fault=True
    If ``True``, X will be copied; else, it may be overwritten.

coef_init : array-like of shape (n_features, ), default=None
    The initial values of the coefficients.

verbose : bool or int, default=False
    Amount of verbosity.

return_n_iter : bool, default=False
    Whether to return the number of iterations or not.

positive : bool, default=False
    If set to True, forces coefficients to be positive.
    (Only allowed when ``y.ndim == 1``).

**params : kwargs
    Keyword arguments passed to the coordinate descent solver.

Returns
-------
alphas : ndarray of shape (n_alphas,)
    The alphas along the path where models are computed.

coefs : ndarray of shape (n_features, n_alphas) or             (n_targets, n_features, n_alphas)
    Coefficients along the path.

dual_gaps : ndarray of shape (n_alphas,)
    The dual gaps at the end of the optimization for each alpha.

n_iters : list of int
    The number of iterations taken by the coordinate descent optimizer to
    reach the specified tolerance for each alpha.

See Also
--------
lars_path : Compute Least Angle Regression or Lasso path using LARS
    algorithm.
Lasso : The Lasso is a linear model that estimates sparse coefficients.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
LassoCV : Lasso linear model with iterative fitting along a regularization
    path.
LassoLarsCV : Cross-validated Lasso using the LARS algorithm.
sklearn.decomposition.sparse_encode : Estimator that can be used to
    transform signals into sparse linear combination of atoms from a fixed.

Notes
-----
For an example, see
:ref:`examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py
<sphx_glr_auto_examples_linear_model_plot_lasso_lasso_lars_elasticnet_path.py>`.

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

Note that in certain cases, the Lars solver may be significantly
faster to implement this functionality. In particular, linear
interpolation can be used to retrieve model coefficients between the
values output by lars_path

Examples
--------

Comparing lasso_path and lars_path with interpolation:

>>> import numpy as np
>>> from sklearn.linear_model import lasso_path
>>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
>>> y = np.array([1, 2, 3.1])
>>> # Use lasso_path to compute a coefficient path
>>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5])
>>> print(coef_path)
[[0.         0.         0.46874778]
 [0.2159048  0.4425765  0.23689075]]

>>> # Now use lars_path and 1D linear interpolation to compute the
>>> # same path
>>> from sklearn.linear_model import lars_path
>>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
>>> from scipy import interpolate
>>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
...                                             coef_path_lars[:, ::-1])
>>> print(coef_path_continuous([5., 1., .5]))
[[0.         0.         0.46915237]
 [0.2159048  0.4425765  0.23668876]]
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