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logic edits for copy
This commit is contained in:
Max Zwiessele
parent
441a9f524d
commit
a98334e009
5 changed files with 144 additions and 138 deletions
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@ -247,7 +247,8 @@ class Param(OptimizationHandlable, ObservableArray, Gradcheckable):
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#===========================================================================
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@property
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def _description_str(self):
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if self.size <= 1: return ["%f" % self]
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if self.size <= 1:
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return [str(numpy.take(self, 0))]
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else: return [str(self.shape)]
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def parameter_names(self, add_self=False, adjust_for_printing=False):
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if adjust_for_printing:
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@ -2,7 +2,7 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from transformations import Transformation, Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
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import heapq
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import numpy as np
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__updated__ = '2013-12-16'
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@ -22,7 +22,7 @@ class Observable(object):
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self._observer_callables_ = []
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def add_observer(self, observer, callble, priority=0):
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heapq.heappush(self._observer_callables_, (priority, observer, callble))
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self._insert_sorted(priority, observer, callble)
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def remove_observer(self, observer, callble=None):
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to_remove = []
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@ -51,10 +51,21 @@ class Observable(object):
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if which is None:
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which = self
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if min_priority is None:
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[callble(which) for _, _, callble in heapq.nlargest(len(self._observer_callables_), self._observer_callables_)]
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[callble(which) for _, _, callble in self._observer_callables_]
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else:
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[callble(which) for p, _, callble in heapq.nlargest(len(self._observer_callables_), self._observer_callables_) if p > min_priority]
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for p, _, callble in self._observer_callables_:
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if p <= min_priority:
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break
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callble(which)
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def _insert_sorted(self, p, o, c):
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ins = 0
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for pr, _, _ in self._observer_callables_:
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if p > pr:
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break
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ins += 1
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self._observer_callables_.insert(ins, (p, o, c))
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class Pickleable(object):
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def _getstate(self):
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"""
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@ -202,20 +213,17 @@ class Constrainable(Nameable, Indexable):
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unfix = unconstrain_fixed
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def _set_fixed(self, index):
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import numpy as np
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if not self._has_fixes(): self._fixes_ = np.ones(self.size, dtype=bool)
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self._fixes_[index] = FIXED
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if np.all(self._fixes_): self._fixes_ = None # ==UNFIXED
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def _set_unfixed(self, index):
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import numpy as np
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if not self._has_fixes(): self._fixes_ = np.ones(self.size, dtype=bool)
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# rav_i = self._raveled_index_for(param)[index]
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self._fixes_[index] = UNFIXED
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if np.all(self._fixes_): self._fixes_ = None # ==UNFIXED
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def _connect_fixes(self):
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import numpy as np
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fixed_indices = self.constraints[__fixed__]
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if fixed_indices.size > 0:
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self._fixes_ = np.ones(self.size, dtype=bool) * UNFIXED
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@ -245,7 +253,6 @@ class Constrainable(Nameable, Indexable):
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def _log_prior_gradients(self):
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"""evaluate the gradients of the priors"""
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import numpy as np
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if self.priors.size > 0:
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x = self._get_params()
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ret = np.zeros(x.size)
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@ -342,7 +349,6 @@ class Constrainable(Nameable, Indexable):
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def _remove_from_index_operations(self, which, transforms):
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if len(transforms) == 0:
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transforms = which.properties()
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import numpy as np
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removed = np.empty((0,), dtype=int)
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for t in transforms:
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unconstrained = which.remove(t, self._raveled_index())
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@ -404,7 +410,6 @@ class OptimizationHandlable(Constrainable, Observable):
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Randomize the model.
