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  1. AUTHORS

    Matthew Chase Whittemore <******@****.***>
    Matthew King <******@****.***>
    Matthew Mueller <******@****.***>
    Mengdi Gao <******@****.***>
    Michaël Zasso <******@****.***>
    Michał Gołębiowski-Owczarek <******@****.***>
    Nathan Rajlich <******@****.***>
    New Now Nohow <******@****.***>
    Nick McCurdy <******@****.***>
    Nicolae Vartolomei <******@****.***>
    Plain Text
    - Registered: 2021-09-22 08:22
    - Last Modified: 2018-07-12 06:15
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  2. README.md

      **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>
    Plain Text
    - Registered: 2021-09-21 21:40
    - Last Modified: 2021-09-20 12:01
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  3. .mailmap

    Maurice Hayward <******@****.***>
    Michael Bernstein <******@****.***>
    Michael Dawson <******@****.***> <******@****.***>
    Michaël Zasso <******@****.***> <******@****.***>
    Michael-Rainabba Richardson <******@****.***>
    Michał Gołębiowski-Owczarek <******@****.***>
    Micheil Smith <******@****.***> <******@****.***>
    Plain Text
    - Registered: 2021-09-21 21:40
    - Last Modified: 2021-09-19 06:57
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  4. benchmarks/bench_lasso.py

                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)
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2020-06-24 14:51
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  5. examples/linear_model/plot_lasso_lars.py

    #!/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 <******@****.***>
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2019-08-25 03:17
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  6. examples/linear_model/plot_lasso_and_elasticnet.py

    # #############################################################################
    # 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)
    
    # #############################################################################
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2019-07-13 16:35
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  7. sklearn/covariance/_graph_lasso.py

        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,
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2020-12-21 13:09
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  8. benchmarks/bench_plot_lasso_path.py

                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='')
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2020-06-24 14:51
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  9. sklearn/covariance/tests/test_graphical_lasso.py

    """ 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)
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2020-12-18 16:09
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  10. examples/linear_model/plot_lasso_model_selection.py

              '(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
    Python
    - Registered: 2021-09-03 09:24
    - Last Modified: 2020-04-23 09:43
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