diff --git a/GPy/core/gp.py b/GPy/core/gp.py index 916aaa81..1add8268 100644 --- a/GPy/core/gp.py +++ b/GPy/core/gp.py @@ -31,7 +31,7 @@ class GP(Model): super(GP, self).__init__(name) assert X.ndim == 2 - if isinstance(X, ObservableArray) or isinstance(X, VariationalPosterior): + if isinstance(X, (ObservableArray, VariationalPosterior)): self.X = X else: self.X = ObservableArray(X) @@ -65,8 +65,6 @@ class GP(Model): self.add_parameter(self.kern) self.add_parameter(self.likelihood) - if self.__class__ is GP: - self.parameters_changed() def parameters_changed(self): self.posterior, self._log_marginal_likelihood, grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, Y_metadata=self.Y_metadata) @@ -224,13 +222,9 @@ class GP(Model): self.kern, self.likelihood, self.output_dim, - self._Xoffset, - self._Xscale, ] def _setstate(self, state): - self._Xscale = state.pop() - self._Xoffset = state.pop() self.output_dim = state.pop() self.likelihood = state.pop() self.kern = state.pop() diff --git a/GPy/core/model.py b/GPy/core/model.py index 6514d73a..a858a62d 100644 --- a/GPy/core/model.py +++ b/GPy/core/model.py @@ -15,6 +15,7 @@ import itertools class Model(Parameterized): _fail_count = 0 # Count of failed optimization steps (see objective) _allowed_failures = 10 # number of allowed failures + def __init__(self, name): super(Model, self).__init__(name) # Parameterized.__init__(self) self.optimization_runs = [] @@ -25,14 +26,8 @@ class Model(Parameterized): raise NotImplementedError, "this needs to be implemented to use the model class" def _log_likelihood_gradients(self): - g = np.zeros(self.size) - try: - [p._collect_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed] - except ValueError: - raise ValueError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name) - return g - raise NotImplementedError, "this needs to be implemented to use the model class" - + return self.gradient + def _getstate(self): """ Get the current state of the class. @@ -60,20 +55,6 @@ class Model(Parameterized): self.priors = state.pop() Parameterized._setstate(self, state) - def randomize(self): - """ - Randomize the model. - Make this draw from the prior if one exists, else draw from N(0,1) - """ - # first take care of all parameters (from N(0,1)) - # x = self._get_params_transformed() - x = np.random.randn(self.size_transformed) - x = self._untransform_params(x) - # now draw from prior where possible - [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None] - self._set_params(x) - # self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...) - def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs): """ Perform random restarts of the model, and set the model to the best @@ -161,6 +142,12 @@ class Model(Parameterized): """ raise DeprecationWarning, 'parameters now have default constraints' + def input_sensitivity(self): + """ + Returns the sensitivity for each dimension of this kernel. + """ + return self.kern.input_sensitivity() + def objective_function(self, x): """ The objective function passed to the optimizer. It combines @@ -216,8 +203,8 @@ class Model(Parameterized): try: self._set_params_transformed(x) obj_f = -float(self.log_likelihood()) - self.log_prior() - self._fail_count = 0 obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()) + self._fail_count = 0 except (LinAlgError, ZeroDivisionError, ValueError) as e: if self._fail_count >= self._allowed_failures: raise e @@ -240,6 +227,11 @@ class Model(Parameterized): TODO: valid args """ + if self.is_fixed: + raise RuntimeError, "Cannot optimize, when everything is fixed" + if self.size == 0: + raise RuntimeError, "Model without parameters cannot be minimized" + if optimizer is None: optimizer = self.preferred_optimizer @@ -278,9 +270,8 @@ class Model(Parameterized): The gradient is considered correct if the ratio of the analytical and numerical gradients is within of unity. """ - x = self._get_params_transformed().copy() - + if not verbose: # make sure only to test the selected parameters if target_param is None: @@ -297,7 +288,7 @@ class Model(Parameterized): return # just check the global ratio - dx = np.zeros_like(x) + dx = np.zeros(x.shape) dx[transformed_index] = step * np.sign(np.random.uniform(-1, 1, transformed_index.size)) # evaulate around the point x @@ -307,10 +298,12 @@ class Model(Parameterized): dx = dx[transformed_index] gradient = gradient[transformed_index] - - 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) + + denominator = (2 * np.dot(dx, gradient)) + global_ratio = (f1 - f2) / np.where(denominator==0., 1e-32, denominator) + gloabl_diff = (f1 - f2) - denominator + + return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gloabl_diff) < tolerance) else: # check the gradient of each parameter individually, and do some pretty printing try: @@ -356,7 +349,8 @@ class Model(Parameterized): xx[xind] -= 2.*step f2 = self.objective_function(xx) numerical_gradient = (f1 - f2) / (2 * step) - ratio = (f1 - f2) / (2 * step * gradient[xind]) + if np.all(gradient[xind]==0): ratio = (f1-f2) == gradient[xind] + else: ratio = (f1 - f2) / (2 * step * gradient[xind]) difference = np.abs((f1 - f2) / 2 / step - gradient[xind]) if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance: @@ -372,6 +366,7 @@ class Model(Parameterized): 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 + self._set_params_transformed(x) return ret diff --git a/GPy/core/parameterization/array_core.py b/GPy/core/parameterization/array_core.py index a338ceed..27801e23 100644 --- a/GPy/core/parameterization/array_core.py +++ b/GPy/core/parameterization/array_core.py @@ -6,19 +6,6 @@ __updated__ = '2013-12-16' import numpy as np from parameter_core import Observable -class ParamList(list): - """ - List to store ndarray-likes in. - It will look for 'is' instead of calling __eq__ on each element. - """ - def __contains__(self, other): - for el in self: - if el is other: - return True - return False - - pass - class ObservableArray(np.ndarray, Observable): """ An ndarray which reports changes to its observers. @@ -62,10 +49,11 @@ class ObservableArray(np.ndarray, Observable): def __setitem__(self, s, val): if self._s_not_empty(s): super(ObservableArray, self).__setitem__(s, val) - self._notify_observers() + self.notify_observers(self[s]) def __getslice__(self, start, stop): return self.__getitem__(slice(start, stop)) + def __setslice__(self, start, stop, val): return self.__setitem__(slice(start, stop), val) @@ -77,149 +65,149 @@ class ObservableArray(np.ndarray, Observable): def __ilshift__(self, *args, **kwargs): r = np.ndarray.__ilshift__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __irshift__(self, *args, **kwargs): r = np.ndarray.__irshift__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __ixor__(self, *args, **kwargs): r = np.ndarray.__ixor__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __ipow__(self, *args, **kwargs): r = np.ndarray.__ipow__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __ifloordiv__(self, *args, **kwargs): r = np.ndarray.__ifloordiv__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __isub__(self, *args, **kwargs): r = np.ndarray.__isub__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __ior__(self, *args, **kwargs): r = np.ndarray.__ior__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __itruediv__(self, *args, **kwargs): r = np.ndarray.__itruediv__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __idiv__(self, *args, **kwargs): r = np.ndarray.__idiv__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __iand__(self, *args, **kwargs): r = np.ndarray.__iand__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __imod__(self, *args, **kwargs): r = np.ndarray.__imod__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __iadd__(self, *args, **kwargs): r = np.ndarray.__iadd__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r def __imul__(self, *args, **kwargs): r = np.ndarray.__imul__(self, *args, **kwargs) - self._notify_observers() + self.notify_observers() return r # def __rrshift__(self, *args, **kwargs): # r = np.ndarray.__rrshift__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __ror__(self, *args, **kwargs): # r = np.ndarray.__ror__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rxor__(self, *args, **kwargs): # r = np.ndarray.__rxor__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rdivmod__(self, *args, **kwargs): # r = np.ndarray.__rdivmod__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __radd__(self, *args, **kwargs): # r = np.ndarray.__radd__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rdiv__(self, *args, **kwargs): # r = np.ndarray.__rdiv__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rtruediv__(self, *args, **kwargs): # r = np.ndarray.__rtruediv__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rshift__(self, *args, **kwargs): # r = np.ndarray.__rshift__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rmul__(self, *args, **kwargs): # r = np.ndarray.__rmul__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rpow__(self, *args, **kwargs): # r = np.ndarray.__rpow__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rsub__(self, *args, **kwargs): # r = np.ndarray.__rsub__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r # def __rfloordiv__(self, *args, **kwargs): # r = np.ndarray.__rfloordiv__(self, *args, **kwargs) -# self._notify_observers() +# self.notify_observers() # return r diff --git a/GPy/core/parameterization/index_operations.py b/GPy/core/parameterization/index_operations.py index b5399741..f8f6ab5b 100644 --- a/GPy/core/parameterization/index_operations.py +++ b/GPy/core/parameterization/index_operations.py @@ -5,47 +5,7 @@ Created on Oct 2, 2013 ''' import numpy from numpy.lib.function_base import vectorize -from param import Param -from collections import defaultdict - -class ParamDict(defaultdict): - def __init__(self): - """ - Default will be self._default, if not set otherwise - """ - defaultdict.__init__(self, self.default_factory) - - def __getitem__(self, key): - try: - return defaultdict.__getitem__(self, key) - except KeyError: - for a in self.iterkeys(): - if numpy.all(a==key) and a._parent_index_==key._parent_index_: - return defaultdict.__getitem__(self, a) - raise - - def __contains__(self, key): - if defaultdict.__contains__(self, key): - return True - for a in self.iterkeys(): - if numpy.all(a==key) and a._parent_index_==key._parent_index_: - return True - return False - - def __setitem__(self, key, value): - if isinstance(key, Param): - for a in self.iterkeys(): - if numpy.all(a==key) and a._parent_index_==key._parent_index_: - return super(ParamDict, self).__setitem__(a, value) - defaultdict.__setitem__(self, key, value) - -class SetDict(ParamDict): - def default_factory(self): - return set() - -class IntArrayDict(ParamDict): - def default_factory(self): - return numpy.int_([]) +from lists_and_dicts import IntArrayDict class ParameterIndexOperations(object): ''' @@ -102,6 +62,7 @@ class ParameterIndexOperations(object): def clear(self): self._properties.clear() + @property def size(self): return reduce(lambda a,b: a+b.size, self.iterindices(), 0) @@ -194,14 +155,18 @@ class ParameterIndexOperationsView(object): def shift_right(self, start, size): - raise NotImplementedError, 'Shifting only supported in original ParamIndexOperations' - + self._param_index_ops.shift_right(start+self._offset, size) + def shift_left(self, start, size): + self._param_index_ops.shift_left(start+self._offset, size) + self._offset -= size + self._size -= size + def clear(self): for i, ind in self.items(): self._param_index_ops.remove(i, ind+self._offset) - + @property def size(self): return reduce(lambda a,b: a+b.size, self.iterindices(), 0) @@ -232,9 +197,7 @@ class ParameterIndexOperationsView(object): def __getitem__(self, prop): ind = self._filter_index(self._param_index_ops[prop]) - if ind.size > 0: - return ind - raise KeyError, prop + return ind def __str__(self, *args, **kwargs): import pprint diff --git a/GPy/core/parameterization/lists_and_dicts.py b/GPy/core/parameterization/lists_and_dicts.py new file mode 100644 index 00000000..5b13b3b5 --- /dev/null +++ b/GPy/core/parameterization/lists_and_dicts.py @@ -0,0 +1,35 @@ +''' +Created on 27 Feb 2014 + +@author: maxz +''' + +from collections import defaultdict +class DefaultArrayDict(defaultdict): + def __init__(self): + """ + Default will be self._default, if not set otherwise + """ + defaultdict.__init__(self, self.default_factory) + +class SetDict(DefaultArrayDict): + def default_factory(self): + return set() + +class IntArrayDict(DefaultArrayDict): + def default_factory(self): + import numpy as np + return np.int_([]) + +class ArrayList(list): + """ + List to store ndarray-likes in. + It will look for 'is' instead of calling __eq__ on each element. + """ + def __contains__(self, other): + for el in self: + if el is other: + return True + return False + + pass diff --git a/GPy/core/parameterization/param.py b/GPy/core/parameterization/param.py index 89d3a4e4..3ebeb566 100644 --- a/GPy/core/parameterization/param.py +++ b/GPy/core/parameterization/param.py @@ -3,8 +3,8 @@ import itertools import numpy -from parameter_core import Constrainable, Gradcheckable, Indexable, Parentable, adjust_name_for_printing -from array_core import ObservableArray, ParamList +from parameter_core import OptimizationHandlable, Gradcheckable, adjust_name_for_printing +from array_core import ObservableArray ###### printing __constraints_name__ = "Constraint" @@ -15,7 +15,7 @@ __precision__ = numpy.get_printoptions()['precision'] # numpy printing precision __print_threshold__ = 5 ###### -class Param(Constrainable, ObservableArray, Gradcheckable): +class Param(OptimizationHandlable, ObservableArray): """ Parameter object for GPy models. @@ -50,11 +50,11 @@ class Param(Constrainable, ObservableArray, Gradcheckable): obj._realsize_ = obj.size obj._realndim_ = obj.ndim obj._updated_ = False - from index_operations import SetDict + from lists_and_dicts import SetDict obj._tied_to_me_ = SetDict() obj._tied_to_ = [] obj._original_ = True - obj._gradient_ = None + obj._gradient_array_ = numpy.zeros(obj.shape, dtype=numpy.float64) return obj def __init__(self, name, input_array, default_constraint=None, *a, **kw): @@ -77,7 +77,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable): # 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_ = getattr(obj, '_parent_', None) self._parent_index_ = getattr(obj, '_parent_index_', None) self._default_constraint_ = getattr(obj, '_default_constraint_', None) self._current_slice_ = getattr(obj, '_current_slice_', None) @@ -89,16 +89,18 @@ class Param(Constrainable, ObservableArray, Gradcheckable): self._updated_ = getattr(obj, '_updated_', None) self._original_ = getattr(obj, '_original_', None) self._name = getattr(obj, 'name', None) - self._gradient_ = getattr(obj, '_gradient_', None) + self._gradient_array_ = getattr(obj, '_gradient_array_', None) self.constraints = getattr(obj, 'constraints', None) self.priors = getattr(obj, 'priors', None) - + @property + def _param_array_(self): + return self + @property def gradient(self): - if self._gradient_ is None: - self._gradient_ = numpy.zeros(self._realshape_) - return self._gradient_[self._current_slice_] + return self._gradient_array_[self._current_slice_] + @gradient.setter def gradient(self, val): self.gradient[:] = val @@ -110,7 +112,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable): func, args, state = super(Param, self).__reduce__() return func, args, (state, (self.name, - self._direct_parent_, + self._parent_, self._parent_index_, self._default_constraint_, self._current_slice_, @@ -135,7 +137,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable): self._current_slice_ = state.pop() self._default_constraint_ = state.pop() self._parent_index_ = state.pop() - self._direct_parent_ = state.pop() + self._parent_ = state.pop() self.name = state.pop() def copy(self, *args): @@ -148,17 +150,20 @@ class Param(Constrainable, ObservableArray, Gradcheckable): #=========================================================================== # get/set parameters #=========================================================================== - def _set_params(self, param, update=True): - self.flat = param - - def _get_params(self): - return self.flat - - def _collect_gradient(self, target): - target += self.gradient.flat - - def _set_gradient(self, g): - self.gradient = g.reshape(self._realshape_) +# def _set_params(self, param, trigger_parent=True): +# self.flat = param +# if trigger_parent: min_priority = None +# else: min_priority = -numpy.inf +# self.notify_observers(None, min_priority) +# +# def _get_params(self): +# return self.flat +# +# def _collect_gradient(self, target): +# target += self.gradient.flat +# +# def _set_gradient(self, g): +# self.gradient = g.reshape(self._realshape_) #=========================================================================== # Array operations -> done @@ -172,11 +177,9 @@ class Param(Constrainable, ObservableArray, Gradcheckable): 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 return new_arr + def __setitem__(self, s, val): super(Param, self).__setitem__(s, val) - if self.has_parent(): - self._direct_parent_._notify_parameters_changed() - #self._notify_observers() #=========================================================================== # Index Operations: @@ -204,6 +207,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable): 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) + 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 @@ -230,7 +234,8 @@ class Param(Constrainable, ObservableArray, Gradcheckable): #=========================================================================== @property def is_fixed(self): - return self._highest_parent_._is_fixed(self) + from transformations import __fixed__ + return self.constraints[__fixed__].size == self.size #def round(self, decimals=0, out=None): # view = super(Param, self).round(decimals, out).view(Param) # view.__array_finalize__(self) @@ -244,7 +249,8 @@ class Param(Constrainable, ObservableArray, Gradcheckable): #=========================================================================== @property def _description_str(self): - if self.size <= 1: return ["%f" % self] + if self.size <= 1: + return [str(self.view(numpy.ndarray)[0])] else: return [str(self.shape)] def parameter_names(self, add_self=False, adjust_for_printing=False): if adjust_for_printing: @@ -267,7 +273,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable): return [t._short() for t in self._tied_to_] or [''] def __repr__(self, *args, **kwargs): name = "\033[1m{x:s}\033[0;0m:\n".format( - x=self.hirarchy_name()) + x=self.hierarchy_name()) return name + super(Param, self).__repr__(*args, **kwargs) def _ties_for(self, rav_index): # size = sum(p.size for p in self._tied_to_) @@ -301,12 +307,12 @@ class Param(Constrainable, ObservableArray, Gradcheckable): gen = map(lambda x: " ".join(map(str, x)), gen) return reduce(lambda a, b:max(a, len(b)), gen, len(header)) def _max_len_values(self): - return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.hirarchy_name())) + return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.hierarchy_name())) def _max_len_index(self, ind): return reduce(lambda a, b:max(a, len(str(b))), ind, len(__index_name__)) def _short(self): # short string to print - name = self.hirarchy_name() + name = self.hierarchy_name() if self._realsize_ < 2: return name ind = self._indices() @@ -329,8 +335,8 @@ class Param(Constrainable, ObservableArray, Gradcheckable): if lp is None: lp = self._max_len_names(prirs, __tie_name__) sep = '-' header_format = " {i:{5}^{2}s} | \033[1m{x:{5}^{1}s}\033[0;0m | {c:{5}^{0}s} | {p:{5}^{4}s} | {t:{5}^{3}s}" - if only_name: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hirarchy_name(), c=sep*lc, i=sep*li, t=sep*lt, p=sep*lp) # nice header for printing - else: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hirarchy_name(), c=__constraints_name__, i=__index_name__, t=__tie_name__, p=__priors_name__) # nice header for printing + if only_name: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hierarchy_name(), c=sep*lc, i=sep*li, t=sep*lt, p=sep*lp) # nice header for printing + else: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hierarchy_name(), c=__constraints_name__, i=__index_name__, t=__tie_name__, p=__priors_name__) # nice header for printing if not ties: ties = itertools.cycle(['']) return "\n".join([header] + [" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {p:^{5}s} | {t:^{4}s} ".format(lc, lx, __precision__, li, lt, lp, x=x, c=" ".join(map(str, c)), p=" ".join(map(str, p)), t=(t or ''), i=i) for i, x, c, t, p in itertools.izip(indices, vals, constr_matrix, ties, prirs)]) # return all the constraints with right indices # except: return super(Param, self).