"""
The base classes for PyPOTS forecasting models.
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from abc import abstractmethod
from typing import Optional, Union
import numpy as np
import torch
from torch.utils.data import DataLoader
from ..base import BaseModel, BaseNNModel
from ..data.checking import key_in_data_set
from ..data.dataset.base import BaseDataset
from ..nn.functional import autocast, gather_listed_dicts
from ..nn.modules.loss import Criterion
[docs]
class BaseForecaster(BaseModel):
"""Abstract class for all forecasting models.
Parameters
----------
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.
enable_amp :
Whether to enable automatic mixed precision (AMP), default as False.
If the implemented model is based on LLMs that need large-scale operation and AMP, please set it as True.
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,
device: Optional[Union[str, torch.device, list]] = None,
enable_amp: bool = False,
saving_path: str = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
device=device,
enable_amp=enable_amp,
saving_path=saving_path,
model_saving_strategy=model_saving_strategy,
verbose=verbose,
)
[docs]
@abstractmethod
def fit(
self,
train_set: Union[dict, str],
val_set: Optional[Union[dict, str]] = None,
file_type: str = "hdf5",
) -> None:
"""Train the classifier on the given data.
Parameters
----------
train_set :
The dataset for model training, should be a dictionary including the key 'X',
or a path string locating a data file.
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is time-series data for training, can contain missing values.
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 the key 'X'.
val_set :
The dataset for model validating, should be a dictionary including the key 'X',
or a path string locating a data file.
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is time-series data for validation, can contain missing values.
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 the key 'X'.
file_type :
The type of the given file if train_set and val_set are path strings.
"""
raise NotImplementedError
[docs]
@abstractmethod
def predict(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
**kwargs,
) -> dict:
"""Make predictions for the input data with the trained model.
Parameters
----------
test_set :
The test dataset for model to process, should be a dictionary including keys as 'X',
or a path string locating a data file supported by PyPOTS (e.g. h5 file).
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is the time-series data for processing.
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 'X' key.
file_type :
The type of the given file if test_set is a path string.
Returns
-------
result_dict :
The dictionary containing the forecasting results as key 'forecasting' and latent variables if necessary.
"""
raise NotImplementedError
[docs]
def forecast(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
**kwargs,
) -> np.ndarray:
"""Forecast the future the input with the trained model.
Parameters
----------
test_set :
The data samples for testing, should be array-like with shape [n_samples, 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,
**kwargs,
)
results = result_dict["forecasting"]
return results
[docs]
class BaseNNForecaster(BaseNNModel):
"""The abstract class for all neural-network forecasting models in PyPOTS.
Parameters
----------
batch_size :
Size of the batch input into the model for one step.
epochs :
Training epochs, i.e. the maximum rounds of the model to be trained with.
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.
training_loss:
The customized loss function designed by users for training the model.
If not given, will use the default loss as claimed in the original paper.
validation_metric:
The customized metric function designed by users for validating the model.
If not given, will use the default loss from the original paper as the metric.
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.
enable_amp :
Whether to enable automatic mixed precision (AMP), default as False.
If the implemented model is based on LLMs that need large-scale operation and AMP, please set it as True.
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.
Notes
-----
Optimizers are necessary for training deep-learning neural networks, but we don't put a parameter ``optimizer``
here because some models (e.g. GANs) need more than one optimizer (e.g. one for generator, one for discriminator),
and ``optimizer`` is ambiguous for them. Therefore, we leave optimizers as parameters for concrete model
implementations, and you can pass any number of optimizers to your model when implementing it,
:class:`pypots.clustering.crli.CRLI` for example.
"""
def __init__(
self,
# n_forecasting_steps: int,
training_loss: Union[Criterion, type],
validation_metric: Union[Criterion, type],
batch_size: int,
epochs: int,
patience: Optional[int] = None,
num_workers: int = 0,
device: Optional[Union[str, torch.device, list]] = None,
enable_amp: bool = False,
saving_path: str = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
training_loss=training_loss,
validation_metric=validation_metric,
batch_size=batch_size,
epochs=epochs,
patience=patience,
num_workers=num_workers,
device=device,
enable_amp=enable_amp,
saving_path=saving_path,
model_saving_strategy=model_saving_strategy,
verbose=verbose,
)
def _assemble_input_for_training(self, data: list) -> dict:
"""Assemble the given data into a dictionary for training input.
