random_output_trees.transformer.FixedStateTransformer¶

class random_output_trees.transformer.FixedStateTransformer(transformer, random_seed=0)¶

Fixe the random_state of the transformer

This meta-transformer is usefull when you want to fix the random_state of a transformer, which is modified by some meta-estimator.

Parameters:	transformer : scikit-learn transformer random_seed : int, RandomState instance, optional (default=0) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator;

Attributes

transformer_

transformer

A clone of the fitted transformer

Methods

fit(X[, y])	Fit estimator.
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)	Transform dataset.

__init__(transformer, random_seed=0)¶

fit(X, y=None)¶

Fit estimator.

Parameters:	X : array-like, shape=(n_samples, n_features) Input data used to build forests.
Returns:	self : object 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)¶

Transform dataset.

Parameters:	X : array-like, shape=(n_samples, n_features) Input data to be transformed.
Returns:	X_transformed: sparse matrix, shape=(n_samples, n_out) : Transformed dataset.