sklearn.StochTreeBARTRegressor

sklearn.StochTreeBARTRegressor(
    num_gfr=10,
    num_burnin=0,
    num_mcmc=100,
    general_params=None,
    mean_forest_params=None,
    variance_forest_params=None,
    rfx_params=None,
)

A scikit-learn-compatible estimator that implements a BART regression model.

Parameters

Name	Type	Description	Default
num_gfr	int	The number of grow-from-root (GFR) iterations to run of the BART model.	10
num_burnin	int	The number of MCMC iterations of the BART model that will be discarded as “burn-in” samples.	0
num_mcmc	int	The number of retained MCMC iterations to run of the BART model.	100
general_params	dict	General parameters for the BART model.	None
mean_forest_params	dict	Parameters for the mean forest.	None
variance_forest_params	dict	Parameters for the variance forest.	None
rfx_params	dict	Parameters for the random effects.	None

Attributes

Name	Type	Description
X_	(ndarray, shape(n_samples, n_features))	The covariates (or features) used to define tree partitions.
y_	(ndarray, shape(n_samples))	The outcome variable (or labels) used to evaluate tree partitions.
leaf_regression_basis_	(ndarray, shape(n_samples, n_bases))	The basis functions used for leaf regression model if requested.
rfx_group_ids_	(ndarray, shape(n_samples))	The group IDs for random effects if requested.
rfx_basis_	(ndarray, shape(n_samples, n_rfx_bases))	The basis functions used for random effects if requested.
n_features_in_	int	Number of features seen during :term:fit.
feature_names_in_	ndarray of shape (n_features_in_,)	Names of features seen during :term:fit. Defined only when X has feature names that are all strings.

Examples

>>> from sklearn.datasets import load_boston
>>> from stochtree import StochTreeBARTRegressor
>>> data = load_boston()
>>> X = data.data
>>> y = data.target
>>> reg = StochTreeBARTRegressor()
>>> reg.fit(X, y)
>>> reg.predict(X)

Methods

Name	Description
fit	Fit a BART regressor by sampling from its posterior.
predict	Predict the outcome based on the provided test data.
score	Compute and return the R2 for a BART regression model

fit

sklearn.StochTreeBARTRegressor.fit(
    X,
    y,
    leaf_regression_basis=None,
    rfx_group_ids=None,
    rfx_basis=None,
)

Fit a BART regressor by sampling from its posterior.

Parameters

Name	Type	Description	Default
X	array-like, sparse matrix	The covariates used to train a BART forest.	array-like
y	(array - like, shape(n_samples) or (n_samples, n_outputs))	The continuous outcomes used to train a BART forest.	required
leaf_regression_basis	optional array-like, (n_samples, n_bases)	The basis functions to use for leaf regression model, if requested.	None
rfx_group_ids	optional array-like, (n_samples,)	The group IDs for random effects, if requested.	None
rfx_basis	optional array-like, (n_samples, n_rfx_bases)	The basis functions to use for random effects, if requested.	None

Returns

Name	Type	Description
self	object	Returns self.

predict

sklearn.StochTreeBARTRegressor.predict(
    X,
    leaf_regression_basis=None,
    rfx_group_ids=None,
    rfx_basis=None,
)

Predict the outcome based on the provided test data.

Parameters

Name	Type	Description	Default
X	array-like, sparse matrix	The covariates used to predict from a BART forest.	array-like
leaf_regression_basis	optional array-like, (n_samples, n_bases)	The basis functions to use for leaf regression model if requested.	None
rfx_group_ids	optional array-like, (n_samples,)	The group IDs for random effects if requested.	None
rfx_basis	optional array-like, (n_samples, n_rfx_bases)	The basis functions to use for random effects if requested.	None

Returns

Name	Type	Description
y	(ndarray, shape(n_samples))	Returns an array of predicted target values.

score

sklearn.StochTreeBARTRegressor.score(
    X,
    y,
    leaf_regression_basis=None,
    rfx_group_ids=None,
    rfx_basis=None,
)

Compute and return the R2 for a BART regression model

Parameters

Name	Type	Description	Default
X	array-like, sparse matrix	The covariates used to train a BART forest.	array-like
y	(array - like, shape(n_samples) or (n_samples, n_outputs))	The continuous outcomes used to train a BART forest.	required
leaf_regression_basis	optional array-like, (n_samples, n_bases)	The basis functions to use for leaf regression model, if requested.	None
rfx_group_ids	optional array-like, (n_samples,)	The group IDs for random effects, if requested.	None
rfx_basis	optional array-like, (n_samples, n_rfx_bases)	The basis functions to use for random effects, if requested.	None

Returns

Name	Type	Description
score	float	R^2 of self.predict(X, leaf_regression_basis, rfx_group_ids, rfx_basis) with respect to y.