Main training function. Performs model fitting given configuration params, dtrain, target_name and other optional kwargs.
Arguments
params::EvoTypes: configuration info providing hyper-paramters. EvoTypes can be one of: dtrain: A Tables compatible training data (named tuples, DataFrame...) containing features and target variables. Keyword arguments
target_name: name of target variable. fnames = nothing: the names of the x_train features. If provided, should be a vector of string with length(fnames) = size(x_train, 2).w_name = nothing: name of the variable containing weights. If nothing, common weights on one will be used.offset_name = nothing: name of the offset variable.deval: A Tables compatible evaluation data containing features and target variables. metric: The evaluation metric that wil be tracked on deval. Supported metrics are: :mse: mean-squared error. Adapted for general regression models.:rmse: root-mean-squared error (CPU only). Adapted for general regression models.:mae: mean absolute error. Adapted for general regression models.:logloss: Adapted for :logistic regression models.:mlogloss: Multi-class cross entropy. Adapted to EvoTreeClassifier classification models. :poisson: Poisson deviance. Adapted to EvoTreeCount count models.:gamma: Gamma deviance. Adapted to regression problem on Gamma like, positively distributed targets.:tweedie: Tweedie deviance. Adapted to regression problem on Tweedie like, positively distributed targets with probability mass at y == 0.:gaussian_mle: Gaussian maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :gaussian_mle. :logistic_mle: Logistic maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :logistic_mle. early_stopping_rounds::Integer: number of consecutive rounds without metric improvement after which fitting in stopped. print_every_n: sets at which frequency logging info should be printed. verbosity: set to 1 to print logging info during training.return_logger::Bool = false: if set to true (default), fit_evotree return a tuple (m, logger) where logger is a dict containing various tracking information.device="cpu": Hardware device to use for computations. Can be either "cpu" or "gpu". Following losses are not GPU supported at the moment:l1, :quantile, :logistic_mle.fit_evotree(
+ device="cpu")Main training function. Performs model fitting given configuration params, dtrain, target_name and other optional kwargs.
Arguments
params::EvoTypes: configuration info providing hyper-paramters. EvoTypes can be one of: dtrain: A Tables compatible training data (named tuples, DataFrame...) containing features and target variables. Keyword arguments
target_name: name of target variable. fnames = nothing: the names of the x_train features. If provided, should be a vector of string with length(fnames) = size(x_train, 2).w_name = nothing: name of the variable containing weights. If nothing, common weights on one will be used.offset_name = nothing: name of the offset variable.deval: A Tables compatible evaluation data containing features and target variables. metric: The evaluation metric that wil be tracked on deval. Supported metrics are: :mse: mean-squared error. Adapted for general regression models.:rmse: root-mean-squared error (CPU only). Adapted for general regression models.:mae: mean absolute error. Adapted for general regression models.:logloss: Adapted for :logistic regression models.:mlogloss: Multi-class cross entropy. Adapted to EvoTreeClassifier classification models. :poisson: Poisson deviance. Adapted to EvoTreeCount count models.:gamma: Gamma deviance. Adapted to regression problem on Gamma like, positively distributed targets.:tweedie: Tweedie deviance. Adapted to regression problem on Tweedie like, positively distributed targets with probability mass at y == 0.:gaussian_mle: Gaussian maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :gaussian_mle. :logistic_mle: Logistic maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :logistic_mle. early_stopping_rounds::Integer: number of consecutive rounds without metric improvement after which fitting in stopped. print_every_n: sets at which frequency logging info should be printed. verbosity: set to 1 to print logging info during training.return_logger::Bool = false: if set to true (default), fit_evotree return a tuple (m, logger) where logger is a dict containing various tracking information.device="cpu": Hardware device to use for computations. Can be either "cpu" or "gpu". Following losses are not GPU supported at the moment:l1, :quantile, :logistic_mle.fit_evotree(
params::EvoTypes{L};
x_train::AbstractMatrix,
y_train::AbstractVector,
@@ -24,4 +24,4 @@
offset_eval=nothing,
early_stopping_rounds=9999,
print_every_n=9999,
- verbosity=1)Main training function. Performs model fitting given configuration params, x_train, y_train and other optional kwargs.
Arguments
params::EvoTypes: configuration info providing hyper-paramters. EvoTypes can be one of: Keyword arguments
x_train::Matrix: training data of size [#observations, #features]. y_train::Vector: vector of train targets of length #observations.w_train::Vector: vector of train weights of length #observations. If nothing, a vector of ones is assumed.offset_train::VecOrMat: offset for the training data. Should match the size of the predictions.x_eval::Matrix: evaluation data of size [#observations, #features]. y_eval::Vector: vector of evaluation targets of length #observations.w_eval::Vector: vector of evaluation weights of length #observations. Defaults to nothing (assumes a vector of 1s).offset_eval::VecOrMat: evaluation data offset. Should match the size of the predictions.metric: The evaluation metric that wil be tracked on x_eval, y_eval and optionally w_eval / offset_eval data. Supported metrics are: :mse: mean-squared error. Adapted for general regression models.:rmse: root-mean-squared error (CPU only). Adapted for general regression models.:mae: mean absolute error. Adapted for general regression models.:logloss: Adapted for :logistic regression models.:mlogloss: Multi-class cross entropy. Adapted to EvoTreeClassifier classification models. :poisson: Poisson deviance. Adapted to EvoTreeCount count models.:gamma: Gamma deviance. Adapted to regression problem on Gamma like, positively distributed targets.:tweedie: Tweedie deviance. Adapted to regression problem on Tweedie like, positively distributed targets with probability mass at y == 0.:gaussian_mle: Gaussian maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :gaussian_mle. :logistic_mle: Logistic maximum log-likelihood. Adapted to EvoTreeMLE models with loss = :logistic_mle. early_stopping_rounds::Integer: number of consecutive rounds without metric improvement after which fitting in stopped. print_every_n: sets at which frequency logging info should be printed. verbosity: set to 1 to print logging info during training.fnames: the names of the x_train features. If provided, should be a vector of string with length(fnames) = size(x_train, 2).return_logger::Bool = false: if set to true (default), fit_evotree return a tuple (m, logger) where logger is a dict containing various tracking information.device="cpu": Hardware device to use for computations. Can be either "cpu" or "gpu". Following losses are not GPU supported at the moment:l1, :quantile, :logistic_mle.MLJModelInterface.predict — Functionpredict(model::EvoTree, X::AbstractMatrix; ntree_limit = length(model.trees))Predictions from an EvoTree model - sums the predictions from all trees composing the model. Use ntree_limit=N to only predict with the first N trees.
EvoTrees.importance — Functionimportance(model::EvoTree; fnames=model.info[:fnames])Sorted normalized feature importance based on loss function gain. Feature names associated to the model are stored in model.info[:fnames] as a string Vector and can be updated at any time. Eg: model.info[:fnames] = new_fnames_vec.
Settings
This document was generated with Documenter.jl version 1.1.0 on Tuesday 10 October 2023. Using Julia version 1.6.7.