Update daal4py 2023.2.1 (#5)

* Update model builders

* Fix daaal4py

Author: aepanchi

daal4py/_modules/index.html

@@ -52,7 +52,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../index.html">About daal4py</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../data.html">Data</a></li>

daal4py/_sources/algorithms.rst.txt

@@ -18,6 +18,8 @@
 Algorithms
 ##########
 
+.. include:: note.rst
+
 Classification
 --------------
 See also |onedal-dg-classification|_.

daal4py/_sources/contents.rst.txt

@@ -1,27 +1,10 @@
-.. ******************************************************************************
-.. * 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.
-.. *******************************************************************************/
+.. _contents::
 
-.. _contents:
+.. include:: note.rst
 
-########
-Contents
-########
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
+   :caption: Contents
 
    About daal4py <index>
    Data <data>
@@ -30,4 +13,4 @@ Contents
    Distributed Mode <scaling>
    Streaming Mode <streaming>
    Examples <examples>
-   Scikit-Learn API <sklearn>
+   Scikit-Learn API <sklearn>

daal4py/_sources/data.rst.txt

@@ -19,6 +19,9 @@
 ##########
 Input Data
 ##########
+
+.. include:: note.rst
+
 All array arguments to compute functions and to algorithm constructors can be
 provided in different formats. daal4py will automatically do its best to work on
 the provided data with minimal overhead, most notably without copying the data.

daal4py/_sources/examples.rst.txt

@@ -18,6 +18,8 @@
 Examples
 ##########
 
+.. include:: note.rst
+
 Below are examples on how to utilize daal4py for various usage styles.
 
 General usage

daal4py/_sources/index.rst.txt

@@ -19,6 +19,9 @@
 #####################################################
 Fast, Scalable and Easy Machine Learning With DAAL4PY
 #####################################################
+
+.. include:: note.rst
+    
 Daal4py makes your Machine Learning algorithms in Python lightning fast and easy to use. It provides
 highly configurable Machine Learning kernels, some of which support streaming input data and/or can
 be easily and efficiently scaled out to clusters of workstations.  Internally it uses Intel(R)

