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https://issues.apache.org/jira/browse/MADLIB-986?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-986:
-----------------------------------
Description:
Story
As a data scientist, I want to sample a data table in proportion to the number
of rows in each group, so that I can do model building on the sampled data sets.
The MVP for this story is:
* sample proportion is global, i.e., single fractional value between 0 and 1
* allow option to sample without replacement (default) and sample with
replacement
* allow option to output a subset of columns to the output table
Proposed Interface
{code}
stratified_sample (
source_table,
output_table,
proportion,
grouping_col -- optional
with_replacement, -- optional
target_cols -- optional
)
source_table
TEXT. The name of the table containing the input data.
output_table
TEXT. Name of output table that contains the sampled data.
The output table contains all the columns present in the source table
unless otherwise specified in the 'target_cols' parameter below.
proportion
FLOAT8 in the range (0,1). The size of the sample in each stratum will
be taken in proportion to the size of the stratum.
grouping_col (optional)
TEXT, default: NULL. A single column or a list of comma-separated columns
that defines how to stratify. When this parameter is NULL,
no grouping is used so the sampling is non-stratified.
with_replacement (optional)
BOOLEAN, default FALSE. Determines whether to sample with replacement
or without replacement (default).
target_cols (optional)
TEXT, default NULL. A comma-separated list of columns to appear in the
'output_table'.
If NULL, all columns from the 'source_table' will appear in the 'output_table'.
{code}
Other notes
PDL tools is one example implementation of stratified sampling to review [2].
Please review existing MADlib sample functions [3] to see if these can be used
as a basis, or built on, for this stratified sample story.
References
[2] PDL tools sampling modules incl stratified sampling
http://pivotalsoftware.github.io/PDLTools/group__grp__sampling.html
[3] Existing MADlib sample function
http://madlib.incubator.apache.org/docs/latest/group__grp__sample.html
[4] Pandas/Selecting Random Samples
http://pandas.pydata.org/pandas-docs/stable/indexing.html#selecting-random-samples
[5] General
https://en.wikipedia.org/wiki/Stratified_sampling
was:
Story
As a data scientist, I want to sample a data table in proportion to the number
of rows in each group, so that I can do model building on the sampled data sets.
The MVP for this story is:
* sample proportion is global, i.e., single fractional value between 0 and 1
* allow option to sample without replacement (default) and sample with
replacement
* allow option to output a subset of columns to the output table
Proposed Interface
{code}
stratified_sample (
source_table,
output_table,
proportion,
grouping_col -- optional
with_replacement, -- optional
target_cols -- optional
)
source_table
TEXT. The name of the table containing the input data.
output_table
TEXT. Name of output table that contains the sampled data.
The output table contains all the columns present in the source table
unless otherwise specified in the 'target_cols' parameter below.
proportion
FLOAT8 in the range (0,1). The size of the sample in each stratum will
be taken in proportion to the size of the stratum.
grouping_col (optional)
TEXT, default: NULL. A single column or a list of comma-separated columns
that defines how to stratify. When this parameter is NULL,
no grouping is used so the sampling is non-stratified.
with_replacement (optional)
BOOLEAN, default FALSE. Determines whether to sample with replacement
or without replacement (default).
target_cols (optional)
TEXT, default NULL. A comma-separated list of columns to appear in the
'output_table'.
If NULL, all columns from the 'source_table' will appear in the 'output_table'.
{code}
Other notes
PDL tools is one example implementation of stratified sampling to review [2].
Please review existing MADlib sample functions [3] to see if these can be used
as a basis, or built on, for this stratified sample story.
References
[2] PDL tools sampling modules incl stratified sampling
http://pivotalsoftware.github.io/PDLTools/group__grp__sampling.html
[3] Existing MADlib sample function
http://madlib.incubator.apache.org/docs/latest/group__grp__sample.html
[4] Pandas/Selecting Random Samples
http://pandas.pydata.org/pandas-docs/stable/indexing.html#selecting-random-samples
[5] General
https://en.wikipedia.org/wiki/Stratified_sampling
> Stratified sampling
> -------------------
>
> Key: MADLIB-986
> URL: https://issues.apache.org/jira/browse/MADLIB-986
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Sampling
> Reporter: Frank McQuillan
> Labels: starter
> Fix For: v1.12
>
>
> Story
> As a data scientist, I want to sample a data table in proportion to the
> number of rows in each group, so that I can do model building on the sampled
> data sets.
> The MVP for this story is:
> * sample proportion is global, i.e., single fractional value between 0 and 1
> * allow option to sample without replacement (default) and sample with
> replacement
> * allow option to output a subset of columns to the output table
> Proposed Interface
> {code}
> stratified_sample (
> source_table,
> output_table,
> proportion,
> grouping_col -- optional
> with_replacement, -- optional
> target_cols -- optional
> )
> source_table
> TEXT. The name of the table containing the input data.
> output_table
> TEXT. Name of output table that contains the sampled data.
> The output table contains all the columns present in the source table
> unless otherwise specified in the 'target_cols' parameter below.
> proportion
> FLOAT8 in the range (0,1). The size of the sample in each stratum will
> be taken in proportion to the size of the stratum.
> grouping_col (optional)
> TEXT, default: NULL. A single column or a list of comma-separated columns
> that defines how to stratify. When this parameter is NULL,
> no grouping is used so the sampling is non-stratified.
> with_replacement (optional)
> BOOLEAN, default FALSE. Determines whether to sample with replacement
> or without replacement (default).
> target_cols (optional)
> TEXT, default NULL. A comma-separated list of columns to appear in the
> 'output_table'.
> If NULL, all columns from the 'source_table' will appear in the
> 'output_table'.
> {code}
> Other notes
> PDL tools is one example implementation of stratified sampling to review [2].
>
> Please review existing MADlib sample functions [3] to see if these can be
> used as a basis, or built on, for this stratified sample story.
> References
> [2] PDL tools sampling modules incl stratified sampling
> http://pivotalsoftware.github.io/PDLTools/group__grp__sampling.html
> [3] Existing MADlib sample function
> http://madlib.incubator.apache.org/docs/latest/group__grp__sample.html
> [4] Pandas/Selecting Random Samples
> http://pandas.pydata.org/pandas-docs/stable/indexing.html#selecting-random-samples
> [5] General
> https://en.wikipedia.org/wiki/Stratified_sampling
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