Github user kaknikhil commented on a diff in the pull request:

    https://github.com/apache/madlib/pull/230#discussion_r165515209
  
    --- Diff: src/ports/postgres/modules/sample/balance_sample.py_in ---
    @@ -0,0 +1,748 @@
    +# 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.
    +
    +m4_changequote(`<!', `!>')
    +
    +import math
    +
    +if __name__ != "__main__":
    +    import plpy
    +    from utilities.control import MinWarning
    +    from utilities.utilities import _assert
    +    from utilities.utilities import extract_keyvalue_params
    +    from utilities.utilities import unique_string
    +    from utilities.validate_args import columns_exist_in_table
    +    from utilities.validate_args import get_cols
    +    from utilities.validate_args import table_exists
    +    from utilities.validate_args import table_is_empty
    +else:
    +    # Used only for Unit Testing
    +    # FIXME: repeating a function from utilities that is needed by the 
unit test.
    +    # This should be removed once a unittest framework in used for testing.
    +    import random
    +    import time
    +
    +    def unique_string(desp='', **kwargs):
    +        """
    +        Generate random remporary names for temp table and other names.
    +        It has a SQL interface so both SQL and Python functions can call 
it.
    +        """
    +        r1 = random.randint(1, 100000000)
    +        r2 = int(time.time())
    +        r3 = int(time.time()) % random.randint(1, 100000000)
    +        u_string = "__madlib_temp_" + desp + str(r1) + "_" + str(r2) + "_" 
+ str(r3) + "__"
    +        return u_string
    +# 
------------------------------------------------------------------------------
    +
    +UNIFORM = 'uniform'
    +UNDERSAMPLE = 'undersample'
    +OVERSAMPLE = 'oversample'
    +NOSAMPLE = 'nosample'
    +
    +NEW_ID_COLUMN = '__madlib_id__'
    +NULL_IDENTIFIER = '__madlib_null_id__'
    +
    +def _get_frequency_distribution(source_table, class_col):
    +    """ Returns a dict containing the number of rows associated with each 
class
    +        level. Each class level value is converted to a string using 
::text.
    +    """
    +    query_result = plpy.execute("""
    +                    SELECT {class_col}::text AS classes,
    +                           count(*) AS class_count
    +                    FROM {source_table}
    +                    GROUP BY {class_col}
    +                 """.format(**locals()))
    +    actual_level_counts = {}
    +    for each_row in query_result:
    +        level = each_row['classes']
    +        if level:
    +            level = level.strip()
    +        actual_level_counts[level] = each_row['class_count']
    +    return actual_level_counts
    +
    +
    +def _validate_and_get_sampling_strategy(sampling_strategy_str, 
output_table_size,
    +                            supported_strategies=None, default=UNIFORM):
    +    """ Returns the sampling strategy based on the class_sizes input param.
    +        @param sampling_strategy_str The sampling strategy specified by the
    +                                         user (class_sizes param)
    +        @returns:
    +            Str. One of [UNIFORM, UNDERSAMPLE, OVERSAMPLE]. Default is 
UNIFORM.
    +    """
    +    if not sampling_strategy_str:
    +        sampling_strategy_str = default
    +    else:
    +        if len(sampling_strategy_str) < 3:
    +            # Require at least 3 characters since UNIFORM and UNDERSAMPLE 
have
    +            # common prefix substring
    +            plpy.error("Sample: Invalid class_sizes parameter")
    +
    +        if not supported_strategies:
    +            supported_strategies = [UNIFORM, UNDERSAMPLE, OVERSAMPLE]
    +        try:
    +            # allow user to specify a prefix substring of
    +            # supported strategies.
