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James Hensman 2014-02-11 13:44:18 +00:00
commit 103aaebb29
60 changed files with 916 additions and 826 deletions
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74
GPy/core/model.py
View file

@ -260,17 +260,18 @@ class Model(Parameterized):
"""
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa', 'sensitivity']
# param_names = self._get_param_names()
for s in positive_strings:
paramlist = self.grep_param_names(".*"+s)
for param in paramlist:
for p in param.flattened_parameters:
rav_i = set(self._raveled_index_for(p))
for constraint in self.constraints.iter_properties():
rav_i -= set(self._constraint_indices(p, constraint))
rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
if ind.size != 0:
p[np.unravel_index(ind, p.shape)].constrain_positive()
# for s in positive_strings:
# paramlist = self.grep_param_names(".*"+s)
# for param in paramlist:
# for p in param.flattened_parameters:
# rav_i = set(self._raveled_index_for(p))
# for constraint in self.constraints.iter_properties():
# rav_i -= set(self._constraint_indices(p, constraint))
# rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
# ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
# if ind.size != 0:
# p[np.unravel_index(ind, p.shape)].constrain_positive(warning=warning)
# if paramlist:
# self.__getitem__(None, paramlist).constrain_positive(warning=warning)
# currently_constrained = self.all_constrained_indices()
@ -380,7 +381,7 @@ class Model(Parameterized):
sgd.run()
self.optimization_runs.append(sgd)
def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
"""
Check the gradient of the ,odel by comparing to a numerical
estimate. If the verbose flag is passed, invividual
@ -405,18 +406,18 @@ class Model(Parameterized):
dx = step * np.sign(np.random.uniform(-1, 1, x.size))
# evaulate around the point x
f1, g1 = self.objective_and_gradients(x + dx)
f2, g2 = self.objective_and_gradients(x - dx)
f1 = self.objective_function(x + dx)
f2 = self.objective_function(x - dx)
gradient = self.objective_function_gradients(x)
numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient==0, 1e-32, gradient)))
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() < tolerance)
else:
# check the gradient of each parameter individually, and do some pretty printing
try:
names = self._get_param_names_transformed()
names = self._get_param_names()
except NotImplementedError:
names = ['Variable %i' % i for i in range(len(x))]
# Prepare for pretty-printing
@ -430,39 +431,46 @@ class Model(Parameterized):
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
if target_param is None:
param_list = range(len(x))
else:
param_list = self.grep_param_names(target_param, transformed=True, search=True)
if not np.any(param_list):
param_list = self._raveled_index_for(target_param)
if self._has_fixes():
param_list = np.intersect1d(param_list, np.r_[:self.size][self._fixes_], True)
if param_list.size == 0:
print "No free parameters to check"
return
for i in param_list:
gradient = self.objective_function_gradients(x)
np.where(gradient==0, 1e-312, gradient)
ret = True
for i, ind in enumerate(param_list):
xx = x.copy()
xx[i] += step
f1, g1 = self.objective_and_gradients(xx)
xx[i] -= 2.*step
f2, g2 = self.objective_and_gradients(xx)
gradient = self.objective_function_gradients(x)[i]
xx[ind] += step
f1 = self.objective_function(xx)
xx[ind] -= 2.*step
f2 = self.objective_function(xx)
numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * np.where(gradient==0, 1e-312, gradient))
difference = np.abs((f1 - f2) / 2 / step - gradient)
ratio = (f1 - f2) / (2 * step * gradient[ind])
difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[i])
formatted_name = "\033[92m {0} \033[0m".format(names[ind])
ret &= True
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])
formatted_name = "\033[91m {0} \033[0m".format(names[ind])
ret &= False
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient
g = '%.6f' % gradient[ind]
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string
return ret
def input_sensitivity(self):
"""
return an array describing the sesitivity of the model to each input

57
GPy/core/parameterization/array_core.py
View file

@ -15,8 +15,36 @@ class ListArray(np.ndarray):
def __new__(cls, input_array):
obj = np.asanyarray(input_array).view(cls)
return obj
def __eq__(self, other):
return other is self
#def __eq__(self, other):
# return other is self
class ParamList(list):
def __contains__(self, other):
for el in self:
if el is other:
return True
return False
pass
class C(np.ndarray):
__array_priority__ = 1.
def __new__(cls, array):
obj = array.view(cls)
return obj
#def __array_finalize__(self, obj):
# #print 'finalize'
# return obj
def __array_prepare__(self, out_arr, context):
#print 'prepare'
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
def __array_wrap__(self, out_arr, context):
#print 'wrap', type(self), type(out_arr), context
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
class ObservableArray(ListArray, Observable):
"""
@ -25,7 +53,7 @@ class ObservableArray(ListArray, Observable):
will be called every time this array changes. The callable
takes exactly one argument, which is this array itself.
"""
__array_priority__ = 0 # Never give back Param
__array_priority__ = -1 # Never give back ObservableArray
def __new__(cls, input_array):
cls.__name__ = "ObservableArray\n "
obj = super(ObservableArray, cls).__new__(cls, input_array).view(cls)
@ -36,16 +64,19 @@ class ObservableArray(ListArray, Observable):
if obj is None: return
self._observers_ = getattr(obj, '_observers_', None)
def __setitem__(self, s, val, update=True):
if self.ndim:
if not np.all(np.equal(self[s], val)):
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
else:
if not np.all(np.equal(self, val)):
super(ObservableArray, self).__setitem__(Ellipsis, val)
if update:
self._notify_observers()
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
# if self.ndim:
# if not np.all(np.equal(self[s], val)):
# super(ObservableArray, self).__setitem__(s, val)
# if update:
# self._notify_observers()
# else:
# if not np.all(np.equal(self, val)):
# super(ObservableArray, self).__setitem__(Ellipsis, val)
# if update:
# self._notify_observers()
def __getslice__(self, start, stop):
return self.__getitem__(slice(start, stop))
def __setslice__(self, start, stop, val):

64
GPy/core/parameterization/index_operations.py
View file

@ -90,11 +90,6 @@ class ParameterIndexOperations(object):
return self._properties.values()
def properties_for(self, index):
# already_seen = dict()
# for ni in index:
# if ni not in already_seen:
# already_seen[ni] = [prop for prop in self.iter_properties() if ni in self._properties[prop]]
# yield already_seen[ni]
return vectorize(lambda i: [prop for prop in self.iter_properties() if i in self._properties[prop]], otypes=[list])(index)
def add(self, prop, indices):
@ -111,70 +106,11 @@ class ParameterIndexOperations(object):
self._properties[prop] = diff
else:
del self._properties[prop]
#[self._reverse[i].remove(prop) for i in removed if prop in self._reverse[i]]
return removed.astype(int)
# else:
# for a in self.properties():
# if numpy.all(a==prop) and a._parent_index_ == prop._parent_index_:
# ind = create_raveled_indices(indices, shape, offset)
# diff = remove_indices(self[a], ind)
# removed = numpy.intersect1d(self[a], ind, True)
# if not index_empty(diff):
# self._properties[a] = diff
# else:
# del self._properties[a]
# [self._reverse[i].remove(a) for i in removed if a in self._reverse[i]]
# return removed.astype(int)
return numpy.array([]).astype(int)
def __getitem__(self, prop):
return self._properties[prop]
# class TieIndexOperations(object):
# def __init__(self, params):
# self.params = params
# self.tied_from = ParameterIndexOperations()
# self.tied_to = ParameterIndexOperations()
# def add(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# self.tied_from.add(tied_to, rav_from)
# self.tied_to.add(tied_to, rav_to)
# return rav_from, rav_to
# def remove(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# rem_from = self.tied_from.remove(tied_to, rav_from)
# rem_to = self.tied_to.remove(tied_to, rav_to)
# left_from = self.tied_from._properties.pop(tied_to)
# left_to = self.tied_to._properties.pop(tied_to)
# self.tied_from[numpy.delete(tied_to, rem_from)] = left_from
# self.tied_to[numpy.delete(tied_to, rem_to)] = left_to
# return rav_from, rav_to
# def from_to_for(self, index):
# return self.tied_from.properties_for(index), self.tied_to.properties_for(index)
# def iter_from_to_indices(self):
# for k, f in self.tied_from.iteritems():
# yield f, self.tied_to[k]
# def iter_to_indices(self):
# return self.tied_to.iterindices()
# def iter_from_indices(self):
# return self.tied_from.iterindices()
# def iter_from_items(self):
# for f, i in self.tied_from.iteritems():
# yield f, i
# def iter_properties(self):
# return self.tied_from.iter_properties()
# def properties(self):
# return self.tied_from.properties()
# def from_to_indices(self, param):
# return self.tied_from[param], self.tied_to[param]
#
# # def create_raveled_indices(index, shape, offset=0):
# # if isinstance(index, (tuple, list)): i = [slice(None)] + list(index)
# # else: i = [slice(None), index]
# # ind = numpy.array(numpy.ravel_multi_index(numpy.indices(shape)[i], shape)).flat + numpy.int_(offset)
# # return ind
def combine_indices(arr1, arr2):
return numpy.union1d(arr1, arr2)

