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https://github.com/SheffieldML/GPy.git
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1077 lines
46 KiB
Python
1077 lines
46 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy; np = numpy
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import copy
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import cPickle
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import transformations
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import itertools
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from re import compile, _pattern_type
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import re
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class Parentable(object):
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_direct_parent_ = None
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_parent_index_ = None
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def has_parent(self):
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return self._direct_parent_ is not None
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class Nameable(Parentable):
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_name = None
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def __init__(self, name):
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self._name = name or self.__class__.__name__
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self.name = name
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@property
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def name(self):
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return self._name
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@name.setter
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def name(self, name):
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from_name = self.name
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self._name = name
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if self.has_parent():
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self._direct_parent_._name_changed(self, from_name)
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class Pickleable(object):
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def getstate(self):
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"""
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Returns the state of this class in a memento pattern.
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The state must be a list-like structure of all the fields
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this class need to run
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"""
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raise NotImplementedError, "To be able to use pickling you need to implement this method"
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def setstate(self, state):
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"""
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Set the state (memento pattern) of this class to the given state.
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Usually this is just the counterpart to getstate, such that
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an object is a copy of another when calling
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copy = <classname>.__new__(*args,**kw).setstate(<to_be_copied>.getstate())
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"""
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raise NotImplementedError, "To be able to use pickling you need to implement this method"
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class Observable(object):
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_observers_ = {}
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def add_observer(self, observer, callble):
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self._observers_[observer] = callble
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callble(self)
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def remove_observer(self, observer):
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del self._observers_[observer]
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def _notify_observers(self):
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[callble(self) for callble in self._observers_.itervalues()]
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def _adjust_name_for_printing(name):
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return name.replace(" ", "_").replace(".", "_")
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from parameter import ParamConcatenation, Param
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from index_operations import ParameterIndexOperations,\
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index_empty
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#===============================================================================
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# Printing:
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__fixed__ = "fixed"
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#===============================================================================
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#===============================================================================
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# constants
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FIXED = False
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UNFIXED = True
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#===============================================================================
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class Parameterized(Nameable, Pickleable, Observable):
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"""
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Parameterized class
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Say m is a handle to a parameterized class.
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Printing parameters:
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- print m: prints a nice summary over all parameters
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- print m.name: prints details for all the parameters
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which start with name
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- print m['.*name']: prints details for all the parameters
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which contain "name"
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- print m['']: prints details for all parameters
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Fields:
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Name: The name of the parameter, can be renamed!
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Value: Shape or value, if one-valued
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Constrain: constraint of the parameter, curly "{c}" brackets indicate
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some parameters are constrained by c. See detailed print
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to get exact constraints.
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Tied_to: which paramter it is tied to.
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Getting and setting parameters:
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Two ways to get parameters:
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- m.name regular expression matches all parameters beginning with name
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- m['name'] regular expression matches all parameters with name
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Handling of constraining, fixing and tieing parameters:
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You can constrain parameters by calling the constrain on the parameter itself, e.g:
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- m.name[:,1].constrain_positive()
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- m.name[0].tie_to(m.name[1])
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Fixing parameters will fix them to the value they are right now. If you change
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the parameters value, the parameter will be fixed to the new value!
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If you want to operate on all parameters use m[''] to wildcard select all paramters
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and concatenate them. Printing m[''] will result in printing of all parameters in detail.
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"""
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def __init__(self, name=None):
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super(Parameterized, self).__init__(name)
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self._in_init_ = True
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self._constraints_ = None#ParameterIndexOperations()
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self._fixes_ = None
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if not hasattr(self, "_parameters_"):
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self._parameters_ = []
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#else:
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# self._parameters_.extend(parameters)
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self._connect_parameters()
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self.gradient_mapping = {}
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self._added_names_ = set()
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del self._in_init_
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@property
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def constraints(self):
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if self._constraints_ is None:
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self._constraints_ = ParameterIndexOperations()
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return self._constraints_
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#===========================================================================
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# Parameter connection for model creation:
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#===========================================================================
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# def set_as_parameter(self, name, array, gradient, index=None, gradient_parent=None):
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# """
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# :param name: name of the parameter (in print and plots), can be callable without parameters
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# :type name: str, callable
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# :param array: array which the parameter consists of
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# :type array: array-like
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# :param gradient: gradient method of the parameter
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# :type gradient: callable
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# :param index: (optional) index of the parameter when printing
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#
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# (:param gradient_parent: connect these parameters to this class, but tell
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# updates to highest_parent, this is needed when parameterized classes
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# contain parameterized classes, but want to access the parameters
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# of their children)
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#
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#
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# Set array (e.g. self.X) as parameter with name and gradient.
