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.
- Clustering algorithms used to group data points and to identify patterns in their distributions. The algorithms make use of a k-dimensional tree to store points and scoring functions to analyze how well they performed.
Over time the machine-learning functionality in this library will be extended to include;
- q-specific implementations of machine-learning algorithms
- broader functionality
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
conda install --file requirements.txt
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