sklearn.random_projection
.GaussianRandomProjection¶
- class sklearn.random_projection.GaussianRandomProjection(n_components='auto', *, eps=0.1, random_state=None)[source]¶
Reduce dimensionality through Gaussian random projection.
The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the User Guide.
New in version 0.13.
- Parameters
- n_componentsint or ‘auto’, default=’auto’
Dimensionality of the target projection space.
n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the
eps
parameter.It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset.
- epsfloat, default=0.1
Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when
n_components
is set to ‘auto’. The value should be strictly positive.Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space.
- random_stateint, RandomState instance or None, default=None
Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See Glossary.
- Attributes
- n_components_int
Concrete number of components computed when n_components=”auto”.
- components_ndarray of shape (n_components, n_features)
Random matrix used for the projection.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
SparseRandomProjection
Reduce dimensionality through sparse random projection.
Examples
>>> import numpy as np >>> from sklearn.random_projection import GaussianRandomProjection >>> rng = np.random.RandomState(42) >>> X = rng.rand(100, 10000) >>> transformer = GaussianRandomProjection(random_state=rng) >>> X_new = transformer.fit_transform(X) >>> X_new.shape (100, 3947)
Methods
fit
(X[, y])Generate a sparse random projection matrix.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Project the data by using matrix product with the random matrix.
- fit(X, y=None)[source]¶
Generate a sparse random projection matrix.
- Parameters
- X{ndarray, sparse matrix} of shape (n_samples, n_features)
Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers.
- yIgnored
Not used, present here for API consistency by convention.
- Returns
- selfobject
BaseRandomProjection class instance.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
- transform(X)[source]¶
Project the data by using matrix product with the random matrix.
- Parameters
- X{ndarray, sparse matrix} of shape (n_samples, n_features)
The input data to project into a smaller dimensional space.
- Returns
- X_new{ndarray, sparse matrix} of shape (n_samples, n_components)
Projected array.