API
Regressors
EvoLinear.Linear.EvoLinearRegressor — TypeEvoLinearRegressor(; kwargs...)A model type for constructing a EvoLinearRegressor, based on EvoLinear.jl, and implementing both an internal API and the MLJ model interface.
Keyword arguments
loss=:mse: loss function to be minimised. Can be one of::mse:logistic:poisson:gamma:tweedie
nrounds=10: maximum number of training rounds.eta=1: Learning rate. Typically in the range[1e-2, 1].L1=0: Regularization penalty applied by shrinking to 0 weight update if update is < L1. No penalty if update > L1. Results in sparse feature selection. Typically in the[0, 1]range on normalized features.L2=0: Regularization penalty applied to the squared of the weight update value. Restricts large parameter values. Typically in the[0, 1]range on normalized features.rng=123: random seed. Not used at the moment.updater=:all: training method. Only:allis supported at the moment. Gradients for each feature are computed simultaneously, then bias is updated based on all features update.device=:cpu: Only:cpuis supported at the moment.
Internal API
Do config = EvoLinearRegressor() to construct an hyper-parameter struct with default hyper-parameters. Provide keyword arguments as listed above to override defaults, for example:
EvoLinearRegressor(loss=:logistic, L1=1e-3, L2=1e-2, nrounds=100)Training model
A model is built using fit:
config = EvoLinearRegressor()
-m = fit(config; x, y, w)Inference
Fitted results is an EvoLinearModel which acts as a prediction function when passed a features matrix as argument.
preds = m(x)MLJ Interface
From MLJ, the type can be imported using:
EvoLinearRegressor = @load EvoLinearRegressor pkg=EvoLinearDo model = EvoLinearRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoLinearRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where:
X: any table of input features (eg, aDataFrame) whose columns each have one of the following element scitypes:Continuous,Count, or<:OrderedFactor; check column scitypes withschema(X)y: is the target, which can be anyAbstractVectorwhose element scitype is<:Continuous; check the scitype withscitype(y)
Train the machine using fit!(mach, rows=...).
Operations
predict(mach, Xnew): return predictions of the target given
features Xnew having the same scitype as X above. Predictions are deterministic.
Fitted parameters
The fields of fitted_params(mach) are:
:fitresult: theEvoLinearModelobject returned by EvoLnear.jl fitting algorithm.
Report
The fields of report(mach) are:
:coef: Vector of coefficients (βs) associated to each of the features.:bias: Value of the bias.:names: Names of each of the features.
EvoLinear.Splines.EvoSplineRegressor — TypeEvoSplineRegressor(; kwargs...)A model type for constructing a EvoSplineRegressor, based on EvoLinear.jl, and implementing both an internal API and the MLJ model interface.
Keyword arguments
loss=:mse: loss function to be minimised. Can be one of::mse:logistic:poisson:gamma:tweedie
nrounds=10: maximum number of training rounds.eta=1: Learning rate. Typically in the range[1e-2, 1].L1=0: Regularization penalty applied by shrinking to 0 weight update if update is < L1. No penalty if update > L1. Results in sparse feature selection. Typically in the[0, 1]range on normalized features.L2=0: Regularization penalty applied to the squared of the weight update value. Restricts large parameter values. Typically in the[0, 1]range on normalized features.rng=123: random seed. Not used at the moment.updater=:all: training method. Only:allis supported at the moment. Gradients for each feature are computed simultaneously, then bias is updated based on all features update.device=:cpu: Only:cpuis supported at the moment.
