Usage¶
Requirements¶
To use HoNCAML, it is required to have Python >= 3.10.
Install¶
To install HoNCAML, run: pip install honcaml
Command line execution¶
Quick execution with example data¶
For a quick usage with example data and configuration, just run:
honcaml -e {example_directory}
This would create a directory containing sample data and configuration
to see how HoNCAML works in a straightforward manner. Just enter the
specified directory: cd {example_directory}
and run one of the
pipelines located in files directory. For example, a benchmark for a
classification task:
honcaml -c files/classification_benchmark.yaml
Standard execution¶
To start a HoNCAML execution for a particular pipeline, first it is needed to generate the configuration file for it. It may be easy to start with a template, which is provided by the CLI itself.
In case a basic configuration file is enough, with the minimum required options, the following should be invoked:
honcaml -b {config_file} -t {pipeline_type}
On the other hand, there is the possibility of generating an advanced configuration file, with all the supported options:
honcaml -a {config_file} -t {pipeline_type}
In both cases, {config_file}
should be a path to the file containing
the configuration in yaml extension, and {pipeline_type}
one of the
supported: train, predict or benchmark.
When having a filled configuration file to run the pipeline, it is just a matter of executing it:
honcaml -c {config_file}
For example, the following basic configuration would train a default model for classification and store it.
global:
problem_type: classification
steps:
data:
extract:
filepath: data/dataset.csv
target: class
transform:
model:
transform:
fit:
load:
filepath: default_model.sav
GUI execution¶
To run the HoNCAML GUI locally in a web browser tab, run the following command:
honcaml -g
It allows to execute HoNCAML by interactively selecting pipeline options, although it is possible to run a pipeline by uploading its configuration file as well.