mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-08 19:42:39 +02:00
Priors added
This commit is contained in:
Max Zwiessele
parent
922e72af94
commit
b1c98c2c3d
8 changed files with 206 additions and 356 deletions
|
|
@ -10,6 +10,7 @@ from array_core import ObservableArray, ParamList
|
|||
__constraints_name__ = "Constraint"
|
||||
__index_name__ = "Index"
|
||||
__tie_name__ = "Tied to"
|
||||
__priors_name__ = "Prior"
|
||||
__precision__ = numpy.get_printoptions()['precision'] # numpy printing precision used, sublassing numpy ndarray after all
|
||||
__print_threshold__ = 5
|
||||
######
|
||||
|
|
@ -77,9 +78,10 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
self._name = getattr(obj, 'name', None)
|
||||
self.gradient = getattr(obj, 'gradient', None)
|
||||
self.constraints = getattr(obj, 'constraints', None)
|
||||
self.priors = getattr(obj, 'priors', None)
|
||||
|
||||
def __array_wrap__(self, out_arr, context=None):
|
||||
return out_arr.view(numpy.ndarray)
|
||||
#def __array_wrap__(self, out_arr, context=None):
|
||||
# return out_arr.view(numpy.ndarray)
|
||||
#===========================================================================
|
||||
# Pickling operations
|
||||
#===========================================================================
|
||||
|
|
@ -118,154 +120,17 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
#===========================================================================
|
||||
# get/set parameters
|
||||
#===========================================================================
|
||||
|
||||
def _set_params(self, param, update=True):
|
||||
self.flat = param
|
||||
self._notify_tied_parameters()
|
||||
#self._notify_tied_parameters()
|
||||
self._notify_observers()
|
||||
|
||||
def _get_params(self):
|
||||
return self.flat
|
||||
# @property
|
||||
# def name(self):
|
||||
# """
|
||||
# Name of this parameter.
|
||||
# This can be a callable without parameters. The callable will be called
|
||||
# every time the name property is accessed.
|
||||
# """
|
||||
# if callable(self.name):
|
||||
# return self.name()
|
||||
# return self.name
|
||||
# @name.setter
|
||||
# def name(self, new_name):
|
||||
# from_name = self.name
|
||||
# self.name = new_name
|
||||
# self._direct_parent_._name_changed(self, from_name)
|
||||
|
||||
def _collect_gradient(self, target):
|
||||
target[:] = self.gradient.flat
|
||||
#===========================================================================
|
||||
# Tying operations -> bugged, TODO
|
||||
#===========================================================================
|
||||
def tie_to(self, param):
|
||||
"""
|
||||
:param param: the parameter object to tie this parameter to.
|
||||
Can be ParamConcatenation (retrieved by regexp search)
|
||||
|
||||
Tie this parameter to the given parameter.
|
||||
Broadcasting is not allowed, but you can tie a whole dimension to
|
||||
one parameter: self[:,0].tie_to(other), where other is a one-value
|
||||
parameter.
|
||||
|
||||
Note: For now only one parameter can have ties, so all of a parameter
|
||||
will be removed, when re-tieing!
|
||||
"""
|
||||
#Note: this method will tie to the parameter which is the last in
|
||||
# the chain of ties. Thus, if you tie to a tied parameter,
|
||||
# this tie will be created to the parameter the param is tied
|
||||
# to.
|
||||
|
||||
assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param, param.__class__)
|
||||
param = numpy.atleast_1d(param)
|
||||
if param.size != 1:
|
||||
raise NotImplementedError, "Broadcast tying is not implemented yet"
|
||||
try:
|
||||
if self._original_:
|
||||
self[:] = param
|
||||
else: # this happens when indexing created a copy of the array
|
||||
self._direct_parent_._get_original(self)[self._current_slice_] = param
|
||||
except ValueError:
|
||||
raise ValueError("Trying to tie {} with shape {} to {} with shape {}".format(self.name, self.shape, param.name, param.shape))
|
||||
if param is self:
|
||||
raise RuntimeError, 'Cyclic tieing is not allowed'
|
||||
# if len(param._tied_to_) > 0:
|
||||
# if (self._direct_parent_._get_original(self) is param._direct_parent_._get_original(param)
|
||||
# and len(set(self._raveled_index())&set(param._tied_to_[0]._raveled_index()))!=0):
|
||||
# raise RuntimeError, 'Cyclic tieing is not allowed'
|
||||
# self.tie_to(param._tied_to_[0])
|
||||
# return
|
||||
if not param in self._direct_parent_._get_original(self)._tied_to_:
|
||||
self._direct_parent_._get_original(self)._tied_to_ += [param]
|
||||
param._add_tie_listener(self)
|
||||
self._highest_parent_._set_fixed(self)
|
||||
cs = self._highest_parent_._constraints_for(param, param._raveled_index())
|
||||
for cs in self._highest_parent_._constraints_for(param, param._raveled_index()):
|
||||
[self.constrain(c, warning=False) for c in cs]
|
||||
# for t in self._tied_to_me_.keys():
|
||||
# if t is not self:
|
||||
# t.untie(self)
|
||||
# t.tie_to(param)
|
||||
|
||||
def untie(self, *ties):
|
||||
"""
|
||||
remove all ties.
