package sklearn

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package sklearn
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type tag = [
  1. | `FeatureUnion
]
type t = [ `BaseEstimator | `FeatureUnion | `Object | `TransformerMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_transformer : t -> [ `TransformerMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val create : ?n_jobs:int -> ?transformer_weights:Dict.t -> ?verbose:int -> transformer_list:(string * [> `TransformerMixin ] Np.Obj.t) list -> unit -> t

Concatenates results of multiple transformer objects.

This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer.

Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to 'drop'.

Read more in the :ref:`User Guide <feature_union>`.

.. versionadded:: 0.13

Parameters ---------- transformer_list : list of (string, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer.

.. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'.

n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

.. versionchanged:: v0.20 `n_jobs` default changed from 1 to None

transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights.

verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed.

See Also -------- sklearn.pipeline.make_union : Convenience function for simplified feature union construction.

Examples -------- >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA, TruncatedSVD >>> union = FeatureUnion(('pca', PCA(n_components=1)), ... ('svd', TruncatedSVD(n_components=2))) >>> X = [0., 1., 3], [2., 2., 5] >>> union.fit_transform(X) array([ 1.5 , 3.0..., 0.8...], [-1.5 , 5.7..., -0.4...])

val fit : ?y:[> `ArrayLike ] Np.Obj.t -> ?fit_params:(string * Py.Object.t) list -> x:Py.Object.t -> [> tag ] Obj.t -> t

Fit all transformers using X.

Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers.

y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning.

Returns ------- self : FeatureUnion This estimator

val fit_transform : ?y:[> `ArrayLike ] Np.Obj.t -> ?fit_params:(string * Py.Object.t) list -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Fit all transformers, transform the data and concatenate results.

Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed.

y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning.

Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

val get_feature_names : [> tag ] Obj.t -> string list

Get feature names from all transformers.

Returns ------- feature_names : list of strings Names of the features produced by transform.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val set_params : ?kwargs:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set the parameters of this estimator.

Valid parameter keys can be listed with ``get_params()``.

Returns ------- self

val transform : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Transform X separately by each transformer, concatenate results.

Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed.

Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.