.. ******************************************************************************
.. * Copyright 2020 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
.. *
.. *     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,
.. * 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.
.. *******************************************************************************/

.. _sklearn_algorithms:

####################
Supported Algorithms
####################

Applying |intelex| impacts the following scikit-learn algorithms:

on CPU
------

Classification
**************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `SVC`
     - All parameters are supported
     - No limitations
   * - `NuSVC`
     - All parameters are supported
     - No limitations
   * - `RandomForestClassifier`
     - All parameters are supported except:

       - ``warm_start`` = `True`
       - ``cpp_alpha`` != `0`
       - ``criterion`` != `'gini'`
     - Multi-output and sparse data are not supported
   * - `KNeighborsClassifier`
     - 
       - For ``algorithm`` == `'kd_tree'`:
       
         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
       - For ``algorithm`` == `'brute'`:
         
         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
     - Multi-output and sparse data are not supported
   * - `LogisticRegression`
     - All parameters are supported except:

       - ``solver`` not in [`'lbfgs'`, `'newton-cg'`]
       - ``class_weight`` != `None`
       - ``sample_weight`` != `None`
     - Only dense data is supported

Regression
**********

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `SVR`
     - All parameters are supported
     - No limitations
   * - `NuSVR`
     - All parameters are supported
     - No limitations
   * - `RandomForestRegressor`
     - All parameters are supported except:

       - ``warm_start`` = `True`
       - ``cpp_alpha`` != `0`
       - ``criterion`` != `'mse'`
     - Multi-output and sparse data are not supported
   * - `KNeighborsRegressor`
     - All parameters are supported except:

       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
     - Multi-output and sparse data are not supported
   * - `LinearRegression`
     - All parameters are supported except:

       - ``normalize`` != `False`
       - ``sample_weight`` != `None`
     - Only dense data is supported, `#observations` should be >= `#features`.
   * - `Ridge`
     - All parameters are supported except:

       - ``normalize`` != `False`
       - ``solver`` != `'auto'`
       - ``sample_weight`` != `None`
     - Only dense data is supported, `#observations` should be >= `#features`.
   * - `ElasticNet`
     - All parameters are supported except:

       - ``sample_weight`` != `None`
     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
   * - `Lasso`
     - All parameters are supported except:

       - ``sample_weight`` != `None`
     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.

Clustering
**********

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `KMeans`
     - All parameters are supported except:

       - ``precompute_distances``
       - ``sample_weight`` != `None`
     - No limitations
   * - `DBSCAN`
     - All parameters are supported except:

       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
       - ``algorithm`` not in [`'brute'`, `'auto'`]
     - Only dense data is supported

Dimensionality reduction
************************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `PCA`
     - All parameters are supported except:

       - ``svd_solver`` != `'full'`
     - Sparse data is not supported
   * - `TSNE`
     - All parameters are supported except:

       - ``metric`` != 'euclidean' or `'minkowski'` with ``p`` != `2`

       Refer to :ref:`TSNE acceleration details <acceleration_tsne>` to learn more.
     - Sparse data is not supported

Nearest Neighbors
*****************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `NearestNeighbors`
     - 
       - For ``algorithm`` == 'kd_tree':
         
         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
       - For ``algorithm`` == 'brute':
         
         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
     - Sparse data is not supported

Other tasks
***********

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `train_test_split`
     - All parameters are supported
     - Only dense data is supported
   * - `assert_all_finite`
     - All parameters are supported
     - Only dense data is supported
   * - `pairwise_distance`
     - All parameters are supported except:
     
       - ``metric`` not in [`'cosine'`, `'correlation'`]
     - Only dense data is supported
   * - `roc_auc_score`
     - All parameters are supported except:
       
       - ``average`` != `None`
       - ``sample_weight`` != `None`
       - ``max_fpr`` != `None`
       - ``multi_class`` != `None`
     - No limitations

on GPU
------

.. seealso:: :ref:`oneapi_gpu`

Classification
**************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `SVC`
     - All parameters are supported except:

       - ``kernel`` = `'sigmoid_poly'`
       - ``class_weight`` != `None`
     - Only binary dense data is supported
   * - `RandomForestClassifier`
     - All parameters are supported except:

       - ``warm_start`` = `True`
       - ``cpp_alpha`` != `0`
       - ``criterion`` != `'gini'`
       - ``oob_score`` = `True`
       - ``sample_weight`` != `None`
     - Multi-output and sparse data are not supported
   * - `KNeighborsClassifier`
     - All parameters are supported except:

       - ``algorithm`` != `'brute'`
       - ``weights`` = `'callable'`
       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
     - Only dense data is supported
   * - `LogisticRegression`
     - All parameters are supported except:

       - ``solver`` != `'newton-cg'`
       - ``class_weight`` != `None`
       - ``sample_weight`` != `None`
       - ``penalty`` != `'l2'`
     - Only dense data is supported

Regression
**********

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `RandomForestRegressor`
     - All parameters are supported except:

       - ``warm_start`` = `True`
       - ``cpp_alpha`` != `0`
       - ``criterion`` != `'mse'`
       - ``oob_score`` = `True`
       - ``sample_weight`` != `None`
     - Multi-output and sparse data are not supported
   * - `KNeighborsRegressor`
     - All parameters are supported except:

       - ``algorithm`` != `'brute'`
       - ``weights`` = `'callable'`
       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
     - Only dense data is supported
   * - `LinearRegression`
     - All parameters are supported except:

       - ``normalize`` != `False`
       - ``sample_weight`` != `None`
     - Only dense data is supported, `#observations` should be >= `#features`.

Clustering
**********

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `KMeans`
     - All parameters are supported except:

       - ``precompute_distances``
       - ``sample_weight`` != `None`
       
       ``Init`` = `'k-means++'` fallbacks to CPU.
     - Sparse data is not supported
   * - `DBSCAN`
     - All parameters are supported except:

       - ``metric`` != `'euclidean'`
       - ``algorithm`` not in [`'brute'`, `'auto'`]
     - Only dense data is supported

Dimensionality reduction
************************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `PCA`
     - All parameters are supported except:
     
       - ``svd_solver`` != `'full'`
     - Sparse data is not supported

Nearest Neighbors
*****************

.. list-table::
   :widths: 10 30 20
   :header-rows: 1
   :align: left

   * - Algorithm
     - Parameters
     - Data formats
   * - `NearestNeighbors`
     - All parameters are supported except:

       - ``algorithm`` != `'brute'`
       - ``weights`` = `'callable'`
       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
     - Only dense data is supported

Scikit-learn tests
------------------

Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
suite, with few exceptions, specified in `deselected_tests.yaml
<https://github.com/intel/scikit-learn-intelex/blob/master/deselected_tests.yaml>`__.

The results of the entire latest scikit-learn test suite with |intelex|: `CircleCI
<https://circleci.com/gh/intel/scikit-learn-intelex>`_.