41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
import numpy as np
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import pylab as pl
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from scikits.learn import cross_val, datasets, linear_model
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diabetes = datasets.load_diabetes()
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X = diabetes.data
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y = diabetes.target
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lasso = linear_model.Lasso()
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alphas = np.logspace(-4, -1, 20)
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scores = list()
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scores_std = list()
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for alpha in alphas:
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lasso.alpha = alpha
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this_scores = cross_val.cross_val_score(lasso, X, y, n_jobs=-1)
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scores.append(np.mean(this_scores))
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scores_std.append(np.std(this_scores))
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pl.figure(1, figsize=(2.5, 2))
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pl.clf()
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pl.axes([.1, .25, .8, .7])
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pl.semilogx(alphas, scores)
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pl.semilogx(alphas, np.array(scores) + np.array(scores_std)/20, 'b--')
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pl.semilogx(alphas, np.array(scores) - np.array(scores_std)/20, 'b--')
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pl.yticks(())
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pl.ylabel('CV score')
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pl.xlabel('alpha')
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pl.axhline(np.max(scores), linestyle='--', color='.5')
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pl.text(2e-4, np.max(scores)+1e-4, '.489')
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################################################################################
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# Bonus: how much can you trust the selection of alpha?
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from scikits.learn import cross_val
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k_fold = cross_val.KFold(len(X), 3)
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print [lasso.fit(X[train], y[train]).alpha for train, _ in k_fold]
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