From 5e3a7e0611809cad231ba51346d8a30a0393f6bd Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Thu, 29 Apr 2010 11:49:28 +0000
Subject: [PATCH] Remove examples from module glm.coordinate_descent.
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@716 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
scikits/learn/glm/coordinate_descent.py | 134 ------------------------
1 file changed, 134 deletions(-)
diff --git a/scikits/learn/glm/coordinate_descent.py b/scikits/learn/glm/coordinate_descent.py
index f63826c75a..570b97a468 100644
--- a/scikits/learn/glm/coordinate_descent.py
+++ b/scikits/learn/glm/coordinate_descent.py
@@ -156,137 +156,3 @@ def enet_path(X, y, factor=0.95, n_alphas=10, beta=1.0, **kwargs):
alphas = np.asarray(alphas)
weights = np.asarray(weights)
return alphas, weights
-
-if __name__ == '__main__':
- import time
- import pylab as pl
-
- n_samples, n_features, maxit = 5, 10, 30
- np.random.seed(0)
- y = np.random.randn(n_samples)
- X = np.random.randn(n_samples, n_features)
-
- """Tests Lasso implementations (python and cython)
- """
-
- alpha = 1.0
-
- tol = 1e-5
-
- # Callbacks to store objective values and densities
- callback_objective = IterationCallbackFunc(lasso_objective)
- callback_density = IterationCallbackFunc(density)
-
- t0 = time.time()
- lasso_slow = Lasso(alpha=alpha, callbacks=[callback_objective,
- callback_density])
- lasso_slow.learner = lasso_coordinate_descent_slow
- lasso_slow.fit(X, y, maxit=maxit, tol=tol)
- print time.time() - t0
-
- objective_convergence_slow = callback_objective.values
- density_slow = callback_density.values
-
- print "Duality gap Lasso (should be small): %f" % \
- lasso_dual_gap(X, y, lasso_slow.coef_, alpha)[0]
-
- t0 = time.time()
- lasso_fast = Lasso(alpha=alpha, callbacks=[callback_objective,
- callback_density])
- lasso_fast.learner = lasso_coordinate_descent_fast
- lasso_fast.fit(X, y, maxit=maxit, tol=tol)
- print time.time() - t0
-
- print "Duality gap Lasso (should be small): %f" % \
- lasso_dual_gap(X, y, lasso_slow.coef_, alpha)[0]
-
- objective_convergence_fast = callback_objective.values
- density_fast = callback_density.values
-
- pl.close('all')
- pl.plot(objective_convergence_fast,"rx-")
- pl.plot(objective_convergence_slow,"bo--")
- pl.xlabel('Iteration')
- pl.ylabel('Cost function')
- pl.legend(['Fast', 'Slow'])
- pl.title('Lasso')
-
- pl.figure()
- pl.plot(density_fast,"rx-")
- pl.plot(density_slow,"bo--")
- pl.xlabel('Iteration')
- pl.ylabel('Density')
- pl.legend(['Fast', 'Slow'])
- pl.title('Lasso')
-
- """Tests Elastic-Net implementations (python and cython)
- """
-
- alpha = 1.0
- beta = 1.0
-
- callback_objective = IterationCallbackFunc(enet_objective)
-
- import time
- t0 = time.time()
- enet_slow = ElasticNet(alpha=alpha, beta=beta, callbacks=[callback_objective,
- callback_density])
- enet_slow.learner = enet_coordinate_descent_slow
- enet_slow.fit(X, y, maxit=maxit)
- print time.time() - t0
-
- print "Duality gap (should be small): %f" % \
- enet_dual_gap(X, y, enet_slow.coef_, alpha)[0]
-
- objective_convergence_slow = callback_objective.values
- density_slow = callback_density.values
-
- t0 = time.time()
- enet_fast = ElasticNet(alpha=alpha, beta=beta, callbacks=[callback_objective,
- callback_density])
-
- enet_fast.learner = enet_coordinate_descent_fast
- enet_fast.fit(X, y, maxit=maxit)
- print time.time() - t0
-
- print "Duality gap (should be small): %f" % \
- enet_dual_gap(X, y, enet_fast.coef_, alpha)[0]
-
- objective_convergence_fast = callback_objective.values
- density_fast = callback_density.values
-
- pl.figure()
- pl.plot(objective_convergence_fast,"rx-")
- pl.plot(objective_convergence_slow,"bo--")
- pl.xlabel('Iteration')
- pl.ylabel('Cost function')
- pl.legend(['Fast', 'Slow'])
- pl.title('Elastic-Net')
-
- pl.figure()
- pl.plot(density_fast,"rx-")
- pl.plot(density_slow,"bo--")
- pl.xlabel('Iteration')
- pl.ylabel('Density')
- pl.legend(['Fast', 'Slow'])
- pl.title('Elastic-Net')
-
- """Test path functions
- """
-
- alphas_lasso, weights_lasso = lasso_path(X, y, factor=0.97, n_alphas = 100, tol=1-2)
- alphas_enet, weights_enet = enet_path(X, y, factor=0.97, n_alphas = 100, beta=0.1, tol=1-2)
-
- from itertools import cycle
- color_iter = cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
-
- pl.figure()
- for color, weight_lasso, weight_enet in zip(color_iter, weights_lasso.T, weights_enet.T):
- pl.plot(-np.log(alphas_lasso), weight_lasso, color)
- pl.plot(-np.log(alphas_enet), weight_enet, color+'x')
- pl.xlabel('-log(lambda)')
- pl.ylabel('weights')
- pl.title('Lasso and Elastic-Net Paths')
- pl.legend(['Lasso','Elastic-Net'])
- pl.show()
-
--
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