Machine learning toolkit

Introduction

This repository contains the following sections:

• An implementation of the FRESH (FeatuRe Extraction and Scalable Hypothesis testing) algorithm for use in the extraction of features from time series data and the reduction in the number of features through statistical testing.
• Utility functions relating to areas including statistical analysis, data preprocessing and array manipulation.
• Cross validation and grid-search functions allowing for testing of the stability of models to changes in the volume of data or the specific subsets of data used in training.

The contents of these sections are explained in greater depth within FRESH, Utilities and Cross Validation documentation.

Requirements

• embedPy

The python packages required to allow successful exectution of all functions within the machine learning toolkit can be installed via:

pip:

pip install -r requirements.txt

or via conda:

conda install --file requirements.txt

Installation

Place the library file in $QHOME and load into a q instance using ml/ml.q

This will load all the functions contained within the .ml namespace

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

Examples

Examples showing implementations of several components of this toolkit can be found here. These notebooks include examples of the following sections of the toolkit.

• Pre-processing functions
• Implementations of the FRESH algorithm
• Cross validation and grid search capabilities
• Results Scoring functionality

Documentation

Documentation for all sections of the machine learning toolkit are available here.

Status

The machine learning toolkit 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.

If you have any issues, questions or suggestions, please write to [email protected].