apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-27 14:25:16 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'params' of github.com:SheffieldML/GPy into params

Browse source
This commit is contained in:
James Hensman 2014-03-27 10:08:53 +00:00
commit 34ec8e08bf
6 changed files with 156 additions and 152 deletions
Download patch file Download diff file Expand all files Collapse all files

15
GPy/core/gp.py
View file

@ -222,15 +222,16 @@ class GP(Model):
""" """
return Model._getstate(self) + [self.X, return []#Model._getstate(self) + [self.X,
self.num_data, # self.num_data,
self.input_dim, # self.input_dim,
self.kern, # self.kern,
self.likelihood, # self.likelihood,
self.output_dim, # self.output_dim,
] # ]
def _setstate(self, state): def _setstate(self, state):
return
self.output_dim = state.pop() self.output_dim = state.pop()
self.likelihood = state.pop() self.likelihood = state.pop()
self.kern = state.pop() self.kern = state.pop()

7
GPy/core/parameterization/lists_and_dicts.py
View file

@ -28,4 +28,11 @@ class ArrayList(list):
return True return True
return False return False
def index(self, item):
index = 0
for el in self:
if el is item:
return index
index += 1
raise ValueError, "{} is not in list".format(item)
pass pass

13
GPy/core/parameterization/parameter_core.py
View file

@ -902,15 +902,19 @@ class Parameterizable(OptimizationHandlable):
#=========================================================================== #===========================================================================
def copy(self): def copy(self):
"""Returns a (deep) copy of the current model""" """Returns a (deep) copy of the current model"""
raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy" #raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy"
import copy import copy
from .index_operations import ParameterIndexOperations, ParameterIndexOperationsView from .index_operations import ParameterIndexOperations, ParameterIndexOperationsView
from .lists_and_dicts import ArrayList from .lists_and_dicts import ArrayList
param_mapping = [[] for _ in range(self.num_params)]
dc = dict() dc = dict()
for k, v in self.__dict__.iteritems(): for k, v in self.__dict__.iteritems():
if k not in ['_parent_', '_parameters_', '_parent_index_', '_observer_callables_'] + self.parameter_names(recursive=False): if k not in ['_parent_', '_parameters_', '_parent_index_', '_observer_callables_'] + self.parameter_names(recursive=False):
if isinstance(v, (Constrainable, ParameterIndexOperations, ParameterIndexOperationsView)): if v in self._parameters_:
param_mapping[self._parameters_.index(v)] += [k]
elif isinstance(v, (Constrainable, ParameterIndexOperations, ParameterIndexOperationsView)):
dc[k] = v.copy() dc[k] = v.copy()
else: else:
dc[k] = copy.deepcopy(v) dc[k] = copy.deepcopy(v)
@ -928,9 +932,10 @@ class Parameterizable(OptimizationHandlable):
s = self.__new__(self.__class__) s = self.__new__(self.__class__)
s.__dict__ = dc s.__dict__ = dc
for p in params: for p, mlist in zip(params, param_mapping):
s.add_parameter(p, _ignore_added_names=True) s.add_parameter(p, _ignore_added_names=True)
for m in mlist:
setattr(s, m, p)
return s return s
#=========================================================================== #===========================================================================

18
GPy/core/parameterization/parameterized.py
View file

@ -110,29 +110,15 @@ class Parameterized(Parameterizable, Pickleable):
Allways append the state of the inherited object Allways append the state of the inherited object
and call down to the inherited object in _setstate!! and call down to the inherited object in _setstate!!
""" """
return [ return []
self._fixes_,
self.priors,
self.constraints,
self._parameters_,
self._name,
self._added_names_,
]
def _setstate(self, state): def _setstate(self, state):
self._added_names_ = state.pop()
self._name = state.pop()
self._parameters_ = state.pop()
self.constraints = state.pop()
self.priors = state.pop()
self._fixes_ = state.pop()
self._connect_parameters()
self.parameters_changed() self.parameters_changed()
#=========================================================================== #===========================================================================
# Override copy to handle programmatically added observers # Override copy to handle programmatically added observers
#=========================================================================== #===========================================================================
def copy(self): def copy(self):
c = super(Pickleable, self).copy() c = super(Parameterized, self).copy()
c.add_observer(c, c._parameters_changed_notification, -100) c.add_observer(c, c._parameters_changed_notification, -100)
return c return c