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# I.e: self.set_as_parameter('curvature', self.lengthscale, self.dK_dlengthscale)
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#
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# Note: the order in which parameters are added can be adjusted by
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# giving an index, of where to put this parameter in printing
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# """
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# if index is None:
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# self._parameters_.append(Param(name, array, gradient))
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# else:
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# self._parameters_.insert(index, Param(name, array, gradient))
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# self._connect_parameters(gradient_parent=gradient_parent)
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def add_parameter(self, parameter, gradient=None, index=None):
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"""
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:param parameters: the parameters to add
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:type parameters: list of or one :py:class:`GPy.core.parameter.Param`
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:param [gradients]: gradients for each parameter,
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one gradient per parameter
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:param [index]: index of where to put parameters
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Add all parameters to this parameter class, you can insert parameters
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at any given index using the :py:func:`list.insert` syntax
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"""
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if parameter in self._parameters_ and index is not None:
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del self._parameters_[parameter._parent_index_]
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self._parameters_.insert(index, parameter)
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elif parameter not in self._parameters_:
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if index is None:
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self._parameters_.append(parameter)
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else:
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self._parameters_.insert(index, parameter)
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self._connect_parameters()
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if gradient:
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self.gradient_mapping[parameter] = gradient
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def add_parameters(self, *parameters):
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"""
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convenience method for adding several
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parameters without gradient specification
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"""
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[self.add_parameter(p) for p in parameters]
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def remove_parameter(self, *names_params_indices):
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"""
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:param names_params_indices: mix of parameter_names, parameter objects, or indices
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to remove from being a parameter of this parameterized object.
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note: if it is a string object it will not (!) be regexp-matched
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automatically.
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"""
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self._parameters_ = [p for p in self._parameters_
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if not (p._parent_index_ in names_params_indices
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or p.name in names_params_indices
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or p in names_params_indices)]
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self._connect_parameters()
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def parameters_changed(self):
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"""
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This method gets called when parameters have changed.
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Another way of listening to parameter changes is to
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add self as a listener to the parameter, such that
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updates get passed through. See :py:function:``GPy.core.parameter.Observable.add_observer``
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"""
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# will be called as soon as paramters have changed
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pass
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def _connect_parameters(self):
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# connect parameterlist to this parameterized object
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# This just sets up the right connection for the params objects
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# to be used as parameters
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if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
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# no parameters for this class
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return
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i = 0
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for p in self._parameters_:
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#if p._parent_ is None:
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p._direct_parent_ = self
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p._parent_index_ = i
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i += 1
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for pi in p.flattened_parameters:
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pi._highest_parent_ = self
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not_unique = []
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# for k,v in self.__dict__.iteritems():
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# try:
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# if fast_array_equal(v,p):
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# self.__dict__[k] = p
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# except: # parameter comparison, just for convenience
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# pass
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pname = _adjust_name_for_printing(p.name)
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if pname in self.__dict__:
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if isinstance(self.__dict__[pname], (Parameterized, Param)):
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if not p is self.__dict__[pname]:
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not_unique.append(pname)
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del self.__dict__[pname]
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elif not (pname in not_unique):
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self.__dict__[pname] = p
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self._added_names_.add(pname)
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sizes = numpy.cumsum([0] + self._parameter_sizes_)
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self.size = sizes[-1]
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self._param_slices_ = [slice(start, stop) for start,stop in zip(sizes, sizes[1:])]
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# self.parameters_changed()
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#===========================================================================
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# Pickling operations
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#===========================================================================
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def pickle(self, f, protocol=-1):
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"""
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:param f: either filename or open file object to write to.
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if it is an open buffer, you have to make sure to close
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it properly.
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:param protocol: pickling protocol to use, python-pickle for details.
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"""
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if isinstance(f, str):
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with open(f, 'w') as f:
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cPickle.dump(self, f, protocol)
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else:
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cPickle.dump(self, f, protocol)
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def copy(self):
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"""Returns a (deep) copy of the current model """
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return copy.deepcopy(self)
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def __getstate__(self):
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if self._has_get_set_state():
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return self.getstate()
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return self.__dict__
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def __setstate__(self, state):
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if self._has_get_set_state():
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self.setstate(state) # set state
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#self._set_params(self._get_params()) # restore all values
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return
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self.__dict__ = state
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def _has_get_set_state(self):
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return 'getstate' in vars(self.__class__) and 'setstate' in vars(self.__class__)
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def getstate(self):
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"""
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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For inheriting from Parameterized:
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Allways append the state of the inherited object
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and call down to the inherited object in setstate!!
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"""
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return [
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self._fixes_,
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self._constraints_,
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self._priors_,
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self._parameters_,
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self._name,
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self.gradient_mapping,
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self._added_names_,
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]
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def setstate(self, state):
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self._added_names_ = state.pop()
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self.gradient_mapping = state.pop(),
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self._name = state.pop()
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self._parameters_ = state.pop()
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self._connect_parameters()
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self._priors = state.pop()
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self._constraints_ = state.pop()
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self._fixes_ = state.pop()
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self.parameters_changed()
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#===========================================================================
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# Gradient control
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#===========================================================================
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def _transform_gradients(self, g):
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if self.has_parent():
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return g
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x = self._get_params()
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#g = g.copy()
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#for constraint, index in self.constraints.iteritems():
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# if constraint != __fixed__:
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# g[index] = g[index] * constraint.gradfactor(x[index])
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[numpy.put(g, i, g[i]*c.gradfactor(x[i])) for c,i in self.constraints.iteritems() if c != __fixed__]
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#[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
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for p in self.flattened_parameters:
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for t,i in p._tied_to_me_.iteritems():
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g[self._offset_for(p) + numpy.array(list(i))] += g[self._raveled_index_for(t)]
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#[g[self._offset_for(t) + numpy.array(list(i))].__iadd__(v) for i, v in [[i, g[self._raveled_index_for(p)].sum()] for p in self.flattened_parameters for t,i in p._tied_to_me_.iteritems()]]
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# if len(self.tied_indices) or len(self.fixed_indices):
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# to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
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# return np.delete(g, to_remove)
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# else:
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if self._fixes_ is not None: return g[self._fixes_]
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return g
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#===========================================================================
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# Optimization handles:
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#===========================================================================
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def _get_param_names_transformed(self):
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n = numpy.array([p.name_hirarchical+'['+str(i)+']' for p in self.flattened_parameters for i in p._indices()])
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if self._fixes_ is not None:
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return n[self._fixes_]
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return n
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def _get_params(self):
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# don't overwrite this anymore!