__str__() @@ -345,7 +351,8 @@ class ParamConcatenation(object): See :py:class:`GPy.core.parameter.Param` for more details on constraining. """ # self.params = params - self.params = ParamList([]) + from lists_and_dicts import ArrayList + self.params = ArrayList([]) for p in params: for p in p.flattened_parameters: if p not in self.params: @@ -353,6 +360,21 @@ class ParamConcatenation(object): 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:])] + + parents = dict() + for p in self.params: + if p.has_parent(): + parent = p._parent_ + level = 0 + while parent is not None: + if parent in parents: + parents[parent] = max(level, parents[parent]) + else: + parents[parent] = level + level += 1 + parent = parent._parent_ + import operator + self.parents = map(lambda x: x[0], sorted(parents.iteritems(), key=operator.itemgetter(1))) #=========================================================================== # Get/set items, enable broadcasting #=========================================================================== @@ -366,24 +388,26 @@ class ParamConcatenation(object): val = val._vals() 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 update and p._notify_observers() + [numpy.place(p, ind[ps], vals[ps]) for p, ps in zip(self.params, self._param_slices_)] + if update: + self.update_all_params() def _vals(self): - return numpy.hstack([p._get_params() for p in self.params]) + return numpy.hstack([p._param_array_ for p in self.params]) #=========================================================================== # parameter operations: #=========================================================================== def update_all_params(self): - for p in self.params: - p._notify_observers() - + for par in self.parents: + par.notify_observers(-numpy.inf) + def constrain(self, constraint, warning=True): - [param.constrain(constraint, update=False) for param in self.params] + [param.constrain(constraint, trigger_parent=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, update=False) for param in self.params] + [param.constrain_positive(warning, trigger_parent=False) for param in self.params] self.update_all_params() constrain_positive.__doc__ = Param.constrain_positive.__doc__ @@ -393,12 +417,12 @@ class ParamConcatenation(object): fix = constrain_fixed def constrain_negative(self, warning=True): - [param.constrain_negative(warning, update=False) for param in self.params] + [param.constrain_negative(warning, trigger_parent=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, update=False) for param in self.params] + [param.constrain_bounded(lower, upper, warning, trigger_parent=False) for param in self.params] self.update_all_params() constrain_bounded.__doc__ = Param.constrain_bounded.__doc__ diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py index 6afa94cb..a78cf02d 100644 --- a/GPy/core/parameterization/parameter_core.py +++ b/GPy/core/parameterization/parameter_core.py @@ -1,37 +1,132 @@ # Copyright (c) 2012, GPy authors (see AUTHORS.txt). # Licensed under the BSD 3-clause license (see LICENSE.txt) +""" +Core module for parameterization. +This module implements all parameterization techniques, split up in modular bits. + +HierarchyError: +raised when an error with the hierarchy occurs (circles etc.) + +Observable: +Observable Pattern for patameterization + + +""" from transformations import Transformation, Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED +import numpy as np +import itertools __updated__ = '2013-12-16' +class HierarchyError(Exception): + """ + Gets thrown when something is wrong with the parameter hierarchy. + """ + def adjust_name_for_printing(name): + """ + Make sure a name can be printed, alongside used as a variable name. + """ if name is not None: - return name.replace(" ", "_").replace(".", "_").replace("-", "").replace("+", "").replace("!", "").replace("*", "").replace("/", "") + return name.replace(" ", "_").replace(".", "_").replace("-", "_m_").replace("+", "_p_").replace("!", "_I_").replace("**", "_xx_").replace("*", "_x_").replace("/", "_l_").replace("@",'_at_') return '' class Observable(object): + """ + Observable pattern for parameterization. + + This Object allows for observers to register with self and a (bound!) function + as an observer. Every time the observable changes, it sends a notification with + self as only argument to all its observers. + """ + _updated = True def __init__(self, *args, **kwargs): - from collections import defaultdict - self._observer_callables_ = defaultdict(list) - - def add_observer(self, observer, callble): - self._observer_callables_[observer].append(callble) - + self._observer_callables_ = [] + def __del__(self, *args, **kwargs): + del self._observer_callables_ + + def add_observer(self, observer, callble, priority=0): + self._insert_sorted(priority, observer, callble) + def remove_observer(self, observer, callble=None): - if observer in self._observer_callables_: - if callble is None: - del self._observer_callables_[observer] - elif callble in self._observer_callables_[observer]: - self._observer_callables_[observer].remove(callble) - if len(self._observer_callables_[observer]) == 0: - self.remove_observer(observer) - - def _notify_observers(self): - [[callble(self) for callble in callables] - for callables in self._observer_callables_.itervalues()] + to_remove = [] + for p, obs, clble in self._observer_callables_: + if callble is not None: + if (obs == observer) and (callble == clble): + to_remove.append((p, obs, clble)) + else: + if obs is observer: + to_remove.append((p, obs, clble)) + for r in to_remove: + self._observer_callables_.remove(r) + + def notify_observers(self, which=None, min_priority=None): + """ + Notifies all observers. Which is the element, which kicked off this + notification loop. + + NOTE: notifies only observers with priority p > min_priority! + ^^^^^^^^^^^^^^^^ + + :param which: object, which started this notification loop + :param min_priority: only notify observers with priority > min_priority + if min_priority is None, notify all observers in order + """ + if which is None: + which = self + if min_priority is None: + [callble(which) for _, _, callble in self._observer_callables_] + else: + for p, _, callble in self._observer_callables_: + if p <= min_priority: + break + callble(which) + def _insert_sorted(self, p, o, c): + ins = 0 + for pr, _, _ in self._observer_callables_: + if p > pr: + break + ins += 1 + self._observer_callables_.insert(ins, (p, o, c)) + class Pickleable(object): + """ + Make an object pickleable (See python doc 'pickling'). + + This class allows for pickling support by Memento pattern. + _getstate returns a memento of the class, which gets pickled. + _setstate() (re-)sets the state of the class to the memento + """ + #=========================================================================== + # Pickling operations + #=========================================================================== + def pickle(self, f, protocol=-1): + """ + :param f: either filename or open file object to write to. + if it is an open buffer, you have to make sure to close + it properly. + :param protocol: pickling protocol to use, python-pickle for details. + """ + import cPickle + if isinstance(f, str): + with open(f, 'w') as f: + cPickle.dump(self, f, protocol) + else: + cPickle.dump(self, f, protocol) + def __getstate__(self): + if self._has_get_set_state(): + return self._getstate() + return self.__dict__ + def __setstate__(self, state): + if self._has_get_set_state(): + self._setstate(state) + # TODO: maybe parameters_changed() here? + return + self.__dict__ = state + def _has_get_set_state(self): + return '_getstate' in vars(self.__class__) and '_setstate' in vars(self.__class__) def _getstate(self): """ Returns the state of this class in a memento pattern. @@ -58,70 +153,145 @@ class Pickleable(object): #=============================================================================== class Parentable(object): - _direct_parent_ = None + """ + Enable an Object to have a parent. + + Additionally this adds the parent_index, which is the index for the parent + to look for in its parameter list. + """ + _parent_ = None _parent_index_ = None def has_parent(self): - return self._direct_parent_ is not None - - def _notify_parent_change(self): - for p in self._parameters_: - p._parent_changed(self) + """ + Return whether this parentable object currently has a parent. + """ + return self._parent_ is not None def _parent_changed(self): + """ + Gets called, when the parent changed, so we can adjust our + inner attributes according to the new parent. + """ raise NotImplementedError, "shouldnt happen, Parentable objects need to be able to change their parent" + def _disconnect_parent(self, *args, **kw): + """ + Disconnect this object from its parent + """ + raise NotImplementedError, "Abstaract superclass" + @property def _highest_parent_(self): - if self._direct_parent_ is None: + """ + Gets the highest parent by traversing up to the root node of the hierarchy. + """ + if self._parent_ is None: return self - return self._direct_parent_._highest_parent_ + return self._parent_._highest_parent_ - def _notify_parameters_changed(self): - raise NotImplementedError, "shouldnt happen, abstract superclass" + def _notify_parent_change(self): + """ + Dont do anything if in leaf node + """ + pass + +class Gradcheckable(Parentable): + """ + Adds the functionality for an object to be gradcheckable. + It is just a thin wrapper of a call to the highest parent for now. + TODO: Can be done better, by only changing parameters of the current parameter handle, + such that object hierarchy only has to change for those. + """ + def __init__(self, *a, **kw): + super(Gradcheckable, self).__init__(*a, **kw) + def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3): + """ + Check the gradient of this parameter with respect to the highest parent's + objective function. + This is a three point estimate of the gradient, wiggling at the parameters + with a stepsize step. + The check passes if either the ratio or the difference between numerical and + analytical gradient is smaller then tolerance. -class Nameable(Parentable): + :param bool verbose: whether each parameter shall be checked individually. + :param float step: the stepsize for the numerical three point gradient estimate. + :param flaot tolerance: the tolerance for the gradient ratio or difference. + """ + 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): + """ + Perform the checkgrad on the model. + TODO: this can be done more efficiently, when doing it inside here + """ + raise NotImplementedError, "Need log likelihood to check gradient against" + + +class Nameable(Gradcheckable): + """ + Make an object nameable inside the hierarchy. + """ def __init__(self, name, *a, **kw): super(Nameable, self).__init__(*a, **kw) self._name = name or self.__class__.__name__ @property def name(self): + """ + The name of this object + """ return self._name @name.setter def name(self, name): + """ + Set the name of this object. + Tell the parent if the name has changed. + """ from_name = self.name assert isinstance(name, str) self._name = name if self.has_parent(): - self._direct_parent_._name_changed(self, from_name) - def hirarchy_name(self, adjust_for_printing=True): + self._parent_._name_changed(self, from_name) + def hierarchy_name(self, adjust_for_printing=True): + """ + return the name for this object with the parents names attached by dots. + + :param bool adjust_for_printing: whether to call :func:`~adjust_for_printing()` + on the names, recursively + """ if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x) else: adjust = lambda x: x if self.has_parent(): - return self._direct_parent_.hirarchy_name() + "." + adjust(self.name) + return self._parent_.hierarchy_name() + "." + adjust(self.name) return adjust(self.name) - -class Gradcheckable(Parentable): - def __init__(self, *a, **kw): - super(Gradcheckable, self).__init__(*a, **kw) - 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 Indexable(object): + """ + Enable enraveled indexes and offsets for this object. + The raveled index of an object is the index for its parameters in a flattened int array. + """ def _raveled_index(self): + """ + Flattened array of ints, specifying the index of this object. + This has to account for shaped parameters! + """ raise NotImplementedError, "Need to be able to get the raveled Index" def _internal_offset(self): + """ + The offset for this parameter inside its parent. + This has to account for shaped parameters! + """ return 0 def _offset_for(self, param): + """ + Return the offset of the param inside this parameterized object. + This does not need to account for shaped parameters, as it + basically just sums up the parameter sizes which come before param. + """ raise NotImplementedError, "shouldnt happen, offset required from non parameterization object?" def _raveled_index_for(self, param): @@ -134,6 +304,15 @@ class Indexable(object): class Constrainable(Nameable, Indexable): + """ + Make an object constrainable with Priors and Transformations. + TODO: Mappings!! + Adding a constraint to a Parameter means to tell the highest parent that + the constraint was added and making sure that all parameters covered + by this object are indeed conforming to the constraint. + + :func:`constrain()` and :func:`unconstrain()` are main methods here + """ def __init__(self, name, default_constraint=None, *a, **kw): super(Constrainable, self).__init__(name=name, *a, **kw) self._default_constraint_ = default_constraint @@ -143,12 +322,16 @@ class Constrainable(Nameable, Indexable): if self._default_constraint_ is not None: self.constrain(self._default_constraint_) - def _disconnect_parent(self, constr=None): + def _disconnect_parent(self, constr=None, *args, **kw): + """ + From Parentable: + disconnect the parent and set the new constraints to constr + """ if constr is None: constr = self.constraints.copy() self.constraints.clear() self.constraints = constr - self._direct_parent_ = None + self._parent_ = None self._parent_index_ = None self._connect_fixes() self._notify_parent_change() @@ -156,15 +339,15 @@ class Constrainable(Nameable, Indexable): #=========================================================================== # Fixing Parameters: #=========================================================================== - def constrain_fixed(self, value=None, warning=True): + def constrain_fixed(self, value=None, warning=True, trigger_parent=True): """ - Constrain this paramter to be fixed to the current value it carries. + Constrain this parameter 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.constrain(__fixed__, warning=warning) + self.constrain(__fixed__, warning=warning, trigger_parent=trigger_parent) rav_i = self._highest_parent_._raveled_index_for(self) self._highest_parent_._set_fixed(rav_i) fix = constrain_fixed @@ -178,20 +361,17 @@ class Constrainable(Nameable, Indexable): unfix = unconstrain_fixed def _set_fixed(self, index): - import numpy as np if not self._has_fixes(): self._fixes_ = np.ones(self.size, dtype=bool) self._fixes_[index] = FIXED if np.all(self._fixes_): self._fixes_ = None # ==UNFIXED def _set_unfixed(self, index): - import numpy as np if not self._has_fixes(): self._fixes_ = np.ones(self.size, dtype=bool) # rav_i = self._raveled_index_for(param)[index] self._fixes_[index] = UNFIXED if np.all(self._fixes_): self._fixes_ = None # ==UNFIXED def _connect_fixes(self): - import numpy as np fixed_indices = self.constraints[__fixed__] if fixed_indices.size > 0: self._fixes_ = np.ones(self.size, dtype=bool) * UNFIXED @@ -205,11 +385,20 @@ class Constrainable(Nameable, Indexable): #=========================================================================== # Prior Operations #=========================================================================== - def set_prior(self, prior, warning=True, update=True): + def set_prior(self, prior, warning=True): + """ + Set the prior for this object to prior. + :param :class:`~GPy.priors.Prior` prior: a prior to set for this parameter + :param bool warning: whether to warn if another prior was set for this parameter + """ repriorized = self.unset_priors() - self._add_to_index_operations(self.priors, repriorized, prior, warning, update) + self._add_to_index_operations(self.priors, repriorized, prior, warning) def unset_priors(self, *priors): + """ + Un-set all priors given from this parameter handle. + + """ return self._remove_from_index_operations(self.priors, priors) def log_prior(self): @@ -221,7 +410,6 @@ class Constrainable(Nameable, Indexable): def _log_prior_gradients(self): """evaluate the gradients of the priors""" - import numpy as np if self.priors.size > 0: x = self._get_params() ret = np.zeros(x.size) @@ -233,7 +421,7 @@ class Constrainable(Nameable, Indexable): # Constrain operations -> done #=========================================================================== - def constrain(self, transform, warning=True, update=True): + def constrain(self, transform, warning=True, trigger_parent=True): """ :param transform: the :py:class:`GPy.core.transformations.Transformation` to constrain the this parameter to. @@ -243,9 +431,9 @@ class Constrainable(Nameable, Indexable): :py:class:`GPy.core.transformations.Transformation`. """ if isinstance(transform, Transformation): - self._set_params(transform.initialize(self._get_params()), update=False) + self._param_array_[:] = transform.initialize(self._param_array_) reconstrained = self.unconstrain() - self._add_to_index_operations(self.constraints, reconstrained, transform, warning, update) + self._add_to_index_operations(self.constraints, reconstrained, transform, warning) def unconstrain(self, *transforms): """ @@ -256,30 +444,30 @@ class Constrainable(Nameable, Indexable): """ return self._remove_from_index_operations(self.constraints, transforms) - def constrain_positive(self, warning=True, update=True): + def constrain_positive(self, warning=True, trigger_parent=True): """ :param warning: print a warning if re-constraining parameters. Constrain this parameter to the default positive constraint. """ - self.constrain(Logexp(), warning=warning, update=update) + self.constrain(Logexp(), warning=warning, trigger_parent=trigger_parent) - def constrain_negative(self, warning=True, update=True): + def constrain_negative(self, warning=True, trigger_parent=True): """ :param warning: print a warning if re-constraining parameters. Constrain this parameter to the default negative constraint. """ - self.constrain(NegativeLogexp(), warning=warning, update=update) + self.constrain(NegativeLogexp(), warning=warning, trigger_parent=trigger_parent) - def constrain_bounded(self, lower, upper, warning=True, update=True): + def constrain_bounded(self, lower, upper, warning=True, trigger_parent=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=warning, update=update) + self.constrain(Logistic(lower, upper), warning=warning, trigger_parent=trigger_parent) def unconstrain_positive(self): """ @@ -302,6 +490,10 @@ class Constrainable(Nameable, Indexable): self.unconstrain(Logistic(lower, upper)) def _parent_changed(self, parent): + """ + From Parentable: + Called when the parent changed + """ from index_operations import ParameterIndexOperationsView self.constraints = ParameterIndexOperationsView(parent.constraints, parent._offset_for(self), self.size) self.priors = ParameterIndexOperationsView(parent.priors, parent._offset_for(self), self.size) @@ -309,17 +501,26 @@ class Constrainable(Nameable, Indexable): for p in self._parameters_: p._parent_changed(parent) - def _add_to_index_operations(self, which, reconstrained, transform, warning, update): + def _add_to_index_operations(self, which, reconstrained, what, warning): + """ + Helper preventing copy code. + This addes the given what (transformation, prior etc) to parameter index operations which. + revonstrained are reconstrained indices. + warn when reconstraining parameters if warning is True. + TODO: find out which parameters have changed specifically + """ if warning and reconstrained.size > 0: + # TODO: figure out which parameters have changed and only print those print "WARNING: reconstraining parameters {}".format(self.parameter_names() or self.name) - which.add(transform, self._raveled_index()) - if update: - self._notify_observers() + which.add(what, self._raveled_index()) - def _remove_from_index_operations(self, which, transforms): - if len(transforms) == 0: + def _remove_from_index_operations(self, which, what): + """ + Helper preventing copy code. + Remove given what (transform prior etc) from which param index ops. + """ + if len(what) == 0: transforms = which.properties() - import numpy as np removed = np.empty((0,), dtype=int) for t in transforms: unconstrained = which.remove(t, self._raveled_index()) @@ -329,15 +530,119 @@ class Constrainable(Nameable, Indexable): return removed +class OptimizationHandlable(Constrainable, Observable): + """ + This enables optimization handles on an Object as done in GPy 0.4. -class Parameterizable(Constrainable, Observable): + transformed: make sure the transformations and constraints etc are handled + """ + def transform(self): + [np.put(self._param_array_, ind, c.finv(self._param_array_[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] + + def untransform(self): + [np.put(self._param_array_, ind, c.f(self._param_array_[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] + + def _get_params_transformed(self): + # transformed parameters (apply transformation rules) + p = self._param_array_.copy() + [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] + if self._has_fixes(): + return p[self._fixes_] + return p + + def _set_params_transformed(self, p): + if p is self._param_array_: + p = p.copy() + if self._has_fixes(): self._param_array_[self._fixes_] = p + else: self._param_array_[:] = p + self.untransform() + self._trigger_params_changed() + + def _trigger_params_changed(self, trigger_parent=True): + [p._trigger_params_changed(trigger_parent=False) for p in self._parameters_] + if trigger_parent: min_priority = None + else: min_priority = -np.inf + self.notify_observers(None, min_priority) + + def _size_transformed(self): + return self.size - self.constraints[__fixed__].size +# +# def _untransform_params(self, p): +# # inverse apply transformations for parameters +# #p = p.copy() +# if self._has_fixes(): tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp +# [np.put(p, ind, c.f(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] +# return p +# +# def _get_params(self): +# """ +# get all parameters +# """ +# return self._param_array_ +# p = np.empty(self.size, dtype=np.float64) +# if self.size == 0: +# return p +# [np.put(p, ind, par._get_params()) for ind, par in itertools.izip(self._param)] +# return p + +# def _set_params(self, params, trigger_parent=True): +# self._param_array_.flat = params +# if trigger_parent: min_priority = None +# else: min_priority = -np.inf +# self.notify_observers(None, min_priority) + # don't overwrite this anymore! + #raise NotImplementedError, "Abstract superclass: This needs to be implemented in Param and Parameterizable" + + #=========================================================================== + # Optimization handles: + #=========================================================================== + def _get_param_names(self): + n = np.array([p.hierarchy_name() + '[' + 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 + + #=========================================================================== + # Randomizeable + #=========================================================================== + def randomize(self, rand_gen=np.random.normal, loc=0, scale=1, *args, **kwargs): + """ + Randomize the model. + Make this draw from the prior if one exists, else draw from given random generator + + :param rand_gen: numpy random number generator which takes args and kwargs + :param flaot loc: loc parameter for random number generator + :param float scale: scale parameter for random number generator + :param args, kwargs: will be passed through to random number generator + """ + # first take care of all parameters (from N(0,1)) + x = rand_gen(loc=loc, scale=scale, size=self._size_transformed(), *args, **kwargs) + # now draw from prior where possible + [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None] + self._set_params_transformed(x) # makes sure all of the tied parameters get the same init (since there's only one prior object...) + +class Parameterizable(OptimizationHandlable): def __init__(self, *args, **kwargs): super(Parameterizable, self).