250
GPy/kern/_src/kernel_slice_operations.py
View file

@ -5,130 +5,134 @@ Created on 11 Mar 2014
''' '''
from ...core.parameterization.parameterized import ParametersChangedMeta from ...core.parameterization.parameterized import ParametersChangedMeta
import numpy as np import numpy as np
from functools import wraps
def put_clean(dct, name, func):
if name in dct:
dct['_clean_{}'.format(name)] = dct[name]
dct[name] = func(dct[name])
class KernCallsViaSlicerMeta(ParametersChangedMeta): class KernCallsViaSlicerMeta(ParametersChangedMeta):
def __call__(self, *args, **kw): def __new__(cls, name, bases, dct):
instance = super(ParametersChangedMeta, self).__call__(*args, **kw) put_clean(dct, 'K', _slice_K)
instance.K = _slice_wrapper(instance, instance.K) put_clean(dct, 'Kdiag', _slice_Kdiag)
instance.Kdiag = _slice_wrapper(instance, instance.Kdiag, diag=True) put_clean(dct, 'update_gradients_full', _slice_update_gradients_full)
instance.update_gradients_full = _slice_wrapper(instance, instance.update_gradients_full, diag=False, derivative=True) put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag)
instance.update_gradients_diag = _slice_wrapper(instance, instance.update_gradients_diag, diag=True, derivative=True) put_clean(dct, 'gradients_X', _slice_gradients_X)
instance.gradients_X = _slice_wrapper(instance, instance.gradients_X, diag=False, derivative=True, ret_X=True) put_clean(dct, 'gradients_X_diag', _slice_gradients_X_diag)
instance.gradients_X_diag = _slice_wrapper(instance, instance.gradients_X_diag, diag=True, derivative=True, ret_X=True)
instance.psi0 = _slice_wrapper(instance, instance.psi0, diag=False, derivative=False)
instance.psi1 = _slice_wrapper(instance, instance.psi1, diag=False, derivative=False)
instance.psi2 = _slice_wrapper(instance, instance.psi2, diag=False, derivative=False)
instance.update_gradients_expectations = _slice_wrapper(instance, instance.update_gradients_expectations, derivative=True, psi_stat=True)
instance.gradients_Z_expectations = _slice_wrapper(instance, instance.gradients_Z_expectations, derivative=True, psi_stat_Z=True, ret_X=True)
instance.gradients_qX_expectations = _slice_wrapper(instance, instance.gradients_qX_expectations, derivative=True, psi_stat=True, ret_X=True)
instance.parameters_changed()
return instance
def _slice_wrapper(kern, operation, diag=False, derivative=False, psi_stat=False, psi_stat_Z=False, ret_X=False): put_clean(dct, 'psi0', _slice_psi)
""" put_clean(dct, 'psi1', _slice_psi)
This method wraps the functions in kernel to make sure all kernels allways see their respective input dimension. put_clean(dct, 'psi2', _slice_psi)
The different switches are: put_clean(dct, 'update_gradients_expectations', _slice_update_gradients_expectations)
diag: if X2 exists put_clean(dct, 'gradients_Z_expectations', _slice_gradients_Z_expectations)
derivative: if first arg is dL_dK put_clean(dct, 'gradients_qX_expectations', _slice_gradients_qX_expectations)
psi_stat: if first 3 args are dL_dpsi0..2 return super(KernCallsViaSlicerMeta, cls).__new__(cls, name, bases, dct)
psi_stat_Z: if first 2 args are dL_dpsi1..2
""" class _Slice_wrap(object):
if derivative: def __init__(self, k, X, X2=None):
if diag: self.k = k
def x_slice_wrapper(dL_dKdiag, X): self.shape = X.shape
ret_X_not_sliced = ret_X and kern._sliced_X == 0 if self.k._sliced_X == 0:
if ret_X_not_sliced: self.X = self.k._slice_X(X)
ret = np.zeros(X.shape) self.X2 = self.k._slice_X(X2) if X2 is not None else None
X = kern._slice_X(X) if not kern._sliced_X else X self.ret = True
# if the return value is of shape X.shape, we need to make sure to return the right shape
kern._sliced_X += 1
try:
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dKdiag, X)
else: ret = operation(dL_dKdiag, X)
except:
raise
finally:
kern._sliced_X -= 1
return ret
elif psi_stat:
def x_slice_wrapper(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced:
ret1, ret2 = np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
kern._sliced_X += 1
# if the return value is of shape X.shape, we need to make sure to return the right shape
try:
if ret_X_not_sliced:
ret = list(operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior))
r2 = ret[:2]
ret[0] = ret1
ret[1] = ret2
ret[0][:, kern.active_dims] = r2[0]
ret[1][:, kern.active_dims] = r2[1]
del r2
