diff --git a/scikits/learn/glm/coordinate_descent.py b/scikits/learn/glm/coordinate_descent.py
index f63826c75a9874130cb5877554ade77e51ac14b7..570b97a468f5476131ce0bdbf3fb2e6615757b2f 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()
-