package sklearn
Module Pipeline.FeatureUnionSource
Source
val create :
?n_jobs:[ `Int of int | `None ] ->
?transformer_weights:Py.Object.t ->
?verbose:bool ->
transformer_list:(string * Py.Object.t) list ->
unit ->
tConcatenates 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 or None, optional (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.
transformer_weights : dict, optional Multiplicative weights for features per transformer. Keys are transformer names, values the weights.
verbose : boolean, optional(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...])
Source
val fit :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
tFit all transformers using X.
Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers.
y : array-like, shape (n_samples, ...), optional Targets for supervised learning.
Returns ------- self : FeatureUnion This estimator
Source
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
Ndarray.tFit 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, shape (n_samples, ...), optional Targets for supervised learning.
Returns ------- X_t : array-like or sparse matrix, shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.
Get feature names from all transformers.
Returns ------- feature_names : list of strings Names of the features produced by transform.
Get parameters for this estimator.
Parameters ---------- deep : boolean, optional 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.
Set the parameters of this estimator.
Valid parameter keys can be listed with ``get_params()``.
Returns ------- self
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, shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.
Pretty-print the object to a formatter.