else: ret = operation(dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
kern._sliced_X -= 1
return ret
elif psi_stat_Z:
def x_slice_wrapper(dL_dpsi1, dL_dpsi2, Z, variational_posterior):
ret_X_not_sliced = ret_X and kern._sliced_X == 0
if ret_X_not_sliced: ret = np.zeros(Z.shape)
Z, variational_posterior = kern._slice_X(Z) if not kern._sliced_X else Z, kern._slice_X(variational_posterior) if not kern._sliced_X else variational_posterior
kern._sliced_X += 1
try:
if ret_X_not_sliced:
ret[:, kern.active_dims] = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
else: ret = operation(dL_dpsi1, dL_dpsi2, Z, variational_posterior)
except:
raise
finally:
kern._sliced_X -= 1
return ret
else: else:
def x_slice_wrapper(dL_dK, X, X2=None): self.X = X
ret_X_not_sliced = ret_X and kern._sliced_X == 0 self.X2 = X2
if ret_X_not_sliced: self.ret = False
ret = np.zeros(X.shape) def __enter__(self):
X, X2 = kern._slice_X(X) if not kern._sliced_X else X, kern._slice_X(X2) if X2 is not None and not kern._sliced_X else X2 self.k._sliced_X += 1
kern._sliced_X += 1 return self
try: def __exit__(self, *a):
if ret_X_not_sliced: ret[:, kern.active_dims] = operation(dL_dK, X, X2) self.k._sliced_X -= 1
else: ret = operation(dL_dK, X, X2) def handle_return_array(self, return_val):
except: if self.ret:
raise ret = np.zeros(self.shape)
finally: ret[:, self.k.active_dims] = return_val
kern._sliced_X -= 1 return ret
return ret return return_val
else:
if diag: def _slice_K(f):
def x_slice_wrapper(X, *args, **kw): @wraps(f)
X = kern._slice_X(X) if not kern._sliced_X else X def wrap(self, X, X2 = None, *a, **kw):
kern._sliced_X += 1 with _Slice_wrap(self, X, X2) as s:
try: ret = f(self, s.X, s.X2, *a, **kw)
ret = operation(X, *args, **kw) return ret
except: return wrap
raise
finally: def _slice_Kdiag(f):
kern._sliced_X -= 1 @wraps(f)
return ret def wrap(self, X, *a, **kw):
else: with _Slice_wrap(self, X, None) as s:
def x_slice_wrapper(X, X2=None, *args, **kw): ret = f(self, s.X, *a, **kw)
X, X2 = kern._slice_X(X) if not kern._sliced_X else X, kern._slice_X(X2) if X2 is not None and not kern._sliced_X else X2 return ret
kern._sliced_X += 1 return wrap
try:
ret = operation(X, X2, *args, **kw) def _slice_update_gradients_full(f):
except: raise @wraps(f)
finally: def wrap(self, dL_dK, X, X2=None):
kern._sliced_X -= 1 with _Slice_wrap(self, X, X2) as s:
return ret ret = f(self, dL_dK, s.X, s.X2)
x_slice_wrapper._operation = operation return ret
x_slice_wrapper.__name__ = ("slicer("+str(operation) return wrap
+(","+str(bool(diag)) if diag else'')
+(','+str(bool(derivative)) if derivative else '') def _slice_update_gradients_diag(f):
+')') @wraps(f)
x_slice_wrapper.__doc__ = "**sliced**\n" + (operation.__doc__ or "") def wrap(self, dL_dKdiag, X):
return x_slice_wrapper with _Slice_wrap(self, X, None) as s:
ret = f(self, dL_dKdiag, s.X)
return ret
return wrap
def _slice_gradients_X(f):
@wraps(f)
def wrap(self, dL_dK, X, X2=None):
with _Slice_wrap(self, X, X2) as s:
ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2))
return ret
return wrap
def _slice_gradients_X_diag(f):
@wraps(f)
def wrap(self, dL_dKdiag, X):
with _Slice_wrap(self, X, None) as s:
ret = s.handle_return_array(f(self, dL_dKdiag, s.X))
return ret
return wrap
def _slice_psi(f):
@wraps(f)
def wrap(self, Z, variational_posterior):
with _Slice_wrap(self, Z, variational_posterior) as s:
ret = f(self, s.X, s.X2)
return ret
return wrap
def _slice_update_gradients_expectations(f):
@wraps(f)
def wrap(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
with _Slice_wrap(self, Z, variational_posterior) as s:
ret = f(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, s.X, s.X2)
return ret
return wrap
def _slice_gradients_Z_expectations(f):
@wraps(f)
def wrap(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
with _Slice_wrap(self, Z, variational_posterior) as s:
ret = s.handle_return_array(f(self, dL_dpsi1, dL_dpsi2, s.X, s.X2))
return ret
return wrap
def _slice_gradients_qX_expectations(f):
@wraps(f)
def wrap(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
with _Slice_wrap(self, variational_posterior, Z) as s:
ret = list(f(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, s.X2, s.X))
r2 = ret[:2]
ret[0] = s.handle_return_array(r2[0])
ret[1] = s.handle_return_array(r2[1])
del r2
return ret
return wrap