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return numpy.hstack([x._get_params() for x in self._parameters_])#numpy.fromiter(itertools.chain(*itertools.imap(lambda x: x._get_params(), self._parameters_)), dtype=numpy.float64, count=sum(self._parameter_sizes_))
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def _set_params(self, params, update=True):
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# don't overwrite this anymore!
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[p._set_params(params[s], update=update) for p,s in itertools.izip(self._parameters_,self._param_slices_)]
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self.parameters_changed()
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def _get_params_transformed(self):
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p = self._get_params()
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[numpy.put(p, ind, c.finv(p[ind])) for c,ind in self.constraints.iteritems() if c != __fixed__]
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if self._fixes_ is not None:
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return p[self._fixes_]
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return p
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def _set_params_transformed(self, p):
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p = p.copy()
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if self._fixes_ is not None: tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp
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[numpy.put(p, ind, c.f(p[ind])) for c,ind in self.constraints.iteritems() if c != __fixed__]
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self._set_params(p)
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def _name_changed(self, param, old_name):
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if hasattr(self, old_name) and old_name in self._added_names_:
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delattr(self, old_name)
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self._added_names_.remove(old_name)
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pname = _adjust_name_for_printing(param.name)
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if pname not in self.__dict__:
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self._added_names_.add(pname)
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self.__dict__[pname] = param
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#===========================================================================
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# Index Handling
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#===========================================================================
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def _backtranslate_index(self, param, ind):
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# translate an index in parameterized indexing into the index of param
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ind = ind-self._offset_for(param)
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ind = ind[ind >= 0]
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internal_offset = param._internal_offset()
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ind = ind[ind < param.size + internal_offset]
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return ind
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def _offset_for(self, param):
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# get the offset in the parameterized index array for param
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if param._direct_parent_._get_original(param) in self._parameters_:
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return self._param_slices_[param._direct_parent_._get_original(param)._parent_index_].start
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if param.has_parent():
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return self._offset_for(param._direct_parent_) + param._direct_parent_._offset_for(param)
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return 0
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def _raveled_index_for(self, param):
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return param._raveled_index() + self._offset_for(param)
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#===========================================================================
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# Handle ties:
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#===========================================================================
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def _set_fixed(self, param_or_index):
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if self._fixes_ is None: self._fixes_ = numpy.ones(self.size, dtype=bool)
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try:
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param_or_index = self._raveled_index_for(param_or_index)
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except AttributeError:
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pass
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self._fixes_[param_or_index] = FIXED
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if numpy.all(self._fixes_): self._fixes_ = None # ==UNFIXED
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def _set_unfixed(self, param_or_index):
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if self._fixes_ is None: self._fixes_ = numpy.ones(self.size, dtype=bool)
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try:
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param_or_index = self._raveled_index_for(param_or_index)
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except AttributeError:
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pass
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self._fixes_[param_or_index] = UNFIXED
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if numpy.all(self._fixes_): self._fixes_ = None # ==UNFIXED
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# def _add_tie(self, param, tied_to):
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# # tie param to tie_to, if the values match (with broadcasting)
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# self._remove_tie(param) # delete if multiple ties should be allowed
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# f, _ = self._fixes_.add(param, tied_to)
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# if self._fixes_ is None: self._fixes_ = numpy.ones(self.size, dtype=bool)
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# self._fixes_[f] = False
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# def _remove_tie(self, param, *params):
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# # remove the tie from param to all *params (can be None, so all ties get deleted for param)
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# if len(params) == 0:
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# params = self._fixes_.properties()
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# for p in params:
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# _, t = self._fixes_.remove(param, p)
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# self._fixes_[t] = True
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# if numpy.all(self._fixes_): self._fixes_ = None # ==UNFIXED
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# def _ties_iter_items(self, param):
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# for tied_to, ind in self._fixes_.iter_from_items():
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# ind = self._backtranslate_index(param, ind)
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# if not index_empty(ind):
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# yield tied_to, ind
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# def _ties_iter(self, param):
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# for constr, _ in self._ties_iter_items(param):
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# yield constr
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# def _ties_iter_indices(self, param):
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# for _, ind in self._ties_iter_items(param):
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# yield ind
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# def _ties_for(self, param, rav_index):
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# return self._fixes_.from_to_for(rav_index+self._offset_for(param))
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#===========================================================================
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# Fixing parameters:
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#===========================================================================
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def _fix(self, param, warning=True):
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f = self._add_constrain(param, __fixed__, warning)
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self._set_fixed(f)
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def _unfix(self, param):
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if self._fixes_ is not None:
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f = self._remove_constrain(param, __fixed__)
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self._set_unfixed(f)
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#===========================================================================
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# Convenience for fixed, tied checking of parameter:
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#===========================================================================
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def _is_fixed(self, param):
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# returns if the whole parameter is fixed
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if self._fixes_ is None:
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return False
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return not self._fixes_[self._offset_for(param): self._offset_for(param)+param._realsize_].any()
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@property
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def is_fixed(self):
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for p in self._parameters_:
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if not p.is_fixed: return False
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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 parameter through this method, by passing
|
|
# the copy here
|
|
return self._parameters_[param._parent_index_]
|
|
def hirarchy_name(self):
|
|
if self.has_parent():
|
|
return self._direct_parent_.hirarchy_name() + _adjust_name_for_printing(self.name) + "."
