## Not run: ------------------------------------
# # Load libraries
# library(data.table)
# library(Matrix)
# library(xgboost)
#
# # Create data
# data(agaricus.train, package = "lightgbm")
# data(agaricus.test, package = "lightgbm")
# agaricus_data_train <- data.table(as.matrix(agaricus.train$data))
# agaricus_data_test <- data.table(as.matrix(agaricus.test$data))
# agaricus_label_train <- agaricus.train$label
# agaricus_label_test <- agaricus.test$label
# folds <- Laurae::kfold(agaricus_label_train, 5)
#
# # Train a model (binary classification)
# model <- CascadeForest(training_data = agaricus_data_train, # Training data
# validation_data = agaricus_data_test, # Validation data
# training_labels = agaricus_label_train, # Training labels
# validation_labels = agaricus_label_test, # Validation labels
# folds = folds, # Folds for cross-validation
# boosting = FALSE, # Do not touch this unless you are expert
# nthread = 1, # Change this to use more threads
# cascade_lr = 1, # Do not touch this unless you are expert
# training_start = NULL, # Do not touch this unless you are expert
# validation_start = NULL, # Do not touch this unless you are expert
# cascade_forests = rep(4, 5), # Number of forest models
# cascade_trees = 10, # Number of trees per forest
# cascade_rf = 2, # Number of Random Forest in models
# cascade_seeds = 0, # Seed per layer
# objective = "binary:logistic",
# eval_metric = Laurae::df_logloss,
# multi_class = 2, # Modify this for multiclass problems
# early_stopping = 2, # stop after 2 bad combos of forests
# maximize = FALSE, # not a maximization task
# verbose = TRUE, # print information during training
# low_memory = FALSE)
#
# # Predict from model
# new_preds <- CascadeForest_pred(model, agaricus_data_test, prediction = FALSE)
#
# # We can check whether we have equal predictions, it's all TRUE!
# all.equal(model$train_means, CascadeForest_pred(model,
# agaricus_data_train,
# folds = folds))
# all.equal(model$valid_means, CascadeForest_pred(model,
# agaricus_data_test))
#
# # Attempt to perform fake multiclass problem
# agaricus_label_train[1:100] <- 2
#
# # Train a model (multiclass classification)
# model <- CascadeForest(training_data = agaricus_data_train, # Training data
# validation_data = agaricus_data_test, # Validation data
# training_labels = agaricus_label_train, # Training labels
# validation_labels = agaricus_label_test, # Validation labels
# folds = folds, # Folds for cross-validation
# boosting = FALSE, # Do not touch this unless you are expert
# nthread = 1, # Change this to use more threads
# cascade_lr = 1, # Do not touch this unless you are expert
# training_start = NULL, # Do not touch this unless you are expert
# validation_start = NULL, # Do not touch this unless you are expert
# cascade_forests = rep(4, 5), # Number of forest models
# cascade_trees = 10, # Number of trees per forest
# cascade_rf = 2, # Number of Random Forest in models
# cascade_seeds = 0, # Seed per layer
# objective = "multi:softprob",
# eval_metric = Laurae::df_logloss,
# multi_class = 3, # Modify this for multiclass problems
# early_stopping = 2, # stop after 2 bad combos of forests
# maximize = FALSE, # not a maximization task
# verbose = TRUE, # print information during training
# low_memory = FALSE)
#
# # Predict from model for mutliclass problems
# new_preds <- CascadeForest_pred(model, agaricus_data_test, prediction = FALSE)
#
# # We can check whether we have equal predictions, it's all TRUE!
# all.equal(model$train_means, CascadeForest_pred(model,
# agaricus_data_train,
# folds = folds))
# all.equal(model$valid_means, CascadeForest_pred(model,
# agaricus_data_test))
## ---------------------------------------------
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