__init__(*args, **kwargs) - from GPy.core.parameterization.array_core import ParamList - _parameters_ = ParamList() + from GPy.core.parameterization.lists_and_dicts import ArrayList + _parameters_ = ArrayList() + self.size = 0 + self._param_array_ = np.empty(self.size, dtype=np.float64) + self._gradient_array_ = np.empty(self.size, dtype=np.float64) self._added_names_ = set() def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True): + """ + Get the names of all parameters of this model. + + :param bool add_self: whether to add the own name in front of names + :param bool adjust_for_printing: whether to call `adjust_name_for_printing` on names + :param bool recursive: whether to traverse through hierarchy and append leaf node names + """ if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x) else: adjust = lambda x: x if recursive: names = [xi for x in self._parameters_ for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)] @@ -349,15 +654,18 @@ class Parameterizable(Constrainable, Observable): def num_params(self): return len(self._parameters_) - def _add_parameter_name(self, param): + def _add_parameter_name(self, param, ignore_added_names=False): pname = adjust_name_for_printing(param.name) + if ignore_added_names: + self.__dict__[pname] = param + return # and makes sure to not delete programmatically added parameters if pname in self.__dict__: if not (param is self.__dict__[pname]): if pname in self._added_names_: del self.__dict__[pname] self._add_parameter_name(param) - else: + elif pname not in dir(self): self.__dict__[pname] = param self._added_names_.add(pname) @@ -372,47 +680,187 @@ class Parameterizable(Constrainable, Observable): def _name_changed(self, param, old_name): self._remove_parameter_name(None, old_name) self._add_parameter_name(param) + + #========================================================================= + # Gradient handling + #========================================================================= + @property + def gradient(self): + return self._gradient_array_ + + @gradient.setter + def gradient(self, val): + self._gradient_array_[:] = val + #=========================================================================== + # def _collect_gradient(self, target): + # [p._collect_gradient(target[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)] + #=========================================================================== + + #=========================================================================== + # def _set_params(self, params, trigger_parent=True): + # [p._set_params(params[s], trigger_parent=False) for p, s in itertools.izip(self._parameters_, self._param_slices_)] + # if trigger_parent: min_priority = None + # else: min_priority = -np.inf + # self.notify_observers(None, min_priority) + #=========================================================================== + + #=========================================================================== + # def _set_gradient(self, g): + # [p._set_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)] + #=========================================================================== + + def add_parameter(self, param, index=None, _ignore_added_names=False): + """ + :param parameters: the parameters to add + :type parameters: list of or one :py:class:`GPy.core.param.Param` + :param [index]: index of where to put parameters + + :param bool _ignore_added_names: whether the name of the parameter overrides a possibly existing field + + Add all parameters to this param class, you can insert parameters + at any given index using the :func:`list.insert` syntax + """ + # if param.has_parent(): + # raise AttributeError, "parameter {} already in another model, create new object (or copy) for adding".format(param._short()) + if param in self._parameters_ and index is not None: + self.remove_parameter(param) + self.add_parameter(param, index) + elif param not in self._parameters_: + if param.has_parent(): + parent = param._parent_ + while parent is not None: + if parent is self: + raise HierarchyError, "You cannot add a parameter twice into the hierarchy" + parent = parent._parent_ + param._parent_.remove_parameter(param) + # make sure the size is set + if index is None: + self.constraints.update(param.constraints, self.size) + self.priors.update(param.priors, self.size) + self._parameters_.append(param) + else: + start = sum(p.size for p in self._parameters_[:index]) + self.constraints.shift_right(start, param.size) + self.priors.shift_right(start, param.size) + self.constraints.update(param.constraints, start) + self.priors.update(param.priors, start) + self._parameters_.insert(index, param) - def _collect_gradient(self, target): - import itertools - [p._collect_gradient(target[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)] + param.add_observer(self, self._pass_through_notify_observers, -np.inf) + + self.size += param.size - def _set_gradient(self, g): - import itertools - [p._set_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)] + self._connect_parameters(ignore_added_names=_ignore_added_names) + self._notify_parent_change() + self._connect_fixes() + else: + raise RuntimeError, """Parameter exists already added and no copy made""" - def _get_params(self): - import numpy as np - # don't overwrite this anymore! - if not self.size: - return np.empty(shape=(0,), dtype=np.float64) - return np.hstack([x._get_params() for x in self._parameters_ if x.size > 0]) - def _set_params(self, params, update=True): - # don't overwrite this anymore! - import itertools - [p._set_params(params[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)] - self._notify_parameters_changed() + def add_parameters(self, *parameters): + """ + convenience method for adding several + parameters without gradient specification + """ + [self.add_parameter(p) for p in parameters] + def remove_parameter(self, param): + """ + :param param: param object to remove from being a parameter of this parameterized object. + """ + if not param in self._parameters_: + raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short()) + + start = sum([p.size for p in self._parameters_[:param._parent_index_]]) + self._remove_parameter_name(param) + self.size -= param.size + del self._parameters_[param._parent_index_] + + param._disconnect_parent() + param.remove_observer(self, self._pass_through_notify_observers) + self.constraints.shift_left(start, param.size) + + self._connect_fixes() + self._connect_parameters() + self._notify_parent_change() + + parent = self._parent_ + while parent is not None: + parent._connect_fixes() + parent._connect_parameters() + parent._notify_parent_change() + parent = parent._parent_ + + def _connect_parameters(self, ignore_added_names=False): + # connect parameterlist to this parameterized object + # This just sets up the right connection for the params objects + # to be used as parameters + # it also sets the constraints for each parameter to the constraints + # of their respective parents + if not hasattr(self, "_parameters_") or len(self._parameters_) < 1: + # no parameters for this class + return + old_size = 0 + self._param_array_ = np.empty(self.size, dtype=np.float64) + self._gradient_array_ = np.empty(self.size, dtype=np.float64) + + self._param_slices_ = [] + + for i, p in enumerate(self._parameters_): + p._parent_ = self + p._parent_index_ = i + + pslice = slice(old_size, old_size+p.size) + pi_old_size = old_size + for pi in p.flattened_parameters: + pislice = slice(pi_old_size, pi_old_size+pi.size) + + self._param_array_[pislice] = pi._param_array_.flat + self._gradient_array_[pislice] = pi._gradient_array_.flat + + pi._param_array_.data = self._param_array_[pislice].data + pi._gradient_array_.data = self._gradient_array_[pislice].data + + pi_old_size += pi.size + + p._param_array_.data = self._param_array_[pslice].data + p._gradient_array_.data = self._gradient_array_[pslice].data + + self._param_slices_.append(pslice) + self._add_parameter_name(p, ignore_added_names=ignore_added_names) + old_size += p.size + + #=========================================================================== + # notification system + #=========================================================================== + def _parameters_changed_notification(self, which): + self.parameters_changed() + def _pass_through_notify_observers(self, which): + self.notify_observers(which) + + #=========================================================================== + # TODO: not working yet + #=========================================================================== def copy(self): """Returns a (deep) copy of the current model""" import copy from .index_operations import ParameterIndexOperations, ParameterIndexOperationsView - from .array_core import ParamList + from .lists_and_dicts import ArrayList dc = dict() for k, v in self.__dict__.iteritems(): - if k not in ['_direct_parent_', '_parameters_', '_parent_index_'] + self.parameter_names(): + if k not in ['_parent_', '_parameters_', '_parent_index_', '_observer_callables_'] + self.parameter_names(recursive=False): if isinstance(v, (Constrainable, ParameterIndexOperations, ParameterIndexOperationsView)): dc[k] = v.copy() else: dc[k] = copy.deepcopy(v) if k == '_parameters_': params = [p.copy() for p in v] - - dc['_direct_parent_'] = None + + dc['_parent_'] = None dc['_parent_index_'] = None - dc['_parameters_'] = ParamList() + dc['_observer_callables_'] = [] + dc['_parameters_'] = ArrayList() dc['constraints'].clear() dc['priors'].clear() dc['size'] = 0 @@ -421,16 +869,20 @@ class Parameterizable(Constrainable, Observable): s.__dict__ = dc for p in params: - s.add_parameter(p) + s.add_parameter(p, _ignore_added_names=True) return s + + #=========================================================================== + # From being parentable, we have to define the parent_change notification + #=========================================================================== + def _notify_parent_change(self): + """ + Notify all parameters that the parent has changed + """ + for p in self._parameters_: + p._parent_changed(self) - def _notify_parameters_changed(self): - self.parameters_changed() - self._notify_observers() - if self.has_parent(): - self._direct_parent_._notify_parameters_changed() - def parameters_changed(self): """ This method gets called when parameters have changed. diff --git a/GPy/core/parameterization/parameterized.py b/GPy/core/parameterization/parameterized.py index f5fcc6ad..6d06018a 100644 --- a/GPy/core/parameterization/parameterized.py +++ b/GPy/core/parameterization/parameterized.py @@ -7,11 +7,17 @@ import cPickle import itertools from re import compile, _pattern_type from param import ParamConcatenation -from parameter_core import Constrainable, Pickleable, Parentable, Observable, Parameterizable, adjust_name_for_printing, Gradcheckable +from parameter_core import Pickleable, Parameterizable, adjust_name_for_printing from transformations import __fixed__ -from array_core import ParamList +from lists_and_dicts import ArrayList -class Parameterized(Parameterizable, Pickleable, Gradcheckable): +class ParametersChangedMeta(type): + def __call__(self, *args, **kw): + instance = super(ParametersChangedMeta, self).__call__(*args, **kw) + instance.parameters_changed() + return instance + +class Parameterized(Parameterizable, Pickleable): """ Parameterized class @@ -53,11 +59,18 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): If you want to operate on all parameters use m[''] to wildcard select all paramters and concatenate them. Printing m[''] will result in printing of all parameters in detail. """ + #=========================================================================== + # Metaclass for parameters changed after init. + # This makes sure, that parameters changed will always be called after __init__ + # **Never** call parameters_changed() yourself + __metaclass__ = ParametersChangedMeta + #=========================================================================== def __init__(self, name=None, *a, **kw): super(Parameterized, self).__init__(name=name, parent=None, parent_index=None, *a, **kw) self._in_init_ = True - self._parameters_ = ParamList() + self._parameters_ = ArrayList() self.size = sum(p.size for p in self._parameters_) + self.add_observer(self, self._parameters_changed_notification, -100) if not self._has_fixes(): self._fixes_ = None self._param_slices_ = [] @@ -65,7 +78,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): del self._in_init_ def build_pydot(self, G=None): - import pydot + import pydot # @UnresolvedImport iamroot = False if G is None: G = pydot.Dot(graph_type='digraph') @@ -87,116 +100,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): return G return node - - def add_parameter(self, param, index=None): - """ - :param parameters: the parameters to add - :type parameters: list of or one :py:class:`GPy.core.param.Param` - :param [index]: index of where to put parameters - - - Add all parameters to this param class, you can insert parameters - at any given index using the :func:`list.insert` syntax - """ - # if param.has_parent(): - # raise AttributeError, "parameter {} already in another model, create new object (or copy) for adding".format(param._short()) - if param in self._parameters_ and index is not None: - self.remove_parameter(param) - self.add_parameter(param, index) - elif param not in self._parameters_: - # make sure the size is set - if index is None: - self.constraints.update(param.constraints, self.size) - self.priors.update(param.priors, self.size) - self._parameters_.append(param) - else: - start = sum(p.size for p in self._parameters_[:index]) - self.constraints.shift_right(start, param.size) - self.priors.shift_right(start, param.size) - self.constraints.update(param.constraints, start) - self.priors.update(param.priors, start) - self._parameters_.insert(index, param) - self.size += param.size - else: - raise RuntimeError, """Parameter exists already added and no copy made""" - self._connect_parameters() - self._notify_parent_change() - self._connect_fixes() - - - def add_parameters(self, *parameters): - """ - convenience method for adding several - parameters without gradient specification - """ - [self.add_parameter(p) for p in parameters] - - def remove_parameter(self, param): - """ - :param param: param object to remove from being a parameter of this parameterized object. - """ - if not param in self._parameters_: - raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short()) - - start = sum([p.size for p in self._parameters_[:param._parent_index_]]) - self._remove_parameter_name(param) - self.size -= param.size - del self._parameters_[param._parent_index_] - - param._disconnect_parent() - param.remove_observer(self, self._notify_parameters_changed) - self.constraints.shift_left(start, param.size) - self._connect_fixes() - self._connect_parameters() - self._notify_parent_change() - - - def _connect_parameters(self): - # connect parameterlist to this parameterized object - # This just sets up the right connection for the params objects - # to be used as parameters - # it also sets the constraints for each parameter to the constraints - # of their respective parents - if not hasattr(self, "_parameters_") or len(self._parameters_) < 1: - # no parameters for this class - return - sizes = [0] - self._param_slices_ = [] - for i, p in enumerate(self._parameters_): - p._direct_parent_ = self - p._parent_index_ = i - sizes.append(p.size + sizes[-1]) - self._param_slices_.append(slice(sizes[-2], sizes[-1])) - self._add_parameter_name(p) - - #=========================================================================== - # Pickling operations - #=========================================================================== - def pickle(self, f, protocol=-1): - """ - :param f: either filename or open file object to write to. - if it is an open buffer, you have to make sure to close - it properly. - :param protocol: pickling protocol to use, python-pickle for details. - """ - if isinstance(f, str): - with open(f, 'w') as f: - cPickle.dump(self, f, protocol) - else: - cPickle.dump(self, f, protocol) - - def __getstate__(self): - if self._has_get_set_state(): - return self._getstate() - return self.__dict__ - def __setstate__(self, state): - if self._has_get_set_state(): - self._setstate(state) # set state - # self._set_params(self._get_params()) # restore all values - return - self.__dict__ = state - def _has_get_set_state(self): - return '_getstate' in vars(self.__class__) and '_setstate' in vars(self.__class__) + def _getstate(self): """ Get the current state of the class, @@ -225,60 +129,33 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): self._connect_parameters() self.parameters_changed() #=========================================================================== + # Override copy to handle programmatically added observers + #=========================================================================== + def copy(self): + c = super(Pickleable, self).copy() + c.add_observer(c, c._parameters_changed_notification, -100) + return c + + #=========================================================================== # Gradient control #=========================================================================== def _transform_gradients(self, g): if self.has_parent(): return g - x = self._get_params() - [numpy.put(g, i, g[i] * c.gradfactor(x[i])) for c, i in self.constraints.iteritems() if c != __fixed__] - for p in self.flattened_parameters: - for t, i in p._tied_to_me_.iteritems(): - g[self._offset_for(p) + numpy.array(list(i))] += g[self._raveled_index_for(t)] + [numpy.put(g, i, g[i] * c.gradfactor(self._param_array_[i])) for c, i in self.constraints.iteritems() if c != __fixed__] if self._has_fixes(): return g[self._fixes_] return g + + #=========================================================================== - # Optimization handles: + # Indexable #=========================================================================== - def _get_param_names(self): - n = numpy.array([p.hirarchy_name() + '[' + 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 - def _get_params_transformed(self): - # transformed parameters (apply transformation rules) - p = self._get_params() - [numpy.