|
||||
"""
|
||||
[t._direct_parent_._get_original(t)._remove_tie_listener(self) for t in self._tied_to_]
|
||||
new_ties = []
|
||||
for t in self._direct_parent_._get_original(self)._tied_to_:
|
||||
for tied in t._tied_to_me_.keys():
|
||||
if t._parent_index_ is tied._parent_index_:
|
||||
new_ties.append(tied)
|
||||
self._direct_parent_._get_original(self)._tied_to_ = new_ties
|
||||
self._direct_parent_._get_original(self)._highest_parent_._set_unfixed(self)
|
||||
# self._direct_parent_._remove_tie(self, *params)
|
||||
def _notify_tied_parameters(self):
|
||||
for tied, ind in self._tied_to_me_.iteritems():
|
||||
tied._on_tied_parameter_changed(self.base, list(ind))
|
||||
def _add_tie_listener(self, tied_to_me):
|
||||
for t in self._tied_to_me_.keys():
|
||||
if tied_to_me._parent_index_ is t._parent_index_:
|
||||
t_rav_i = t._raveled_index()
|
||||
tr_rav_i = tied_to_me._raveled_index()
|
||||
new_index = list(set(t_rav_i) | set(tr_rav_i))
|
||||
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
|
||||
self._tied_to_me_[tmp] = self._tied_to_me_[t] | set(self._raveled_index())
|
||||
del self._tied_to_me_[t]
|
||||
return
|
||||
self._tied_to_me_[tied_to_me] = set(self._raveled_index())
|
||||
def _remove_tie_listener(self, to_remove):
|
||||
for t in self._tied_to_me_.keys():
|
||||
if t._parent_index_ == to_remove._parent_index_:
|
||||
t_rav_i = t._raveled_index()
|
||||
tr_rav_i = to_remove._raveled_index()
|
||||
import ipdb;ipdb.set_trace()
|
||||
new_index = list(set(t_rav_i) - set(tr_rav_i))
|
||||
if new_index:
|
||||
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
|
||||
self._tied_to_me_[tmp] = self._tied_to_me_[t]
|
||||
del self._tied_to_me_[t]
|
||||
if len(self._tied_to_me_[tmp]) == 0:
|
||||
del self._tied_to_me_[tmp]
|
||||
else:
|
||||
del self._tied_to_me_[t]
|
||||
def _on_tied_parameter_changed(self, val, ind):
|
||||
if not self._updated_: # not fast_array_equal(self, val[ind]):
|
||||
val = numpy.atleast_1d(val)
|
||||
self._updated_ = True
|
||||
if self._original_:
|
||||
self.__setitem__(slice(None), val[ind], update=False)
|
||||
else: # this happens when indexing created a copy of the array
|
||||
self._direct_parent_._get_original(self).__setitem__(self._current_slice_, val[ind], update=False)
|
||||
self._notify_tied_parameters()
|
||||
self._updated_ = False
|
||||
#===========================================================================
|
||||
# Prior Operations
|
||||
#===========================================================================
|
||||
def set_prior(self, prior):
|
||||
"""
|
||||
:param prior: prior to be set for this parameter
|
||||
|
||||
Set prior for this parameter.
