.. Copyright 2024 Intel Corporation
..
.. Licensed under the Apache License, Version 2.0 (the "License");
.. you may not use this file except in compliance with the License.
.. You may obtain a copy of the License at
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..     http://www.apache.org/licenses/LICENSE-2.0
..
.. Unless required by applicable law or agreed to in writing, software
.. distributed under the License is distributed on an "AS IS" BASIS,
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.. See the License for the specific language governing permissions and
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.. _input_types:

=====================
Supported input types
=====================

Just like Scikit-Learn estimators, estimators from Intel(R)'s Extension are able to accept
and work with different classes of input data, including:

- `Numpy <https://numpy.org/>`__ arrays.
- Other array classes implementing the `Array API <https://data-apis.org/array-api/latest/>`__ protocol
  (see :ref:`array_api` for details).
- `SciPy sparse arrays and sparse matrices <https://docs.scipy.org/doc/scipy/tutorial/sparse.html>`__ (depending on the estimator).
- `Pandas <https://pandas.pydata.org>`__ ``DataFrame`` and ``Series`` classes.

In addition, Intel(R)'s Extension also supports:

- `dpnp <https://github.com/IntelPython/dpnp>`__ arrays.
- `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ arrays.

Stock Scikit-Learn estimators, depending on the version, might offer support for additional
input types beyond this list, such as ``DataFrame`` and ``Series`` classes from other libraries
like `Polars <https://pola.rs>`__.

Intel(R)'s Extension currently does not offer accelerated routines for input types not listed
here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
handle it, so make sure to convert them to a supported type when using Intel(R)'s Extension.