mojolearn.MultiOutputRegressor

class mojolearn.MultiOutputRegressor(estimator, *, n_jobs=None)

Bases: _TreesWrapperBase

Fit one regressor per target column.

Follows scikit-learn’s MultiOutputRegressor: fit clones estimator once per column of Y and fits each clone on that column; predict stacks their predictions. The columns fit one after another.

Parameters:

• estimator (estimator) – A mojolearn regressor; it is cloned, never fitted in place.

• n_jobs (None) – Only None is accepted; any other value raises NotImplementedError.

Variables:

• estimators_ (list) – One fitted clone per column of Y.

• n_features_in_ (int) – Number of features seen in fit.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> X = rng.random((1000, 8), dtype=np.float32)
>>> Y = np.stack([X[:, 0] + X[:, 1], X[:, 2] - X[:, 3]], axis=1)
>>> model = mojolearn.MultiOutputRegressor(mojolearn.Ridge()).fit(X, Y)
>>> model.predict(X[:5]).shape
(5, 2)

__init__(estimator, *, n_jobs=None)

Methods

fit(X, Y[, sample_weight])	Fit one clone of estimator per column of Y.
get_params([deep])
numeric_mode_used()	The numeric mode this estimator runs in.
predict(X)	Every target's prediction, one column per output.
set_params(**params)	Set constructor parameters.
vendor_used()	The GPU backend this estimator runs on.

fit(X, Y, sample_weight=None)

Fit one clone of estimator per column of Y.

Parameters:

• X (array of shape (n_samples, n_features)) – Training data, converted to float32.

• Y (array of shape (n_samples, n_outputs)) – Numeric targets, converted to float32. A row count that differs from X raises ValueError.

• sample_weight (array of shape (n_samples,), default None) – Passed to every clone’s fit when given.

Returns:

self

predict(X)

Every target’s prediction, one column per output.

Returns:

array of shape (n_samples, n_outputs)