random_output_trees.random_projection.RademacherRandomProjection¶

class random_output_trees.random_projection.RademacherRandomProjection(n_components='auto', eps=0.1, random_state=None)¶

Rademacher random projection

The components of the random matrix are drawn from:

• -sqrt(s) / sqrt(n_components) with probability 1 / 2
• +sqrt(s) / sqrt(n_components) with probability 1 / 2

Parameters:

n_components : int or ‘auto’, optional (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. eps : strictly positive float, optional (default=0.1) Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n_components is set to ‘auto’. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. random_state : integer, RandomState instance or None (default=None) Control the pseudo random number generator used to generate the matrix at fit time.

Attributes

n_component_	int	Concrete number of components computed when n_components=”auto”.
components_	numpy array of shape [n_components, n_features]	Random matrix used for the projection.

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[, y])	Project the data by using matrix product with the random matrix

__init__(n_components='auto', eps=0.1, random_state=None)¶

fit(X, y=None)¶

Generate a sparse random projection matrix

Parameters:	X : numpy array or scipy.sparse 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. y : is not used: placeholder to allow for usage in a Pipeline.
Returns:	self :

fit_transform(X, y=None, **fit_params)¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True)¶

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_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

transform(X, y=None)¶

Project the data by using matrix product with the random matrix

Parameters:	X : numpy array or scipy.sparse of shape [n_samples, n_features] The input data to project into a smaller dimensional space. y : is not used: placeholder to allow for usage in a Pipeline.
Returns:	X_new : numpy array or scipy sparse of shape [n_samples, n_components] Projected array.