Matthew Chase Whittemore <******@****.***>
Matthew King <******@****.***>
Matthew Mueller <******@****.***>
Mengdi Gao <******@****.***>
Michaël Zasso <******@****.***>
Michał Gołębiowski-Owczarek <******@****.***>
Nathan Rajlich <******@****.***>
New Now Nohow <******@****.***>
Nick McCurdy <******@****.***>
Nicolae Vartolomei <******@****.***>

  **Myles Borins** \<******@****.***> (he/him)
* [ronag](https://github.com/ronag) -
  **Robert Nagy** \<******@****.***>
* [targos](https://github.com/targos) -
  **Michaël Zasso** \<******@****.***> (he/him)
* [tniessen](https://github.com/tniessen) -
  **Tobias Nießen** \<******@****.***>
* [Trott](https://github.com/Trott) -
  **Rich Trott** \<******@****.***> (he/him)

<details>

Maurice Hayward <******@****.***>
Michael Bernstein <******@****.***>
Michael Dawson <******@****.***> <******@****.***>
Michaël Zasso <******@****.***> <******@****.***>
Michael-Rainabba Richardson <******@****.***>
Michał Gołębiowski-Owczarek <******@****.***>
Micheil Smith <******@****.***> <******@****.***>

            clf.fit(X, Y)
            lars_lasso_results.append(time() - tstart)

    return lasso_results, lars_lasso_results


if __name__ == '__main__':
    from sklearn.linear_model import Lasso, LassoLars
    import matplotlib.pyplot as plt

    alpha = 0.01  # regularization parameter

    n_features = 10
    list_n_samples = np.linspace(100, 1000000, 5).astype(int)

#!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================

Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regularization parameter.

"""
print(__doc__)

# Author: Fabian Pedregosa <******@****.***>

# #############################################################################
# Lasso
from sklearn.linear_model import Lasso

alpha = 0.1
lasso = Lasso(alpha=alpha)

y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
r2_score_lasso = r2_score(y_test, y_pred_lasso)
print(lasso)
print("r^2 on test data : %f" % r2_score_lasso)

# #############################################################################

    This results from the bound for the all the Lasso that are solved
    in GraphicalLasso: each time, the row of cov corresponds to Xy. As the
    bound for alpha is given by `max(abs(Xy))`, the result follows.
    """
    A = np.copy(emp_cov)
    A.flat[::A.shape[0] + 1] = 0
    return np.max(np.abs(A))


# The g-lasso algorithm
@_deprecate_positional_args
def graphical_lasso(emp_cov, alpha, *, cov_init=None, mode='cd', tol=1e-4,

            gc.collect()
            print("benchmarking lasso_path (with Gram):", end='')
            sys.stdout.flush()
            tstart = time()
            lasso_path(X, y, precompute=True)
            delta = time() - tstart
            print("%0.3fs" % delta)
            results['lasso_path (with Gram)'].append(delta)

            gc.collect()
            print("benchmarking lasso_path (without Gram):", end='')

""" Test the graphical_lasso module.
"""
import sys
import pytest

import numpy as np
from scipy import linalg

from numpy.testing import assert_allclose
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_less

from sklearn.covariance import (graphical_lasso, GraphicalLasso,
                                GraphicalLassoCV, empirical_covariance)

          '(train time: %.2fs)' % t_lasso_cv)
plt.axis('tight')
plt.ylim(ymin, ymax)

# #############################################################################
# LassoLarsCV: least angle regression

# Compute paths
print("Computing regularization path using the Lars lasso...")
t1 = time.time()
model = LassoLarsCV(cv=20).fit(X, y)
t_lasso_lars_cv = time.time() - t1

# Display results