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Refactored tests
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Alan Saul
parent
42f8180c4e
commit
888a1ff0f7
1 changed files with 119 additions and 37 deletions
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@ -22,6 +22,45 @@ def dparam_partial(inst_func, *args):
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return inst_func(*args)
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return functools.partial(param_func, inst_func=inst_func, args=args)
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def grad_checker_wrt_params(func, dfunc, params, args, randomize=False, verbose=False):
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"""
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checkgrad expects a f: R^N -> R^1 and df: R^N -> R^N
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However if we are holding other parameters fixed and moving something else
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We need to check the gradient of each of the fixed parameters (f and y for example) seperately
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Whilst moving another parameter. otherwise f: gives back R^N and df: gives back R^NxM where M is
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The number of parameters and N is the number of data
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Need to take a slice out from f and a slice out of df
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"""
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print "{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
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func.__name__, dfunc.__name__)
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partial_f = dparam_partial(func, *args)
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partial_df = dparam_partial(dfunc, *args)
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gradchecked = False
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for param in params:
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fnum = np.atleast_1d(partial_f(param)).shape[0]
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dfnum = np.atleast_1d(partial_df(param)).shape[0]
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for fixed_val in range(dfnum):
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f_ind = min(fnum, fixed_val+1) - 1 #dlik and dlik_dvar gives back 1 value for each
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grad = GradientChecker(lambda x: np.atleast_1d(partial_f(x))[f_ind],
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lambda x : np.atleast_1d(partial_df(x))[fixed_val],
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param, 'p')
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grad.constrain_positive('p')
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if randomize:
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grad.randomize()
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if verbose:
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grad.checkgrad(verbose=1)
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cg = grad.checkgrad()
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print cg
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if cg:
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print "True"
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gradchecked = True
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else:
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print "False"
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return False
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print str(gradchecked)
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return gradchecked
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class LaplaceTests(unittest.TestCase):
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def setUp(self):
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self.N = 5
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@ -34,72 +73,115 @@ class LaplaceTests(unittest.TestCase):
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self.f = np.random.rand(self.N, 1)
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self.var = 0.1
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self.stu_t = GPy.likelihoods.functions.StudentT(deg_free=5, sigma2=self.var)
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self.gauss = GPy.likelihoods.functions.Gaussian(self.var, self.D, self.N)
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def tearDown(self):
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self.stu_t = None
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self.gauss = None
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def test_gaussian_dlik_df(self):
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var = 0.1
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gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
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link = functools.partial(gauss.link_function, self.Y)
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dlik_df = functools.partial(gauss.dlik_df, self.Y)
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link = functools.partial(self.gauss.link_function, self.Y)
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dlik_df = functools.partial(self.gauss.dlik_df, self.Y)
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grad = GradientChecker(link, dlik_df, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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self.assertTrue(grad.checkgrad())
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def test_gaussian_d2lik_d2f(self):
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var = 0.1
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gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
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dlik_df = functools.partial(gauss.dlik_df, self.Y)
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d2lik_d2f = functools.partial(gauss.d2lik_d2f, self.Y)
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dlik_df = functools.partial(self.gauss.dlik_df, self.Y)
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d2lik_d2f = functools.partial(self.gauss.d2lik_d2f, self.Y)
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grad = GradientChecker(dlik_df, d2lik_d2f, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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self.assertTrue(grad.checkgrad())
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def test_gaussian_d3lik_d3f(self):
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var = 0.1
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gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
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d2lik_d2f = functools.partial(gauss.d2lik_d2f, self.Y)
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d3lik_d3f = functools.partial(gauss.d3lik_d3f, self.Y)
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d2lik_d2f = functools.partial(self.gauss.d2lik_d2f, self.Y)
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d3lik_d3f = functools.partial(self.gauss.d3lik_d3f, self.Y)
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grad = GradientChecker(d2lik_d2f, d3lik_d3f, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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self.assertTrue(grad.checkgrad())
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def test_gaussian_dlik_dvar(self):
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var = 0.1
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gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
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link = dparam_partial(gauss.link_function, self.Y, self.f)
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dlik_dvar = dparam_partial(gauss.dlik_dvar, self.Y, self.f)
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grad = GradientChecker(link, dlik_dvar, var, 'v')
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grad.constrain_positive('v')
