Models

Because TMLE.jl is based on top of MLJ, we can support any model respecting the MLJ interface. At the moment, we readily support all models from the following packages:

  • MLJLinearModels: Generalized Linear Models in Julia.
  • XGBoost.jl: Julia wrapper of the famous XGBoost package.
  • EvoTrees.jl: A pure Julia implementation of histogram based gradient boosting trees (subset of XGBoost)
  • GLMNet: A Julia wrapper of the glmnet package. See the GLMNet section.
  • MLJModels: General utilities such as the OneHotEncoder or InteractionTransformer.

Further support for more packages can be added on request, please fill an issue.

Also, because the estimator file used by the TMLE CLI is a pure Julia file, it is possible to use it in order to install additional package that can be used to define additional models.

Finally, we also provide some additional models described in Additional models provided by TMLECLI.jl.

Additional models provided by TMLECLI.jl

GLMNet

This is a simple wrapper around the glmnetcv function from the GLMNet.jl package. The only difference is that the resampling is made based on MLJ resampling strategies.

TMLECLI.GLMNetRegressor — Method
GLMNetRegressor(;resampling=CV(), params...)

A GLMNet regressor for continuous outcomes based on the glmnetcv function from the GLMNet.jl package.

Arguments:

Examples:

A glmnet with alpha=0.


model = GLMNetRegressor(resampling=CV(nfolds=3), alpha=0)
mach = machine(model, X, y)
fit!(mach, verbosity=0)
source
TMLECLI.GLMNetClassifier — Method
GLMNetClassifier(;resampling=StratifiedCV(), params...)

A GLMNet classifier for binary/multinomial outcomes based on the glmnetcv function from the GLMNet.jl package.

Arguments:

Examples:

A glmnet with alpha=0.


model = GLMNetClassifier(resampling=StratifiedCV(nfolds=3), alpha=0)
mach = machine(model, X, y)
fit!(mach, verbosity=0)
source

RestrictedInteractionTransformer

This transformer generates interaction terms based on a set of primary variables in order to limit the combinatorial explosion.

TMLECLI.RestrictedInteractionTransformer — Type
RestrictedInteractionTransformer(;order=2, primary_variables=Symbol[], primary_variables_patterns=Regex[])

Definition

This transformer generates interaction terms based on a set of primary variables. All generated interaction terms are composed of a set of primary variables and at most one remaining variable in the provided table. If (T₁, T₂) are defining the set of primary variables and (W₁, W₂) are reamining variables in the table, the generated interaction terms at order 2 will be:

  • T₁xT₂
  • T₁xW₂
  • W₁xT₂

but W₁xW₂ will not be generated because it would contain 2 remaining variables.

Arguments:

  • order: All interaction features up to the given order will be computed
  • primary_variables: A set of column names to generate the interactions
  • primaryvariablespatterns: A set of regular expression that can additionally

be used to identify primary_variables.

source

BiAllelicSNPEncoder

This transformer, mostly useful for genetic studies, converts bi-allelic single nucleotide polyphormism columns, encoded as Strings to a count of one of the two alleles.

TMLECLI.BiAllelicSNPEncoder — Type
BiAllelicSNPEncoder(patterns=Symbol[])

Encodes bi-allelic SNP columns, identified by the provided patterns Regex, as a count of a reference allele determined dynamically (not necessarily the minor allele).

source