collate_fn=data_utils.custom_collate_fn,
shuffle=True,
batch_size=100,
num_workers=4)
dl_val = DataLoader(dataset=data_val,
collate_fn=data_utils.custom_collate_fn,
shuffle=False,
batch_size=len(data_val),
num_workers=1)
## the neural negative feedback with observation jumps
model = gru_ode_bayes.NNFOwithBayesianJumps(
input_size=params_dict["input_size"],
hidden_size=params_dict["hidden_size"],
p_hidden=params_dict["p_hidden"],
prep_hidden=params_dict["prep_hidden"],
logvar=params_dict["logvar"],
mixing=params_dict["mixing"],
full_gru_ode=params_dict["full_gru_ode"],
solver=params_dict["solver"],
impute=params_dict["impute"])
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.0005)
epoch_max = 200
params_dict = dict()
#Training
for epoch in range(epoch_max):
model.train()
optimizer.zero_grad()
def train_gruode_mimic(simulation_name,
params_dict,
device,
train_idx,
val_idx,
test_idx,
epoch_max=40,
binned60=False):
dir_path = r"D:/mimic_iii/clean_data/"
if binned60:
##csv_file_path = "../../Datasets/MIMIC/Processed_MIMIC60.csv"
##csv_file_tags = "../../Datasets/MIMIC/MIMIC_tags60.csv"
csv_file_path = dir_path + r"GRU_ODE_Dataset.csv"
csv_file_tags = dir_path + r"GRU_ODE_death_tags.csv"
else:
##csv_file_path = "../../Datasets/MIMIC/Processed_MIMIC.csv"
##csv_file_tags = "../../Datasets/MIMIC/MIMIC_tags.csv"
#print("Todo: make simplified Dataset")
#sys.exit()
csv_file_path = dir_path + r"GRU_ODE_Dataset.csv"
csv_file_tags = dir_path + r"GRU_ODE_death_tags.csv"
if params_dict["no_cov"]:
csv_file_cov = None
else:
if binned60:
#csv_file_cov = "../../Datasets/MIMIC/MIMIC_covs60.csv"
csv_file_cov = dir_path + r"GRU_ODE_covariates.csv"
else:
#csv_file_cov = "../../Datasets/MIMIC/MIMIC_covs.csv"
csv_file_cov = dir_path + r"GRU_ODE_covariates.csv"
N = pd.read_csv(csv_file_tags)["ID"].nunique()
if params_dict["lambda"] == 0:
validation = True
val_options = {"T_val": 75, "max_val_samples": 3}
else:
validation = False
val_options = None
if params_dict["lambda"] == 0:
#logger = Logger(f'../../Logs/Regression/{simulation_name}')
logger = Logger(
f'D:/mimic_iii/clean_data/Logs/gru_ode_bayse/Regression/{simulation_name}'
)
else:
#logger = Logger(f'../../Logs/Classification/{simulation_name}')
logger = Logger(
f'D:/mimic_iii/clean_data/Logs/gru_ode_bayse/Classification/{simulation_name}'
)
data_train = data_utils.ODE_Dataset(csv_file=csv_file_path,
label_file=csv_file_tags,
cov_file=csv_file_cov,
idx=train_idx)
data_val = data_utils.ODE_Dataset(csv_file=csv_file_path,
label_file=csv_file_tags,
cov_file=csv_file_cov,
idx=val_idx,
validation=validation,
val_options=val_options)
data_test = data_utils.ODE_Dataset(csv_file=csv_file_path,
label_file=csv_file_tags,
cov_file=csv_file_cov,
idx=test_idx,
validation=validation,
val_options=val_options)
dl = DataLoader(dataset=data_train,
collate_fn=data_utils.custom_collate_fn,
shuffle=True,
batch_size=150,
num_workers=5)
dl_val = DataLoader(dataset=data_val,
collate_fn=data_utils.custom_collate_fn,
shuffle=True,
batch_size=150)
dl_test = DataLoader(dataset=data_test,
collate_fn=data_utils.custom_collate_fn,
shuffle=True,
batch_size=150) #len(test_idx))
params_dict["input_size"] = data_train.variable_num
params_dict["cov_size"] = data_train.cov_dim
