Index

MetaICVIModule

Stateful information for a single MetaICVI module.

Fields

• opts::MetaICVIOpts: options for construction.
• cvis::Vector{CVI}: list of cvis used for computing the CVIs.
• criterion_values::RealVector: list of outputs of the cvis used for computing correlations.
• correlations::RealVector: list of outputs of the rank correlations.
• features::RealVector: list of outputs of the rocket feature kernels.
• rocket::RocketModule: time-series random feature kernels module.
• classifier::MetaICVIClassifier: ScikitLearn classifier.
• performance::RealFP: final output of the most recent the Meta-ICVI step.
• is_pretrained::Bool: internal flag for if the classifier is trained and ready for inference.

MetaICVIModule(opts::MetaICVIOpts)

Instantiate a MetaICVIModule with given options.

Arguments

• opts::MetaICVIOpts: options struct for the MetaICVI object.

MetaICVIModule()

Default constructor for the MetaICVIModule.

MetaICVIOpts()

Meta-ICVI module options.

Examples

julia> MetaICVIOpts()

Base.show(io::IO, metaicvi::MetaICVIModule)

Display a metaicvi module to the command line.

Arguments

• io::IO: default io stream.
• metaicvi::MetaICVIModule: metaicvi object about which to display info.

construct_classifier(opts::MetaICVIOpts)

Construct a new classifier for the MetaICVI module with metaprogramming.

Arguments

• opts::MetaICVIOpts: options containing the classifier type and options for instantiation.

get_correlations(metaicvi::MetaICVIModule)

Compute and store the rank correlations from the cvi values.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.

get_cvi_data(data_file::String)

Get the cvi data specified by the data_file path.

Arguments

• data_file::String: file containing clustered data for cvi processing.

get_features(metaicvi::MetaICVIModule, sample::RealVector, label::Integer)

Compute only the features on the sample and label without classifier inference.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.
• sample::RealVector: the sample used for clustering.
• label::Integer: the label prescribed to the sample by the clustering algorithm.

get_icvis(metaicvi::MetaICVIModule, sample::RealVector, label::Integer)

Compute and store the icvi criterion values.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.
• sample::RealVector: the sample used for clustering.
• label::Integer: the label prescribed to the sample by the clustering algorithm.

get_metaicvi(metaicvi::MetaICVIModule, sample::RealVector, label::Integer)

Compute and return the meta-icvi value.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.
• sample::RealVector: the sample used for clustering.
• label::Integer: the label prescribed to the sample by the clustering algorithm.

get_probability(metaicvi::MetaICVIModule)

Compute and store the metaicvi value from the classifier.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.

get_rocket_features(metaicvi::MetaICVIModule)

Compute and store the rocket features.

Arguments

• metaicvi::MetaICVIModule: the Meta-ICVI module.

get_training_features(metaicvi::MetaICVIModule, data_path::String)

Arguments

is_pretrained(metaicvi::MetaICVIModule)

Checks if the classifier is pretrained to permit inference.

Arguments

• metaicvi::MetaICVIModule: metaicvi module containing the classifier to check.

load_classifier(filepath::String)

Load the classifier at the filepath.

Arguments

• filepath::String: location of the classifier .jld file.

safe_save_classifier(metaicvi::MetaICVIModule)

Error handle saving of the metaicvi classifier.

Arguments

• metaicvi::MetaICVIModule: metaicvi module containing the classifier and path for saving.

safe_save_rocket(metaicvi::MetaICVIModule)

Error handle saving of the metaicvi rocket kernels.

Arguments

• metaicvi::MetaICVIModule: metaicvi module containing the classifier and path for saving.

save_classifier(classifier::MetaICVIClassifier, filepath::String)

Save the classifier at the filepath.

Arguments

• classifier::MetaICVIClassifier: classifier object to save.
• filepath::String: name/path to save the classifier .jld file.

train_and_save(metaicvi::MetaICVIModule, x::RealMatrix, y::IntegerVector)

Train the classifier on x/y and save the kernels and classifier.

Arguments

• metaicvi::MetaICVIModule: metaicvi module to save with.
• x::RealMatrix: features to train on.
• y::IntegerVector: correct/over/under partition targets.

RocketKernel

Structure containing information about one rocket kernel.

RocketModule

Structure containing a vector of rocket kernels.

RocketModule(input_length::Integer, n_kernels::Integer)

Create a new RocketModule structure, requiring feature length and the number of kernels.

RocketModule()

Default constructor for the RocketModule object.

apply_kernel(kernel::RocketKernel, x::RealVector)

Apply a single RocketModule kernel to the sequence x.

Arguments

• kernel::RocketKernel: rocket kernel used for computing features.
• x::RealVector: data sequence for computing rocket features.

apply_kernels(rocket::RocketModule, x::RealVector)

Run a vector of rocket kernels along a sequence x.

Arguments

• rocket::RocketModule: rocket module containing many kernels for processing.
• x::RealVector: data sequence for computing rocket features.

load_rocket(filepath::String="rocket.jld2")

Load and return a rocket module with existing parameters from a .jld2 file.

Arguments

filepath::String: path to .jld2 containing rocket parameters. Defaults to rocket.jld2.

save_rocket(rocket::RocketModule, filepath::String="rocket.jld2")

Save the rocket parameters to a .jld2 file.

Arguments

rocket::RocketModule: rocket module to save. filepath::String: path to .jld2 for saving rocket parameters. Defaults to rocket.jld2.