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All gradients now gradcheck
This commit is contained in:
Alan Saul
parent
6e405319b0
commit
e36ffcba6e
2 changed files with 82 additions and 77 deletions
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@ -291,6 +291,7 @@ class StudentT(LikelihoodFunction):
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"""
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assert y.shape == f.shape
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e = y - f
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#FIXME: OUT BY SOME FUNCTION OF N
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dlik_dvar = self.v*(e**2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e**2))
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return dlik_dvar
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@ -442,7 +443,7 @@ class Gaussian(LikelihoodFunction):
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self.I = np.eye(self.N)
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self.covariance_matrix = self.I * self._variance
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self.Ki = self.I*(1.0 / self._variance)
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self.ln_K = np.trace(self.covariance_matrix)
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self.ln_det_K = np.sum(np.log(np.diag(self.covariance_matrix)))
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def link_function(self, y, f, extra_data=None):
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"""link_function $\ln p(y|f)$
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@ -458,11 +459,11 @@ class Gaussian(LikelihoodFunction):
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e = y - f
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eeT = np.dot(e, e.T)
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objective = (- 0.5*self.D*np.log(2*np.pi)
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- 0.5*self.ln_K
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#- 0.5*np.sum(np.multiply(self.Ki, eeT))
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- 0.5*np.dot(np.dot(e.T, self.Ki), e)
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- 0.5*self.ln_det_K
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#- 0.5*np.dot(np.dot(e.T, self.Ki), e)
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- (0.5/self._variance)*np.dot(e.T, e) # As long as K is diagonal
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)
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return np.sum(objective) # FIXME: put this back!
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return np.sum(objective)
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def dlik_df(self, y, f, extra_data=None):
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"""
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@ -514,7 +515,8 @@ class Gaussian(LikelihoodFunction):
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assert y.shape == f.shape
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e = y - f
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s_4 = 1.0/(self._variance**2)
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dlik_dsigma = -0.5*self.N/self._variance + 0.5*s_4*np.trace(np.dot(e.T, np.dot(self.I, e)))
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dlik_dsigma = -0.5*self.N/self._variance + 0.5*s_4*np.dot(e.T, e)
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#dlik_dsigma = -0.5*self.N + 0.5*s_4*np.dot(e.T, e)
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return dlik_dsigma
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def dlik_df_dvar(self, y, f, extra_data=None):
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@ -523,7 +525,7 @@ class Gaussian(LikelihoodFunction):
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"""
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assert y.shape == f.shape
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s_4 = 1.0/(self._variance**2)
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dlik_grad_dsigma = -np.dot(s_4, np.dot(self.I, y)) + 0.5*np.dot(s_4, np.dot(self.I, f))
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dlik_grad_dsigma = -np.dot(s_4*self.I, y) + np.dot(s_4*self.I, f)
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return dlik_grad_dsigma
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def d2lik_d2f_dvar(self, y, f, extra_data=None):
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@ -533,7 +535,7 @@ class Gaussian(LikelihoodFunction):
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$$\frac{d}{d\sigma}(\frac{d^{2}p(y_{i}|f_{i})}{d^{2}f}) = \frac{2\sigma v(v + 1)(\sigma^2 v - 3(y-f)^2)}{((y-f)^2 + \sigma^2 v)^3}$$
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"""
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assert y.shape == f.shape
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dlik_hess_dsigma = 0.5*np.diag((1.0/(self._variance**2))*self.I)[:, None]
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dlik_hess_dsigma = np.diag((1.0/(self._variance**2))*self.I)[:, None]
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return dlik_hess_dsigma
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def _gradients(self, y, f, extra_data=None):
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@ -3,6 +3,7 @@ import unittest
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import GPy
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from GPy.models import GradientChecker
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import functools
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import inspect
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def dparam_partial(inst_func, *args):
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"""
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@ -22,66 +23,71 @@ 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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def dparam_checkgrad(func, dfunc, params, args, constrain_positive=True, 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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We need to check the gradient of each of the fixed parameters
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(f and y for example) seperately.
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Whilst moving another parameter. otherwise f: gives back R^N and
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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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#print "\n{} 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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gradchecking = True
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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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#dlik and dlik_dvar gives back 1 value for each
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f_ind = min(fnum, fixed_val+1) - 1
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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 constrain_positive:
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grad.constrain_positive('p')
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if randomize:
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grad.randomize()
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print grad
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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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if not grad.checkgrad():
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gradchecking = False
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return gradchecking
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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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self.D = 1
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self.N = 1
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self.D = 5
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self.X = np.linspace(0, 1, self.N)[:, None]
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self.real_std = 0.2
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noise = np.random.randn(*self.X.shape)*self.real_std
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self.Y = np.sin(self.X*2*np.pi) + noise
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#self.Y = np.array([[1.0]])#np.sin(self.X*2*np.pi) + noise
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self.f = np.random.rand(self.N, 1)
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#self.f = np.array([[3.0]])#np.sin(self.X*2*np.pi) + noise
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self.var = 0.1
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self.var = np.random.rand(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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self.Y = None
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self.f = None
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self.X = None
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def test_gaussian_dlik_df(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -90,6 +96,7 @@ class LaplaceTests(unittest.TestCase):
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self.assertTrue(grad.checkgrad())
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def test_gaussian_d2lik_d2f(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -98,6 +105,7 @@ class LaplaceTests(unittest.TestCase):
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self.assertTrue(grad.checkgrad())
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def test_gaussian_d3lik_d3f(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -106,28 +114,31 @@ class LaplaceTests(unittest.TestCase):
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self.assertTrue(grad.checkgrad())
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def test_gaussian_dlik_dvar(self):
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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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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.gauss.link_function, self.gauss.dlik_dvar,
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[self.var], args=(self.Y, self.f), constrain_positive=True,
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randomize=False, verbose=True)
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)
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def test_gaussian_dlik_df_dvar(self):
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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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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.gauss.dlik_df, self.gauss.dlik_df_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()), constrain_positive=True,
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randomize=False, verbose=True)
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)
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def test_gaussian_d2lik_d2f_dvar(self):
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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.gauss.d2lik_d2f, self.gauss.d2lik_d2f_dvar,
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[self.var], args=(self.Y, self.f), constrain_positive=True,
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randomize=True, verbose=True)
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)
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def test_studentt_dlik_df(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -135,6 +146,7 @@ class LaplaceTests(unittest.TestCase):
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grad.checkgrad(verbose=1)
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def test_studentt_d2lik_d2f(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -142,6 +154,7 @@ class LaplaceTests(unittest.TestCase):
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grad.checkgrad(verbose=1)
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def test_studentt_d3lik_d3f(self):
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print "\n{}".format(inspect.stack()[0][3])
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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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@ -149,39 +162,29 @@ class LaplaceTests(unittest.TestCase):
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grad.checkgrad(verbose=1)
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def test_studentt_dlik_dvar(self):
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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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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.stu_t.link_function, self.stu_t.dlik_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()),
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constrain_positive=True, randomize=True, verbose=True)
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)
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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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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.stu_t.dlik_df, self.stu_t.dlik_df_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()),
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constrain_positive=True, randomize=True, verbose=True)
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)
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def test_studentt_d2lik_d2f_dvar(self):
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print "\n{}".format(inspect.stack()[0][3])
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self.assertTrue(
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dparam_checkgrad(self.stu_t.d2lik_d2f, self.stu_t.d2lik_d2f_dvar,
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[self.var], args=(self.Y.copy(), self.f.copy()),
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constrain_positive=True, randomize=True, verbose=True)
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)
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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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