model_dir = dir_path + "trained_models/"
if not os.path.exists(model_dir):
os.makedirs(model_dir)
np.save(model_dir + f"/{simulation_name}_params.npy", params_dict)
nnfwobj = gru_ode_bayes.NNFOwithBayesianJumps(
input_size=params_dict["input_size"],
hidden_size=params_dict["hidden_size"],
p_hidden=params_dict["p_hidden"],
prep_hidden=params_dict["prep_hidden"],
logvar=params_dict["logvar"],
mixing=params_dict["mixing"],
classification_hidden=params_dict["classification_hidden"],
cov_size=params_dict["cov_size"],
cov_hidden=params_dict["cov_hidden"],
dropout_rate=params_dict["dropout_rate"],
full_gru_ode=params_dict["full_gru_ode"],
impute=params_dict["impute"])
nnfwobj.to(device)
optimizer = torch.optim.Adam(nnfwobj.parameters(),
lr=params_dict["lr"],
weight_decay=params_dict["weight_decay"])
class_criterion = torch.nn.BCEWithLogitsLoss(reduction='sum')
print("Start Training")
val_metric_prev = -1000
for epoch in range(epoch_max):
nnfwobj.train()
total_train_loss = 0
auc_total_train = 0
for i, b in enumerate(tqdm.tqdm(dl)):
optimizer.zero_grad()
times = b["times"]
time_ptr = b["time_ptr"]
X = b["X"].to(device)
M = b["M"].to(device)
obs_idx = b["obs_idx"]
cov = b["cov"].to(device)
labels = b["y"].to(device)
batch_size = labels.size(0)
h0 = 0 # torch.zeros(labels.shape[0], params_dict["hidden_size"]).to(device)
hT, loss, class_pred, loss_pre, loss_post = nnfwobj(
times,
time_ptr,
X,
M,
obs_idx,
delta_t=params_dict["delta_t"],
T=params_dict["T"],
cov=cov)
total_loss = (loss + params_dict["lambda"] *
class_criterion(class_pred, labels)) / batch_size
total_train_loss += total_loss
try:
auc_total_train += roc_auc_score(
labels.detach().cpu(),
torch.sigmoid(class_pred).detach().cpu())
except ValueError:
print("Single CLASS ! AUC is erroneous")
pass
total_loss.backward()
optimizer.step()
info = {
'training_loss': total_train_loss.detach().cpu().numpy() / (i + 1),
'AUC_training': auc_total_train / (i + 1)
}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch)
data_utils.adjust_learning_rate(optimizer, epoch, params_dict["lr"])
with torch.no_grad():
nnfwobj.eval()
total_loss_val = 0
auc_total_val = 0
loss_val = 0
mse_val = 0
corr_val = 0
pre_jump_loss = 0
post_jump_loss = 0
num_obs = 0
for i, b in enumerate(dl_val):
times = b["times"]
time_ptr = b["time_ptr"]
X = b["X"].to(device)
M = b["M"].to(device)
obs_idx = b["obs_idx"]
cov = b["cov"].to(device)
labels = b["y"].to(device)
batch_size = labels.size(0)
if b["X_val"] is not None:
X_val = b["X_val"].to(device)
M_val = b["M_val"].to(device)
times_val = b["times_val"]
times_idx = b["index_val"]
h0 = 0 #torch.zeros(labels.shape[0], params_dict["hidden_size"]).to(device)
hT, loss, class_pred, t_vec, p_vec, h_vec, _, _, loss1, loss2 = nnfwobj(
times,
time_ptr,
X,
M,
obs_idx,
delta_t=params_dict["delta_t"],
T=params_dict["T"],
cov=cov,
return_path=True)
total_loss = (loss + params_dict["lambda"] *
class_criterion(class_pred, labels)) / batch_size
try:
auc_val = roc_auc_score(labels.cpu(),
torch.sigmoid(class_pred).cpu())
except ValueError:
auc_val = 0.5
print("Only one class : AUC is erroneous")
pass
if params_dict["lambda"] == 0:
t_vec = np.around(
t_vec,
str(params_dict["delta_t"])[::-1].find('.')).astype(
np.float32
) #Round floating points error in the time vector.
