pypots.imputation package¶
pypots.imputation.saits¶
The package of the partially-observed time-series imputation model SAITS.
Refer to the paper Wenjie Du, David Cote, and Yan Liu. SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
Notes
This implementation is inspired by the official one https://github.com/WenjieDu/SAITS
- class pypots.imputation.saits.SAITS(n_steps, n_features, n_layers, d_model, n_heads, d_k, d_v, d_ffn, dropout=0, attn_dropout=0, diagonal_attention_mask=True, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, customized_loss_func=<function calc_mae>, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the SAITS model [1].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the 1st and 2nd DMSA blocks in the SAITS model.d_model (
int
) – The dimension of the model’s backbone. It is the input dimension of the multi-head DMSA layers.n_heads (
int
) – The number of heads in the multi-head DMSA mechanism.d_model
must be divisible byn_heads
, and the result should be equal tod_k
.d_k (
int
) – The dimension of the keys (K) and the queries (Q) in the DMSA mechanism.d_k
should be the result ofd_model
divided byn_heads
. Althoughd_k
can be directly calculated with givend_model
andn_heads
, we want it be explicitly given together withd_v
by users to ensure users be aware of them and to avoid any potential mistakes.d_v (
int
) – The dimension of the values (V) in the DMSA mechanism.d_ffn (
int
) – The dimension of the layer in the Feed-Forward Networks (FFN).dropout (
float
) – The dropout rate for all fully-connected layers in the model.attn_dropout (
float
) – The dropout rate for DMSA.diagonal_attention_mask (
bool
) – Whether to apply a diagonal attention mask to the self-attention mechanism. If so, the attention layers will use DMSA. Otherwise, the attention layers will use the original.ORT_weight (
int
) – The weight for the ORT loss.MIT_weight (
int
) – The weight for the MIT loss.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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.customized_loss_func (
Callable
) – The customized loss function designed by users for the model to optimize. If not given, will use the default MAE loss as claimed in the original paper.optimizer (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5', diagonal_attention_mask=True, return_latent_vars=False)[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.diagonal_attention_mask (
bool
) – Whether to apply a diagonal attention mask to the self-attention mechanism in the testing stage.return_latent_vars (
bool
) – Whether to return the latent variables in SAITS, e.g. attention weights of two DMSA blocks and the weight matrix from the combination block, etc.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.transformer¶
The package of the partially-observed time-series imputation model Transformer.
Refer to the papers Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. and Wenjie Du, David Cote, and Yan Liu. SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
Notes
This implementation is inspired by https://github.com/WenjieDu/SAITS
- class pypots.imputation.transformer.Transformer(n_steps, n_features, n_layers, d_model, n_heads, d_k, d_v, d_ffn, dropout=0, attn_dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Transformer model. Transformer is originally proposed by Vaswani et al. in [40], and gets re-implemented as a time-series imputation model by Du et al. in [1]. Here you should refer to [1] for details about this Transformer imputation model.
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the 1st and 2nd DMSA blocks in the SAITS model.d_model (
int
) – The dimension of the model’s backbone. It is the input dimension of the multi-head self-attention layers.n_heads (
int
) – The number of heads in the multi-head self-attention mechanism.d_model
must be divisible byn_heads
, and the result should be equal tod_k
.d_k (
int
) – The dimension of the keys (K) and the queries (Q) in the DMSA mechanism.d_k
should be the result ofd_model
divided byn_heads
. Althoughd_k
can be directly calculated with givend_model
andn_heads
, we want it be explicitly given together withd_v
by users to ensure users be aware of them and to avoid any potential mistakes.d_v (
int
) – The dimension of the values (V) in the DMSA mechanism.d_ffn (
int
) – The dimension of the layer in the Feed-Forward Networks (FFN).dropout (
float
) – The dropout rate for all fully-connected layers in the model.attn_dropout (
float
) – The dropout rate for the attention mechanism.ORT_weight (
int
) – The weight for the ORT loss.MIT_weight (
int
) – The weight for the MIT loss.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.tefn¶
The package of the forecasting model TEFN.
Refer to the paper Tianxiang Zhan, Yuanpeng He, Yong Deng, and Zhen Li. Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting. In Arxiv, 2024.
Notes
This implementation is transfered from the official one https://github.com/ztxtech/Time-Evidence-Fusion-Network
- class pypots.imputation.tefn.TEFN(n_steps, n_features, n_fod=2, apply_nonstationary_norm=True, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the TEFN model. TEFN is originally proposed by Zhan et al. in [2].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_fod (
int
) – The number of FOD (frame of discernment) in the TEFN model.apply_nonstationary_norm (
bool
) – Whether to apply non-stationary normalization to the input data for TimesNet. Please refer to [20] for details about non-stationary normalization.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.csai¶
The package including the modules of CSAI.
Notes
This implementation is inspired by the official one the official implementation https://github.com/LinglongQian/CSAI.