223
GPy/core/parameterization/param.py
View file

@ -3,8 +3,8 @@
import itertools
import numpy
from parameter_core import Constrainable, adjust_name_for_printing
from array_core import ObservableArray
from parameter_core import Constrainable, Gradcheckable, adjust_name_for_printing
from array_core import ObservableArray, ParamList
###### printing
__constraints_name__ = "Constraint"
@ -12,47 +12,41 @@ __index_name__ = "Index"
__tie_name__ = "Tied to"
__precision__ = numpy.get_printoptions()['precision'] # numpy printing precision used, sublassing numpy ndarray after all
__print_threshold__ = 5
######
######
class Float(numpy.float64, Constrainable):
def __init__(self, f, base):
super(Float,self).__init__(f)
self._base = base
class Param(ObservableArray, Constrainable):
class Param(ObservableArray, Constrainable, Gradcheckable):
"""
Parameter object for GPy models.
:param name: name of the parameter to be printed
:param input_array: array which this parameter handles
:param str name: name of the parameter to be printed
:param input_array: array which this parameter handles
:type input_array: numpy.ndarray
:param default_constraint: The default constraint for this parameter
:type default_constraint:
You can add/remove constraints by calling constrain on the parameter itself, e.g:
- self[:,1].constrain_positive()
- self[0].tie_to(other)
- self.untie()
- self[:3,:].unconstrain()
- self[1].fix()
Fixing parameters will fix them to the value they are right now. If you change
the fixed value, it will be fixed to the new value!
See :py:class:`GPy.core.parameterized.Parameterized` for more details on constraining etc.
This ndarray can be stored in lists and checked if it is in.
>>> import numpy as np
>>> x = np.random.normal(size=(10,3))
>>> x in [[1], x, [3]]
True
WARNING: This overrides the functionality of x==y!!!
Use numpy.equal(x,y) for element-wise equality testing.
"""
__array_priority__ = 0 # Never give back Param
__array_priority__ = -1 # Never give back Param
_fixes_ = None
def __new__(cls, name, input_array, *args, **kwargs):
def __new__(cls, name, input_array, default_constraint=None):
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
obj._current_slice_ = (slice(obj.shape[0]),)
obj._realshape_ = obj.shape
@ -66,16 +60,16 @@ class Param(ObservableArray, Constrainable):
obj.gradient = None
return obj
def __init__(self, name, input_array):
super(Param, self).__init__(name=name)
def __init__(self, name, input_array, default_constraint=None):
super(Param, self).__init__(name=name, default_constraint=default_constraint)
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
super(Param, self).__array_finalize__(obj)
self._direct_parent_ = getattr(obj, '_direct_parent_', None)
self._parent_index_ = getattr(obj, '_parent_index_', None)
self._highest_parent_ = getattr(obj, '_highest_parent_', None)
self._default_constraint_ = getattr(obj, '_default_constraint_', None)
self._current_slice_ = getattr(obj, '_current_slice_', None)
self._tied_to_me_ = getattr(obj, '_tied_to_me_', None)
self._tied_to_ = getattr(obj, '_tied_to_', None)
@ -86,7 +80,7 @@ class Param(ObservableArray, Constrainable):
self._original_ = getattr(obj, '_original_', None)
self._name = getattr(obj, 'name', None)
self.gradient = getattr(obj, 'gradient', None)
def __array_wrap__(self, out_arr, context=None):
return out_arr.view(numpy.ndarray)
#===========================================================================
@ -94,11 +88,11 @@ class Param(ObservableArray, Constrainable):
#===========================================================================
def __reduce_ex__(self):
func, args, state = super(Param, self).__reduce__()
return func, args, (state,
return func, args, (state,
(self.name,
self._direct_parent_,
self._parent_index_,
self._highest_parent_,
self._default_constraint_,
self._current_slice_,
self._realshape_,
self._realsize_,
@ -119,7 +113,7 @@ class Param(ObservableArray, Constrainable):
self._realsize_ = state.pop()
self._realshape_ = state.pop()
self._current_slice_ = state.pop()
self._highest_parent_ = state.pop()
self._default_constraint_ = state.pop()
self._parent_index_ = state.pop()
self._direct_parent_ = state.pop()
self.name = state.pop()
@ -132,13 +126,13 @@ class Param(ObservableArray, Constrainable):
self.flat = param
self._notify_tied_parameters()
self._notify_observers()
def _get_params(self):
return self.flat
# @property
# def name(self):
# """
# Name of this parameter.
# Name of this parameter.
# This can be a callable without parameters. The callable will be called
# every time the name property is accessed.
# """
@ -153,28 +147,9 @@ class Param(ObservableArray, Constrainable):
@property
def _parameters_(self):
return []
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
def _collect_gradient(self, target):
target[:] = self.gradient.flat
#===========================================================================
# Fixing Parameters:
#===========================================================================
def constrain_fixed(self, warning=True):
"""
Constrain this paramter to be fixed to the current value it carries.
:param warning: print a warning for overwriting constraints.
"""
self._highest_parent_._fix(self,warning)
fix = constrain_fixed
def unconstrain_fixed(self):
"""
This parameter will no longer be fixed.
"""
self._highest_parent_._unfix(self)
unfix = unconstrain_fixed
#===========================================================================
# Tying operations -> bugged, TODO
#===========================================================================
def tie_to(self, param):
@ -190,22 +165,22 @@ class Param(ObservableArray, Constrainable):
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
#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__)
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_:
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))
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:
@ -248,7 +223,7 @@ class Param(ObservableArray, Constrainable):
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_)]
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
@ -261,7 +236,7 @@ class Param(ObservableArray, Constrainable):
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_)]
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:
@ -269,12 +244,12 @@ class Param(ObservableArray, Constrainable):
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]):
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
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
@ -293,7 +268,7 @@ class Param(ObservableArray, Constrainable):
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)
@ -303,11 +278,11 @@ class Param(ObservableArray, Constrainable):
def __getitem__(self, s, *args, **kwargs):
if not isinstance(s, tuple):
s = (s,)
if not reduce(lambda a,b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
s += (Ellipsis,)
new_arr = super(Param, self).__getitem__(s, *args, **kwargs)
try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
except AttributeError: pass# returning 0d array or float, double etc
except AttributeError: pass # returning 0d array or float, double etc
return new_arr
def __setitem__(self, s, val, update=True):
super(Param, self).__setitem__(s, val, update=update)
@ -324,12 +299,12 @@ class Param(ObservableArray, Constrainable):
if numpy.all(si == Ellipsis):
continue
if isinstance(si, slice):
a = si.indices(self._realshape_[i])[0]
a = si.indices(self._realshape_[i])[0]
elif isinstance(si, (list,numpy.ndarray,tuple)):
a = si[0]
else: a = si
if a<0:
a = self._realshape_[i]+a
if a < 0:
a = self._realshape_[i] + a
internal_offset += a * extended_realshape[i]
return internal_offset
def _raveled_index(self, slice_index=None):
@ -337,8 +312,8 @@ class Param(ObservableArray, Constrainable):
# of this object
extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
ind = self._indices(slice_index)
if ind.ndim < 2: ind=ind[:,None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape*x), 1, ind), dtype=int)
if ind.ndim < 2: ind = ind[:, None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape * x), 1, ind), dtype=int)
def _expand_index(self, slice_index=None):
# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
# it basically translates slices to their respective index arrays and turns negative indices around
@ -351,11 +326,11 @@ class Param(ObservableArray, Constrainable):
if isinstance(a, slice):
start, stop, step = a.indices(b)
return numpy.r_[start:stop:step]
elif isinstance(a, (list,numpy.ndarray,tuple)):
elif isinstance(a, (list, numpy.ndarray, tuple)):
a = numpy.asarray(a, dtype=int)
a[a<0] = b + a[a<0]
elif a<0:
a = b+a
a[a < 0] = b + a[a < 0]
elif a < 0:
a = b + a
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)))
@ -379,7 +354,7 @@ class Param(ObservableArray, Constrainable):
#===========================================================================
@property
def _description_str(self):
if self.size <= 1: return ["%f"%self]
if self.size <= 1: return ["%f" % self]
else: return [str(self.shape)]
def _parameter_names(self, add_name):
return [self.name]
@ -391,31 +366,31 @@ class Param(ObservableArray, Constrainable):
return [self.shape]
@property
def _constraints_str(self):
return [' '.join(map(lambda c: str(c[0]) if c[1].size==self._realsize_ else "{"+str(c[0])+"}", self._highest_parent_._constraints_iter_items(self)))]
return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self._highest_parent_._constraints_iter_items(self)))]
@property
def _ties_str(self):
return [t._short() for t in self._tied_to_] or ['']
@property
def name_hirarchical(self):
if self.has_parent():
return self._direct_parent_.hirarchy_name()+adjust_name_for_printing(self.name)
return self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name)
return adjust_name_for_printing(self.name)
def __repr__(self, *args, **kwargs):
name = "\033[1m{x:s}\033[0;0m:\n".format(
x=self.name_hirarchical)
return name + super(Param, self).__repr__(*args,**kwargs)
return name + super(Param, self).__repr__(*args, **kwargs)
def _ties_for(self, rav_index):
#size = sum(p.size for p in self._tied_to_)
# size = sum(p.size for p in self._tied_to_)
ties = numpy.empty(shape=(len(self._tied_to_), numpy.size(rav_index)), dtype=Param)
for i, tied_to in enumerate(self._tied_to_):
for t, ind in tied_to._tied_to_me_.iteritems():
if t._parent_index_ == self._parent_index_:
matches = numpy.where(rav_index[:,None] == t._raveled_index()[None, :])
matches = numpy.where(rav_index[:, None] == t._raveled_index()[None, :])
tt_rav_index = tied_to._raveled_index()
ind_rav_matches = numpy.where(tt_rav_index == numpy.array(list(ind)))[0]
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_)]))
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._highest_parent_._constraints_for(self, rav_index)
def _indices(self, slice_index=None):
@ -424,13 +399,13 @@ class Param(ObservableArray, Constrainable):
slice_index = self._current_slice_
if isinstance(slice_index, (tuple, list)):
clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)]
if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice)
and len(set(map(len,clean_curr_slice))) <= 1):
if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice)
and len(set(map(len, clean_curr_slice))) <= 1):
return numpy.fromiter(itertools.izip(*clean_curr_slice),
dtype=[('',int)]*self._realndim_,count=len(clean_curr_slice[0])).view((int, self._realndim_))
dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
expanded_index = list(self._expand_index(slice_index))
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_))
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):
return reduce(lambda a, b:max(a, len(b)), gen, len(header))
def _max_len_values(self):
@ -443,9 +418,9 @@ class Param(ObservableArray, Constrainable):
if self._realsize_ < 2:
return name
ind = self._indices()
if ind.size > 4: indstr = ','.join(map(str,ind[:2])) + "..." + ','.join(map(str,ind[-2:]))
else: indstr = ','.join(map(str,ind))
return name+'['+indstr+']'
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):
filter_ = self._current_slice_
vals = self.flat
@ -458,10 +433,10 @@ class Param(ObservableArray, Constrainable):
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
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 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
#except: return super(Param, self).__str__()
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
# except: return super(Param, self).__str__()
class ParamConcatenation(object):
def __init__(self, params):
@ -472,12 +447,12 @@ class ParamConcatenation(object):
See :py:class:`GPy.core.parameter.Param` for more details on constraining.
"""
#self.params = params
self.params = []
# self.params = params
self.params = ParamList([])
for p in params:
for p in p.flattened_parameters:
if p not in self.params:
self.params.append(p)
self.params.append(p)
self._param_sizes = [p.size for p in self.params]
startstops = numpy.cumsum([0] + self._param_sizes)
self._param_slices_ = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
@ -485,15 +460,15 @@ class ParamConcatenation(object):
# Get/set items, enable broadcasting
#===========================================================================
def __getitem__(self, s):
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
params = [p._get_params()[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p._get_params()[ind[ps]])]
if len(params)==1: return params[0]
return ParamConcatenation(params)
def __setitem__(self, s, val, update=True):
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
vals = self._vals(); vals[s] = val; del val
[numpy.place(p, ind[ps], vals[ps]) and p._notify_tied_parameters()
for p, ps in zip(self.params, self._param_slices_)]
[numpy.place(p, ind[ps], vals[ps]) and p._notify_tied_parameters()
for p, ps in zip(self.params, self._param_slices_)]
if update:
self.params[0]._highest_parent_.parameters_changed()
def _vals(self):
@ -501,46 +476,68 @@ class ParamConcatenation(object):
#===========================================================================
# parameter operations:
#===========================================================================
def update_all_params(self):
self.params[0]._highest_parent_.parameters_changed()
def constrain(self, constraint, warning=True):
[param.constrain(constraint) for param in self.params]
[param.constrain(constraint, update=False) for param in self.params]
self.update_all_params()
constrain.__doc__ = Param.constrain.__doc__
def constrain_positive(self, warning=True):
[param.constrain_positive(warning) for param in self.params]
[param.constrain_positive(warning, update=False) for param in self.params]
self.update_all_params()
constrain_positive.__doc__ = Param.constrain_positive.__doc__
def constrain_fixed(self, warning=True):
[param.constrain_fixed(warning) for param in self.params]
constrain_fixed.__doc__ = Param.constrain_fixed.__doc__
fix = constrain_fixed
def constrain_negative(self, warning=True):
[param.constrain_negative(warning) for param in self.params]
[param.constrain_negative(warning, update=False) for param in self.params]
self.update_all_params()
constrain_negative.__doc__ = Param.constrain_negative.__doc__
def constrain_bounded(self, lower, upper, warning=True):
[param.constrain_bounded(lower, upper, warning) for param in self.params]
[param.constrain_bounded(lower, upper, warning, update=False) for param in self.params]
self.update_all_params()
constrain_bounded.__doc__ = Param.constrain_bounded.__doc__
def unconstrain(self, *constraints):
[param.unconstrain(*constraints) for param in self.params]
unconstrain.__doc__ = Param.unconstrain.__doc__
def unconstrain_negative(self):
[param.unconstrain_negative() for param in self.params]
unconstrain_negative.__doc__ = Param.unconstrain_negative.__doc__
def unconstrain_positive(self):
[param.unconstrain_positive() for param in self.params]
unconstrain_positive.__doc__ = Param.unconstrain_positive.__doc__
def unconstrain_fixed(self):
[param.unconstrain_fixed() for param in self.params]
unconstrain_fixed.__doc__ = Param.unconstrain_fixed.__doc__
unfix = unconstrain_fixed
def unconstrain_bounded(self, lower, upper):
[param.unconstrain_bounded(lower, upper) for param in self.params]
unconstrain_bounded.__doc__ = Param.unconstrain_bounded.__doc__
def untie(self, *ties):
[param.untie(*ties) for param in self.params]
__lt__ = lambda self, val: self._vals()<val
__le__ = lambda self, val: self._vals()<=val
__eq__ = lambda self, val: self._vals()==val
__ne__ = lambda self, val: self._vals()!=val
__gt__ = lambda self, val: self._vals()>val
__ge__ = lambda self, val: self._vals()>=val
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance)
#checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__
__lt__ = lambda self, val: self._vals() < val
__le__ = lambda self, val: self._vals() <= val
__eq__ = lambda self, val: self._vals() == val
__ne__ = lambda self, val: self._vals() != val
__gt__ = lambda self, val: self._vals() > val
__ge__ = lambda self, val: self._vals() >= val
def __str__(self, *args, **kwargs):
def f(p):
ind = p._raveled_index()
@ -557,9 +554,9 @@ class ParamConcatenation(object):
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
@ -569,17 +566,17 @@ if __name__ == '__main__':
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))
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"

99
GPy/core/parameterization/parameter_core.py
View file

@ -20,7 +20,7 @@ class Observable(object):
def _notify_observers(self):
[callble(self) for callble in self._observers_.itervalues()]
class Pickleable(object):
def _getstate(self):
"""
@ -36,9 +36,9 @@ class Pickleable(object):
Set the state (memento pattern) of this class to the given state.
Usually this is just the counterpart to _getstate, such that
an object is a copy of another when calling
copy = <classname>.__new__(*args,**kw)._setstate(<to_be_copied>._getstate())
See python doc "pickling" (`__getstate__` and `__setstate__`) for details.
"""
raise NotImplementedError, "To be able to use pickling you need to implement this method"
@ -48,19 +48,24 @@ class Pickleable(object):
#===============================================================================
class Parentable(object):
def __init__(self, direct_parent=None, highest_parent=None, parent_index=None):
super(Parentable,self).__init__()
def __init__(self, direct_parent=None, parent_index=None):
super(Parentable,self).__init__()
self._direct_parent_ = direct_parent
self._parent_index_ = parent_index
self._highest_parent_ = highest_parent
def has_parent(self):
return self._direct_parent_ is not None and self._highest_parent_ is not None
return self._direct_parent_ is not None
@property
def _highest_parent_(self):
if self._direct_parent_ is None:
return self
return self._direct_parent_._highest_parent_
class Nameable(Parentable):
_name = None
def __init__(self, name, direct_parent=None, highest_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, highest_parent, parent_index)
def __init__(self, name, direct_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, parent_index)
self._name = name or self.__class__.__name__
@property
@ -69,13 +74,45 @@ class Nameable(Parentable):
@name.setter
def name(self, name):
from_name = self.name
self._name = name
self._name = name
if self.has_parent():
self._direct_parent_._name_changed(self, from_name)
self._direct_parent_._name_changed(self, from_name)
class Gradcheckable(Parentable):
#===========================================================================
# Gradchecking
#===========================================================================
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
if self.has_parent():
return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance)
def _checkgrad(self, param):
raise NotImplementedError, "Need log likelihood to check gradient against"
class Constrainable(Nameable):
def __init__(self, name):
def __init__(self, name, default_constraint=None):
super(Constrainable,self).__init__(name)
self._default_constraint_ = default_constraint
#===========================================================================
# Fixing Parameters:
#===========================================================================
def constrain_fixed(self, value=None, warning=True):
"""
Constrain this paramter to be fixed to the current value it carries.
:param warning: print a warning for overwriting constraints.
"""
if value is not None:
self[:] = value
self._highest_parent_._fix(self,warning)
fix = constrain_fixed
def unconstrain_fixed(self):
"""
This parameter will no longer be fixed.
"""
self._highest_parent_._unfix(self)
unfix = unconstrain_fixed
#===========================================================================
# Constrain operations -> done
#===========================================================================
@ -84,7 +121,7 @@ class Constrainable(Nameable):
:param transform: the :py:class:`GPy.core.transformations.Transformation`
to constrain the this parameter to.
:param warning: print a warning if re-constraining parameters.
Constrain the parameter to the given
:py:class:`GPy.core.transformations.Transformation`.
"""
@ -97,37 +134,37 @@ class Constrainable(Nameable):
self._add_constrain(p, transform, warning)
if update:
self.parameters_changed()
def constrain_positive(self, warning=True):
def constrain_positive(self, warning=True, update=True):
"""
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to the default positive constraint.
"""
self.constrain(Logexp(), warning)
self.constrain(Logexp(), warning=warning, update=update)
def constrain_negative(self, warning=True):
def constrain_negative(self, warning=True, update=True):
"""
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to the default negative constraint.
"""
self.constrain(NegativeLogexp(), warning)
self.constrain(NegativeLogexp(), warning=warning, update=update)
def constrain_bounded(self, lower, upper, warning=True):
def constrain_bounded(self, lower, upper, warning=True, update=True):
"""
:param lower, upper: the limits to bound this parameter to
:param warning: print a warning if re-constraining parameters.
Constrain this parameter to lie within the given range.
"""
self.constrain(Logistic(lower, upper), warning)
self.constrain(Logistic(lower, upper), warning=warning, update=update)
def unconstrain(self, *transforms):
"""
:param transforms: The transformations to unconstrain from.
remove all :py:class:`GPy.core.transformations.Transformation`
remove all :py:class:`GPy.core.transformations.Transformation`
transformats of this parameter object.
"""
if self.has_parent():
@ -138,20 +175,20 @@ class Constrainable(Nameable):
def unconstrain_positive(self):
"""
Remove positive constraint of this parameter.
Remove positive constraint of this parameter.
"""
self.unconstrain(Logexp())
def unconstrain_negative(self):
"""
Remove negative constraint of this parameter.
Remove negative constraint of this parameter.
"""
self.unconstrain(NegativeLogexp())
def unconstrain_bounded(self, lower, upper):
"""
:param lower, upper: the limits to unbound this parameter from
Remove (lower, upper) bounded constrain from this parameter/
"""
self.unconstrain(Logistic(lower, upper))