daal4py/_sources/model-builders.rst.txt

@@ -1,102 +1,97 @@
-.. ******************************************************************************
-.. * Copyright 2023 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.
-.. *******************************************************************************/
-
-.. _model-builders:
-
-###############################################
-Model Builders for the Gradient Boosting Frameworks
-###############################################
-Introduction
-------------------
-Gradient boosting on decision trees is one of the most accurate and efficient 
-machine learning algorithms for classification and regression. 
-The most popular implementations of it are the XGBoost*, 
-LightGBM*, and CatBoost* frameworks.
-daal4py Model Builders deliver the accelerated
-models inference of those frameworks. The inference is performed by the oneDAL GBT implementation tuned 
-for the best performance on the Intel(R) Architecture. 
-
-Conversion
----------
-The first step is to convert already trained model. There are similar 
-APIs for different frameworks. 
-XGBoost::
-
-  import daal4py as d4p
-  d4p_model = d4p.get_gbt_model_from_xgboost(xgb_model)
-
-LightGBM::
-
-  import daal4py as d4p
-  d4p_model = d4p.get_gbt_model_from_lightgbm(lgb_model)
-
-CatBoost::
-
-  import daal4py as d4p
-  d4p_model = d4p.get_gbt_model_from_catboost(cb_model)
-
-.. note:: Convert model only once and then use it for the inference.
-
-Classification and Regression Inference
----------
-GBT implementation in daal4py assumes separate APIs for the classification and regression.
-Specify the corresponding API and match the corresponding problem 
-in the initial framework.
-
-Classification::
-
-    d4p_cls_algo = d4p.gbt_classification_prediction(
-        nClasses=params['classes_count'],
-        resultsToEvaluate="computeClassLabels",
-        fptype='float'
-    )
-
-Regression::
-
-    d4p_reg_algo = d4p.gbt_regression_prediction()
-
-Next, daal4py algorithm object needs compute method to be called. 
-Both the data and the previously converted model should be passed with the results of the prediction 
-available within the  ``.prediction`` parameter.
-
-Compute::
-
-    d4p_predictions = d4p_reg_algo.compute(X_test, d4p_model).prediction
-
-The one-line variant of the same code::
-
-    d4p_prediction = d4p.gbt_regression_prediction().compute(X_test, d4p_model).prediction
-
-
-Limitations
----------------------------------
-Missing Values (NaN)
-Note that there is temporary limitation on the use of missing values 
-(NaN) during training and prediction. This problem is addressed on 
-the master branch and to be available in the 2023.2 release.
-
-Examples
----------------------------------
-Model Builders models conversion
-
-- `XGBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/gbt_cls_model_create_from_xgboost_batch.py>`_
-- `LightGBM model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/gbt_cls_model_create_from_lightgbm_batch.py>`_
-- `CatBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/gbt_cls_model_create_from_catboost_batch.py>`_
-
-Articles and Blog Posts
----------------------------------
-
--  `Improving the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_
+.. ******************************************************************************
+.. * Copyright 2023 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.
+.. *******************************************************************************/
+
+.. _model-builders:
+
+###############################################
+Model Builders for the Gradient Boosting Frameworks
+###############################################
+
+.. include:: note.rst
+
+Introduction
+------------------
+Gradient boosting on decision trees is one of the most accurate and efficient 
+machine learning algorithms for classification and regression. 
+The most popular implementations of it are: 
+
+* XGBoost*
+* LightGBM*
+* CatBoost*
+
+daal4py Model Builders deliver the accelerated
+models inference of those frameworks. The inference is performed by the oneDAL GBT implementation tuned 
+for the best performance on the Intel(R) Architecture. 
+
+Conversion
+---------
+The first step is to convert already trained model. The
+API usage for different frameworks is the same:
+
+XGBoost::
+
+  import daal4py as d4p
+  d4p_model = d4p.mb.convert_model(xgb_model)
+
+LightGBM::
+
+  import daal4py as d4p
+  d4p_model = d4p.mb.convert_model(lgb_model)
+
+CatBoost::
+
+  import daal4py as d4p
+  d4p_model = d4p.mb.convert_model(cb_model)
+
+.. note:: Convert model only once and then use it for the inference.
+
+Classification and Regression Inference
+----------------------------------------
+
+The API is the same for classification and regression inference. 
+Based on the original model passed to the ``convert_model``, ``d4p_prediction`` is either the classification or regression output. 
+    
+    ::
+      
+      d4p_prediction = d4p_model.predict(test_data)
+
+Here, the ``predict()`` method of ``d4p_model`` is being used to make predictions on the ``test_data`` dataset.
+The ``d4p_prediction`` variable stores the predictions made by the ``predict()`` method. 
+
+Scikit-learn-style Estimators
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can also use the scikit-learn-style classes ``GBTDAALClassifier`` and ``GBTDAALRegressor`` to convert and infer your models. For example:
+
+:: 
+
+  from daal4py.sklearn.ensemble import GBTDAALRegressor
+  reg = xgb.XGBRegressor()
+  reg.fit(X, y)
+  d4p_predt = GBTDAALRegressor.convert_model(reg).predict(X)
+
+Examples
+---------------------------------
+Model Builders models conversion
+
+- `XGBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/model_builders_xgboost.py>`_
+- `LightGBM model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/model_builders_lightgbm.py>`_
+- `CatBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/model_builders_catboost.py>`_
+
+Articles and Blog Posts
+---------------------------------
+
+-  `Improving the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_

daal4py/_sources/note.rst.txt

@@ -0,0 +1,4 @@
+.. _note:
+
+.. note:: Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, `Intel(R) Extension for Scikit-learn* <https://github.com/intel/scikit-learn-intelex>`_. 
+                All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.

daal4py/_sources/scaling.rst.txt

@@ -19,6 +19,9 @@
 ###############################################
 Scaling on Distributed Memory (Multiprocessing)
 ###############################################
+
+.. include:: note.rst
+
 It's Easy
 ---------
 daal4py operates in SPMD style (Single Program Multiple Data), which means your

daal4py/_sources/sklearn.rst.txt

@@ -20,6 +20,8 @@
 Scikit-Learn API and patching
 #############################
 
+.. include:: note.rst
+
 Python interface to efficient Intel(R) oneAPI Data Analytics Library provided by daal4py allows one
 to create scikit-learn compatible estimators, transformers, clusterers, etc. powered by oneDAL which
 are nearly as efficient as native programs.