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Machine-learning toolkit

KxSystems/ml

The toolkit contains libraries and scripts that provide kdb+/q users with general-use functions and procedures to perform machine-learning tasks on a wide variety of datasets.

The toolkit contains:

  • Utility functions relating to important aspects of machine-learning including data preprocessing, statistical metrics, and various other functionality useful in many machine-learning applications contained under utils.
  • An implementation of the FRESH (FeatuRe Extraction and Scalable Hypothesis testing) algorithm in q. This lets a kdb+/q user perform feature-extraction and feature-significance tests on structured time-series data for forecasting, regression and classification.
  • Implementations of a number of cross validation and grid search procedures. These allow kdb+/q users to validate the performance of machine learning models when exposed to new data, test the stability of models over time or find the best hyper-parameters for tuning their models.

Over time the machine-learning functionality in this library will be extended to include;

  • q-specific implementations of machine-learning algorithms
  • broader functionality

Requirements

The following requirements cover all those needed to run the libraries in the current build of the toolkit.

A number of Python dependencies also exist for the running of embedPy functions within both the the machine-learning utilities and FRESH libraries. These can be installed as outlined at

KxSystems/ml using Pip

pip install -r requirements.txt

or Conda

conda install --file requirements.txt

Running notebooks

Running notebooks within the FRESH section requires both JupyterQ and embedPy. However this is not a requirement for the toolkit itself.

Installation

Install and load all libraries.

q)\l ml/ml.q
q).ml.loadfile`:init.q

This can be achieved by one command. Copy a link to the library into $QHOME. Then:

q)\l ml/init.q