70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
"""
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==============================
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Lasso on dense and sparse data
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==============================
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We show that linear_model.Lasso provides the same results for dense and sparse
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data and that in the case of sparse data the speed is improved.
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"""
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from time import time
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from scipy import sparse
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from scipy import linalg
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from sklearn.datasets import make_regression
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from sklearn.linear_model import Lasso
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# #############################################################################
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# The two Lasso implementations on Dense data
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print("--- Dense matrices")
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X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
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X_sp = sparse.coo_matrix(X)
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alpha = 1
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sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
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dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
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t0 = time()
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sparse_lasso.fit(X_sp, y)
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print("Sparse Lasso done in %fs" % (time() - t0))
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t0 = time()
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dense_lasso.fit(X, y)
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print("Dense Lasso done in %fs" % (time() - t0))
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print(
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"Distance between coefficients : %s"
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% linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
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)
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# #############################################################################
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# The two Lasso implementations on Sparse data
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print("--- Sparse matrices")
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Xs = X.copy()
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Xs[Xs < 2.5] = 0.0
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Xs = sparse.coo_matrix(Xs)
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Xs = Xs.tocsc()
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print("Matrix density : %s %%" % (Xs.nnz / float(X.size) * 100))
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alpha = 0.1
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sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
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dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
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t0 = time()
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sparse_lasso.fit(Xs, y)
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print("Sparse Lasso done in %fs" % (time() - t0))
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t0 = time()
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dense_lasso.fit(Xs.toarray(), y)
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print("Dense Lasso done in %fs" % (time() - t0))
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print(
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"Distance between coefficients : %s"
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% linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
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)
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