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] - if self._has_fixes(): - return p[self._fixes_] - return p - def _set_params_transformed(self, p): - # inverse apply transformations for parameters and set the resulting parameters - self._set_params(self._untransform_params(p)) - def _untransform_params(self, p): - p = p.copy() - if self._has_fixes(): tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp - [numpy.put(p, ind, c.f(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] - return p - #=========================================================================== - # Indexable Handling - #=========================================================================== - def _backtranslate_index(self, param, ind): - # translate an index in parameterized indexing into the index of param - ind = ind - self._offset_for(param) - ind = ind[ind >= 0] - internal_offset = param._internal_offset() - ind = ind[ind < param.size + internal_offset] - return ind def _offset_for(self, param): # get the offset in the parameterized index array for param if param.has_parent(): - if param._direct_parent_._get_original(param) in self._parameters_: - return self._param_slices_[param._direct_parent_._get_original(param)._parent_index_].start - return self._offset_for(param._direct_parent_) + param._direct_parent_._offset_for(param) + if param._parent_._get_original(param) in self._parameters_: + return self._param_slices_[param._parent_._get_original(param)._parent_index_].start + return self._offset_for(param._parent_) + param._parent_._offset_for(param) return 0 def _raveled_index_for(self, param): @@ -297,34 +174,22 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): this is not in the global view of things! """ return numpy.r_[:self.size] - #=========================================================================== - # Fixing parameters: - #=========================================================================== - def _fixes_for(self, param): - if self._has_fixes(): - return self._fixes_[self._raveled_index_for(param)] - return numpy.ones(self.size, dtype=bool)[self._raveled_index_for(param)] + #=========================================================================== # 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(): - return False - return not self._fixes_[self._raveled_index_for(param)].any() - # return not self._fixes_[self._offset_for(param): self._offset_for(param)+param._realsize_].any() @property def is_fixed(self): for p in self._parameters_: if not p.is_fixed: return False return True + def _get_original(self, param): # if advanced indexing is activated it happens that the array is a copy # you can retrieve the original param through this method, by passing # the copy here return self._parameters_[param._parent_index_] + #=========================================================================== # Get/set parameters: #=========================================================================== @@ -352,9 +217,13 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): return ParamConcatenation(paramlist) def __setitem__(self, name, value, paramlist=None): - try: param = self.__getitem__(name, paramlist) - except AttributeError as a: raise a - param[:] = value + if isinstance(name, (slice, tuple, np.ndarray)): + self._param_array_[name] = value + else: + try: param = self.__getitem__(name, paramlist) + except AttributeError as a: raise a + param[:] = value + def __setattr__(self, name, val): # override the default behaviour, if setting a param, so broadcasting can by used if hasattr(self, '_parameters_'): @@ -365,7 +234,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): # Printing: #=========================================================================== def _short(self): - return self.hirarchy_name() + return self.hierarchy_name() @property def flattened_parameters(self): return [xi for x in self._parameters_ for xi in x.flattened_parameters] @@ -373,11 +242,6 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): def _parameter_sizes_(self): return [x.size for x in self._parameters_] @property - def size_transformed(self): - if self._has_fixes(): - return sum(self._fixes_) - return self.size - @property def parameter_shapes(self): return [xi for x in self._parameters_ for xi in x.parameter_shapes] @property @@ -404,7 +268,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable): cl = max([len(str(x)) if x else 0 for x in constrs + ["Constraint"]]) tl = max([len(str(x)) if x else 0 for x in ts + ["Tied to"]]) pl = max([len(str(x)) if x else 0 for x in prirs + ["Prior"]]) - format_spec = " \033[1m{{name:<{0}s}}\033[0;0m | {{desc:^{1}s}} | {{const:^{2}s}} | {{pri:^{3}s}} | {{t:^{4}s}}".format(nl, sl, cl, pl, tl) + format_spec = " \033[1m{{name:<{0}s}}\033[0;0m | {{desc:>{1}s}} | {{const:^{2}s}} | {{pri:^{3}s}} | {{t:^{4}s}}".format(nl, sl, cl, pl, tl) to_print = [] for n, d, c, t, p in itertools.izip(names, desc, constrs, ts, prirs): to_print.append(format_spec.format(name=n, desc=d, const=c, t=t, pri=p)) diff --git a/GPy/core/parameterization/priors.py b/GPy/core/parameterization/priors.py index 906fe003..29adc923 100644 --- a/GPy/core/parameterization/priors.py +++ b/GPy/core/parameterization/priors.py @@ -64,6 +64,36 @@ class Gaussian(Prior): return np.random.randn(n) * self.sigma + self.mu +class Uniform(Prior): + domain = _REAL + _instances = [] + def __new__(cls, lower, upper): # Singleton: + if cls._instances: + cls._instances[:] = [instance for instance in cls._instances if instance()] + for instance in cls._instances: + if instance().lower == lower and instance().upper == upper: + return instance() + o = super(Prior, cls).__new__(cls, lower, upper) + cls._instances.append(weakref.ref(o)) + return cls._instances[-1]() + + def __init__(self, lower, upper): + self.lower = float(lower) + self.upper = float(upper) + + def __str__(self): + return "[" + str(np.round(self.lower)) + ', ' + str(np.round(self.upper)) + ']' + + def lnpdf(self, x): + region = (x>=self.lower) * (x<=self.upper) + return region + + def lnpdf_grad(self, x): + return np.zeros(x.shape) + + def rvs(self, n): + return np.random.uniform(self.lower, self.upper, size=n) + class LogGaussian(Prior): """ Implementation of the univariate *log*-Gaussian probability function, coupled with random variables. diff --git a/GPy/core/parameterization/transformations.py b/GPy/core/parameterization/transformations.py index 36291ca3..5cda8d46 100644 --- a/GPy/core/parameterization/transformations.py +++ b/GPy/core/parameterization/transformations.py @@ -6,8 +6,11 @@ import numpy as np from domains import _POSITIVE,_NEGATIVE, _BOUNDED import weakref +import sys +#_lim_val = -np.log(sys.float_info.epsilon) + _exp_lim_val = np.finfo(np.float64).max -_lim_val = np.log(_exp_lim_val)#-np.log(sys.float_info.epsilon) +_lim_val = np.log(_exp_lim_val) #=============================================================================== # Fixing constants @@ -35,7 +38,6 @@ class Transformation(object): """ produce a sensible initial value for f(x)""" raise NotImplementedError def plot(self, xlabel=r'transformed $\theta$', ylabel=r'$\theta$', axes=None, *args,**kw): - import sys assert "matplotlib" in sys.modules, "matplotlib package has not been imported." import matplotlib.pyplot as plt from ...plotting.matplot_dep import base_plots @@ -52,10 +54,10 @@ class Transformation(object): class Logexp(Transformation): domain = _POSITIVE def f(self, x): - return np.where(x>_lim_val, x, np.log(1. + np.exp(np.clip(x, -np.inf, _lim_val)))) + return np.where(x>_lim_val, x, np.log(1. + np.exp(np.clip(x, -_lim_val, _lim_val)))) #raises overflow warning: return np.where(x>_lim_val, x, np.log(1. + np.exp(x))) def finv(self, f): - return np.where(f>_lim_val, f, np.log(np.exp(f) - 1.)) + return np.where(f>_lim_val, f, np.log(np.exp(f+1e-20) - 1.)) def gradfactor(self, f): return np.where(f>_lim_val, 1., 1 - np.exp(-f)) def initialize(self, f): diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py index c51a8021..8bc7ca59 100644 --- a/GPy/core/parameterization/variational.py +++ b/GPy/core/parameterization/variational.py @@ -7,10 +7,10 @@ Created on 6 Nov 2013 import numpy as np from parameterized import Parameterized from param import Param -from transformations import Logexp +from transformations import Logexp, Logistic class VariationalPrior(Parameterized): - def __init__(self, name=None, **kw): + def __init__(self, name='latent space', **kw): super(VariationalPrior, self).__init__(name=name, **kw) def KL_divergence(self, variational_posterior): @@ -34,12 +34,12 @@ class NormalPrior(VariationalPrior): variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5 class SpikeAndSlabPrior(VariationalPrior): - def __init__(self, variance = 1.0, pi = 0.5, name='SpikeAndSlabPrior', **kw): + def __init__(self, pi, variance = 1.0, name='SpikeAndSlabPrior', **kw): super(VariationalPrior, self).__init__(name=name, **kw) assert variance==1.0, "Not Implemented!" - self.pi = Param('pi', pi) + self.pi = Param('pi', pi, Logistic(1e-10,1.-1e-10)) self.variance = Param('variance',variance) - self.add_parameters(self.pi, self.variance) + self.add_parameters(self.pi) def KL_divergence(self, variational_posterior): mu = variational_posterior.mean @@ -58,6 +58,8 @@ class SpikeAndSlabPrior(VariationalPrior): gamma.gradient -= np.log((1-self.pi)/self.pi*gamma/(1.-gamma))+(np.square(mu)+S-np.log(S)-1.)/2. mu.gradient -= gamma*mu S.gradient -= (1. - (1. / (S))) * gamma /2. + self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0) + class VariationalPosterior(Parameterized): @@ -103,7 +105,7 @@ class SpikeAndSlabPosterior(VariationalPosterior): binary_prob : the probability of the distribution on the slab part. """ super(SpikeAndSlabPosterior, self).__init__(means, variances, name) - self.gamma = Param("binary_prob",binary_prob,) + self.gamma = Param("binary_prob",binary_prob, Logistic(1e-10,1.-1e-10)) self.add_parameter(self.gamma) def plot(self, *args): @@ -115,4 +117,4 @@ class SpikeAndSlabPosterior(VariationalPosterior): import sys assert "matplotlib" in sys.modules, "matplotlib package has not been imported." from ...plotting.matplot_dep import variational_plots - return variational_plots.plot(self,*args) + return variational_plots.plot_SpikeSlab(self,*args) diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py index 751a295d..f4f34a5e 100644 --- a/GPy/core/sparse_gp.py +++ b/GPy/core/sparse_gp.py @@ -48,7 +48,6 @@ class SparseGP(GP): GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name) self.add_parameter(self.Z, index=0) - self.parameters_changed() def has_uncertain_inputs(self): return isinstance(self.X, VariationalPosterior) @@ -60,11 +59,9 @@ class SparseGP(GP): #gradients wrt kernel dL_dKmm = self.grad_dict.pop('dL_dKmm') self.kern.update_gradients_full(dL_dKmm, self.Z, None) - target = np.zeros(self.kern.size) - self.kern._collect_gradient(target) + target = self.kern.gradient.copy() self.kern.update_gradients_expectations(variational_posterior=self.X, Z=self.Z, **self.grad_dict) - self.kern._collect_gradient(target) - self.kern._set_gradient(target) + self.kern.gradient += target #gradients wrt Z self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z) @@ -72,24 +69,22 @@ class SparseGP(GP): self.grad_dict['dL_dpsi1'], self.grad_dict['dL_dpsi2'], Z=self.Z, variational_posterior=self.X) else: #gradients wrt kernel - target = np.zeros(self.kern.size) self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X) - self.kern._collect_gradient(target) + target = self.kern.gradient.copy() self.kern.update_gradients_full(self.grad_dict['dL_dKnm'], self.X, self.Z) - self.kern._collect_gradient(target) + target += self.kern.gradient self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None) - self.kern._collect_gradient(target) - self.kern._set_gradient(target) + self.kern.gradient += target #gradients wrt Z self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z) self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X) - def _raw_predict(self, Xnew, X_variance_new=None, full_cov=False): + def _raw_predict(self, Xnew, full_cov=False): """ Make a prediction for the latent function values """ - if X_variance_new is None: + if not isinstance(Xnew, VariationalPosterior): Kx = self.kern.K(self.Z, Xnew) mu = np.dot(Kx.T, self.posterior.woodbury_vector) if full_cov: @@ -100,13 +95,13 @@ class SparseGP(GP): Kxx = self.kern.Kdiag(Xnew) var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T else: - Kx = self.kern.psi1(self.Z, Xnew, X_variance_new) - mu = np.dot(Kx, self.Cpsi1V) + Kx = self.kern.psi1(self.Z, Xnew) + mu = np.dot(Kx, self.posterior.woodbury_vector) if full_cov: raise NotImplementedError, "TODO" else: - Kxx = self.kern.psi0(self.Z, Xnew, X_variance_new) - psi2 = self.kern.psi2(self.Z, Xnew, X_variance_new) + Kxx = self.kern.psi0(self.Z, Xnew) + psi2 = self.kern.psi2(self.Z, Xnew) var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1) return mu, var @@ -114,14 +109,12 @@ class SparseGP(GP): def _getstate(self): """ Get the current state of the class, - here just all the indices, rest can get recomputed """ - return GP._getstate(self) + [self.Z, - self.num_inducing, - self.X_variance] + return GP._getstate(self) + [ + self.Z, + self.num_inducing] def _setstate(self, state): - self.X_variance = state.pop() self.num_inducing = state.pop() self.Z = state.pop() GP._setstate(self, state) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index 2044f08d..818dff69 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -187,10 +187,10 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False): _np.random.seed(1234) x = _np.linspace(0, 4 * _np.pi, N)[:, None] - s1 = _np.vectorize(lambda x: -_np.sin(_np.exp(x))) + s1 = _np.vectorize(lambda x: _np.sin(x)) s2 = _np.vectorize(lambda x: _np.cos(x)**2) s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x))) - sS = _np.vectorize(lambda x: x*_np.sin(x)) + sS = _np.vectorize(lambda x: _np.cos(x)) s1 = s1(x) s2 = s2(x) @@ -202,7 +202,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False): s3 -= s3.mean(); s3 /= s3.std(0) sS -= sS.mean(); sS /= sS.std(0) - S1 = _np.hstack([s1, s2, sS]) + S1 = _np.hstack([s1, sS]) S2 = _np.hstack([s2, s3, sS]) S3 = _np.hstack([s3, sS]) @@ -270,7 +270,7 @@ def bgplvm_simulation(optimize=True, verbose=1, from GPy import kern from GPy.models import BayesianGPLVM - D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9 + D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9 _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim) Y = Ylist[0] k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) @@ -294,7 +294,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1, from GPy.models import BayesianGPLVM from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData - D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9 + D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 7, 9 _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim) Y = Ylist[0] k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) @@ -515,3 +515,28 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose lvm_visualizer.close() return m + +def ssgplvm_simulation_linear(): + import numpy as np + import GPy + N, D, Q = 1000, 20, 5 + pi = 0.2 + + def sample_X(Q, pi): + x = np.empty(Q) + dies = np.random.rand(Q) + for q in xrange(Q): + if dies[q]n',self.variances,gamma,np.square(mu)+S) +# return (self.variances*gamma*(np.square(mu)+S)).sum(axis=1) + else: + return np.sum(self.variances * self._mu2S(variational_posterior), 1) def psi1(self, Z, variational_posterior): - return self.K(variational_posterior.mean, Z) #the variance, it does nothing + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + gamma = variational_posterior.binary_prob + mu = variational_posterior.mean + return np.einsum('nq,q,mq,nq->nm',gamma,self.variances,Z,mu) +# return (self.variances*gamma*mu).sum(axis=1) + else: + return self.K(variational_posterior.mean, Z) #the variance, it does nothing @Cache_this(limit=1) def psi2(self, Z, variational_posterior): - ZA = Z * self.variances - ZAinner = self._ZAinner(variational_posterior, Z) - return np.dot(ZAinner, ZA.T) + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + gamma = variational_posterior.binary_prob + mu = variational_posterior.mean + S = variational_posterior.variance + mu2 = np.square(mu) + variances2 = np.square(self.variances) + tmp = np.einsum('nq,q,mq,nq->nm',gamma,self.variances,Z,mu) + return np.einsum('nq,q,mq,oq,nq->nmo',gamma,variances2,Z,Z,mu2+S)+\ + np.einsum('nm,no->nmo',tmp,tmp) - np.einsum('nq,q,mq,oq,nq->nmo',np.square(gamma),variances2,Z,Z,mu2) + else: + ZA = Z * self.variances + ZAinner = self._ZAinner(variational_posterior, Z) + return np.dot(ZAinner, ZA.T) def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - #psi1 - self.update_gradients_full(dL_dpsi1, variational_posterior.mean, Z) - # psi0: - tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior) - if self.ARD: self.variances.gradient += tmp.sum(0) - else: self.variances.gradient += tmp.sum() - #psi2 - if self.ARD: - tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * Z[None, None, :, :]) - self.variances.gradient += 2.*tmp.sum(0).sum(0).sum(0) + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + gamma = variational_posterior.binary_prob + mu = variational_posterior.mean + S = variational_posterior.variance + mu2S = np.square(mu)+S + + _dpsi2_dvariance, _, _, _, _ = linear_psi_comp._psi2computations(self.variances, Z, mu, S, gamma) + grad = np.einsum('n,nq,nq->q',dL_dpsi0,gamma,mu2S) + np.einsum('nm,nq,mq,nq->q',dL_dpsi1,gamma,Z,mu) +\ + np.einsum('nmo,nmoq->q',dL_dpsi2,_dpsi2_dvariance) + if self.ARD: + self.variances.gradient = grad + else: + self.variances.gradient = grad.sum() else: - self.variances.gradient += 2.*np.sum(dL_dpsi2 * self.psi2(Z, variational_posterior))/self.variances + #psi1 + self.update_gradients_full(dL_dpsi1, variational_posterior.mean, Z) + # psi0: + tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior) + if self.ARD: self.variances.gradient += tmp.sum(0) + else: self.variances.gradient += tmp.sum() + #psi2 + if self.ARD: + tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * Z[None, None, :, :]) + self.variances.gradient += 2.*tmp.sum(0).sum(0).sum(0) + else: + self.variances.gradient += 2.*np.sum(dL_dpsi2 * self.psi2(Z, variational_posterior))/self.variances def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - #psi1 - grad = self.gradients_X(dL_dpsi1.T, Z, variational_posterior.mean) - #psi2 - self._weave_dpsi2_dZ(dL_dpsi2, Z, variational_posterior, grad) - return grad + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + gamma = variational_posterior.binary_prob + mu = variational_posterior.mean + S = variational_posterior.variance + _, _, _, _, _dpsi2_dZ = linear_psi_comp._psi2computations(self.variances, Z, mu, S, gamma) + + grad = np.einsum('nm,nq,q,nq->mq',dL_dpsi1,gamma, self.variances,mu) +\ + np.einsum('nmo,noq->mq',dL_dpsi2,_dpsi2_dZ) + + return grad + else: + #psi1 + grad = self.gradients_X(dL_dpsi1.T, Z, variational_posterior.mean) + #psi2 + self._weave_dpsi2_dZ(dL_dpsi2, Z, variational_posterior, grad) + return grad def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape) - # psi0 - grad_mu += dL_dpsi0[:, None] * (2.0 * variational_posterior.mean * self.variances) - grad_S += dL_dpsi0[:, None] * self.variances - # psi1 - grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1) - # psi2 - self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S) - - return grad_mu, grad_S + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + gamma = variational_posterior.binary_prob + mu = variational_posterior.mean + S = variational_posterior.variance + mu2S = np.square(mu)+S + _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _ = linear_psi_comp._psi2computations(self.variances, Z, mu, S, gamma) + + grad_gamma = np.einsum('n,q,nq->nq',dL_dpsi0,self.variances,mu2S) + np.einsum('nm,q,mq,nq->nq',dL_dpsi1,self.variances,Z,mu) +\ + np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dgamma) + grad_mu = np.einsum('n,nq,q,nq->nq',dL_dpsi0,gamma,2.*self.variances,mu) + np.einsum('nm,nq,q,mq->nq',dL_dpsi1,gamma,self.variances,Z) +\ + np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dmu) + grad_S = np.einsum('n,nq,q->nq',dL_dpsi0,gamma,self.variances) + np.einsum('nmo,nmoq->nq',dL_dpsi2,_dpsi2_dS) + + return grad_mu, grad_S, grad_gamma + else: + grad_mu, grad_S = np.zeros(variational_posterior.mean.shape), np.zeros(variational_posterior.mean.shape) + # psi0 + grad_mu += dL_dpsi0[:, None] * (2.0 * variational_posterior.mean * self.variances) + grad_S += dL_dpsi0[:, None] * self.variances + # psi1 + grad_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1) + # psi2 + self._weave_dpsi2_dmuS(dL_dpsi2, Z, variational_posterior, grad_mu, grad_S) + + return grad_mu, grad_S #--------------------------------------------------# # Helpers for psi statistics # diff --git a/GPy/kern/_src/periodic.py b/GPy/kern/_src/periodic.py index e4e659a2..36ff3527 100644 --- a/GPy/kern/_src/periodic.py +++ b/GPy/kern/_src/periodic.py @@ -34,7 +34,6 @@ class Periodic(Kern): self.lengthscale = Param('lengthscale', np.float64(lengthscale), Logexp()) self.period = Param('period', np.float64(period), Logexp()) self.add_parameters(self.variance, self.lengthscale, self.period) - self.parameters_changed() def _cos(self, alpha, omega, phase): def f(x): diff --git a/GPy/kern/_src/prod.py b/GPy/kern/_src/prod.py index bb809356..51490687 100644 --- a/GPy/kern/_src/prod.py +++ b/GPy/kern/_src/prod.py @@ -15,14 +15,16 @@ class Prod(Kern): :rtype: kernel object """ - def __init__(self, k1, k2, tensor=False): + def __init__(self, k1, k2, tensor=False,name=None): if tensor: - super(Prod, self).__init__(k1.input_dim + k2.input_dim, k1.name + '_xx_' + k2.name) + name = k1.name + '_xx_' + k2.name if name is None else name + super(Prod, self).__init__(k1.input_dim + k2.input_dim, name) self.slice1 = slice(0,k1.input_dim) self.slice2 = slice(k1.input_dim,k1.input_dim+k2.input_dim) else: assert k1.input_dim == k2.input_dim, "Error: The input spaces of the kernels to multiply don't have the same dimension." - super(Prod, self).__init__(k1.input_dim, k1.name + '_x_' + k2.name) + name = k1.name + '_x_' + k2.name if name is None else name + super(Prod, self).