    +            sampling_strategy_str = next(x for x in supported_strategies
    +                                         if 
x.startswith(sampling_strategy_str.lower()))
    +        except StopIteration:
    +            # next() returns a StopIteration if no element found
    +            plpy.error("Sample: Invalid class_sizes parameter: "
    +                       "{0}. Supported class_size parameters are ({1})"
    +                       .format(sampling_strategy_str, 
','.join(sorted(supported_strategies))))
    +
    +    _assert(sampling_strategy_str.lower() in (UNIFORM, UNDERSAMPLE, 
OVERSAMPLE) or
    +            (sampling_strategy_str.find('=') > 0),
    +            "Sample: Invalid class size 
({sampling_strategy_str}).".format(**locals()))
    +
    +    _assert(not(sampling_strategy_str.lower() == 'oversample' and 
output_table_size),
    +            "Sample: Cannot set output_table_size with oversampling.")
    +
    +    _assert(not(sampling_strategy_str.lower() == 'undersample' and 
output_table_size),
    +            "Sample: Cannot set output_table_size with undersampling.")
    +
    +    return sampling_strategy_str
    +# 
------------------------------------------------------------------------------
    +
    +
    +def _choose_strategy(actual_count, desired_count):
    +    """ Choose sampling strategy by comparing actual and desired sample 
counts
    +
    +    @param actual_count: Actual number of samples for some level
    +    @param desired_count: Desired number of sample for the level
    +    @returns:
    +        Str. Sampling strategy string (either UNDERSAMPlE or OVERSAMPLE)
    +    """
    +    # OVERSAMPLE when the actual count is less than the desired count
    +    # UNDERSAMPLE when the actual count is more than the desired count
    +
    +    # If the actual count for a class level is the same as desired count, 
then
    +    # we could potentially return the input rows as is.  This, however,
    +    # precludes the case of bootstrapping (i.e. returning same  number of 
rows
    +    # but after sampling with replacement).  Hence, we treat the 
actual=desired
    +    # as UNDERSAMPLE.  It's specifically set to UNDERSAMPLE since it 
provides
    +    # both 'with' and 'without' replacement  (OVERSAMPLE is always with
    +    # replacement and NOSAMPLE is always without replacement)
    +    if actual_count < desired_count:
    +        return OVERSAMPLE
    +    else:
    +        return UNDERSAMPLE
    +# -------------------------------------------------------------------------
    +
    +def _get_target_level_counts(sampling_strategy_str, desired_level_counts,
    +                             actual_level_counts, output_table_size):
    +    """
    +    @param sampling_strategy_str: one of [UNIFORM, UNDERSAMPLE, 
OVERSAMPLE, None].
    +                               This is 'None' only if this is 
user-defined, i.e.,
    +                               a comma separated list of class levels and 
number of
    +                               rows desired pairs.
    +    @param desired_level_counts: Dict that is defined and populated only 
when
    +                                    sampling_strategy_str is None.
    +    @param actual_level_counts: Dict of various class levels and number of 
rows
    +                                  in each of them in the input table
    +    @param output_table_size: Size of the desired output table (NULL or 
Integer)
    +
    +    @returns:
    +        Dict. Number of samples to be drawn, and the sampling strategy to 
be
    +              used for each class level.
    +    """
    +    target_level_counts = {}
    +    if not sampling_strategy_str:
    +        # This case implies user has provided a desired count for one or 
more
    +        # levels. Counts for the rest of the levels depend on 
'output_table_size'.
    +        #   if 'output_table_size' = NULL, unspecified level counts remain 
as is
    +        #   if 'output_table_size' = <Integer>, divide remaining row count
    +        #                             uniformly among unspecified level 
counts
    +        for each_level, desired_count in desired_level_counts.items():
    +            sample_strategy = 
_choose_strategy(actual_level_counts[each_level],
    +                                               desired_count)
    +            target_level_counts[each_level] = (desired_count, 
sample_strategy)
    +
    +        remaining_levels = (set(actual_level_counts.keys()) -
    +                            set(desired_level_counts.keys()))
    +        if output_table_size:
    +            # Uniformly distribute across the remaining class levels
    +            remaining_rows = output_table_size - 
sum(desired_level_counts.values())
    +            if remaining_rows > 0:
    +                rows_per_level = math.ceil(float(remaining_rows) /
    +                                           len(remaining_levels))
    +                for each_level in remaining_levels:
    +                    sample_strategy = _choose_strategy(
    +                        actual_level_counts[each_level], rows_per_level)
    +                    target_level_counts[each_level] = (rows_per_level,
    +                                                       sample_strategy)
    +        else:
    +            # When output_table_size is unspecified, rows from the input 
table
    +            # are sampled as is for remaining class levels. This is same 
as the
    +            # NOSAMPLE strategy.
