Source code for pycbc.inference.models.base

# Copyright (C) 2016  Collin Capano
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# =============================================================================
#                                   Preamble
# =============================================================================

"""Base class for models.

import numpy
import logging
from abc import (ABCMeta, abstractmethod)
from six.moves.configparser import NoSectionError
from six import (add_metaclass, string_types)
from pycbc import (transforms, distributions)
from import FieldArray

# =============================================================================
#                               Support classes
# =============================================================================

class _NoPrior(object):
    """Dummy class to just return 0 if no prior is given to a model.
    def apply_boundary_conditions(**params):
        return params

    def __call__(self, **params):
        return 0.

[docs]class ModelStats(object): """Class to hold model's current stat values.""" @property def statnames(self): """Returns the names of the stats that have been stored.""" return list(self.__dict__.keys())
[docs] def getstats(self, names, default=numpy.nan): """Get the requested stats as a tuple. If a requested stat is not an attribute (implying it hasn't been stored), then the default value is returned for that stat. Parameters ---------- names : list of str The names of the stats to get. default : float, optional What to return if a requested stat is not an attribute of self. Default is ``numpy.nan``. Returns ------- tuple A tuple of the requested stats. """ return tuple(getattr(self, n, default) for n in names)
[docs] def getstatsdict(self, names, default=numpy.nan): """Get the requested stats as a dictionary. If a requested stat is not an attribute (implying it hasn't been stored), then the default value is returned for that stat. Parameters ---------- names : list of str The names of the stats to get. default : float, optional What to return if a requested stat is not an attribute of self. Default is ``numpy.nan``. Returns ------- dict A dictionary of the requested stats. """ return dict(zip(names, self.getstats(names, default=default)))
[docs]class SamplingTransforms(object): """Provides methods for transforming between sampling parameter space and model parameter space. """ def __init__(self, variable_params, sampling_params, replace_parameters, sampling_transforms): assert len(replace_parameters) == len(sampling_params), ( "number of sampling parameters must be the " "same as the number of replace parameters") # pull out the replaced parameters self.sampling_params = [arg for arg in variable_params if arg not in replace_parameters] # add the sampling parameters self.sampling_params += sampling_params # sort to make sure we have a consistent order self.sampling_params.sort() self.sampling_transforms = sampling_transforms
[docs] def logjacobian(self, **params): r"""Returns the log of the jacobian needed to transform pdfs in the ``variable_params`` parameter space to the ``sampling_params`` parameter space. Let :math:`\mathbf{x}` be the set of variable parameters, :math:`\mathbf{y} = f(\mathbf{x})` the set of sampling parameters, and :math:`p_x(\mathbf{x})` a probability density function defined over :math:`\mathbf{x}`. The corresponding pdf in :math:`\mathbf{y}` is then: .. math:: p_y(\mathbf{y}) = p_x(\mathbf{x})\left|\mathrm{det}\,\mathbf{J}_{ij}\right|, where :math:`\mathbf{J}_{ij}` is the Jacobian of the inverse transform :math:`\mathbf{x} = g(\mathbf{y})`. This has elements: .. math:: \mathbf{J}_{ij} = \frac{\partial g_i}{\partial{y_j}} This function returns :math:`\log \left|\mathrm{det}\,\mathbf{J}_{ij}\right|`. Parameters ---------- \**params : The keyword arguments should specify values for all of the variable args and all of the sampling args. Returns ------- float : The value of the jacobian. """ return numpy.log(abs(transforms.compute_jacobian( params, self.sampling_transforms, inverse=True)))
[docs] def apply(self, samples, inverse=False): """Applies the sampling transforms to the given samples. Parameters ---------- samples : dict or FieldArray The samples to apply the transforms to. inverse : bool, optional Whether to apply the inverse transforms (i.e., go from the sampling args to the ``variable_params``). Default is False. Returns ------- dict or FieldArray The transformed samples, along with the original samples. """ return transforms.apply_transforms(samples, self.sampling_transforms, inverse=inverse)
[docs] @classmethod def from_config(cls, cp, variable_params): """Gets sampling transforms specified in a config file. Sampling parameters and the parameters they replace are read from the ``sampling_params`` section, if it exists. Sampling transforms are read from the ``sampling_transforms`` section(s), using ``transforms.read_transforms_from_config``. An ``AssertionError`` is raised if no ``sampling_params`` section exists in the config file. Parameters ---------- cp : WorkflowConfigParser Config file parser to read. variable_params : list List of parameter names of the original variable params. Returns ------- SamplingTransforms A sampling transforms class. """ # Check if a sampling_params section is provided try: sampling_params, replace_parameters = \ read_sampling_params_from_config(cp) except NoSectionError as e: logging.warning("No sampling_params section read from config file") raise e # get sampling transformations sampling_transforms = transforms.read_transforms_from_config( cp, 'sampling_transforms')"Sampling in {} in place of {}".format( ', '.join(sampling_params), ', '.join(replace_parameters))) return cls(variable_params, sampling_params, replace_parameters, sampling_transforms)