5
GPy/util/caching.py
View file

@ -48,7 +48,7 @@ class Cacher(object):
if k in kw and kw[k] is not None: if k in kw and kw[k] is not None:
return self.operation(*args, **kw) return self.operation(*args, **kw)
# TODO: WARNING !!! Cache OFFSWITCH !!! WARNING # TODO: WARNING !!! Cache OFFSWITCH !!! WARNING
#return self.operation(*args) # return self.operation(*args, **kw)
#if the result is cached, return the cached computation #if the result is cached, return the cached computation
state = [all(a is b for a, b in itertools.izip_longest(args, cached_i)) for cached_i in self.cached_inputs] state = [all(a is b for a, b in itertools.izip_longest(args, cached_i)) for cached_i in self.cached_inputs]
@ -101,7 +101,7 @@ class Cacher(object):
def __name__(self): def __name__(self):
return self.operation.__name__ return self.operation.__name__
from functools import partial from functools import partial, update_wrapper
class Cacher_wrap(object): class Cacher_wrap(object):
def __init__(self, f, limit, ignore_args, force_kwargs): def __init__(self, f, limit, ignore_args, force_kwargs):
@ -109,6 +109,7 @@ class Cacher_wrap(object):
self.ignore_args = ignore_args self.ignore_args = ignore_args
self.force_kwargs = force_kwargs self.force_kwargs = force_kwargs
self.f = f self.f = f
update_wrapper(self, self.f)
def __get__(self, obj, objtype=None): def __get__(self, obj, objtype=None):
return partial(self, obj) return partial(self, obj)
def __call__(self, *args, **kwargs): def __call__(self, *args, **kwargs):