|
|
return ''
|
|
#===========================================================================
|
|
# Constraint Handling:
|
|
#===========================================================================
|
|
def _add_constrain(self, param, transform, warning=True):
|
|
rav_i = self._raveled_index_for(param)
|
|
reconstrained = self._remove_constrain(param, index=rav_i) # remove constraints before
|
|
# if removing constraints before adding new is not wanted, just delete the above line!
|
|
self.constraints.add(transform, rav_i)
|
|
if warning and any(reconstrained):
|
|
# if you want to print the whole params object, which was reconstrained use:
|
|
# m = str(param[self._backtranslate_index(param, reconstrained)])
|
|
print "Warning: re-constraining parameters:\n{}".format(param._short())
|
|
return rav_i
|
|
def _remove_constrain(self, param, *transforms, **kwargs):
|
|
if not transforms:
|
|
transforms = self.constraints.properties()
|
|
removed_indices = numpy.array([]).astype(int)
|
|
if "index" in kwargs: index = kwargs['index']
|
|
else: index = self._raveled_index_for(param)
|
|
for constr in transforms:
|
|
removed = self.constraints.remove(constr, index)
|
|
if constr is __fixed__:
|
|
self._set_unfixed(removed)
|
|
removed_indices = numpy.union1d(removed_indices, removed)
|
|
return removed_indices
|
|
# convienience for iterating over items
|
|
def _constraints_iter_items(self, param):
|
|
for constr, ind in self.constraints.iteritems():
|
|
ind = self._backtranslate_index(param, ind)
|
|
if not index_empty(ind):
|
|
yield constr, ind
|
|
def _constraints_iter(self, param):
|
|
for constr, _ in self._constraints_iter_items(param):
|
|
yield constr
|
|
def _contraints_iter_indices(self, param):
|
|
for _, ind in self._constraints_iter_items(param):
|
|
yield ind
|
|
def _constraint_indices(self, param, constraint):
|
|
return self._backtranslate_index(param, self.constraints[constraint])
|
|
def _constraints_for(self, param, rav_index):
|
|
return self.constraints.properties_for(rav_index+self._offset_for(param))
|
|
#===========================================================================
|
|
# Get/set parameters:
|
|
#===========================================================================
|
|
def grep_param_names(self, regexp):
|
|
"""
|
|
create a list of parameters, matching regular expression regexp
|
|
"""
|
|
if not isinstance(regexp, _pattern_type): regexp = compile(regexp)
|
|
found_params = []
|
|
for p in self._parameters_:
|
|
if regexp.match(p.name) is not None:
|
|
found_params.append(p)
|
|
if isinstance(p, Parameterized):
|
|
found_params.extend(p.grep_param_names(regexp))
|
|
return found_params
|
|
return [param for param in self._parameters_ if regexp.match(param.name) is not None]
|
|
def __getitem__(self, name, paramlist=None):
|
|
if paramlist is None:
|
|
paramlist = self.grep_param_names(name)
|
|
if len(paramlist) < 1: raise AttributeError, name
|
|
if len(paramlist) == 1:
|
|
if isinstance(paramlist[-1], Parameterized):
|
|
paramlist = paramlist[-1].flattened_parameters
|
|
if len(paramlist) != 1:
|
|
return ParamConcatenation(paramlist)
|
|
return paramlist[-1]
|
|
return ParamConcatenation(paramlist)
|
|
def __setitem__(self, name, value, paramlist=None):
|
|
try: param = self.__getitem__(name, paramlist)
|
|
except AttributeError as a: raise a
|
|
param[:] = value
|
|
# def __getattr__(self, name):
|
|
# return self.__getitem__(name)
|
|
# def __getattribute__(self, name):
|
|
# #try:
|
|
# return object.__getattribute__(self, name)
|
|
#except AttributeError:
|
|
# _, a, tb = sys.exc_info()
|
|
# try:
|
|
# return self.__getitem__(name)
|
|
# except AttributeError:
|
|
# raise AttributeError, a.message, tb
|
|
def __setattr__(self, name, val):
|
|
# override the default behaviour, if setting a parameter, so broadcasting can by used
|
|
if hasattr(self, "_parameters_"):
|
|
paramlist = self.grep_param_names(name)
|
|
if len(paramlist) == 1: self.__setitem__(name, val, paramlist); return
|
|
object.__setattr__(self, name, val);
|
|
#===========================================================================
|
|
# Printing:
|
|