__init__(k1.input_dim, name) self.slice1 = slice(0, self.input_dim) self.slice2 = slice(0, self.input_dim) self.k1 = k1 @@ -39,17 +41,17 @@ class Prod(Kern): return self.k1.Kdiag(X[:,self.slice1]) * self.k2.Kdiag(X[:,self.slice2]) def update_gradients_full(self, dL_dK, X): - self.k1.update_gradients_full(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1]) - self.k2.update_gradients_full(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2]) + self.k1.update_gradients_full(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1]) + self.k2.update_gradients_full(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2]) def gradients_X(self, dL_dK, X, X2=None): target = np.zeros(X.shape) if X2 is None: - target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1], None) - target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2], None) + target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1], None) + target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2], None) else: - target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1]) - target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2]) + target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1]) + target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2]) return target def gradients_X_diag(self, dL_dKdiag, X): diff --git a/GPy/kern/_src/psi_comp/__init__.py b/GPy/kern/_src/psi_comp/__init__.py new file mode 100644 index 00000000..4c0d373d --- /dev/null +++ b/GPy/kern/_src/psi_comp/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) 2012, GPy authors (see AUTHORS.txt). +# Licensed under the BSD 3-clause license (see LICENSE.txt) diff --git a/GPy/kern/_src/psi_comp/linear_psi_comp.py b/GPy/kern/_src/psi_comp/linear_psi_comp.py new file mode 100644 index 00000000..22147366 --- /dev/null +++ b/GPy/kern/_src/psi_comp/linear_psi_comp.py @@ -0,0 +1,51 @@ +# Copyright (c) 2012, GPy authors (see AUTHORS.txt). +# Licensed under the BSD 3-clause license (see LICENSE.txt) + +""" +The package for the Psi statistics computation of the linear kernel for SSGPLVM +""" + +import numpy as np +from GPy.util.caching import Cache_this + +#@Cache_this(limit=1) +def _psi2computations(variance, Z, mu, S, gamma): + """ + Z - MxQ + mu - NxQ + S - NxQ + gamma - NxQ + """ + # here are the "statistics" for psi1 and psi2 + # Produced intermediate results: + # _psi2 NxMxM + # _psi2_dvariance NxMxMxQ + # _psi2_dZ NxMxQ + # _psi2_dgamma NxMxMxQ + # _psi2_dmu NxMxMxQ + # _psi2_dS NxMxMxQ + + mu2 = np.square(mu) + gamma2 = np.square(gamma) + variance2 = np.square(variance) + mu2S = mu2+S # NxQ + common_sum = np.einsum('nq,q,mq,nq->nm',gamma,variance,Z,mu) # NxM + + _dpsi2_dvariance = np.einsum('nq,q,mq,oq->nmoq',2.*(gamma*mu2S-gamma2*mu2),variance,Z,Z)+\ + np.einsum('nq,mq,nq,no->nmoq',gamma,Z,mu,common_sum)+\ + np.einsum('nq,oq,nq,nm->nmoq',gamma,Z,mu,common_sum) + + _dpsi2_dgamma = np.einsum('q,mq,oq,nq->nmoq',variance2,Z,Z,(mu2S-2.*gamma*mu2))+\ + np.einsum('q,mq,nq,no->nmoq',variance,Z,mu,common_sum)+\ + np.einsum('q,oq,nq,nm->nmoq',variance,Z,mu,common_sum) + + _dpsi2_dmu = np.einsum('q,mq,oq,nq,nq->nmoq',variance2,Z,Z,mu,2.*(gamma-gamma2))+\ + np.einsum('nq,q,mq,no->nmoq',gamma,variance,Z,common_sum)+\ + np.einsum('nq,q,oq,nm->nmoq',gamma,variance,Z,common_sum) + + _dpsi2_dS = np.einsum('nq,q,mq,oq->nmoq',gamma,variance2,Z,Z) + + _dpsi2_dZ = 2.*(np.einsum('nq,q,mq,nq->nmq',gamma,variance2,Z,mu2S)+np.einsum('nq,q,nq,nm->nmq',gamma,variance,mu,common_sum) + -np.einsum('nq,q,mq,nq->nmq',gamma2,variance2,Z,mu2)) + + return _dpsi2_dvariance, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _dpsi2_dZ \ No newline at end of file diff --git a/GPy/kern/_src/psi_comp/ssrbf_psi_comp.py b/GPy/kern/_src/psi_comp/ssrbf_psi_comp.py new file mode 100644 index 00000000..d8414cfb --- /dev/null +++ b/GPy/kern/_src/psi_comp/ssrbf_psi_comp.py @@ -0,0 +1,107 @@ +# Copyright (c) 2012, GPy authors (see AUTHORS.txt). +# Licensed under the BSD 3-clause license (see LICENSE.txt) + +""" +The package for the psi statistics computation +""" + +import numpy as np +from GPy.util.caching import Cache_this + +@Cache_this(limit=1) +def _Z_distances(Z): + Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q + Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q + return Zhat, Zdist + +@Cache_this(limit=1) +def _psi1computations(variance, lengthscale, Z, mu, S, gamma): + """ + Z - MxQ + mu - NxQ + S - NxQ + gamma - NxQ + """ + # here are the "statistics" for psi1 and psi2 + # Produced intermediate results: + # _psi1 NxM + # _dpsi1_dvariance NxM + # _dpsi1_dlengthscale NxMxQ + # _dpsi1_dZ NxMxQ + # _dpsi1_dgamma NxMxQ + # _dpsi1_dmu NxMxQ + # _dpsi1_dS NxMxQ + + lengthscale2 = np.square(lengthscale) + + # psi1 + _psi1_denom = S[:, None, :] / lengthscale2 + 1. # Nx1xQ + _psi1_denom_sqrt = np.sqrt(_psi1_denom) #Nx1xQ + _psi1_dist = Z[None, :, :] - mu[:, None, :] # NxMxQ + _psi1_dist_sq = np.square(_psi1_dist) / (lengthscale2 * _psi1_denom) # NxMxQ + _psi1_common = gamma[:,None,:] / (lengthscale2*_psi1_denom*_psi1_denom_sqrt) #Nx1xQ + _psi1_exponent1 = np.log(gamma[:,None,:]) -0.5 * (_psi1_dist_sq + np.log(_psi1_denom)) # NxMxQ + _psi1_exponent2 = np.log(1.-gamma[:,None,:]) -0.5 * (np.square(Z[None,:,:])/lengthscale2) # NxMxQ + _psi1_exponent_max = np.maximum(_psi1_exponent1,_psi1_exponent2) + _psi1_exponent = _psi1_exponent_max+np.log(np.exp(_psi1_exponent1-_psi1_exponent_max) + np.exp(_psi1_exponent2-_psi1_exponent_max)) #NxMxQ + _psi1_exp_sum = _psi1_exponent.sum(axis=-1) #NxM + _psi1_exp_dist_sq = np.exp(-0.5*_psi1_dist_sq) # NxMxQ + _psi1_exp_Z = np.exp(-0.5*np.square(Z[None,:,:])/lengthscale2) # 1xMxQ + _psi1_q = variance * np.exp(_psi1_exp_sum[:,:,None] - _psi1_exponent) # NxMxQ + _psi1 = variance * np.exp(_psi1_exp_sum) # NxM + _dpsi1_dvariance = _psi1 / variance # NxM + _dpsi1_dgamma = _psi1_q * (_psi1_exp_dist_sq/_psi1_denom_sqrt-_psi1_exp_Z) # NxMxQ + _dpsi1_dmu = _psi1_q * (_psi1_exp_dist_sq * _psi1_dist * _psi1_common) # NxMxQ + _dpsi1_dS = _psi1_q * (_psi1_exp_dist_sq * _psi1_common * 0.5 * (_psi1_dist_sq - 1.)) # NxMxQ + _dpsi1_dZ = _psi1_q * (- _psi1_common * _psi1_dist * _psi1_exp_dist_sq - (1-gamma[:,None,:])/lengthscale2*Z[None,:,:]*_psi1_exp_Z) # NxMxQ + _dpsi1_dlengthscale = 2.*lengthscale*_psi1_q * (0.5*_psi1_common*(S[:,None,:]/lengthscale2+_psi1_dist_sq)*_psi1_exp_dist_sq + 0.5*(1-gamma[:,None,:])*np.square(Z[None,:,:]/lengthscale2)*_psi1_exp_Z) # NxMxQ + + return _psi1, _dpsi1_dvariance, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _dpsi1_dZ, _dpsi1_dlengthscale + +@Cache_this(limit=1) +def _psi2computations(variance, lengthscale, Z, mu, S, gamma): + """ + Z - MxQ + mu - NxQ + S - NxQ + gamma - NxQ + """ + # here are the "statistics" for psi1 and psi2 + # Produced intermediate results: + # _psi2 NxMxM + # _psi2_dvariance NxMxM + # _psi2_dlengthscale NxMxMxQ + # _psi2_dZ NxMxMxQ + # _psi2_dgamma NxMxMxQ + # _psi2_dmu NxMxMxQ + # _psi2_dS NxMxMxQ + + lengthscale2 = np.square(lengthscale) + + _psi2_Zhat, _psi2_Zdist = _Z_distances(Z) + _psi2_Zdist_sq = np.square(_psi2_Zdist / lengthscale) # M,M,Q + _psi2_Z_sq_sum = (np.square(Z[:,None,:])+np.square(Z[None,:,:]))/lengthscale2 # MxMxQ + + # psi2 + _psi2_denom = 2.*S[:, None, None, :] / lengthscale2 + 1. # Nx1x1xQ + _psi2_denom_sqrt = np.sqrt(_psi2_denom) + _psi2_mudist = mu[:,None,None,:]-_psi2_Zhat #N,M,M,Q + _psi2_mudist_sq = np.square(_psi2_mudist)/(lengthscale2*_psi2_denom) + _psi2_common = gamma[:,None,None,:]/(lengthscale2 * _psi2_denom * _psi2_denom_sqrt) # Nx1x1xQ + _psi2_exponent1 = -_psi2_Zdist_sq -_psi2_mudist_sq -0.5*np.log(_psi2_denom)+np.log(gamma[:,None,None,:]) #N,M,M,Q + _psi2_exponent2 = np.log(1.-gamma[:,None,None,:]) - 0.5*(_psi2_Z_sq_sum) # NxMxMxQ + _psi2_exponent_max = np.maximum(_psi2_exponent1, _psi2_exponent2) + _psi2_exponent = _psi2_exponent_max+np.log(np.exp(_psi2_exponent1-_psi2_exponent_max) + np.exp(_psi2_exponent2-_psi2_exponent_max)) + _psi2_exp_sum = _psi2_exponent.sum(axis=-1) #NxM + _psi2_q = np.square(variance) * np.exp(_psi2_exp_sum[:,:,:,None]-_psi2_exponent) # NxMxMxQ + _psi2_exp_dist_sq = np.exp(-_psi2_Zdist_sq -_psi2_mudist_sq) # NxMxMxQ + _psi2_exp_Z = np.exp(-0.5*_psi2_Z_sq_sum) # MxMxQ + _psi2 = np.square(variance) * np.exp(_psi2_exp_sum) # N,M,M + _dpsi2_dvariance = 2. * _psi2/variance # NxMxM + _dpsi2_dgamma = _psi2_q * (_psi2_exp_dist_sq/_psi2_denom_sqrt - _psi2_exp_Z) # NxMxMxQ + _dpsi2_dmu = _psi2_q * (-2.*_psi2_common*_psi2_mudist * _psi2_exp_dist_sq) # NxMxMxQ + _dpsi2_dS = _psi2_q * (_psi2_common * (2.*_psi2_mudist_sq - 1.) * _psi2_exp_dist_sq) # NxMxMxQ + _dpsi2_dZ = 2.*_psi2_q * (_psi2_common*(-_psi2_Zdist*_psi2_denom+_psi2_mudist)*_psi2_exp_dist_sq - (1-gamma[:,None,None,:])*Z[:,None,:]/lengthscale2*_psi2_exp_Z) # NxMxMxQ + _dpsi2_dlengthscale = 2.*lengthscale* _psi2_q * (_psi2_common*(S[:,None,None,:]/lengthscale2+_psi2_Zdist_sq*_psi2_denom+_psi2_mudist_sq)*_psi2_exp_dist_sq+(1-gamma[:,None,None,:])*_psi2_Z_sq_sum*0.5/lengthscale2*_psi2_exp_Z) # NxMxMxQ + + return _psi2, _dpsi2_dvariance, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _dpsi2_dZ, _dpsi2_dlengthscale diff --git a/GPy/kern/_src/rbf.py b/GPy/kern/_src/rbf.py index cf5ea0c4..cd6c41e9 100644 --- a/GPy/kern/_src/rbf.py +++ b/GPy/kern/_src/rbf.py @@ -7,6 +7,8 @@ from scipy import weave from ...util.misc import param_to_array from stationary import Stationary from GPy.util.caching import Cache_this +from ...core.parameterization import variational +from psi_comp import ssrbf_psi_comp class RBF(Stationary): """ @@ -18,7 +20,7 @@ class RBF(Stationary): """ - def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='RBF'): + def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='rbf'): super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, name) self.weave_options = {} @@ -36,76 +38,145 @@ class RBF(Stationary): return self.Kdiag(variational_posterior.mean) def psi1(self, Z, variational_posterior): - _, _, _, psi1 = self._psi1computations(Z, variational_posterior) + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + else: + _, _, _, psi1 = self._psi1computations(Z, variational_posterior) return psi1 def psi2(self, Z, variational_posterior): - _, _, _, _, _, psi2 = self._psi2computations(Z, variational_posterior) + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + else: + _, _, _, _, psi2 = self._psi2computations(Z, variational_posterior) return psi2 def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - l2 = self.lengthscale **2 + # Spike-and-Slab GPLVM + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + _, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + + #contributions from psi0: + self.variance.gradient = np.sum(dL_dpsi0) + + #from psi1 + self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance) + if self.ARD: + self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) + else: + self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum() + + + #from psi2 + self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum() + if self.ARD: + self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) + else: + self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum() + + elif isinstance(variational_posterior, variational.NormalPosterior): + + l2 = self.lengthscale **2 - #contributions from psi0: - self.variance.gradient = np.sum(dL_dpsi0) - self.lengthscale.gradient = 0. + #contributions from psi0: + self.variance.gradient = np.sum(dL_dpsi0) + self.lengthscale.gradient = 0. + + #from psi1 + denom, _, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) + d_length = psi1[:,:,None] * ((dist_sq - 1.)/(self.lengthscale*denom) +1./self.lengthscale) + dpsi1_dlength = d_length * dL_dpsi1[:, :, None] + if self.ARD: + self.lengthscale.gradient += dpsi1_dlength.sum(0).sum(0) + else: + self.lengthscale.gradient += dpsi1_dlength.sum() + self.variance.gradient += np.sum(dL_dpsi1 * psi1) / self.variance + + #from psi2 + S = variational_posterior.variance + _, Zdist_sq, _, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) + + if not self.ARD: + self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2).sum() + else: + self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2) + + self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance - #from psi1 - denom, _, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) - d_length = psi1[:,:,None] * ((dist_sq - 1.)/(self.lengthscale*denom) +1./self.lengthscale) - dpsi1_dlength = d_length * dL_dpsi1[:, :, None] - if not self.ARD: - self.lengthscale.gradient += dpsi1_dlength.sum() else: - self.lengthscale.gradient += dpsi1_dlength.sum(0).sum(0) - self.variance.gradient += np.sum(dL_dpsi1 * psi1) / self.variance - - #from psi2 - S = variational_posterior.variance - denom, _, Zdist_sq, _, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) - d_length = 2.*psi2[:, :, :, None] * (Zdist_sq * denom + mudist_sq + S[:, None, None, :] / l2) / (self.lengthscale * denom) - #TODO: combine denom and l2 as denom_l2?? - #TODO: tidy the above! - #TODO: tensordot below? - - dpsi2_dlength = d_length * dL_dpsi2[:, :, :, None] - if not self.ARD: - self.lengthscale.gradient += dpsi2_dlength.sum() - else: - self.lengthscale.gradient += dpsi2_dlength.sum(0).sum(0).sum(0) - - self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance + raise ValueError, "unknown distriubtion received for psi-statistics" def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - l2 = self.lengthscale **2 + # Spike-and-Slab GPLVM + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + _, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + + #psi1 + grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0) + + #psi2 + grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1) + + return grad - #psi1 - denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) - denominator = l2 * denom - dpsi1_dZ = -psi1[:, :, None] * (dist / denominator) - grad = np.sum(dL_dpsi1[:, :, None] * dpsi1_dZ, 0) + elif isinstance(variational_posterior, variational.NormalPosterior): + + l2 = self.lengthscale **2 - #psi2 - denom, Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) - term1 = Zdist / l2 # M, M, Q - term2 = mudist / denom / l2 # N, M, M, Q - dZ = psi2[:, :, :, None] * (term1[None, :, :, :] + term2) #N,M,M,Q - grad += 2*(dL_dpsi2[:, :, :, None] * dZ).sum(0).sum(0) + #psi1 + denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) + grad = np.einsum('ij,ij,ijk,ijk->jk', dL_dpsi1, psi1, dist, -1./(denom*l2)) - return grad + #psi2 + Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) + term1 = Zdist / l2 # M, M, Q + S = variational_posterior.variance + term2 = mudist / (2.*S[:,None,None,:] + l2) # N, M, M, Q + + grad += 2.*np.einsum('ijk,ijk,ijkl->kl', dL_dpsi2, psi2, term1[None,:,:,:] + term2) + + return grad + else: + raise ValueError, "unknown distriubtion received for psi-statistics" def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): - l2 = self.lengthscale **2 - #psi1 - denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) - tmp = psi1[:, :, None] / l2 / denom - grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1) - grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1) - #psi2 - denom, Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) - tmp = psi2[:, :, :, None] / l2 / denom - grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * mudist).sum(1).sum(1) - grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*mudist_sq - 1)).sum(1).sum(1) + # Spike-and-Slab GPLVM + if isinstance(variational_posterior, variational.SpikeAndSlabPosterior): + ndata = variational_posterior.mean.shape[0] + + _, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + + #psi1 + grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1) + grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1) + grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1) + #psi2 + grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1) + grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1) + grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1) + + return grad_mu, grad_S, grad_gamma + + elif isinstance(variational_posterior, variational.NormalPosterior): + + l2 = self.lengthscale **2 + #psi1 + denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior) + tmp = psi1[:, :, None] / l2 / denom + grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1) + grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1) + #psi2 + _, _, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior) + S = variational_posterior.variance + tmp = psi2[:, :, :, None] / (2.*S[:,None,None,:] + l2) + grad_mu += -2.*np.einsum('ijk,ijkl,ijkl->il', dL_dpsi2, tmp , mudist) + grad_S += np.einsum('ijk,ijkl,ijkl->il', dL_dpsi2 , tmp , (2.*mudist_sq - 1)) + + else: + raise ValueError, "unknown distriubtion received for psi-statistics" return grad_mu, grad_S @@ -113,61 +184,6 @@ class RBF(Stationary): # Precomputations # #---------------------------------------# - #TODO: this function is unused, but it will be useful in the stationary class - def _dL_dlengthscales_via_K(self, dL_dK, X, X2): - """ - A helper function for update_gradients_* methods - - Computes the derivative of the objective L wrt the lengthscales via - - dL_dl = sum_{i,j}(dL_dK_{ij} dK_dl) - - assumes self._K_computations has just been called. - - This is only valid if self.ARD=True - """ - target = np.zeros(self.input_dim) - dvardLdK = self._K_dvar * dL_dK - var_len3 = self.variance / np.power(self.lengthscale, 3) - if X2 is None: - # save computation for the symmetrical case - dvardLdK = dvardLdK + dvardLdK.T - code = """ - int q,i,j; - double tmp; - for(q=0; ql', dL_dpsi2, psi2, Zdist_sq * (2.*S[:,None,None,:]/l2 + 1.) + mudist_sq + S[:, None, None, :] / l2, 1./(2.*S + l2))*self.lengthscale + + result = np.zeros(self.input_dim) + code = """ + double tmp; + for(int q=0; q + #include + """ + N,Q = S.shape + M = psi2.shape[-1] + + S = param_to_array(S) + weave.inline(code, support_code=support_code, libraries=['gomp'], + arg_names=['psi2', 'dL_dpsi2', 'N', 'M', 'Q', 'mudist_sq', 'l2', 'Zdist_sq', 'S', 'result'], + type_converters=weave.converters.blitz, **self.weave_options) + + return 2.*result*self.lengthscale diff --git a/GPy/kern/_src/ssrbf.py b/GPy/kern/_src/ssrbf.py index cd921acb..c566c414 100644 --- a/GPy/kern/_src/ssrbf.py +++ b/GPy/kern/_src/ssrbf.py @@ -7,6 +7,7 @@ import numpy as np from ...util.linalg import tdot from ...util.config import * from stationary import Stationary +from psi_comp import ssrbf_psi_comp class SSRBF(Stationary): """ @@ -54,101 +55,63 @@ class SSRBF(Stationary): # PSI statistics # #---------------------------------------# - def psi0(self, Z, posterior_variational): - ret = np.empty(posterior_variational.mean.shape[0]) + def psi0(self, Z, variational_posterior): + ret = np.empty(variational_posterior.mean.shape[0]) ret[:] = self.variance return ret - def psi1(self, Z, posterior_variational): - self._psi_computations(Z, posterior_variational.mean, posterior_variational.variance, posterior_variational.binary_prob) - return self._psi1 + def psi1(self, Z, variational_posterior): + _psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + return _psi1 - def psi2(self, Z, posterior_variational): - self._psi_computations(Z, posterior_variational.mean, posterior_variational.variance, posterior_variational.binary_prob) - return self._psi2 + def psi2(self, Z, variational_posterior): + _psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + return _psi2 - def dL_dpsi0_dmuSgamma(self, dL_dpsi0, Z, mu, S, gamma, target_mu, target_S, target_gamma): - pass - - - def dL_dpsi1_dmuSgamma(self, dL_dpsi1, Z, mu, S, gamma, target_mu, target_S, target_gamma): - self._psi_computations(Z, mu, S, gamma) - target_mu += (dL_dpsi1[:, :, None] * self._dpsi1_dmu).sum(axis=1) - target_S += (dL_dpsi1[:, :, None] * self._dpsi1_dS).sum(axis=1) - target_gamma += (dL_dpsi1[:,:,None] * self._dpsi1_dgamma).sum(axis=1) - - - def dL_dpsi2_dmuSgamma(self, dL_dpsi2, Z, mu, S, gamma, target_mu, target_S, target_gamma): - """Think N,num_inducing,num_inducing,input_dim """ - self._psi_computations(Z, mu, S, gamma) - target_mu += (dL_dpsi2[:, :, :, None] * self._dpsi2_dmu).reshape(mu.shape[0],-1,mu.shape[1]).sum(axis=1) - target_S += (dL_dpsi2[:, :, :, None] * self._dpsi2_dS).reshape(S.shape[0],-1,S.shape[1]).sum(axis=1) - target_gamma += (dL_dpsi2[:,:,:, None] *self._dpsi2_dgamma).reshape(gamma.shape[0],-1,gamma.shape[1]).sum(axis=1) - - def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational): - self._psi_computations(Z, posterior_variational.mean, posterior_variational.variance, posterior_variational.binary_prob) + def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): + _, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) #contributions from psi0: self.variance.gradient = np.sum(dL_dpsi0) #from psi1 - self.variance.gradient += np.sum(dL_dpsi1 * self._dpsi1_dvariance) - self.lengthscale.gradient = (dL_dpsi1[:,:,None]*self._dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) + self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance) + self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) #from psi2 - self.variance.gradient += (dL_dpsi2 * self._dpsi2_dvariance).sum() - self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * self._dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) - - #from Kmm - self._K_computations(Z, None) - dvardLdK = self._K_dvar * dL_dKmm - var_len3 = self.variance / (np.square(self.lengthscale)*self.lengthscale) - - self.variance.gradient += np.sum(dvardLdK) - self.lengthscale.gradient += (np.square(Z[:,None,:]-Z[None,:,:])*dvardLdK[:,:,None]).reshape(-1,self.input_dim).sum(axis=0)*var_len3 + self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum() + self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0) - - def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational): - self._psi_computations(Z, posterior_variational.mean, posterior_variational.variance, posterior_variational.binary_prob) + def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior): + _, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) #psi1 - grad = (dL_dpsi1[:, :, None] * self._dpsi1_dZ).sum(axis=0) + grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0) #psi2 - grad += (dL_dpsi2[:, :, :, None] * self._dpsi2_dZ).sum(axis=0).sum(axis=1) - - grad += self.gradients_X(dL_dKmm, Z, None) + grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1) return grad - def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational): - ndata = posterior_variational.mean.shape[0] - self._psi_computations(Z, posterior_variational.mean, posterior_variational.variance, posterior_variational.binary_prob) + def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior): + ndata = variational_posterior.mean.shape[0] + + _, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + _, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) + #psi1 - grad_mu = (dL_dpsi1[:, :, None] * self._dpsi1_dmu).sum(axis=1) - grad_S = (dL_dpsi1[:, :, None] * self._dpsi1_dS).sum(axis=1) - grad_gamma = (dL_dpsi1[:,:,None] * self._dpsi1_dgamma).sum(axis=1) + grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1) + grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1) + grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1) #psi2 - grad_mu += (dL_dpsi2[:, :, :, None] * self._dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1) - grad_S += (dL_dpsi2[:, :, :, None] * self._dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1) - grad_gamma += (dL_dpsi2[:,:,:, None] *self._dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1) + grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1) + grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1) + grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1) return grad_mu, grad_S, grad_gamma - - def gradients_X(self, dL_dK, X, X2=None): - #if self._X is None or X.base is not self._X.base or X2 is not None: - if X2==None: - _K_dist = X[:,None,:] - X[None,:,:] - _K_dist2 = np.square(_K_dist/self.lengthscale).sum(axis=-1) - dK_dX = self.variance*np.exp(-0.5 * self._K_dist2[:,:,None]) * (-2.