p_val = data_utils.extract_from_path(
t_vec, p_vec, times_val, times_idx)
m, v = torch.chunk(p_val, 2, dim=1)
last_loss = (data_utils.log_lik_gaussian(X_val, m, v) *
M_val).sum()
mse_loss = (torch.pow(X_val - m, 2) * M_val).sum()
corr_val_loss = data_utils.compute_corr(X_val, m, M_val)
loss_val += last_loss.cpu().numpy()
num_obs += M_val.sum().cpu().numpy()
mse_val += mse_loss.cpu().numpy()
corr_val += corr_val_loss.cpu().numpy()
else:
num_obs = 1
pre_jump_loss += loss1.cpu().detach().numpy()
post_jump_loss += loss2.cpu().detach().numpy()
total_loss_val += total_loss.cpu().detach().numpy()
auc_total_val += auc_val
loss_val /= num_obs
mse_val /= num_obs
info = {
'validation_loss': total_loss_val / (i + 1),
'AUC_validation': auc_total_val / (i + 1),
'loglik_loss': loss_val,
'validation_mse': mse_val,
'correlation_mean': np.nanmean(corr_val),
'correlation_max': np.nanmax(corr_val),
'correlation_min': np.nanmin(corr_val),
'pre_jump_loss': pre_jump_loss / (i + 1),
'post_jump_loss': post_jump_loss / (i + 1)
}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch)
if params_dict["lambda"] == 0:
val_metric = -loss_val
else:
val_metric = auc_total_val / (i + 1)
if val_metric > val_metric_prev:
print(
f"New highest validation metric reached ! : {val_metric}")
print("Saving Model")
torch.save(
nnfwobj.state_dict(),
dir_path + f"trained_models/{simulation_name}_MAX.pt")
val_metric_prev = val_metric
test_loglik, test_auc, test_mse = test_evaluation(
nnfwobj, params_dict, class_criterion, device, dl_test)
print(f"Test loglik loss at epoch {epoch} : {test_loglik}")
print(f"Test AUC loss at epoch {epoch} : {test_auc}")
print(f"Test MSE loss at epoch{epoch} : {test_mse}")
else:
if epoch % 10:
torch.save(
nnfwobj.state_dict(),
dir_path + f"trained_models/{simulation_name}.pt")
print(
f"Total validation loss at epoch {epoch}: {total_loss_val/(i+1)}")
print(f"Validation AUC at epoch {epoch}: {auc_total_val/(i+1)}")
print(
f"Validation loss (loglik) at epoch {epoch}: {loss_val:.5f}. MSE : {mse_val:.5f}. Correlation : {np.nanmean(corr_val):.5f}. Num obs = {num_obs}"
)
print(
f"Finished training GRU-ODE for MIMIC. Saved in /trained_models/{simulation_name}"
)
return (info, val_metric_prev, test_loglik, test_auc, test_mse)
def plot_trained_model(model_name="paper_random_r",
format_image="pdf",
random_r=True,
max_lag=0,
jitter=0,
random_theta=False,
data_type="double_OU"):
style = "fill"
summary_dict = np.load(f"./../trained_models/{model_name}_params.npy",
allow_pickle=True).item()
params_dict = summary_dict["model_params"]
metadata = summary_dict["metadata"]
if type(params_dict) == np.ndarray:
## converting np array to dictionary:
params_dict = params_dict.tolist()
#Loading model
model = gru_ode_bayes.NNFOwithBayesianJumps(
input_size=params_dict["input_size"],
hidden_size=params_dict["hidden_size"],
p_hidden=params_dict["p_hidden"],
prep_hidden=params_dict["prep_hidden"],
logvar=params_dict["logvar"],
mixing=params_dict["mixing"],
full_gru_ode=params_dict["full_gru_ode"],
impute=params_dict["impute"],
solver=params_dict["solver"],
store_hist=True)
model.load_state_dict(torch.load(f"./../trained_models/{model_name}.pt"))
model.eval()
#Test data :
N = 10
T = metadata["T"]
delta_t = metadata["delta_t"]
theta = metadata.pop("theta", None)
sigma = metadata["sigma"]
rho = metadata["rho"]
r_mu = metadata.pop("r_mu", None)
sample_rate = metadata["sample_rate"]
sample_rate = 1
dual_sample_rate = metadata["dual_sample_rate"]
r_std = metadata.pop("r_std", None)
#print(f"R std :{r_std}")
