A Julia implementation of the Meta-ICVI method as a separate package.

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• MetaICVI
  • Table of Contents
  • Usage
    • Installation
    • Basic Usage
    • Advanced Usage
  • Contributing
  • Credits
    • Authors
    • License

You must install PyCallJLD.jl alongside MetaICVI.jl for correct classifier module loading and saving. This is because the ScikitLearn.jl dependency requires saving/loading with the JLD.jl package on PyCall.jl objects, and PyCallJLD correctly loads the serialized object definitions into the current workspace. Otherwise, the classifier is loaded a memory block wrapped in a PyObject type, breaking inference and other operations.

PyCallJLD.jl is distributed as a Julia package, available on JuliaHub. Its installation follows the usual Julia package installation procedure, interactively:

] add PyCallJLD

or programmatically:

using Pkg
Pkg.add("PyCallJLD")

At the time of this writing, MetaICVI.jl is not released on JuliaHub. To install MetaICVI.jl, you may add the package directly from GitHub:

] add https://github.com/AP6YC/MetaICVI.jl

or programmatically, also with the GitHub link:

using Pkg
Pkg.add("https://github.com/AP6YC/MetaICVI.jl")

First, load both PyCall and MetaICVI with

using PyCall, MetaICVI

Then, create a MetaICVI module with the default constructor

    metaicvi = MetaICVIModule()

and retrieve the MetaICVI value iteratively with

    get_metaicvi(metaicvi, sample, label)

where sample is a real-valued vector and label is an integer.

After loading both PyCall and MetaICVI

using PyCall, MetaICVI

you can specify the MetaICVI options with

    opts = MetaICVIOpts(
        classifier_selection = :SGDClassifier,
        classifier_opts = (loss="log", max_iter=30),
        icvi_window = 5,
        correlation_window = 5,
        n_rocket = 5,
        rocket_file = "data/models/rocket.jld2",
        classifier_file = "data/models/classifier.jld",
        display = true,
        fail_on_missing = false
    )
    metaicvi = MetaICVIModule(opts)

The options are

• classifier_selection: a symbol for a linear classifier from ScikitLearn.jl (only used if you are creating and training a new classifier).
• classifier_opts: the options passed to the classifier during instantiation (also only used if creating and training a new classifier).
• icvi_window: the number of ICVI criterion values to compute rank correlation across.
• correlation_window: the number of correlations to compute rocket features across.
• rocket_file: filename of a saved RocketModule.
• classifier_file: filename of a saved linear classifier.
• display: boolean flag for logging info.
• fail_on_missing: boolean flag for crashing if missing rocket and/or classifier files.

Please raise an issue.

• Sasha Petrenko sap625@mst.edu

This software is developed by the Applied Computational Intelligence Laboratory (ACIL) of the Missouri University of Science and Technology (S&T) under the supervision of Teledyne Technologies for the DARPA L2M program. Read the License.