- class pypots.imputation.csai.CSAI(n_steps, n_features, rnn_hidden_size, imputation_weight, consistency_weight, removal_percent, increase_factor, compute_intervals, step_channels, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the CSAI model [17].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.rnn_hidden_size (
int
) – The size of the GRU hidden state, also the number of hidden units in the GRU cell.imputation_weight (
float
) – The weight assigned to the reconstruction loss during training.consistency_weight (
float
) – The weight assigned to the consistency loss during training.removal_percent (
int
) – The percentage of data to be removed during training for imputation tasks.increase_factor (
float
) – A scaling factor used to adjust the amount of missing data during training.compute_intervals (
bool
) – Whether to compute time intervals between observations for handling irregular time-series.step_channels (
int
) – The number of channels for each step in the sequence.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
Notes
CSAI (Consistent Sequential Imputation) is a bidirectional model designed for time-series imputation. It employs a forward and backward GRU network to handle missing data, using consistency and reconstruction losses to improve accuracy. The model supports various training configurations, such as interval computations, early-stopping, and multiple devices for training. Results can be saved based on the specified saving strategy, and tensorboard files are generated for tracking the model’s performance over time.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer 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 of shape [n_samples, sequence length (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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.segrnn¶
The package including the modules of SegRNN.
Notes
This implementation is inspired by the official one https://github.com/lss-1138/SegRNN
- class pypots.imputation.segrnn.SegRNN(n_steps, n_features, seg_len=24, d_model=512, dropout=0.5, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the SegRNN model. SegRNN is originally proposed by Shengsheng Lin et al. in [18]. See detail in https://arxiv.org/abs/2308.11200 or https://github.com/lss-1138/SegRNN.
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.seg_len (
int
) – The segment length for input of RNN.d_model (
int
) – The dimension of RNN cell.dropout (
float
) – The dropout rate of the output layer of SegRNN.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.fits¶
The package including the modules of FITS.
Refer to the paper Zhijian Xu, Ailing Zeng, and Qiang Xu. FITS: Modeling Time Series with 10k parameters. In The Twelfth International Conference on Learning Representations, 2024.
Notes
This implementation is inspired by the official one https://github.com/VEWOXIC/FITS
- class pypots.imputation.fits.FITS(n_steps, n_features, cut_freq, individual=False, ORT_weight=1, MIT_weight=1, apply_nonstationary_norm=False, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the FITS model. FITS is originally proposed by Xu et al. in [3].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers – The number of layers in the FITS model.
d_model – The dimension of the model.
n_heads – The number of heads in each layer of FITS.
d_ffn – The dimension of the feed-forward network.
factor – The factor of the auto correlation mechanism for the FITS model.
moving_avg_window_size – The window size of moving average.
dropout – The dropout rate for the model.
ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.timemixer¶
The package including the modules of TimeMixer.
Notes
This implementation is inspired by the official one https://github.com/kwuking/TimeMixer
- class pypots.imputation.timemixer.TimeMixer(n_steps, n_features, n_layers, d_model, d_ffn, top_k, dropout=0, channel_independence=False, decomp_method='moving_avg', moving_avg=5, downsampling_layers=3, downsampling_window=2, apply_nonstationary_norm=False, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the TimeMixer model. TimeMixer is originally proposed by Wang et al. in [4].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the TimeMixer model.d_model (
int
) – The dimension of the model.d_ffn (
int
) – The dimension of the feed-forward network.top_k (
int
) – The number of top-k amplitude values to be selected to obtain the most significant frequencies.dropout (
float
) – The dropout rate for the model.channel_independence (
bool
) – Whether to use channel independence in the model.decomp_method (
str
) – The decomposition method for the model. It has to be one of [‘moving_avg’, ‘dft_decomp’].moving_avg (
int
) – The window size for moving average decomposition.downsampling_layers (
int
) – The number of downsampling layers in the model.downsampling_window (
int
) – The window size for downsampling.apply_nonstationary_norm (
bool
) – Whether to apply non-stationary normalization to the input data for TimeMixer. Please refer to [20] for details about non-stationary normalization, which is not the idea of the original TimeMixer paper. Hence, we make it optional and default not to use here.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.moderntcn¶
The package of the partially-observed time-series imputation model ModernTCN.
Notes
This implementation is inspired by the official one https://github.com/luodhhh/ModernTCN
- class pypots.imputation.moderntcn.ModernTCN(n_steps, n_features, patch_size, patch_stride, downsampling_ratio, ffn_ratio, num_blocks, large_size, small_size, dims, small_kernel_merged=False, backbone_dropout=0.1, head_dropout=0.1, use_multi_scale=True, individual=False, apply_nonstationary_norm=False, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the ModernTCN model. ModernTCN is originally proposed by Luo et al. in [6].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.patch_size (
int
) – The size of the patch for the patching mechanism.patch_stride (
int
) – The stride for the patching mechanism.downsampling_ratio (
float
) – The downsampling ratio for the downsampling mechanism.ffn_ratio (
float
) – The ratio for the feed-forward neural network in the model.num_blocks (
list
) – The number of blocks for the model. It should be a list of integers.large_size (
list
) – The size of the large kernel. It should be a list of odd integers.small_size (
list
) – The size of the small kernel. It should be a list of odd integers.dims (
list
) – The dimensions for the model. It should be a list of integers.small_kernel_merged (
bool
) – Whether the small kernel is merged.backbone_dropout (
float
) – The dropout rate for the backbone of the model.head_dropout (
float
) – The dropout rate for the head of the model.use_multi_scale (
bool
) – Whether to use multi-scale fusing.individual (
bool
) – Whether to make a linear layer for each variate/channel/feature individually.apply_nonstationary_norm (
bool
) – Whether to apply non-stationary normalization.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.imputeformer¶
The package of the partially-observed time-series imputation model ImputeFormer.
