GitLocker: The Coding Marketplace

Description:

honcaml 0.2.1

HoNCAML
Introduction
HoNCAML (Holistic No Code Automated Machine Learning) is a tool aimed to run
automated machine learning pipelines, and specifically focused on finding the
best model and hyperparameters for the problem at hand.
Following the no code
paradigm, no
Python knowledge is needed. There are two ways to define pipelines:

Through the Graphical User Interface
Through YAML configuration files

Pipelines
There are three types of provided pipelines.
Train
Train a specific model with the hyperparameters specified.

Input: A dataset for the training.
Output: The model object stored to disk.

Predict
Use a model to generate predictions for a specific dataset.

Input: A dataset for the test, together with a model object.
Output: A tabular file with the predictions.

Benchmark
Search for the best model and hyperparameters for the dataset at hand.

Input: A dataset for the benchmark.
Output: Main output is a configuration file with the best model and
hyperparameters, and a tabular file with the results for all configurations
tested.

Focus
HoNCAML has been designed having the following aspects in mind:

Ease of use
Modularity
Extensibility
Simpler is better

Users
HoNCAML does not assume any kind of technical knowledge, but at the same time
it is designed to be extended by expert people. Therefore, its user base may
range from:

Basic users: In terms of programming experience and/or machine learning
knowledge. It would be possible for them to get results in an easy way.

Advanced users: It is possible to customize experiments in order to
adapt to a specific use case that may be needed by an expert person.

Support
Regarding each of the following concepts, HoNCAML supports specific sets of
them; nevertheless, due to its nature, extend the library further should be not
only feasible, but intuitive.
Data structure
For now only data with tabular format is supported. However, HoNCAML provides special
preprocessing methods if needed:

Normalization
One hot encoding of categorical features

Problem type
At this moment, the following types of problems are supported:

Regression
Classification

Model type
Regarding available models, the following are supported:

Sklearn models (ML)
Pytorch models (DL)

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.
```yaml
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.
Contribute
All contributions are more than welcome! For further information, please refer
to the contribution
documentation.
Bugs
If you find any bug, please check if there is any existing
issues, and if not,
open a new one with a clear description.
Contact
Should you have any inquiry regarding the library or its development, please
contact the Applied Machine Learning team.