Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/madlib/pull/241#discussion_r175593796
--- Diff:
src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in ---
@@ -0,0 +1,559 @@
+# 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.
+
+
+"""
+@file minibatch_preprocessing.py_in
+
+"""
+from math import ceil
+import plpy
+
+from utilities import add_postfix
+from utilities import _assert
+from utilities import get_seg_number
+from utilities import is_platform_pg
+from utilities import is_psql_numeric_type
+from utilities import is_string_formatted_as_array_expression
+from utilities import py_list_to_sql_string
+from utilities import split_quoted_delimited_str
+from utilities import _string_to_array
+from utilities import validate_module_input_params
+from mean_std_dev_calculator import MeanStdDevCalculator
+from validate_args import get_expr_type
+from validate_args import output_tbl_valid
+from validate_args import _tbl_dimension_rownum
+
+m4_changequote(`<!', `!>')
+
+# These are readonly variables, do not modify
+MINIBATCH_OUTPUT_DEPENDENT_COLNAME = "dependent_varname"
+MINIBATCH_OUTPUT_INDEPENDENT_COLNAME = "independent_varname"
+
+class MiniBatchPreProcessor:
+ """
+ This class is responsible for executing the main logic of mini batch
+ preprocessing, which packs multiple rows of selected columns from the
+ source table into one row based on the buffer size
+ """
+ def __init__(self, schema_madlib, source_table, output_table,
+ dependent_varname, independent_varname, buffer_size,
**kwargs):
+ self.schema_madlib = schema_madlib
+ self.source_table = source_table
+ self.output_table = output_table
+ self.dependent_varname = dependent_varname
+ self.independent_varname = independent_varname
+ self.buffer_size = buffer_size
+
+ self.module_name = "minibatch_preprocessor"
+ self.output_standardization_table = add_postfix(self.output_table,
+ "_standardization")
+ self.output_summary_table = add_postfix(self.output_table,
"_summary")
+ self._validate_minibatch_preprocessor_params()
+
+ def minibatch_preprocessor(self):
+ # Get array expressions for both dep and indep variables from the
+ # MiniBatchQueryFormatter class
+ dependent_var_dbtype = get_expr_type(self.dependent_varname,
+ self.source_table)
+ qry_formatter = MiniBatchQueryFormatter(self.source_table)
+ dep_var_array_str, dep_var_classes_str = qry_formatter.\
+ get_dep_var_array_and_classes(self.dependent_varname,
+ dependent_var_dbtype)
+ indep_var_array_str = qry_formatter.get_indep_var_array_str(
+ self.independent_varname)
+
+ standardizer = MiniBatchStandardizer(self.schema_madlib,
+ self.source_table,
+ dep_var_array_str,
+ indep_var_array_str,
+
self.output_standardization_table)
+ standardize_query = standardizer.get_query_for_standardizing()
+
+ num_rows_processed, num_missing_rows_skipped = self.\
+
_get_skipped_rows_processed_count(
+ dep_var_array_str,
+ indep_var_array_str)
+ calculated_buffer_size = MiniBatchBufferSizeCalculator.\
+ calculate_default_buffer_size(
+ self.buffer_size,
+ num_rows_processed,
+
standardizer.independent_var_dimension)
+ """
+ This query does the following:
+ 1. Standardize the independent variables in the input table
+ (see MiniBatchStandardizer for more details)
+ 2. Filter out rows with null values either in dependent/independent
+ variables
+ 3. Converts the input dependent/independent variables into arrays
+ (see MiniBatchQueryFormatter for more details)
+ 4. Based on the buffer size, pack the dependent/independent arrays
into
+ matrices
+
+ Notes
+ 1. we are ignoring null in x because
+ a. matrix_agg does not support null
+ b. __utils_normalize_data returns null if any element of the
array
+ contains NULL
+ 2. Please keep the null checking where clause of this query in
sync with
+ the query in _get_skipped_rows_processed_count. We are doing this
null
+ check in two places to prevent another pass of the entire dataset.
+ """
+
+ # This ID is the unique row id that get assigned to each row after
preprocessing
+ unique_row_id = "__id__"
+ sql = """
+ CREATE TABLE {output_table} AS
+ SELECT {row_id},
+ {schema_madlib}.matrix_agg({dep_colname}) as
{dep_colname},
+ {schema_madlib}.matrix_agg({ind_colname}) as
{ind_colname}
+ FROM (
+ SELECT (row_number() OVER (ORDER BY random()) - 1) /
{buffer_size}
+ as {row_id}, * FROM
+ (
+ {standardize_query}
+ ) sub_query_1
+ WHERE NOT
{schema_madlib}.array_contains_null({dep_colname})
+ AND NOT {schema_madlib}.array_contains_null({ind_colname})
+ ) sub_query_2
+ GROUP BY {row_id}
+ {distributed_by_clause}
+ """.format(
+ schema_madlib=self.schema_madlib,
+ source_table=self.source_table,
+ output_table=self.output_table,
+ dependent_varname=self.dependent_varname,
+ independent_varname=self.independent_varname,
+ buffer_size = calculated_buffer_size,
+ dep_colname=MINIBATCH_OUTPUT_DEPENDENT_COLNAME,
+ ind_colname=MINIBATCH_OUTPUT_INDEPENDENT_COLNAME,
+ row_id = unique_row_id,
+ distributed_by_clause = '' if is_platform_pg() else
'DISTRIBUTED RANDOMLY',
+ **locals())
+ plpy.execute(sql)
+
+
+ standardizer.create_output_standardization_table()
+ MiniBatchSummarizer.create_output_summary_table(
+ self.source_table,
+ self.output_table,
+ self.dependent_varname,
+ self.independent_varname,
+ calculated_buffer_size,
+ dep_var_classes_str,
+ num_rows_processed,
+ num_missing_rows_skipped,
+ self.output_summary_table)
+
+ def _validate_minibatch_preprocessor_params(self):
+ # Test if the independent variable can be typecasted to a double
precision
+ # array and let postgres validate the expression
+
+ # Note that this will not fail for 2d arrays but the standardizer
will
+ # fail because utils_normalize_data will throw an error
+ typecasted_ind_varname = "{0}::double precision[]".format(
+
self.independent_varname)
+ validate_module_input_params(self.source_table, self.output_table,
+ typecasted_ind_varname,
+ self.dependent_varname,
self.module_name)
+
+ self._validate_other_output_tables()
--- End diff --
This is linked to the last comment on this PR. We could use an optional
param in `validate_module_input_params` to validate other output tables too (a
list of suffixes such as `['_summary', '_standardization']`).
---