Refer to the papers Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun. “ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation” KDD 2024.
- class pypots.imputation.imputeformer.ImputeFormer(n_steps, n_features, n_layers, d_input_embed, d_learnable_embed, d_proj, d_ffn, n_temporal_heads, dropout=0.0, input_dim=1, output_dim=1, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the ImputeFormer model. ImputeFormer is originally proposed by Nie et al. in KDD’24: cite:nie2024imputeformer.
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the 1st and 2nd DMSA blocks in the SAITS model.d_input_embed (
int
) – The dimension of the input embedding. It is the input dimension of the input embedding layer.d_learnable_embed (
int
) – The dimension of the learnable node embedding. It is the dimension of the learnable node embedding (spatial positional embedding) used in spatial attention layers.d_proj (
int
) – The dimension of the learnable projector. It is the dimension of the learnable projector used in temporal attention layers.d_ffn (
int
) – The dimension of the layer in the Feed-Forward Networks (FFN).dropout (
float
) – The dropout rate for all fully-connected layers in the model.n_temporal_heads (
int
) – The number of attention heads in temporal attention layers.input_dim (
int
) – The dimension of the input feature dimension, default is 1.output_dim (
int
) – The dimension of the output feature dimension, default is 1.ORT_weight (
float
) – The weight for the ORT loss.MIT_weight (
float
) – The weight for the MIT loss.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(X, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
Warning
The method impute is deprecated. Please use predict() instead.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (time steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.itransformer¶
The package of the partially-observed time-series imputation model iTransformer.
Refer to the papers Liu, Yong, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. “iTransformer: Inverted transformers are effective for time series forecasting.” ICLR 2024.
Notes
This implementation is inspired by the official one https://github.com/thuml/iTransformer
- class pypots.imputation.itransformer.iTransformer(n_steps, n_features, n_layers, d_model, n_heads, d_k, d_v, d_ffn, dropout=0, attn_dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the iTransformer model. iTransformer is originally proposed by Liu et al. in [5].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the 1st and 2nd DMSA blocks in the SAITS model.d_model (
int
) – The dimension of the model’s backbone. It is the input dimension of the multi-head self-attention layers.n_heads (
int
) – The number of heads in the multi-head self-attention mechanism.d_model
must be divisible byn_heads
, and the result should be equal tod_k
.d_k (
int
) – The dimension of the keys (K) and the queries (Q) in the DMSA mechanism.d_k
should be the result ofd_model
divided byn_heads
. Althoughd_k
can be directly calculated with givend_model
andn_heads
, we want it be explicitly given together withd_v
by users to ensure users be aware of them and to avoid any potential mistakes.d_v (
int
) – The dimension of the values (V) in the DMSA mechanism.d_ffn (
int
) – The dimension of the layer in the Feed-Forward Networks (FFN).dropout (
float
) – The dropout rate for all fully-connected layers in the model.attn_dropout (
float
) – The dropout rate for DMSA.ORT_weight (
int
) – The weight for the ORT loss.MIT_weight (
int
) – The weight for the MIT loss.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(X, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
Warning
The method impute is deprecated. Please use predict() instead.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (time steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.koopa¶
The package of the partially-observed time-series imputation model Koopa.
Notes
This implementation is inspired by the official one https://github.com/thuml/Koopa
- class pypots.imputation.koopa.Koopa(n_steps, n_features, n_seg_steps, d_dynamic, d_hidden, n_hidden_layers, n_blocks, multistep=False, alpha=0.2, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Koopa model. Koopa is originally proposed by Liu et al. in [9].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_seg_steps (
int
) – Segment length of time series.d_dynamic (
int
) – Latent dimension of koopman embedding.d_hidden (
int
) – The dimension of the hidden layers in the model.n_hidden_layers (
int
) – The number of hidden layers in the model.n_blocks (
int
) – The number of blocks in the model.multistep (
bool
) – Whether to use the multistep approximation for multistep K in the model.alpha (
int
) – spectrum filter ratioORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.frets¶
The package of the partially-observed time-series imputation model FreTS.