*_K_dist/np.square(self.lengthscale)) - dL_dX = (dL_dK[:,:,None] * dK_dX).sum(axis=1) - else: - _K_dist = X[:,None,:] - X2[None,:,:] - _K_dist2 = np.square(_K_dist/self.lengthscale).sum(axis=-1) - dK_dX = self.variance*np.exp(-0.5 * self._K_dist2[:,:,None]) * (-_K_dist/np.square(self.lengthscale)) - dL_dX = (dL_dK[:,:,None] * dK_dX).sum(axis=1) - return dL_dX #---------------------------------------# # Precomputations # @@ -174,78 +137,3 @@ class SSRBF(Stationary): self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), axis=1)[:, None] + np.sum(np.square(X2), axis=1)[None, :]) self._K_dvar = np.exp(-0.5 * self._K_dist2) - #@cache_this(1) - def _psi_computations(self, Z, mu, S, gamma): - """ - Z - MxQ - mu - NxQ - S - NxQ - gamma - NxQ - """ - # here are the "statistics" for psi1 and psi2 - # Produced intermediate results: - # _psi1 NxM - # _dpsi1_dvariance NxM - # _dpsi1_dlengthscale NxMxQ - # _dpsi1_dZ NxMxQ - # _dpsi1_dgamma NxMxQ - # _dpsi1_dmu NxMxQ - # _dpsi1_dS NxMxQ - # _psi2 NxMxM - # _psi2_dvariance NxMxM - # _psi2_dlengthscale NxMxMxQ - # _psi2_dZ NxMxMxQ - # _psi2_dgamma NxMxMxQ - # _psi2_dmu NxMxMxQ - # _psi2_dS NxMxMxQ - - lengthscale2 = np.square(self.lengthscale) - - _psi2_Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q - _psi2_Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q - _psi2_Zdist_sq = np.square(_psi2_Zdist / self.lengthscale) # M,M,Q - _psi2_Z_sq_sum = (np.square(Z[:,None,:])+np.square(Z[None,:,:]))/lengthscale2 # MxMxQ - - # psi1 - _psi1_denom = S[:, None, :] / lengthscale2 + 1. # Nx1xQ - _psi1_denom_sqrt = np.sqrt(_psi1_denom) #Nx1xQ - _psi1_dist = Z[None, :, :] - mu[:, None, :] # NxMxQ - _psi1_dist_sq = np.square(_psi1_dist) / (lengthscale2 * _psi1_denom) # NxMxQ - _psi1_common = gamma[:,None,:] / (lengthscale2*_psi1_denom*_psi1_denom_sqrt) #Nx1xQ - _psi1_exponent1 = np.log(gamma[:,None,:]) -0.5 * (_psi1_dist_sq + np.log(_psi1_denom)) # NxMxQ - _psi1_exponent2 = np.log(1.-gamma[:,None,:]) -0.5 * (np.square(Z[None,:,:])/lengthscale2) # NxMxQ - _psi1_exponent = np.log(np.exp(_psi1_exponent1) + np.exp(_psi1_exponent2)) #NxMxQ - _psi1_exp_sum = _psi1_exponent.sum(axis=-1) #NxM - _psi1_exp_dist_sq = np.exp(-0.5*_psi1_dist_sq) # NxMxQ - _psi1_exp_Z = np.exp(-0.5*np.square(Z[None,:,:])/lengthscale2) # 1xMxQ - _psi1_q = self.variance * np.exp(_psi1_exp_sum[:,:,None] - _psi1_exponent) # NxMxQ - self._psi1 = self.variance * np.exp(_psi1_exp_sum) # NxM - self._dpsi1_dvariance = self._psi1 / self.variance # NxM - self._dpsi1_dgamma = _psi1_q * (_psi1_exp_dist_sq/_psi1_denom_sqrt-_psi1_exp_Z) # NxMxQ - self._dpsi1_dmu = _psi1_q * (_psi1_exp_dist_sq * _psi1_dist * _psi1_common) # NxMxQ - self._dpsi1_dS = _psi1_q * (_psi1_exp_dist_sq * _psi1_common * 0.5 * (_psi1_dist_sq - 1.)) # NxMxQ - self._dpsi1_dZ = _psi1_q * (- _psi1_common * _psi1_dist * _psi1_exp_dist_sq - (1-gamma[:,None,:])/lengthscale2*Z[None,:,:]*_psi1_exp_Z) # NxMxQ - self._dpsi1_dlengthscale = 2.*self.lengthscale*_psi1_q * (0.5*_psi1_common*(S[:,None,:]/lengthscale2+_psi1_dist_sq)*_psi1_exp_dist_sq + 0.5*(1-gamma[:,None,:])*np.square(Z[None,:,:]/lengthscale2)*_psi1_exp_Z) # NxMxQ - - - # psi2 - _psi2_denom = 2.*S[:, None, None, :] / lengthscale2 + 1. # Nx1x1xQ - _psi2_denom_sqrt = np.sqrt(_psi2_denom) - _psi2_mudist = mu[:,None,None,:]-_psi2_Zhat #N,M,M,Q - _psi2_mudist_sq = np.square(_psi2_mudist)/(lengthscale2*_psi2_denom) - _psi2_common = gamma[:,None,None,:]/(lengthscale2 * _psi2_denom * _psi2_denom_sqrt) # Nx1x1xQ - _psi2_exponent1 = -_psi2_Zdist_sq -_psi2_mudist_sq -0.5*np.log(_psi2_denom)+np.log(gamma[:,None,None,:]) #N,M,M,Q - _psi2_exponent2 = np.log(1.-gamma[:,None,None,:]) - 0.5*(_psi2_Z_sq_sum) # NxMxMxQ - _psi2_exponent = np.log(np.exp(_psi2_exponent1) + np.exp(_psi2_exponent2)) - _psi2_exp_sum = _psi2_exponent.sum(axis=-1) #NxM - _psi2_q = np.square(self.variance) * np.exp(_psi2_exp_sum[:,:,:,None]-_psi2_exponent) # NxMxMxQ - _psi2_exp_dist_sq = np.exp(-_psi2_Zdist_sq -_psi2_mudist_sq) # NxMxMxQ - _psi2_exp_Z = np.exp(-0.5*_psi2_Z_sq_sum) # MxMxQ - self._psi2 = np.square(self.variance) * np.exp(_psi2_exp_sum) # N,M,M - self._dpsi2_dvariance = 2. * self._psi2/self.variance # NxMxM - self._dpsi2_dgamma = _psi2_q * (_psi2_exp_dist_sq/_psi2_denom_sqrt - _psi2_exp_Z) # NxMxMxQ - self._dpsi2_dmu = _psi2_q * (-2.*_psi2_common*_psi2_mudist * _psi2_exp_dist_sq) # NxMxMxQ - self._dpsi2_dS = _psi2_q * (_psi2_common * (2.*_psi2_mudist_sq - 1.) * _psi2_exp_dist_sq) # NxMxMxQ - self._dpsi2_dZ = 2.*_psi2_q * (_psi2_common*(-_psi2_Zdist*_psi2_denom+_psi2_mudist)*_psi2_exp_dist_sq - (1-gamma[:,None,None,:])*Z[:,None,:]/lengthscale2*_psi2_exp_Z) # NxMxMxQ - self._dpsi2_dlengthscale = 2.*self.lengthscale* _psi2_q * (_psi2_common*(S[:,None,None,:]/lengthscale2+_psi2_Zdist_sq*_psi2_denom+_psi2_mudist_sq)*_psi2_exp_dist_sq+(1-gamma[:,None,None,:])*_psi2_Z_sq_sum*0.5/lengthscale2*_psi2_exp_Z) # NxMxMxQ - \ No newline at end of file diff --git a/GPy/kern/_src/stationary.py b/GPy/kern/_src/stationary.py index 8d8ae476..44e17d8a 100644 --- a/GPy/kern/_src/stationary.py +++ b/GPy/kern/_src/stationary.py @@ -12,6 +12,35 @@ from scipy import integrate from ...util.caching import Cache_this class Stationary(Kern): + """ + Stationary kernels (covariance functions). + + Stationary covariance fucntion depend only on r, where r is defined as + + r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 } + + The covariance function k(x, x' can then be written k(r). + + In this implementation, r is scaled by the lengthscales parameter(s): + + r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }. + + By default, there's only one lengthscale: seaprate lengthscales for each + dimension can be enables by setting ARD=True. + + To implement a stationary covariance function using this class, one need + only define the covariance function k(r), and it derivative. + + ... + def K_of_r(self, r): + return foo + def dK_dr(self, r): + return bar + + The lengthscale(s) and variance parameters are added to the structure automatically. + + """ + def __init__(self, input_dim, variance, lengthscale, ARD, name): super(Stationary, self).__init__(input_dim, name) self.ARD = ARD @@ -20,11 +49,11 @@ class Stationary(Kern): lengthscale = np.ones(1) else: lengthscale = np.asarray(lengthscale) - assert lengthscale.size == 1, "Only lengthscale needed for non-ARD kernel" + assert lengthscale.size == 1, "Only 1 lengthscale needed for non-ARD kernel" else: if lengthscale is not None: lengthscale = np.asarray(lengthscale) - assert lengthscale.size in [1, input_dim], "Bad lengthscales" + assert lengthscale.size in [1, input_dim], "Bad number of lengthscales" if lengthscale.size != input_dim: lengthscale = np.ones(input_dim)*lengthscale else: @@ -35,42 +64,43 @@ class Stationary(Kern): self.add_parameters(self.variance, self.lengthscale) def K_of_r(self, r): - raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class" + raise NotImplementedError, "implement the covariance function as a fn of r to use this class" def dK_dr(self, r): - raise NotImplementedError, "implement the covaraiance function as a fn of r to use this class" + raise NotImplementedError, "implement derivative of the covariance function wrt r to use this class" - #@Cache_this(limit=5, ignore_args=()) + @Cache_this(limit=5, ignore_args=()) def K(self, X, X2=None): r = self._scaled_dist(X, X2) return self.K_of_r(r) - #@Cache_this(limit=5, ignore_args=(0,)) - def _dist(self, X, X2): - if X2 is None: - X2 = X - return X[:, None, :] - X2[None, :, :] + @Cache_this(limit=3, ignore_args=()) + def dK_dr_via_X(self, X, X2): + #a convenience function, so we can cache dK_dr + return self.dK_dr(self._scaled_dist(X, X2)) - #@Cache_this(limit=5, ignore_args=(0,)) + @Cache_this(limit=5, ignore_args=(0,)) def _unscaled_dist(self, X, X2=None): """ - Compute the square distance between each row of X and X2, or between + Compute the Euclidean distance between each row of X and X2, or between each pair of rows of X if X2 is None. """ if X2 is None: Xsq = np.sum(np.square(X),1) - return np.sqrt(-2.*tdot(X) + (Xsq[:,None] + Xsq[None,:])) + r2 = -2.*tdot(X) + (Xsq[:,None] + Xsq[None,:]) + util.diag.view(r2)[:,]= 0. # force diagnoal to be zero: sometime numerically a little negative + return np.sqrt(r2) else: X1sq = np.sum(np.square(X),1) X2sq = np.sum(np.square(X2),1) return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:])) - #@Cache_this(limit=5, ignore_args=()) + @Cache_this(limit=5, ignore_args=()) def _scaled_dist(self, X, X2=None): """ Efficiently compute the scaled distance, r. - r = \sum_{q=1}^Q (x_q - x'q)^2/l_q^2 + r = \sqrt( \sum_{q=1}^Q (x_q - x'q)^2/l_q^2 ) Note that if thre is only one lengthscale, l comes outside the sum. In this case we compute the unscaled distance first (in a separate @@ -84,7 +114,6 @@ class Stationary(Kern): else: return self._unscaled_dist(X, X2)/self.lengthscale - def Kdiag(self, X): ret = np.empty(X.shape[0]) ret[:] = self.variance @@ -95,20 +124,23 @@ class Stationary(Kern): self.lengthscale.gradient = 0. def update_gradients_full(self, dL_dK, X, X2=None): - r = self._scaled_dist(X, X2) - K = self.K_of_r(r) - rinv = self._inv_dist(X, X2) - dL_dr = self.dK_dr(r) * dL_dK + self.variance.gradient = np.einsum('ij,ij,i', self.K(X, X2), dL_dK, 1./self.variance) + #now the lengthscale gradient(s) + dL_dr = self.dK_dr_via_X(X, X2) * dL_dK if self.ARD: - x_xl3 = np.square(self._dist(X, X2)) / self.lengthscale**3 - self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0) + #rinv = self._inv_dis# this is rather high memory? Should we loop instead?t(X, X2) + #d = X[:, None, :] - X2[None, :, :] + #x_xl3 = np.square(d) + #self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3 + tmp = dL_dr*self._inv_dist(X, X2) + if X2 is None: X2 = X + self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)]) else: - x_xl3 = np.square(self._dist(X, X2)) / self.lengthscale**3 - self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum() + r = self._scaled_dist(X, X2) + self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale - self.variance.gradient = np.sum(K * dL_dK)/self.variance def _inv_dist(self, X, X2=None): """ @@ -116,7 +148,7 @@ class Stationary(Kern): diagonal, where we return zero (the distance on the diagonal is zero). This term appears in derviatives. """ - dist = self._scaled_dist(X, X2) + dist = self._scaled_dist(X, X2).copy() if X2 is None: nondiag = util.diag.offdiag_view(dist) nondiag[:] = 1./nondiag @@ -128,10 +160,11 @@ class Stationary(Kern): """ Given the derivative of the objective wrt K (dL_dK), compute the derivative wrt X """ - r = self._scaled_dist(X, X2) invdist = self._inv_dist(X, X2) - dL_dr = self.dK_dr(r) * dL_dK - #The high-memory numpy way: ret = np.sum((invdist*dL_dr)[:,:,None]*self._dist(X, X2),1)/self.lengthscale**2 + dL_dr = self.dK_dr_via_X(X, X2) * dL_dK + #The high-memory numpy way: + #d = X[:, None, :] - X2[None, :, :] + #ret = np.sum((invdist*dL_dr)[:,:,None]*d,1)/self.lengthscale**2 #if X2 is None: #ret *= 2. @@ -141,7 +174,7 @@ class Stationary(Kern): tmp *= 2. X2 = X ret = np.empty(X.shape, dtype=np.float64) - [np.copyto(ret[:,q], np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), 1)) for q in xrange(self.input_dim)] + [np.einsum('ij,ij->i', tmp, X[:,q][:,None]-X2[:,q][None,:], out=ret[:,q]) for q in xrange(self.input_dim)] ret /= self.lengthscale**2 return ret @@ -214,7 +247,7 @@ class Matern52(Stationary): .. math:: - k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \ \ \ \ \ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} } + k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) """ def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Mat52'): super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, name) @@ -225,7 +258,7 @@ class Matern52(Stationary): def dK_dr(self, r): return self.variance*(10./3*r -5.*r -5.*np.sqrt(5.)/3*r**2)*np.exp(-np.sqrt(5.)*r) - def Gram_matrix(self,F,F1,F2,F3,lower,upper): + def Gram_matrix(self, F, F1, F2, F3, lower, upper): """ Return the Gram matrix of the vector of functions F with respect to the RKHS norm. The use of this function is limited to input_dim=1. diff --git a/GPy/kern/_src/sympykern.py b/GPy/kern/_src/sympykern.py index cf6838c4..9878ec68 100644 --- a/GPy/kern/_src/sympykern.py +++ b/GPy/kern/_src/sympykern.py @@ -1,11 +1,10 @@ # Check Matthew Rocklin's blog post. -try: +try: import sympy as sp sympy_available=True from sympy.utilities.lambdify import lambdify except ImportError: sympy_available=False - exit() import numpy as np from kern import Kern @@ -36,7 +35,7 @@ class Sympykern(Kern): super(Sympykern, self).__init__(input_dim, name) self._sp_k = k - + # pull the variable names out of the symbolic covariance function. sp_vars = [e for e in k.atoms() if e.is_Symbol] self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:])) @@ -51,7 +50,7 @@ class Sympykern(Kern): self._sp_kdiag = k for x, z in zip(self._sp_x, self._sp_z): self._sp_kdiag = self._sp_kdiag.subs(z, x) - + # If it is a multi-output covariance, add an input for indexing the outputs. self._real_input_dim = x_dim # Check input dim is number of xs + 1 if output_dim is >1 @@ -73,47 +72,44 @@ class Sympykern(Kern): # Extract names of shared parameters (those without a subscript) self._sp_theta = [theta for theta in thetas if theta not in self._sp_theta_i and theta not in self._sp_theta_j] - + self.num_split_params = len(self._sp_theta_i) self._split_theta_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i] + # Add split parameters to the model. for theta in self._split_theta_names: + # TODO: what if user has passed a parameter vector, how should that be stored and interpreted? setattr(self, theta, Param(theta, np.ones(self.output_dim), None)) - self.add_parameters(getattr(self, theta)) + self.add_parameter(getattr(self, theta)) + - #setattr(self, theta, np.ones(self.output_dim)) - self.num_shared_params = len(self._sp_theta) for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j): self._sp_kdiag = self._sp_kdiag.subs(theta_j, theta_i) - #self.num_params = self.num_shared_params+self.num_split_params*self.output_dim - + else: self.num_split_params = 0 self._split_theta_names = [] self._sp_theta = thetas self.num_shared_params = len(self._sp_theta) - #self.num_params = self.num_shared_params # Add parameters to the model. for theta in self._sp_theta: val = 1.0 + # TODO: what if user has passed a parameter vector, how should that be stored and interpreted? This is the old way before params class. if param is not None: if param.has_key(theta): val = param[theta] setattr(self, theta.name, Param(theta.name, val, None)) self.add_parameters(getattr(self, theta.name)) - #deal with param - #self._set_params(self._get_params()) # Differentiate with respect to parameters. derivative_arguments = self._sp_x + self._sp_theta if self.output_dim > 1: derivative_arguments += self._sp_theta_i - + self.derivatives = {theta.name : sp.diff(self._sp_k,theta).simplify() for theta in derivative_arguments} self.diag_derivatives = {theta.name : sp.diff(self._sp_kdiag,theta).simplify() for theta in derivative_arguments} - - + # This gives the parameters for the arg list. self.arg_list = self._sp_x + self._sp_z + self._sp_theta self.diag_arg_list = self._sp_x + self._sp_theta @@ -127,14 +123,11 @@ class Sympykern(Kern): # generate the code for the covariance functions self._gen_code() - self.parameters_changed() # initializes caches - - def __add__(self,other): return spkern(self._sp_k+other._sp_k) def _gen_code(self): - + #fn_theano = theano_function([self.arg_lists], [self._sp_k + self.derivatives], dims={x: 1}, dtypes={x_0: 'float64', z_0: 'float64'}) self._K_function = lambdify(self.arg_list, self._sp_k, 'numpy') for key in self.derivatives.keys(): setattr(self, '_K_diff_' + key, lambdify(self.arg_list, self.derivatives[key], 'numpy')) @@ -143,7 +136,7 @@ class Sympykern(Kern): for key in self.derivatives.keys(): setattr(self, '_Kdiag_diff_' + key, lambdify(self.diag_arg_list, self.diag_derivatives[key], 'numpy')) - def K(self,X,X2=None): + def K(self,X,X2=None): self._K_computations(X, X2) return self._K_function(**self._arguments) @@ -151,11 +144,11 @@ class Sympykern(Kern): def Kdiag(self,X): self._K_computations(X) return self._Kdiag_function(**self._diag_arguments) - + def _param_grad_helper(self,partial,X,Z,target): pass - + def gradients_X(self, dL_dK, X, X2=None): #if self._X is None or X.base is not self._X.base or X2 is not None: self._K_computations(X, X2) @@ -174,7 +167,7 @@ class Sympykern(Kern): gf = getattr(self, '_Kdiag_diff_' + x.name) dX[:, i] = gf(**self._diag_arguments)*dL_dK return dX - + def update_gradients_full(self, dL_dK, X, X2=None): # Need to extract parameters to local variables first self._K_computations(X, X2) @@ -199,7 +192,7 @@ class Sympykern(Kern): gradient += np.asarray([A[np.where(self._output_ind2==i)].T.sum() for i in np.arange(self.output_dim)]) setattr(parameter, 'gradient', gradient) - + def update_gradients_diag(self, dL_dKdiag, X): self._K_computations(X) @@ -215,7 +208,7 @@ class Sympykern(Kern): setattr(parameter, 'gradient', np.asarray([a[np.where(self._output_ind==i)].sum() for i in np.arange(self.output_dim)])) - + def _K_computations(self, X, X2=None): """Set up argument lists for the derivatives.""" # Could check if this needs doing or not, there could diff --git a/GPy/mappings/kernel.py b/GPy/mappings/kernel.py index 94ce203f..74fa344f 100644 --- a/GPy/mappings/kernel.py +++ b/GPy/mappings/kernel.py @@ -17,7 +17,7 @@ class Kernel(Mapping): :type X: ndarray :param output_dim: dimension of output. :type output_dim: int - :param kernel: a GPy kernel, defaults to GPy.kern.rbf + :param kernel: a GPy kernel, defaults to GPy.kern.RBF :type kernel: GPy.kern.kern """ @@ -25,7 +25,7 @@ class Kernel(Mapping): def __init__(self, X, output_dim=1, kernel=None): Mapping.__init__(self, input_dim=X.shape[1], output_dim=output_dim) if kernel is None: - kernel = GPy.kern.rbf(self.input_dim) + kernel = GPy.kern.RBF(self.input_dim) self.kern = kernel self.X = X self.num_data = X.shape[0] @@ -43,7 +43,7 @@ class Kernel(Mapping): def _set_params(self, x): self.A = x[:self.num_data * self.output_dim].reshape(self.num_data, self.output_dim).copy() self.bias = x[self.num_data*self.output_dim:].copy() - + def randomize(self): self.A = np.random.randn(self.num_data, self.output_dim)/np.sqrt(self.num_data+1) self.bias = np.random.randn(self.output_dim)/np.sqrt(self.num_data+1) diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py index 18a08e5d..3617e260 100644 --- a/GPy/models/bayesian_gplvm.py +++ b/GPy/models/bayesian_gplvm.py @@ -49,7 +49,6 @@ class BayesianGPLVM(SparseGP): SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs) self.add_parameter(self.X, index=0) - self.parameters_changed() def _getstate(self): """ @@ -150,37 +149,6 @@ class BayesianGPLVM(SparseGP): return dim_reduction_plots.plot_steepest_gradient_map(self,*args,**kwargs) -class BayesianGPLVMWithMissingData(BayesianGPLVM): - def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10, - Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', **kwargs): - from ..util.subarray_and_sorting import common_subarrays - self.subarrays = common_subarrays(Y) - import ipdb;ipdb.set_trace() - BayesianGPLVM.