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psistattests update
This commit is contained in:
Max Zwiessele
parent
4948fb1345
commit
76bfbee545
3 changed files with 51 additions and 32 deletions
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@ -412,6 +412,9 @@ class kern(Parameterized):
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[p.dpsi0_dtheta(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target[ps]) for p, ps, i_s in zip(self.parts, self.param_slices, self.input_slices)]
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return self._transform_gradients(target)
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def dpsi0_dZ(self, dL_dpsi0, Z, mu, S):
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return np.zeros_like(Z)
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def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S):
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target_mu, target_S = np.zeros_like(mu), np.zeros_like(S)
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[p.dpsi0_dmuS(dL_dpsi0, Z[:, i_s], mu[:, i_s], S[:, i_s], target_mu[:, i_s], target_S[:, i_s]) for p, i_s in zip(self.parts, self.input_slices)]
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@ -28,8 +28,8 @@ def ard(p):
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class Test(unittest.TestCase):
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input_dim = 9
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num_inducing = 13
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N = 30
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Nsamples = 9e6
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N = 300
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Nsamples = 1e6
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def setUp(self):
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i_s_dim_list = [2,4,3]
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@ -50,20 +50,20 @@ class Test(unittest.TestCase):
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# GPy.kern.linear(self.input_dim, ARD=True) +
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# GPy.kern.bias(self.input_dim) +
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# GPy.kern.white(self.input_dim)),
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# (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
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(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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+GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True)
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# GPy.kern.bias(self.input_dim) +
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# GPy.kern.white(self.input_dim)),
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(#GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True)
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+GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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# +GPy.kern.bias(self.input_dim)
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# +GPy.kern.white(self.input_dim)),
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),
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(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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+GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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#+GPy.kern.bias(self.input_dim, np.random.rand())
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#+GPy.kern.white(self.input_dim, np.random.rand())),
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),
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(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
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GPy.kern.bias(self.input_dim, np.random.rand()) +
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GPy.kern.white(self.input_dim, np.random.rand())),
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# (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
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# GPy.kern.bias(self.input_dim, np.random.rand())),
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# (GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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# +GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True)
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# #+GPy.kern.bias(self.input_dim, np.random.rand())
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# #+GPy.kern.white(self.input_dim, np.random.rand())),
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# ),
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# GPy.kern.white(self.input_dim, np.random.rand())),
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# GPy.kern.rbf(self.input_dim), GPy.kern.rbf(self.input_dim, ARD=True),
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# GPy.kern.linear(self.input_dim, ARD=False), GPy.kern.linear(self.input_dim, ARD=True),
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# GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim),
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@ -120,25 +120,25 @@ class Test(unittest.TestCase):
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diffs = []
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for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
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K = kern.K(q_x_sample_stripe, self.Z)
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K = (K[:, :, None] * K[:, None, :]).mean(0)
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K_ += K
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diffs.append(((psi2 - (K_ / (i + 1)))**2).mean())
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K_ /= self.Nsamples / Nsamples
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K = (K[:, :, None] * K[:, None, :])
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K_ += K.sum(0) / self.Nsamples
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diffs.append(((psi2 - (K_*self.Nsamples/((i+1)*Nsamples)))**2).mean())
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#K_ /= self.Nsamples / Nsamples
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msg = "psi2: {}".format("+".join([p.name + ard(p) for p in kern.parts]))
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try:
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import pylab
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pylab.figure(msg)
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pylab.plot(diffs, marker='x', mew=1.3)
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pylab.plot(diffs, marker='x', mew=.2)
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# print msg, np.allclose(psi2.squeeze(), K_, rtol=1e-1, atol=.1)
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self.assertTrue(np.allclose(psi2.squeeze(), K_),
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#rtol=1e-1, atol=.1),
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msg=msg + ": not matching")
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# sys.stdout.write(".")
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except:
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# import ipdb;ipdb.set_trace()
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# kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
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# sys.stdout.write("E")
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print msg + ": not matching"
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import ipdb;ipdb.set_trace()
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pass
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if __name__ == "__main__":
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@ -40,10 +40,9 @@ class PsiStatModel(Model):
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return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
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def _log_likelihood_gradients(self):
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psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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try:
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psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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except AttributeError:
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psiZ = numpy.zeros(self.num_inducing * self.input_dim)
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#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
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psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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#psiZ = numpy.ones(self.num_inducing * self.input_dim)
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thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
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return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
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@ -116,9 +115,9 @@ if __name__ == "__main__":
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# m.randomize()
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# # self.assertTrue(m.checkgrad())
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numpy.random.seed(0)
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input_dim = 5
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N = 50
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num_inducing = 10
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input_dim = 3
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N = 3
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num_inducing = 2
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D = 15
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X = numpy.random.randn(N, input_dim)
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X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
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@ -143,10 +142,27 @@ if __name__ == "__main__":
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# num_inducing=num_inducing, kernel=kernel)
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# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
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m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
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# m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
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# + GPy.kern.bias(input_dim))
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# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
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# m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing,
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# kernel=(
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# GPy.kern.rbf(input_dim, ARD=1)
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# +GPy.kern.linear(input_dim, ARD=1)
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# +GPy.kern.bias(input_dim))
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# )
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# m.ensure_default_constraints()
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m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=(
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GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.linear(input_dim, numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0)
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+GPy.kern.bias(input_dim)
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+GPy.kern.white(input_dim)
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)
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)
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m2.ensure_default_constraints()
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else:
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unittest.main()
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