Notes
This implementation is inspired by the official one https://github.com/aikunyi/FreTS
- class pypots.imputation.frets.FreTS(n_steps, n_features, embed_size=128, hidden_size=256, channel_independence=False, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the FreTS model. FreTS is originally proposed by Yi et al. in [8].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.embed_size (
int
) – The size of the embedding layer in the FreTS model.hidden_size (
int
) – The size of the hidden layer in the FreTS model.channel_independence (
bool
) – Whether to use the channel independence mechanism in the FreTS model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.crossformer¶
The package of the partially-observed time-series imputation model Transformer.
Refer to the paper Yunhao Zhang and Junchi Yan. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The 11th ICLR, 2023.
Notes
This implementation is inspired by the official one https://github.com/Thinklab-SJTU/Crossformer
- class pypots.imputation.crossformer.Crossformer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, factor, seg_len, win_size, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Crossformer model. Crossformer is originally proposed by Zhang et al. in [10].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the 1st and 2nd DMSA blocks in the SAITS model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in the multi-head attention mechanism.d_ffn (
int
) – The dimension of the feed-forward network.factor (
int
) – The num of routers in Cross-Dimension Stage of TSA (c).seg_len (
int
) – The length of the segment in the model.win_size (
int
) – The window size for merging segment.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.timesnet¶
The package of the partially-observed time-series imputation model TimesNet.
Refer to the paper Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In ICLR, 2023.
Notes
This implementation is inspired by the official one https://github.com/thuml/Time-Series-Library
- class pypots.imputation.timesnet.TimesNet(n_steps, n_features, n_layers, top_k, d_model, d_ffn, n_kernels, dropout=0, apply_nonstationary_norm=False, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the TimesNet model. TimesNet is originally proposed by Wu et al. in [11].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the TimesNet model.top_k (
int
) – The number of top-k amplitude values to be selected to obtain the most significant frequencies.d_model (
int
) – The dimension of the model.d_ffn (
int
) – The dimension of the feed-forward network.n_kernels (
int
) – The number of 2D kernels (2D convolutional layers) to use in the submodule InceptionBlockV1.dropout (
float
) – The dropout rate for the model.apply_nonstationary_norm (
bool
) – Whether to apply non-stationary normalization to the input data for TimesNet. Please refer to [20] for details about non-stationary normalization, which is not the idea of the original TimesNet paper. Hence, we make it optional and default not to use here.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.patchtst¶
The package of the partially-observed time-series imputation model PatchTST.
Refer to the paper Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In ICLR, 2023.
Notes
This implementation is inspired by the official one https://github.com/yuqinie98/PatchTST
- class pypots.imputation.patchtst.PatchTST(n_steps, n_features, patch_len, stride, n_layers, d_model, n_heads, d_k, d_v, d_ffn, dropout, attn_dropout, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the PatchTST model. TimesNet is originally proposed by Wu et al. in [12].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.patch_len (
int
) – The patch length for patch embedding.stride (
int
) – The stride for patch embedding.n_layers (
int
) – The number of layers in the PatchTST model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of PatchTST.d_k (
int
) – The dimension of the keys (K) and the queries (Q) in the DMSA mechanism.d_k
should be the result ofd_model
divided byn_heads
. Althoughd_k
can be directly calculated with givend_model
andn_heads
, we want it be explicitly given together withd_v
by users to ensure users be aware of them and to avoid any potential mistakes.d_v (
int
) – The dimension of the values (V) in the DMSA mechanism.d_ffn (
int
) – The dimension of the feed-forward network.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.etsformer¶
The package of the partially-observed time-series imputation model ETSformer.
Refer to the paper Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi. ETSformer: Exponential smoothing transformers for time-series forecasting. In ICLR, 2023.
Notes
This implementation is inspired by the official one https://github.com/salesforce/ETSformer
- class pypots.imputation.etsformer.ETSformer(n_steps, n_features, n_e_layers, n_d_layers, d_model, n_heads, d_ffn, top_k, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the ETSformer model. ETSformer is originally proposed by Woo et al. in [13].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_e_layers (
int
) – The number of layers in the ETSformer encoder.n_d_layers (
int
) – The number of layers in the ETSformer decoder.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of ETSformer.d_ffn (
int
) – The dimension of the feed-forward network.top_k (
int
) – Top-K Fourier bases.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.micn¶
The package of the partially-observed time-series imputation model MICN.
Notes
This implementation is inspired by the official one https://github.com/wanghq21/MICN
- class pypots.imputation.micn.MICN(n_steps, n_features, n_layers, d_model, conv_kernel, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the MICN model. MICN is originally proposed by Huang et al. in [14].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the MICN model.d_model (
int
) – The dimension of the model.conv_kernel (
list
) – The kernel size for the convolutional layers in the model. It should be a list of integers, and the maximum value in the list should be less than or equal to the minimum value of n_steps and n_features.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.dlinear¶
The package of the partially-observed time-series imputation model DLinear.
Refer to the paper Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? In AAAI, volume 37, pages 11121–11128, Jun. 2023.
