[MINOR] Update Python API

src/main/python/systemds/operator/algorithm/__init__.py

@@ -24,6 +24,7 @@
 from .builtin.WoE import WoE 
 from .builtin.WoEApply import WoEApply 
 from .builtin.abstain import abstain 
+from .builtin.adasyn import adasyn 
 from .builtin.als import als 
 from .builtin.alsCG import alsCG 
 from .builtin.alsDS import alsDS 
@@ -199,6 +200,7 @@
 __all__ = ['WoE',
  'WoEApply',
  'abstain',
+ 'adasyn',
  'als',
  'alsCG',
  'alsDS',

src/main/python/systemds/operator/algorithm/builtin/adasyn.py

@@ -0,0 +1,62 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+# -------------------------------------------------------------
+
+# Autogenerated By   : src/main/python/generator/generator.py
+# Autogenerated From : scripts/builtin/adasyn.dml
+
+from typing import Dict, Iterable
+
+from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
+from systemds.utils.consts import VALID_INPUT_TYPES
+
+
+def adasyn(X: Matrix,
+           Y: Matrix,
+           **kwargs: Dict[str, VALID_INPUT_TYPES]):
+    """
+     Builtin function for handing class imbalance using Adaptive Synthetic Sampling (ADASYN)
+     by Haibo He et. al. In International Joint Conference on Neural Networks (2008). 1322-1328
+    
+    
+    
+    :param X: Feature matrix [shape: n-by-m]
+    :param Y: Class labels [shape: n-by-1]
+    :param k: Number of nearest neighbors
+    :param beta: Desired balance level after generation of synthetic data [0, 1]
+    :param dth: Distribution threshold
+    :param seed: Seed for randomized data point selection
+    :return: Feature matrix of n original rows followed by G = (ml-ms)*beta synthetic rows
+    :return: Class labels aligned with output X
+    """
+
+    params_dict = {'X': X, 'Y': Y}
+    params_dict.update(kwargs)
+
+    vX_0 = Matrix(X.sds_context, '')
+    vX_1 = Matrix(X.sds_context, '')
+    output_nodes = [vX_0, vX_1, ]
+
+    op = MultiReturn(X.sds_context, 'adasyn', output_nodes, named_input_nodes=params_dict)
+
+    vX_0._unnamed_input_nodes = [op]
+    vX_1._unnamed_input_nodes = [op]
+
+    return op

src/main/python/systemds/operator/algorithm/builtin/incSliceLine.py

@@ -65,8 +65,9 @@ def incSliceLine(addedX: Matrix,
     :param prevTK: previous top-k slices (for incremental updates)
     :param prevTKC: previous top-k scores (for incremental updates)
     :param encodeLat: flag for encoding output lattice for less memory consumption
-    :param pruningStrat: flag for disabling certain pruning strategies
-        (0 all, 1 all exact (score and size), 2 no score, 3 no size, 4 none)
+    :param pruningStrat: pruning strategy: 0 all pruning, 1 all exact pruning,
+        2 only score pruning, 3 only max score pruning,
+        4 only size pruning, 5 no pruning
     :return: top-k slices (k x ncol(totalX) if successful)
     :return: score, size, error of slices (k x 3)
     :return: debug matrix, populated with enumeration stats if verbose