@yawetse/js-grid-search
v1.3.2
Published
JsGridSearch (without colors and dependencies) Simple mechanism for performing grid-search in node.js - parameter tuning for machine learning algorithms.
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this is a version of JsGridSearch without colors and dependencies - https://github.com/bergloman/JsGridSearch
JsGridSearch
Simple mechanism for performing grid-search in node.js
-
parameter tuning for machine learning algorithms.
Basic idea
Simple class that creates easy-to-use platform for performing parameter tuning using grid-search (an exhaustive search over parameter space).
When results are collected, it can output heat-map of the custom result evaluation to console or plain string.
Basic usage
Run different parameters combinations
let options = {
params: {
a: [1, 2], // all possible values for parameter a
b: ["none", "tf"], // all possible values for parameter b
c: [0, 100] // all possible values for parameter c
},
run_callback: (comb) => {
// comb parameter contains one of the parameter combinations
// e.g. { a: 2, b: "tf", c: 0 }
// here one would run his algorithm (using comb values)
// and collect the result
// return the result - shape and content don't matter
return {
some_metric: Math.random(),
some_other_metric: Math.random()
};
}
};
let grid_search = new gs.GridSearch(options);
grid_search.run();
Display the result
Call methods displayTableOfResults
or getTableOfResults
to
display heat-map of collected data.
User needs to provide callback that evaluates each result - e.g. from the same set of results one can display heat-map for accuracy, recall, precision or F1 measure.
grid_search.displayTableOfResults(
["a"], // columns
["b", "c"], // rows
x => +(x.results.some_metric.toFixed(3)) // evaluation function
);
The result would look something like this:
| | a=1 | a=2
|-----------------|---------|---------
| b=none,c=0 | 0.009 | 0.501
| b=none,c=100 | 0.872 | 0.088
| b=tf,c=0 | 0.3 | 0.733
| b=tf,c=100 | 0.672 | 0.663
This is easily copy-paste-able into markdown
:
| | a=1 | a=2 |-----------------|---------|--------- | b=none,c=0 | 0.009 | 0.501 | b=none,c=100 | 0.872 | 0.088 | b=tf,c=0 | 0.3 | 0.733 | b=tf,c=100 | 0.672 | 0.663