Notes
This implementation is inspired by the official one https://github.com/cure-lab/LTSF-Linear
- class pypots.imputation.dlinear.DLinear(n_steps, n_features, moving_avg_window_size, individual=False, d_model=None, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the DLinear model. DLinear is originally proposed by Zeng et al. in [15].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.moving_avg_window_size (
int
) – The window size of moving average.individual (
bool
) – Whether to make a linear layer for each variate/channel/feature individually.d_model (
Optional
[int
]) – The dimension of the space in which the time-series data will be embedded and modeled. It is necessary only for DLinear in the non-individual mode.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.tide¶
The package of the partially-observed time-series imputation model TiDE.
Notes
This implementation is inspired by the official one https://github.com/google-research/google-research/blob/master/tide and https://github.com/lich99/TiDE
- class pypots.imputation.tide.TiDE(n_steps, n_features, n_layers, d_model, d_hidden, d_feature_encode, d_temporal_decoder_hidden, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the TiDE model. TiDE is originally proposed by Das et al. in [16].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the TiDE model.d_model (
int
) – The dimension of the model.d_hidden (
int
) – The dimension of the hidden layer in the model.d_feature_encode (
int
) – The dimension of the feature encoder.d_temporal_decoder_hidden (
int
) – The dimension of the hidden layer in the temporal decoder.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.scinet¶
The package of the partially-observed time-series imputation model SCINet.
Notes
This implementation is inspired by the official one https://github.com/cure-lab/SCINet
- class pypots.imputation.scinet.SCINet(n_steps, n_features, n_stacks, n_levels, n_groups, n_decoder_layers, d_hidden, kernel_size=3, concat_len=0, dropout=0.5, pos_enc=False, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the SCINet model. SCINet is originally proposed by Liu et al. in [19].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_stacks (
int
) – The number of stacks in the model.n_levels (
int
) – The number of levels in the model.n_groups (
int
) – The number of groups in the model.n_decoder_layers (
int
) – The number of decoder layers in the model.d_hidden (
int
) – The hidden dimension for the model.kernel_size (
int
) – The kernel size for the model.dropout (
float
) – The dropout rate for the model.concat_len (
int
) – The length for concatenation.pos_enc (
bool
) – Whether to use positional encoding in the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.nonstationary_transformer¶
The package of the partially-observed time-series imputation model Nonstationary-Transformer.
Notes
This implementation is inspired by the official one https://github.com/thuml/Nonstationary_Transformers
- class pypots.imputation.nonstationary_transformer.NonstationaryTransformer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, d_projector_hidden, n_projector_hidden_layers, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Nonstationary-Transformer model. NonstationaryTransformer is originally proposed by Liu et al. in [20].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the NonstationaryTransformer model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of NonstationaryTransformer.d_ffn (
int
) – The dimension of the feed-forward network.d_projector_hidden (
list
) – The dimensions of hidden layers in MLP projectors. It should be a list of integers and the length of the list should be equal to n_projector_hidden_layers.n_projector_hidden_layers (
int
) – The number of hidden layers in MLP projectors.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 of shape [n_samples, sequence length (time 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(X, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
Warning
The method impute is deprecated. Please use predict() instead.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (time steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.film¶
The package of the partially-observed time-series imputation model FiLM.
Notes
This implementation is inspired by the official one https://github.com/tianzhou2011/FiLM
- class pypots.imputation.film.FiLM(n_steps, n_features, window_size, multiscale, modes1=32, dropout=0.5, mode_type=0, d_model=128, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the FiLM model. FiLM is originally proposed by Zhou et al. in [21].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.window_size (
list
) – A list including the window sizes for the HiPPO projection layers.multiscale (
list
) – A list including the multiscale factors for the HiPPO projection layers.dropout (
float
) – The dropout ratio for the HiPPO projection layers. It only works when mode_type == 1.mode_type (
int
) – The mode type of the SpectralConv1d layers. It has to be one of [0, 1, 2].d_model (
int
) – The dimension of the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.revin_scinet¶
The package of the partially-observed time-series imputation model FiLM.
Notes
This implementation is inspired by the official one https://github.com/tianzhou2011/FiLM
- class pypots.imputation.film.FiLM(n_steps, n_features, window_size, multiscale, modes1=32, dropout=0.5, mode_type=0, d_model=128, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the FiLM model. FiLM is originally proposed by Zhou et al. in [21].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.window_size (
list
) – A list including the window sizes for the HiPPO projection layers.multiscale (
list
) – A list including the multiscale factors for the HiPPO projection layers.dropout (
float
) – The dropout ratio for the HiPPO projection layers. It only works when mode_type == 1.mode_type (
int
) – The mode type of the SpectralConv1d layers. It has to be one of [0, 1, 2].d_model (
int
) – The dimension of the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.pyraformer¶
The package of the partially-observed time-series imputation model Pyraformer.