Internal API
Do config = EvoSplineRegressor() to construct an hyper-parameter struct with default hyper-parameters. Provide keyword arguments as listed above to override defaults, for example:
EvoSplineRegressor(loss=:logistic, L1=1e-3, L2=1e-2, nrounds=100)Training model
A model is built using fit:
config = EvoSplineRegressor()
-m = fit(config; x, y, w)Inference
Fitted results is an EvoLinearModel which acts as a prediction function when passed a features matrix as argument.
preds = m(x)MLJ Interface
From MLJ, the type can be imported using:
EvoSplineRegressor = @load EvoSplineRegressor pkg=EvoLinearDo model = EvoLinearRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoSplineRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where:
X: any table of input features (eg, aDataFrame) whose columns each have one of the following element scitypes:Continuous,Count, or<:OrderedFactor; check column scitypes withschema(X)y: is the target, which can be anyAbstractVectorwhose element scitype is<:Continuous; check the scitype withscitype(y)
Train the machine using fit!(mach, rows=...).
Operations
predict(mach, Xnew): return predictions of the target given
features Xnew having the same scitype as X above. Predictions are deterministic.
Fitted parameters
The fields of fitted_params(mach) are:
:fitresult: theSplineModelobject returned by EvoSplineRegressor fitting algorithm.
Report
The fields of report(mach) are:
:coef: Vector of coefficients (βs) associated to each of the features.:bias: Value of the bias.:names: Names of each of the features.
Training
MLJModelInterface.fit — Functionfit(config::EvoLinearRegressor;
+m = fit(config; x, y, w)Inference
Fitted results is an EvoLinearModel which acts as a prediction function when passed a features matrix as argument.
preds = m(x)MLJ Interface
From MLJ, the type can be imported using:
EvoLinearRegressor = @load EvoLinearRegressor pkg=EvoLinearDo model = EvoLinearRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoLinearRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where:
X: any table of input features (eg, aDataFrame) whose columns each have one of the following element scitypes:Continuous,Count, or<:OrderedFactor; check column scitypes withschema(X)y: is the target, which can be anyAbstractVectorwhose element scitype is<:Continuous; check the scitype withscitype(y)
Train the machine using fit!(mach, rows=...).
Operations
predict(mach, Xnew): return predictions of the target given
features Xnew having the same scitype as X above. Predictions are deterministic.
Fitted parameters
The fields of fitted_params(mach) are:
:fitresult: theEvoLinearModelobject returned by EvoLnear.jl fitting algorithm.
Report
The fields of report(mach) are:
:coef: Vector of coefficients (βs) associated to each of the features.:bias: Value of the bias.:names: Names of each of the features.
EvoLinear.Splines.EvoSplineRegressor — TypeEvoSplineRegressor(; kwargs...)A model type for constructing a EvoSplineRegressor, based on EvoLinear.jl, and implementing both an internal API and the MLJ model interface.
Keyword arguments
loss=:mse: loss function to be minimised. Can be one of::mse:logistic:poisson:gamma:tweedie
nrounds=10: maximum number of training rounds.eta=1: Learning rate. Typically in the range[1e-2, 1].L1=0: Regularization penalty applied by shrinking to 0 weight update if update is < L1. No penalty if update > L1. Results in sparse feature selection. Typically in the[0, 1]range on normalized features.L2=0: Regularization penalty applied to the squared of the weight update value. Restricts large parameter values. Typically in the[0, 1]range on normalized features.rng=123: random seed. Not used at the moment.updater=:all: training method. Only:allis supported at the moment. Gradients for each feature are computed simultaneously, then bias is updated based on all features update.device=:cpu: Only:cpuis supported at the moment.
Internal API
Do config = EvoSplineRegressor() to construct an hyper-parameter struct with default hyper-parameters. Provide keyword arguments as listed above to override defaults, for example:
EvoSplineRegressor(loss=:logistic, L1=1e-3, L2=1e-2, nrounds=100)Training model
A model is built using fit:
config = EvoSplineRegressor()
+m = fit(config; x, y, w)Inference
Fitted results is an EvoLinearModel which acts as a prediction function when passed a features matrix as argument.
preds = m(x)MLJ Interface
From MLJ, the type can be imported using:
EvoSplineRegressor = @load EvoSplineRegressor pkg=EvoLinearDo model = EvoLinearRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoSplineRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where:
X: any table of input features (eg, aDataFrame) whose columns each have one of the following element scitypes:Continuous,Count, or<:OrderedFactor; check column scitypes withschema(X)y: is the target, which can be anyAbstractVectorwhose element scitype is<:Continuous; check the scitype withscitype(y)
Train the machine using fit!(mach, rows=...).