#===========================================================================
|
|
def _parameter_names(self, add_name=False):
|
|
if add_name:
|
|
return [_adjust_name_for_printing(self.name) + "." + xi for x in self._parameters_ for xi in x._parameter_names(add_name=True)]
|
|
return [xi for x in self._parameters_ for xi in x._parameter_names(add_name=True)]
|
|
parameter_names = property(_parameter_names, doc="Names for all parameters handled by this parameterization object -- will add hirarchy name entries for printing")
|
|
@property
|
|
def flattened_parameters(self):
|
|
return [xi for x in self._parameters_ for xi in x.flattened_parameters]
|
|
@property
|
|
def _parameter_sizes_(self):
|
|
return [x.size for x in self._parameters_]
|
|
@property
|
|
def size_transformed(self):
|
|
if self._fixes_ is not None:
|
|
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
|
|
def _constraints_str(self):
|
|
return [cs for p in self._parameters_ for cs in p._constraints_str]
|
|
@property
|
|
def _description_str(self):
|
|
return [xi for x in self._parameters_ for xi in x._description_str]
|
|
@property
|
|
def _ties_str(self):
|
|
return [xi for x in self._parameters_ for xi in x._ties_str]
|
|
def __str__(self, header=True):
|
|
constrs = self._constraints_str; ts = self._ties_str
|
|
desc = self._description_str; names = self.parameter_names
|
|
nl = max([len(str(x)) for x in names + ["Name"]])
|
|
sl = max([len(str(x)) for x in desc + ["Value"]])
|
|
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"]])
|
|
format_spec = " \033[1m{{name:<{0}s}}\033[0;0m | {{desc:^{1}s}} | {{const:^{2}s}} | {{t:^{3}s}}".format(nl, sl, cl, tl)
|
|
to_print = []
|
|
for n, d, c, t in itertools.izip(names, desc, constrs, ts):
|
|
to_print.append(format_spec.format(name=n, desc=d, const=c, t=t))
|
|
#to_print = [format_spec.format(p=p, const=c, t=t) if isinstance(p, Param) else p.__str__(header=False) for p, c, t in itertools.izip(self._parameters_, constrs, ts)]
|
|
sep = '-'*(nl+sl+cl+tl+8*2+3)
|
|
if header:
|
|
header = " {{0:<{0}s}} | {{1:^{1}s}} | {{2:^{2}s}} | {{3:^{3}s}}".format(nl, sl, cl, tl).format("Name", "Value", "Constraint", "Tied to")
|
|
#header += '\n' + sep
|
|
to_print.insert(0, header)
|
|
return '\n'.format(sep).join(to_print)
|
|
pass
|
|
|
|
|
|
class Parameterized_old(object):
|
|
def __init__(self):
|
|
"""
|
|
This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
|
|
"""
|
|
self.tied_indices = []
|
|
self.fixed_indices = []
|
|
self.fixed_values = []
|
|
self.constrained_indices = []
|
|
self.constraints = []
|
|
|
|
def _get_params(self):
|
|
raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
|
|
def _set_params(self, x):
|
|
raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
|
|
|
|
def _get_param_names(self):
|
|
raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
|
|
#def _get_print_names(self):
|
|
# """ Override for which parameter_names to print out, when using print m """
|
|
# return self._get_param_names()
|
|
|
|
def pickle(self, filename, protocol=None):
|
|
if protocol is None:
|
|
if self._has_get_set_state():
|
|
protocol = 0
|
|
else:
|
|
protocol = -1
|
|
with open(filename, 'w') as f:
|
|
cPickle.dump(self, f, protocol)
|
|
|
|
def copy(self):
|
|
"""Returns a (deep) copy of the current model """
|
|
return copy.deepcopy(self)
|
|
|
|
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,
|
|
here just all the indices, rest can get recomputed
|
|
For inheriting from Parameterized:
|
|
|
|
Allways append the state of the inherited object
|
|
and call down to the inherited object in setstate!!
|
|
"""
|
|
return [self.tied_indices,
|
|
self.fixed_indices,
|
|
self.fixed_values,
|
|
self.constrained_indices,
|
|
self.constraints]
|
|
|
|
def setstate(self, state):
|
|
self.constraints = state.pop()
|
|
self.constrained_indices = state.pop()
|
|
self.fixed_values = state.pop()
|
|
self.fixed_indices = state.pop()
|
|
self.tied_indices = state.pop()
|
|
|
|
def __getitem__(self, regexp, return_names=False):
|
|
"""
|
|
Get a model parameter by name. The name is applied as a regular
|
|
expression and all parameters that match that regular expression are
|
|
returned.