51
GPy/core/parameterization/parameterized.py
View file

@ -8,9 +8,10 @@ import cPickle
import itertools
from re import compile, _pattern_type
from param import ParamConcatenation, Param
from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing
from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing, Gradcheckable
from index_operations import ParameterIndexOperations,\
index_empty
from array_core import ParamList
#===============================================================================
# Printing:
@ -23,7 +24,7 @@ FIXED = False
UNFIXED = True
#===============================================================================
class Parameterized(Constrainable, Pickleable, Observable):
class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
"""
Parameterized class
@ -69,7 +70,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
super(Parameterized, self).__init__(name=name)
self._in_init_ = True
self._constraints_ = None#ParameterIndexOperations()
self._parameters_ = []
self._parameters_ = ParamList()
self.size = sum(p.size for p in self._parameters_)
if not self._has_fixes():
self._fixes_ = None
@ -170,6 +171,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
for t, ind in param._constraints_.iteritems():
self.constraints.add(t, ind+self._offset_for(param))
param._constraints_.clear()
if param._default_constraint_ is not None:
self._add_constrain(param, param._default_constraint_, False)
if self._has_fixes() and np.all(self._fixes_): # ==UNFIXED
self._fixes_= None
@ -188,10 +191,10 @@ class Parameterized(Constrainable, Pickleable, Observable):
note: if it is a string object it will not (!) be regexp-matched
automatically.
"""
self._parameters_ = [p for p in self._parameters_
self._parameters_ = ParamList([p for p in self._parameters_
if not (p._parent_index_ in names_params_indices
or p.name in names_params_indices
or p in names_params_indices)]
or p in names_params_indices)])
self._connect_parameters()
def parameters_changed(self):
@ -216,7 +219,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
for i,p in enumerate(self._parameters_):
p._direct_parent_ = self
p._parent_index_ = i
p._connect_highest_parent(self)
not_unique = []
sizes.append(p.size+sizes[-1])
self._param_slices_.append(slice(sizes[-2], sizes[-1]))
@ -230,15 +232,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
elif not (pname in not_unique):
self.__dict__[pname] = p
self._added_names_.add(pname)
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
# no parameters for this class
return
for p in self._parameters_:
p._connect_highest_parent(highest_parent)
#===========================================================================
# Pickling operations
#===========================================================================
@ -256,6 +250,16 @@ class Parameterized(Constrainable, Pickleable, Observable):
cPickle.dump(self, f, protocol)
def copy(self):
"""Returns a (deep) copy of the current model """
#dc = dict()
#for k, v in self.__dict__.iteritems():
#if k not in ['_highest_parent_', '_direct_parent_']:
#dc[k] = copy.deepcopy(v)
#dc = copy.deepcopy(self.__dict__)
#dc['_highest_parent_'] = None
#dc['_direct_parent_'] = None
#s = self.__class__.new()
#s.__dict__ = dc
return copy.deepcopy(self)
def __getstate__(self):
if self._has_get_set_state():
@ -310,8 +314,11 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Optimization handles:
#===========================================================================
def _get_param_names_transformed(self):
def _get_param_names(self):
n = numpy.array([p.name_hirarchical+'['+str(i)+']' for p in self.flattened_parameters for i in p._indices()])
return n
def _get_param_names_transformed(self):
n = self._get_param_names()
if self._has_fixes():
return n[self._fixes_]
return n
@ -372,6 +379,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
that is an int array, containing the indexes for the flattened
param inside this parameterized logic.
"""
if isinstance(param, ParamConcatenation):
return numpy.hstack((self._raveled_index_for(p) for p in param.params))
return param._raveled_index() + self._offset_for(param)
def _raveled_index(self):
@ -381,7 +390,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
"""
return numpy.r_[:self.size]
#===========================================================================
# Handle ties:
# Fixing parameters:
#===========================================================================
def _set_fixed(self, param_or_index):
if not self._has_fixes(): self._fixes_ = numpy.ones(self.size, dtype=bool)
@ -406,9 +415,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
if self._has_fixes():
return self._fixes_[self._raveled_index_for(param)]
return numpy.ones(self.size, dtype=bool)[self._raveled_index_for(param)]
#===========================================================================
# Fixing parameters:
#===========================================================================
def _fix(self, param, warning=True):
f = self._add_constrain(param, __fixed__, warning)
self._set_fixed(f)
@ -419,6 +425,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Convenience for fixed, tied checking of param:
#===========================================================================
def fixed_indices(self):
return np.array([x.is_fixed for x in self._parameters_])
def _is_fixed(self, param):
# returns if the whole param is fixed
if not self._has_fixes():
@ -448,7 +456,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
# if removing constraints before adding new is not wanted, just delete the above line!
self.constraints.add(transform, rav_i)
param = self._get_original(param)
param._set_params(transform.initialize(param._get_params()))
if not (transform == __fixed__):
param._set_params(transform.initialize(param._get_params()), update=False)
if warning and any(reconstrained):
# if you want to print the whole params object, which was reconstrained use:
# m = str(param[self._backtranslate_index(param, reconstrained)])

12
GPy/core/parameterization/transformations.py
View file

@ -4,9 +4,9 @@
import numpy as np
from domains import _POSITIVE,_NEGATIVE, _BOUNDED
import sys
import sys
import weakref
_lim_val = -np.log(sys.float_info.epsilon)
_lim_val = -np.log(sys.float_info.epsilon)
class Transformation(object):
domain = None
@ -94,7 +94,7 @@ class LogexpClipped(Logexp):
def __str__(self):
return '+ve_c'
class Exponent(Transformation):
# TODO: can't allow this to go to zero, need to set a lower bound. Similar with negative Exponent below. See old MATLAB code.
domain = _POSITIVE
@ -162,9 +162,11 @@ class Logistic(Transformation):
def initialize(self, f):
if np.any(np.logical_or(f < self.lower, f > self.upper)):
print "Warning: changing parameters to satisfy constraints"
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
#return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
#FIXME: Max, zeros_like right?
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(np.zeros_like(f)), f)
def __str__(self):
return '{},{}'.format(self.lower, self.upper)

5
GPy/core/parameterization/variational.py
View file

@ -3,10 +3,8 @@ Created on 6 Nov 2013
@author: maxz
'''
import numpy as np
from parameterized import Parameterized
from param import Param
from ...util.misc import param_to_array
class Normal(Parameterized):
'''
@ -26,6 +24,7 @@ class Normal(Parameterized):
See GPy.plotting.matplot_dep.variational_plots
"""
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import variational_plots
from ...plotting.matplot_dep import variational_plots
return variational_plots.plot(self,*args)

4
GPy/core/svigp.py
View file

@ -154,13 +154,13 @@ class SVIGP(GP):
self.psi2 = None
def dL_dtheta(self):
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
dL_dtheta = self.kern._param_grad_helper(self.dL_dKmm, self.Z)
if self.has_uncertain_inputs:
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X_batch, self.X_variance_batch)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1, self.Z, self.X_batch, self.X_variance_batch)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X_batch, self.X_variance_batch)
else:
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.X_batch, self.Z)
dL_dtheta += self.kern._param_grad_helper(self.dL_dpsi1, self.X_batch, self.Z)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X_batch)
return dL_dtheta

34
GPy/examples/dimensionality_reduction.py
View file

@ -15,54 +15,54 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
num_inducing = 5
if plot:
output_dim = 1
input_dim = 2
input_dim = 3
else:
input_dim = 2
input_dim = 1
output_dim = 25
# generate GPLVM-like data
X = _np.random.rand(num_inputs, input_dim)
lengthscales = _np.random.rand(input_dim)
k = (GPy.kern.rbf(input_dim, .5, lengthscales, ARD=True)
+ GPy.kern.white(input_dim, 0.01))
#+ GPy.kern.white(input_dim, 0.01)
)
K = k.K(X)
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.rbf(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m = GPy.models.BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
#===========================================================================
# randomly obstruct data with percentage p
p = .8
Y_obstruct = Y.copy()
Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
#===========================================================================
m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
if plot:
import matplotlib.pyplot as pb
m.plot()
pb.title('PCA initialisation')
m2.plot()
pb.title('PCA initialisation')
#m2.plot()
#pb.title('PCA initialisation')
if optimize:
m.optimize('scg', messages=verbose)
m2.optimize('scg', messages=verbose)
#m2.optimize('scg', messages=verbose)
if plot:
m.plot()
pb.title('After optimisation')
m2.plot()
pb.title('After optimisation')
#m2.plot()
#pb.title('After optimisation')
return m, m2
return m
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
import GPy
@ -264,16 +264,16 @@ def bgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m['noise'] = Y.var() / 100.
m.Gaussian_noise = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.q.plot("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m

34
GPy/examples/non_gaussian.py
View file

@ -37,39 +37,43 @@ def student_t_approx(optimize=True, plot=True):
# Kernel object
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
kernel3 = kernel1.copy()
kernel4 = kernel1.copy()
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
#Gaussian GP model on clean data
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1.ensure_default_constraints()
m1.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m1.randomize()
#Gaussian GP model on corrupt data
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m2.ensure_default_constraints()
m2.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m2.randomize()
#Student t GP model on clean data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution)
m3 = GPy.models.GPRegression(X, Y.copy(), kernel3, likelihood=stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
m3.ensure_default_constraints()
m3.constrain_bounded('t_noise', 1e-6, 10.)
m3.constrain_fixed('white', 1e-5)
m3['t_noise'].constrain_bounded(1e-6, 10.)
m3['white'] = 1e-5
m3['white'].constrain_fixed()
m3.randomize()
#Student t GP model on corrupt data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
m4 = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
m4.ensure_default_constraints()
m4.constrain_bounded('t_noise', 1e-6, 10.)
m4.constrain_fixed('white', 1e-5)
m4['t_noise'].constrain_bounded(1e-6, 10.)
m4['white'] = 1e-5
m4['white'].constrain_fixed()
m4.randomize()
if optimize:

7
GPy/examples/regression.py
View file

@ -281,11 +281,12 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.Laplace(Y,noise_model)
kern = GPy.kern.rbf(1)
poisson_lik = GPy.likelihoods.Poisson()
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
# create simple GP Model
m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)
if optimize:
m.optimize(optimizer)

16
GPy/inference/latent_function_inference/fitc.py
View file

@ -97,10 +97,10 @@ class VarDTC(object):
K_pp_K = np.dot(Kmmipsi1[:,i:(i+1)],Kmmipsi1[:,i:(i+1)].T)
_dpsi1 = (-V_n**2 - alpha_n + 2.*gamma_k - gamma_n**2) * Kmmipsi1.T[i:(i+1),:]
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
self._dKmm_dX += self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
self._dpsi1_dtheta += self.kern._param_grad_helper(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern._param_grad_helper(_dKmm,self.Z)
self._dKmm_dX += self.kern.gradients_X(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.gradients_X(_dpsi1.T,self.Z,self.X[i:i+1,:])
# the partial derivative vector for the likelihood
if self.likelihood.num_params == 0:
@ -144,15 +144,15 @@ class VarDTC(object):
def dL_dtheta(self):
dL_dtheta = self.kern.dKdiag_dtheta(self._dL_dpsi0,self.X)
dL_dtheta += self.kern.dK_dtheta(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern.dK_dtheta(self._dL_dKmm,X=self.Z)
dL_dtheta += self.kern._param_grad_helper(self._dL_dpsi1,self.X,self.Z)
dL_dtheta += self.kern._param_grad_helper(self._dL_dKmm,X=self.Z)
dL_dtheta += self._dKmm_dtheta
dL_dtheta += self._dpsi1_dtheta
return dL_dtheta
def dL_dZ(self):
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.dK_dX(self._dL_dKmm,X=self.Z)
dL_dZ = self.kern.gradients_X(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.gradients_X(self._dL_dKmm,X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
return dL_dZ

