IntelPython · napetrov · May 7, 2024 · May 3, 2024 · May 6, 2024
diff --git a/daal4py/.buildinfo b/daal4py/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: b3f9dd5f9dffbf1afeba6d7177901aa9
+config: e1589d1aed29dd35620ebcc85aa22a53
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/daal4py/.nojekyll b/daal4py/.nojekyll
diff --git a/daal4py/_modules/index.html b/daal4py/_modules/index.html
@@ -1,15 +1,20 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Overview: module code &mdash; daal4py 2021 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+  <title>Overview: module code &mdash; daal4py 2021.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/style.css" />
+
+
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+        <script src="../_static/jquery.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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@@ -44,6 +49,9 @@
           <a href="../contents.html" class="icon icon-home">
             daal4py
           </a>
+              <div class="version">
+                2021
+              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -52,7 +60,7 @@
   </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>
@@ -98,7 +106,7 @@ <h1>All modules for which code is available</h1>
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, Intel.</p>
+    <p>&#169; Copyright Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a

diff --git a/daal4py/_sources/algorithms.rst.txt b/daal4py/_sources/algorithms.rst.txt
diff --git a/daal4py/_sources/contents.rst.txt b/daal4py/_sources/contents.rst.txt
@@ -1,10 +1,30 @@
-.. _contents::
+.. ******************************************************************************
+.. * 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.
+.. *******************************************************************************/
 
-.. include:: note.rst
+.. _contents:
+
+########
+Contents
+########
 
+.. include:: note.rst
+
 .. toctree::
    :maxdepth: 2
-   :caption: Contents
+   :caption: Contents:
 
    About daal4py <index>
    Data <data>
@@ -13,4 +33,4 @@
    Distributed Mode <scaling>
    Streaming Mode <streaming>
    Examples <examples>
-   Scikit-Learn API <sklearn>
+   Scikit-Learn API <sklearn>
diff --git a/daal4py/_sources/data.rst.txt b/daal4py/_sources/data.rst.txt
@@ -21,7 +21,7 @@ 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.

diff --git a/daal4py/_sources/examples.rst.txt b/daal4py/_sources/examples.rst.txt
diff --git a/daal4py/_sources/index.rst.txt b/daal4py/_sources/index.rst.txt
@@ -21,7 +21,7 @@ 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)
@@ -102,7 +102,7 @@ Last but not least, daal4py allows :ref:`getting input data from streams <stream
 
 oneAPI and GPU support in daal4py
 ---------------------------------
-daal4py oneAPI and GPU support is deprecated. Use `scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/oneapi-gpu.html#>`_ 
+daal4py oneAPI and GPU support is deprecated. Use `scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#>`_ 
 instead.
 
 
@@ -146,11 +146,11 @@ daal4py is available at the `Python Package Index <https://pypi.org/project/daal
 on Anaconda Cloud in `Conda Forge channel <https://anaconda.org/conda-forge/daal4py>`_
 and in `Intel channel <https://anaconda.org/intel/daal4py>`_.
 Sources and build instructions are available in
-`daal4py repository <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_.
+`daal4py repository <https://github.com/intel/scikit-learn-intelex/tree/main/daal4py>`_.
 
 The daal4py package is available via same distribution channels and platforms as scikit-learn-intelex. 
 See  
-`scikit-learn-intelex requirements <https://intel.github.io/scikit-learn-intelex/system-requirements.html>` _
+`scikit-learn-intelex requirements <https://intel.github.io/scikit-learn-intelex/latest/system-requirements.html>` _
 
 - Install from PyPI::
 
@@ -194,11 +194,11 @@ Scikit-Learn API and patching
 -----------------------------
 .. tip::
     We recommend using 
-    the 'scikit-learn-intelex package patching <https://intel.github.io/scikit-learn-intelex/what-is-patching.html>' _ for the scikit-learn patching. daal4py exposes some oneDAL solvers using a scikit-learn compatible API.
+    the 'scikit-learn-intelex package patching <https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html>' _ for the scikit-learn patching.
+daal4py exposes some oneDAL solvers using a scikit-learn compatible API.
 
 daal4py can furthermore monkey-patch the ``sklearn`` package to use the DAAL
 solvers as drop-in replacement without any code change.
 