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Make this draw from the prior if one exists, else draw from N(0,1)
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"""
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import numpy as np
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# first take care of all parameters (from N(0,1))
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# x = self._get_params_transformed()
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x = np.random.randn(self._size_transformed())
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@ -413,9 +418,6 @@ class OptimizationHandlable(Constrainable, Observable):
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[np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None]
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self._set_params(x)
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# self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
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import numpy as np
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class Parameterizable(OptimizationHandlable):
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def __init__(self, *args, **kwargs):
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@ -474,7 +476,113 @@ class Parameterizable(OptimizationHandlable):
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def _set_gradient(self, g):
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import itertools
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[p._set_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
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def add_parameter(self, param, index=None):
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"""
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:param parameters: the parameters to add
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:type parameters: list of or one :py:class:`GPy.core.param.Param`
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:param [index]: index of where to put parameters
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Add all parameters to this param class, you can insert parameters
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at any given index using the :func:`list.insert` syntax
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"""
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# if param.has_parent():
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# raise AttributeError, "parameter {} already in another model, create new object (or copy) for adding".format(param._short())
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if param in self._parameters_ and index is not None:
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self.remove_parameter(param)
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self.add_parameter(param, index)
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elif param not in self._parameters_:
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if param.has_parent():
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parent = param._direct_parent_
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while parent is not None:
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if parent is self:
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raise HierarchyError, "You cannot add a parameter twice into the hirarchy"
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parent = parent._direct_parent_
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param._direct_parent_.remove_parameter(param)
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# make sure the size is set
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if index is None:
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self.constraints.update(param.constraints, self.size)
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self.priors.update(param.priors, self.size)
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self._parameters_.append(param)
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else:
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start = sum(p.size for p in self._parameters_[:index])
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self.constraints.shift_right(start, param.size)
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self.priors.shift_right(start, param.size)
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self.constraints.update(param.constraints, start)
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self.priors.update(param.priors, start)
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self._parameters_.insert(index, param)
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param.add_observer(self, self._pass_through_notify_observers, -np.inf)
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self.size += param.size
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self._connect_parameters()
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self._notify_parent_change()
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self._connect_fixes()
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else:
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raise RuntimeError, """Parameter exists already added and no copy made"""
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def add_parameters(self, *parameters):
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"""
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convenience method for adding several
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parameters without gradient specification
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"""
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[self.add_parameter(p) for p in parameters]
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def remove_parameter(self, param):
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"""
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:param param: param object to remove from being a parameter of this parameterized object.
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"""
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if not param in self._parameters_:
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raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short())
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start = sum([p.size for p in self._parameters_[:param._parent_index_]])
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self._remove_parameter_name(param)
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self.size -= param.size
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del self._parameters_[param._parent_index_]
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param._disconnect_parent()
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param.remove_observer(self, self._pass_through_notify_observers)
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self.constraints.shift_left(start, param.size)
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self._connect_fixes()
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self._connect_parameters()
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self._notify_parent_change()
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parent = self._direct_parent_
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while parent is not None:
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parent._connect_fixes()
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parent._connect_parameters()
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parent._notify_parent_change()
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parent = parent._direct_parent_
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def _connect_parameters(self):
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# connect parameterlist to this parameterized object
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# This just sets up the right connection for the params objects
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# to be used as parameters
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# it also sets the constraints for each parameter to the constraints
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# of their respective parents
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if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
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# no parameters for this class
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return
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sizes = [0]
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self._param_slices_ = []
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for i, p in enumerate(self._parameters_):
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p._direct_parent_ = self
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p._parent_index_ = i
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sizes.append(p.size + sizes[-1])
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self._param_slices_.append(slice(sizes[-2], sizes[-1]))
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self._add_parameter_name(p)
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#===========================================================================
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# notification system
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#===========================================================================
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def _parameters_changed_notification(self, which):
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self.parameters_changed()
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def _pass_through_notify_observers(self, which):
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self._notify_observers(which)
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#===========================================================================
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# TODO: not working yet
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@ -487,16 +595,17 @@ class Parameterizable(OptimizationHandlable):
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dc = dict()
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for k, v in self.__dict__.iteritems():
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if k not in ['_direct_parent_', '_parameters_', '_parent_index_'] + self.parameter_names():
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if k not in ['_direct_parent_', '_parameters_', '_parent_index_', '_observer_callables_'] + self.parameter_names():
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if isinstance(v, (Constrainable, ParameterIndexOperations, ParameterIndexOperationsView)):
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dc[k] = v.copy()
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else:
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dc[k] = copy.deepcopy(v)
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if k == '_parameters_':
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params = [p.copy() for p in v]
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dc['_direct_parent_'] = None
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dc['_parent_index_'] = None
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dc['_observer_callables_'] = []
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dc['_parameters_'] = ArrayList()
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dc['constraints'].clear()
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dc['priors'].clear()
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@ -506,6 +615,7 @@ class Parameterizable(OptimizationHandlable):
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s.__dict__ = dc
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for p in params:
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import ipdb;ipdb.set_trace()
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s.add_parameter(p)
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return s
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@ -88,114 +88,6 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
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return G
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return node
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def add_parameter(self, param, index=None):
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"""
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:param parameters: the parameters to add
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:type parameters: list of or one :py:class:`GPy.core.param.Param`
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:param [index]: index of where to put parameters
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Add all parameters to this param class, you can insert parameters
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at any given index using the :func:`list.insert` syntax
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"""
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# if param.has_parent():
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# raise AttributeError, "parameter {} already in another model, create new object (or copy) for adding".format(param._short())
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if param in self._parameters_ and index is not None:
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self.remove_parameter(param)
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self.add_parameter(param, index)
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elif param not in self._parameters_:
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if param.has_parent():
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parent = param._direct_parent_
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while parent is not None:
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if parent is self:
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from GPy.core.parameterization.parameter_core import HierarchyError
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raise HierarchyError, "You cannot add a parameter twice into the hirarchy"
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parent = parent._direct_parent_
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param._direct_parent_.remove_parameter(param)
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# make sure the size is set
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if index is None:
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self.constraints.update(param.constraints, self.size)
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self.priors.update(param.priors, self.size)
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self._parameters_.append(param)
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else:
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start = sum(p.size for p in self._parameters_[:index])
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self.constraints.shift_right(start, param.size)
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self.priors.shift_right(start, param.size)
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self.constraints.update(param.constraints, start)
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self.priors.update(param.priors, start)
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self._parameters_.insert(index, param)
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param.add_observer(self, self._pass_through_notify_observers, -np.inf)
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self.size += param.size
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self._connect_parameters()
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self._notify_parent_change()
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self._connect_fixes()
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else:
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raise RuntimeError, """Parameter exists already added and no copy made"""
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def add_parameters(self, *parameters):
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"""
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convenience method for adding several
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parameters without gradient specification
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"""
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[self.add_parameter(p) for p in parameters]
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def remove_parameter(self, param):
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"""
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:param param: param object to remove from being a parameter of this parameterized object.