Parameters
----------
data :
Input data from dataloader, should be list.
Returns
-------
dict,
A python dictionary contains the input data for model training.
"""
(
indices,
X,
missing_mask,
X_pred,
X_pred_missing_mask,
) = self._send_data_to_given_device(data)
inputs = {
"X": X,
"missing_mask": missing_mask,
"X_pred": X_pred,
"X_pred_missing_mask": X_pred_missing_mask,
}
return inputs
def _assemble_input_for_validating(self, data: list) -> dict:
"""Assemble the given data into a dictionary for validating input.
Parameters
----------
data :
Data output from dataloader, should be list.
Returns
-------
dict,
A python dictionary contains the input data for model validating.
"""
return self._assemble_input_for_training(data)
def _assemble_input_for_testing(self, data: list) -> dict:
"""Assemble the given data into a dictionary for testing input.
Notes
-----
The processing functions of train/val/test stages are separated for the situation that the input of
the three stages are different, and this situation usually happens when the Dataset/Dataloader classes
used in the train/val/test stages are not the same, e.g. the training data and validating data in a
classification task contains labels, but the testing data (from the production environment) generally
doesn't have labels.
Parameters
----------
data :
Data output from dataloader, should be list.
Returns
-------
dict,
A python dictionary contains the input data for model testing.
"""
(
indices,
X,
missing_mask,
) = self._send_data_to_given_device(data)
inputs = {
"X": X,
"missing_mask": missing_mask,
}
return inputs
[docs]
def fit(
self,
train_set: Union[dict, str],
val_set: Optional[Union[dict, str]] = None,
file_type: str = "hdf5",
) -> None:
"""Train the classifier on the given data.
Parameters
----------
train_set :
The dataset for model training, should be a dictionary including the key 'X',
or a path string locating a data file.
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is time-series data for training, can contain missing values.
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 the key 'X'.
val_set :
The dataset for model validating, should be a dictionary including the key 'X',
or a path string locating a data file.
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is time-series data for validation, can contain missing values.
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 the key 'X'.
file_type :
The type of the given file if train_set and val_set are path strings.
"""
# Step 1: wrap the input data with classes Dataset and DataLoader
train_dataset = BaseDataset(
train_set,
return_X_ori=False,
return_X_pred=True,
return_y=False,
file_type=file_type,
)
train_dataloader = DataLoader(
train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
)
val_dataloader = 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_dataset = BaseDataset(
val_set,
return_X_ori=False,
return_X_pred=True,
return_y=False,
file_type=file_type,
)
val_dataloader = DataLoader(
val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
# Step 2: train the model and freeze it
self._train_model(train_dataloader, val_dataloader)
self.model.load_state_dict(self.best_model_dict)
# Step 3: save the model if necessary
self._auto_save_model_if_necessary(confirm_saving=self.model_saving_strategy == "best")
[docs]
@torch.no_grad()
def predict(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
**kwargs,
) -> dict:
"""Make predictions for the input data with the trained model.
Parameters
----------
test_set :
The test dataset for model to process, should be a dictionary including keys as 'X',
or a path string locating a data file supported by PyPOTS (e.g. h5 file).
If it is a dict, X should be array-like with shape [n_samples, n_steps, n_features],
which is the time-series data for processing.
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 'X' key.
file_type :
The type of the given file if test_set is a path string.
Returns
-------
result_dict :
The dictionary containing the forecasting results as key 'forecasting' and latent variables if necessary.
"""
self.model.eval() # set the model to evaluation mode
# Step 1: wrap the input data with classes Dataset and DataLoader
test_dataset = BaseDataset(
test_set,
return_X_ori=False,
return_X_pred=False,
return_y=False,
file_type=file_type,
)
test_dataloader = DataLoader(
test_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
# Step 2: process the data with the model
dict_result_collector = []
for idx, data in enumerate(test_dataloader):
inputs = self._assemble_input_for_testing(data)
with autocast(enabled=self.amp_enabled):
results = self.model(inputs, **kwargs)
dict_result_collector.append(results)
# Step 3: output collection and return
result_dict = gather_listed_dicts(dict_result_collector)
return result_dict
[docs]
def forecast(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
**kwargs,
) -> np.ndarray:
"""Forecast the future the input with the trained model.
Parameters
----------
test_set :
The data samples for testing, should be array-like with shape [n_samples, 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,
**kwargs,
)
results = result_dict["forecasting"]
return results