**************************************
:py:mod:`wildboar.datasets.preprocess`
**************************************

.. py:module:: wildboar.datasets.preprocess

.. autoapi-nested-parse::

   Utilities for preprocessing time series.

   ..
       !! processed by numpydoc !!



Classes
-------

.. autoapisummary::

   wildboar.datasets.preprocess.Interpolate
   wildboar.datasets.preprocess.MaxAbsScale
   wildboar.datasets.preprocess.MinMaxScale
   wildboar.datasets.preprocess.Standardize
   wildboar.datasets.preprocess.Truncate



Functions
---------

.. autoapisummary::

   wildboar.datasets.preprocess.interpolate
   wildboar.datasets.preprocess.maxabs_scale
   wildboar.datasets.preprocess.minmax_scale
   wildboar.datasets.preprocess.named_preprocess
   wildboar.datasets.preprocess.standardize
   wildboar.datasets.preprocess.truncate




.. raw:: html

   <br />

.. py:class:: Interpolate(method='linear')



   
   Interpolate missing (`np.nan`) values.


   :Parameters:

       **method** : str, optional
           The interpolation method to use. Default is "linear".











   .. rubric:: Notes

   If scipy < 1.4, valid `method` values include "linear", "pchip", and
   "cubic". Otherwise, `method` also supports "akima" and "makima".



   ..
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   .. py:method:: fit(X, y=None)

      
      Fit the model to the provided data.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to fit the model.

          **y** : array-like, optional
              The target values. Ignored.



      :Returns:

          object
              Returns the instance of the fitted model.











      ..
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   .. py:method:: fit_transform(X, y=None, **fit_params)

      
      Fit to data, then transform it.

      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.

      :Parameters:

          **X** : array-like of shape (n_samples, n_features)
              Input samples.

          **y** :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None
              Target values (None for unsupervised transformations).

          **\*\*fit_params** : dict
              Additional fit parameters.



      :Returns:

          **X_new** : ndarray array of shape (n_samples, n_features_new)
              Transformed array.











      ..
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   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











      ..
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   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











      ..
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   .. py:method:: set_output(*, transform=None)

      
      Set output container.

      See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
      for an example on how to use the API.

      :Parameters:

          **transform** : {"default", "pandas", "polars"}, default=None
              Configure output of `transform` and `fit_transform`.
              
              - `"default"`: Default output format of a transformer
              - `"pandas"`: DataFrame output
              - `"polars"`: Polars output
              - `None`: Transform configuration is unchanged
              
              .. versionadded:: 1.4
                  `"polars"` option was added.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
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   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
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   .. py:method:: transform(X)

      
      Transform the data using the specified interpolation method.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to be transformed.



      :Returns:

          ndarray of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The transformed data after applying the interpolation method.











      ..
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.. py:class:: MaxAbsScale



   
   Scale each time series by its maximum absolute value.
















   ..
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   .. py:method:: fit(X, y=None)

      
      Fit the model to the provided data.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to fit the model.

          **y** : array-like, optional
              The target values. Ignored.



      :Returns:

          object
              Returns the instance of the fitted model.











      ..
          !! processed by numpydoc !!


   .. py:method:: fit_transform(X, y=None, **fit_params)

      
      Fit to data, then transform it.

      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.

      :Parameters:

          **X** : array-like of shape (n_samples, n_features)
              Input samples.

          **y** :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None
              Target values (None for unsupervised transformations).

          **\*\*fit_params** : dict
              Additional fit parameters.



      :Returns:

          **X_new** : ndarray array of shape (n_samples, n_features_new)
              Transformed array.











      ..
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   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











      ..
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   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_output(*, transform=None)

      
      Set output container.

      See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
      for an example on how to use the API.

      :Parameters:

          **transform** : {"default", "pandas", "polars"}, default=None
              Configure output of `transform` and `fit_transform`.
              
              - `"default"`: Default output format of a transformer
              - `"pandas"`: DataFrame output
              - `"polars"`: Polars output
              - `None`: Transform configuration is unchanged
              
              .. versionadded:: 1.4
                  `"polars"` option was added.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: transform(X)

      
      Transform the data using the specified interpolation method.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to be transformed.



      :Returns:

          ndarray of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The transformed data after applying the interpolation method.











      ..
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.. py:class:: MinMaxScale(min=0, max=1)



   
   Normalize time series, ensuring that each value within a specified minimum and maximum range.


   :Parameters:

       **min** : float, optional
           The minimum value.

       **max** : float, optional
           The maximum value.













   .. rubric:: Examples

   >>> from wildboar.datasets import load_gun_point
   >>> from wildboar.datasets.preprocess import MinMaxScale
   >>> X, _ = load_gun_point()
   >>> MinMaxScale().fit_transform(X).shape
   (200, 150)

   ..
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   .. py:method:: fit(X, y=None)

      
      Fit the model to the provided data.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to fit the model.

          **y** : array-like, optional
              The target values. Ignored.



      :Returns:

          object
              Returns the instance of the fitted model.











      ..
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   .. py:method:: fit_transform(X, y=None, **fit_params)

      
      Fit to data, then transform it.

      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.

      :Parameters:

          **X** : array-like of shape (n_samples, n_features)
              Input samples.

          **y** :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None
              Target values (None for unsupervised transformations).

          **\*\*fit_params** : dict
              Additional fit parameters.



      :Returns:

          **X_new** : ndarray array of shape (n_samples, n_features_new)
              Transformed array.











      ..
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   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











      ..
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   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_output(*, transform=None)

      
      Set output container.

