pypots/forecasting/csdi/model.py
"""
The implementation of CSDI for the partially-observed time-series forecasting task.
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
import os
from typing import Union, Optional
import numpy as np
import torch
from torch.utils.data import DataLoader
try:
import nni
except ImportError:
pass
from .core import _CSDI
from .data import DatasetForForecastingCSDI, TestDatasetForForecastingCSDI
from ..base import BaseNNForecaster
from ...data.checking import key_in_data_set
from ...optim.adam import Adam
from ...optim.base import Optimizer
from ...utils.logging import logger
class CSDI(BaseNNForecaster):
"""The PyTorch implementation of the CSDI model :cite:`tashiro2021csdi`.
Parameters
----------
n_steps :
The number of time steps in the time-series data sample.
n_features :
The number of features in the time-series data sample.
n_pred_steps :
The number of steps in the forecasting time series.
n_pred_features :
The number of features in the forecasting time series.
n_layers :
The number of layers in the CSDI model.
n_heads :
The number of heads in the multi-head attention mechanism.
n_channels :
The number of residual channels.
d_time_embedding :
The dimension number of the time (temporal) embedding.
d_feature_embedding :
The dimension number of the feature embedding.
d_diffusion_embedding :
The dimension number of the diffusion embedding.
is_unconditional :
Whether the model is unconditional or conditional.
target_strategy :
The strategy for selecting the target for the diffusion process. It has to be one of ["mix", "random"].
n_diffusion_steps :
The number of the diffusion step T in the original paper.
schedule:
The schedule for other noise levels. It has to be one of ["quad", "linear"].
beta_start:
The minimum noise level.
beta_end:
The maximum noise level.
batch_size :
The batch size for training and evaluating the model.
epochs :
The number of epochs for training the model.
patience :
The patience for the early-stopping mechanism. Given a positive integer, the training process will be
stopped when the model does not perform better after that number of epochs.
Leaving it default as None will disable the early-stopping.
optimizer :
The optimizer for model training.
If not given, will use a default Adam optimizer.
num_workers :
The number of subprocesses to use for data loading.
`0` means data loading will be in the main process, i.e. there won't be subprocesses.
device :
The device for the model to run on. It can be a string, a :class:`torch.device` object, or a list of them.
If not given, will try to use CUDA devices first (will use the default CUDA device if there are multiple),
then CPUs, considering CUDA and CPU are so far the main devices for people to train ML models.
If given a list of devices, e.g. ['cuda:0', 'cuda:1'], or [torch.device('cuda:0'), torch.device('cuda:1')] , the
model will be parallely trained on the multiple devices (so far only support parallel training on CUDA devices).
Other devices like Google TPU and Apple Silicon accelerator MPS may be added in the future.
saving_path :
The path for automatically saving model checkpoints and tensorboard files (i.e. loss values recorded during
training into a tensorboard file). Will not save if not given.
model_saving_strategy :
The strategy to save model checkpoints. It has to be one of [None, "best", "better", "all"].
No model will be saved when it is set as None.
The "best" strategy will only automatically save the best model after the training finished.
The "better" strategy will automatically save the model during training whenever the model performs
better than in previous epochs.
The "all" strategy will save every model after each epoch training.
verbose :
Whether to print out the training logs during the training process.
"""
def __init__(
self,
n_steps: int,
n_features: int,
n_pred_steps: int,
n_pred_features: int,
n_layers: int,
n_heads: int,
n_channels: int,
d_time_embedding: int,
d_feature_embedding: int,
d_diffusion_embedding: int,
n_diffusion_steps: int = 50,
target_strategy: str = "random",
is_unconditional: bool = False,
schedule: str = "quad",
beta_start: float = 0.0001,
beta_end: float = 0.5,
batch_size: int = 32,
epochs: int = 100,
patience: Optional[int] = None,
optimizer: Optional[Optimizer] = Adam(),
num_workers: int = 0,
device: Optional[Union[str, torch.device, list]] = None,
saving_path: Optional[str] = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
batch_size,
epochs,
patience,
num_workers,
device,
saving_path,
model_saving_strategy,
verbose,
)
assert n_pred_features == n_features, (
f"currently n_pred_features of CSDI forecasting model should be equal to n_features, "
f"but got {n_pred_features} and {n_features}."