Notes
This implementation is inspired by the official one https://github.com/ant-research/Pyraformer
- class pypots.imputation.pyraformer.Pyraformer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, window_size, inner_size, dropout=0, attn_dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Pyraformer model. Pyraformer is originally proposed by Liu et al. in [23].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the Pyraformer model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of Pyraformer.d_ffn (
int
) – The dimension of the feed-forward network.window_size (
list
) – The downsample window size in pyramidal attention.inner_size (
int
) – The size of neighbour attentiondropout (
float
) – The dropout rate for the model.attn_dropout (
float
) – The dropout rate for the attention mechanism.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.fedformer¶
The package of the partially-observed time-series imputation model FEDformer.
Notes
This implementation is inspired by the official one https://github.com/MAZiqing/FEDformer
- class pypots.imputation.fedformer.FEDformer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, moving_avg_window_size, dropout=0, version='Fourier', modes=32, mode_select='random', ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the FEDformer model. FEDformer is originally proposed by Woo et al. in [25].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the FEDformer.n_heads (
int
) – The number of heads in the multi-head attention mechanism.d_model (
int
) – The dimension of the model.d_ffn (
int
) – The dimension of the feed-forward network.moving_avg_window_size (
int
) – The window size of moving average.dropout (
float
) – The dropout rate for the model.version – The version of the model. It has to be one of [“Wavelets”, “Fourier”]. The default value is “Fourier”.
modes – The number of modes to be selected. The default value is 32.
mode_select – Get modes on frequency domain. It has to “random” or “low”. The default value is “random”. ‘random’ means sampling randomly; ‘low’ means sampling the lowest modes;
ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.informer¶
The package of the partially-observed time-series imputation model Informer.
Notes
This implementation is inspired by the official one https://github.com/zhouhaoyi/Informer2020
- class pypots.imputation.informer.Informer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, factor, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Informer model. Informer is originally proposed by Wu et al. in [28].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the Informer model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of Informer.d_ffn (
int
) – The dimension of the feed-forward network.factor (
int
) – The factor of the ProbSparse self-attention mechanism for the Informer model.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.autoformer¶
The package of the partially-observed time-series imputation model Autoformer.
Notes
This implementation is inspired by the official one https://github.com/thuml/Autoformer
- class pypots.imputation.autoformer.Autoformer(n_steps, n_features, n_layers, d_model, n_heads, d_ffn, factor, moving_avg_window_size, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Autoformer model. Autoformer is originally proposed by Wu et al. in [26].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the Autoformer model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of Autoformer.d_ffn (
int
) – The dimension of the feed-forward network.factor (
int
) – The factor of the auto correlation mechanism for the Autoformer model.moving_avg_window_size (
int
) – The window size of moving average.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.csdi¶
The implementation of CSDI for the partially-observed time-series imputation task.
Refer to the paper Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. In NeurIPS, 2021.
Notes
This implementation is inspired by the official one the official implementation https://github.com/ermongroup/CSDI.
- class pypots.imputation.csdi.CSDI(n_steps, n_features, n_layers, n_heads, n_channels, d_time_embedding, d_feature_embedding, d_diffusion_embedding, n_diffusion_steps=50, target_strategy='random', is_unconditional=False, schedule='quad', beta_start=0.0001, beta_end=0.5, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the CSDI model [27].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the CSDI model.n_heads (
int
) – The number of heads in the multi-head attention mechanism.n_channels (
int
) – The number of residual channels.d_time_embedding (
int
) – The dimension number of the time (temporal) embedding.d_feature_embedding (
int
) – The dimension number of the feature embedding.d_diffusion_embedding (
int
) – The dimension number of the diffusion embedding.is_unconditional (
bool
) – Whether the model is unconditional or conditional.target_strategy (
str
) – The strategy for selecting the target for the diffusion process. It has to be one of [“mix”, “random”].n_diffusion_steps (
int
) – The number of the diffusion step T in the original paper.schedule (
str
) – The schedule for other noise levels. It has to be one of [“quad”, “linear”].beta_start (
float
) – The minimum noise level.beta_end (
float
) – The maximum noise level.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5', n_sampling_times=1)[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5', n_sampling_times=1)[source]¶
- 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 (
str
) – The type of the given file if test_set is a path string.n_sampling_times (
int
) – The number of sampling times for the model to sample from the diffusion process.
- Returns:
result_dict – Prediction results in a Python Dictionary for the given samples. It should be a dictionary including a key named ‘imputation’.
- Return type:
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.usgan¶
The package of the partially-observed time-series imputation method USGAN.
- class pypots.imputation.usgan.USGAN(n_steps, n_features, rnn_hidden_size, lambda_mse=1, hint_rate=0.7, dropout=0.0, G_steps=1, D_steps=1, batch_size=32, epochs=100, patience=None, G_optimizer=<pypots.optim.adam.Adam object>, D_optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the USGAN model. Refer to [29].
- Parameters:
n_steps (int) – The number of time steps in the time-series data sample.
n_features (int) – The number of features in the time-series data sample.
rnn_hidden_size (int) – The hidden size of the RNN cell
lambda_mse (float) – The weight of the reconstruction loss
hint_rate (float) – The hint rate for the discriminator
dropout (float) – The dropout rate for the last layer in Discriminator
G_steps (int) – The number of steps to train the generator in each iteration.
