Source file Multioutput.ml

let () = Wrap_utils.init ();;
let __wrap_namespace = Py.import "sklearn.multioutput"

let get_py name = Py.Module.get __wrap_namespace name
module ClassifierChain = struct
type tag = [`ClassifierChain]
type t = [`BaseEstimator | `ClassifierChain | `ClassifierMixin | `MetaEstimatorMixin | `Object] Obj.t
let of_pyobject x = ((Obj.of_pyobject x) : t)
let to_pyobject x = Obj.to_pyobject x
let as_classifier x = (x :> [`ClassifierMixin] Obj.t)
let as_estimator x = (x :> [`BaseEstimator] Obj.t)
let as_meta_estimator x = (x :> [`MetaEstimatorMixin] Obj.t)
                  let create ?order ?cv ?random_state ~base_estimator () =
                     Py.Module.get_function_with_keywords __wrap_namespace "ClassifierChain"
                       [||]
                       (Wrap_utils.keyword_args [("order", Wrap_utils.Option.map order (function
| `Arr x -> Np.Obj.to_pyobject x
| `Random -> Py.String.of_string "random"
)); ("cv", Wrap_utils.Option.map cv (function
| `Arr x -> Np.Obj.to_pyobject x
| `BaseCrossValidator x -> Np.Obj.to_pyobject x
| `I x -> Py.Int.of_int x
)); ("random_state", Wrap_utils.Option.map random_state Py.Int.of_int); ("base_estimator", Some(base_estimator |> Np.Obj.to_pyobject))])
                       |> of_pyobject
let decision_function ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "decision_function"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let fit ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "fit"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject)); ("Y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let get_params ?deep self =
   Py.Module.get_function_with_keywords (to_pyobject self) "get_params"
     [||]
     (Wrap_utils.keyword_args [("deep", Wrap_utils.Option.map deep Py.Bool.of_bool)])
     |> Dict.of_pyobject
let predict ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let predict_proba ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict_proba"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let score ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "score"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> Py.Float.to_float
let set_params ?params self =
   Py.Module.get_function_with_keywords (to_pyobject self) "set_params"
     [||]
     (match params with None -> [] | Some x -> x)
     |> of_pyobject

let classes_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "classes_" with
  | None -> failwith "attribute classes_ not found"
  | Some x -> if Py.is_none x then None else Some ((fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t)) x)

let classes_ self = match classes_opt self with
  | None -> raise Not_found
  | Some x -> x

let estimators_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "estimators_" with
  | None -> failwith "attribute estimators_ not found"
  | Some x -> if Py.is_none x then None else Some ((fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t)) x)

let estimators_ self = match estimators_opt self with
  | None -> raise Not_found
  | Some x -> x

let order_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "order_" with
  | None -> failwith "attribute order_ not found"
  | Some x -> if Py.is_none x then None else Some ((fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t)) x)

let order_ self = match order_opt self with
  | None -> raise Not_found
  | Some x -> x
let to_string self = Py.Object.to_string (to_pyobject self)
let show self = to_string self
let pp formatter self = Format.fprintf formatter "%s" (show self)

end
module MultiOutputClassifier = struct
type tag = [`MultiOutputClassifier]
type t = [`BaseEstimator | `ClassifierMixin | `MetaEstimatorMixin | `MultiOutputClassifier | `Object] Obj.t
let of_pyobject x = ((Obj.of_pyobject x) : t)
let to_pyobject x = Obj.to_pyobject x
let as_classifier x = (x :> [`ClassifierMixin] Obj.t)
let as_estimator x = (x :> [`BaseEstimator] Obj.t)
let as_meta_estimator x = (x :> [`MetaEstimatorMixin] Obj.t)
let create ?n_jobs ~estimator () =
   Py.Module.get_function_with_keywords __wrap_namespace "MultiOutputClassifier"
     [||]
     (Wrap_utils.keyword_args [("n_jobs", Wrap_utils.Option.map n_jobs Py.Int.of_int); ("estimator", Some(estimator |> Np.Obj.to_pyobject))])
     |> of_pyobject
let fit ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "fit"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("Y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let get_params ?deep self =
   Py.Module.get_function_with_keywords (to_pyobject self) "get_params"
     [||]
     (Wrap_utils.keyword_args [("deep", Wrap_utils.Option.map deep Py.Bool.of_bool)])
     |> Dict.of_pyobject
let partial_fit ?classes ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "partial_fit"
     [||]
     (Wrap_utils.keyword_args [("classes", Wrap_utils.Option.map classes (fun ml -> Py.List.of_list_map Np.Obj.to_pyobject ml)); ("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let predict ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let score ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "score"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> Py.Float.to_float
let set_params ?params self =
   Py.Module.get_function_with_keywords (to_pyobject self) "set_params"
     [||]
     (match params with None -> [] | Some x -> x)
     |> of_pyobject

