R package wrapping EvoTrees.jl, a pure Julia tree boosting library.
EvoTrees.jl needs first to be installed and available from main Julia
environment. It’s available on the official Julia registry. Can be
installed from REPL: ] add EvoTrees. Then, install EvoTrees R package:
devtools::install_github("Evovest/EvoTrees").
- loss: {“linear”, “logistic”, “poisson”, “L1”, “quantile”, “softmax”, “gaussian”}
- alpha: float, optional, [0, 1], set the quantile and L1 level, default=0.5
- nrounds: 10L
- lambda: 0.0
- gamma: 0.0
- eta: 0.1
- max_depth: integer, default 5L
- min_weight: float >= 0 default=1.0,
- rowsample: float [0,1] default=1.0
- colsample: float [0,1] default=1.0
- nbins: integer [2,250] default=250
- metric: {“mse”, “rmse”, “mae”, “logloss”, “mlogloss”, “poisson”, “quantile”, “gaussian”}, default=“none”
- device: {“cpu”, “gpu”}
set.seed(1234)
x <- runif(10000, -6, 6)
y <- sin(x) * 0.5 + 0.5
y <- log(y/(1 - y)) + rnorm(length(y))
y <- 1 / (1 + exp(-y))
data_train <- matrix(x)
target_train <- y
weights_train <- y
# linear regression
params <- list(loss = "linear", nrounds = 200, eta = 0.05, lambda = 0.1, gamma = 0.1, max_depth = 5, min_weight = 1, rowsample = 0.5, colsample = 1, nbins = 64, metric = "mse")
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_linear <- predict(model = model, data = data_train)
model <- evo_train(data_train = data_train, target_train = target_train, weights_train = weights_train, params = params)
pred_linear_w <- predict(model = model, data = data_train)
# logistic / cross-engtropy regression
params <- list(loss = "logistic", nrounds = 200, eta = 0.05, lambda = 0.1, gamma = 0.1, max_depth = 5, min_weight = 1, rowsample = 0.5, colsample = 1, nbins = 64, metric = "logloss")
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_logistic <- predict(model = model, data = data_train)
# poisson regression
params <- list(loss = "poisson", nrounds = 200, eta = 0.05, lambda = 0.1, gamma = 0.1, max_depth = 5, min_weight = 1, rowsample = 0.5, colsample = 1, nbins = 64, metric = "poisson")
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_poisson <- predict(model = model, data = data_train)
# xgboost reference
params <- list(max_depth = 4, eta = 0.05, subsample = 0.5, colsample_bytree = 1.0, min_child_weight = 1, lambda = 0.1, alpha = 0, gamma = 0.1, max_bin = 64, tree_method = "hist", objective = "reg:squarederror", eval_metric = "rmse")
xgb_train <- xgb.DMatrix(data = data_train, label = target_train)
model <- xgb.train(data = xgb_train, params = params, nrounds = 200, verbose = 1, print_every_n = 10L, early_stopping_rounds = NULL)
pred_xgb <- predict(model, xgb_train)
set.seed(1234)
x <- runif(10000, 0, 6)
y <- sin(x) * 0.5 + 0.5
y <- log(y/(1 - y)) + rnorm(length(y))
y <- 1 / (1 + exp(-y))
data_train <- matrix(x)
target_train <- y
# quantile regression - q50
params <- list(loss = "quantile", alpha = 0.5, nrounds = 200, eta = 0.05, nbins = 64, lambda = 0.2, gamma = 0.0, max_depth = 4, min_weight = 1, rowsample = 0.5, colsample = 1)
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_q50 <- predict(model = model, data = data_train)
# quantile regression - q20
params <- list(loss = "quantile", alpha = 0.2, nrounds = 200, eta = 0.05, nbins = 64, lambda = 0.2, gamma = 0.0, max_depth = 4, min_weight = 1, rowsample = 0.5, colsample = 1)
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_q20 <- predict(model = model, data = data_train)
# quantile regression - q80
params <- list(loss = "quantile", alpha = 0.8, nrounds = 200, eta = 0.05, nbins = 64, lambda = 0.2, gamma = 0.0, max_depth = 4, min_weight = 1, rowsample = 0.5, colsample = 1)
model <- evo_train(data_train = data_train, target_train = target_train, params = params)
pred_q80 <- predict(model = model, data = data_train)
# lightgbm
params <- list(objective = "quantile", alpha = 0.2, eta = 0.05, max_bin = 64, lambda_l1 = 0, lambda_l2 = 0.1, min_split_gain = 0.0, max_depth = 4, min_data_in_leaf = 1, subsample = 0.5, subsample_freq = 1, feature_fraction = 1)
dtrain <- lgb.Dataset(data = data_train, label = target_train, free_raw_data = FALSE)
model <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 100, verbose = -1)
pred_q20_lgb <- predict(model, data_train)
params <- list(objective = "quantile", alpha = 0.8, eta = 0.05, max_bin = 64, lambda_l1 = 0, lambda_l2 = 0.1, min_split_gain = 0.0, max_depth = 4, min_data_in_leaf = 1, subsample = 0.5, subsample_freq = 1, feature_fraction = 1)
dtrain <- lgb.Dataset(data = data_train, label = target_train, free_raw_data = FALSE)
model <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 100, verbose = -1)
pred_q80_lgb <- predict(model, data_train)