mojolearn.LassoLars

class mojolearn.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None)

Bases: _LinearRegressorMixin, NumericModeMixin

Lasso fitted by least angle regression.

Follows scikit-learn’s LassoLars: the LARS path with the lasso modification, stopped at alpha. jitter must be None (any other value raises ValueError).

Parameters:

• alpha (float, default 1.0) – L1 regularization strength (>= 0); 0 runs the full path.

• fit_intercept (bool, default True) – Whether to fit an intercept.

• verbose (bool, default False) – Accepted for compatibility; ignored.

• precompute (bool, ‘auto’ or array, default ‘auto’) – Accepted for compatibility; the fit always uses the Gram matrix.

• max_iter (int, default 500) – Maximum number of path steps.

• eps (float, default 2.220446049250313e-16) – Accepted for compatibility; ignored.

• copy_X (bool, default True) – Accepted for compatibility; X is never modified.

• fit_path (bool, default True) – Accepted for compatibility; the path is not stored.

• positive (bool, default False) – Restrict the weights to be non-negative.

• jitter (None, default None) – Not supported: any value other than None raises ValueError.

• random_state (int or None, default None) – Accepted for compatibility; ignored.

• numeric_mode (str or None, default None) – Numeric mode for this model ('identical', 'deterministic' or 'fast', which runs on Apple GPUs only); None uses the process default (see mojolearn.set_numeric_mode()).

Variables:

• coef_ (float32 array of shape (n_features,)) – Weights.

• intercept_ (float) – Intercept.

• alpha_ (float) – Regularization value where the path stopped.

• active_ (list of int) – Indices of the active features.

• n_iter_ (int) – Number of steps run.

• 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 = 3 * X[:, 0] - 2 * X[:, 1]
>>> model = mojolearn.LassoLars(alpha=0.01).fit(X, y)

__init__(alpha=1.0, *, fit_intercept=True, verbose=False, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None)

Methods

fit(X, y)	Fit the model.
get_params([deep])
numeric_mode_used()	The numeric mode this estimator runs in.
predict(X)	Predict targets for X.
score(X, y)	Coefficient of determination R^2 of predict(X) against y.
set_params(**params)	Set constructor parameters.
vendor_used()	The GPU backend this estimator runs on.

fit(X, y)

Fit the model.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data, converted to float32; values must be finite.

• y (array-like of shape (n_samples,)) – Targets.

Returns:

self (LassoLars) – The fitted model.

Raises:

ValueError – If alpha is negative or jitter is not None.