Operations
predict(mach, Xnew): return predictions of the target given
features Xnew having the same scitype as X above. Predictions are deterministic.
Fitted parameters
The fields of fitted_params(mach) are:
:fitresult: theSplineModelobject returned by EvoSplineRegressor fitting algorithm.
Report
The fields of report(mach) are:
:coef: Vector of coefficients (βs) associated to each of the features.:bias: Value of the bias.:names: Names of each of the features.
Training
MLJModelInterface.fit — Functionfit(config::EvoSplineRegressor; x_train, y_train, x_eval = nothing, y_eval = nothing)Train a splined linear model.
fit(config::EvoLinearRegressor;
x, y, w=nothing,
x_eval=nothing, y_eval=nothing, w_eval=nothing,
metric=:none,
- print_every_n=1)Provided a config, EvoLinear.fit takes x and y as features and target inputs, plus optionally w as weights and train a Linear boosted model.
Arguments
config::EvoLinearRegressor:
Keyword arguments
x::AbstractMatrix: Features matrix. Dimensions are[nobs, num_features].y::AbstractVector: Vector of observed targets.w=nothing: Vector of weights. Can be be either aVectorornothing. Ifnothing, assumes a vector of 1s.metric=nothing: Evaluation metric to be tracked through each iteration. Default tonothing. Can be one of::mse:logistic:poisson_deviance:gamma_deviance:tweedie_deviance
fit(config::EvoSplineRegressor; x_train, y_train, x_eval = nothing, y_eval = nothing)Train a splined linear model.
Metrics
EvoLinear.Metrics.gamma_deviance — Methodgamma_deviance(p, y)
-gamma_deviance(p, y, w)Gamma deviance evaluation metric. 𝐷 = 2 * (log(μ/y) + y/μ - 1)
Arguments
p: predicted value. Assumes that p is on a projected basis (ie. in the[0-Inf]range).y: observed target variable.w: vector of weights.
EvoLinear.Metrics.logloss — Methodlogloss(p, y)
-logloss(p, y, w)Logloss evaluation metric. ylog(p) + (1-y)log(1-p)
Arguments
p: predicted value. Assumes that p is on a projected basis (ie. in the[0-1]range).y: observed target variable.w: vector of weights.
EvoLinear.Metrics.mae — Methodmae(p, y)
-mae(p, y, w)Mean absolute error evaluation metric.
Arguments
p: predicted value.y: observed target variable.w: vector of weights.
EvoLinear.Metrics.mse — Methodmse(p, y)
-mse(p, y, w)Mean squared error evaluation metric.
Arguments
p: predicted value.y: observed target variable.w: vector of weights.
EvoLinear.Metrics.poisson_deviance — Methodpoisson_deviance(p, y)
-poisson_deviance(p, y, w)Poisson deviance evaluation metric. 𝐷 = 2 * (y * log(y/p) + p - y)
Arguments
p: predicted value. Assumes that p is on a projected basis (ie. in the[0-Inf]range).y: observed target variable.w: vector of weights.
EvoLinear.Metrics.tweedie_deviance — Methodtweedie_deviance(p, y)
-tweedie_deviance(p, y, w)Tweedie deviance evaluation metric. Fixed rho (ρ) of 1.5. 𝐷 = 2 * (y²⁻ʳʰᵒ/(1-rho)(2-rho) - yμ¹⁻ʳʰᵒ/(1-rho) + μ²⁻ʳʰᵒ/(2-rho))
Arguments
p: predicted value. Assumes that p is on a projected basis (ie. in the[0-Inf]range).y: observed target variable.w: vector of weights.