package caisar

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From https://github.com/dmlc/xgboost/raw/master/doc/model.schema

type int_option = int option
val show_int_option : int_option -> Ppx_deriving_runtime.string
val int_option_none : int32
val int_option_of_yojson : Yojson.Safe.t -> (int option, string) result
val int_option_to_yojson : int option -> [> `Intlit of string ]
type gbtree_model_param = {
  1. num_trees : string;
  2. num_parallel_tree : string;
  3. size_leaf_vector : string;
}
and tree_param = {
  1. num_nodes : string;
  2. size_leaf_vector : string;
  3. num_feature : string;
}
and reg_loss_param = {
  1. scale_pos_weight : string;
}
and pseudo_huber_param = {
  1. huber_slope : string;
}
and aft_loss_param = {
  1. aft_loss_distribution : string;
  2. aft_loss_distribution_scale : string;
}
and softmax_multiclass_param = {
  1. num_class : string;
}
and lambda_rank_param = {
  1. num_pairsample : string;
  2. fix_list_weight : string;
}
and tree = {
  1. tree_param : tree_param;
  2. id : int;
  3. loss_changes : float array;
  4. sum_hessian : float array;
  5. base_weights : float array;
  6. left_children : int array;
  7. right_children : int array;
  8. parents : int_option array;
  9. split_indices : int array;
  10. split_conditions : float array;
  11. split_type : int array;
  12. default_left : bool array;
  13. categories : int array;
  14. categories_nodes : int array;
  15. categories_segments : int array;
  16. categories_sizes : int array;
}
and gbtree = {
  1. gbtree_model_param : gbtree_model_param;
  2. trees : tree array;
  3. tree_info : int array;
}
and gblinear = {
  1. weights : float array;
}
and dart = {
  1. gbtree : gbtree;
  2. weight_drop : float array;
}
and learner_model_param = {
  1. base_score : string;
  2. num_class : string;
  3. num_feature : string;
}
val show_gbtree_model_param : gbtree_model_param -> Ppx_deriving_runtime.string
val show_tree_param : tree_param -> Ppx_deriving_runtime.string
val show_reg_loss_param : reg_loss_param -> Ppx_deriving_runtime.string
val show_pseudo_huber_param : pseudo_huber_param -> Ppx_deriving_runtime.string
val show_aft_loss_param : aft_loss_param -> Ppx_deriving_runtime.string
val show_softmax_multiclass_param : softmax_multiclass_param -> Ppx_deriving_runtime.string
val show_lambda_rank_param : lambda_rank_param -> Ppx_deriving_runtime.string
val show_gbtree : gbtree -> Ppx_deriving_runtime.string
val show_gblinear : gblinear -> Ppx_deriving_runtime.string
val show_learner_model_param : learner_model_param -> Ppx_deriving_runtime.string
val gbtree_model_param_to_yojson : gbtree_model_param -> Yojson.Safe.t
val tree_param_to_yojson : tree_param -> Yojson.Safe.t
val reg_loss_param_to_yojson : reg_loss_param -> Yojson.Safe.t
val pseudo_huber_param_to_yojson : pseudo_huber_param -> Yojson.Safe.t
val aft_loss_param_to_yojson : aft_loss_param -> Yojson.Safe.t
val softmax_multiclass_param_to_yojson : softmax_multiclass_param -> Yojson.Safe.t
val lambda_rank_param_to_yojson : lambda_rank_param -> Yojson.Safe.t
val tree_to_yojson : tree -> Yojson.Safe.t
val gbtree_to_yojson : gbtree -> Yojson.Safe.t
val gblinear_to_yojson : gblinear -> Yojson.Safe.t
val dart_to_yojson : dart -> Yojson.Safe.t
val learner_model_param_to_yojson : learner_model_param -> Yojson.Safe.t
type gradient_booster =
  1. | Gbtree of gbtree
  2. | Gblinear of gblinear
  3. | Dart of dart
val show_gradient_booster : gradient_booster -> Ppx_deriving_runtime.string
val gradient_booster_to_yojson : gradient_booster -> [> `Assoc of (string * Yojson.Safe.t) list ]
type objective =
  1. | Reg_squarederror of reg_loss_param
  2. | Reg_pseudohubererror of reg_loss_param
  3. | Reg_squaredlogerror of reg_loss_param
  4. | Reg_linear of reg_loss_param
  5. | Binary_logistic of reg_loss_param
val show_objective : objective -> Ppx_deriving_runtime.string
val objective_to_yojson : objective -> [> `Assoc of (string * Yojson.Safe.t) list ]
type learner = {
  1. feature_names : string array;
  2. feature_types : string array;
  3. gradient_booster : gradient_booster;
  4. objective : objective;
  5. learner_model_param : learner_model_param;
}
and t = {
  1. version : int * int * int;
  2. learner : learner;
}
val show_learner : learner -> Ppx_deriving_runtime.string
val learner_to_yojson : learner -> Yojson.Safe.t
val to_yojson : t -> Yojson.Safe.t
val _ : Yojson.Safe.t -> t Ppx_deriving_yojson_runtime.error_or
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