D_steps (int) – The number of steps to train the discriminator in each iteration.
batch_size (int) – The batch size for training and evaluating the model.
epochs (int) – The number of epochs for training the model.
patience (int) – 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.
G_optimizer (
pypots.optim.Optimizer
) – The optimizer for the generator training. If not given, will use a default Adam optimizer.D_optimizer (
pypots.optim.Optimizer
) – The optimizer for the discriminator training. If not given, will use a default Adam optimizer.num_workers (int) – 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 (Union[str, torch.device, list]) – The device for the model to run on. It can be a string, a
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 (str) – 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 (str) – The strategy to save model checkpoints. It has to be one of [None, “best”, “better”]. 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.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.stemgnn¶
The package of the partially-observed time-series imputation model StemGNN.
Notes
This implementation is inspired by the official one https://github.com/microsoft/StemGNN
- class pypots.imputation.stemgnn.StemGNN(n_steps, n_features, n_layers, n_stacks, d_model, dropout=0.5, leaky_rate=0.2, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the StemGNN model. StemGNN is originally proposed by Cao et al. in [32].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in each block stack of the StemGNN model.n_stacks (
int
) – The number of stacks in the StemGNN model.d_model (
int
) – The dimension of the embedding layer and the model.dropout (
float
) – The dropout rate for the model.: (leaky_rate) – The leaky rate for the model.
ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.reformer¶
The package of the partially-observed time-series imputation model Reformer.
Refer to the paper Kitaev, Nikita, Łukasz Kaiser, and Anselm Levskaya. Reformer: The Efficient Transformer. International Conference on Learning Representations, 2020.
Notes
This implementation is inspired by the official one https://github.com/google/trax/tree/master/trax/models/reformer and https://github.com/lucidrains/reformer-pytorch
- class pypots.imputation.reformer.Reformer(n_steps, n_features, n_layers, d_model, n_heads, bucket_size, n_hashes, causal, d_ffn, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the Reformer model. Reformer is originally proposed by Kitaev et al. in [33].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_layers (
int
) – The number of layers in the Reformer model.d_model (
int
) – The dimension of the model.n_heads (
int
) – The number of heads in each layer of Reformer.bucket_size (
int
) – Average size of qk per bucket, 64 was recommended in paper.n_hashes (
int
) – 4 is permissible per author, 8 is the best but slower.causal (
bool
) – Auto-regressive or not.d_ffn (
int
) – The dimension of the feed-forward network. The window size of moving average.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.gpvae¶
The package of the partially-observed time-series imputation model GP-VAE.
Notes
This implementation is inspired by the official one https://github.com/ratschlab/GP-VAE
- class pypots.imputation.gpvae.GPVAE(n_steps, n_features, latent_size, encoder_sizes=(64, 64), decoder_sizes=(64, 64), kernel='cauchy', beta=0.2, M=1, K=1, sigma=1.0, length_scale=7.0, kernel_scales=1, window_size=3, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the GPVAE model [34].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.latent_size (int,) – The feature dimension of the latent embedding
encoder_sizes (tuple,) – The tuple of the network size in encoder
decoder_sizes (tuple,) – The tuple of the network size in decoder
beta (float,) – The weight of KL divergence in ELBO.
M (int,) – The number of Monte Carlo samples for ELBO estimation during training.
K (int,) – The number of importance weights for IWAE model training loss.
kernel (str) – The type of kernel function chosen in the Gaussain Process Proir. [“cauchy”, “diffusion”, “rbf”, “matern”]
sigma (float,) – The scale parameter for a kernel function
length_scale (float,) – The length scale parameter for a kernel function
kernel_scales (int,) – The number of different length scales over latent space dimensions
window_size (int,) – Window size for the inference CNN.
batch_size (int) – The batch size for training and evaluating the model.
epochs (int) – The number of epochs for training the model.
patience (int) – 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 (pypots.optim.base.Optimizer) – The optimizer for model training. If not given, will use a default Adam optimizer.
num_workers (int) – 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 (
torch.device
or list) – The device for the model to run on. It can be a string, atorch.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 (str) – 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 (str) – The strategy to save model checkpoints. It has to be one of [None, “best”, “better”]. 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.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5', n_sampling_times=1)[source]¶
- 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 (
str
) – The type of the given file if test_set is a path string.n_sampling_times (
int
) – The number of sampling times for the model to produce predictions.
- Returns:
result_dict – Prediction results in a Python Dictionary for the given samples. It should be a dictionary including a key named ‘imputation’.
- Return type:
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.brits¶
The package of the partially-observed time-series imputation model BRITS.
Notes
This implementation is inspired by the official one https://github.com/caow13/BRITS The bugs in the original implementation are fixed here.