99
GPy/inference/latent_function_inference/laplace.py
View file

@ -11,9 +11,8 @@
#http://gaussianprocess.org/gpml/code.
import numpy as np
from ...util.linalg import mdot, jitchol, pddet, dpotrs, dtrtrs, dpotri, symmetrify
from ...util.linalg import mdot, jitchol, dpotrs, dtrtrs, dpotri, symmetrify
from ...util.misc import param_to_array
from functools import partial as partial_func
from posterior import Posterior
import warnings
from scipy import optimize
@ -32,7 +31,7 @@ class LaplaceInference(object):
self._mode_finding_tolerance = 1e-7
self._mode_finding_max_iter = 40
self.bad_fhat = True
self._previous_Ki_fhat = None
def inference(self, kern, X, likelihood, Y, Y_metadata=None):
"""
@ -50,16 +49,17 @@ class LaplaceInference(object):
Ki_f_init = np.zeros_like(Y)
else:
Ki_f_init = self._previous_Ki_fhat
f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
#Compute hessian and other variables at mode
log_marginal, Ki_W_i, K_Wi_i, dL_dK, woodbury_vector = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, Y_metadata)
log_marginal, woodbury_vector, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)
#likelihood.gradient = self.likelihood_gradients()
kern.update_gradients_full(dL_dK, X)
likelihood.update_gradients(dL_dthetaL)
self._previous_Ki_fhat = Ki_fhat.copy()
return Posterior(woodbury_vector=woodbury_vector, woodbury_inv = K_Wi_i, K=K), log_marginal, {'dL_dK':dL_dK}
return Posterior(woodbury_vector=woodbury_vector, woodbury_inv=woodbury_inv, K=K), log_marginal, {'dL_dK':dL_dK}
def rasm_mode(self, K, Y, likelihood, Ki_f_init, Y_metadata=None):
"""
@ -84,7 +84,6 @@ class LaplaceInference(object):
Ki_f = Ki_f_init.copy()
f = np.dot(K, Ki_f)
#define the objective function (to be maximised)
def obj(Ki_f, f):
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, extra_data=Y_metadata)
@ -133,13 +132,15 @@ class LaplaceInference(object):
return f, Ki_f
def mode_computations(self, f_hat, Ki_f, K, Y, likelihood, Y_metadata):
def mode_computations(self, f_hat, Ki_f, K, Y, likelihood, kern, Y_metadata):
"""
At the mode, compute the hessian and effective covariance matrix.
returns: logZ : approximation to the marginal likelihood
Cov : the approximation to the covariance matrix
woodbury_vector : variable required for calculating the approximation to the covariance matrix
woodbury_inv : variable required for calculating the approximation to the covariance matrix
dL_dthetaL : array of derivatives (1 x num_kernel_params)
dL_dthetaL : array of derivatives (1 x num_likelihood_params)
"""
#At this point get the hessian matrix (or vector as W is diagonal)
W = -likelihood.d2logpdf_df2(f_hat, Y, extra_data=Y_metadata)
@ -153,45 +154,54 @@ class LaplaceInference(object):
#compute the log marginal
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, extra_data=Y_metadata) - np.sum(np.log(np.diag(L)))
#compute dL_dK
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
d3lik_d3fhat = likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata)
dL_dfhat = 0.5*(np.diag(Ki_W_i)[:, None]*d3lik_d3fhat) #why isn't this -0.5? s2 in R&W p126 line 9.
#Compute vival matrices for derivatives
dW_df = -likelihood.d3logpdf_df3(f_hat, Y, extra_data=Y_metadata) # -d3lik_d3fhat
woodbury_vector = likelihood.dlogpdf_df(f_hat, Y, extra_data=Y_metadata)
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(np.eye(Y.shape[0]) - np.dot(K, K_Wi_i))
dL_dfhat = -0.5*(np.diag(Ki_W_i)[:, None]*dW_df) #why isn't this -0.5? s2 in R&W p126 line 9.
#BiK, _ = dpotrs(L, K, lower=1)
#dL_dfhat = 0.5*np.diag(BiK)[:, None]*dW_df
I_KW_i = np.eye(Y.shape[0]) - np.dot(K, K_Wi_i)
dL_dK = explicit_part + implicit_part
return log_marginal, Ki_W_i, K_Wi_i, dL_dK, woodbury_vector
def likelihood_gradients(self):
"""
Gradients with respect to likelihood parameters (dL_dthetaL)
:rtype: array of derivatives (1 x num_likelihood_params)
"""
dL_dfhat, I_KW_i = self._shared_gradients_components()
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(self.f_hat, self.data, extra_data=self.extra_data)
num_params = len(self._get_param_names())
# make space for one derivative for each likelihood parameter
dL_dthetaL = np.zeros(num_params)
for thetaL_i in range(num_params):
####################
#compute dL_dK#
####################
if kern.size > 0 and not kern.is_fixed:
#Explicit
dL_dthetaL_exp = ( np.sum(dlik_dthetaL[:, thetaL_i])
#- 0.5*np.trace(mdot(self.Ki_W_i, (self.K, np.diagflat(dlik_hess_dthetaL[thetaL_i]))))
+ np.dot(0.5*np.diag(self.Ki_W_i)[:,None].T, dlik_hess_dthetaL[:, thetaL_i])
)
explicit_part = 0.5*(np.dot(Ki_f, Ki_f.T) - K_Wi_i)
#Implicit
dfhat_dthetaL = mdot(I_KW_i, self.K, dlik_grad_dthetaL[:, thetaL_i])
dL_dthetaL_imp = np.dot(dL_dfhat, dfhat_dthetaL)
dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
implicit_part = np.dot(woodbury_vector, dL_dfhat.T).dot(I_KW_i)
return dL_dthetaL
dL_dK = explicit_part + implicit_part
else:
dL_dK = np.zeros(likelihood.size)
####################
#compute dL_dthetaL#
####################
if likelihood.size > 0 and not likelihood.is_fixed:
dlik_dthetaL, dlik_grad_dthetaL, dlik_hess_dthetaL = likelihood._laplace_gradients(f_hat, Y, extra_data=Y_metadata)
num_params = likelihood.size
# make space for one derivative for each likelihood parameter
dL_dthetaL = np.zeros(num_params)
for thetaL_i in range(num_params):
#Explicit
dL_dthetaL_exp = ( np.sum(dlik_dthetaL[thetaL_i])
# The + comes from the fact that dlik_hess_dthetaL == -dW_dthetaL
+ 0.5*np.sum(np.diag(Ki_W_i).flatten()*dlik_hess_dthetaL[:, thetaL_i].flatten())
)
#Implicit
dfhat_dthetaL = mdot(I_KW_i, K, dlik_grad_dthetaL[:, thetaL_i])
#dfhat_dthetaL = mdot(Ki_W_i, dlik_grad_dthetaL[:, thetaL_i])
dL_dthetaL_imp = np.dot(dL_dfhat.T, dfhat_dthetaL)
dL_dthetaL[thetaL_i] = dL_dthetaL_exp + dL_dthetaL_imp
else:
dL_dthetaL = np.zeros(likelihood.size)
return log_marginal, woodbury_vector, K_Wi_i, dL_dK, dL_dthetaL
def _compute_B_statistics(self, K, W, log_concave):
"""
@ -219,12 +229,11 @@ class LaplaceInference(object):
LiW12, _ = dtrtrs(L, np.diagflat(W_12), lower=1, trans=0)
K_Wi_i = np.dot(LiW12.T, LiW12) # R = W12BiW12, in R&W p 126, eq 5.25
#here's a better way to compute the required matrix.
#here's a better way to compute the required matrix.
# you could do the model finding witha backsub, instead of a dot...
#L2 = L/W_12
#K_Wi_i_2 , _= dpotri(L2)
#symmetrify(K_Wi_i_2)
return K_Wi_i, L, LiW12

2
GPy/inference/latent_function_inference/varDTC.py
View file

@ -80,7 +80,7 @@ class VarDTC(object):
# no backsubstitution because of bound explosion on tr(A) if not...
LmInv, _ = dtrtri(Lm, lower=1)
A = LmInv.T.dot(psi2_beta.dot(LmInv))
print A.sum()
#print A.sum()
else:
if het_noise:
tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))

68
GPy/kern/kern.py
View file

@ -8,6 +8,7 @@ from parts.prod import Prod as prod
from parts.linear import Linear
from parts.kernpart import Kernpart
from ..core.parameterization import Parameterized
from GPy.core.parameterization.param import Param
class kern(Parameterized):
def __init__(self, input_dim, parts=[], input_slices=None):
@ -84,7 +85,7 @@ class kern(Parameterized):
# represents the gradient in the transformed space (i.e. that given by
# get_params_transformed())
#
# :param g: the gradient vector for the current model, usually created by dK_dtheta
# :param g: the gradient vector for the current model, usually created by _param_grad_helper
# """
# x = self._get_params()
# [np.place(g, index, g[index] * constraint.gradfactor(x[index]))
@ -160,6 +161,9 @@ class kern(Parameterized):
return newkern
def __call__(self, X, X2=None):
return self.K(X, X2)
def __mul__(self, other):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)
@ -291,7 +295,7 @@ class kern(Parameterized):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
[p.update_gradients_variational(dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z) for p in self._parameters_]
def dK_dtheta(self, dL_dK, X, X2=None):
def _param_grad_helper(self, dL_dK, X, X2=None):
"""
Compute the gradient of the covariance function with respect to the parameters.
@ -307,9 +311,9 @@ class kern(Parameterized):
assert X.shape[1] == self.input_dim
target = np.zeros(self.size)
if X2 is None:
[p.dK_dtheta(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
[p._param_grad_helper(dL_dK, X[:, i_s], None, target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
else:
[p.dK_dtheta(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
[p._param_grad_helper(dL_dK, X[:, i_s], X2[:, i_s], target[ps]) for p, i_s, ps, in zip(self._parameters_, self.input_slices, self._param_slices_)]
return self._transform_gradients(target)
@ -481,6 +485,7 @@ from GPy.core.model import Model
class Kern_check_model(Model):
"""This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Model.__init__(self, 'kernel_test_model')
num_samples = 20
num_samples2 = 10
if kernel==None:
@ -492,14 +497,12 @@ class Kern_check_model(Model):
dL_dK = np.ones((X.shape[0], X.shape[0]))
else:
dL_dK = np.ones((X.shape[0], X2.shape[0]))
self.kernel=kernel
self.add_parameter(kernel)
self.X = X
self.X2 = X2
self.dL_dK = dL_dK
#self.constrained_indices=[]
#self.constraints=[]
Model.__init__(self, 'kernel_test_model')
def is_positive_definite(self):
v = np.linalg.eig(self.kernel.K(self.X))[0]
@ -508,15 +511,6 @@ class Kern_check_model(Model):
else:
return True
def _get_params(self):
return self.kernel._get_params()
def _get_param_names(self):
return self.kernel._get_param_names()
def _set_params(self, x):
self.kernel._set_params(x)
def log_likelihood(self):
return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()
@ -529,7 +523,7 @@ class Kern_check_dK_dtheta(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
def _log_likelihood_gradients(self):
return self.kernel.dK_dtheta(self.dL_dK, self.X, self.X2)
return self.kernel._param_grad_helper(self.dL_dK, self.X, self.X2)
class Kern_check_dKdiag_dtheta(Kern_check_model):
"""This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters."""
@ -537,6 +531,8 @@ class Kern_check_dKdiag_dtheta(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0]))
def parameters_changed(self):
self.kernel.update_gradients_full(self.dL_dK, self.X)
def log_likelihood(self):
return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
@ -548,41 +544,25 @@ class Kern_check_dK_dX(Kern_check_model):
"""This class allows gradient checks for the gradient of a kernel with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.remove_parameter(kernel)
self.X = Param('X', self.X)
self.add_parameter(self.X)
def _log_likelihood_gradients(self):
return self.kernel.dK_dX(self.dL_dK, self.X, self.X2).flatten()
return self.kernel.gradients_X(self.dL_dK, self.X, self.X2).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)
class Kern_check_dKdiag_dX(Kern_check_model):
class Kern_check_dKdiag_dX(Kern_check_dK_dX):
"""This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
Kern_check_dK_dX.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0]))
def log_likelihood(self):
return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
def _log_likelihood_gradients(self):
return self.kernel.dKdiag_dX(self.dL_dK, self.X).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)
def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
"""
This function runs on kernels to check the correctness of their
@ -657,7 +637,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -673,7 +653,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -689,7 +669,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:

4
GPy/kern/parts/Brownian.py
View file

@ -43,7 +43,7 @@ class Brownian(Kernpart):
def Kdiag(self,X,target):
target += self.variance*X.flatten()
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
if X2 is None:
X2 = X
target += np.sum(np.fmin(X,X2.T)*dL_dK)
@ -51,7 +51,7 @@ class Brownian(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target += np.dot(X.flatten(), dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError, "TODO"
#target += self.variance
#target -= self.variance*theta(X-X2.T)

8
GPy/kern/parts/Matern32.py
View file

@ -74,7 +74,7 @@ class Matern32(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
@ -96,7 +96,7 @@ class Matern32(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X[None, :, :]) / self.lengthscale), -1))[:, :, None]
@ -105,8 +105,8 @@ class Matern32(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

8
GPy/kern/parts/Matern52.py
View file

@ -74,7 +74,7 @@ class Matern52(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target,self.variance,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))
@ -96,7 +96,7 @@ class Matern52(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X[None,:,:])/self.lengthscale),-1))[:,:,None]
@ -104,8 +104,8 @@ class Matern52(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
gradients_X = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(gradients_X*dL_dK.T[:,:,None],0)
def dKdiag_dX(self,dL_dKdiag,X,target):
pass

2
GPy/kern/parts/ODE_1.py
View file

@ -90,7 +90,7 @@ class ODE_1(Kernpart):
np.add(self.varianceU*self.varianceY*(k1+k2+k3), target, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.abs(X - X2.T)

4
GPy/kern/parts/eq_ode1.py
View file

@ -124,7 +124,7 @@ class Eq_ode1(Kernpart):
#target += np.diag(self.B)[np.asarray(index,dtype=np.int).flatten()]
pass
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
# First extract times and indices.
self._extract_t_indices(X, X2, dL_dK=dL_dK)
@ -193,7 +193,7 @@ class Eq_ode1(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,index,target):
pass
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
pass
def _extract_t_indices(self, X, X2=None, dL_dK=None):

8
GPy/kern/parts/exponential.py
View file

@ -75,7 +75,7 @@ class Exponential(Kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))
@ -95,13 +95,13 @@ class Exponential(Kernpart):
# NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

