[MINOR] Correct dedub builtin docs

Author: Baunsgaard

scripts/builtin/dedup.dml

@@ -28,11 +28,11 @@
 #
 # INPUT:
 # --------------------------------------------------------------------------------------
-# X                 Input Frame[String] with n rows and d columns (raw tuples)
-# gloveMatrix       Matrix[Double] of size |V| × e (pretrained GloVe embeddings) -> |V| number of words and e = embedding dimesnion
-# vocab             Frame[String] of size |V| × 1 (vocabulary aligned with gloveMatrix)
-# similarityMeasure (optional) String specifying similarity metric: "cosine", "euclidean"
-# threshold         (optional) Double: threshold value above which tuples are considered duplicates
+# X                  Input Frame[String] with n rows and d columns (raw tuples)
+# gloveMatrix        Matrix[Double] of size |V| × e (pretrained GloVe embeddings) -> |V| number of words and e = embedding dimesnion
+# vocab              Frame[String] of size |V| × 1 (vocabulary aligned with gloveMatrix)
+# similarityMeasure  (optional) String specifying similarity metric: "cosine", "euclidean"
+# threshold          (optional) Double: threshold value above which tuples are considered duplicates
 # --------------------------------------------------------------------------------------
 #
 # OUTPUT:

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

@@ -53,6 +53,7 @@
 from .builtin.dbscanApply import dbscanApply 
 from .builtin.decisionTree import decisionTree 
 from .builtin.decisionTreePredict import decisionTreePredict 
+from .builtin.dedup import dedup 
 from .builtin.deepWalk import deepWalk 
 from .builtin.denialConstraints import denialConstraints 
 from .builtin.differenceStatistics import differenceStatistics 
@@ -251,6 +252,7 @@
  'dbscanApply',
  'decisionTree',
  'decisionTreePredict',
+ 'dedup',
  'deepWalk',
  'denialConstraints',
  'differenceStatistics',

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

@@ -0,0 +1,68 @@
+# -------------------------------------------------------------
+#
+# 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/dedup.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 dedup(X: Frame,
+          gloveMatrix: Matrix,
+          vocab: Frame,
+          **kwargs: Dict[str, VALID_INPUT_TYPES]):
+    """
+     Builtin for deduplication using distributed representations (DRs) and
+     locality-sensitive hashing (LSH) based blocking.
+    
+     The function encodes each input tuple as a dense vector using pre-trained GloVe embeddings (simple averaging), 
+     groups semantically similar tuples via LSH into buckets, and compares only those pairs for deduplication.
+     
+    
+    
+    
+    :param X: Input Frame[String] with n rows and d columns (raw tuples)
+    :param gloveMatrix: Matrix[Double] of size |V| × e (pretrained GloVe embeddings) -> |V| number of words and e = embedding dimesnion
+    :param vocab: Frame[String] of size |V| × 1 (vocabulary aligned with gloveMatrix)
+    :param similarityMeasure: (optional) String specifying similarity metric: "cosine", "euclidean"
+    :param threshold: (optional) Double: threshold value above which tuples are considered duplicates
+    :return: Frame[String] with deduplicated tuples
+        (first occurrence of each duplicate group is retained)
+    :return: Frame[String] with all detected duplicates
+        (i.e., tuples removed from the input)
+    """
+
+    params_dict = {'X': X, 'gloveMatrix': gloveMatrix, 'vocab': vocab}
+    params_dict.update(kwargs)
+    
+    vX_0 = Frame(X.sds_context, '')
+    vX_1 = Frame(X.sds_context, '')
+    output_nodes = [vX_0, vX_1, ]
+
+    op = MultiReturn(X.sds_context, 'dedup', 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/glove.py

@@ -42,6 +42,7 @@ def glove(input: Frame,
           distanceWeighting: bool,
           symmetric: bool):
     """
+     Computes the vector embeddings for words in a large text corpus.