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Extension descriptor (YAML)

Connecting duckdb to the mlpack C++ machine learning library

Compatibility Matrix

This table covers all DuckDB releases that have not reached end-of-life, including LTS releases, and shows the 2 most recent patch releases of each. Older patch releases are not listed, but the extension may be available for them as well.

Installing and Loading

INSTALL mlpack FROM community;
LOAD mlpack;

Example

-- Perform adaBoost (using weak learner 'Perceptron' by default)
-- Read 'features' into 'X', 'labels' into 'Y', use optional parameters
-- from 'Z', and prepare model storage in 'M'
CREATE TABLE X AS SELECT * FROM read_csv("https://eddelbuettel.github.io/duckdb-mlpack/data/iris.csv");
CREATE TABLE Y AS SELECT * FROM read_csv("https://eddelbuettel.github.io/duckdb-mlpack/data/iris_labels.csv");
CREATE TABLE Z (name VARCHAR, value VARCHAR);
INSERT INTO Z VALUES ('iterations', '50'), ('tolerance', '1e-7');
CREATE TABLE M (key VARCHAR, json VARCHAR);

-- Train model for 'Y' on 'X' using parameters 'Z', store in 'M'
CREATE TEMP TABLE A AS SELECT * FROM mlpack_adaboost_train("X", "Y", "Z", "M");

-- Count by predicted group
SELECT COUNT(*) as n, predicted FROM A GROUP BY predicted;

-- Model 'M' can be used to predict
CREATE TABLE N (x1 DOUBLE, x2 DOUBLE, x3 DOUBLE, x4 DOUBLE);
-- inserting approximate column mean values
INSERT INTO N VALUES (5.843, 3.054, 3.759, 1.199);
-- inserting approximate column mean values, min values, max values
INSERT INTO N VALUES (5.843, 3.054, 3.759, 1.199), (4.3, 2.0, 1.0, 0.1), (7.9, 4.4, 6.9, 2.5);
-- and this predict one element each
SELECT * FROM mlpack_adaboost_pred("N", "M");

About mlpack

Supervised Learning

The mlpack extension allows to fit (or train) and predict (or classify) from the models implemented, currently adaBoost, Bayesian linear regression, decision tree, LARS, linear regression, logistic regression, naive Bayes, random forests as well as softmax regression. The format is the same for these methods: four tables, say, "X", "Y", "Z" and "M" provide input for, respectively, features "X", labels "Y", optional parameters varying by model in "Z" as well as an output table "M" for the JSON-serialized model. For all methods, following a model fit (or training), a prediction (or classification) can be made using "M" and new predictor values "N" as shown in the example. All these "fit" (or "train") methods take four parameter tables, all "predict" methods take two.

Unsupervised Learning

A kMeans clustering method is also available. It uses three tables for data, parameters and results.

General Information

A pair of parameters "mlpack_verbose" (to show additional data) and "mlpack_silent" (to suppress display of minimal summaries) can also be set.

The implementation is wraps several supervised and unsupervised machine learning methods, and provides Linux and macOS builds. More methods, options or parameters can be added quite easily.

For more, please see the repo.

Added Functions

Overloaded Functions

This extension does not add any function overloads.

Added Types

This extension does not add any types.

Added Settings