2
GPy/kern/parts/finite_dimensional.py
View file

@ -50,7 +50,7 @@ class FiniteDimensional(Kernpart):
def Kdiag(self,X,target):
product = np.diag(self.K(X, X))
np.add(target,product,target)
def dK_dtheta(self,X,X2,target):
def _param_grad_helper(self,X,X2,target):
"""Return shape is NxMx(Ntheta)"""
if X2 is None: X2 = X
FX = np.column_stack([f(X) for f in self.F])

4
GPy/kern/parts/fixed.py
View file

@ -31,10 +31,10 @@ class Fixed(Kernpart):
def K(self, X, X2, target):
target += self.variance * self.fixed_K
def dK_dtheta(self, partial, X, X2, target):
def _param_grad_helper(self, partial, X, X2, target):
target += (partial * self.fixed_K).sum()
def dK_dX(self, partial, X, X2, target):
def gradients_X(self, partial, X, X2, target):
pass
def dKdiag_dX(self, partial, X, target):

8
GPy/kern/parts/gibbs.py
View file

@ -85,7 +85,7 @@ class Gibbs(Kernpart):
"""Compute the diagonal of the covariance matrix for X."""
np.add(target, self.variance, target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
self._dK_computations(dL_dK)
@ -97,7 +97,7 @@ class Gibbs(Kernpart):
target+= np.hstack([(dL_dK*self._K_dvar).sum(), gmapping])
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
# First account for gradients arising from presence of X in exponent.
self._K_computations(X, X2)
@ -105,8 +105,8 @@ class Gibbs(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_co
dK_dX = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(dK_dX*dL_dK.T[:, :, None], 0)
gradients_X = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(gradients_X*dL_dK.T[:, :, None], 0)
# Now account for gradients arising from presence of X in lengthscale.
self._dK_computations(dL_dK)
if X2 is None:

4
GPy/kern/parts/hetero.py
View file

@ -80,7 +80,7 @@ class Hetero(Kernpart):
"""Helper function for computing the diagonal elements of the covariance."""
return self.mapping.f(X).flatten()**2
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
if (X2 is None) or (X2 is X):
dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]
@ -90,7 +90,7 @@ class Hetero(Kernpart):
"""Gradient of diagonal of covariance with respect to parameters."""
target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
if X2==None or X2 is X:
dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]

8
GPy/kern/parts/hierarchical.py
View file

@ -50,19 +50,19 @@ class Hierarchical(Kernpart):
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
X, X2, slices, slices2 = self._sort_slices(X,X2)
[[[[k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
[[[[k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#if X2 is None:
#X2,slices2 = X,slices
#else:
#X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
#[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#
def dKdiag_dX(self,dL_dKdiag,X,target):
raise NotImplementedError

8
GPy/kern/parts/independent_outputs.py
View file

@ -70,22 +70,22 @@ class IndependentOutputs(Kernpart):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[self.k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
[[[self.k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dKdiag_dX(self,dL_dKdiag,X,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])

20
GPy/kern/parts/kernpart.py
View file

@ -20,7 +20,6 @@ class Kernpart(Parameterized):
# the number of optimisable parameters
# the name of the covariance function.
# link to parameterized objects
self._parameters_ = []
#self._X = None
def connect_input(self, X):
@ -76,14 +75,14 @@ class Kernpart(Parameterized):
raise NotImplementedError
def Kdiag(self,X,target):
raise NotImplementedError
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
raise NotImplementedError
def dKdiag_dtheta(self,dL_dKdiag,X,target):
# In the base case compute this by calling dK_dtheta. Need to
# In the base case compute this by calling _param_grad_helper. Need to
# override for stationary covariances (for example) to save
# time.
for i in range(X.shape[0]):
self.dK_dtheta(dL_dKdiag[i], X[i, :][None, :], X2=None, target=target)
self._param_grad_helper(dL_dKdiag[i], X[i, :][None, :], X2=None, target=target)
def psi0(self,Z,mu,S,target):
raise NotImplementedError
def dpsi0_dtheta(self,dL_dpsi0,Z,mu,S,target):
@ -106,12 +105,19 @@ class Kernpart(Parameterized):
raise NotImplementedError
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
raise NotImplementedError
def dKdiag_dX(self, dL_dK, X, target):
raise NotImplementedError
def update_gradients_full(self, dL_dK, X):
"""Set the gradients of all parameters when doing full (N) inference."""
raise NotImplementedError
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
"""Set the gradients of all parameters when doing sparse (M) inference."""
raise NotImplementedError
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
"""Set the gradients of all parameters when doing variational (M) inference with uncertain inputs."""
raise NotImplementedError
class Kernpart_stationary(Kernpart):
def __init__(self, input_dim, lengthscale=None, ARD=False):

91
GPy/kern/parts/linear.py
View file

@ -8,6 +8,7 @@ from kernpart import Kernpart
from ...util.linalg import tdot
from ...util.misc import fast_array_equal, param_to_array
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
class Linear(Kernpart):
"""
@ -43,8 +44,9 @@ class Linear(Kernpart):
else:
variances = np.ones(self.input_dim)
self.variances = Param('variances', variances)
self.add_parameters(self.variances)
self.variances = Param('variances', variances, Logexp())
self.variances.gradient = np.zeros(self.variances.shape)
self.add_parameter(self.variances)
self.variances.add_observer(self, self.update_variance)
# initialize cache
@ -57,21 +59,35 @@ class Linear(Kernpart):
def on_input_change(self, X):
self._K_computations(X, None)
# def _get_params(self):
# return self.variances
#
# def _set_params(self, x):
# assert x.size == (self.num_params)
# self.variances = x
#def parameters_changed(self):
# self.variances2 = np.square(self.variances)
#
# def _get_param_names(self):
# if self.num_params == 1:
# return ['variance']
# else:
# return ['variance_%i' % i for i in range(self.variances.size)]
def update_gradients_full(self, dL_dK, X):
#self.variances.gradient[:] = 0
self._param_grad_helper(dL_dK, X, self.variances.gradient)
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
tmp = dL_dKdiag[:, None] * X ** 2
if self.ARD:
self.variances.gradient = tmp.sum(0)
else:
self.variances.gradient = tmp.sum()
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
self._param_grad_helper(dL_dKnm, X, Z, self.variances.gradient)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
self._psi_computations(Z, mu, S)
# psi0:
tmp = dL_dpsi0[:, None] * self.mu2_S
if self.ARD: self.variances.gradient[:] = tmp.sum(0)
else: self.variances.gradient[:] = tmp.sum()
#psi1
self._param_grad_helper(dL_dpsi1, mu, Z, self.variances.gradient)
#psi2
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
else: self.variances.gradient += tmp.sum()
#from Kmm
self._K_computations(Z, None)
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
def K(self, X, X2, target):
if self.ARD:
XX = X * np.sqrt(self.variances)
@ -88,7 +104,7 @@ class Linear(Kernpart):
def Kdiag(self, X, target):
np.add(target, np.sum(self.variances * np.square(X), -1), target)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
if self.ARD:
if X2 is None:
[np.add(target[i:i + 1], np.sum(dL_dK * tdot(X[:, i:i + 1])), target[i:i + 1]) for i in range(self.input_dim)]
@ -100,14 +116,7 @@ class Linear(Kernpart):
self._K_computations(X, X2)
target += np.sum(self._dot_product * dL_dK)
def dKdiag_dtheta(self, dL_dKdiag, X, target):
tmp = dL_dKdiag[:, None] * X ** 2
if self.ARD:
target += tmp.sum(0)
else:
target += tmp.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
if X2 is None:
target += 2*(((X[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
else:
@ -124,14 +133,6 @@ class Linear(Kernpart):
self._psi_computations(Z, mu, S)
target += np.sum(self.variances * self.mu2_S, 1)
def dpsi0_dtheta(self, dL_dpsi0, Z, mu, S, target):
self._psi_computations(Z, mu, S)
tmp = dL_dpsi0[:, None] * self.mu2_S
if self.ARD:
target += tmp.sum(0)
else:
target += tmp.sum()
def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
target_mu += dL_dpsi0[:, None] * (2.0 * mu * self.variances)
target_S += dL_dpsi0[:, None] * self.variances
@ -140,17 +141,13 @@ class Linear(Kernpart):
"""the variance, it does nothing"""
self._psi1 = self.K(mu, Z, target)
def dpsi1_dtheta(self, dL_dpsi1, Z, mu, S, target):
"""the variance, it does nothing"""
self.dK_dtheta(dL_dpsi1, mu, Z, target)
def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
"""Do nothing for S, it does not affect psi1"""
self._psi_computations(Z, mu, S)
target_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
self.dK_dX(dL_dpsi1.T, Z, mu, target)
self.gradients_X(dL_dpsi1.T, Z, mu, target)
def psi2(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
@ -164,25 +161,17 @@ class Linear(Kernpart):
def dpsi2_dtheta_new(self, dL_dpsi2, Z, mu, S, target):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
self.K(mu,Z,tmp)
self.dK_dtheta(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
self._param_grad_helper(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target)
result= 2.*(dL_dpsi2[:,:,:,None]*S[:,None,None,:]*self.variances*Z[None,:,None,:]*Z[None,None,:,:]).sum(0).sum(0).sum(0)
if self.ARD:
target += result.sum(0).sum(0).sum(0)
else:
target += result.sum()
def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
self._psi_computations(Z, mu, S)
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
if self.ARD:
target += tmp.sum(0).sum(0).sum(0)
else:
target += tmp.sum()
def dpsi2_dmuS_new(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
self.K(mu,Z,tmp)
self.dK_dX(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
self.gradients_X(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
Zs = Z*self.variances
Zs_sq = Zs[:,None,:]*Zs[None,:,:]
@ -288,11 +277,11 @@ class Linear(Kernpart):
if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2)):
self._X = X.copy()
if X2 is None:
self._dot_product = tdot(X)
self._dot_product = tdot(param_to_array(X))
self._X2 = None
else:
self._X2 = X2.copy()
self._dot_product = np.dot(X, X2.T)
self._dot_product = np.dot(param_to_array(X), param_to_array(X2.T))
def _psi_computations(self, Z, mu, S):
# here are the "statistics" for psi1 and psi2

4
GPy/kern/parts/mlp.py
View file

@ -77,7 +77,7 @@ class MLP(Kernpart):
self._K_diag_computations(X)
target+= self.variance*self._K_diag_dvar
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
denom3 = self._K_denom*self._K_denom*self._K_denom
@ -107,7 +107,7 @@ class MLP(Kernpart):
target[0] += np.sum(self._K_dvar*dL_dK)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_asin_arg

2
GPy/kern/parts/periodic_Matern32.py
View file

@ -112,7 +112,7 @@ class PeriodicMatern32(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

2
GPy/kern/parts/periodic_Matern52.py
View file

@ -114,7 +114,7 @@ class PeriodicMatern52(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is num_data x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

2
GPy/kern/parts/periodic_exponential.py
View file

@ -110,7 +110,7 @@ class PeriodicExponential(Kernpart):
np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
@silence_errors
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters (shape is N x num_inducing x num_params)"""
if X2 is None: X2 = X
FX = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)

4
GPy/kern/parts/poly.py
View file

@ -86,7 +86,7 @@ class POLY(Kernpart):
self._K_diag_computations(X)
target+= self.variance*self._K_diag_dvar
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
"""Derivative of the covariance with respect to the parameters."""
self._K_computations(X, X2)
base = self.variance*self.degree*self._K_poly_arg**(self.degree-1)
@ -99,7 +99,7 @@ class POLY(Kernpart):
target[2] += base_cov_grad.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_poly_arg

30
GPy/kern/parts/prod.py
View file

@ -54,15 +54,15 @@ class Prod(Kernpart):
self.k1.update_gradients_full(dL_dK*self._K2, X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self._K1, X[:,self.slice2])
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""Derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,self.slice1], None, target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.slice2], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -81,21 +81,21 @@ class Prod(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
if X2 is None:
if not isinstance(self.k1,Coregionalize) and not isinstance(self.k2,Coregionalize):
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
else:#if isinstance(self.k1,Coregionalize) or isinstance(self.k2,Coregionalize):
#NOTE The indices column in the inputs makes the ki.dK_dX fail when passing None instead of X[:,self.slicei]
#NOTE The indices column in the inputs makes the ki.gradients_X fail when passing None instead of X[:,self.slicei]
X2 = X
self.k1.dK_dX(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
else:
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -103,8 +103,8 @@ class Prod(Kernpart):
self.k1.Kdiag(X[:,self.slice1],K1)
self.k2.Kdiag(X[:,self.slice2],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

20
GPy/kern/parts/prod_orthogonal.py
View file

@ -41,15 +41,15 @@ class prod_orthogonal(Kernpart):
self._K_computations(X,X2)
target += self._K1 * self._K2
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2)
if X2 is None:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], None, target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], None, target[self.k1.num_params:])
else:
self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
self.k1._param_grad_helper(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target[:self.k1.num_params])
self.k2._param_grad_helper(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target[self.k1.num_params:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
@ -67,11 +67,11 @@ class prod_orthogonal(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.input_dim],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.input_dim:],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -79,8 +79,8 @@ class prod_orthogonal(Kernpart):
self.k1.Kdiag(X[:,0:self.k1.input_dim],K1)
self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

4
GPy/kern/parts/rational_quadratic.py
View file

@ -52,7 +52,7 @@ class RationalQuadratic(Kernpart):
def Kdiag(self,X,target):
target += self.variance
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
if X2 is None: X2 = X
dist2 = np.square((X-X2.T)/self.lengthscale)
@ -68,7 +68,7 @@ class RationalQuadratic(Kernpart):
target[0] += np.sum(dL_dKdiag)
# here self.lengthscale and self.power have no influence on Kdiag so target[1:] are unchanged
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist2 = np.square((X-X.T)/self.lengthscale)