)
assert target_strategy in ["mix", "random"]
assert schedule in ["quad", "linear"]
self.n_steps = n_steps
self.n_features = n_features
self.n_pred_steps = n_pred_steps
self.n_pred_features = n_pred_features
self.target_strategy = target_strategy
# set up the model
self.model = _CSDI(
n_features,
n_pred_features,
n_layers,
n_heads,
n_channels,
d_time_embedding,
d_feature_embedding,
d_diffusion_embedding,
is_unconditional,
n_diffusion_steps,
schedule,
beta_start,
beta_end,
)
self._print_model_size()
self._send_model_to_given_device()
# set up the optimizer
self.optimizer = optimizer
self.optimizer.init_optimizer(self.model.parameters())
def _assemble_input_for_training(self, data: list) -> dict:
(
indices,
X_ori,
indicating_mask,
cond_mask,
observed_tp,
feature_id,
) = self._send_data_to_given_device(data)
inputs = {
"X_ori": X_ori.permute(0, 2, 1), # ori observed part for model hint
"indicating_mask": indicating_mask.permute(0, 2, 1), # for loss calc
"cond_mask": cond_mask.permute(0, 2, 1), # for masking X_ori
"observed_tp": observed_tp,
"feature_id": feature_id,
}
return inputs
def _assemble_input_for_validating(self, data: list) -> dict:
return self._assemble_input_for_training(data)
def _assemble_input_for_testing(self, data: list) -> dict:
(
indices,
X,
cond_mask,
observed_tp,
feature_id,
) = self._send_data_to_given_device(data)
inputs = {
"X": X.permute(0, 2, 1), # for model input
"cond_mask": cond_mask.permute(0, 2, 1), # missing mask
"observed_tp": observed_tp,
"feature_id": feature_id,
}
return inputs
def _train_model(
self,
training_loader: DataLoader,
val_loader: DataLoader = None,
) -> None:
# each training starts from the very beginning, so reset the loss and model dict here
self.best_loss = float("inf")
self.best_model_dict = None
try:
training_step = 0
for epoch in range(1, self.epochs + 1):
self.model.train()
epoch_train_loss_collector = []
for idx, data in enumerate(training_loader):
training_step += 1
inputs = self._assemble_input_for_training(data)
self.optimizer.zero_grad()
results = self.model.forward(inputs)
# use sum() before backward() in case of multi-gpu training
results["loss"].sum().backward()
self.optimizer.step()
epoch_train_loss_collector.append(results["loss"].sum().item())
# save training loss logs into the tensorboard file for every step if in need
if self.summary_writer is not None:
self._save_log_into_tb_file(training_step, "training", results)
# mean training loss of the current epoch
mean_train_loss = np.mean(epoch_train_loss_collector)
if val_loader is not None:
self.model.eval()
val_loss_collector = []
with torch.no_grad():
for idx, data in enumerate(val_loader):
inputs = self._assemble_input_for_validating(data)
results = self.model.forward(inputs, training=False, n_sampling_times=0)
val_loss_collector.append(results["loss"].sum().item())
mean_val_loss = np.asarray(val_loss_collector).mean()
# save validation loss logs into the tensorboard file for every epoch if in need
if self.summary_writer is not None:
val_loss_dict = {
"validating_loss": mean_val_loss,
}
self._save_log_into_tb_file(epoch, "validating", val_loss_dict)
logger.info(
f"Epoch {epoch:03d} - "
f"training loss: {mean_train_loss:.4f}, "
f"validation loss: {mean_val_loss:.4f}"
)
mean_loss = mean_val_loss
else:
logger.info(f"Epoch {epoch:03d} - training loss: {mean_train_loss:.4f}")
mean_loss = mean_train_loss
if np.isnan(mean_loss):
logger.warning(f"‼️ Attention: got NaN loss in Epoch {epoch}. This may lead to unexpected errors.")
if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
else:
self.patience -= 1
# save the model if necessary
self._auto_save_model_if_necessary(
confirm_saving=self.best_epoch == epoch and self.model_saving_strategy == "better",
saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss:.4f}",
)
if os.getenv("enable_tuning", False):
nni.report_intermediate_result(mean_loss)
if epoch == self.epochs - 1 or self.patience == 0:
nni.report_final_result(self.best_loss)
if self.patience == 0:
logger.info("Exceeded the training patience. Terminating the training procedure...")
break
except KeyboardInterrupt: # if keyboard interrupt, only warning
logger.warning("‼️ Training got interrupted by the user. Exist now ...")
except Exception as e: # other kind of exception follows below processing
logger.error(f"❌ Exception: {e}")
if self.best_model_dict is None: # if no best model, raise error
raise RuntimeError(
"Training got interrupted. Model was not trained. Please investigate the error printed above."
