Source Code for Module paramz.tests.examples_tests

 1  #=============================================================================== 
 2  # Copyright (c) 2015, Max Zwiessele 
 3  # All rights reserved. 
 4  # 
 5  # Redistribution and use in source and binary forms, with or without 
 6  # modification, are permitted provided that the following conditions are met: 
 7  # 
 8  # * Redistributions of source code must retain the above copyright notice, this 
 9  #   list of conditions and the following disclaimer. 
10  # 
11  # * Redistributions in binary form must reproduce the above copyright notice, 
12  #   this list of conditions and the following disclaimer in the documentation 
13  #   and/or other materials provided with the distribution. 
14  # 
15  # * Neither the name of paramax nor the names of its 
16  #   contributors may be used to endorse or promote products derived from 
17  #   this software without specific prior written permission. 
18  # 
19  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
20  # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
21  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 
22  # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 
23  # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 
24  # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 
25  # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 
26  # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 
27  # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
28  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
29  #=============================================================================== 
30   
31  import unittest 
32  import numpy as np 
33  from ..examples import RidgeRegression, Lasso, Polynomial 
34   
35 -class Test2D(unittest.TestCase): 
36   
37 -    def testRidgeRegression(self): 
38          np.random.seed(1000) 
39          X = np.random.normal(0,1,(20,2)) 
40          beta = np.random.uniform(0,1,(2,1)) 
41          Y = X.dot(beta) 
42          #Y += np.random.normal(0, .001, Y.shape) 
43          m = RidgeRegression(X, Y) 
44          m.regularizer.lambda_ = 0.00001 
45          self.assertTrue(m.checkgrad()) 
46          m.optimize('scg', gtol=0, ftol=0, xtol=0,max_iters=10) 
47          m.optimize(max_iters=10) 
48          np.testing.assert_array_almost_equal(m.regularizer.weights[1], beta[:,0], 4) 
49          np.testing.assert_array_almost_equal(m.regularizer.weights[0], [0,0], 4) 
50          np.testing.assert_array_almost_equal(m.gradient, np.zeros(m.weights.size), 4) 
51           
52          xpred = np.repeat(np.linspace(0,1,50)[:,None], 2, axis=1) 
53          xpred[:, 1] = xpred[::-1, 1] 
54          phi = m.phi(xpred) 
55          np.testing.assert_array_almost_equal(phi[0], np.zeros_like(xpred), 4) 
56          np.testing.assert_array_almost_equal(phi[1], xpred*beta.T) 
57          for d in range(2): 
58              phid = m.phi(xpred, [d]) 
59              np.testing.assert_array_equal(phi[d], phid[0]) 
60           
61          ypred = m.predict(xpred) 
62          np.testing.assert_array_almost_equal(ypred, xpred.dot(beta)) 
63   
64 -    def testLassoRegression(self): 
65          np.random.seed(12345) 
66          X = np.random.uniform(0,1,(20,2)) 
67          beta = np.random.normal(0,1,(2,1)) 
68          Y = X.dot(beta) 
69          #Y += np.random.normal(0, .001, Y.shape) 
70   
71          m = RidgeRegression(X, Y, regularizer=Lasso(.00001), basis=Polynomial(1)) 
72          self.assertTrue(m.checkgrad()) 
73          m.optimize(max_iters=10) 
74          np.testing.assert_array_almost_equal(m.regularizer.weights[1], beta[:,0], 3) 
75          np.testing.assert_array_almost_equal(m.regularizer.weights[0], [0,0], 3) 
76          np.testing.assert_array_almost_equal(m.gradient, np.zeros(m.weights.size), 3) 
77   
78  #         m = RidgeRegression(X, Y, regularizer=Lasso(.00001, np.ones(X.shape[1]))) 
79  #         self.assertTrue(m.checkgrad()) 
80  #         m.optimize() 
81  #         np.testing.assert_array_almost_equal(m.regularizer.weights[1], beta, 4) 
82  #         np.testing.assert_array_almost_equal(m.gradient, np.zeros(m.weights.shape[0]), 4) 
83   
84  if __name__ == "__main__": 
85      #import sys;sys.argv = ['', 'Test.testRidgeRegression'] 
86      unittest.main() 
87