18
GPy/kern/parts/rbf.py
View file

@ -8,6 +8,7 @@ from kernpart import Kernpart
from ...util.linalg import tdot
from ...util.misc import fast_array_equal, param_to_array
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
class RBF(Kernpart):
"""
@ -50,9 +51,12 @@ class RBF(Kernpart):
else:
lengthscale = np.ones(self.input_dim)
self.variance = Param('variance', variance)
self.variance = Param('variance', variance, Logexp())
self.lengthscale = Param('lengthscale', lengthscale)
self.lengthscale.add_observer(self, self.update_lengthscale)
self.update_lengthscale(self.lengthscale)
self.add_parameters(self.variance, self.lengthscale)
self.parameters_changed() # initializes cache
@ -114,7 +118,7 @@ class RBF(Kernpart):
self._K_computations(X, Z)
self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
@ -123,7 +127,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -138,7 +142,7 @@ class RBF(Kernpart):
d_length = self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) +1./self.lengthscale)
dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
if not self.ARD:
self.lengthscale.gradeint = dpsi1_dlength.sum()
self.lengthscale.gradient = dpsi1_dlength.sum()
else:
self.lengthscale.gradient = dpsi1_dlength.sum(0).sum(0)
@ -157,7 +161,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -168,8 +172,8 @@ class RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

8
GPy/kern/parts/rbf_inv.py
View file

@ -97,7 +97,7 @@ class RBFInv(RBF):
# return ['variance'] + ['inv_lengthscale%i' % i for i in range(self.inv_lengthscale.size)]
# TODO: Rewrite computations so that lengthscale is not needed (but only inv. lengthscale)
def dK_dtheta(self, dL_dK, X, X2, target):
def _param_grad_helper(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
target[0] += np.sum(self._K_dvar * dL_dK)
if self.ARD:
@ -142,14 +142,14 @@ class RBFInv(RBF):
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK) * (-self.lengthscale2)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if X2 is None:
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

4
GPy/kern/parts/rbfcos.py
View file

@ -73,7 +73,7 @@ class RBFCos(Kernpart):
def Kdiag(self,X,target):
np.add(target,self.variance,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
target[0] += np.sum(dL_dK*self._dvar)
if self.ARD:
@ -88,7 +88,7 @@ class RBFCos(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
#TODO!!!
raise NotImplementedError

2
GPy/kern/parts/spline.py
View file

@ -50,7 +50,7 @@ class Spline(Kernpart):
def Kdiag(self,X,target):
target += self.variance*X.flatten()**3/3.
def dK_dtheta(self,X,X2,target):
def _param_grad_helper(self,X,X2,target):
target += 0.5*(t*s**2) - s**3/6. + (s_t)**3*theta(s_t)/6.
def dKdiag_dtheta(self,X,target):

4
GPy/kern/parts/ss_rbf.py
View file

@ -144,8 +144,8 @@ class SS_RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

20
GPy/kern/parts/symmetric.py
View file

@ -40,7 +40,7 @@ class Symmetric(Kernpart):
self.k.K(X,AX2,target)
self.k.K(AX,AX2,target)
def dK_dtheta(self,dL_dK,X,X2,target):
def _param_grad_helper(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters."""
AX = np.dot(X,self.transform)
if X2 is None:
@ -48,13 +48,13 @@ class Symmetric(Kernpart):
ZX2 = AX
else:
AX2 = np.dot(X2, self.transform)
self.k.dK_dtheta(dL_dK,X,X2,target)
self.k.dK_dtheta(dL_dK,AX,X2,target)
self.k.dK_dtheta(dL_dK,X,AX2,target)
self.k.dK_dtheta(dL_dK,AX,AX2,target)
self.k._param_grad_helper(dL_dK,X,X2,target)
self.k._param_grad_helper(dL_dK,AX,X2,target)
self.k._param_grad_helper(dL_dK,X,AX2,target)
self.k._param_grad_helper(dL_dK,AX,AX2,target)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
AX = np.dot(X,self.transform)
if X2 is None:
@ -62,10 +62,10 @@ class Symmetric(Kernpart):
ZX2 = AX
else:
AX2 = np.dot(X2, self.transform)
self.k.dK_dX(dL_dK, X, X2, target)
self.k.dK_dX(dL_dK, AX, X2, target)
self.k.dK_dX(dL_dK, X, AX2, target)
self.k.dK_dX(dL_dK, AX ,AX2, target)
self.k.gradients_X(dL_dK, X, X2, target)
self.k.gradients_X(dL_dK, AX, X2, target)
self.k.gradients_X(dL_dK, X, AX2, target)
self.k.gradients_X(dL_dK, AX ,AX2, target)
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""

4
GPy/kern/parts/sympykern.py
View file

@ -348,7 +348,7 @@ class spkern(Kernpart):
def Kdiag(self,X,target):
self._weave_inline(self._Kdiag_code, X, target)
def dK_dtheta(self,partial,X,Z,target):
def _param_grad_helper(self,partial,X,Z,target):
if Z is None:
self._weave_inline(self._dK_dtheta_code_X, X, target, Z, partial)
else:
@ -357,7 +357,7 @@ class spkern(Kernpart):
def dKdiag_dtheta(self,partial,X,target):
self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
def dK_dX(self,partial,X,Z,target):
def gradients_X(self,partial,X,Z,target):
if Z is None:
self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
else:

19
GPy/likelihoods/gaussian.py
View file

@ -5,7 +5,7 @@
"""
A lot of this code assumes that the link function is the identity.
I think laplace code is okay, but I'm quite sure that the EP moments will only work if the link is identity.
I think laplace code is okay, but I'm quite sure that the EP moments will only work if the link is identity.
Furthermore, exact Guassian inference can only be done for the identity link, so we should be asserting so for all calls which relate to that.
@ -130,7 +130,10 @@ class Gaussian(Likelihood):
:rtype: float
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
return -0.5*(np.sum((y-link_f)**2/self.variance) + self.ln_det_K + self.N*np.log(2.*np.pi))
N = y.shape[0]
ln_det_cov = N*np.log(self.variance)
return -0.5*(np.sum((y-link_f)**2/self.variance) + ln_det_cov + N*np.log(2.*np.pi))
def dlogpdf_dlink(self, link_f, y, extra_data=None):
"""
@ -175,7 +178,8 @@ class Gaussian(Likelihood):
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
hess = -(1.0/self.variance)*np.ones((self.N, 1))
N = y.shape[0]
hess = -(1.0/self.variance)*np.ones((N, 1))
return hess
def d3logpdf_dlink3(self, link_f, y, extra_data=None):
@ -194,7 +198,8 @@ class Gaussian(Likelihood):
:rtype: Nx1 array
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
d3logpdf_dlink3 = np.diagonal(0*self.I)[:, None]
N = y.shape[0]
d3logpdf_dlink3 = np.zeros((N,1))
return d3logpdf_dlink3
def dlogpdf_link_dvar(self, link_f, y, extra_data=None):
@ -215,7 +220,8 @@ class Gaussian(Likelihood):
assert np.asarray(link_f).shape == np.asarray(y).shape
e = y - link_f
s_4 = 1.0/(self.variance**2)
dlik_dsigma = -0.5*self.N/self.variance + 0.5*s_4*np.sum(np.square(e))
N = y.shape[0]
dlik_dsigma = -0.5*N/self.variance + 0.5*s_4*np.sum(np.square(e))
return np.sum(dlik_dsigma) # Sure about this sum?
def dlogpdf_dlink_dvar(self, link_f, y, extra_data=None):
@ -255,7 +261,8 @@ class Gaussian(Likelihood):
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
s_4 = 1.0/(self.variance**2)
d2logpdf_dlink2_dvar = np.diag(s_4*self.I)[:, None]
N = y.shape[0]
d2logpdf_dlink2_dvar = np.ones((N,1))*s_4
return d2logpdf_dlink2_dvar
def dlogpdf_link_dtheta(self, f, y, extra_data=None):

33
GPy/likelihoods/likelihood.py
View file

@ -13,12 +13,12 @@ from ..core.parameterization import Parameterized
class Likelihood(Parameterized):
"""
Likelihood base class, used to defing p(y|f).
Likelihood base class, used to defing p(y|f).
All instances use _inverse_ link functions, which can be swapped out. It is
expected that inherriting classes define a default inverse link function
To use this class, inherrit and define missing functionality.
To use this class, inherrit and define missing functionality.
Inherriting classes *must* implement:
pdf_link : a bound method which turns the output of the link function into the pdf
@ -27,7 +27,7 @@ class Likelihood(Parameterized):
To enable use with EP, inherriting classes *must* define:
TODO: a suitable derivative function for any parameters of the class
It is also desirable to define:
moments_match_ep : a function to compute the EP moments If this isn't defined, the moments will be computed using 1D quadrature.
moments_match_ep : a function to compute the EP moments If this isn't defined, the moments will be computed using 1D quadrature.
To enable use with Laplace approximation, inherriting classes *must* define:
Some derivative functions *AS TODO*
@ -36,7 +36,7 @@ class Likelihood(Parameterized):
"""
def __init__(self, gp_link, name):
super(Likelihood, self).__init__(name)
super(Likelihood, self).__init__(name)
assert isinstance(gp_link,link_functions.GPTransformation), "gp_link is not a valid GPTransformation."
self.gp_link = gp_link
self.log_concave = False
@ -44,6 +44,10 @@ class Likelihood(Parameterized):
def _gradients(self,partial):
return np.zeros(0)
def update_gradients(self, partial):
if self.size > 0:
raise NotImplementedError('Must be implemented for likelihoods with parameters to be optimized')
def _preprocess_values(self,Y):
"""
In case it is needed, this function assess the output values or makes any pertinent transformation on them.
@ -303,31 +307,31 @@ class Likelihood(Parameterized):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
return self.dlogpdf_link_dtheta(link_f, y, extra_data=extra_data)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([1, 0])
return np.zeros([1, 0])
def dlogpdf_df_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
dlogpdf_dlink_dtheta = self.dlogpdf_dlink_dtheta(link_f, y, extra_data=extra_data)
return chain_1(dlogpdf_dlink_dtheta, dlink_df)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
return np.zeros([f.shape[0], 0])
def d2logpdf_df2_dtheta(self, f, y, extra_data=None):
"""
TODO: Doc strings
"""
if len(self._get_param_names()) > 0:
if self.size > 0:
link_f = self.gp_link.transf(f)
dlink_df = self.gp_link.dtransf_df(f)
d2link_df2 = self.gp_link.d2transf_df2(f)
@ -336,7 +340,7 @@ class Likelihood(Parameterized):
return chain_2(d2logpdf_dlink2_dtheta, dlink_df, dlogpdf_dlink_dtheta, d2link_df2)
else:
#Is no parameters so return an empty array for its derivatives
return np.empty([f.shape[0], 0])
return np.zeros([f.shape[0], 0])
def _laplace_gradients(self, f, y, extra_data=None):
dlogpdf_dtheta = self.dlogpdf_dtheta(f, y, extra_data=extra_data)
@ -345,9 +349,12 @@ class Likelihood(Parameterized):
#Parameters are stacked vertically. Must be listed in same order as 'get_param_names'
# ensure we have gradients for every parameter we want to optimize
assert dlogpdf_dtheta.shape[1] == len(self._get_param_names())
assert dlogpdf_df_dtheta.shape[1] == len(self._get_param_names())
assert d2logpdf_df2_dtheta.shape[1] == len(self._get_param_names())
try:
assert len(dlogpdf_dtheta) == self.size #1 x num_param array
assert dlogpdf_df_dtheta.shape[1] == self.size #f x num_param matrix
assert d2logpdf_df2_dtheta.shape[1] == self.size #f x num_param matrix
except Exception as e:
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta

7
GPy/likelihoods/poisson.py
View file

@ -19,8 +19,11 @@ class Poisson(Likelihood):
.. Note::
Y is expected to take values in {0,1,2,...}
"""
def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
super(Poisson, self).__init__(gp_link,analytical_mean,analytical_variance)
def __init__(self, gp_link=None):
if gp_link is None:
gp_link = link_functions.Log_ex_1()
super(Poisson, self).__init__(gp_link, name='Poisson')
def _preprocess_values(self,Y):
return Y

60
GPy/likelihoods/student_t.py
View file

@ -8,6 +8,7 @@ import link_functions
from scipy import stats, integrate
from scipy.special import gammaln, gamma
from likelihood import Likelihood
from ..core.parameterization import Param
class StudentT(Likelihood):
"""
@ -19,26 +20,30 @@ class StudentT(Likelihood):
p(y_{i}|\\lambda(f_{i})) = \\frac{\\Gamma\\left(\\frac{v+1}{2}\\right)}{\\Gamma\\left(\\frac{v}{2}\\right)\\sqrt{v\\pi\\sigma^{2}}}\\left(1 + \\frac{1}{v}\\left(\\frac{(y_{i} - f_{i})^{2}}{\\sigma^{2}}\\right)\\right)^{\\frac{-v+1}{2}}
"""
def __init__(self,gp_link=None,analytical_mean=True,analytical_variance=True, deg_free=5, sigma2=2):
self.v = deg_free
self.sigma2 = sigma2
def __init__(self,gp_link=None, deg_free=5, sigma2=2):
if gp_link is None:
gp_link = link_functions.Identity()
super(StudentT, self).__init__(gp_link, name='Student_T')
self.sigma2 = Param('t_noise', float(sigma2))
self.v = Param('deg_free', float(deg_free))
self.add_parameter(self.sigma2)
self.add_parameter(self.v)
self.v.constrain_fixed()
self._set_params(np.asarray(sigma2))
super(StudentT, self).__init__(gp_link,analytical_mean,analytical_variance)
self.log_concave = False
def _get_params(self):
return np.asarray(self.sigma2)
def parameters_changed(self):
self.variance = (self.v / float(self.v - 2)) * self.sigma2
def _get_param_names(self):
return ["t_noise_std2"]
def _set_params(self, x):
self.sigma2 = float(x)
@property
def variance(self, extra_data=None):
return (self.v / float(self.v - 2)) * self.sigma2
def update_gradients(self, partial):
"""
Pull out the gradients, be careful as the order must match the order
in which the parameters are added
"""
self.sigma2.gradient = partial[0]
self.v.gradient = partial[1]
def pdf_link(self, link_f, y, extra_data=None):
"""
@ -82,10 +87,14 @@ class StudentT(Likelihood):
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
#FIXME:
#Why does np.log(1 + (1/self.v)*((y-link_f)**2)/self.sigma2) suppress the divide by zero?!
#But np.log(1 + (1/float(self.v))*((y-link_f)**2)/self.sigma2) throws it correctly
#print - 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
objective = (+ gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
- 0.5*np.log(self.sigma2 * self.v * np.pi)
- 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
- gammaln(self.v * 0.5)
- 0.5*np.log(self.sigma2 * self.v * np.pi)
- 0.5*(self.v + 1)*np.log(1 + (1/np.float(self.v))*((e**2)/self.sigma2))
)
return np.sum(objective)
@ -222,17 +231,20 @@ class StudentT(Likelihood):
def dlogpdf_link_dtheta(self, f, y, extra_data=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, extra_data=extra_data)
return np.asarray([[dlogpdf_dvar]])
dlogpdf_dv = np.zeros_like(dlogpdf_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dvar, dlogpdf_dv))
def dlogpdf_dlink_dtheta(self, f, y, extra_data=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)
return dlogpdf_dlink_dvar
dlogpdf_dlink_dv = np.zeros_like(dlogpdf_dlink_dvar) #FIXME: Not done yet
return np.hstack((dlogpdf_dlink_dvar, dlogpdf_dlink_dv))
def d2logpdf_dlink2_dtheta(self, f, y, extra_data=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)
return d2logpdf_dlink2_dvar
d2logpdf_dlink2_dv = np.zeros_like(d2logpdf_dlink2_dvar) #FIXME: Not done yet
return np.hstack((d2logpdf_dlink2_dvar, d2logpdf_dlink2_dv))
def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
def predictive_variance(self, mu, sigma, predictive_mean=None):
"""
Compute predictive variance of student_t*normal p(y*|f*)p(f*)
@ -252,7 +264,7 @@ class StudentT(Likelihood):
return true_var
def _predictive_mean_analytical(self, mu, sigma):
def predictive_mean(self, mu, sigma):
"""
Compute mean of the prediction
"""

