Add SHAP calculation to GBT regression

.ci/pipeline/build-and-test-mac.yml

@@ -40,7 +40,7 @@ steps:
   - script: |
       source activate CB
       bash .ci/scripts/setup_sklearn.sh $(SKLEARN_VERSION)
-      pip install --upgrade -r requirements-test.txt -r requirements-test-optional.txt
+      pip install --upgrade -r requirements-test.txt
       pip install $(python .ci/scripts/get_compatible_scipy_version.py)
       pip list
     displayName: 'Install testing requirements'

.ci/pipeline/build-and-test-win.yml

@@ -43,7 +43,7 @@ steps:
       set PATH=C:\msys64\usr\bin;%PATH%
       call activate CB
       bash .ci/scripts/setup_sklearn.sh $(SKLEARN_VERSION)
-      pip install --upgrade -r requirements-test.txt -r requirements-test-optional.txt
+      pip install --upgrade -r requirements-test.txt
       cd ..
       for /f "delims=" %%c in ('python s\.ci\scripts\get_compatible_scipy_version.py') do set SCIPY_VERSION=%%c
       pip install %SCIPY_VERSION%

.ci/pipeline/nightly.yml

@@ -64,7 +64,7 @@ jobs:
       conda activate CB
       pip install -r dependencies-dev
       pip install -r requirements-doc.txt
-      pip install -r requirements-test.txt -r requirements-test-optional.txt
+      pip install -r requirements-test.txt
       pip install jupyter matplotlib requests
     displayName: 'Install requirements'
   - script: |

.github/CODEOWNERS

@@ -13,17 +13,16 @@ requirements-doc.txt          @maria-Petrova @napetrov @aepanchi @Alexsandruss
 onedal/                       @Alexsandruss @samir-nasibli @KulikovNikita
 sklearnex/                    @Alexsandruss @samir-nasibli @KulikovNikita
 
-# Examples 
+# Examples
 examples/                     @maria-Petrova @Alexsandruss @samir-nasibli @napetrov
 
 # Dependencies
 setup.py                      @napetrov @Alexsandruss @samir-nasibli
 requirements*                 @napetrov @Alexsandruss @samir-nasibli @homksei @ahuber21 @ethanglaser
-conda-recipe/                 @napetrov @Alexsandruss 
+conda-recipe/                 @napetrov @Alexsandruss
 
 # Model builders
 *model_builders*              @razdoburdin @ahuber21 @avolkov-intel
-requirements-test-optional.txt  @razdoburdin @ahuber21 @avolkov-intel
 
 # Forests
 *ensemble*                    @ahuber21 @icfaust

daal4py/mb/model_builders.py

@@ -200,7 +200,9 @@ def _predict_classification(self, X, fptype, resultsToEvaluate):
         else:
             return predict_result.probabilities
 
-    def _predict_regression(self, X, fptype):
+    def _predict_regression(
+        self, X, fptype, pred_contribs=False, pred_interactions=False
+    ):
         if X.shape[1] != self.n_features_in_:
             raise ValueError("Shape of input is different from what was seen in `fit`")
 
@@ -212,22 +214,64 @@ def _predict_regression(self, X, fptype):
                 ).format(type(self).__name__)
             )
 
-        # Prediction
+        try:
+            return self._predict_regression_with_results_to_compute(
+                X, fptype, pred_contribs, pred_interactions
+            )
+        except TypeError as e:
+            if "unexpected keyword argument 'resultsToCompute'" in str(e):
+                if pred_contribs or pred_interactions:
+                    # SHAP values requested, but not supported by this version
+                    raise TypeError(
+                        f"{'pred_contribs' if pred_contribs else 'pred_interactions'} not supported by this version of daalp4y"
+                    ) from e
+            else:
+                # unknown type error
+                raise
+
+        # fallback to calculation without `resultsToCompute`
         predict_algo = d4p.gbt_regression_prediction(fptype=fptype)
         predict_result = predict_algo.compute(X, self.daal_model_)
-
         return predict_result.prediction.ravel()
 
+    def _predict_regression_with_results_to_compute(
+        self, X, fptype, pred_contribs=False, pred_interactions=False
+    ):
+        """Assume daal4py supports the resultsToCompute kwarg"""
+        resultsToCompute = ""
+        if pred_contribs:
+            resultsToCompute = "shapContributions"
+        elif pred_interactions:
+            resultsToCompute = "shapInteractions"
+
+        predict_algo = d4p.gbt_regression_prediction(
+            fptype=fptype, resultsToCompute=resultsToCompute
+        )
+        predict_result = predict_algo.compute(X, self.daal_model_)
+
+        if pred_contribs:
+            return predict_result.prediction.ravel().reshape((-1, X.shape[1] + 1))
+        elif pred_interactions:
+            return predict_result.prediction.ravel().reshape(
+                (-1, X.shape[1] + 1, X.shape[1] + 1)
+            )
+        else:
+            return predict_result.prediction.ravel()
+
 
 class GBTDAALModel(GBTDAALBaseModel):
     def __init__(self):
         pass
 
-    def predict(self, X):
+    def predict(self, X, pred_contribs=False, pred_interactions=False):
         fptype = getFPType(X)
         if self._is_regression:
-            return self._predict_regression(X, fptype)
+            return self._predict_regression(X, fptype, pred_contribs, pred_interactions)
         else:
+            if pred_contribs or pred_interactions:
+                raise NotImplementedError(
+                    f"{'pred_contribs' if pred_contribs else 'pred_interactions'} is not implemented for classification models"
+                )
             return self._predict_classification(X, fptype, "computeClassLabels")
 
     def predict_proba(self, X):

doc/daal4py/model-builders.rst

@@ -24,17 +24,17 @@ 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.
 