let estimators_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "estimators_" with
  | None -> failwith "attribute estimators_ not found"
  | Some x -> if Py.is_none x then None else Some (Wrap_utils.id x)

let estimators_ self = match estimators_opt self with
  | None -> raise Not_found
  | Some x -> x
let to_string self = Py.Object.to_string (to_pyobject self)
let show self = to_string self
let pp formatter self = Format.fprintf formatter "%s" (show self)

end
module MultiOutputEstimator = struct
type tag = [`MultiOutputEstimator]
type t = [`BaseEstimator | `MetaEstimatorMixin | `MultiOutputEstimator | `Object] Obj.t
let of_pyobject x = ((Obj.of_pyobject x) : t)
let to_pyobject x = Obj.to_pyobject x
let as_estimator x = (x :> [`BaseEstimator] Obj.t)
let as_meta_estimator x = (x :> [`MetaEstimatorMixin] Obj.t)
let fit ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "fit"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let get_params ?deep self =
   Py.Module.get_function_with_keywords (to_pyobject self) "get_params"
     [||]
     (Wrap_utils.keyword_args [("deep", Wrap_utils.Option.map deep Py.Bool.of_bool)])
     |> Dict.of_pyobject
let partial_fit ?classes ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "partial_fit"
     [||]
     (Wrap_utils.keyword_args [("classes", Wrap_utils.Option.map classes (fun ml -> Py.List.of_list_map Np.Obj.to_pyobject ml)); ("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let predict ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let set_params ?params self =
   Py.Module.get_function_with_keywords (to_pyobject self) "set_params"
     [||]
     (match params with None -> [] | Some x -> x)
     |> of_pyobject
let to_string self = Py.Object.to_string (to_pyobject self)
let show self = to_string self
let pp formatter self = Format.fprintf formatter "%s" (show self)

end
module MultiOutputRegressor = struct
type tag = [`MultiOutputRegressor]
type t = [`BaseEstimator | `MetaEstimatorMixin | `MultiOutputRegressor | `Object | `RegressorMixin] Obj.t
let of_pyobject x = ((Obj.of_pyobject x) : t)
let to_pyobject x = Obj.to_pyobject x
let as_regressor x = (x :> [`RegressorMixin] Obj.t)
let as_estimator x = (x :> [`BaseEstimator] Obj.t)
let as_meta_estimator x = (x :> [`MetaEstimatorMixin] Obj.t)
let create ?n_jobs ~estimator () =
   Py.Module.get_function_with_keywords __wrap_namespace "MultiOutputRegressor"
     [||]
     (Wrap_utils.keyword_args [("n_jobs", Wrap_utils.Option.map n_jobs Py.Int.of_int); ("estimator", Some(estimator |> Np.Obj.to_pyobject))])
     |> of_pyobject
let fit ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "fit"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let get_params ?deep self =
   Py.Module.get_function_with_keywords (to_pyobject self) "get_params"
     [||]
     (Wrap_utils.keyword_args [("deep", Wrap_utils.Option.map deep Py.Bool.of_bool)])
     |> Dict.of_pyobject
let partial_fit ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "partial_fit"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let predict ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let score ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "score"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> Py.Float.to_float
let set_params ?params self =
   Py.Module.get_function_with_keywords (to_pyobject self) "set_params"
     [||]
     (match params with None -> [] | Some x -> x)
     |> of_pyobject

let estimators_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "estimators_" with
  | None -> failwith "attribute estimators_ not found"
  | Some x -> if Py.is_none x then None else Some (Wrap_utils.id x)

