random_output_trees.ensemble.LazyBaggingRegressor¶

class random_output_trees.ensemble.LazyBaggingRegressor(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, random_state=None, verbose=0)¶

A lazy bagging regressor.

Everything is done lazily, models are built at prediction time and are not kept in memory. Since the models is thrown away, this allows to highly reduce the memory consumption and allows to build very large ensemble.

Parameters:

base_estimator : object or None, optional (default=None) The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree. n_estimators : int, optional (default=10) The number of base estimators in the ensemble. max_samples : int or float, optional (default=1.0) The number of samples to draw from X to train each base estimator. If int, then draw max_samples samples. If float, then draw max_samples * X.shape[0] samples. max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator. If int, then draw max_features features. If float, then draw max_features * X.shape[1] features. bootstrap : boolean, optional (default=True) Whether samples are drawn with replacement. bootstrap_features : boolean, optional (default=False) Whether features are drawn with replacement. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. verbose : int, optional (default=0) Controls the verbosity of the building process.

References

[R9]	L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.

[R10]

L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.

[R11]

T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.

[R12]

G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.

Attributes

n_features_	int,	Number of features of the fitted input matrix
n_outputs_	int,	Number of outputs of the fitted ouput matrix

Methods

fit(X, y[, sample_weight])	Build a lazy a bagging ensemble of estimators from the training set
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict regression target for X.
score(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, random_state=None, verbose=0)¶

fit(X, y, sample_weight=None)¶

Build a lazy a bagging ensemble of estimators from the training set

Parameters:

X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like, shape = [n_samples] The target values (class labels in classification, real numbers in regression). sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.

Returns:

self : object Returns self.

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.

predict(X)¶

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble.

Parameters:	X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:	y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted values.

score(X, y, sample_weight=None)¶

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float R^2 of self.predict(X) wrt. y.

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 :