Github user kaknikhil commented on a diff in the pull request:
https://github.com/apache/madlib/pull/218#discussion_r157892828
--- Diff: src/ports/postgres/modules/sample/balance_sample.py_in ---
@@ -0,0 +1,322 @@
+# coding=utf-8
+#
+# 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.
+import plpy
+from utilities.control import MinWarning
+from utilities.utilities import _assert
+from utilities.utilities import unique_string
+from utilities.validate_args import table_exists
+from utilities.validate_args import columns_exist_in_table
+from utilities.validate_args import table_is_empty
+from utilities.validate_args import get_cols
+from utilities.utilities import py_list_to_sql_string
+
+m4_changequote(`<!', `!>')
+
+def balance_sample(schema_madlib, source_table, output_table, class_col,
+ class_sizes, output_table_size, grouping_cols, with_replacement,
**kwargs):
+
+ """
+ Balance sampling function
+ Args:
+ @param source_table Input table name.
+ @param output_table Output table name.
+ @param class_col Name of the column containing the class to
be
+ balanced.
+ @param with_replacement (Default: FALSE) The sampling method.
+
+ """
+ with MinWarning("warning"):
+
+ class_counts = unique_string(desp='class_counts')
+
+ _validate_strs(source_table, output_table, class_col, class_sizes,
+ output_table_size, grouping_cols)
+ source_table_columns = ','.join(get_cols(source_table))
+ grp_by = "GROUP BY {0}".format(class_col)
+ """
+ Frequency table for classes
+ """
+ plpy.execute(""" CREATE VIEW {class_counts} AS (
+ SELECT
+ {class_col} AS classes,
+ count(*) AS class_count
+ FROM {source_table}
+ {grp_by})
+ """.format(**locals()))
+
+ if class_sizes.lower() == 'undersample':
+
+ if not with_replacement:
+ """
+ Random undersample without replacement
+ """
+ output_sql = """
+ CREATE TABLE {output_table} AS (
+ SELECT
+ {source_table_columns}
+ FROM
+ (SELECT
+ *,
+ row_number() OVER(PARTITION BY
+ {class_col} ORDER BY random())
+ AS __row_no
+ FROM {source_table}) AS foo
+ WHERE __row_no <=
+ (SELECT
+ MIN(class_count)
+ FROM {class_counts}))
+ """.format(**locals())
+ plpy.execute(output_sql)
+ else:
+ """
+ Random undersample with replacement
--- End diff --
I think the comments can be more verbose and it might be easier to
understand the implementation if the comments are consolidated and explained in
a bit more detail. It's a bit hard to understand by just looking at these
comments to understand what the algorithm is doing.
---