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"""
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if not param in self._parameters_:
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raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short())
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start = sum([p.size for p in self._parameters_[:param._parent_index_]])
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self._remove_parameter_name(param)
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self.size -= param.size
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del self._parameters_[param._parent_index_]
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param._disconnect_parent()
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param.remove_observer(self, self._pass_through_notify_observers)
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self.constraints.shift_left(start, param.size)
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self._connect_fixes()
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self._connect_parameters()
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self._notify_parent_change()
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parent = self._direct_parent_
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while parent is not None:
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parent._connect_fixes()
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parent._connect_parameters()
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parent._notify_parent_change()
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parent = parent._direct_parent_
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def _connect_parameters(self):
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# connect parameterlist to this parameterized object
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# This just sets up the right connection for the params objects
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# to be used as parameters
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# it also sets the constraints for each parameter to the constraints
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# of their respective parents
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if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
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# no parameters for this class
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return
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sizes = [0]
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self._param_slices_ = []
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for i, p in enumerate(self._parameters_):
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p._direct_parent_ = self
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p._parent_index_ = i
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sizes.append(p.size + sizes[-1])
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self._param_slices_.append(slice(sizes[-2], sizes[-1]))
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self._add_parameter_name(p)
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#===========================================================================
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# notification system
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#===========================================================================
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def _parameters_changed_notification(self, which):
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self.parameters_changed()
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def _pass_through_notify_observers(self, which):
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self._notify_observers(which)
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#===========================================================================
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# Pickling operations
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#===========================================================================
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@ -212,6 +104,11 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
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else:
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cPickle.dump(self, f, protocol)
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def copy(self):
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c = super(Parameterized, self).copy()
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c.add_observer(c, c._parameters_changed_notification, -100)
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return c
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def __getstate__(self):
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if self._has_get_set_state():
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return self._getstate()
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@ -332,9 +229,13 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
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return ParamConcatenation(paramlist)
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def __setitem__(self, name, value, paramlist=None):
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try: param = self.__getitem__(name, paramlist)
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except AttributeError as a: raise a
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param[:] = value
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if isinstance(name, slice):
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self[''][name] = value
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else:
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try: param = self.__getitem__(name, paramlist)
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except AttributeError as a: raise a
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param[:] = value
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def __setattr__(self, name, val):
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# override the default behaviour, if setting a param, so broadcasting can by used
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if hasattr(self, '_parameters_'):
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@ -379,7 +280,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
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cl = max([len(str(x)) if x else 0 for x in constrs + ["Constraint"]])
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tl = max([len(str(x)) if x else 0 for x in ts + ["Tied to"]])
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pl = max([len(str(x)) if x else 0 for x in prirs + ["Prior"]])
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format_spec = " \033[1m{{name:<{0}s}}\033[0;0m | {{desc:^{1}s}} | {{const:^{2}s}} | {{pri:^{3}s}} | {{t:^{4}s}}".format(nl, sl, cl, pl, tl)
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format_spec = " \033[1m{{name:<{0}s}}\033[0;0m | {{desc:>{1}s}} | {{const:^{2}s}} | {{pri:^{3}s}} | {{t:^{4}s}}".format(nl, sl, cl, pl, tl)
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to_print = []
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for n, d, c, t, p in itertools.izip(names, desc, constrs, ts, prirs):
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to_print.append(format_spec.format(name=n, desc=d, const=c, t=t, pri=p))
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