2
GPy/mappings/kernel.py
View file

@ -57,4 +57,4 @@ class Kernel(Mapping):
return np.hstack((self._df_dA.flatten(), self._df_dbias))
def df_dX(self, dL_df, X):
return self.kern.dK_dX((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)
return self.kern.gradients_X((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)

55
GPy/models/bayesian_gplvm.py
View file

@ -2,7 +2,6 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import itertools
from gplvm import GPLVM
from .. import kern
from ..core import SparseGP
@ -23,15 +22,10 @@ class BayesianGPLVM(SparseGP, GPLVM):
:type init: 'PCA'|'random'
"""
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, name='bayesian gplvm', **kwargs):
if type(likelihood_or_Y) is np.ndarray:
likelihood = Gaussian(likelihood_or_Y)
else:
likelihood = likelihood_or_Y
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=Gaussian(), name='bayesian gplvm', **kwargs):
if X == None:
X = self.initialise_latent(init, input_dim, likelihood.Y)
X = self.initialise_latent(init, input_dim, Y)
self.init = init
if X_variance is None:
@ -44,9 +38,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
if kernel is None:
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
SparseGP.__init__(self, X=X, likelihood=likelihood, kernel=kernel, Z=Z, X_variance=X_variance, name=name, **kwargs)
self.q = Normal(self.X, self.X_variance)
self.add_parameter(self.q, gradient=self._dbound_dmuS, index=0)
self.q = Normal(X, X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, X_variance, name, **kwargs)
self.add_parameter(self.q, index=0)
self.ensure_default_constraints()
def _getstate(self):
@ -94,9 +88,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
return dKL_dmu, dKL_dS
def dL_dmuS(self):
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.dL_dpsi0, self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.dL_dpsi2, self.Z, self.X, self.X_variance)
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.grad_dict['dL_dpsi0'], self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu = dL_dmu_psi0 + dL_dmu_psi1 + dL_dmu_psi2
dL_dS = dL_dS_psi0 + dL_dS_psi1 + dL_dS_psi2
@ -107,10 +101,25 @@ class BayesianGPLVM(SparseGP, GPLVM):
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = SparseGP.log_likelihood(self)
kl = self.KL_divergence()
return ll - kl
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
self._log_marginal_likelihood -= self.KL_divergence()
#The derivative of the bound wrt the inducing inputs Z
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu, dL_dS = self.dL_dmuS()
dKL_dmu, dKL_dS = self.dKL_dmuS()
self.q.means.gradient = dL_dmu - dKL_dmu
self.q.variances.gradient = dL_dS - dKL_dS
# def log_likelihood(self):
# ll = SparseGP.log_likelihood(self)
# kl = self.KL_divergence()
# return ll - kl
def _dbound_dmuS(self):
dKL_dmu, dKL_dS = self.dKL_dmuS()
@ -181,18 +190,18 @@ class BayesianGPLVM(SparseGP, GPLVM):
"""
dmu_dX = np.zeros_like(Xnew)
for i in range(self.Z.shape[0]):
dmu_dX += self.kern.dK_dX(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
dmu_dX += self.kern.gradients_X(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
return dmu_dX
def dmu_dXnew(self, Xnew):
"""
Individual gradient of prediction at Xnew w.r.t. each sample in Xnew
"""
dK_dX = np.zeros((Xnew.shape[0], self.num_inducing))
gradients_X = np.zeros((Xnew.shape[0], self.num_inducing))
ones = np.ones((1, 1))
for i in range(self.Z.shape[0]):
dK_dX[:, i] = self.kern.dK_dX(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(dK_dX, self.Cpsi1Vf)
gradients_X[:, i] = self.kern.gradients_X(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(gradients_X, self.Cpsi1Vf)
def plot_steepest_gradient_map(self, *args, ** kwargs):
"""

2
GPy/models/bcgplvm.py
View file

@ -44,7 +44,7 @@ class BCGPLVM(GPLVM):
GP._set_params(self, x[self.mapping.num_params:])
def _log_likelihood_gradients(self):
dL_df = self.kern.dK_dX(self.dL_dK, self.X)
dL_df = self.kern.gradients_X(self.dL_dK, self.X)
dL_dtheta = self.mapping.df_dtheta(dL_df, self.likelihood.Y)
return np.hstack((dL_dtheta.flatten(), GP._log_likelihood_gradients(self)))

2
GPy/models/gplvm.py
View file

@ -60,7 +60,7 @@ class GPLVM(GP):
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
target[:,:,i]=self.kern.gradients_X(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):

59
GPy/models/gradient_checker.py
View file

@ -1,9 +1,10 @@
# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from GPy.core.model import Model
from ..core.model import Model
import itertools
import numpy
from ..core.parameterization import Param
def get_shape(x):
if isinstance(x, numpy.ndarray):
@ -24,42 +25,42 @@ class GradientChecker(Model):
"""
:param f: Function to check gradient for
:param df: Gradient of function to check
:param x0:
:param x0:
Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
Can be a list of arrays, if takes a list of arrays. This list will be passed
Can be a list of arrays, if takes a list of arrays. This list will be passed
to f and df in the same order as given here.
If only one argument, make sure not to pass a list!!!
:type x0: [array-like] | array-like | float | int
:param names:
Names to print, when performing gradcheck. If a list was passed to x0
a list of names with the same length is expected.
:param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
Examples:
---------
from GPy.models import GradientChecker
N, M, Q = 10, 5, 3
Sinusoid:
X = numpy.random.rand(N, Q)
grad = GradientChecker(numpy.sin,numpy.cos,X,'x')
grad.checkgrad(verbose=1)
Using GPy:
X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
grad = GradientChecker(kern.K,
grad = GradientChecker(kern.K,
lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
x0 = X.copy(),
names='X')
names='X')
grad.checkgrad(verbose=1)
grad.randomize()
grad.checkgrad(verbose=1)
grad.checkgrad(verbose=1)
"""
Model.__init__(self)
Model.__init__(self, 'GradientChecker')
if isinstance(x0, (list, tuple)) and names is None:
self.shapes = [get_shape(xi) for xi in x0]
self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
@ -72,8 +73,10 @@ class GradientChecker(Model):
else:
self.names = names
self.shapes = [get_shape(x0)]
for name, xi in zip(self.names, at_least_one_element(x0)):
self.__setattr__(name, xi)
self.__setattr__(name, Param(name, xi))
self.add_parameter(self.__getattribute__(name))
# self._param_names = []
# for name, shape in zip(self.names, self.shapes):
# self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
@ -93,20 +96,18 @@ class GradientChecker(Model):
def _log_likelihood_gradients(self):
return numpy.atleast_1d(self.df(*self._get_x(), **self.kwargs)).flatten()
#def _get_params(self):
#return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
def _get_params(self):
return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
#def _set_params(self, x):
#current_index = 0
#for name, shape in zip(self.names, self.shapes):
#current_size = numpy.prod(shape)
#self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
#current_index += current_size
def _set_params(self, x):
current_index = 0
for name, shape in zip(self.names, self.shapes):
current_size = numpy.prod(shape)
self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
current_index += current_size
def _get_param_names(self):
_param_names = []
for name, shape in zip(self.names, self.shapes):
_param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
return _param_names
#def _get_param_names(self):
#_param_names = []
#for name, shape in zip(self.names, self.shapes):
#_param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
#return _param_names

2
GPy/models/sparse_gplvm.py
View file

@ -52,7 +52,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
def dL_dX(self):
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0, self.X)
dL_dX += self.kern.dK_dX(self.dL_dpsi1, self.X, self.Z)
dL_dX += self.kern.gradients_X(self.dL_dpsi1, self.X, self.Z)
return dL_dX

3
GPy/plotting/matplot_dep/variational_plots.py
View file

@ -1,4 +1,5 @@
import pylab as pb
import pylab as pb, numpy as np
from ...util.misc import param_to_array
def plot(parameterized, fignum=None, ax=None, colors=None):
"""

2
GPy/testing/kernel_tests.py
View file

@ -5,7 +5,7 @@ import unittest
import numpy as np
import GPy
verbose = False
verbose = True
try:
import sympy