)
else:
RuntimeWarning(
"Training got interrupted. Please investigate the error printed above.\n"
"Model got trained and will load the best checkpoint so far for testing.\n"
"If you don't want it, please try fit() again."
)
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")
logger.info(f"Finished training. The best model is from epoch#{self.best_epoch}.")
def fit(
self,
train_set: Union[dict, str],
val_set: Optional[Union[dict, str]] = None,
file_type: str = "hdf5",
n_sampling_times: int = 1,
) -> None:
# Step 1: wrap the input data with classes Dataset and DataLoader
training_set = DatasetForForecastingCSDI(
train_set,
file_type=file_type,
)
training_loader = DataLoader(
training_set,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
)
val_loader = None
if val_set is not None:
if not key_in_data_set("X_pred", val_set):
raise ValueError("val_set must contain 'X_pred' for model validation.")
val_set = DatasetForForecastingCSDI(
val_set,
file_type=file_type,
)
val_loader = DataLoader(
val_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
# Step 2: train the model and freeze it
self._train_model(training_loader, val_loader)
self.model.load_state_dict(self.best_model_dict)
self.model.eval() # set the model as eval status to freeze it.
# Step 3: save the model if necessary
self._auto_save_model_if_necessary(confirm_saving=self.model_saving_strategy == "best")
def predict(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
n_sampling_times: int = 1,
) -> dict:
"""
Parameters
----------
test_set : dict or str
The dataset for model validating, should be a dictionary including keys as 'X' and 'y',
or a path string locating a data file.
If it is a dict, X should be array-like of shape [n_samples, sequence length (n_steps), n_features],
which is time-series data for validating, can contain missing values, and y should be array-like of shape
[n_samples], which is classification labels of X.
If it is a path string, the path should point to a data file, e.g. a h5 file, which contains
key-value pairs like a dict, and it has to include keys as 'X' and 'y'.
file_type :
The type of the given file if test_set is a path string.
n_sampling_times:
The number of sampling times for the model to sample from the diffusion process.
Returns
-------
result_dict: dict
Prediction results in a Python Dictionary for the given samples.
It should be a dictionary including a key named 'imputation'.
"""
assert n_sampling_times > 0, "n_sampling_times should be greater than 0."
# Step 1: wrap the input data with classes Dataset and DataLoader
self.model.eval() # set the model as eval status to freeze it.
test_set = TestDatasetForForecastingCSDI(
test_set,
self.n_pred_steps,
self.n_pred_features,
file_type=file_type,
)
test_loader = DataLoader(
test_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
forecasting_collector = []
# Step 2: process the data with the model
with torch.no_grad():
for idx, data in enumerate(test_loader):
inputs = self._assemble_input_for_testing(data)
results = self.model(
inputs,
training=False,
n_sampling_times=n_sampling_times,
)
forecasting_data = results["forecasting_data"][:, :, -self.n_pred_steps :]
forecasting_collector.append(forecasting_data)
# Step 3: output collection and return
forecasting_data = torch.cat(forecasting_collector).cpu().detach().numpy()
result_dict = {
"forecasting": forecasting_data, # [bz, n_sampling_times, n_pred_steps, n_features]
}
return result_dict
def forecast(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
) -> np.ndarray:
"""Forecast the future of the input with the trained model.
Parameters
----------
test_set :
The data samples for testing, should be array-like of shape [n_samples, sequence length (n_steps),
n_features], or a path string locating a data file, e.g. h5 file.
file_type :
The type of the given file if X is a path string.
Returns
-------
array-like, shape [n_samples, n_pred_steps, n_features],
Forecasting results.
"""
result_dict = self.predict(test_set, file_type=file_type)
return result_dict["forecasting"]