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the merging of two tie objects are done
This commit is contained in:
Zhenwen Dai
parent
2463a954f7
commit
39327e0789
4 changed files with 55 additions and 126 deletions
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@ -21,10 +21,6 @@ class Model(Parameterized):
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self.optimization_runs = []
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self.sampling_runs = []
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self.preferred_optimizer = 'bfgs'
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from .parameterization.ties_and_remappings import Tie
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self.tie = Tie()
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self.add_parameter(self.tie, -1)
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self.add_observer(self.tie, self.tie._parameters_changed_notification, priority=-500)
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def log_likelihood(self):
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raise NotImplementedError, "this needs to be implemented to use the model class"
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@ -517,14 +517,16 @@ class Indexable(Nameable, Observable):
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#===========================================================================
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def _has_ties(self):
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if self._highest_parent_.tie.tied_param is None:
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if self._highest_parent_.ties.tied_param is None:
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return False
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if self.has_parent():
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return self._highest_parent_.tie.label_buf[self._highest_parent_._raveled_index_for(self)].sum()>0
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return self._highest_parent_.ties.label_buf[self._highest_parent_._raveled_index_for(self)].sum()>0
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return True
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def tie_together(self):
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self._highest_parent_.tie.tie_together([self])
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def tie_together(self, *plist):
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plist = list(plist)
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plist.append(self)
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self._highest_parent_.ties.tie_together(plist)
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self._highest_parent_._set_fixed(self,self._raveled_index())
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self._trigger_params_changed()
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@ -681,7 +683,7 @@ class OptimizationHandlable(Indexable):
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if self.has_parent() and (self.constraints[__fixed__].size != 0 or self._has_ties()):
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fixes = np.ones(self.size).astype(bool)
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fixes[self.constraints[__fixed__]] = FIXED
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return self._optimizer_copy_[np.logical_and(fixes, self._highest_parent_.tie.getTieFlag(self))]
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return self._optimizer_copy_[np.logical_and(fixes, self._highest_parent_.ties.getTieFlag(self))]
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elif self._has_fixes():
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return self._optimizer_copy_[self._fixes_]
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@ -711,7 +713,7 @@ class OptimizationHandlable(Indexable):
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self.param_array.flat[f] = p
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[np.put(self.param_array, ind[f[ind]], c.f(self.param_array.flat[ind[f[ind]]]))
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for c, ind in self.constraints.iteritems() if c != __fixed__]
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self._highest_parent_.tie.propagate_val()
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self._highest_parent_.ties.propagate_val()
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self._optimizer_copy_transformed = False
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self._trigger_params_changed()
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@ -744,7 +746,7 @@ class OptimizationHandlable(Indexable):
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Transform the gradients by multiplying the gradient factor for each
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constraint to it.
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"""
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self._highest_parent_.tie.collate_gradient()
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self._highest_parent_.ties.collate_gradient()
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[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
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if self._has_fixes(): return g[self._fixes_]
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return g
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@ -82,6 +82,12 @@ class Parameterized(Parameterizable):
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self._param_slices_ = []
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#self._connect_parameters()
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self.add_parameters(*parameters)
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from ties_and_remappings import Tie
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if not isinstance(self,Tie):
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self.ties = Tie()
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self.add_parameter(self.ties, -1)
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self.add_observer(self.ties, self.ties._parameters_changed_notification, priority=-500)
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def build_pydot(self, G=None):
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import pydot # @UnresolvedImport
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@ -163,6 +169,12 @@ class Parameterized(Parameterizable):
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self._highest_parent_._notify_parent_change()
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self._highest_parent_._connect_fixes()
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if isinstance(param,Parameterized):
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from ties_and_remappings import Tie
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if not isinstance(param,Tie):
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self._highest_parent_.ties.mergeTies(param)
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else:
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self._highest_parent_.ties._update_label_buf()
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else:
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raise HierarchyError, """Parameter exists already, try making a copy"""
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@ -29,8 +29,6 @@ class Fix(Remapping):
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pass
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class Tie(Parameterized):
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"""
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The new parameter tie framework. (under development)
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@ -61,25 +59,39 @@ class Tie(Parameterized):
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4. Properly handling initialization
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"""
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def __init__(self, name='tie'):
