Machine learning library in C++
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 (json VARCHAR);
-- Train model for 'Y' on 'X' using parameters 'Z', store in 'M'
CREATE TEMP TABLE A AS SELECT * FROM mlpack_adaboost("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");
EOF
About mlpack
The mlpack extension allows to fit (or train) and predict (or classify) from the models implemented, currently adaBoost and (regularized) linear regression. The format is the same for both: 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. Following a model fit (or training), a prediction (or classification) can be made using "M" and new predictor values as shown in the example.
The implementation is still stressing the 'minimal' part of 'a MVP demo': currently only Linux is supported. It should be considered experimental, interfaces may change while we work out how to automate interface generation from the mlpack-side.
For more, see the repo.
Added Functions
| function_name | function_type | description | comment | examples |
|---|---|---|---|---|
| mlpack_adaboost_pred | table | NULL | NULL | |
| mlpack_adaboost_train | table | NULL | NULL | |
| mlpack_linear_regression_fit | table | NULL | NULL | |
| mlpack_linear_regression_pred | table | NULL | NULL | |
| mlpack_table | table | NULL | NULL |