Add test for xgboost modelbuiler

tests/test_xgboost_mb.py

@@ -0,0 +1,87 @@
+#===============================================================================
+# 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.
+#===============================================================================
+
+import unittest
+import importlib.util
+import daal4py as d4p
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from daal4py.sklearn._utils import daal_check_version
+
+from daal4py import _get__daal_link_version__ as dv
+# First item is major version - 2021,
+# second is minor+patch - 0110,
+# third item is status - B
+daal_version = (int(dv()[0:4]), dv()[10:11], int(dv()[4:8]))
+reason = str(((2021, 'P', 1))) + " not supported in this library version "
+reason += str(daal_version)
+
+
+class XgboostModelBuilder(unittest.TestCase):
+    @unittest.skipUnless(all([
+        hasattr(d4p, 'get_gbt_model_from_xgboost'),
+        hasattr(d4p, 'gbt_classification_prediction'),
+        daal_check_version(((2021, 'P', 1)))]), reason)
+    @unittest.skipUnless(importlib.util.find_spec('xgboost')
+                         is not None, 'xgoost library is not installed')
+    def test_earlystop(self):
+        import xgboost as xgb
+        num_classes = 3
+        X, y = make_classification(n_samples=1000,
+                                   n_features=10,
+                                   n_informative=3,
+                                   n_classes=num_classes,
+                                   random_state=42)
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=0.3, random_state=42)
+
+        # training parameters setting
+        params = {
+            'n_estimators': 100,
+            'max_bin': 256,
+            'scale_pos_weight': 2,
+            'lambda_l2': 1,
+            'alpha': 0.9,
+            'max_depth': 8,
+            'num_leaves': 2**8,
+            'verbosity': 0,
+            'objective': 'multi:softproba',
+            'learning_rate': 0.3,
+            'num_class': num_classes,
+            'early_stopping_rounds': 5
+        }
+
+        xgb_clf = xgb.XGBClassifier(**params)
+        xgb_clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
+        booster = xgb_clf.get_booster()
+
+        xgb_prediction = xgb_clf.predict(X_test)
+        xgb_proba = xgb_clf.predict_proba(X_test)
+        xgb_errors_count = np.count_nonzero(xgb_prediction - np.ravel(y_test))
+
+        daal_model = d4p.mb.convert_model(booster)
+
+        daal_prediction = daal_model.predict(X_test)
+        daal_proba = daal_model.predict_proba(X_test)
+        daal_errors_count = np.count_nonzero(daal_prediction - np.ravel(y_test))
+
+        self.assertTrue(np.absolute(xgb_errors_count - daal_errors_count) == 0)
+        self.assertTrue(np.allclose(xgb_proba, daal_proba))
+
+
+if __name__ == '__main__':
+    unittest.main()