|
||||
"""
|
||||
if not hasattr(self._highest_parent_, '_set_prior'):
|
||||
raise AttributeError("Parent of type {} does not support priors".format(self._highest_parent_.__class__))
|
||||
self._highest_parent_._set_prior(self, prior)
|
||||
def unset_prior(self, *priors):
|
||||
"""
|
||||
:param priors: priors to remove from this parameter
|
||||
|
||||
Remove all priors from this parameter
|
||||
"""
|
||||
self._highest_parent_._remove_prior(self, *priors)
|
||||
#===========================================================================
|
||||
# Array operations -> done
|
||||
#===========================================================================
|
||||
|
|
@ -283,6 +148,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
self._notify_tied_parameters()
|
||||
if update:
|
||||
self._highest_parent_.parameters_changed()
|
||||
|
||||
#===========================================================================
|
||||
# Index Operations:
|
||||
#===========================================================================
|
||||
|
|
@ -328,8 +194,9 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
return numpy.r_[a]
|
||||
return numpy.r_[:b]
|
||||
return itertools.imap(f, itertools.izip_longest(slice_index[:self._realndim_], self._realshape_, fillvalue=slice(self.size)))
|
||||
|
||||
#===========================================================================
|
||||
# Convienience
|
||||
# Convenience
|
||||
#===========================================================================
|
||||
@property
|
||||
def is_fixed(self):
|
||||
|
|
@ -338,11 +205,10 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
view = super(Param, self).round(decimals, out).view(Param)
|
||||
view.__array_finalize__(self)
|
||||
return view
|
||||
def _has_fixes(self):
|
||||
return False
|
||||
round.__doc__ = numpy.round.__doc__
|
||||
def _get_original(self, param):
|
||||
return self
|
||||
|
||||
#===========================================================================
|
||||
# Printing -> done
|
||||
#===========================================================================
|
||||
|
|
@ -362,6 +228,9 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
def _constraints_str(self):
|
||||
return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self.constraints.iteritems()))]
|
||||
@property
|
||||
def _priors_str(self):
|
||||
return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self.priors.iteritems()))]
|
||||
@property
|
||||
def _ties_str(self):
|
||||
return [t._short() for t in self._tied_to_] or ['']
|
||||
@property
|
||||
|
|
@ -385,8 +254,6 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
if len(ind) != 1: ties[i, matches[0][ind_rav_matches]] = numpy.take(tt_rav_index, matches[1], mode='wrap')[ind_rav_matches]
|
||||
else: ties[i, matches[0]] = numpy.take(tt_rav_index, matches[1], mode='wrap')
|
||||
return map(lambda a: sum(a, []), zip(*[[[tie.flatten()] if tx != None else [] for tx in t] for t, tie in zip(ties, self._tied_to_)]))
|
||||
def _constraints_for(self, rav_index):
|
||||
return self.constraints.properties_for(rav_index)
|
||||
def _indices(self, slice_index=None):
|
||||
# get a int-array containing all indices in the first axis.
|
||||
if slice_index is None:
|
||||
|
|
@ -404,6 +271,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
return numpy.fromiter(itertools.product(*expanded_index),
|
||||
dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
|
||||
def _max_len_names(self, gen, header):
|
||||
gen = map(lambda x: " ".join(map(str, x)), gen)
|
||||
return reduce(lambda a, b:max(a, len(b)), gen, len(header))
|
||||
def _max_len_values(self):
|
||||
return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.name_hirarchical))
|
||||
|
|
@ -418,21 +286,26 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
|
|||
if ind.size > 4: indstr = ','.join(map(str, ind[:2])) + "..." + ','.join(map(str, ind[-2:]))
|
||||
else: indstr = ','.join(map(str, ind))
|
||||
return name + '[' + indstr + ']'
|
||||
def __str__(self, constr_matrix=None, indices=None, ties=None, lc=None, lx=None, li=None, lt=None):
|
||||
def __str__(self, constr_matrix=None, indices=None, prirs=None, ties=None, lc=None, lx=None, li=None, lp=None, lt=None, only_name=False):
|
||||
filter_ = self._current_slice_
|
||||
vals = self.flat
|
||||
if indices is None: indices = self._indices(filter_)
|
||||
ravi = self._raveled_index(filter_)
|
||||
if constr_matrix is None: constr_matrix = self._constraints_for(ravi)
|
||||
if constr_matrix is None: constr_matrix = self.constraints.properties_for(ravi)
|
||||
if prirs is None: prirs = self.priors.properties_for(ravi)
|
||||
if ties is None: ties = self._ties_for(ravi)
|
||||
