mojolearn.Lars#

class mojolearn.Lars(*, fit_intercept=True, verbose=False, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, jitter=None, random_state=None)#

Least angle regression (LARS).

Follows scikit-learn’s Lars: adds features to the active set one at a time, up to n_nonzero_coefs of them. jitter must be None (any other value raises ValueError).

Parameters:

  • 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.

  • n_nonzero_coefs (int, default 500) – Maximum number of active features.

  • 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.

  • 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' 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 at the end of the path.

  • intercept_ (float) – Intercept.

  • alpha_ (float) – Regularization value at the end of the path.

  • 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.Lars(n_nonzero_coefs=2).fit(X, y)

See also

User guide: Linear models

Choosing an estimator

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 (Lars) – The fitted model.

numeric_mode_used()#

The numeric mode this estimator runs in.

Returns:

str – 'identical' or 'fast', read from the compiled binary the estimator uses, not from the argument it was given.

predict(X)#

Predict targets for X.

Returns X @ coef_ + intercept_; models with a log link (Poisson, Gamma and Tweedie with power > 0) return its exponential.

Parameters:

X (array-like of shape (n_samples, n_features)) – Samples, converted to float32. The feature count must match the one seen in fit.

Returns:

y_pred (float32 array of shape (n_samples,)) – Predicted values.

Raises:

  • RuntimeError – If the model is not fitted.

  • ValueError – If X has a different number of features than in fit.

score(X, y)#

Coefficient of determination R^2 of predict(X) against y.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples.

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

Returns:

score (float) – R^2; 1.0 is a perfect fit.

set_params(**params)#

Set constructor parameters. The estimator is rebuilt through its constructor, so every constructor check runs again and any fitted state is dropped, as a new setting invalidates it.

vendor_used()#

The GPU backend this estimator runs on.

Returns:

str – 'metal', 'cuda' or 'hip', read from the compiled binary the estimator uses.