|
|
"""
|
|
matches = self.grep_param_names(regexp)
|
|
if len(matches):
|
|
if return_names:
|
|
return self._get_params()[matches], np.asarray(self._get_param_names())[matches].tolist()
|
|
else:
|
|
return self._get_params()[matches]
|
|
else:
|
|
raise AttributeError, "no parameter matches %s" % regexp
|
|
|
|
def __setitem__(self, name, val):
|
|
"""
|
|
Set model parameter(s) by name. The name is provided as a regular
|
|
expression. All parameters matching that regular expression are set to
|
|
the given value.
|
|
"""
|
|
matches = self.grep_param_names(name)
|
|
if len(matches):
|
|
val = np.array(val)
|
|
assert (val.size == 1) or val.size == len(matches), "Shape mismatch: {}:({},)".format(val.size, len(matches))
|
|
x = self._get_params()
|
|
x[matches] = val
|
|
self._set_params(x)
|
|
else:
|
|
raise AttributeError, "no parameter matches %s" % name
|
|
|
|
def tie_params(self, regexp):
|
|
"""
|
|
Tie (all!) parameters matching the regular expression `regexp`.
|
|
"""
|
|
matches = self.grep_param_names(regexp)
|
|
assert matches.size > 0, "need at least something to tie together"
|
|
if len(self.tied_indices):
|
|
assert not np.any(matches[:, None] == np.hstack(self.tied_indices)), "Some indices are already tied!"
|
|
self.tied_indices.append(matches)
|
|
# TODO only one of the priors will be evaluated. Give a warning message if the priors are not identical
|
|
if hasattr(self, 'prior'):
|
|
pass
|
|
|
|
self._set_params_transformed(self._get_params_transformed()) # sets tied parameters to single value
|
|
|
|
def untie_everything(self):
|
|
"""Unties all parameters by setting tied_indices to an empty list."""
|
|
self.tied_indices = []
|
|
|
|
def grep_param_names(self, regexp, transformed=False, search=False):
|
|
"""
|
|
:param regexp: regular expression to select parameter parameter_names
|
|
:type regexp: re | str | int
|
|
:rtype: the indices of self._get_param_names which match the regular expression.
|
|
|
|
Note:-
|
|
Other objects are passed through - i.e. integers which weren't meant for grepping
|
|
"""
|
|
|
|
if transformed:
|
|
parameter_names = self._get_param_names_transformed()
|
|
else:
|
|
parameter_names = self._get_param_names()
|
|
|
|
if type(regexp) in [str, np.string_, np.str]:
|
|
regexp = re.compile(regexp)
|
|
elif type(regexp) is re._pattern_type:
|
|
pass
|
|
else:
|
|
return regexp
|
|
if search:
|
|
return np.nonzero([regexp.search(name) for name in parameter_names])[0]
|
|
else:
|
|
return np.nonzero([regexp.match(name) for name in parameter_names])[0]
|
|
|
|
def num_params_transformed(self):
|
|
removed = 0
|
|
for tie in self.tied_indices:
|
|
removed += tie.size - 1
|
|
|
|
for fix in self.fixed_indices:
|
|
removed += fix.size
|
|
|
|
return len(self._get_params()) - removed
|
|
|
|
def unconstrain(self, regexp):
|
|
"""Unconstrain matching parameters. Does not untie parameters"""
|
|
matches = self.grep_param_names(regexp)
|
|
|
|
# tranformed contraints:
|
|
for match in matches:
|
|
self.constrained_indices = [i[i <> match] for i in self.constrained_indices]
|
|
|
|
# remove empty constraints
|
|
tmp = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
|
|
if tmp:
|
|
self.constrained_indices, self.constraints = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
|
|
self.constrained_indices, self.constraints = list(self.constrained_indices), list(self.constraints)
|
|
|
|
# fixed:
|
|
self.fixed_values = [np.delete(values, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices, values in zip(self.fixed_indices, self.fixed_values)]
|
|
self.fixed_indices = [np.delete(indices, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices in self.fixed_indices]
|
|
|
|
# remove empty elements
|
|
tmp = [(i, v) for i, v in zip(self.fixed_indices, self.fixed_values) if len(i)]
|
|
if tmp:
|
|
self.fixed_indices, self.fixed_values = zip(*tmp)
|
|
self.fixed_indices, self.fixed_values = list(self.fixed_indices), list(self.fixed_values)
|
|
else:
|
|
self.fixed_indices, self.fixed_values = [], []
|
|
|
|
def constrain_negative(self, regexp, warning=True):
|
|
""" Set negative constraints. """
|
|
self.constrain(regexp, transformations.NegativeLogexp(), warning)
|
|
|
|
def constrain_positive(self, regexp, warning=True):
|
|
""" Set positive constraints. """
|
|
self.constrain(regexp, transformations.Logexp(), warning)
|
|
|
|
def constrain_bounded(self, regexp, lower, upper, warning=True):
|
|
""" Set bounded constraints. """
|
|
self.constrain(regexp, transformations.Logistic(lower, upper), warning)
|
|
|
|
def all_constrained_indices(self):
|
|
if len(self.constrained_indices) or len(self.fixed_indices):
|
|
return np.hstack(self.constrained_indices + self.fixed_indices)
|
|
else:
|
|
return np.empty(shape=(0,))
|
|
|
|
def constrain(self, regexp, transform, warning=True):
|
|
assert isinstance(transform, transformations.Transformation)
|
|
|
|
matches = self.grep_param_names(regexp)
|
|
overlap = set(matches).intersection(set(self.all_constrained_indices()))
|
|
if overlap:
|
|
self.unconstrain(np.asarray(list(overlap)))
|
|
if warning:
|
|
print 'Warning: re-constraining these parameters'
|
|
pn = self._get_param_names()
|
|
for i in overlap:
|
|
print pn[i]
|
|
|
|
self.constrained_indices.append(matches)
|
|
self.constraints.append(transform)
|
|
x = self._get_params()
|
|
x[matches] = transform.initialize(x[matches])
|
|
self._set_params(x)
|
|
|
|
def constrain_fixed(self, regexp, value=None, warning=True):
|
|
"""
|
|
|
|
:param regexp: which parameters need to be fixed.
