"""
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================

This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
"""
print __doc__

import numpy as np
import pylab as pl

from sklearn import cross_validation, datasets, linear_model

diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target

lasso = linear_model.Lasso()

alphas = np.logspace(-4, -1, 20)

scores = list()
scores_std = list()

for alpha in alphas:
    lasso.alpha = alpha
    this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
    scores.append(np.mean(this_scores))
    scores_std.append(np.std(this_scores))

pl.figure(1, figsize=(2.5, 2))
pl.clf()
pl.axes([.1, .25, .8, .7])
pl.semilogx(alphas, scores)
pl.semilogx(alphas, np.array(scores) + np.array(scores_std) / 20, 'b--')
pl.semilogx(alphas, np.array(scores) - np.array(scores_std) / 20, 'b--')
pl.yticks(())
pl.ylabel('CV score')
pl.xlabel('alpha')
pl.axhline(np.max(scores), linestyle='--', color='.5')
pl.text(2e-4, np.max(scores) + 1e-4, '.489')

##############################################################################
# Bonus: how much can you trust the selection of alpha?
k_fold = cross_validation.KFold(len(X), 3)
print [lasso.fit(X[train], y[train]).alpha for train, _ in k_fold]