[docs]def read_sampling_params_from_config(cp, section_group=None, section='sampling_params'): """Reads sampling parameters from the given config file. Parameters are read from the `[({section_group}_){section}]` section. The options should list the variable args to transform; the parameters they point to should list the parameters they are to be transformed to for sampling. If a multiple parameters are transformed together, they should be comma separated. Example: .. code-block:: ini [sampling_params] mass1, mass2 = mchirp, logitq spin1_a = logitspin1_a Note that only the final sampling parameters should be listed, even if multiple intermediate transforms are needed. (In the above example, a transform is needed to go from mass1, mass2 to mchirp, q, then another one needed to go from q to logitq.) These transforms should be specified in separate sections; see ``transforms.read_transforms_from_config`` for details. Parameters ---------- cp : WorkflowConfigParser An open config parser to read from. section_group : str, optional Append `{section_group}_` to the section name. Default is None. section : str, optional The name of the section. Default is 'sampling_params'. Returns ------- sampling_params : list The list of sampling parameters to use instead. replaced_params : list The list of variable args to replace in the sampler. """ if section_group is not None: section_prefix = '{}_'.format(section_group) else: section_prefix = '' section = section_prefix + section replaced_params = set() sampling_params = set() for args in cp.options(section): map_args = cp.get(section, args) sampling_params.update(set(map(str.strip, map_args.split(',')))) replaced_params.update(set(map(str.strip, args.split(',')))) return sorted(sampling_params), sorted(replaced_params)
# # ============================================================================= # # Base model definition # # ============================================================================= #
[docs]@add_metaclass(ABCMeta) class BaseModel(object): r"""Base class for all models. Given some model :math:`h` with parameters :math:`\Theta`, Bayes Theorem states that the probability of observing parameter values :math:`\vartheta` is: .. math:: p(\vartheta|h) = \frac{p(h|\vartheta) p(\vartheta)}{p(h)}. Here: * :math:`p(\vartheta|h)` is the **posterior** probability; * :math:`p(h|\vartheta)` is the **likelihood**; * :math:`p(\vartheta)` is the **prior**; * :math:`p(h)` is the **evidence**. This class defines properties and methods for evaluating the log likelihood, log prior, and log posteror. A set of parameter values is set using the ``update`` method. Calling the class's ``log(likelihood|prior|posterior)`` properties will then evaluate the model at those parameter values. Classes that inherit from this class must implement a ``_loglikelihood`` function that can be called by ``loglikelihood``. Parameters ---------- variable_params : (tuple of) string(s) A tuple of parameter names that will be varied. static_params : dict, optional A dictionary of parameter names -> values to keep fixed. prior : callable, optional A callable class or function that computes the log of the prior. If None provided, will use ``_noprior``, which returns 0 for all parameter values. sampling_params : list, optional Replace one or more of the ``variable_params`` with the given parameters for sampling. replace_parameters : list, optional The ``variable_params`` to replace with sampling parameters. Must be the same length as ``sampling_params``. sampling_transforms : list, optional List of transforms to use to go between the ``variable_params`` and the sampling parameters. Required if ``sampling_params`` is not None. waveform_transforms : list, optional A list of transforms to convert the ``variable_params`` into something understood by the likelihood model. This is useful if the prior is more easily parameterized in parameters that are different than what the likelihood is most easily defined in. Since these are used solely for converting parameters, and not for rescaling the parameter space, a Jacobian is not required for these transforms. Properties ---------- logjacobian : Returns the log of the jacobian needed to go from the parameter space of the ``variable_params`` to the sampling params. logprior : Returns the log of the prior. loglikelihood : A function that returns the log of the likelihood function. logposterior : A function that returns the log of the posterior. loglr : A function that returns the log of the likelihood ratio. logplr : A function that returns the log of the prior-weighted likelihood ratio. """ name = None def __init__(self, variable_params, static_params=None, prior=None, sampling_transforms=None, waveform_transforms=None): # store variable and static args if isinstance(variable_params, string_types): variable_params = (variable_params,) if not isinstance(variable_params, tuple): variable_params = tuple(variable_params) self._variable_params = variable_params if static_params is None: static_params = {} self._static_params = static_params # store prior if prior is None: self.prior_distribution = _NoPrior() else: assert prior.variable_args == variable_params, ( "variable params of prior and model must be the same") self.prior_distribution = prior # store transforms self.sampling_transforms = sampling_transforms self.waveform_transforms = waveform_transforms # initialize current params to None self._current_params = None # initialize a model stats self._current_stats = ModelStats() @property def variable_params(self): """Returns the model parameters.""" return self._variable_params @property def static_params(self): """Returns the model's static arguments.""" return self._static_params @property def sampling_params(self): """Returns the sampling parameters. If ``sampling_transforms`` is None, this is the same as the ``variable_params``. """ if self.sampling_transforms is None: sampling_params = self.variable_params else: sampling_params = self.sampling_transforms.sampling_params return sampling_params
[docs] def update(self, **params): """Updates the current parameter positions and resets stats. If any sampling transforms are specified, they are applied to the params before being stored. """ # add the static params params.update(self.static_params) self._current_params = self._transform_params(**params) self._current_stats = ModelStats()
@property def current_params(self): if self._current_params is None: raise ValueError("no parameters values currently stored; " "run update to add some") return self._current_params @property def default_stats(self): """The stats that ``get_current_stats`` returns by default.""" return ['logjacobian', 'logprior', 'loglikelihood'] + self._extra_stats @property def _extra_stats(self): """Allows child classes to add more stats to the default stats. This returns an empty list; classes that inherit should override this property if they want to add extra stats. """ return []
[docs] def get_current_stats(self, names=None): """Return one or more of the current stats as a tuple. This function does no computation. It only returns what has already been calculated. If a stat hasn't been calculated, it will be returned as ``numpy.nan``. Parameters ---------- names : list of str, optional Specify the names of the stats to retrieve. If ``None`` (the default), will return ``default_stats``. Returns ------- tuple : The current values of the requested stats, as a tuple. The order of the stats is the same as the names. """ if names is None: names = self.default_stats return self._current_stats.getstats(names)
@property def current_stats(self): """Return the ``default_stats`` as a dict. This does no computation. It only returns what has already been calculated. If a stat hasn't been calculated, it will be returned as ``numpy.nan``. Returns ------- dict : Dictionary of stat names -> current stat values. """ return self._current_stats.getstatsdict(self.default_stats) def _trytoget(self, statname, fallback, apply_transforms=False, **kwargs): r"""Helper function to get a stat from ``_current_stats``. If the statistic hasn't been calculated, ``_current_stats`` will raise an ``AttributeError``. In that case, the ``fallback`` function will be called. If that call is successful, the ``statname`` will be added to ``_current_stats`` with the returned value. Parameters ---------- statname : str The stat to get from ``current_stats``. fallback : method of self The function to call if the property call fails. apply_transforms : bool, optional Apply waveform transforms to the current parameters before calling the fallback function. Default is False. \**kwargs : Any other keyword arguments are passed through to the function. Returns ------- float : The value of the property. """ try: return getattr(self._current_stats, statname) except AttributeError: # apply waveform transforms if requested if apply_transforms and self.waveform_transforms is not None: self._current_params = transforms.apply_transforms( self._current_params, self.waveform_transforms, inverse=False) val = fallback(**kwargs) setattr(self._current_stats, statname, val) return val @property def loglikelihood(self): """The log likelihood at the current parameters. This will initially try to return the ``current_stats.loglikelihood``. If that raises an ``AttributeError``, will call `_loglikelihood`` to calculate it and store it to ``current_stats``. """ return self._trytoget('loglikelihood', self._loglikelihood, apply_transforms=True) @abstractmethod def _loglikelihood(self): """Low-level function that calculates the log likelihood of the current params.""" pass @property def logjacobian(self): """The log jacobian of the sampling transforms at the current postion. If no sampling transforms were provided, will just return 0. Parameters ---------- \**params : The keyword arguments should specify values for all of the variable args and all of the sampling args. Returns ------- float : The value of the jacobian. """ return self._trytoget('logjacobian', self._logjacobian) def _logjacobian(self): """Calculates the logjacobian of the current parameters.""" if self.sampling_transforms is None: logj = 0. else: logj = self.sampling_transforms.logjacobian( **self.current_params) return logj @property def logprior(self): """Returns the log prior at the current parameters.""" return self._trytoget('logprior', self._logprior) def _logprior(self): """Calculates the log prior at the current parameters.""" logj = self.logjacobian logp = self.prior_distribution(**self.current_params) + logj if numpy.isnan(logp): logp = -numpy.inf return logp @property def logposterior(self): """Returns the log of the posterior of the current parameter values. The logprior is calculated first. If the logprior returns ``-inf`` (possibly indicating a non-physical point), then the ``loglikelihood`` is not called. """ logp = self.logprior if logp == -numpy.inf: return logp else: return logp + self.loglikelihood