 Please refer to the section on :ref:`scikit-learn API and patching <sklearn>`
 for more details.
-
diff --git a/daal4py/_sources/model-builders.rst.txt b/daal4py/_sources/model-builders.rst.txt
@@ -24,20 +24,26 @@ 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: 
+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. 
+models inference of those frameworks. The inference is performed by the oneDAL GBT implementation tuned
+for the best performance on the Intel(R) Architecture.
+
+.. note::
+
+   Currently, experimental support for XGBoost* and LightGBM* categorical data is not supported.
+   For the model conversion to work with daal4py, convert non-numeric data to numeric data 
+   before training and converting the model.
 
 Conversion
----------
+----------
 The first step is to convert already trained model. The
 API usage for different frameworks is the same:
 
@@ -61,37 +67,76 @@ CatBoost::
 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. 
-    
+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. 
+The ``d4p_prediction`` variable stores the predictions made by the ``predict()`` method.
+
+SHAP Value Calculation for Regression Models
+------------------------------------------------------------
+
+SHAP contribution and interaction value calculation are natively supported by models created with daal4py Model Builders.
+For these models, the ``predict()`` method takes additional keyword arguments:
+
+    ::
+
+      d4p_model.predict(test_data, pred_contribs=True)      # for SHAP contributions
+      d4p_model.predict(test_data, pred_interactions=True)  # for SHAP interactions
+
+The returned prediction has the shape:
+
+   * ``(n_rows, n_features + 1)``  for SHAP contributions 
+   * ``(n_rows, n_features + 1, n_features + 1)`` for SHAP interactions
+Here, ``n_rows`` is the number of rows (i.e., observations) in
+``test_data``, and ``n_features`` is the number of features in the dataset.
+
+The prediction result for SHAP contributions includes a feature attribution value for each feature and a bias term for each observation.
+
+The prediction result for SHAP interactions comprises ``(n_features + 1) x (n_features + 1)`` values for all possible
+feature combinations, along with their corresponding bias terms.
+
+.. note:: The shapes of SHAP contributions and interactions are consistent with the XGBoost results.
+  In contrast, the `SHAP Python package <https://shap.readthedocs.io/en/latest/>`_ drops bias terms, resulting
+  in SHAP contributions (SHAP interactions) with one fewer column (one fewer column and row) per observation.
 
 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)
 
+
+Limitations
+------------------
+Model Builders support only base inference with prediction and probabilities prediction. The functionality is to be extended.
+Therefore, there are the following limitations:
+- The categorical features are not supported for conversion and prediction.
+- The multioutput models are not supported for conversion and prediction.
+- SHAP values can be calculated for regression models only.
+
+
 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>`_
+- `XGBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/main/examples/daal4py/model_builders_xgboost.py>`_
+- `SHAP value prediction from an XGBoost model <https://github.com/intel/scikit-learn-intelex/blob/main/examples/daal4py/model_builders_xgboost_shap.py>`_
+- `LightGBM model conversion <https://github.com/intel/scikit-learn-intelex/blob/main/examples/daal4py/model_builders_lightgbm.py>`_
+- `CatBoost model conversion <https://github.com/intel/scikit-learn-intelex/blob/main/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>`_
+
diff --git a/daal4py/_sources/note.rst.txt b/daal4py/_sources/note.rst.txt
diff --git a/daal4py/_sources/scaling.rst.txt b/daal4py/_sources/scaling.rst.txt
@@ -76,36 +76,36 @@ The following algorithms support distribution:
 
 - PCA (pca)
 
-  - `PCA <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/pca_spmd.py>`_
+  - `PCA <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/pca_spmd.py>`_
 