      See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
      for an example on how to use the API.

      :Parameters:

          **transform** : {"default", "pandas", "polars"}, default=None
              Configure output of `transform` and `fit_transform`.
              
              - `"default"`: Default output format of a transformer
              - `"pandas"`: DataFrame output
              - `"polars"`: Polars output
              - `None`: Transform configuration is unchanged
              
              .. versionadded:: 1.4
                  `"polars"` option was added.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: transform(X)

      
      Transform the data using the specified interpolation method.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to be transformed.



      :Returns:

          ndarray of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The transformed data after applying the interpolation method.











      ..
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.. py:class:: Standardize



   
   Standardize time series with zero mean and unit standard deviation.
















   ..
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   .. py:method:: fit(X, y=None)

      
      Fit the model to the provided data.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to fit the model.

          **y** : array-like, optional
              The target values. Ignored.



      :Returns:

          object
              Returns the instance of the fitted model.











      ..
          !! processed by numpydoc !!


   .. py:method:: fit_transform(X, y=None, **fit_params)

      
      Fit to data, then transform it.

      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.

      :Parameters:

          **X** : array-like of shape (n_samples, n_features)
              Input samples.

          **y** :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None
              Target values (None for unsupervised transformations).

          **\*\*fit_params** : dict
              Additional fit parameters.



      :Returns:

          **X_new** : ndarray array of shape (n_samples, n_features_new)
              Transformed array.











      ..
          !! processed by numpydoc !!


   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











      ..
          !! processed by numpydoc !!


   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_output(*, transform=None)

      
      Set output container.

      See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
      for an example on how to use the API.

      :Parameters:

          **transform** : {"default", "pandas", "polars"}, default=None
              Configure output of `transform` and `fit_transform`.
              
              - `"default"`: Default output format of a transformer
              - `"pandas"`: DataFrame output
              - `"polars"`: Polars output
              - `None`: Transform configuration is unchanged
              
              .. versionadded:: 1.4
                  `"polars"` option was added.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: transform(X)

      
      Transform the data using the specified interpolation method.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to be transformed.



      :Returns:

          ndarray of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The transformed data after applying the interpolation method.











      ..
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.. py:class:: Truncate



   
   A transformer that truncates the input data based on the end of series indicators.
















   ..
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   .. py:method:: fit(X, y=None)

      
      Fit the model to the provided data.


      :Parameters:

          **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
              The input data to fit the model.

          **y** : array-like, optional
              The target values. Ignored.



      :Returns:

          object
              Returns the instance of the fitted model.











      ..
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   .. py:method:: fit_transform(X, y=None, **fit_params)

      
      Fit to data, then transform it.

      Fits transformer to `X` and `y` with optional parameters `fit_params`
      and returns a transformed version of `X`.

      :Parameters:

          **X** : array-like of shape (n_samples, n_features)
              Input samples.

          **y** :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None
              Target values (None for unsupervised transformations).

          **\*\*fit_params** : dict
              Additional fit parameters.



      :Returns:

          **X_new** : ndarray array of shape (n_samples, n_features_new)
              Transformed array.











      ..
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   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











      ..
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   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_output(*, transform=None)

      
      Set output container.

      See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
      for an example on how to use the API.

      :Parameters:

          **transform** : {"default", "pandas", "polars"}, default=None
              Configure output of `transform` and `fit_transform`.
              
              - `"default"`: Default output format of a transformer
              - `"pandas"`: DataFrame output
              - `"polars"`: Polars output
              - `None`: Transform configuration is unchanged
              
              .. versionadded:: 1.4
                  `"polars"` option was added.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
          !! processed by numpydoc !!


   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











      ..
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   .. py:method:: transform(X)

      
      Transform the input data X according to the fitted model.


      :Parameters:

          **X** : array-like
              Input data to transform.



      :Returns:

          array-like
              Transformed input data.











      ..
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.. py:function:: interpolate(X, method='linear')

   
   Interpolate the given time series using the specified method.


   :Parameters:

       **X** : array-like of shape (n_samples, n_dims, n_timestep) or (n_samples, n_timestep)
           The input data to be interpolated. It can be of any shape but must
           have at least dimension.

       **method** : str, optional
           The interpolation method to use. Default is "linear".



   :Returns:

       ndarray
           The interpolated data.








   .. rubric:: Notes

   If scipy < 1.4, valid `method` values include "linear", "pchip", and
   "cubic". Otherwise, `method` also supports "akima" and "makima".



   ..
       !! processed by numpydoc !!

.. py:function:: maxabs_scale(x)

   
   Scale each time series by its maximum absolute value.


   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.



   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The transformed samples.











   ..
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.. py:function:: minmax_scale(x, min=0, max=1)

   
   Scale x along the time dimension.

   Each time series is scaled such that each value is between min and max.

   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.

       **min** : float, optional
           The minimum value.

       **max** : float, optional
           The maximum value.



   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The transformed samples.











   ..
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.. py:function:: named_preprocess(name)

   
   Get a named preprocessor.


   :Parameters:

       **name** : str
           The name of the preprocessor.



   :Returns:

       callable
           The preprocessor function.











   ..
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.. py:function:: standardize(x)

   
   Scale x along the time dimension.

   The resulting array will have zero mean and unit standard deviation.

   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.



   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The standardized samples.











   ..
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.. py:function:: truncate(x)

   
   Truncate x to the shortest sequence.


   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.



   :Returns:

       ndarray of shape (n_samples, n_shortest) or (n_samples, n_dims, n_shortest)
           The truncated samples.











   ..
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