Run LassoCV for cross-validation on the Leukemia dataset

 Code 1

(.env) [boris@fedora34server NUMPY]$ cat 

"""

========================================

Run LassoCV for cross-validation on the Leukemia dataset

========================================

The example runs the LassoCV scikit-learn like estimator

using the Celer algorithm.

"""

plot_linear_model_cv01.py

import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets import fetch_openml

from sklearn.model_selection import KFold

from celer import LassoCV

from celer.plot_utils import configure_plt

print(__doc__)

configure_plt()

print("Loading data...")

dataset = fetch_openml("leukemia")

X = np.asfortranarray(dataset.data.astype(float))

y = 2 * ((dataset.target == "AML") - 0.5)

y -= np.mean(y)

y /= np.std(y)

kf = KFold(shuffle=True, n_splits=3, random_state=0)

# cv appearance

model = LassoCV(cv=kf, n_jobs=3)

model.fit(X, y)

print("Estimated regularization parameter alpha: %s" % model.alpha_)

plt.figure(figsize=(5, 3), constrained_layout=True)

plt.semilogx(model.alphas_, model.mse_path_, ':')

plt.semilogx(model.alphas_, model.mse_path_.mean(axis=-1), 'k',

             label='Average across the folds', linewidth=2)

plt.axvline(model.alpha_, linestyle='--', color='k',label='alpha: CV estimate')

plt.legend()

plt.xlabel(r'$\alpha$')

plt.ylabel('Mean square prediction error')

plt.show()

References

https://mathurinm.github.io/celer/auto_examples/plot_lasso_cv.html