let estimators_ self = match estimators_opt self with
  | None -> raise Not_found
  | Some x -> x
let to_string self = Py.Object.to_string (to_pyobject self)
let show self = to_string self
let pp formatter self = Format.fprintf formatter "%s" (show self)

end
module RegressorChain = struct
type tag = [`RegressorChain]
type t = [`BaseEstimator | `MetaEstimatorMixin | `Object | `RegressorChain | `RegressorMixin] Obj.t
let of_pyobject x = ((Obj.of_pyobject x) : t)
let to_pyobject x = Obj.to_pyobject x
let as_regressor x = (x :> [`RegressorMixin] Obj.t)
let as_estimator x = (x :> [`BaseEstimator] Obj.t)
let as_meta_estimator x = (x :> [`MetaEstimatorMixin] Obj.t)
                  let create ?order ?cv ?random_state ~base_estimator () =
                     Py.Module.get_function_with_keywords __wrap_namespace "RegressorChain"
                       [||]
                       (Wrap_utils.keyword_args [("order", Wrap_utils.Option.map order (function
| `Arr x -> Np.Obj.to_pyobject x
| `Random -> Py.String.of_string "random"
)); ("cv", Wrap_utils.Option.map cv (function
| `Arr x -> Np.Obj.to_pyobject x
| `BaseCrossValidator x -> Np.Obj.to_pyobject x
| `I x -> Py.Int.of_int x
)); ("random_state", Wrap_utils.Option.map random_state Py.Int.of_int); ("base_estimator", Some(base_estimator |> Np.Obj.to_pyobject))])
                       |> of_pyobject
let fit ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "fit"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject)); ("Y", Some(y |> Np.Obj.to_pyobject))])
     |> of_pyobject
let get_params ?deep self =
   Py.Module.get_function_with_keywords (to_pyobject self) "get_params"
     [||]
     (Wrap_utils.keyword_args [("deep", Wrap_utils.Option.map deep Py.Bool.of_bool)])
     |> Dict.of_pyobject
let predict ~x self =
   Py.Module.get_function_with_keywords (to_pyobject self) "predict"
     [||]
     (Wrap_utils.keyword_args [("X", Some(x |> Np.Obj.to_pyobject))])
     |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let score ?sample_weight ~x ~y self =
   Py.Module.get_function_with_keywords (to_pyobject self) "score"
     [||]
     (Wrap_utils.keyword_args [("sample_weight", Wrap_utils.Option.map sample_weight Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
     |> Py.Float.to_float
let set_params ?params self =
   Py.Module.get_function_with_keywords (to_pyobject self) "set_params"
     [||]
     (match params with None -> [] | Some x -> x)
     |> of_pyobject

let estimators_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "estimators_" with
  | None -> failwith "attribute estimators_ not found"
  | Some x -> if Py.is_none x then None else Some ((fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t)) x)

let estimators_ self = match estimators_opt self with
  | None -> raise Not_found
  | Some x -> x

let order_opt self =
  match Py.Object.get_attr_string (to_pyobject self) "order_" with
  | None -> failwith "attribute order_ not found"
  | Some x -> if Py.is_none x then None else Some ((fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t)) x)

let order_ self = match order_opt self with
  | None -> raise Not_found
  | Some x -> x
let to_string self = Py.Object.to_string (to_pyobject self)
let show self = to_string self
let pp formatter self = Format.fprintf formatter "%s" (show self)

end
let abstractmethod funcobj =
   Py.Module.get_function_with_keywords __wrap_namespace "abstractmethod"
     [||]
     (Wrap_utils.keyword_args [("funcobj", Some(funcobj ))])

