Since 9th October 2024 (the 4.0.0 release of the ML Toolkit), this project has been merged into the ml toolkit mono-repo. For the latest updates and active development, please visit https://github.com/KxSystems/ml.
This repository is preserved only to maintain old links and project history but will no longer be actively maintained.
The automated machine learning library described here is built largely on the tools available within the Machine Learning Toolkit. The purpose of this framework is help you automate the process of applying machine learning techniques to real-world problems. In the absence of expert machine-learning engineers this handles the following processes within a traditional workflow.
- Data preprocessing
- Feature engineering and feature selection
- Model selection
- Hyperparameter Tuning
- Report generation and model persistence
Each of these steps is outlined in depth within the documentation. This allows you to understand how decisions are being made and the transformations which their data undergo during the production of the output models.
At present the machine learning frameworks supported for this are based on:
- One-to-one feature to target non time-series
- FRESH based feature extraction and model production
- NLP-based feature creation and word2vec transformation.
The problems which can be solved by this framework will be expanded over time as will the available functionality.
The following requirements cover all those needed to run the libraries in the current build of the toolkit.
- embedPy
- ML-Toolkit ≥ 3.0.0
A number of Python dependencies also exist for the running of embedPy functions within both the the machine-learning utilities and FRESH libraries. Install as follows:
pip:
pip install -r requirements.txtor via conda:
conda install --file requirements.txtThe above requirements allow you to access the base functionality of AutoML. Additional modules are available – including Sobol sequence hyperparameter search, LaTeX report generation and Keras, PyTorch and NLP models. However, given the large memory requirement for the dependencies of these modules, they are not included in the base functionality and must be installed if required.
Sobol search - via pip (see package details here):
sobol-seqLaTeX - via conda or pip:
pylatexKeras - via conda or pip:
keras
tensorflowPyTorch - via conda or pip:
torchTheano - via conda or pip:
theanoNLP
The NLP functionality in AutoML requires the KX NLP library along with gensim which can be installed using conda or pip.
Place the library file in $QHOME and load into a q instance using automl/automl.q
This will load all the functions contained within the .ml namespace
$q automl/automl.q
q).automl.loadfile`:init.q📂 docs
Automated machine learning in kdb+ is still in development and is available here as a beta release, further functionality and improvements will be made to the library in the coming months.
Any issues with the framework should be raised in the issues section of this repository. Functionality suggestions or more general questions should be submitted via email to ai@kx.com