add clickable links in supported input docs (#2711)

Author: david-cortes-intel

doc/sources/conf.py

@@ -75,6 +75,7 @@
 intersphinx_mapping = {
     "sklearn": ("https://scikit-learn.org/stable/", None),
     "dpctl": ("https://intelpython.github.io/dpctl/latest", None),
+    "dpnp": ("https://intelpython.github.io/dpnp", None),
     "mpi4py": ("https://mpi4py.readthedocs.io/en/stable/", None),
     "xgboost": ("https://xgboost.readthedocs.io/en/stable/", None),
     # from scikit-learn, in case some object in sklearnex points to them:

doc/sources/input-types.rst

@@ -11,42 +11,45 @@
 .. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 .. See the License for the specific language governing permissions and
 .. limitations under the License.
-
+.. include:: substitutions.rst
 .. _input_types:
 
 =====================
 Supported input types
 =====================
 
-Just like Scikit-Learn estimators, estimators from Extension are able to accept
+Just like |sklearn| estimators, estimators from the |sklearnex| 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.
+- SciPy :external+scipy:doc:`sparse arrays and sparse matrices <tutorial/sparse>` (depending on the estimator).
+- Pandas :external+pandas:doc:`DataFrame and Series <user_guide/dsintro>` classes.
 
-In addition, Extension also supports:
+In addition, |sklearnex| also supports:
 
-- `dpnp <https://github.com/IntelPython/dpnp>`__ arrays.
-- `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ arrays.
+- :external+dpnp:doc:`dpnp.ndarray <reference/ndarray>`.
+- :external+dpctl:doc:`dpctl.tensor <api_reference/dpctl/tensor>`.
 
 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>`__.
 
-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 Extension.
+|sklearnex| currently does not offer accelerated routines for input types not listed here - when
+receiving an unsupported class, estimators will either convert to a supported class under some
+circumstances (e.g. PyArrow tables might get converted to NumPy arrays when passed to data
+validators from stock |sklearn|), throw an error (e.g. when passing some data format not that's
+not recognized by |sklearn|), or fall back to stock |sklearn| to handle it (when array API is
+enabled the input is unsupported).
 
 .. warning::
   In some cases data passed to estimators might be copied/duplicated during calls to methods such as fit/predict.
   The affected cases are listed below.
 
   - Non-contiguous NumPy array - i.e. where strides are wider than one element across both rows and columns
-  - For SciPy CSR matrix / CSR array index array is always copied.
+  - For SciPy CSR matrix / array, index arrays are always copied.
   - Heterogeneous NumPy array
-  - If SYCL queue is provided for device without `float64` support but data are `float64`, data are copied with reduced precision.
+  - If SYCL queue is provided for device without ``float64`` support but data are ``float64``, data are copied with reduced precision.
   - If :ref:`Array API <array_api>` is not enabled then data from GPU devices are always copied to the host device and then result table 
     (for applicable methods) is copied to the source device.