250
GPy/testing/likelihood_tests.py
View file

@ -4,10 +4,11 @@ import GPy
from GPy.models import GradientChecker
import functools
import inspect
from GPy.likelihoods.noise_models import gp_transformations
from GPy.likelihoods import link_functions
from ..core.parameterization import Param
from functools import partial
#np.random.seed(300)
np.random.seed(7)
#np.random.seed(7)
def dparam_partial(inst_func, *args):
"""
@ -22,12 +23,14 @@ def dparam_partial(inst_func, *args):
the f or Y that are being used in the function whilst we tweak the
param
"""
def param_func(param, inst_func, args):
inst_func.im_self._set_params(param)
def param_func(param_val, param_name, inst_func, args):
#inst_func.im_self._set_params(param)
#inst_func.im_self.add_parameter(Param(param_name, param_val))
inst_func.im_self[param_name] = param_val
return inst_func(*args)
return functools.partial(param_func, inst_func=inst_func, args=args)
def dparam_checkgrad(func, dfunc, params, args, constraints=None, randomize=False, verbose=False):
def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None, randomize=False, verbose=False):
"""
checkgrad expects a f: R^N -> R^1 and df: R^N -> R^N
However if we are holding other parameters fixed and moving something else
@ -38,27 +41,34 @@ def dparam_checkgrad(func, dfunc, params, args, constraints=None, randomize=Fals
The number of parameters and N is the number of data
Need to take a slice out from f and a slice out of df
"""
#print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
#func.__name__, dfunc.__name__)
print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
func.__name__, dfunc.__name__)
partial_f = dparam_partial(func, *args)
partial_df = dparam_partial(dfunc, *args)
gradchecking = True
for param in params:
fnum = np.atleast_1d(partial_f(param)).shape[0]
dfnum = np.atleast_1d(partial_df(param)).shape[0]
zipped_params = zip(params, params_names)
for param_ind, (param_val, param_name) in enumerate(zipped_params):
#Check one parameter at a time, make sure it is 2d (as some gradients only return arrays) then strip out the parameter
fnum = np.atleast_2d(partial_f(param_val, param_name))[:, param_ind].shape[0]
dfnum = np.atleast_2d(partial_df(param_val, param_name))[:, param_ind].shape[0]
for fixed_val in range(dfnum):
#dlik and dlik_dvar gives back 1 value for each
f_ind = min(fnum, fixed_val+1) - 1
print "fnum: {} dfnum: {} f_ind: {} fixed_val: {}".format(fnum, dfnum, f_ind, fixed_val)
#Make grad checker with this param moving, note that set_params is NOT being called
#The parameter is being set directly with __setattr__
grad = GradientChecker(lambda x: np.atleast_1d(partial_f(x))[f_ind],
lambda x : np.atleast_1d(partial_df(x))[fixed_val],
param, 'p')
#This is not general for more than one param...
#Check only the parameter and function value we wish to check at a time
grad = GradientChecker(lambda p_val: np.atleast_2d(partial_f(p_val, param_name))[f_ind, param_ind],
lambda p_val: np.atleast_2d(partial_df(p_val, param_name))[fixed_val, param_ind],
param_val, [param_name])
if constraints is not None:
for constraint in constraints:
constraint('p', grad)
for constrain_param, constraint in constraints:
if grad.grep_param_names(constrain_param):
constraint(constrain_param, grad)
else:
print "parameter didn't exist"
print constrain_param, " ", constraint
if randomize:
grad.randomize()
if verbose:
@ -76,7 +86,7 @@ class TestNoiseModels(object):
Generic model checker
"""
def setUp(self):
self.N = 5
self.N = 15
self.D = 3
self.X = np.random.rand(self.N, self.D)*10
@ -94,7 +104,7 @@ class TestNoiseModels(object):
self.var = np.random.rand(1)
#Make a bigger step as lower bound can be quite curved
self.step = 1e-3
self.step = 1e-4
def tearDown(self):
self.Y = None
@ -107,17 +117,20 @@ class TestNoiseModels(object):
####################################################
# Constraint wrappers so we can just list them off #
####################################################
def constrain_fixed(regex, model):
model[regex].constrain_fixed()
def constrain_negative(regex, model):
model.constrain_negative(regex)
model[regex].constrain_negative()
def constrain_positive(regex, model):
model.constrain_positive(regex)
model[regex].constrain_positive()
def constrain_bounded(regex, model, lower, upper):
"""
Used like: partial(constrain_bounded, lower=0, upper=1)
"""
model.constrain_bounded(regex, lower, upper)
model[regex].constrain_bounded(lower, upper)
"""
Dictionary where we nest models we would like to check
@ -134,71 +147,81 @@ class TestNoiseModels(object):
}
"""
noise_models = {"Student_t_default": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
#"constraints": [("t_noise", constrain_positive), ("deg_free", partial(constrain_fixed, value=5))]
},
"laplace": True
},
"Student_t_1_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [1.0],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_small_deg_free": {
"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_small_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [0.01],
"constraints": [constrain_positive]
"vals": [0.0001],
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_large_var": {
"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [10.0],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_approx_gauss": {
"model": GPy.likelihoods.student_t(deg_free=1000, sigma2=self.var),
"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Student_t_log": {
"model": GPy.likelihoods.student_t(gp_link=gp_transformations.Log(), deg_free=5, sigma2=self.var),
"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
"grad_params": {
"names": ["t_noise"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
},
"laplace": True
},
"Gaussian_default": {
"model": GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N),
"model": GPy.likelihoods.Gaussian(variance=self.var),
"grad_params": {
"names": ["noise_model_variance"],
"names": ["variance"],
"vals": [self.var],
"constraints": [constrain_positive]
"constraints": [("variance", constrain_positive)]
},
"laplace": True,
"ep": True
},
#"Gaussian_log": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -207,7 +230,7 @@ class TestNoiseModels(object):
#"laplace": True
#},
#"Gaussian_probit": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Probit(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Probit(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -216,7 +239,7 @@ class TestNoiseModels(object):
#"laplace": True
#},
#"Gaussian_log_ex": {
#"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
#"model": GPy.likelihoods.gaussian(gp_link=link_functions.Log_ex_1(), variance=self.var, D=self.D, N=self.N),
#"grad_params": {
#"names": ["noise_model_variance"],
#"vals": [self.var],
@ -225,31 +248,31 @@ class TestNoiseModels(object):
#"laplace": True
#},
"Bernoulli_default": {
"model": GPy.likelihoods.bernoulli(),
"model": GPy.likelihoods.Bernoulli(),
"link_f_constraints": [partial(constrain_bounded, lower=0, upper=1)],
"laplace": True,
"Y": self.binary_Y,
"ep": True
},
"Exponential_default": {
"model": GPy.likelihoods.exponential(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True,
},
"Poisson_default": {
"model": GPy.likelihoods.poisson(),
"link_f_constraints": [constrain_positive],
"Y": self.integer_Y,
"laplace": True,
"ep": False #Should work though...
},
"Gamma_default": {
"model": GPy.likelihoods.gamma(),
"link_f_constraints": [constrain_positive],
"Y": self.positive_Y,
"laplace": True
}
#"Exponential_default": {
#"model": GPy.likelihoods.exponential(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True,
#},
#"Poisson_default": {
#"model": GPy.likelihoods.poisson(),
#"link_f_constraints": [constrain_positive],
#"Y": self.integer_Y,
#"laplace": True,
#"ep": False #Should work though...
#},
#"Gamma_default": {
#"model": GPy.likelihoods.gamma(),
#"link_f_constraints": [constrain_positive],
#"Y": self.positive_Y,
#"laplace": True
#}
}
for name, attributes in noise_models.iteritems():
@ -286,8 +309,8 @@ class TestNoiseModels(object):
else:
ep = False
if len(param_vals) > 1:
raise NotImplementedError("Cannot support multiple params in likelihood yet!")
#if len(param_vals) > 1:
#raise NotImplementedError("Cannot support multiple params in likelihood yet!")
#Required by all
#Normal derivatives
@ -302,13 +325,13 @@ class TestNoiseModels(object):
yield self.t_d3logpdf_df3, model, Y, f
yield self.t_d3logpdf_dlink3, model, Y, f, link_f_constraints
#Params
yield self.t_dlogpdf_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_df_dparams, model, Y, f, param_vals, param_constraints
yield self.t_d2logpdf2_df2_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_dlogpdf_df_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_d2logpdf2_df2_dparams, model, Y, f, param_vals, param_names, param_constraints
#Link params
yield self.t_dlogpdf_link_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_dlink_dparams, model, Y, f, param_vals, param_constraints
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, param_vals, param_constraints
yield self.t_dlogpdf_link_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_dlogpdf_dlink_dparams, model, Y, f, param_vals, param_names, param_constraints
yield self.t_d2logpdf2_dlink2_dparams, model, Y, f, param_vals, param_names, param_constraints
#laplace likelihood gradcheck
yield self.t_laplace_fit_rbf_white, model, self.X, Y, f, self.step, param_vals, param_names, param_constraints
@ -370,33 +393,33 @@ class TestNoiseModels(object):
# df_dparams #
##############
@with_setup(setUp, tearDown)
def t_dlogpdf_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_dlogpdf_df_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_df_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_d2logpdf2_df2_dparams(self, model, Y, f, params, param_constraints):
def t_d2logpdf2_df2_dparams(self, model, Y, f, params, params_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
params, args=(f, Y), constraints=param_constraints,
randomize=True, verbose=True)
params, params_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
################
@ -454,32 +477,32 @@ class TestNoiseModels(object):
# dlink_dparams #
#################
@with_setup(setUp, tearDown)
def t_dlogpdf_link_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_link_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.logpdf_link, model.dlogpdf_link_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_dlogpdf_dlink_dparams(self, model, Y, f, params, param_constraints):
def t_dlogpdf_dlink_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.dlogpdf_dlink, model.dlogpdf_dlink_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@with_setup(setUp, tearDown)
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, params, param_constraints):
def t_d2logpdf2_dlink2_dparams(self, model, Y, f, params, param_names, param_constraints):
print "\n{}".format(inspect.stack()[0][3])
print model
assert (
dparam_checkgrad(model.d2logpdf_dlink2, model.d2logpdf_dlink2_dtheta,
params, args=(f, Y), constraints=param_constraints,
params, param_names, args=(f, Y), constraints=param_constraints,
randomize=False, verbose=True)
)
@ -493,18 +516,23 @@ class TestNoiseModels(object):
Y = Y/Y.max()
white_var = 1e-6
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), model)
m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=laplace_likelihood)
laplace_likelihood = GPy.inference.latent_function_inference.LaplaceInference()
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, inference_method=laplace_likelihood)
m.ensure_default_constraints()
m.constrain_fixed('white', white_var)
m['white'].constrain_fixed(white_var)
for param_num in range(len(param_names)):
name = param_names[param_num]
m[name] = param_vals[param_num]
constraints[param_num](name, m)
#Set constraints
for constrain_param, constraint in constraints:
constraint(constrain_param, m)
print m
m.randomize()
#Set params
for param_num in range(len(param_names)):
name = param_names[param_num]
m[name] = param_vals[param_num]
#m.optimize(max_iters=8)
print m
m.checkgrad(verbose=1, step=step)
@ -525,10 +553,10 @@ class TestNoiseModels(object):
Y = Y/Y.max()
white_var = 1e-6
kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
ep_likelihood = GPy.likelihoods.EP(Y.copy(), model)
m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=ep_likelihood)
ep_inf = GPy.inference.latent_function_inference.EP()
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, inference_method=ep_inf)
m.ensure_default_constraints()
m.constrain_fixed('white', white_var)
m['white'].constrain_fixed(white_var)
for param_num in range(len(param_names)):
name = param_names[param_num]
@ -559,8 +587,8 @@ class LaplaceTests(unittest.TestCase):
self.var = 0.2
self.var = np.random.rand(1)
self.stu_t = GPy.likelihoods.student_t(deg_free=5, sigma2=self.var)
self.gauss = GPy.likelihoods.gaussian(gp_transformations.Log(), variance=self.var, D=self.D, N=self.N)
self.stu_t = GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var)
self.gauss = GPy.likelihoods.Gaussian(gp_link=link_functions.Log(), variance=self.var)
#Make a bigger step as lower bound can be quite curved
self.step = 1e-6
@ -584,7 +612,7 @@ class LaplaceTests(unittest.TestCase):
noise = np.random.randn(*self.X.shape)*self.real_std
self.Y = np.sin(self.X*2*np.pi) + noise
self.f = np.random.rand(self.N, 1)
self.gauss = GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N)
self.gauss = GPy.likelihoods.Gaussian(variance=self.var)
dlogpdf_df = functools.partial(self.gauss.dlogpdf_df, y=self.Y)
d2logpdf_df2 = functools.partial(self.gauss.d2logpdf_df2, y=self.Y)
@ -605,23 +633,27 @@ class LaplaceTests(unittest.TestCase):
#Yc = Y.copy()
#Yc[75:80] += 1
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
#FIXME: Make sure you can copy kernels when params is fixed
#kernel2 = kernel1.copy()
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
m1.constrain_fixed('white', 1e-6)
m1['noise'] = initial_var_guess
m1.constrain_bounded('noise', 1e-4, 10)
m1.constrain_bounded('rbf', 1e-4, 10)
gauss_distr1 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
exact_inf = GPy.inference.latent_function_inference.ExactGaussianInference()
m1 = GPy.core.GP(X, Y.copy(), kernel=kernel1, likelihood=gauss_distr1, inference_method=exact_inf)
m1['white'].constrain_fixed(1e-6)
m1['variance'] = initial_var_guess
m1['variance'].constrain_bounded(1e-4, 10)
m1['rbf'].constrain_bounded(1e-4, 10)
m1.ensure_default_constraints()
m1.randomize()
gauss_distr = GPy.likelihoods.gaussian(variance=initial_var_guess, D=1, N=Y.shape[0])
laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), gauss_distr)
m2 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel2, likelihood=laplace_likelihood)
gauss_distr2 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m2 = GPy.core.GP(X, Y.copy(), kernel=kernel2, likelihood=gauss_distr2, inference_method=laplace_inf)
m2.ensure_default_constraints()
m2.constrain_fixed('white', 1e-6)
m2.constrain_bounded('rbf', 1e-4, 10)
m2.constrain_bounded('noise', 1e-4, 10)
m2['white'].constrain_fixed(1e-6)
m2['rbf'].constrain_bounded(1e-4, 10)
m2['variance'].constrain_bounded(1e-4, 10)
m2.randomize()
if debug:
@ -667,7 +699,7 @@ class LaplaceTests(unittest.TestCase):
#Check Y's are the same
np.testing.assert_almost_equal(Y, m2.likelihood.Y, decimal=5)
np.testing.assert_almost_equal(m1.Y, m2.Y, decimal=5)
#Check marginals are the same
np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
#Check marginals are the same with random

85
GPy/testing/psi_stat_gradient_tests.py
View file

@ -9,42 +9,44 @@ import numpy
import GPy
import itertools
from GPy.core import Model
from GPy.core.parameterization.param import Param
from GPy.core.parameterization.transformations import Logexp
class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
super(PsiStatModel, self).__init__(name='psi stat test')
self.which = which
self.X = X
self.X_variance = X_variance
self.Z = Z
self.X = Param("X", X)
self.X_variance = Param('X_variance', X_variance, Logexp())
self.Z = Param("Z", Z)
self.N, self.input_dim = X.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
super(PsiStatModel, self).__init__()
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
def _get_param_names(self):
Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.num_inducing), range(self.input_dim))]
return Xnames + Znames + self.kern._get_param_names()
def _get_params(self):
return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
def _set_params(self, x, save_old=True, save_count=0):
start, end = 0, self.X.size
self.X = x[start:end].reshape(self.N, self.input_dim)
start, end = end, end + self.X_variance.size
self.X_variance = x[start: end].reshape(self.N, self.input_dim)
start, end = end, end + self.Z.size
self.Z = x[start: end].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(x[end:])
self.add_parameters(self.X, self.X_variance, self.Z, self.kern)
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def _log_likelihood_gradients(self):
def parameters_changed(self):
psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
self.X.gradient = psimu
self.X_variance.gradient = psiS
#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
except AttributeError: psiZ = numpy.zeros_like(self.Z)
self.Z.gradient = psiZ
#psiZ = numpy.ones(self.num_inducing * self.input_dim)
thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
N,M = self.X.shape[0], self.Z.shape[0]
dL_dpsi0, dL_dpsi1, dL_dpsi2 = numpy.zeros([N]), numpy.zeros([N,M]), numpy.zeros([N,M,M])
if self.which == 'psi0': dL_dpsi0 += 1
if self.which == 'psi1': dL_dpsi1 += 1
if self.which == 'psi2': dL_dpsi2 += 1
self.kern.update_gradients_variational(numpy.zeros([1,1]),
dL_dpsi0,
dL_dpsi1,
dL_dpsi2, self.X, self.X_variance, self.Z)
class DPsiStatTest(unittest.TestCase):
input_dim = 5
@ -57,61 +59,66 @@ class DPsiStatTest(unittest.TestCase):
Y = X.dot(numpy.random.randn(input_dim, input_dim))
# kernels = [GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.rbf(input_dim, ARD=True), GPy.kern.bias(input_dim)]
kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)]
kernels = [
GPy.kern.linear(input_dim),
GPy.kern.rbf(input_dim),
#GPy.kern.bias(input_dim),
#GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
#GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)
]
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
#m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
import ipdb;ipdb.set_trace()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi1(self):
for k in self.kernels:
m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.ensure_default_constraints(warning=0)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
if __name__ == "__main__":

5
GPy/util/caching.py
View file

@ -1,10 +1,9 @@
import numpy as np
from ..core.parameterization.array_core import ObservableArray
from ..core.parameterization.array_core import ObservableArray, ParamList
class Cacher(object):
def __init__(self, operation, limit=5):
self.limit = int(limit)
self.operation=operation
self.cached_inputs = []
self.cached_inputs = ParamList([])
self.cached_outputs = []
self.inputs_changed = []