|
|
:type regexp: ndarray(dtype=int) or regular expression object or string
|
|
:param value: the vlaue to fix the parameters to. If the value is not specified,
|
|
the parameter is fixed to the current value
|
|
:type value: float
|
|
|
|
**Notes**
|
|
|
|
Fixing a parameter which is tied to another, or constrained in some way will result in an error.
|
|
|
|
To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter parameter_names, or pass both of the indexes.
|
|
|
|
"""
|
|
matches = self.grep_param_names(regexp)
|
|
overlap = set(matches).intersection(set(self.all_constrained_indices()))
|
|
if overlap:
|
|
self.unconstrain(np.asarray(list(overlap)))
|
|
if warning:
|
|
print 'Warning: re-constraining these parameters'
|
|
pn = self._get_param_names()
|
|
for i in overlap:
|
|
print pn[i]
|
|
|
|
self.fixed_indices.append(matches)
|
|
if value != None:
|
|
self.fixed_values.append(value)
|
|
else:
|
|
self.fixed_values.append(self._get_params()[self.fixed_indices[-1]])
|
|
|
|
# self.fixed_values.append(value)
|
|
self._set_params_transformed(self._get_params_transformed())
|
|
|
|
def _get_params_transformed(self):
|
|
"""use self._get_params to get the 'true' parameters of the model, which are then tied, constrained and fixed"""
|
|
x = self._get_params()
|
|
[np.put(x, i, t.finv(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
|
|
|
|
to_remove = self.fixed_indices + [t[1:] for t in self.tied_indices]
|
|
if len(to_remove):
|
|
return np.delete(x, np.hstack(to_remove))
|
|
else:
|
|
return x
|
|
|
|
def _set_params_transformed(self, x):
|
|
""" takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params"""
|
|
self._set_params(self._untransform_params(x))
|
|
|
|
def _untransform_params(self, x):
|
|
"""
|
|
The Transformation required for _set_params_transformed.
|
|
|
|
This moves the vector x seen by the optimiser (unconstrained) to the
|
|
valid parameter vector seen by the model
|
|
|
|
Note:
|
|
- This function is separate from _set_params_transformed for downstream flexibility
|
|
"""
|
|
# work out how many places are fixed, and where they are. tricky logic!
|
|
fix_places = self.fixed_indices + [t[1:] for t in self.tied_indices]
|
|
if len(fix_places):
|
|
fix_places = np.hstack(fix_places)
|
|
Nfix_places = fix_places.size
|
|
else:
|
|
Nfix_places = 0
|
|
|
|
free_places = np.setdiff1d(np.arange(Nfix_places + x.size, dtype=np.int), fix_places)
|
|
|
|
# put the models values in the vector xx
|
|
xx = np.zeros(Nfix_places + free_places.size, dtype=np.float64)
|
|
|
|
xx[free_places] = x
|
|
[np.put(xx, i, v) for i, v in zip(self.fixed_indices, self.fixed_values)]
|
|
[np.put(xx, i, v) for i, v in [(t[1:], xx[t[0]]) for t in self.tied_indices] ]
|
|
|
|
[np.put(xx, i, t.f(xx[i])) for i, t in zip(self.constrained_indices, self.constraints)]
|
|
if hasattr(self, 'debug'):
|
|
stop # @UndefinedVariable
|
|
|
|
return xx
|
|
|
|
def _get_param_names_transformed(self):
|
|
"""
|
|
Returns the parameter parameter_names as propagated after constraining,
|
|
tying or fixing, i.e. a list of the same length as _get_params_transformed()
|
|
"""
|
|
n = self._get_param_names()
|
|
|
|
# remove/concatenate the tied parameter parameter_names
|
|
if len(self.tied_indices):
|
|
for t in self.tied_indices:
|
|
n[t[0]] = "<tie>".join([n[tt] for tt in t])
|
|
remove = np.hstack([t[1:] for t in self.tied_indices])
|
|
else:
|
|
remove = np.empty(shape=(0,), dtype=np.int)
|
|
|
|
# also remove the fixed params
|
|
if len(self.fixed_indices):
|
|
remove = np.hstack((remove, np.hstack(self.fixed_indices)))
|
|
|
|
# add markers to show that some variables are constrained
|
|
for i, t in zip(self.constrained_indices, self.constraints):
|
|
for ii in i:
|
|
n[ii] = n[ii] + t.__str__()
|
|
|
|
n = [nn for i, nn in enumerate(n) if not i in remove]
|
|
return n
|
|
|
|
#@property
|
|
#def all(self):
|
|
# return self.__str__(self._get_param_names())
|
|
|
|
|
|
#def __str__(self, parameter_names=None, nw=30):
|
|
def __str__(self, nw=30):
|
|
"""
|
|
Return a string describing the parameter parameter_names and their ties and constraints
|
|
"""
|
|
parameter_names = self._get_param_names()
|
|
#if parameter_names is None:
|
|
# parameter_names = self._get_print_names()
|
|
#name_indices = self.grep_param_names("|".join(parameter_names))
|
|
N = len(parameter_names)
|
|
|
|
if not N:
|
|
return "This object has no free parameters."