__init__(self, Y, input_dim, X=X, X_variance=X_variance, init=init, num_inducing=num_inducing, Z=Z, kernel=kernel, inference_method=inference_method, likelihood=likelihood, name=name, **kwargs) - - - def parameters_changed(self): - super(BayesianGPLVM, self).parameters_changed() - self._log_marginal_likelihood -= self.KL_divergence() - - dL_dmu, dL_dS = self.dL_dmuS() - - # dL: - self.X.mean.gradient = dL_dmu - self.X.variance.gradient = dL_dS - - # dKL: - self.X.mean.gradient -= self.X.mean - self.X.variance.gradient -= (1. - (1. / (self.X.variance))) * 0.5 - -if __name__ == '__main__': - import numpy as np - X = np.random.randn(20,2) - W = np.linspace(0,1,10)[None,:] - Y = (X*W).sum(1) - missing = np.random.binomial(1,.1,size=Y.shape) - - pass def latent_cost_and_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2): """ diff --git a/GPy/models/gp_regression.py b/GPy/models/gp_regression.py index f8957906..5e83db09 100644 --- a/GPy/models/gp_regression.py +++ b/GPy/models/gp_regression.py @@ -28,7 +28,6 @@ class GPRegression(GP): likelihood = likelihoods.Gaussian() super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression') - self.parameters_changed() def _getstate(self): return GP._getstate(self) diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py index 0423aecd..dd1c44ba 100644 --- a/GPy/models/mrd.py +++ b/GPy/models/mrd.py @@ -1,14 +1,17 @@ # ## Copyright (c) 2013, GPy authors (see AUTHORS.txt). # Licensed under the BSD 3-clause license (see LICENSE.txt) -from GPy.core import Model -from GPy.core import SparseGP -from GPy.util.linalg import PCA -import numpy +import numpy as np import itertools import pylab -from GPy.kern import Kern -from GPy.models.bayesian_gplvm import BayesianGPLVM + +from ..core import Model, SparseGP +from ..util.linalg import PCA +from ..kern import Kern +from bayesian_gplvm import BayesianGPLVM +from ..core.parameterization.variational import NormalPosterior, NormalPrior +from ..inference.latent_function_inference.var_dtc import VarDTCMissingData +from ..likelihoods.gaussian import Gaussian class MRD2(Model): """ @@ -20,11 +23,101 @@ class MRD2(Model): to match up, whereas the dimensionality p_d can differ. :param [array-like] Ylist: List of datasets to apply MRD on - :param array-like q_mean: mean of starting latent space q in [n x q] - :param array-like q_variance: variance of starting latent space q in [n x q] - :param :class:`~GPy.inference.latent_function_inference + :param input_dim: latent dimensionality + :type input_dim: int + :param array-like X: mean of starting latent space q in [n x q] + :param array-like X_variance: variance of starting latent space q in [n x q] + :param initx: initialisation method for the latent space : + + * 'concat' - PCA on concatenation of all datasets + * 'single' - Concatenation of PCA on datasets, respectively + * 'random' - Random draw from a Normal(0,1) + + :type initx: ['concat'|'single'|'random'] + :param initz: initialisation method for inducing inputs + :type initz: 'permute'|'random' + :param num_inducing: number of inducing inputs to use + :param Z: initial inducing inputs + :param kernel: list of kernels or kernel to copy for each output + :type kernel: [GPy.kern.kern] | GPy.kern.kern | None (default) + :param :class:`~GPy.inference.latent_function_inference inference_method: the inference method to use + :param :class:`~GPy.likelihoods.likelihood.Likelihood` likelihood: the likelihood to use + :param str name: the name of this model """ + def __init__(self, Ylist, input_dim, X=None, X_variance=None, + initx = 'PCA', initz = 'permute', + num_inducing=10, Z=None, kernel=None, + inference_method=None, likelihood=None, name='mrd'): + super(MRD2, self).__init__(name) + + # sort out the kernels + if kernel is None: + from ..kern import RBF + self.kern = [RBF(input_dim, ARD=1, name='Y_{}'.format(i)) for i in range(len(Ylist))] + elif isinstance(kernel, Kern): + self.kern = [kernel.copy(name='Y_{}'.format(i)) for i in range(len(Ylist))] + else: + assert len(kernel) == len(Ylist), "need one kernel per output" + assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!" + + self.input_dim = input_dim + self.num_inducing = num_inducing + + self._in_init_ = True + X = self._init_X(initx, Ylist) + self.Z = self._init_Z(initz, X) + self.num_inducing = self.Z.shape[0] # ensure M==N if M>N + + if X_variance is None: + X_variance = np.random.uniform(0,.2,X.shape) + + self.variational_prior = NormalPrior() + self.X = NormalPosterior(X, X_variance) + + if likelihood is None: + likelihood = Gaussian() + + if inference_method is None: + if any(np.any(np.isnan(y)) for y in Ylist): + self.inference_method = VarDTCMissingData(limit=len(Ylist)) + + self.Ylist = Ylist + + def parameters_changed(self): + for y in self.Ylist: + pass + + def _init_X(self, init='PCA', likelihood_list=None): + if likelihood_list is None: + likelihood_list = self.likelihood_list + Ylist = [] + for likelihood_or_Y in likelihood_list: + if type(likelihood_or_Y) is np.ndarray: + Ylist.append(likelihood_or_Y) + else: + Ylist.append(likelihood_or_Y.Y) + del likelihood_list + if init in "PCA_concat": + X = PCA(np.hstack(Ylist), self.input_dim)[0] + elif init in "PCA_single": + X = np.zeros((Ylist[0].shape[0], self.input_dim)) + for qs, Y in itertools.izip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist): + X[:, qs] = PCA(Y, len(qs))[0] + else: # init == 'random': + X = np.random.randn(Ylist[0].shape[0], self.input_dim) + self.X = X + return X + + def _init_Z(self, init="permute", X=None): + if X is None: + X = self.X + if init in "permute": + Z = np.random.permutation(X.copy())[:self.num_inducing] + elif init in "random": + Z = np.random.randn(self.num_inducing, self.input_dim) * X.var() + self.Z = Z + return Z class MRD(Model): """ @@ -84,7 +177,7 @@ class MRD(Model): del self._in_init_ self.gref = self.bgplvms[0] - nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms]) + nparams = np.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms]) self.nparams = nparams.cumsum() self.num_data = self.gref.num_data @@ -216,7 +309,7 @@ class MRD(Model): X_var = self.gref.X_variance.ravel() Z = self.gref.Z.ravel() thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms] - params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)]) + params = np.hstack([X, X_var, Z, np.hstack(thetas)]) return params # def _set_var_params(self, g, X, X_var, Z): @@ -239,13 +332,13 @@ class MRD(Model): # set params for all: for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]): - g._set_params(numpy.hstack([X, X_var, Z, thetas[s:e]])) + g._set_params(np.hstack([X, X_var, Z, thetas[s:e]])) # self._set_var_params(g, X, X_var, Z) # self._set_kern_params(g, thetas[s:e].copy()) # g._compute_kernel_matrices() # if self.auto_scale_factor: -# g.scale_factor = numpy.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision) -# # self.scale_factor = numpy.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision) +# g.scale_factor = np.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision) +# # self.scale_factor = np.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision) # g._computations() @@ -264,48 +357,18 @@ class MRD(Model): dKLmu, dKLdS = self.gref.dKL_dmuS() dLdmu -= dKLmu dLdS -= dKLdS - dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten() + dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten() dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms)) - return numpy.hstack((dLdmuS, + return np.hstack((dLdmuS, dldzt1, - numpy.hstack([numpy.hstack([g.dL_dtheta(), + np.hstack([np.hstack([g.dL_dtheta(), g.likelihood._gradients(\ partial=g.partial_for_likelihood)]) \ for g in self.bgplvms]))) - def _init_X(self, init='PCA', likelihood_list=None): - if likelihood_list is None: - likelihood_list = self.likelihood_list - Ylist = [] - for likelihood_or_Y in likelihood_list: - if type(likelihood_or_Y) is numpy.ndarray: - Ylist.append(likelihood_or_Y) - else: - Ylist.append(likelihood_or_Y.Y) - del likelihood_list - if init in "PCA_concat": - X = PCA(numpy.hstack(Ylist), self.input_dim)[0] - elif init in "PCA_single": - X = numpy.zeros((Ylist[0].shape[0], self.input_dim)) - for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist): - X[:, qs] = PCA(Y, len(qs))[0] - else: # init == 'random': - X = numpy.random.randn(Ylist[0].shape[0], self.input_dim) - self.X = X - return X - def _init_Z(self, init="permute", X=None): - if X is None: - X = self.X - if init in "permute": - Z = numpy.random.permutation(X.copy())[:self.num_inducing] - elif init in "random": - Z = numpy.random.randn(self.num_inducing, self.input_dim) * X.var() - self.Z = Z - return Z - def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False): if axes is None: fig = pylab.figure(num=fignum) @@ -358,7 +421,7 @@ class MRD(Model): """ if titles is None: titles = [r'${}$'.format(name) for name in self.names] - ymax = reduce(max, [numpy.ceil(max(g.input_sensitivity())) for g in self.bgplvms]) + ymax = reduce(max, [np.ceil(max(g.input_sensitivity())) for g in self.bgplvms]) def plotf(i, g, ax): ax.set_ylim([0,ymax]) g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs) diff --git a/GPy/models/sparse_gp_regression.py b/GPy/models/sparse_gp_regression.py index 6a76df3f..99176601 100644 --- a/GPy/models/sparse_gp_regression.py +++ b/GPy/models/sparse_gp_regression.py @@ -8,7 +8,7 @@ from .. import likelihoods from .. import kern from ..inference.latent_function_inference import VarDTC from ..util.misc import param_to_array -from ..core.parameterization.variational import VariationalPosterior +from ..core.parameterization.variational import NormalPosterior class SparseGPRegression(SparseGP): """ @@ -47,7 +47,7 @@ class SparseGPRegression(SparseGP): likelihood = likelihoods.Gaussian() if not (X_variance is None): - X = VariationalPosterior(X,X_variance) + X = NormalPosterior(X,X_variance) SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC()) @@ -88,7 +88,7 @@ class SparseGPRegressionUncertainInput(SparseGP): # kern defaults to rbf (plus white for stability) if kernel is None: - kernel = kern.rbf(input_dim) + kern.white(input_dim, variance=1e-3) + kernel = kern.RBF(input_dim) + kern.White(input_dim, variance=1e-3) # Z defaults to a subset of the data if Z is None: @@ -99,5 +99,5 @@ class SparseGPRegressionUncertainInput(SparseGP): likelihood = likelihoods.Gaussian() - SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance) + SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC()) self.ensure_default_constraints() diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py index f21da605..5994814b 100644 --- a/GPy/models/ss_gplvm.py +++ b/GPy/models/ss_gplvm.py @@ -36,7 +36,7 @@ class SSGPLVM(SparseGP): X_variance = np.random.uniform(0,.1,X.shape) gamma = np.empty_like(X) # The posterior probabilities of the binary variable in the variational approximation - gamma[:] = 0.5 + gamma[:] = 0.5 + 0.01 * np.random.randn(X.shape[0], input_dim) if Z is None: Z = np.random.permutation(X.copy())[:num_inducing] @@ -48,24 +48,36 @@ class SSGPLVM(SparseGP): if kernel is None: kernel = kern.SSRBF(input_dim) - self.variational_prior = SpikeAndSlabPrior(pi=0.5) # the prior probability of the latent binary variable b + pi = np.empty((input_dim)) + pi[:] = 0.5 + self.variational_prior = SpikeAndSlabPrior(pi=pi) # the prior probability of the latent binary variable b X = SpikeAndSlabPosterior(X, X_variance, gamma) SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs) self.add_parameter(self.X, index=0) + self.add_parameter(self.variational_prior) def parameters_changed(self): super(SSGPLVM, self).parameters_changed() self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) - self.X.mean.gradient, self.X.variance.gradient, self.X.binary_prob.gradient = self.kern.gradients_q_variational(posterior_variational=self.X, Z=self.Z, **self.grad_dict) + self.X.mean.gradient, self.X.variance.gradient, self.X.binary_prob.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, **self.grad_dict) # update for the KL divergence self.variational_prior.update_gradients_KL(self.X) + def input_sensitivity(self): + if self.kern.ARD: + return self.kern.input_sensitivity() + else: + return self.variational_prior.pi + def plot_latent(self, plot_inducing=True, *args, **kwargs): - pass - #return plot_latent.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs) + import sys + assert "matplotlib" in sys.modules, "matplotlib package has not been imported." + from ..plotting.matplot_dep import dim_reduction_plots + + return dim_reduction_plots.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs) def do_test_latents(self, Y): """ diff --git a/GPy/plotting/matplot_dep/dim_reduction_plots.py b/GPy/plotting/matplot_dep/dim_reduction_plots.py index 10b352d3..bf9297b9 100644 --- a/GPy/plotting/matplot_dep/dim_reduction_plots.py +++ b/GPy/plotting/matplot_dep/dim_reduction_plots.py @@ -20,7 +20,7 @@ def most_significant_input_dimensions(model, which_indices): input_1, input_2 = 0, 1 else: try: - input_1, input_2 = np.argsort(model.kern.input_sensitivity())[::-1][:2] + input_1, input_2 = np.argsort(model.input_sensitivity())[::-1][:2] except: raise ValueError, "cannot automatically determine which dimensions to plot, please pass 'which_indices'" else: diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py index b55a0e53..2b990611 100644 --- a/GPy/plotting/matplot_dep/kernel_plots.py +++ b/GPy/plotting/matplot_dep/kernel_plots.py @@ -23,7 +23,7 @@ def add_bar_labels(fig, ax, bars, bottom=0): xi = patch.get_x() + patch.get_width() / 2. va = 'top' c = 'w' - t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, usetex=True, ha='center') + t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, ha='center') transform = transOffset if patch.get_extents().height <= t.get_extents().height + 3: va = 'bottom' @@ -106,7 +106,7 @@ def plot(kernel, x=None, plot_limits=None, which_parts='all', resolution=None, * raise ValueError, "Bad limits for plotting" Xnew = np.linspace(xmin, xmax, resolution or 201)[:, None] - Kx = kernel.K(Xnew, x, which_parts) + Kx = kernel.K(Xnew, x) pb.plot(Xnew, Kx, *args, **kwargs) pb.xlim(xmin, xmax) pb.xlabel("x") diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py index d72d2a3e..4ca4441e 100644 --- a/GPy/plotting/matplot_dep/models_plots.py +++ b/GPy/plotting/matplot_dep/models_plots.py @@ -56,10 +56,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all', if ax is None: fig = pb.figure(num=fignum) ax = fig.add_subplot(111) - - X, Y = param_to_array(model.X, model.Y) - if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): X_variance = model.X_variance - + + if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): + X = model.X.mean + X_variance = param_to_array(model.X.variance) + else: + X = model.X + X, Y = param_to_array(X, model.Y) if hasattr(model, 'Z'): Z = param_to_array(model.Z) #work out what the inputs are for plotting (1D or 2D) @@ -98,10 +101,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all', #add error bars for uncertain (if input uncertainty is being modelled) - #if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs(): - # ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(), - # xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()), - # ecolor='k', fmt=None, elinewidth=.5, alpha=.5) + if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs(): + ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(), + xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()), + ecolor='k', fmt=None, elinewidth=.5, alpha=.5) #set the limits of the plot to some sensible values diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py index 72b857a6..cf00d8a2 100644 --- a/GPy/plotting/matplot_dep/variational_plots.py +++ b/GPy/plotting/matplot_dep/variational_plots.py @@ -44,3 +44,48 @@ def plot(parameterized, fignum=None, ax=None, colors=None): pb.draw() fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95)) return fig + +def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None): + """ + Plot latent space X in 1D: + + - if fig is given, create input_dim subplots in fig and plot in these + - if ax is given plot input_dim 1D latent space plots of X into each `axis` + - if neither fig nor ax is given create a figure with fignum and plot in there + + colors: + colors of different latent space dimensions input_dim + + """ + if ax is None: + fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1])))) + if colors is None: + colors = pb.gca()._get_lines.color_cycle + pb.clf() + else: + colors = iter(colors) + plots = [] + means, variances, gamma = param_to_array(parameterized.mean, parameterized.variance, parameterized.binary_prob) + x = np.arange(means.shape[0]) + for i in range(means.shape[1]): + # mean and variance plot + a = fig.add_subplot(means.shape[1]*2, 1, 2*i + 1) + a.plot(means, c='k', alpha=.3) + plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i))) + a.fill_between(x, + means.T[i] - 2 * np.sqrt(variances.T[i]), + means.T[i] + 2 * np.sqrt(variances.T[i]), + facecolor=plots[-1].get_color(), + alpha=.3) + a.legend(borderaxespad=0.) + a.set_xlim(x.min(), x.max()) + if i < means.shape[1] - 1: + a.set_xticklabels('') + # binary prob plot + a = fig.add_subplot(means.shape[1]*2, 1, 2*i + 2) + a.bar(x,gamma[:,i],bottom=0.,linewidth=0,align='center') + a.set_xlim(x.min(), x.max()) + a.set_ylim([0.,1.]) + pb.draw() + fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95)) + return fig diff --git a/GPy/testing/likelihood_tests.py b/GPy/testing/likelihood_tests.py index 50adcbff..3a43e478 100644 --- a/GPy/testing/likelihood_tests.py +++ b/GPy/testing/likelihood_tests.py @@ -10,7 +10,7 @@ from functools import partial #np.random.seed(300) #np.random.seed(7) -np.seterr(divide='raise') +#np.seterr(divide='raise') def dparam_partial(inst_func, *args): """ If we have a instance method that needs to be called but that doesn't @@ -350,7 +350,7 @@ class TestNoiseModels(object): def t_logpdf(self, model, Y, f): print "\n{}".format(inspect.stack()[0][3]) print model - print model._get_params() + #print model._get_params() np.testing.assert_almost_equal( model.pdf(f.copy(), Y.copy()), np.exp(model.logpdf(f.copy(), Y.copy())) @@ -664,7 +664,8 @@ class LaplaceTests(unittest.TestCase): print m1 print m2 - m2._set_params(m1._get_params()) + m2.parameters_changed() + #m2._set_params(m1._get_params()) #Predict for training points to get posterior mean and variance post_mean, post_var, _, _ = m1.predict(X) @@ -700,7 +701,8 @@ class LaplaceTests(unittest.TestCase): np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2) #Check marginals are the same with random m1.randomize() - m2._set_params(m1._get_params()) + #m2._set_params(m1._get_params()) + m2.parameters_changed() np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2) #Check they are checkgradding diff --git a/GPy/testing/observable_tests.py b/GPy/testing/observable_tests.py new file mode 100644 index 00000000..ebda1630 --- /dev/null +++ b/GPy/testing/observable_tests.py @@ -0,0 +1,138 @@ +''' +Created on 27 Feb 2014 + +@author: maxz +''' +import unittest +from GPy.core.parameterization.parameterized import Parameterized +from GPy.core.parameterization.param import Param +import numpy + +# One trigger in init +_trigger_start = -1 + +class ParamTestParent(Parameterized): + parent_changed_count = _trigger_start + def parameters_changed(self): + self.parent_changed_count += 1 + +class ParameterizedTest(Parameterized): + # One trigger after initialization + params_changed_count = _trigger_start + def parameters_changed(self): + self.params_changed_count += 1 + def _set_params(self, params, trigger_parent=True): + Parameterized._set_params(self, params, trigger_parent=trigger_parent) + +class Test(unittest.TestCase): + + def setUp(self): + self.parent = ParamTestParent('test parent') + self.par = ParameterizedTest('test model') + self.par2 = ParameterizedTest('test model 2') + self.p = Param('test parameter', numpy.random.normal(1,2,(10,3))) + + self.par.add_parameter(self.p) + self.par.add_parameter(Param('test1', numpy.random.normal(0,1,(1,)))) + self.par.add_parameter(Param('test2', numpy.random.normal(0,1,(1,)))) + + self.par2.add_parameter(Param('par2 test1', numpy.random.normal(0,1,(1,)))) + self.par2.add_parameter(Param('par2 test2', numpy.random.normal(0,1,(1,)))) + + self.parent.add_parameter(self.par) + self.parent.add_parameter(self.par2) + + self._observer_triggered = None + self._trigger_count = 0 + self._first = None + self._second = None + + def _trigger(self, which): + self._observer_triggered = float(which) + self._trigger_count += 1 + if self._first is not None: + self._second = self._trigger + else: + self._first = self._trigger + + def _trigger_priority(self, which): + if self._first is not None: + self._second = self._trigger_priority + else: + self._first = self._trigger_priority + + def test_observable(self): + self.par.add_observer(self, self._trigger, -1) + self.assertEqual(self.par.params_changed_count, 0, 'no params changed yet') + self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param') + + self.p[0,1] = 3 # trigger observers + self.assertEqual(self._observer_triggered, 3, 'observer should have triggered') + self.assertEqual(self._trigger_count, 1, 'observer should have triggered once') + self.assertEqual(self.par.params_changed_count, 1, 'params changed once') + self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param') + + self.par.remove_observer(self) + self.p[2,1] = 4 + self.assertEqual(self._observer_triggered, 3, 'observer should not have triggered') + self.assertEqual(self._trigger_count, 1, 'observer should have triggered once') + self.assertEqual(self.par.params_changed_count, 2, 'params changed second') + self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param') + + self.par.add_observer(self, self._trigger, -1) + self.p[2,1] = 4 + self.assertEqual(self._observer_triggered, 4, 'observer should have triggered') + self.assertEqual(self._trigger_count, 2, 'observer should have triggered once') + self.assertEqual(self.par.params_changed_count, 3, 'params changed second') + self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param') + + self.par.remove_observer(self, self._trigger) + self.p[0,1] = 3 + self.assertEqual(self._observer_triggered, 4, 'observer should not have triggered') + self.assertEqual(self._trigger_count, 2, 'observer should have triggered once') + self.assertEqual(self.par.params_changed_count, 4, 'params changed second') + self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param') + + def test_set_params(self): + self.assertEqual(self.par.params_changed_count, 0, 'no params changed