                  let check_X_y ?accept_sparse ?accept_large_sparse ?dtype ?order ?copy ?force_all_finite ?ensure_2d ?allow_nd ?multi_output ?ensure_min_samples ?ensure_min_features ?y_numeric ?warn_on_dtype ?estimator ~x ~y () =
                     Py.Module.get_function_with_keywords __wrap_namespace "check_X_y"
                       [||]
                       (Wrap_utils.keyword_args [("accept_sparse", Wrap_utils.Option.map accept_sparse (function
| `StringList x -> (Py.List.of_list_map Py.String.of_string) x
| `S x -> Py.String.of_string x
| `Bool x -> Py.Bool.of_bool x
)); ("accept_large_sparse", Wrap_utils.Option.map accept_large_sparse Py.Bool.of_bool); ("dtype", Wrap_utils.Option.map dtype (function
| `S x -> Py.String.of_string x
| `Dtype x -> Np.Dtype.to_pyobject x
| `Dtypes x -> (fun ml -> Py.List.of_list_map Np.Dtype.to_pyobject ml) x
| `None -> Py.none
)); ("order", Wrap_utils.Option.map order (function
| `F -> Py.String.of_string "F"
| `C -> Py.String.of_string "C"
)); ("copy", Wrap_utils.Option.map copy Py.Bool.of_bool); ("force_all_finite", Wrap_utils.Option.map force_all_finite (function
| `Allow_nan -> Py.String.of_string "allow-nan"
| `Bool x -> Py.Bool.of_bool x
)); ("ensure_2d", Wrap_utils.Option.map ensure_2d Py.Bool.of_bool); ("allow_nd", Wrap_utils.Option.map allow_nd Py.Bool.of_bool); ("multi_output", Wrap_utils.Option.map multi_output Py.Bool.of_bool); ("ensure_min_samples", Wrap_utils.Option.map ensure_min_samples Py.Int.of_int); ("ensure_min_features", Wrap_utils.Option.map ensure_min_features Py.Int.of_int); ("y_numeric", Wrap_utils.Option.map y_numeric Py.Bool.of_bool); ("warn_on_dtype", Wrap_utils.Option.map warn_on_dtype Py.Bool.of_bool); ("estimator", Wrap_utils.Option.map estimator Np.Obj.to_pyobject); ("X", Some(x |> Np.Obj.to_pyobject)); ("y", Some(y |> Np.Obj.to_pyobject))])
                       |> (fun x -> ((Wrap_utils.id (Py.Tuple.get x 0)), (Wrap_utils.id (Py.Tuple.get x 1))))
                  let check_array ?accept_sparse ?accept_large_sparse ?dtype ?order ?copy ?force_all_finite ?ensure_2d ?allow_nd ?ensure_min_samples ?ensure_min_features ?warn_on_dtype ?estimator ~array () =
                     Py.Module.get_function_with_keywords __wrap_namespace "check_array"
                       [||]
                       (Wrap_utils.keyword_args [("accept_sparse", Wrap_utils.Option.map accept_sparse (function
| `StringList x -> (Py.List.of_list_map Py.String.of_string) x
| `S x -> Py.String.of_string x
| `Bool x -> Py.Bool.of_bool x
)); ("accept_large_sparse", Wrap_utils.Option.map accept_large_sparse Py.Bool.of_bool); ("dtype", Wrap_utils.Option.map dtype (function
| `S x -> Py.String.of_string x
| `Dtype x -> Np.Dtype.to_pyobject x
| `Dtypes x -> (fun ml -> Py.List.of_list_map Np.Dtype.to_pyobject ml) x
| `None -> Py.none
)); ("order", Wrap_utils.Option.map order (function
| `F -> Py.String.of_string "F"
| `C -> Py.String.of_string "C"
)); ("copy", Wrap_utils.Option.map copy Py.Bool.of_bool); ("force_all_finite", Wrap_utils.Option.map force_all_finite (function
| `Allow_nan -> Py.String.of_string "allow-nan"
| `Bool x -> Py.Bool.of_bool x
)); ("ensure_2d", Wrap_utils.Option.map ensure_2d Py.Bool.of_bool); ("allow_nd", Wrap_utils.Option.map allow_nd Py.Bool.of_bool); ("ensure_min_samples", Wrap_utils.Option.map ensure_min_samples Py.Int.of_int); ("ensure_min_features", Wrap_utils.Option.map ensure_min_features Py.Int.of_int); ("warn_on_dtype", Wrap_utils.Option.map warn_on_dtype Py.Bool.of_bool); ("estimator", Wrap_utils.Option.map estimator Np.Obj.to_pyobject); ("array", Some(array ))])

let check_classification_targets y =
   Py.Module.get_function_with_keywords __wrap_namespace "check_classification_targets"
     [||]
     (Wrap_utils.keyword_args [("y", Some(y |> Np.Obj.to_pyobject))])