|
|
header = ['Name', 'Value', 'Constraints', 'Ties']
|
|
values = self._get_params() # map(str,self._get_params())
|
|
#values = self._get_params()[name_indices] # map(str,self._get_params())
|
|
# sort out the constraints
|
|
constraints = [''] * len(parameter_names)
|
|
#constraints = [''] * len(self._get_param_names())
|
|
for i, t in zip(self.constrained_indices, self.constraints):
|
|
for ii in i:
|
|
constraints[ii] = t.__str__()
|
|
for i in self.fixed_indices:
|
|
for ii in i:
|
|
constraints[ii] = 'Fixed'
|
|
# sort out the ties
|
|
ties = [''] * len(parameter_names)
|
|
for i, tie in enumerate(self.tied_indices):
|
|
for j in tie:
|
|
ties[j] = '(' + str(i) + ')'
|
|
|
|
if values.size == 1:
|
|
values = ['%.4f' %float(values)]
|
|
else:
|
|
values = ['%.4f' % float(v) for v in values]
|
|
max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
|
|
max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
|
|
max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
|
|
max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
|
|
cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
|
|
# columns = cols.sum()
|
|
|
|
header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
|
|
header_string = map(lambda x: '|'.join(x), [header_string])
|
|
separator = '-' * len(header_string[0])
|
|
param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
|
|
|
|
|
|
return ('\n'.join([header_string[0], separator] + param_string)) + '\n'
|
|
|
|
def grep_model(self,regexp):
|
|
regexp_indices = self.grep_param_names(regexp)
|
|
all_names = self._get_param_names()
|
|
|
|
parameter_names = [all_names[pj] for pj in regexp_indices]
|
|
N = len(parameter_names)
|
|
|
|
if not N:
|
|
return "Match not found."
|
|
|
|
header = ['Name', 'Value', 'Constraints', 'Ties']
|
|
all_values = self._get_params()
|
|
values = np.array([all_values[pj] for pj in regexp_indices])
|
|
constraints = [''] * len(parameter_names)
|
|
|
|
_constrained_indices,aux = self._pick_elements(regexp_indices,self.constrained_indices)
|
|
_constraints_ = [self.constraints[pj] for pj in aux]
|
|
|
|
for i, t in zip(_constrained_indices, _constraints_):
|
|
for ii in i:
|
|
iii = regexp_indices.tolist().index(ii)
|
|
constraints[iii] = t.__str__()
|
|
|
|
_fixed_indices,aux = self._pick_elements(regexp_indices,self.fixed_indices)
|
|
for i in _fixed_indices:
|
|
for ii in i:
|
|
iii = regexp_indices.tolist().index(ii)
|
|
constraints[ii] = 'Fixed'
|
|
|
|
_tied_indices,aux = self._pick_elements(regexp_indices,self.tied_indices)
|
|
ties = [''] * len(parameter_names)
|
|
for i,ti in zip(_tied_indices,aux):
|
|
for ii in i:
|
|
iii = regexp_indices.tolist().index(ii)
|
|
ties[iii] = '(' + str(ti) + ')'
|
|
|
|
if values.size == 1:
|
|
values = ['%.4f' %float(values)]
|
|
else:
|
|
values = ['%.4f' % float(v) for v in values]
|
|
|
|
max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
|
|
max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
|
|
max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
|
|
max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
|
|
cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
|
|
|
|
header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
|
|
header_string = map(lambda x: '|'.join(x), [header_string])
|
|
separator = '-' * len(header_string[0])
|
|
param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
|
|
|
|
print header_string[0]
|
|
print separator
|
|
for string in param_string:
|
|
print string
|
|
|
|
def _pick_elements(self,regexp_ind,array_list):
|
|
"""Removes from array_list the elements different from regexp_ind"""
|
|
new_array_list = [] #New list with elements matching regexp_ind
|
|
array_indices = [] #Indices that matches the arrays in new_array_list and array_list
|
|
|
|
array_index = 0
|
|
for array in array_list:
|
|
_new = []
|
|
for ai in array:
|
|
if ai in regexp_ind:
|
|
_new.append(ai)
|
|
if len(_new):
|
|
new_array_list.append(np.array(_new))
|
|
array_indices.append(array_index)
|
|
array_index += 1
|
|
return new_array_list, array_indices
|