- class pypots.imputation.brits.BRITS(n_steps, n_features, rnn_hidden_size, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the BRITS model [37].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.rnn_hidden_size (
int
) – The size of the RNN hidden state, also the number of hidden units in the RNN cell.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.mrnn¶
The package of the partially-observed time-series imputation model M-RNN.
Notes
This implementation is inspired by the official one https://github.com/jsyoon0823/MRNN and https://github.com/WenjieDu/SAITS
- class pypots.imputation.mrnn.MRNN(n_steps, n_features, rnn_hidden_size, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the MRNN model [36].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.rnn_hidden_size (
int
) – The size of the RNN hidden state, also the number of hidden units in the RNN cell.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.grud¶
The package of the partially-observed time-series imputation model GRU-D.
Notes
This implementation is inspired by the official one https://github.com/PeterChe1990/GRU-D
- class pypots.imputation.grud.GRUD(n_steps, n_features, rnn_hidden_size, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the GRU-D model [38].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.rnn_hidden_size (
int
) – The size of the RNN hidden state.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (
Union
[dict
,str
]) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
Prediction results in a Python Dictionary for the given samples. It should be a dictionary including keys as ‘imputation’, ‘classification’, ‘clustering’, and ‘forecasting’. For sure, only the keys that relevant tasks are supported by the model will be returned.
- Return type:
result_dict
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.tcn¶
The package of the partially-observed time-series imputation model TCN.
Notes
This implementation is inspired by the official one https://github.com/locuslab/TCN
- class pypots.imputation.tcn.TCN(n_steps, n_features, n_levels, d_hidden, kernel_size, dropout=0, ORT_weight=1, MIT_weight=1, batch_size=32, epochs=100, patience=None, optimizer=<pypots.optim.adam.Adam object>, num_workers=0, device=None, saving_path=None, model_saving_strategy='best', verbose=True)[source]¶
Bases:
BaseNNImputer
The PyTorch implementation of the TCN model. TCN is originally proposed by Bai et al. in [39].
- Parameters:
n_steps (
int
) – The number of time steps in the time-series data sample.n_features (
int
) – The number of features in the time-series data sample.n_levels (
int
) – The number of levels in the TCN model.d_hidden (
int
) – The hidden dimension for the TCN model.kernel_size (
int
) – The kernel size for the TCN model.dropout (
float
) – The dropout rate for the model.ORT_weight (
float
) – The weight for the ORT loss, the same as SAITS.MIT_weight (
float
) – The weight for the MIT loss, the same as SAITS.batch_size (
int
) – The batch size for training and evaluating the model.epochs (
int
) – The number of epochs for training the model.patience (
Optional
[int
]) – 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 (
Optional
[Optimizer
]) – The optimizer for model training. If not given, will use a default Adam optimizer.num_workers (
int
) – 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 (
Union
[str
,device
,list
,None
]) – The device for the model to run on. It can be a string, atorch.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 (
Optional
[str
]) – 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 (
Optional
[str
]) – 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 (
bool
) – Whether to print out the training logs during the training process.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data.
- Parameters:
train_set (
Union
[dict
,str
]) – 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 of shape [n_samples, sequence length (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 (
Union
[dict
,str
,None
]) – 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 of shape [n_samples, sequence length (n_steps), n_features], which is time-series data for validating, 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 (
str
) – The type of the given file if train_set and val_set are path strings.
- Return type:
- predict(test_set, file_type='hdf5')[source]¶
Make predictions for the input data with the trained model.
- Parameters:
test_set (dict or str) – The dataset for model validating, 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 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 (
str
) – The type of the given file if test_set is a path string.
- Returns:
The dictionary containing the clustering results and latent variables if necessary.
- Return type:
file_type
- impute(test_set, file_type='hdf5')[source]¶
Impute missing values in the given data with the trained model.
- Parameters:
- Returns:
Imputed data.
- Return type:
array-like, shape [n_samples, sequence length (n_steps), n_features],
- load(path)¶
Load the saved model from a disk file.
Notes
If the training environment and the deploying/test environment use the same type of device (GPU/CPU), you can load the model directly with torch.load(model_path).
- save(saving_path, overwrite=False)¶
Save the model with current parameters to a disk file.
A
.pypots
extension will be appended to the filename if it does not already have one. Please note that such an extension is not necessary, but to indicate the saved model is from PyPOTS framework so people can distinguish.
pypots.imputation.lerp¶
The package of the partially-observed time-series imputation method linear interpolation.
- class pypots.imputation.lerp.Lerp[source]¶
Bases:
BaseImputer
Linear interpolation (Lerp) imputation method.
Lerp will linearly interpolate missing values between the nearest non-missing values. If there are missing values at the beginning or end of the series, they will be back-filled or forward-filled with the nearest non-missing value, respectively. If an entire series is empty, all ‘nan’ values will be filled with zeros.
- fit(train_set, val_set=None, file_type='hdf5')[source]¶
Train the imputer on the given data. :rtype:
None
Warning
Linear interpolation class does not need to run fit(). Please run func
predict()
directly.
- predict(test_set,