                  let check_is_fitted ?attributes ?msg ?all_or_any ~estimator () =
                     Py.Module.get_function_with_keywords __wrap_namespace "check_is_fitted"
                       [||]
                       (Wrap_utils.keyword_args [("attributes", Wrap_utils.Option.map attributes (function
| `S x -> Py.String.of_string x
| `Arr x -> Np.Obj.to_pyobject x
| `StringList x -> (Py.List.of_list_map Py.String.of_string) x
)); ("msg", Wrap_utils.Option.map msg Py.String.of_string); ("all_or_any", Wrap_utils.Option.map all_or_any (function
| `Callable x -> Wrap_utils.id x
| `PyObject x -> Wrap_utils.id x
)); ("estimator", Some(estimator |> Np.Obj.to_pyobject))])

                  let check_random_state seed =
                     Py.Module.get_function_with_keywords __wrap_namespace "check_random_state"
                       [||]
                       (Wrap_utils.keyword_args [("seed", Some(seed |> (function
| `Optional x -> (function
| `I x -> Py.Int.of_int x
| `None -> Py.none
) x
| `RandomState x -> Wrap_utils.id x
)))])

let clone ?safe ~estimator () =
   Py.Module.get_function_with_keywords __wrap_namespace "clone"
     [||]
     (Wrap_utils.keyword_args [("safe", Wrap_utils.Option.map safe Py.Bool.of_bool); ("estimator", Some(estimator |> Np.Obj.to_pyobject))])

                  let cross_val_predict ?y ?groups ?cv ?n_jobs ?verbose ?fit_params ?pre_dispatch ?method_ ~estimator ~x () =
                     Py.Module.get_function_with_keywords __wrap_namespace "cross_val_predict"
                       [||]
                       (Wrap_utils.keyword_args [("y", Wrap_utils.Option.map y Np.Obj.to_pyobject); ("groups", Wrap_utils.Option.map groups Np.Obj.to_pyobject); ("cv", Wrap_utils.Option.map cv (function
| `Arr x -> Np.Obj.to_pyobject x
| `BaseCrossValidator x -> Np.Obj.to_pyobject x
| `I x -> Py.Int.of_int x
)); ("n_jobs", Wrap_utils.Option.map n_jobs Py.Int.of_int); ("verbose", Wrap_utils.Option.map verbose Py.Int.of_int); ("fit_params", Wrap_utils.Option.map fit_params Dict.to_pyobject); ("pre_dispatch", Wrap_utils.Option.map pre_dispatch (function
| `S x -> Py.String.of_string x
| `I x -> Py.Int.of_int x
)); ("method", Wrap_utils.Option.map method_ Py.String.of_string); ("estimator", Some(estimator |> Np.Obj.to_pyobject)); ("X", Some(x |> Np.Obj.to_pyobject))])
                       |> (fun py -> (Np.Obj.of_pyobject py : [>`ArrayLike] Np.Obj.t))
let delayed ?check_pickle ~function_ () =
   Py.Module.get_function_with_keywords __wrap_namespace "delayed"
     [||]
     (Wrap_utils.keyword_args [("check_pickle", check_pickle); ("function", Some(function_ ))])

let has_fit_parameter ~estimator ~parameter () =
   Py.Module.get_function_with_keywords __wrap_namespace "has_fit_parameter"
     [||]
     (Wrap_utils.keyword_args [("estimator", Some(estimator |> Np.Obj.to_pyobject)); ("parameter", Some(parameter |> Py.String.of_string))])
     |> Py.Bool.to_bool
                  let if_delegate_has_method delegate =
                     Py.Module.get_function_with_keywords __wrap_namespace "if_delegate_has_method"
                       [||]
                       (Wrap_utils.keyword_args [("delegate", Some(delegate |> (function
| `S x -> Py.String.of_string x
| `StringList x -> (Py.List.of_list_map Py.String.of_string) x
)))])

let is_classifier estimator =
   Py.Module.get_function_with_keywords __wrap_namespace "is_classifier"
     [||]
     (Wrap_utils.keyword_args [("estimator", Some(estimator |> Np.Obj.to_pyobject))])
     |> Py.Bool.to_bool