - intervene_t)*6 # denormalize
elif intervene_var == 't2':
delta_t = (original_t - intervene_t)*8 # +3 denormalize
delta_y, delta_d, _, _ = reverse_scaling(args.scaling, delta_y, y, dataloader.dataset.dataset.a_y, dataloader.dataset.dataset.b_y, dataloader.dataset.dataset.a_d, dataloader.dataset.dataset.b_d)
for i in range(delta_y.size(0)):
data_points_y.append((delta_t[i].item(), delta_y[i].item()))
data_points_d.append((delta_t[i].item(), delta_d[i].item()))
else:
original_t = cont_c[:,:,0].clone() if intervene_var=='t1' else cont_c[:,:,1].clone()
if args.model == 'cevt':
_, _, (original_yd, _), (_, _), (_, _) = model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
else:
original_yd = model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days, is_ce=True)
original_yd = torch.clamp(original_yd, 0, 1)
if args.is_synthetic:
intervene_t_value_range = range(0, 11)
else:
intervene_t_value_range = range(0, 61) if intervene_var == 't1' else range(30, 111)
for intervene_t_value in [x * 0.1 for x in intervene_t_value_range]:
if intervene_var == 't1':
intervene_t_value = intervene_t_value / 6 # t1 norm [0, 6]
cont_c[:,:,0] = intervene_t_value
elif intervene_var == 't2':
intervene_t_value = (intervene_t_value - 3) / 8 # t2 norm [3, 11]
cont_c[:,:,1] = intervene_t_value
intervene_yd = model(cont_p, cont_c, cat_p, cat_c, val_len, diff_days)
# for fair comaparison
intervene_yd = torch.clamp(intervene_yd, 0, 1)
delta_y = original_yd - intervene_yd
# delta_t = (original_t[:,0] - intervene_t_value)*6 # denormalize
if intervene_var == 't1':
delta_t = (original_t[:,0] - intervene_t_value)*6 # denormalize
elif intervene_var == 't2':
delta_t = (original_t[:,0] - intervene_t_value)*8 # +3 denormalize
delta_y, delta_d, _, _ = reverse_scaling(args.scaling, delta_y, y, dataloader.dataset.dataset.a_y, dataloader.dataset.dataset.b_y, dataloader.dataset.dataset.a_d, dataloader.dataset.dataset.b_d)
for i in range(delta_y.size(0)):
data_points_y.append((delta_t[i].item(), delta_y[i].item()))
data_points_d.append((delta_t[i].item(), delta_d[i].item()))
def calculate_gradients_and_effect(data_points, method='coef'):
del_t = data_points[:, 0] # delta_t
del_var = data_points[:, 1] # delta_y or delta_d