max_lag = metadata.pop("max_lag", None)
if data_type == "double_OU":
T = 6
df = double_OU.OU_sample(T=T,
dt=delta_t,
N=N,
sigma=0.1,
theta=theta,
r_mu=r_mu,
r_std=r_std,
rho=rho,
sample_rate=sample_rate,
dual_sample_rate=dual_sample_rate,
max_lag=max_lag,
random_theta=random_theta,
full=True,
seed=432)
## for 10 time-points
times_1 = [1.0, 2.0, 4.0, 5.0, 7.0, 7.5]
times_2 = [2.0, 3.0, 4.0, 6.0]
else:
df = gru_ode_bayes.datasets.BXLator.datagen.BXL_sample(
T=metadata["T"],
dt=metadata["delta_t"],
N=N,
sigma=metadata["sigma"],
a=0.3,
b=1.4,
rho=metadata["rho"],
sample_rate=10,
dual_sample_rate=1,
full=True)
## for 10 time-points
times_1 = [2.0, 5.0, 12.0, 15.0, 23.0, 32.0, 35.0, 41.0, 43.0]
times_2 = [1.0, 7.0, 12.0, 15.0, 25.0, 32.0, 38.0, 45.0]
times = np.union1d(times_1, times_2)
obs = df.loc[df["Time"].isin(times)].copy()
obs[["Mask_1", "Mask_2"]] = 0
obs.loc[df["Time"].isin(times_1), "Mask_1"] = 1
obs.loc[df["Time"].isin(times_2), "Mask_2"] = 1
data = data_utils.ODE_Dataset(panda_df=obs, jitter_time=jitter)
dl = DataLoader(dataset=data,
collate_fn=data_utils.custom_collate_fn,
shuffle=False,
batch_size=1)
with torch.no_grad():
for sample, b in enumerate(dl):
times = b["times"]
time_ptr = b["time_ptr"]
X = b["X"]
M = b["M"]
obs_idx = b["obs_idx"]
cov = b["cov"]
y = b["y"]
hT, loss, _, t_vec, p_vec, _, eval_times, eval_vals = model(
times,
time_ptr,
X,
M,
obs_idx,
delta_t=delta_t,
T=T,
cov=cov,
return_path=True)
if params_dict["solver"] == "dopri5":
p_vec = eval_vals
t_vec = eval_times.cpu().numpy()
observations = X.detach().numpy()
m, v = torch.chunk(p_vec[:, 0, :], 2, dim=1)
if params_dict["logvar"]:
up = m + torch.exp(0.5 * v) * 1.96
down = m - torch.exp(0.5 * v) * 1.96
else:
up = m + torch.sqrt(v) * 1.96
down = m - torch.sqrt(v) * 1.96
plots_dict = dict()
plots_dict["t_vec"] = t_vec
plots_dict["up"] = up.numpy()
plots_dict["down"] = down.numpy()
plots_dict["m"] = m.numpy()
plots_dict["observations"] = observations
plots_dict["mask"] = M.cpu().numpy()
fill_colors = [cm.Blues(0.25), cm.Greens(0.25)]
line_colors = [cm.Blues(0.6), cm.Greens(0.6)]
colors = ["blue", "green"]
## sde trajectory
df_i = df.query(f"ID == {sample}")
plt.figure(figsize=(6.4, 4.8))
if style == "fill":
for dim in range(2):
plt.fill_between(x=t_vec,
y1=down[:, dim].numpy(),
y2=up[:, dim].numpy(),
facecolor=fill_colors[dim],
alpha=1.0,
zorder=1)
plt.plot(t_vec,
m[:, dim].numpy(),
color=line_colors[dim],
linewidth=2,
zorder=2,
label=f"Dimension {dim+1}")
observed_idx = np.where(plots_dict["mask"][:, dim] == 1)[0]
plt.scatter(times[observed_idx],
observations[observed_idx, dim],
color=colors[dim],
alpha=0.5,
s=60)
plt.plot(df_i.Time,
df_i[f"Value_{dim+1}"],
":",
color=colors[dim],
linewidth=1.5,
alpha=0.8,
label="_nolegend_")
else:
for dim in range(2):
plt.plot(t_vec,
up[:, dim].numpy(),
"--",
color="red",
linewidth=2)
plt.plot(t_vec,
down[:, dim].numpy(),
"--",
color="red",
linewidth=2)
plt.plot(t_vec,
m[:, dim].numpy(),
color=colors[dim],
linewidth=2)
observed_idx = np.where(plots_dict["mask"][:, dim] == 1)[0]
plt.scatter(times[observed_idx],
observations[observed_idx, dim],
color=colors[dim],
alpha=0.5,
s=60)
plt.plot(df_i.Time,
df_i[f"Value_{dim+1}"],
":",
color=colors[dim],
linewidth=1.5,
alpha=0.8)
#plt.title("Test trajectory of a double OU process")
plt.xlabel("Time")
plt.grid()
plt.legend(loc="lower right")
plt.ylabel("Predicton (+/- 1.96 st. dev)")
fname = f"{model_name}_sample{sample}_{style}.{format_image}"
plt.tight_layout()
plt.savefig(fname)
plt.close()
print(f"Saved sample into '{fname}'.")