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def __init__(self, name='Ties'):
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# whether it has just propagated tied parameter values during optimization
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# If ture, it does not need to check consistency
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self._PROPAGATE_VAL_ = False
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super(Tie, self).__init__(name)
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self.size = sum(p.size for p in self.parameters)
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self.tied_param = None
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# The buffer keeps track of tie status
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self.label_buf = None
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self.buf_idx = None
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def _get_raveled_index(self,plist):
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indices = []
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def mergeTies(self, p):
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"""Merge the tie tree with another tie tree"""
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assert hasattr(p,'ties') and isinstance(p.ties,Tie)
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self.updates = False
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if p.ties.tied_param is not None:
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tie_labels = self._expand_tie_param(p.ties.tied_param.size)
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self.tied_param[-p.ties.tied_param.size:] = p.ties.tied_param
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pairs = zip(self.tied_param.tie,tie_labels)
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self._replace_labels(p, pairs)
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p.remove_observer(p.ties)
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p.remove_parameter(p.ties)
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del p.ties
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self._update_label_buf()
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self.updates = True
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def _sync_val_group(self, plist):
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val = np.asarray([p.param_array for p in plist]).mean()
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def _set_val(p):
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p[:] = val
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for p in plist:
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indices.extend(self._highest_parent_._raveled_index_for(p))
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return indices
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def _sync_val_group(self, idx):
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self._highest_parent_.param_array[idx] = self._highest_parent_.param_array[idx].mean()
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return self._highest_parent_.param_array[idx][0]
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self._traverse_param(_set_val, p, [])
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return val
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def _traverse_param(self, func, p, res):
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"""
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@ -93,10 +105,11 @@ class Tie(Parameterized):
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for pc in p.parameters:
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self._traverse_param(func,pc,res)
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def _get_labels(self,idx):
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if self.label_buf is None:
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self.label_buf = np.zeros((self._highest_parent_.size,),dtype=np.uint32)
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return np.unique(self.label_buf[idx])
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def _get_labels(self, plist):
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labels = []
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for p in plist:
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self._traverse_param(lambda x: x.tie, p, labels)
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return np.unique(np.asarray(labels))
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def _set_labels(self, plist, labels):
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"""
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@ -133,6 +146,7 @@ class Tie(Parameterized):
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self.tied_param.tie[:old_size] = old_tie_
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self.tied_param.tie[old_size:] = range(start_label,start_label+num)
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self.add_parameter(self.tied_param)
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self.size = sum(p.size for p in self.parameters)
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return range(start_label,start_label+num)
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def _remove_tie_param(self, labels):
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@ -149,7 +163,7 @@ class Tie(Parameterized):
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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.add_parameter(self.tied_param)
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self.size = sum(p.size for p in self.parameters)
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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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@ -169,9 +183,9 @@ class Tie(Parameterized):
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def tie_together(self,plist):
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"""tie a list of parameters"""
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indices = self._get_raveled_index(plist)
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labels = self._get_labels(indices)
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val = self._sync_val_group(indices)
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self.updates = False
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labels = self._get_labels(plist)
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val = self._sync_val_group(plist)
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if labels[0]==0 and labels.size==1:
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# None of parameters in plist has been tied before.
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tie_labels = self._expand_tie_param(1)
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@ -185,102 +199,7 @@ class Tie(Parameterized):
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self._set_labels(plist, [tie_labels[0]])
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self._update_label_buf()
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self.tied_param[self.tied_param.tie==tie_labels[0]] = val
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# def add_tied_parameter(self, p, p2=None):
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# """
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# Tie the list of parameters p together (p2==None) or
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# Tie the list of parameters p with the list of parameters p2 (p2!=None)
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# """
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# self._init_labelBuf()
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# if p2 is None:
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# idx = self._highest_parent_._raveled_index_for(p)
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# val = self._sync_val_group(idx)
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# if np.all(self.label_buf[idx]==0):
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# # None of p has been tied before.