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grad.randomize()
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grad.checkgrad(verbose=1)
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#link = dparam_partial(self.gauss.link_function, self.Y, self.f)
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#dlik_dvar = dparam_partial(self.gauss.dlik_dvar, self.Y, self.f)
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#grad = GradientChecker(link, dlik_dvar, self.var, 'v')
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#grad.constrain_positive('v')
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#grad.randomize()
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#grad.checkgrad(verbose=1)
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#self.assertTrue(grad.checkgrad())
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self.assertTrue(grad_checker_wrt_params(self.gauss.link_function, self.gauss.dlik_dvar,
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[self.var], args=(self.Y, self.f), randomize=True, verbose=True))
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def test_gaussian_dlik_df_dvar(self):
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var = 0.1
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gauss = GPy.likelihoods.functions.Gaussian(var, self.D, self.N)
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#dlik_df = dparam_partial(self.gauss.dlik_df, self.Y, self.f)
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#dlik_df_dvar = dparam_partial(self.gauss.dlik_df_dvar, self.Y, self.f)
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#grad = GradientChecker(dlik_df, dlik_df_dvar, self.var, 'v')
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#grad.constrain_positive('v')
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#grad.randomize()
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#grad.checkgrad(verbose=1)
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#self.assertTrue(grad.checkgrad())
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self.assertTrue(grad_checker_wrt_params(self.gauss.dlik_df, self.gauss.dlik_df_dvar,
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[self.var], args=(self.Y, self.f), randomize=True, verbose=True))
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dlik_df = dparam_partial(gauss.dlik_df, self.Y, self.f)
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dlik_df_dvar = dparam_partial(gauss.dlik_df_dvar, self.Y, self.f)
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grad = GradientChecker(dlik_df, dlik_df_dvar, var, 'v')
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grad.constrain_positive('v')
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def test_studentt_dlik_df(self):
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link = functools.partial(self.stu_t.link_function, self.Y)
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dlik_df = functools.partial(self.stu_t.dlik_df, self.Y)
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grad = GradientChecker(link, dlik_df, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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def test_studentt_d2lik_d2f(self):
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dlik_df = functools.partial(self.stu_t.dlik_df, self.Y)
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d2lik_d2f = functools.partial(self.stu_t.d2lik_d2f, self.Y)
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grad = GradientChecker(dlik_df, d2lik_d2f, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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def test_studentt_d3lik_d3f(self):
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d2lik_d2f = functools.partial(self.stu_t.d2lik_d2f, self.Y)
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d3lik_d3f = functools.partial(self.stu_t.d3lik_d3f, self.Y)
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grad = GradientChecker(d2lik_d2f, d3lik_d3f, self.f.copy(), 'f')
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grad.randomize()
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grad.checkgrad(verbose=1)
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#self.assertTrue(grad.checkgrad())
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def test_studentt_dlik_dvar(self):
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var = 0.1
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stu_t = GPy.likelihoods.functions.StudentT(deg_free=5, sigma2=var)
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link = dparam_partial(stu_t.link_function, self.Y, self.f)
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dlik_dvar = dparam_partial(stu_t.dlik_dvar, self.Y, self.f)
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grad = GradientChecker(link, dlik_dvar, var, 'v')
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grad.constrain_positive('v')
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grad.randomize()
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grad.checkgrad(verbose=1)
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#link = dparam_partial(self.stu_t.link_function, self.Y, self.f)
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#dlik_dvar = dparam_partial(self.stu_t.dlik_dvar, self.Y, self.f)
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#grad = GradientChecker(link, dlik_dvar, self.var, 'v')
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#grad.constrain_positive('v')
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#grad.randomize()
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#grad.checkgrad(verbose=1)
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#self.assertTrue(grad.checkgrad())
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self.assertTrue(grad_checker_wrt_params(self.stu_t.link_function, self.stu_t.dlik_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()), randomize=True, verbose=True))
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def test_studentt_dlik_df_dvar(self):
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#dlik_df = dparam_partial(self.stu_t.dlik_df, self.Y, self.f)
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#dlik_df_dvar = dparam_partial(self.stu_t.dlik_df_dvar, self.Y, self.f)
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#grad = GradientChecker(dlik_df, dlik_df_dvar, self.var, 'v')
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#grad.constrain_positive('v')
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#grad.randomize()
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#grad.checkgrad(verbose=1)
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#self.assertTrue(grad.checkgrad())
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self.assertTrue(grad_checker_wrt_params(self.stu_t.dlik_df, self.stu_t.dlik_df_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()), randomize=True, verbose=True))
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if __name__ == "__main__":
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#N = 5
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#D = 1
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#X = np.linspace(0, 1, N)[:, None]
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#real_std = 0.2
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#noise = np.random.randn(*X.shape)*real_std
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#Y = np.sin(X*2*np.pi) + noise
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#f = np.random.rand(N, 1)
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#var = 0.1
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#stu_t = GPy.likelihoods.functions.StudentT(deg_free=5, sigma2=var)
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#print grad_checker_wrt_params(stu_t.dlik_df, stu_t.dlik_df_dvar, [var], args=(Y, f), randomize=True, verbose=False)
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print "Running unit tests"
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unittest.main()
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