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https://github.com/SheffieldML/GPy.git
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fix the bugs about tie found today
This commit is contained in:
Zhenwen Dai
parent
ca892e32e1
commit
494f38f788
3 changed files with 77 additions and 10 deletions
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@ -240,9 +240,10 @@ class Tie(Parameterized):
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for p in plist:
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self._traverse_param(_set_l1, (p,), [])
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else:
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idx = [0]
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for p in plist:
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self._traverse_param(_set_list, (p,[0]), [])
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self._traverse_param(_set_list, (p,idx), [])
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def _replace_labels(self, p, label_pairs):
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def _replace_l(p):
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for l1,l2 in label_pairs:
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@ -258,6 +259,7 @@ class Tie(Parameterized):
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new_buf = np.empty((num,))
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self.tied_param = Param('tied',new_buf)
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self.tied_param.tie[:] = labellist
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self.link_parameter(self.tied_param)
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else:
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start_label = self.tied_param.tie.max()+1
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new_buf = np.empty((self.tied_param.size+num,))
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@ -266,11 +268,13 @@ class Tie(Parameterized):
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old_size = self.tied_param.size
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labellist = np.array(range(start_label,start_label+num),dtype=np.int)
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idxlist = np.array(range(old_size,old_size+num),dtype=np.int)
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cons = self.tied_param.constraints.copy()
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self.unlink_parameter(self.tied_param)
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self.tied_param = Param('tied',new_buf)
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self.tied_param.tie[:old_size] = old_tie_
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self.tied_param.tie[old_size:] = labellist
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self.link_parameter(self.tied_param)
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self.link_parameter(self.tied_param)
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self.tied_param.constraints.update(cons)
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return labellist, idxlist
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def _remove_tie_param(self, labels):
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@ -283,10 +287,17 @@ class Tie(Parameterized):
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idx = np.logical_not(np.in1d(self.tied_param.tie,labels))
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new_buf[:] = self.tied_param[idx]
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old_tie_ = self.tied_param.tie.copy()
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self.unlink_parameter(self.tied_param)
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cons = {}
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for c,ind in self.tied_param.constraints.iteritems():
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buf = np.zeros((old_tie_.size,),dtype=np.uint8)
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buf[ind] = 1
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if (buf[idx]==1).sum()>0:
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cons[c] = np.where(buf[idx]==1)[0]
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self.unlink_parameter(self.tied_param)
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self.tied_param = Param('tied',new_buf)
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self.tied_param.tie[:] = old_tie_[idx]
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self.link_parameter(self.tied_param)
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[self.tied_param.constraints.add(c,ind) for c,ind in cons.iteritems()]
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def _merge_tie_labels(self, labels):
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"""Merge all the labels in the list to the first one"""
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@ -322,7 +333,10 @@ class Tie(Parameterized):
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assert(np.all(self.tied_param.tie>0))
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def _keepParamList(self,plist):
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return [(p._original_, p._current_slice_) for p in plist]
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paramlist = []
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for p in plist:
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self._traverse_param(lambda p: (p._original_, p._current_slice_), (p,), paramlist)
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return paramlist
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def _updateParamList(self, p_split):
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return [p_org[p_slice] for p_org,p_slice in p_split]
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@ -355,16 +369,20 @@ class Tie(Parameterized):
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"""tie a pair of vectors"""
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self.update_model(False)
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expandlist,removelist,labellist = self._get_labels_vector(p1, p2)
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p_split = self._keepParamList([p1,p2])
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p_split1 = self._keepParamList([p1])
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p_split2 = self._keepParamList([p2])
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if len(expandlist)>0:
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tie_labels,idxlist = self._expand_tie_param(len(expandlist))
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labellist[expandlist] = tie_labels
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if len(removelist[0])>0:
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self._merge_tie_labelpair(removelist)
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p1,p2 = self._updateParamList(p_split)
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self._set_labels([p1,p2], labellist)
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self._sync_val([p1,p2],toTiedParam=True)
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self._sync_constraints([p1,p2], toTiedParam=True)
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p1 = self._updateParamList(p_split1)
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p2 = self._updateParamList(p_split2)
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ps = p1+p2
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self._set_labels(p1, labellist)
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self._set_labels(p2, labellist)
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self._sync_val(ps,toTiedParam=True)
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self._sync_constraints(ps, toTiedParam=True)
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self._update_label_buf()
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self.update_model(True)
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@ -162,6 +162,32 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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from ..util.misc import param_to_array
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import numpy as np
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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if optimize:
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m.optimize('bfgs', messages=verbose, max_iters=max_iters, gtol=.05)
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if plot:
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes, labels=m.data_labels)
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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plt.close(fig)
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return m
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def bgplvm_oil_100(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
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import GPy
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from matplotlib import pyplot as plt
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from ..util.misc import param_to_array
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_np.random.seed(0)
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data = GPy.util.datasets.oil_100()
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@ -2,6 +2,7 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import unittest
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import numpy as np
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import GPy
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class TieTests(unittest.TestCase):
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@ -54,6 +55,28 @@ class TieTests(unittest.TestCase):
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self.assertTrue(m.ties.checkConstraintConsistency())
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self.assertTrue(m.ties.checkTieVector([m.Z[:10],m.Z[10:20],m.Z[20:30],m.Z[30:40]]))
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self.assertTrue(m.checkgrad())
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def test_remove_tie(self):
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x = np.random.rand(100,1)
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y = np.random.rand(100,1)
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m = GPy.models.SparseGPRegression(x,y,kernel=GPy.kern.RBF(1)+GPy.kern.Matern32(1))
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m.kern.rbf.lengthscale.tie_together(m.kern.Mat32.lengthscale)
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m.Z[:1].tie_together(m.Z[1:2])
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m.kern.rbf.variance.tie_together(m.kern.Mat32.variance)
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m.kern.rbf.lengthscale.untie()
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self.assertTrue(m.ties.checkValueConsistency())
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self.assertTrue(m.ties.checkConstraintConsistency())
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self.assertTrue(m.ties.checkTieTogether([m.kern.rbf.variance,m.kern.Mat32.variance]))
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self.assertTrue(m.ties.checkTieVector([m.Z[:1],m.Z[1:2]]))
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self.assertTrue(m.checkgrad())
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def test_tie_variational_posterior(self):
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m = GPy.examples.dimensionality_reduction.bgplvm_oil_100(plot=False,optimize=False)
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m.X[:10].tie_vector(m.X[10:20])
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self.assertTrue(m.ties.checkValueConsistency())
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self.assertTrue(m.ties.checkConstraintConsistency())
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self.assertTrue(m.ties.checkTieVector([m.X[:10],m.X[10:20]]))
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self.assertTrue(m.checkgrad())
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if __name__ == "__main__":
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print "Running unit tests, please be (very) patient..."
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