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utils.py
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import torch
from sklearn.metrics import roc_auc_score, mean_squared_error, f1_score
import numpy as np
from .augmentations import embed_data_mask
import torch.nn as nn
def make_default_mask(x):
mask = np.ones_like(x)
mask[:, -1] = 0
return mask
def tag_gen(tag, y):
return np.repeat(tag, len(y["data"]))
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def get_scheduler(args, optimizer):
if args.scheduler == "cosine":
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)
elif args.scheduler == "linear":
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[args.epochs // 2.667, args.epochs // 1.6, args.epochs // 1.142],
gamma=0.1,
)
return scheduler
def imputations_acc_justy(model, dloader, device):
model.eval()
m = nn.Softmax(dim=1)
y_test = torch.empty(0).to(device)
y_pred = torch.empty(0).to(device)
prob = torch.empty(0).to(device)
with torch.no_grad():
for i, data in enumerate(dloader, 0):
x_categ, x_cont, cat_mask, con_mask = (
data[0].to(device),
data[1].to(device),
data[2].to(device),
data[3].to(device),
)
_, x_categ_enc, x_cont_enc = embed_data_mask(
x_categ, x_cont, cat_mask, con_mask, model
)
reps = model.transformer(x_categ_enc, x_cont_enc)
y_reps = reps[:, model.num_categories - 1, :]
y_outs = model.mlpfory(y_reps)
# import ipdb; ipdb.set_trace()
y_test = torch.cat([y_test, x_categ[:, -1].float()], dim=0)
y_pred = torch.cat([y_pred, torch.argmax(m(y_outs), dim=1).float()], dim=0)
prob = torch.cat([prob, m(y_outs)[:, -1].float()], dim=0)
correct_results_sum = (y_pred == y_test).sum().float()
acc = correct_results_sum / y_test.shape[0] * 100
auc = roc_auc_score(y_score=prob.cpu(), y_true=y_test.cpu())
return acc, auc
def multiclass_acc_justy(model, dloader, device):
model.eval()
vision_dset = True
m = nn.Softmax(dim=1)
y_test = torch.empty(0).to(device)
y_pred = torch.empty(0).to(device)
prob = torch.empty(0).to(device)
with torch.no_grad():
for i, data in enumerate(dloader, 0):
x_categ, x_cont, cat_mask, con_mask = (
data[0].to(device),
data[1].to(device),
data[2].to(device),
data[3].to(device),
)
_, x_categ_enc, x_cont_enc = embed_data_mask(
x_categ, x_cont, cat_mask, con_mask, model, vision_dset
)
reps = model.transformer(x_categ_enc, x_cont_enc)
y_reps = reps[:, model.num_categories - 1, :]
y_outs = model.mlpfory(y_reps)
# import ipdb; ipdb.set_trace()
y_test = torch.cat([y_test, x_categ[:, -1].float()], dim=0)
y_pred = torch.cat([y_pred, torch.argmax(m(y_outs), dim=1).float()], dim=0)
correct_results_sum = (y_pred == y_test).sum().float()
acc = correct_results_sum / y_test.shape[0] * 100
return acc, 0
def get_inference(model, dloader, device, task, vision_dset):
model.eval()
m = nn.Softmax(dim=1)
y_test = torch.empty(0).to(device)
y_pred = torch.empty(0).to(device)
prob = torch.empty(0).to(device)
with torch.no_grad():
for i, data in enumerate(dloader, 0):
x_categ, x_cont, y_gts, cat_mask, con_mask = (
data[0].to(device),
data[1].to(device),
data[2].to(device),
data[3].to(device),
data[4].to(device),
)
_, x_categ_enc, x_cont_enc = embed_data_mask(
x_categ, x_cont, cat_mask, con_mask, model, vision_dset
)
reps = model.transformer(x_categ_enc, x_cont_enc)
y_reps = reps[:, 0, :]
y_outs = model.mlpfory(y_reps)
# import ipdb; ipdb.set_trace()
y_test = torch.cat([y_test, y_gts.squeeze().float()], dim=0)
y_pred = torch.cat([y_pred, torch.argmax(y_outs, dim=1).float()], dim=0)
if task == "binary":
prob = torch.cat([prob, m(y_outs)[:, -1].float()], dim=0)
return y_pred.cpu(), prob.cpu(), y_test.cpu()
def classification_scores(model, dloader, device, task, vision_dset):
model.eval()
m = nn.Softmax(dim=1)
y_test = torch.empty(0).to(device)
y_pred = torch.empty(0).to(device)
prob = torch.empty(0).to(device)
with torch.no_grad():
for i, data in enumerate(dloader, 0):
x_categ, x_cont, y_gts, cat_mask, con_mask = (
data[0].to(device),
data[1].to(device),
data[2].to(device),
data[3].to(device),
data[4].to(device),
)
_, x_categ_enc, x_cont_enc = embed_data_mask(
x_categ, x_cont, cat_mask, con_mask, model, vision_dset
)
reps = model.transformer(x_categ_enc, x_cont_enc)
y_reps = reps[:, 0, :]
y_outs = model.mlpfory(y_reps)
# import ipdb; ipdb.set_trace()
y_test = torch.cat([y_test, y_gts.squeeze().float()], dim=0)
y_pred = torch.cat([y_pred, torch.argmax(y_outs, dim=1).float()], dim=0)
if task == "binary":
prob = torch.cat([prob, m(y_outs)[:, -1].float()], dim=0)
correct_results_sum = (y_pred == y_test).sum().float()
acc = correct_results_sum / y_test.shape[0] * 100
auc = 0
f1 = 0
if task == "binary":
auc = roc_auc_score(y_score=prob.cpu(), y_true=y_test.cpu())
f1 = f1_score(y_test.cpu(), y_pred.cpu())
return acc.cpu().numpy(), auc, f1
def mean_sq_error(model, dloader, device, vision_dset):
model.eval()
y_test = torch.empty(0).to(device)
y_pred = torch.empty(0).to(device)
with torch.no_grad():
for i, data in enumerate(dloader, 0):
x_categ, x_cont, y_gts, cat_mask, con_mask = (
data[0].to(device),
data[1].to(device),
data[2].to(device),
data[3].to(device),
data[4].to(device),
)
_, x_categ_enc, x_cont_enc = embed_data_mask(
x_categ, x_cont, cat_mask, con_mask, model, vision_dset
)
reps = model.transformer(x_categ_enc, x_cont_enc)
y_reps = reps[:, 0, :]
y_outs = model.mlpfory(y_reps)
y_test = torch.cat([y_test, y_gts.squeeze().float()], dim=0)
y_pred = torch.cat([y_pred, y_outs], dim=0)
# import ipdb; ipdb.set_trace()
rmse = mean_squared_error(y_test.cpu(), y_pred.cpu(), squared=False)
return rmse