 Conversion
 ---------
@@ -61,22 +61,49 @@ 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()
@@ -88,16 +115,17 @@ 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 categorical features are not supported for conversion and prediction.
 - The multioutput models are not supported for conversion and prediction.
-- The tree SHAP calculations are not supported.
+- 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>`_
+- `SHAP value prediction from an XGBoost model <https://github.com/intel/scikit-learn-intelex/blob/master/examples/daal4py/model_builders_xgboost_shap.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>`_
 

examples/mb/model_builders_xgboost_shap.py

@@ -0,0 +1,80 @@
+# ==============================================================================
+# 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.
+# ==============================================================================
+
+# daal4py Gradient Boosting Classification model creation and SHAP value
+# prediction example
+
+import numpy as np
+import xgboost as xgb
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+
+import daal4py as d4p
+
+
+def main(*ars, **kwargs):
+    # create data
+    X, y = make_regression(n_samples=10000, n_features=10, random_state=42)
+    X_train, X_test, y_train, _ = train_test_split(X, y, random_state=42)
+
+    # train the model
+    xgb_model = xgb.XGBRegressor(
+        max_depth=6, n_estimators=100, random_state=42, base_score=0.5
+    )
+    xgb_model.fit(X_train, y_train)
+
+    # Conversion to daal4py
+    daal_model = d4p.mb.convert_model(xgb_model.get_booster())
+
+    # SHAP contributions
+    daal_contribs = daal_model.predict(X_test, pred_contribs=True)
+
+    # SHAP interactions
+    daal_interactions = daal_model.predict(X_test, pred_interactions=True)
+
+    # XGBoost reference values
+    xgb_contribs = xgb_model.get_booster().predict(
+        xgb.DMatrix(X_test), pred_contribs=True, validate_features=False
+    )
+    xgb_interactions = xgb_model.get_booster().predict(
+        xgb.DMatrix(X_test), pred_interactions=True, validate_features=False
+    )
+
+    return (
+        daal_contribs,
+        daal_interactions,
+        xgb_contribs,
+        xgb_interactions,
+    )
+
+
+if __name__ == "__main__":
+    daal_contribs, daal_interactions, xgb_contribs, xgb_interactions = main()
+    print(f"XGBoost SHAP contributions shape: {xgb_contribs.shape}")
+    print(f"daal4py SHAP contributions shape: {daal_contribs.shape}")
+
+    print(f"XGBoost SHAP interactions shape: {xgb_interactions.shape}")
+    print(f"daal4py SHAP interactions shape: {daal_interactions.shape}")
+
+    contribution_rmse = np.sqrt(
+        np.mean((daal_contribs.reshape(-1, 1) - xgb_contribs.reshape(-1, 1)) ** 2)
+    )
+    print(f"SHAP contributions RMSE: {contribution_rmse:.2e}")
+
+    interaction_rmse = np.sqrt(
+        np.mean((daal_interactions.reshape(-1, 1) - xgb_interactions.reshape(-1, 1)) ** 2)
+    )
+    print(f"SHAP interactions RMSE: {interaction_rmse:.2e}")

generator/parse.py

@@ -283,8 +283,14 @@ def parse(self, elem, ctxt):
                 ctxt.enum = False
                 return True
             regex = (
-                r"^\s*(\w+)(?:\s*=\s*((\(int\))?\w(\w|:|\s|\+)*))?"
-                + r"(\s*,)?\s*((/\*|//).*)?$"
+                # capture group for value name
+                r"^\s*(\w+)"
+                # capture group for value (different possible formats, 123, 0x1, (1 << 5), etc.)
+                + r"(?:\s*=\s*((\(int\))?(\w|:|\s|\+|\(?\d+\s*<<\s*\d+\)?)*))?"
+                # comma after the value, plus possible comments
+                + r"(\s*,)?\s*((/\*|//).*)?"
+                # EOL
+                + r"$"
             )
             me = re.match(regex, elem)
             if me and not me.group(1).startswith("last"):

requirements-test.txt

@@ -6,3 +6,9 @@ scikit-learn==1.2.2 ; python_version == '3.8'
 scikit-learn==1.3.1 ; python_version >= '3.9'
 pandas==2.0.1 ; python_version == '3.8'
 pandas==2.1.1 ; python_version >= '3.9'
+xgboost==1.7.6; python_version <= '3.9'
+xgboost==2.0.0; python_version >= '3.10'
+lightgbm==4.1.0
+catboost==1.2.2; python_version <= '3.11'
+catboost>=1.2.2; python_version >= '3.12'
+shap==0.42.1