"""
"""
# Created by Linglong Qian, Joseph Arul Raj <linglong.qian@kcl.ac.uk, joseph_arul_raj@kcl.ac.uk>
# License: BSD-3-Clause
from typing import Optional, Union
import numpy as np
import torch
from torch.utils.data import DataLoader
from .core import _BCSAI
from ..base import BaseNNClassifier
from ...data.checking import key_in_data_set
from ...data.saving.h5 import load_dict_from_h5
from ...imputation.csai.data import DatasetForCSAI
from ...nn.functional import gather_listed_dicts
from ...nn.modules.loss import Criterion, CrossEntropy
from ...optim.adam import Adam
from ...optim.base import Optimizer
from ...utils.logging import logger
[docs]
class CSAI(BaseNNClassifier):
"""
The PyTorch implementation of the CSAI model.
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.
rnn_hidden_size :
The size of the RNN hidden state.
imputation_weight :
The loss weight for the imputation task.
consistency_weight :
The loss weight for the consistency task.
classification_weight :
The loss weight for the classification task.
n_classes :
The number of classes in the classification task.
removal_percent :
The percentage of data to be removed during training for simulating missingness.
increase_factor :
The factor to increase the frequency of missing value occurrences.
step_channels :
The number of step channels for the model.
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.
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.
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,
rnn_hidden_size: int,
imputation_weight: float,
consistency_weight: float,
classification_weight: float,
n_classes: int,
removal_percent: int,
increase_factor: float,
step_channels: int,
dropout: float = 0.5,
batch_size: int = 32,
epochs: int = 100,
patience: Optional[int] = None,
training_loss: Union[Criterion, type] = CrossEntropy,
validation_metric: Union[Criterion, type] = CrossEntropy,
optimizer: Union[Optimizer, type] = Adam,
num_workers: int = 0,
device: Optional[Union[str, torch.device, list]] = None,
saving_path: str = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
n_classes=n_classes,
training_loss=training_loss,
validation_metric=validation_metric,
batch_size=batch_size,
epochs=epochs,
patience=patience,
num_workers=num_workers,
device=device,
saving_path=saving_path,
model_saving_strategy=model_saving_strategy,
verbose=verbose,
)
self.n_steps = n_steps
self.n_features = n_features
self.rnn_hidden_size = rnn_hidden_size
self.imputation_weight = imputation_weight
self.consistency_weight = consistency_weight
self.classification_weight = classification_weight
self.removal_percent = removal_percent
self.increase_factor = increase_factor
self.step_channels = step_channels
self.dropout = dropout
# Initialise empty model
self.model = _BCSAI(
n_steps=self.n_steps,
n_features=self.n_features,
rnn_hidden_size=self.rnn_hidden_size,
imputation_weight=self.imputation_weight,
consistency_weight=self.consistency_weight,
classification_weight=self.classification_weight,
n_classes=self.n_classes,
step_channels=self.step_channels,
dropout=self.dropout,
training_loss=self.training_loss,
validation_metric=self.validation_metric,
)
self._send_model_to_given_device()
self._print_model_size()
# set up the optimizer
if isinstance(optimizer, Optimizer):
self.optimizer = optimizer
else:
self.optimizer = optimizer() # instantiate the optimizer if it is a class
assert isinstance(self.optimizer, Optimizer)
self.optimizer.init_optimizer(self.model.parameters())
def _assemble_input_for_training(self, data: list) -> dict:
# extract data
sample = data["sample"]
(indices, X, missing_mask, deltas, last_obs, back_X, back_missing_mask, back_deltas, back_last_obs, labels) = (
self._send_data_to_given_device(sample)
)
inputs = {
"indices": indices,
"y": labels,
"forward": {
"X": X,
"missing_mask": missing_mask,
"deltas": deltas,
"last_obs": last_obs,
},
"backward": {
"X": back_X,
"missing_mask": back_missing_mask,
"deltas": back_deltas,
"last_obs": back_last_obs,
},
"intervals": self.intervals,
}
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:
# extract data
sample = data["sample"]
(
indices,
X,
missing_mask,
deltas,
last_obs,
back_X,
back_missing_mask,
back_deltas,
back_last_obs,
) = self._send_data_to_given_device(sample)
# assemble input data
inputs = {
"indices": indices,
"forward": {
"X": X,
"missing_mask": missing_mask,
"deltas": deltas,
"last_obs": last_obs,
},
"backward": {
"X": back_X,
"missing_mask": back_missing_mask,
"deltas": back_deltas,
"last_obs": back_last_obs,
},
"intervals": self.intervals,
}
return inputs
[docs]
def fit(
self,
train_set,
val_set=None,
file_type: str = "hdf5",
) -> None:
# Create dataset
if isinstance(train_set, str):
logger.warning(
"CSAI does not support lazy loading because intervals need to be calculated ahead. "
"Hence the whole train set will be loaded into memory."
)
train_set = load_dict_from_h5(train_set)
train_dataset = DatasetForCSAI(
data=train_set,
file_type=file_type,
return_X_ori=False,
return_y=True,
removal_percent=self.removal_percent,
increase_factor=self.increase_factor,
)
self.intervals = train_dataset.intervals
self.replacement_probabilities = train_dataset.replacement_probabilities
train_loader = 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 isinstance(val_set, str):
logger.warning(
"CSAI does not support lazy loading because intervals need to be calculated ahead. "
"Hence the whole val set will be loaded into memory."
)
val_set = load_dict_from_h5(val_set)
if not key_in_data_set("X_ori", val_set):
raise ValueError("val_set must contain 'X_ori' for model validation.")
val_dataset = DatasetForCSAI(
data=val_set,
file_type=file_type,
return_X_ori=False,
return_y=True,
removal_percent=self.removal_percent,
increase_factor=self.increase_factor,
replacement_probabilities=self.replacement_probabilities,
)
val_dataloader = DataLoader(
val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
# train the model
self._train_model(train_loader, val_dataloader)
self.model.load_state_dict(self.best_model_dict)
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",
) -> dict:
self.model.eval() # set the model to evaluation mode
if isinstance(test_set, str):
logger.warning(
"CSAI does not support lazy loading because intervals need to be calculated ahead. "
"Hence the whole test set will be loaded into memory."
)
test_set = load_dict_from_h5(test_set)
# Step 1: wrap the input data with classes Dataset and DataLoader
test_dataset = DatasetForCSAI(
data=test_set,
file_type=file_type,
return_X_ori=False,
return_y=False,
removal_percent=self.removal_percent,
increase_factor=self.increase_factor,
replacement_probabilities=self.replacement_probabilities,
)
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)
results = self.model(inputs)
dict_result_collector.append(results)
# Step 3: output collection and return
result_dict = gather_listed_dicts(dict_result_collector)
classification = np.argmax(result_dict["classification_proba"], axis=1)
result_dict["classification"] = classification
return result_dict