cuml4go

const (
	// AlgoAuto choose the algorithm automatically; currently chooses NAIVE for sparse forests
	//  and BatchTreeReorg for dense ones
	AlgoAuto FILInferenceAlgorithm = iota
	// Naive naive algorithm: 1 thread block predicts 1 row; the row is cached in
	//  shared memory, and the trees are distributed cyclically between threads
	Naive
	// TreeReorg tree reorg algorithm: same as naive, but the tree nodes are rearranged
	//  into a more coalescing-friendly layout: for every node position,
	//  nodes of all trees at that position are stored next to each other
	TreeReorg
	// BatchTreeReorg batch tree reorg algorithm: same as tree reorg, but predictions multiple rows (up to 4)
	//  in a single thread block
	BatchTreeReorg
)

const (
	// XGBoost xgboost model (binary model file)
	XGBoost FILModelType = iota
	// XGBoostJSON xgboost model (json model file)
	XGBoostJSON
	// LightGBM lighgbm model (binary model file)
	LightGBM
)

const (
	// Auto decide automatically; currently always builds dense forests
	Auto FILStorageType = iota
	// Dense import the forest as dense
	Dense
	// Sparse import the forest as sparse (currently always with 16-byte nodes)
	Sparse
	// Sparse8 (experimental) import the forest as sparse with 8-byte nodes; can fail if
	//  8-byte nodes are not enough to store the forest, e.g. there are too many
	//  nodes in a tree or too many features; note that the number of bits used to
	//  store the child or feature index can change in the future; this can affect
	//  whether a particular forest can be imported as SPARSE8 */
	Sparse8
)

const (
	Off      LogLevel = 0
	Critical LogLevel = 1
	Error    LogLevel = 2
	Warn     LogLevel = 3
	Info     LogLevel = 4
	Debug    LogLevel = 5
	Trace    LogLevel = 6
)