MLJTestIntegration.jl

Package for applying integration tests to models implementing the MLJ model interface.

To test implementations of the MLJ model interface, use MLJTestInterface.jl instead.

Installation

using Pkg
Pkg.add("MLJTestIntegration")

Usage

This package provides a method for testing a collection of models (types or named tuples with keys :name and :package_name) using the specified training data:

MLJTestIntegration.test(models, data...; mod=Main, level=2, throw=false, verbosity=1) 
    -> failures, summary

For detailed documentation, run using MLJTestIntegration; @doc MLJTestIntegration.test.

For convenience, a number of specializations of this method are also provided:

test_single_target_classifiers
test_single_target_regressors
test_single_target_count_regressors
test_continuous_table_transformers

Query the document strings for details, or see examples/bigtest/notebook.jl.

Example: Testing models filtered from the MLJ model registry

The following applies comprehensive integration tests to all regressors provided by the package GLM.jl appearing in the MLJ Model Registry. Since GLM.jl models are provided through the interface package MLJGLMInterface, this must be in the current environment:

Pkg.add("MLJGLMInterface")
import MLJBase, MLJTestIntegration
using DataFrames # to view summary
X, y = MLJTestIntegration.MLJ.make_regression();
regressors = MLJTestIntegration.MLJ.models(matching(X, y)) do m
    m.package_name == "GLM"
end

# to test code loading:
failures, summary = 
    MLJTestIntegration.test(regressors, X, y, verbosity=2, mod=@__MODULE__, level=1)
@assert isempty(failures)

# comprehensive tests:
failures, summary =
    MLJTestIntegration.test(regressors, X, y, verbosity=2, mod=@__MODULE__, level=4)

summary |> DataFrame

Datasets

The following commands generate datasets of the form (X, y) suitable for integration tests:

MLJTestIntegration.make_binary
MLJTestIntegration.make_multiclass
MLJTestIntegration.make_regression
MLJTestIntegration.make_count