ties = [' '.join(map(lambda x: x._short(), t)) for t in ties]
|
||||
if lc is None: lc = self._max_len_names(constr_matrix, __constraints_name__)
|
||||
if lx is None: lx = self._max_len_values()
|
||||
if li is None: li = self._max_len_index(indices)
|
||||
if lt is None: lt = self._max_len_names(ties, __tie_name__)
|
||||
header = " {i:^{2}s} | \033[1m{x:^{1}s}\033[0;0m | {c:^{0}s} | {t:^{3}s}".format(lc, lx, li, lt, x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__) # nice header for printing
|
||||
if lp is None: lp = self._max_len_names(prirs, __tie_name__)
|
||||
sep = '-'
|
||||
header_format = " {i:{5}^{2}s} | \033[1m{x:{5}^{1}s}\033[0;0m | {c:{5}^{0}s} | {p:{5}^{4}s} | {t:{5}^{3}s}"
|
||||
if only_name: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.name_hirarchical, c=sep*lc, i=sep*li, t=sep*lt, p=sep*lp) # nice header for printing
|
||||
else: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__, p=__priors_name__) # nice header for printing
|
||||
if not ties: ties = itertools.cycle([''])
|
||||
return "\n".join([header] + [" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {t:^{4}s} ".format(lc, lx, __precision__, li, lt, x=x, c=" ".join(map(str, c)), t=(t or ''), i=i) for i, x, c, t in itertools.izip(indices, vals, constr_matrix, ties)]) # return all the constraints with right indices
|
||||
return "\n".join([header] + [" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {p:^{5}s} | {t:^{4}s} ".format(lc, lx, __precision__, li, lt, lp, x=x, c=" ".join(map(str, c)), p=" ".join(map(str, p)), t=(t or ''), i=i) for i, x, c, t, p in itertools.izip(indices, vals, constr_matrix, ties, prirs)]) # return all the constraints with right indices
|
||||
# except: return super(Param, self).__str__()
|
||||
|
||||
class ParamConcatenation(object):
|
||||
|
|
@ -538,53 +411,20 @@ class ParamConcatenation(object):
|
|||
def __str__(self, *args, **kwargs):
|
||||
def f(p):
|
||||
ind = p._raveled_index()
|
||||
return p._constraints_for(ind), p._ties_for(ind)
|
||||
return p.constraints.properties_for(ind), p._ties_for(ind), p.priors.properties_for(ind)
|
||||
params = self.params
|
||||
constr_matrices, ties_matrices = zip(*map(f, params))
|
||||
constr_matrices, ties_matrices, prior_matrices = zip(*map(f, params))
|
||||
indices = [p._indices() for p in params]
|
||||
lc = max([p._max_len_names(cm, __constraints_name__) for p, cm in itertools.izip(params, constr_matrices)])
|
||||
lx = max([p._max_len_values() for p in params])
|
||||
li = max([p._max_len_index(i) for p, i in itertools.izip(params, indices)])
|
||||
lt = max([p._max_len_names(tm, __tie_name__) for p, tm in itertools.izip(params, ties_matrices)])
|
||||
strings = [p.__str__(cm, i, tm, lc, lx, li, lt) for p, cm, i, tm in itertools.izip(params,constr_matrices,indices,ties_matrices)]
|
||||
lp = max([p._max_len_names(pm, __constraints_name__) for p, pm in itertools.izip(params, prior_matrices)])
|
||||
strings = []
|
||||
start = True
|
||||
for p, cm, i, tm, pm in itertools.izip(params,constr_matrices,indices,ties_matrices,prior_matrices):
|
||||
strings.append(p.__str__(constr_matrix=cm, indices=i, prirs=pm, ties=tm, lc=lc, lx=lx, li=li, lp=lp, lt=lt, only_name=(1-start)))
|
||||
start = False
|
||||
return "\n".join(strings)
|
||||
return "\n{}\n".format(" -"+"- | -".join(['-'*l for l in [li,lx,lc,lt]])).join(strings)
|
||||
def __repr__(self):
|
||||
return "\n".join(map(repr,self.params))
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
||||
from GPy.core.parameterized import Parameterized
|
||||
from GPy.core.parameter import Param
|
||||
|
||||
#X = numpy.random.randn(2,3,1,5,2,4,3)
|
||||
X = numpy.random.randn(3,2)
|
||||
print "random done"
|
||||
p = Param("q_mean", X)
|
||||
p1 = Param("q_variance", numpy.random.rand(*p.shape))
|
||||
p2 = Param("Y", numpy.random.randn(p.shape[0], 1))
|
||||
|
||||
p3 = Param("variance", numpy.random.rand())
|
||||
p4 = Param("lengthscale", numpy.random.rand(2))
|
||||
|
||||
m = Parameterized()
|
||||
rbf = Parameterized(name='rbf')
|
||||
|
||||
rbf.add_parameter(p3,p4)
|
||||
m.add_parameter(p,p1,rbf)
|
||||
|
||||
print "setting params"
|
||||
#print m.q_v[3:5,[1,4,5]]
|
||||
print "constraining variance"
|
||||
#m[".*variance"].constrain_positive()
|
||||
#print "constraining rbf"
|
||||
#m.rbf_l.constrain_positive()
|
||||
#m.q_variance[1,[0,5,11,19,2]].tie_to(m.rbf_v)
|
||||
#m.rbf_v.tie_to(m.rbf_l[0])
|
||||
#m.rbf_l[0].tie_to(m.rbf_l[1])
|
||||
#m.q_v.tie_to(m.rbf_v)
|
||||
# m.rbf_l.tie_to(m.rbf_va)
|
||||
# pt = numpy.array(params._get_params_transformed())
|
||||
# ptr = numpy.random.randn(*pt.shape)
|
||||
# params.X.tie_to(params.rbf_v)
|
||||
return "\n".join(map(repr,self.params))
|
||||
Loading…
Add table
Add a link
Reference in a new issue