yet') + self.par._param_array_[:] = 1 + self.par._trigger_params_changed() + self.assertEqual(self.par.params_changed_count, 1, 'now params changed') + self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count) + + self.par._param_array_[:] = 2 + self.par._trigger_params_changed() + self.assertEqual(self.par.params_changed_count, 2, 'now params changed') + self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count) + + + def test_priority_notify(self): + self.assertEqual(self.par.params_changed_count, 0) + self.par.notify_observers(0, None) + self.assertEqual(self.par.params_changed_count, 1) + self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count) + + self.par.notify_observers(0, -numpy.inf) + self.assertEqual(self.par.params_changed_count, 2) + self.assertEqual(self.parent.parent_changed_count, 1) + + def test_priority(self): + self.par.add_observer(self, self._trigger, -1) + self.par.add_observer(self, self._trigger_priority, 0) + self.par.notify_observers(0) + self.assertEqual(self._first, self._trigger_priority, 'priority should be first') + self.assertEqual(self._second, self._trigger, 'priority should be first') + + self.par.remove_observer(self) + self._first = self._second = None + + self.par.add_observer(self, self._trigger, 1) + self.par.add_observer(self, self._trigger_priority, 0) + self.par.notify_observers(0) + self.assertEqual(self._first, self._trigger, 'priority should be second') + self.assertEqual(self._second, self._trigger_priority, 'priority should be second') + + +if __name__ == "__main__": + #import sys;sys.argv = ['', 'Test.testName'] + unittest.main() \ No newline at end of file diff --git a/GPy/testing/parameterized_tests.py b/GPy/testing/parameterized_tests.py index 6f13d294..b2f57144 100644 --- a/GPy/testing/parameterized_tests.py +++ b/GPy/testing/parameterized_tests.py @@ -6,6 +6,7 @@ Created on Feb 13, 2014 import unittest import GPy import numpy as np +from GPy.core.parameterization.parameter_core import HierarchyError class Test(unittest.TestCase): @@ -21,6 +22,10 @@ class Test(unittest.TestCase): self.test1.add_parameter(self.rbf, 0) self.test1.add_parameter(self.param) + x = np.linspace(-2,6,4)[:,None] + y = np.sin(x) + self.testmodel = GPy.models.GPRegression(x,y) + def test_add_parameter(self): self.assertEquals(self.rbf._parent_index_, 0) self.assertEquals(self.white._parent_index_, 1) @@ -37,7 +42,6 @@ class Test(unittest.TestCase): self.test1.add_parameter(self.white, 0) self.assertListEqual(self.test1._fixes_.tolist(),[FIXED,UNFIXED,UNFIXED]) - def test_remove_parameter(self): from GPy.core.parameterization.transformations import FIXED, UNFIXED, __fixed__, Logexp self.white.fix() @@ -65,7 +69,7 @@ class Test(unittest.TestCase): self.assertListEqual(self.test1.constraints[Logexp()].tolist(), [0,1]) def test_add_parameter_already_in_hirarchy(self): - self.test1.add_parameter(self.white._parameters_[0]) + self.assertRaises(HierarchyError, self.test1.add_parameter, self.white._parameters_[0]) def test_default_constraints(self): self.assertIs(self.rbf.variance.constraints._param_index_ops, self.rbf.constraints._param_index_ops) @@ -88,6 +92,18 @@ class Test(unittest.TestCase): self.assertEqual(self.rbf.constraints._offset, 0) self.assertEqual(self.param.constraints._offset, 3) + def test_fixing_randomize(self): + self.white.fix(warning=False) + val = float(self.test1.white.variance) + self.test1.randomize() + self.assertEqual(val, self.white.variance) + + def test_fixing_optimize(self): + self.testmodel.kern.lengthscale.fix() + val = float(self.testmodel.kern.lengthscale) + self.testmodel.randomize() + self.assertEqual(val, self.testmodel.kern.lengthscale) + if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.test_add_parameter'] unittest.main() \ No newline at end of file diff --git a/GPy/testing/psi_stat_expectation_tests.py b/GPy/testing/psi_stat_expectation_tests.py index 90252197..aec0d36d 100644 --- a/GPy/testing/psi_stat_expectation_tests.py +++ b/GPy/testing/psi_stat_expectation_tests.py @@ -9,8 +9,8 @@ import numpy as np from GPy import testing import sys import numpy -from GPy.kern.parts.rbf import RBF -from GPy.kern.parts.linear import Linear +from GPy.kern import RBF +from GPy.kern import Linear from copy import deepcopy __test__ = lambda: 'deep' in sys.argv @@ -36,7 +36,7 @@ class Test(unittest.TestCase): indices = numpy.cumsum(i_s_dim_list).tolist() input_slices = [slice(a,b) for a,b in zip([None]+indices, indices)] #input_slices[2] = deepcopy(input_slices[1]) - input_slice_kern = GPy.kern.kern(9, + input_slice_kern = GPy.kern.kern(9, [ RBF(i_s_dim_list[0], np.random.rand(), np.random.rand(i_s_dim_list[0]), ARD=True), RBF(i_s_dim_list[1], np.random.rand(), np.random.rand(i_s_dim_list[1]), ARD=True), @@ -51,8 +51,8 @@ class Test(unittest.TestCase): # GPy.kern.bias(self.input_dim) + # GPy.kern.white(self.input_dim)), (#GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) - GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) - +GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) + GPy.kern.Linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) + +GPy.kern.RBF(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) # +GPy.kern.bias(self.input_dim) # +GPy.kern.white(self.input_dim)), ), diff --git a/GPy/testing/psi_stat_gradient_tests.py b/GPy/testing/psi_stat_gradient_tests.py index 32986c77..fc189f93 100644 --- a/GPy/testing/psi_stat_gradient_tests.py +++ b/GPy/testing/psi_stat_gradient_tests.py @@ -25,10 +25,10 @@ class PsiStatModel(Model): self.kern = kernel self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance) 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 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 @@ -43,9 +43,9 @@ class PsiStatModel(Model): 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, + 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): @@ -57,14 +57,14 @@ class DPsiStatTest(unittest.TestCase): X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1) Z = numpy.random.permutation(X)[:num_inducing] 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, 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) + 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): @@ -73,7 +73,7 @@ class DPsiStatTest(unittest.TestCase): num_inducing=self.num_inducing, kernel=k) m.randomize() 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, @@ -119,11 +119,11 @@ if __name__ == "__main__": if interactive: # N, num_inducing, input_dim, input_dim = 30, 5, 4, 30 # X = numpy.random.rand(N, input_dim) -# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001) +# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001) # K = k.K(X) # Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T # Y -= Y.mean(axis=0) -# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001) +# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001) # m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing) # m.randomize() # # self.assertTrue(m.checkgrad()) @@ -136,11 +136,11 @@ if __name__ == "__main__": X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1) Z = numpy.random.permutation(X)[:num_inducing] Y = X.dot(numpy.random.randn(input_dim, D)) -# kernel = GPy.kern.bias(input_dim) +# kernel = 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)] # for k in kernels: # m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, @@ -148,32 +148,32 @@ if __name__ == "__main__": # assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts))) # m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z, - num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim)+GPy.kern.bias(input_dim)) + num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim)+GPy.kern.Bias(input_dim)) # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # num_inducing=num_inducing, kernel=kernel) # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # num_inducing=num_inducing, kernel=kernel) # m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, -# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim)) +# num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim)) # m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, -# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim))) - # + GPy.kern.bias(input_dim)) +# num_inducing=num_inducing, kernel=GPy.kern.Linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim))) + # + GPy.kern.Bias(input_dim)) # m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, -# num_inducing=num_inducing, +# num_inducing=num_inducing, # kernel=( -# GPy.kern.rbf(input_dim, ARD=1) -# +GPy.kern.linear(input_dim, ARD=1) -# +GPy.kern.bias(input_dim)) +# GPy.kern.RBF(input_dim, ARD=1) +# +GPy.kern.Linear(input_dim, ARD=1) +# +GPy.kern.Bias(input_dim)) # ) # m.ensure_default_constraints() m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, num_inducing=num_inducing, kernel=( - GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) - #+GPy.kern.linear(input_dim, numpy.random.rand(input_dim), ARD=1) - #+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) - #+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0) - +GPy.kern.bias(input_dim) - +GPy.kern.white(input_dim) + GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) + #+GPy.kern.Linear(input_dim, numpy.random.rand(input_dim), ARD=1) + #+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1) + #+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0) + +GPy.kern.Bias(input_dim) + +GPy.kern.White(input_dim) ) ) m2.ensure_default_constraints() diff --git a/GPy/testing/sparse_gplvm_tests.py b/GPy/testing/sparse_gplvm_tests.py index c3942b95..eb8ccb9c 100644 --- a/GPy/testing/sparse_gplvm_tests.py +++ b/GPy/testing/sparse_gplvm_tests.py @@ -10,10 +10,10 @@ class sparse_GPLVMTests(unittest.TestCase): def test_bias_kern(self): N, num_inducing, input_dim, D = 10, 3, 2, 4 X = np.random.rand(N, input_dim) - k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001) K = k.K(X) Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T - k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001) m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing) m.randomize() self.assertTrue(m.checkgrad()) @@ -21,10 +21,10 @@ class sparse_GPLVMTests(unittest.TestCase): def test_linear_kern(self): N, num_inducing, input_dim, D = 10, 3, 2, 4 X = np.random.rand(N, input_dim) - k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001) K = k.K(X) Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T - k = GPy.kern.linear(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.Linear(input_dim) + GPy.kern.White(input_dim, 0.00001) m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing) m.randomize() self.assertTrue(m.checkgrad()) @@ -32,10 +32,10 @@ class sparse_GPLVMTests(unittest.TestCase): def test_rbf_kern(self): N, num_inducing, input_dim, D = 10, 3, 2, 4 X = np.random.rand(N, input_dim) - k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001) K = k.K(X) Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T - k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001) + k = GPy.kern.RBF(input_dim) + GPy.kern.White(input_dim, 0.00001) m = SparseGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing) m.randomize() self.assertTrue(m.checkgrad()) diff --git a/GPy/testing/unit_tests.py b/GPy/testing/unit_tests.py index 9269a4c4..0cb4cd66 100644 --- a/GPy/testing/unit_tests.py +++ b/GPy/testing/unit_tests.py @@ -33,7 +33,7 @@ class GradientTests(unittest.TestCase): # Get model type (GPRegression, SparseGPRegression, etc) model_fit = getattr(GPy.models, model_type) - # noise = GPy.kern.white(dimension) + # noise = GPy.kern.White(dimension) kern = kern # + noise if uncertain_inputs: m = model_fit(X, Y, kernel=kern, X_variance=np.random.rand(X.shape[0], X.shape[1])) @@ -45,17 +45,17 @@ class GradientTests(unittest.TestCase): def test_GPRegression_rbf_1d(self): ''' Testing the GP regression with rbf kernel with white kernel on 1d data ''' - rbf = GPy.kern.rbf(1) + rbf = GPy.kern.RBF(1) self.check_model(rbf, model_type='GPRegression', dimension=1) def test_GPRegression_rbf_2D(self): ''' Testing the GP regression with rbf kernel on 2d data ''' - rbf = GPy.kern.rbf(2) + rbf = GPy.kern.RBF(2) self.check_model(rbf, model_type='GPRegression', dimension=2) def test_GPRegression_rbf_ARD_2D(self): ''' Testing the GP regression with rbf kernel on 2d data ''' - k = GPy.kern.rbf(2, ARD=True) + k = GPy.kern.RBF(2, ARD=True) self.check_model(k, model_type='GPRegression', dimension=2) def test_GPRegression_mlp_1d(self): @@ -65,7 +65,7 @@ class GradientTests(unittest.TestCase): def test_GPRegression_poly_1d(self): ''' Testing the GP regression with polynomial kernel with white kernel on 1d data ''' - mlp = GPy.kern.poly(1, degree=5) + mlp = GPy.kern.Poly(1, degree=5) self.check_model(mlp, model_type='GPRegression', dimension=1) def test_GPRegression_matern52_1D(self): @@ -100,80 +100,80 @@ class GradientTests(unittest.TestCase): def test_GPRegression_exponential_1D(self): ''' Testing the GP regression with exponential kernel on 1d data ''' - exponential = GPy.kern.exponential(1) + exponential = GPy.kern.Exponential(1) self.check_model(exponential, model_type='GPRegression', dimension=1) def test_GPRegression_exponential_2D(self): ''' Testing the GP regression with exponential kernel on 2d data ''' - exponential = GPy.kern.exponential(2) + exponential = GPy.kern.Exponential(2) self.check_model(exponential, model_type='GPRegression', dimension=2) def test_GPRegression_exponential_ARD_2D(self): ''' Testing the GP regression with exponential kernel on 2d data ''' - exponential = GPy.kern.exponential(2, ARD=True) + exponential = GPy.kern.Exponential(2, ARD=True) self.check_model(exponential, model_type='GPRegression', dimension=2) def test_GPRegression_bias_kern_1D(self): ''' Testing the GP regression with bias kernel on 1d data ''' - bias = GPy.kern.bias(1) + bias = GPy.kern.Bias(1) self.check_model(bias, model_type='GPRegression', dimension=1) def test_GPRegression_bias_kern_2D(self): ''' Testing the GP regression with bias kernel on 2d data ''' - bias = GPy.kern.bias(2) + bias = GPy.kern.Bias(2) self.check_model(bias, model_type='GPRegression', dimension=2) def test_GPRegression_linear_kern_1D_ARD(self): ''' Testing the GP regression with linear kernel on 1d data ''' - linear = GPy.kern.linear(1, ARD=True) + linear = GPy.kern.Linear(1, ARD=True) self.check_model(linear, model_type='GPRegression', dimension=1) def test_GPRegression_linear_kern_2D_ARD(self): ''' Testing the GP regression with linear kernel on 2d data ''' - linear = GPy.kern.linear(2, ARD=True) + linear = GPy.kern.Linear(2, ARD=True) self.check_model(linear, model_type='GPRegression', dimension=2) def test_GPRegression_linear_kern_1D(self): ''' Testing the GP regression with linear kernel on 1d data ''' - linear = GPy.kern.linear(1) + linear = GPy.kern.Linear(1) self.check_model(linear, model_type='GPRegression', dimension=1) def test_GPRegression_linear_kern_2D(self): ''' Testing the GP regression with linear kernel on 2d data ''' - linear = GPy.kern.linear(2) + linear = GPy.kern.Linear(2) self.check_model(linear, model_type='GPRegression', dimension=2) def test_SparseGPRegression_rbf_white_kern_1d(self): ''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data ''' - rbf = GPy.kern.rbf(1) + rbf = GPy.kern.RBF(1) self.check_model(rbf, model_type='SparseGPRegression', dimension=1) def test_SparseGPRegression_rbf_white_kern_2D(self): ''' Testing the sparse GP regression with rbf kernel on 2d data ''' - rbf = GPy.kern.rbf(2) + rbf = GPy.kern.RBF(2) self.check_model(rbf, model_type='SparseGPRegression', dimension=2) def test_SparseGPRegression_rbf_linear_white_kern_1D(self): ''' Testing the sparse GP regression with rbf kernel on 2d data ''' - rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1) + rbflin = GPy.kern.RBF(1) + GPy.kern.Linear(1) self.check_model(rbflin, model_type='SparseGPRegression', dimension=1) def test_SparseGPRegression_rbf_linear_white_kern_2D(self): ''' Testing the sparse GP regression with rbf kernel on 2d data ''' - rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) + rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2) self.check_model(rbflin, model_type='SparseGPRegression', dimension=2) #@unittest.expectedFailure def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self): ''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs''' - rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) + rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2) raise unittest.SkipTest("This is not implemented yet!") self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1) #@unittest.expectedFailure def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self): ''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs''' - rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1) + rbflin = GPy.kern.RBF(1) + GPy.kern.Linear(1) raise unittest.SkipTest("This is not implemented yet!") self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1) @@ -181,7 +181,7 @@ class GradientTests(unittest.TestCase): """ Testing GPLVM with rbf + bias kernel """ N, input_dim, D = 50, 1, 2 X = np.random.rand(N, input_dim) - k = GPy.kern.rbf(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05) + k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) K = k.K(X) Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T m = GPy.models.GPLVM(Y, input_dim, kernel=k) @@ -191,7 +191,7 @@ class GradientTests(unittest.TestCase): """ Testing GPLVM with rbf + bias kernel """ N, input_dim, D = 50, 1, 2 X = np.random.rand(N, input_dim) - k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05) + k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) K = k.K(X) Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k) @@ -201,7 +201,7 @@ class GradientTests(unittest.TestCase): N = 20 X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None] Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None] - kernel = GPy.kern.rbf(1) + kernel = GPy.kern.RBF(1) m = GPy.models.GPClassification(X,Y,kernel=kernel) m.update_likelihood_approximation() self.assertTrue(m.checkgrad()) @@ -211,7 +211,7 @@ class GradientTests(unittest.TestCase): X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None] Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None] Z = np.linspace(0, 15, 4)[:, None] - kernel = GPy.kern.rbf(1) + kernel = GPy.kern.RBF(1) m = GPy.models.SparseGPClassification(X,Y,kernel=kernel,Z=Z) #distribution = GPy.likelihoods.likelihood_functions.Bernoulli() #likelihood = GPy.likelihoods.EP(Y, distribution) @@ -223,7 +223,7 @@ class GradientTests(unittest.TestCase): def test_generalized_FITC(self): N = 20 X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None] - k = GPy.kern.rbf(1) + GPy.kern.white(1) + k = GPy.kern.RBF(1) + GPy.kern.White(1) Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None] m = GPy.models.FITCClassification(X, Y, kernel = k) m.update_likelihood_approximation() @@ -237,7 +237,7 @@ class GradientTests(unittest.TestCase): Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05 Y = np.vstack((Y1, Y2)) - k1 = GPy.kern.rbf(1) + k1 = GPy.kern.RBF(1) m = GPy.models.GPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1]) m.constrain_fixed('.*rbf_var', 1.) self.assertTrue(m.checkgrad()) @@ -250,7 +250,7 @@ class GradientTests(unittest.TestCase): Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05 Y = np.vstack((Y1, Y2)) - k1 = GPy.kern.rbf(1) + k1 = GPy.kern.RBF(1) m = GPy.models.SparseGPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1]) m.constrain_fixed('.*rbf_var', 1.) self.assertTrue(m.checkgrad()) diff --git a/GPy/util/caching.py b/GPy/util/caching.py index 2899cb33..250efe11 100644 --- a/GPy/util/caching.py +++ b/GPy/util/caching.py @@ -1,4 +1,5 @@ from ..core.parameterization.parameter_core import Observable +import itertools class Cacher(object): """ @@ -38,8 +39,11 @@ class Cacher(object): if not all([isinstance(arg, Observable) for arg in observable_args]): return self.operation(*args) + # TODO: WARNING !!! Cache OFFSWITCH !!! WARNING + # return self.operation(*args) + #if the result is cached, return the cached computation - state = [all(a is b for a, b in zip(args, cached_i)) for cached_i in self.cached_inputs] + state = [all(a is b for a, b in itertools.izip_longest(args, cached_i)) for cached_i in self.cached_inputs] if any(state): i = state.index(True) if self.inputs_changed[i]: diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py index f2b372be..4745c4aa 100644 --- a/GPy/util/linalg.py +++ b/GPy/util/linalg.py @@ -78,24 +78,24 @@ def force_F_ordered(A): # return jitchol(A+np.eye(A.shape[0])*jitter, maxtries-1) def jitchol(A, maxtries=5): - A = np.ascontiguousarray(A) - L, info = lapack.dpotrf(A, lower=1) - if info == 0: - return L - else: - diagA = np.diag(A) - if np.any(diagA <= 0.): - raise linalg.LinAlgError, "not pd: non-positive diagonal elements" - jitter = diagA.mean() * 1e-6 - while maxtries > 0 and np.isfinite(jitter): - print 'Warning: adding jitter of {:.10e}'.format(jitter) - try: - return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True) - except: - jitter *= 10 - finally: - maxtries -= 1 - raise linalg.LinAlgError, "not positive definite, even with jitter." + A = np.ascontiguousarray(A) + L, info = lapack.dpotrf(A, lower=1) + if info == 0: + return L + else: + diagA = np.diag(A) + if np.any(diagA <= 0.): + raise linalg.LinAlgError, "not pd: non-positive diagonal elements" + jitter = diagA.mean() * 1e-6 + while maxtries > 0 and np.isfinite(jitter): + print 'Warning: adding jitter of {:.10e}'.format(jitter) + try: + return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True) + except: + jitter *= 10 + finally: + maxtries -= 1 + raise linalg.LinAlgError, "not positive definite, even with jitter."