 - SVD (svd)
 
-  - `SVD <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/svd_spmd.py>`_
+  - `SVD <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/svd_spmd.py>`_
 
 - Linear Regression Training (linear_regression_training)
 
-  - `Linear Regression <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/linear_regression_spmd.py>`_
+  - `Linear Regression <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/linear_regression_spmd.py>`_
 
 - Ridge Regression Training (ridge_regression_training)
 
-  - `Ridge Regression <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/ridge_regression_spmd.py>`_
+  - `Ridge Regression <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/ridge_regression_spmd.py>`_
 
 - Multinomial Naive Bayes Training (multinomial_naive_bayes_training)
 
-  - `Naive Bayes <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/naive_bayes_spmd.py>`_
+  - `Naive Bayes <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/naive_bayes_spmd.py>`_
 
 - K-Means (kmeans_init and kmeans)
 
-  - `K-Means <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/kmeans_spmd.py>`_
+  - `K-Means <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/kmeans_spmd.py>`_
 
 - Correlation and Variance-Covariance Matrices (covariance)
 
-  - `Covariance <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/covariance_spmd.py>`_
+  - `Covariance <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/covariance_spmd.py>`_
 
 - Moments of Low Order (low_order_moments)
 
-  - `Low Order Moments <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/low_order_moms_spmd.py>`_
+  - `Low Order Moments <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/low_order_moms_spmd.py>`_
 
 - QR Decomposition (qr)
 
-  - `QR <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/qr_spmd.py>`_
+  - `QR <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/qr_spmd.py>`_
diff --git a/daal4py/_sources/sklearn.rst.txt b/daal4py/_sources/sklearn.rst.txt
@@ -20,7 +20,6 @@
 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
@@ -160,7 +159,7 @@ algorithms:
 
 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/IntelPython/daal4py/blob/master/deselected_tests.yaml>`__.
+<https://github.com/IntelPython/daal4py/blob/main/deselected_tests.yaml>`__.
 
 In particular the tests execute `check_estimator
 <https://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html>`__

diff --git a/daal4py/_sources/streaming.rst.txt b/daal4py/_sources/streaming.rst.txt
@@ -48,36 +48,36 @@ daal4py's streaming mode is as easy as follows:
 The streaming algorithms also accept arrays and DataFrames as input, e.g. the
 data can come from a stream rather than from multiple files. Here is an example
 which simulates a data stream using a generator which reads a file in chunks:
-`SVD reading stream of data <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/stream.py>`_
+`SVD reading stream of data <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/stream.py>`_
 
 Supported Algorithms and Examples
 ---------------------------------
 The following algorithms support streaming:
 
 - SVD (svd)
 
-  - `SVD <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/svd_streaming.py>`_
+  - `SVD <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/svd_streaming.py>`_
 
 - Linear Regression Training (linear_regression_training)
 
-  - `Linear Regression <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/linear_regression_streaming.py>`_
+  - `Linear Regression <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/linear_regression_streaming.py>`_
 
 - Ridge Regression Training (ridge_regression_training)
 
-  - `Ridge Regression <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/ridge_regression_streaming.py>`_
+  - `Ridge Regression <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/ridge_regression_streaming.py>`_
 
 - Multinomial Naive Bayes Training (multinomial_naive_bayes_training)
 
-  - `Naive Bayes <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/naive_bayes_streaming.py>`_
+  - `Naive Bayes <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/naive_bayes_streaming.py>`_
 
 - Moments of Low Order
 
-  - `Low Order Moments <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/low_order_moms_streaming.py>`_
+  - `Low Order Moments <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/low_order_moms_streaming.py>`_
 
 - Covariance
 
-  - `Covariance <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/covariance_streaming.py>`_
+  - `Covariance <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/covariance_streaming.py>`_
 
 - QR
 
-  - `QR <https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/qr_streaming.py>`_
+  - `QR <https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/qr_streaming.py>`_