-
-
Notifications
You must be signed in to change notification settings - Fork 120
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #317 from WenjieDu/dev
Add mean and median as imputation methods, and update docs
- Loading branch information
Showing
11 changed files
with
495 additions
and
17 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -5,24 +5,33 @@ | |
| # Created by Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| # neural network imputation methods | ||
| from .brits import BRITS | ||
| from .csdi import CSDI | ||
| from .gpvae import GPVAE | ||
| from .locf import LOCF | ||
| from .mrnn import MRNN | ||
| from .saits import SAITS | ||
| from .timesnet import TimesNet | ||
| from .transformer import Transformer | ||
| from .usgan import USGAN | ||
|
|
||
| # naive imputation methods | ||
| from .locf import LOCF | ||
| from .mean import Mean | ||
| from .median import Median | ||
|
|
||
| __all__ = [ | ||
| # neural network imputation methods | ||
| "SAITS", | ||
| "Transformer", | ||
| "TimesNet", | ||
| "BRITS", | ||
| "MRNN", | ||
| "LOCF", | ||
| "GPVAE", | ||
| "USGAN", | ||
| "CSDI", | ||
| # naive imputation methods | ||
| "LOCF", | ||
| "Mean", | ||
| "Median", | ||
| ] | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,12 @@ | ||
| """ | ||
| The package of the partially-observed time-series imputation method Median. | ||
| """ | ||
|
|
||
| # Created by Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| from .model import Mean | ||
|
|
||
| __all__ = [ | ||
| "Mean", | ||
| ] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,143 @@ | ||
| """ | ||
| The implementation of Mean value imputation. | ||
| """ | ||
|
|
||
| # Created by Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| import warnings | ||
| from typing import Union, Optional | ||
|
|
||
| import h5py | ||
| import numpy as np | ||
| import torch | ||
|
|
||
| from ..base import BaseImputer | ||
| from ...utils.logging import logger | ||
|
|
||
|
|
||
| class Mean(BaseImputer): | ||
| """Mean value imputation method.""" | ||
|
|
||
| def __init__( | ||
| self, | ||
| ): | ||
| super().__init__() | ||
|
|
||
| def fit( | ||
| self, | ||
| train_set: Union[dict, str], | ||
| val_set: Optional[Union[dict, str]] = None, | ||
| file_type: str = "h5py", | ||
| ) -> None: | ||
| """Train the imputer on the given data. | ||
| Warnings | ||
| -------- | ||
| Mean imputation class does not need to run fit(). | ||
| Please run func ``predict()`` directly. | ||
| """ | ||
| warnings.warn( | ||
| "Mean imputation class has no parameter to train. " | ||
| "Please run func `predict()` directly." | ||
| ) | ||
|
|
||
| def predict( | ||
| self, | ||
| test_set: Union[dict, str], | ||
| file_type: str = "h5py", | ||
| ) -> dict: | ||
| """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 | ||
| ------- | ||
| result_dict: dict | ||
| 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. | ||
| """ | ||
| if isinstance(test_set, str): | ||
| with h5py.File(test_set, "r") as f: | ||
| X = f["X"][:] | ||
| else: | ||
| X = test_set["X"] | ||
|
|
||
| assert len(X.shape) == 3, ( | ||
| f"Input X should have 3 dimensions [n_samples, n_steps, n_features], " | ||
| f"but the actual shape of X: {X.shape}" | ||
| ) | ||
| if isinstance(X, list): | ||
| X = np.asarray(X) | ||
|
|
||
| n_samples, n_steps, n_features = X.shape | ||
|
|
||
| if isinstance(X, np.ndarray): | ||
| X_imputed_reshaped = np.copy(X).reshape(-1, n_features) | ||
| mean_values = np.nanmean(X_imputed_reshaped, axis=0) | ||
| for i, v in enumerate(mean_values): | ||
| X_imputed_reshaped[:, i] = np.nan_to_num( | ||
| X_imputed_reshaped[:, i], nan=v | ||
| ) | ||
| imputed_data = X_imputed_reshaped.reshape(n_samples, n_steps, n_features) | ||
| elif isinstance(X, torch.Tensor): | ||
| X_imputed_reshaped = torch.clone(X).reshape(-1, n_features) | ||
| mean_values = torch.nanmean(X_imputed_reshaped, dim=0).numpy() | ||
| for i, v in enumerate(mean_values): | ||
| X_imputed_reshaped[:, i] = torch.nan_to_num( | ||
| X_imputed_reshaped[:, i], nan=v | ||
| ) | ||
| imputed_data = X_imputed_reshaped.reshape(n_samples, n_steps, n_features) | ||
| else: | ||
| raise ValueError() | ||
|
|
||
| result_dict = { | ||
| "imputation": imputed_data, | ||
| } | ||
| return result_dict | ||
|
|
||
| def impute( | ||
| self, | ||
| X: Union[dict, str], | ||
| file_type="h5py", | ||
| ) -> np.ndarray: | ||
| """Impute missing values in the given data with the trained model. | ||
| Warnings | ||
| -------- | ||
| The method impute is deprecated. Please use `predict()` instead. | ||
| Parameters | ||
| ---------- | ||
| X : | ||
| The data samples for testing, should be array-like of shape [n_samples, sequence length (time steps), | ||
| n_features], or a path string locating a data file, e.g. h5 file. | ||
| file_type : | ||
| The type of the given file if X is a path string. | ||
| Returns | ||
| ------- | ||
| array-like, shape [n_samples, sequence length (time steps), n_features], | ||
| Imputed data. | ||
| """ | ||
| logger.warning( | ||
| "🚨DeprecationWarning: The method impute is deprecated. Please use `predict` instead." | ||
| ) | ||
| results_dict = self.predict(X, file_type=file_type) | ||
| return results_dict["imputation"] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,12 @@ | ||
| """ | ||
| The package of the partially-observed time-series imputation method Median. | ||
| """ | ||
|
|
||
| # Created by Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| from .model import Median | ||
|
|
||
| __all__ = [ | ||
| "Median", | ||
| ] |
Oops, something went wrong.