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# tie_idx = self._expandTieParam(1)
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# print tie_idx
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# tie_id = self.label_buf.max()+1
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# self.label_buf[tie_idx] = tie_id
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# else:
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# b = self.label_buf[idx]
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# ids = np.unique(b[b>0])
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# tie_id, tie_idx = self._merge_tie_param(ids)
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# self._highest_parent_.param_array[tie_idx] = val
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# idx = self._highest_parent_._raveled_index_for(p)
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# self.label_buf[idx] = tie_id
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# else:
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# pass
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# self._updateTieFlag()
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#
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# def _merge_tie_param(self, ids):
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# """Merge the tie parameters with ids in the list."""
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# if len(ids)==1:
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# id_final_idx = self.buf_idx[self.label_buf[self.buf_idx]==ids[0]][0]
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# return ids[0],id_final_idx
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# id_final = ids[0]
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# ids_rm = ids[1:]
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# label_buf_param = self.label_buf[self.buf_idx]
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# idx_param = [np.where(label_buf_param==i)[0][0] for i in ids_rm]
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# self._removeTieParam(idx_param)
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# [np.put(self.label_buf, np.where(self.label_buf==i), id_final) for i in ids_rm]
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# id_final_idx = self.buf_idx[self.label_buf[self.buf_idx]==id_final][0]
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# return id_final, id_final_idx
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#
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# def _sync_val_group(self, idx):
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# self._highest_parent_.param_array[idx] = self._highest_parent_.param_array[idx].mean()
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# return self._highest_parent_.param_array[idx][0]
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#
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# def _expandTieParam(self, num):
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# """Expand the tie param with the number of *num* parameters"""
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# if self.tied_param is None:
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# new_buf = np.empty((num,))
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# else:
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# new_buf = np.empty((self.tied_param.size+num,))
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# new_buf[:self.tied_param.size] = self.tied_param.param_array.copy()
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# self.remove_parameter(self.tied_param)
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# self.tied_param = Param('tied',new_buf)
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# self.add_parameter(self.tied_param)
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# buf_idx_new = self._highest_parent_._raveled_index_for(self.tied_param)
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# self._expand_label_buf(self.buf_idx, buf_idx_new)
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# self.buf_idx = buf_idx_new
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# return self.buf_idx[-num:]
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#
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# def _removeTieParam(self, idx):
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# """idx within tied_param"""
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# new_buf = np.empty((self.tied_param.size-len(idx),))
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# bool_list = np.ones((self.tied_param.size,),dtype=np.bool)
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# bool_list[idx] = False
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# new_buf[:] = self.tied_param.param_array[bool_list]
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# self.remove_parameter(self.tied_param)
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# self.tied_param = Param('tied',new_buf)
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# self.add_parameter(self.tied_param)
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# buf_idx_new = self._highest_parent_._raveled_index_for(self.tied_param)
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# self._shrink_label_buf(self.buf_idx, buf_idx_new, bool_list)
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# self.buf_idx = buf_idx_new
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#
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# def _expand_label_buf(self, idx_old, idx_new):
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# """Expand label buffer accordingly"""
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# if idx_old is None:
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# self.label_buf = np.zeros(self._highest_parent_.param_array.shape, dtype=np.int)
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# else:
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# bool_old = np.zeros((self.label_buf.size,),dtype=np.bool)
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# bool_old[idx_old] = True
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# bool_new = np.zeros((self._highest_parent_.param_array.size,),dtype=np.bool)
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# bool_new[idx_new] = True
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# label_buf_new = np.zeros(self._highest_parent_.param_array.shape, dtype=np.int)
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# label_buf_new[np.logical_not(bool_new)] = self.label_buf[np.logical_not(bool_old)]
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# label_buf_new[idx_new[:len(idx_old)]] = self.label_buf[idx_old]
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# self.label_buf = label_buf_new
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#
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# def _shrink_label_buf(self, idx_old, idx_new, bool_list):
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# bool_old = np.zeros((self.label_buf.size,),dtype=np.bool)
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# bool_old[idx_old] = True
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# bool_new = np.zeros((self._highest_parent_.param_array.size,),dtype=np.bool)
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# bool_new[idx_new] = True
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# label_buf_new = np.empty(self._highest_parent_.param_array.shape, dtype=np.int)
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# label_buf_new[np.logical_not(bool_new)] = self.label_buf[np.logical_not(bool_old)]
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# label_buf_new[idx_new] = self.label_buf[idx_old[bool_list]]
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# self.label_buf = label_buf_new
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self.updates = True
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def _check_change(self):
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changed = False
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