Learn CLI - variable selection, model development and target dataset processing
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README.md

learn-cli

Codeship Status for tesera/learn-cli

learn-cli performs machine learning tasks, including variable selection, model development and target dataset processing. It uses pylearn, prelurn, and rlearn libraries. The cli invokes rlearn function via rpy2.

Support for developing and using the CLI is only provided if you are using docker, as the CLI has a fairly complex set of requirements (packages, runtimes, etc)

Usage

$ learn --help
Usage:
    learn describe (--xy-data <file>) [--quantile-type <string> --format <string> --output <dir>]
    learn varsel (--xy-data <file> --config <file>) [--yvar <string> --iteration <solutions:x-min:x-max> --criteria <string> --output <dir>]
    learn lda (--xy-data <file> --config <file>) [--yvar <string> --output <dir>]
    learn discrat (--xy-data <file> --x-data <file> --dfunct <file> --idf <file> --varset <int>) [--yvar <string> --output <dir>]

Options:
    --xy-data <file>  The path to the XY reference CSV file. Can be an S3://... path.
    --x-data <file>  The path to the X filtered data CSV file. Can be an S3://... path.
    --config <file>  Variable selection file. Can be an S3://... path. [default: ./config.csv]
    --yvar <string>  Class variable name: CLASS5, CLPRDP [default: Y].
    --iteration <string>  Iteration specific arguments for variable selection. <solutions:x-min:x-max> [default: 10:1:10]
    --criteria <string>  Set the criteria to be applied for variables selection: ccr12, Wilkes, xi2, zeta2 [default: xi2].
    --dfunct <file>  The path to the lda dfunct file to use for the discriminant rating.
    --idf <file>  The path to the IDF Curves file to use for the discriminant rating.
    --varset <int>  The ID of the varset to use for the discriminant rating.
    --format <string>  Output format: json or csv. [default: json]
    --quantile-type <string>  Quantiles: decile or quartile. [default: decile]
    --output <dir>  Output folder. [default: ./]
    -h --help  Show help.

Examples:
    learn describe --xy-data ./folder/xy_reference.csv --quantile-type decile --format json --output ./output/describe
    learn describe --xy-data s3://bucket/xy_reference.csv --quantile-type decile --format json --output s3://bucket/describe
    learn varsel --xy-data ./folder/xy_reference.csv --config ./folder/xvar_sel.csv --output ./output/varsel --iteration 10:1:10
    learn varsel --xy-data s3://bucket/xy_reference.csv --config s3://bucket/xvar_sel.csv --output s3://bucket/varsel --iteration 10:1:10
    learn lda --xy-data ./folder/xy_reference.csv --config ./folder/xvar_sel.csv --output./output/varsel
    learn lda --xy-data s3://bucket/xy_reference.csv --config s3://bucket/xvar_sel.csv --output s3://bucket/varsel
    learn discrat --xy-data ./folder/xy_reference.csv --x-data ./folder/x_filtered.csv --dfunct ./folder/dfunct.csv --idf ./folder/idf.csv --varset 18 --output ./output/varsel
    learn discrat --xy-data s3://bucket/xy_reference.csv --x-data s3://bucket/x_filtered.csv --dfunct s3://bucket/dfunct.csv --idf s3://bucket/idf.csv --varset 18 --output s3://bucket/varsel

Setup with Docker

If you are using docker-machine make sure you have a machine running and that you have evaluated the machine environment.

Creating a Docker Machine Host VM

Windows Powershell

$ docker-machine create --driver virtualbox --virtualbox-host-dns-resolver default
$ docker-machine env --shell powershell default | Invoke-Expression
OSX
$ docker-machine create --driver virtualbox default
$ eval "$(docker-machine env default)"

Running the container

$ docker build -t learn .
$ docker run learn /bin/bash
root@1e36bb3275b5:/opt/learn# learn --help

Development

During development you will want to bring in the codebase with you in the container. You can simply use the Docker Compose command bellow. Once in the container run the install-dependencies.sh script passing in the --dev flag to make the project editable. This wil install all the Python dependencies in the project root under the `pysite folder and the R dependencies under the rlibs folder. You will only need to run this once unless your dependencies change.

You will need to add a dev.env file with at least PYLEARN_REF, RLEARN_REF and PRELURN_REF variables set to the Github ref (branch or tag) of the respective libraries. Optionaly you can also add you AWS Access Keys and region in order to use S3 as a data location.

$ cat dev.env
PYLEARN_REF=master
RLEARN_REF=master
PRELURN_REF=master
AWS_ACCESS_KEY_ID=<your-access-key>
AWS_SECRET_ACCESS_KEY=<your-secret-key>
AWS_REGION=<your-aws-region>
LOG_LEVEL=INFO
$ docker-compose run dev
root@1e36bb3275b5:/opt/learn# bash ./install-dependencies --dev
root@1e36bb3275b5:/opt/learn# learn --help

Testing

You can run the tests, which are written with bats, using the following docker compose task:

docker-compose run tests

You can also enter the contianer and run specific tests as follows:

> dc run dev
root@4d3df46d52c7:/opt/learn# bats tests/integration/
.DS_Store               output/                 test_cli_discrat.bats   test_cli_varsel.bats
input/                  test_cli_describe.bats  test_cli_lda.bats
root@4d3df46d52c7:/opt/learn# bats tests/integration/test_cli_describe.bats
 ✓ describe runs and output expected files

 1 test, 0 failures

Contributing

Refer to the pylearn and rlearn for guides on how to contribute.