    +            for each_level in remaining_levels:
    +                target_level_counts[each_level] = 
(actual_level_counts[each_level],
    +                                                    NOSAMPLE)
    +    else:
    +        def ceil_of_mean(numbers):
    +            return math.ceil(float(sum(numbers)) / max(len(numbers), 1))
    +
    +        # UNIFORM: Ensure all level counts are same (size determined by 
output_table_size)
    +        # UNDERSAMPLE: Ensure all level counts are same as the minimum 
count
    +        # OVERSAMPLE: Ensure all level counts are same as the maximum count
    +        size_function = {UNDERSAMPLE: min,
    +                         OVERSAMPLE: max,
    +                         UNIFORM: ceil_of_mean
    +                         }[sampling_strategy_str]
    +        if sampling_strategy_str == UNIFORM and output_table_size:
    +            # Ignore actual counts for computing target sizes
    +            # if output_table_size is specified
    +            target_size_per_level = math.ceil(float(output_table_size) /
    +                                              len(actual_level_counts))
    +        else:
    +            target_size_per_level = 
size_function(actual_level_counts.values())
    +        for each_level, actual_count in actual_level_counts.items():
    +            sample_strategy = _choose_strategy(actual_count, 
target_size_per_level)
    +            target_level_counts[each_level] = (target_size_per_level,
    +                                               sample_strategy)
    +    return target_level_counts
    +
    +# -------------------------------------------------------------------------
    +
    +
    +def _get_sampling_strategy_specific_dict(target_class_sizes):
    +    """ Return three dicts, one each for undersampling, oversampling, and
    +        nosampling. The dict contains the number of samples to be drawn for
    +        each class level.
    +    """
    +    undersample_level_dict = {}
    +    oversample_level_dict = {}
    +    nosample_level_dict = {}
    +    for level, (count, strategy) in target_class_sizes.items():
    +        if strategy == UNDERSAMPLE:
    +            chosen_strategy = undersample_level_dict
    +        elif strategy == OVERSAMPLE:
    +            chosen_strategy = oversample_level_dict
    +        else:
    +            chosen_strategy = nosample_level_dict
    +        chosen_strategy[level] = count
    +    return (undersample_level_dict, oversample_level_dict, 
nosample_level_dict)
    +# 
------------------------------------------------------------------------------
    +
    +
    +def _get_nosample_subquery(source_table, class_col, nosample_levels):
    +    """ Return the subquery for fetching all rows as is from the input 
table
    +        for specific class levels.
    +    """
    +    if not nosample_levels:
    +        return ''
    +    subquery = """
    +                SELECT *
    +                FROM {0}
    +                WHERE {1} in ({2}) OR {1} IS NULL
    +            """.format(source_table, class_col,
    +                       ','.join(["'{0}'".format(level)
    +                                for level in nosample_levels if level]))
    +    return subquery
    +# 
------------------------------------------------------------------------------
    +
    +
    +def _get_without_replacement_subquery(schema_madlib, source_table,
    +                                      source_table_columns, class_col,
    +                                      actual_level_counts, 
desired_level_counts):
    +    """ Return the subquery for sampling without replacement for specific
    +        class levels.
    +    """
    +    if not desired_level_counts:
    +        return ''
    +    class_col_tmp = unique_string()
    +    row_number_col = unique_string()
    +    desired_count_col = unique_string()
    +
    +    null_value_string = "'{0}'".format(NULL_IDENTIFIER)
    +
    +    desired_level_counts_str = "VALUES " + \
    +            ','.join("({0}, {1})".
    +            format("'{0}'::text".format(k) if k else null_value_string, v)
    +            for k, v in desired_level_counts.items())
    +    subquery = """
    +            SELECT {source_table_columns}
    +            FROM
    +                (
    +                    SELECT {source_table_columns},
    +                           row_number() OVER (PARTITION BY {class_col} 
ORDER BY random()) AS {row_number_col},
    +                           {desired_count_col}
    +                    FROM
    +                    (
    +                        SELECT {source_table_columns},
    +                               {desired_count_col}
    +                        FROM
    +                            {source_table} s,
    +                            ({desired_level_counts_str})
    +                                q({class_col_tmp}, {desired_count_col})
    +                        WHERE {class_col_tmp} = 
coalesce({class_col}::text, '{null_level_val}')
    +                    ) q2
    +                ) q3
    +            WHERE {row_number_col} <= {desired_count_col}
    +        """.format(null_level_val=NULL_IDENTIFIER, **locals())
    +    return subquery
    +# 
------------------------------------------------------------------------------
    +
    +
    +def _get_with_replacement_subquery(schema_madlib, source_table,
    +                                   source_table_columns, class_col,
    +                                   actual_level_counts, 
desired_level_counts):
    +    """ Return the query for sampling with replacement for specific class
    +        levels (always used for oversampling, and used for undersampling if
    +        with_replacement flag is set to TRUE).
    +    """
    +    if not desired_level_counts:
    +        return ''
    +
    +    class_col_tmp = unique_string()
    +    desired_count_col = unique_string()
    +    actual_count_col = unique_string()
    +    q1_row_no = unique_string()
    +    q2_row_no = unique_string()
    +
    +    null_value_string = "'{0}'".format(NULL_IDENTIFIER)
    +
    +    desired_and_actual_level_counts = "VALUES " + \
    +    ','.join("({0}, {1}, {2})".
    +             format("'{0}'::text".format(k) if k else null_value_string,
    +                v, actual_level_counts[k])
    +             for k, v in desired_level_counts.items())
    +    subquery = """
    +            SELECT {source_table_columns}
    +            FROM
    +                (
    +                    SELECT
    +                         {class_col_tmp},
    +                         generate_series(1, {desired_count_col}::int) AS 
_i,
    +                         ((random()*({actual_count_col}-1)+1)::int) AS 
{q1_row_no}
    +                    FROM
    +                        ({desired_and_actual_level_counts})
    +                            q({class_col_tmp}, {desired_count_col}, 
{actual_count_col})
    +                ) q1,
    +                (
    +                    SELECT
    +                        *,
    +                        row_number() OVER(PARTITION BY {class_col}) AS 
{q2_row_no}
    +                    FROM
    +                         {source_table}
    +                ) q2
    +            WHERE {class_col_tmp} = coalesce({class_col}::text, 
'{null_level_val}') AND
    +                  q1.{q1_row_no} = q2.{q2_row_no}
    +        """.format(null_level_val=NULL_IDENTIFIER, **locals())
    +    return subquery
    +# 
------------------------------------------------------------------------------
    +
    +def balance_sample(schema_madlib, source_table, output_table, class_col,
    +                   class_sizes, output_table_size, grouping_cols,
    +                   with_replacement, keep_null, **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 class_sizes        Parameter to define the size of the 
different
    --- End diff --
    
    The name of this variable seems to indicate that it only takes class sizes 
as input. Only after I looked at the sql comments did I understand that this 
can be either 'under/over/uniform` or `class_sizes'. Also I had see the comment 
below which explains in detail the two different forms that this can take.
    
    Can we consider changing the name to be more reflective of it's purpose ? 



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