[docs] def prior_rvs(self, size=1, prior=None): """Returns random variates drawn from the prior. If the ``sampling_params`` are different from the ``variable_params``, the variates are transformed to the `sampling_params` parameter space before being returned. Parameters ---------- size : int, optional Number of random values to return for each parameter. Default is 1. prior : JointDistribution, optional Use the given prior to draw values rather than the saved prior. Returns ------- FieldArray A field array of the random values. """ # draw values from the prior if prior is None: prior = self.prior_distribution p0 = prior.rvs(size=size) # transform if necessary if self.sampling_transforms is not None: ptrans = self.sampling_transforms.apply(p0) # pull out the sampling args p0 = FieldArray.from_arrays([ptrans[arg] for arg in self.sampling_params], names=self.sampling_params) return p0
def _transform_params(self, **params): """Applies sampling transforms and boundary conditions to parameters. Parameters ---------- \**params : Key, value pairs of parameters to apply the transforms to. Returns ------- dict A dictionary of the transformed parameters. """ # apply inverse transforms to go from sampling parameters to # variable args if self.sampling_transforms is not None: params = self.sampling_transforms.apply(params, inverse=True) # apply boundary conditions params = self.prior_distribution.apply_boundary_conditions(**params) return params # # Methods for initiating from a config file. #
[docs] @staticmethod def extra_args_from_config(cp, section, skip_args=None, dtypes=None): """Gets any additional keyword in the given config file. Parameters ---------- cp : WorkflowConfigParser Config file parser to read. section : str The name of the section to read. skip_args : list of str, optional Names of arguments to skip. dtypes : dict, optional A dictionary of arguments -> data types. If an argument is found in the dict, it will be cast to the given datatype. Otherwise, the argument's value will just be read from the config file (and thus be a string). Returns ------- dict Dictionary of keyword arguments read from the config file. """ kwargs = {} if dtypes is None: dtypes = {} if skip_args is None: skip_args = [] read_args = [opt for opt in cp.options(section) if opt not in skip_args] for opt in read_args: val = cp.get(section, opt) # try to cast the value if a datatype was specified for this opt try: val = dtypes[opt](val) except KeyError: pass kwargs[opt] = val return kwargs
[docs] @staticmethod def prior_from_config(cp, variable_params, prior_section, constraint_section): """Gets arguments and keyword arguments from a config file. Parameters ---------- cp : WorkflowConfigParser Config file parser to read. variable_params : list List of of model parameter names. prior_section : str Section to read prior(s) from. constraint_section : str Section to read constraint(s) from. Returns ------- pycbc.distributions.JointDistribution The prior. """ # get prior distribution for each variable parameter"Setting up priors for each parameter") dists = distributions.read_distributions_from_config(cp, prior_section) constraints = distributions.read_constraints_from_config( cp, constraint_section) return distributions.JointDistribution(variable_params, *dists, constraints=constraints)
@classmethod def _init_args_from_config(cls, cp): """Helper function for loading parameters. This retrieves the prior, variable parameters, static parameterss, constraints, sampling transforms, and waveform transforms (if provided). Parameters ---------- cp : ConfigParser Config parser to read. Returns ------- dict : Dictionary of the arguments. Has keys ``variable_params``, ``static_params``, ``prior``, and ``sampling_transforms``. If waveform transforms are in the config file, will also have ``waveform_transforms``. """ section = "model" prior_section = "prior" vparams_section = 'variable_params' sparams_section = 'static_params' constraint_section = 'constraint' # check that the name exists and matches name = cp.get(section, 'name') if name != raise ValueError("section's {} name does not match mine {}".format( name, # get model parameters variable_params, static_params = distributions.read_params_from_config( cp, prior_section=prior_section, vargs_section=vparams_section, sargs_section=sparams_section) # get prior prior = cls.prior_from_config(cp, variable_params, prior_section, constraint_section) args = {'variable_params': variable_params, 'static_params': static_params, 'prior': prior} # try to load sampling transforms try: sampling_transforms = SamplingTransforms.from_config( cp, variable_params) except NoSectionError: sampling_transforms = None args['sampling_transforms'] = sampling_transforms # get any waveform transforms if any(cp.get_subsections('waveform_transforms')):"Loading waveform transforms") args['waveform_transforms'] = \ transforms.read_transforms_from_config( cp, 'waveform_transforms') return args
[docs] @classmethod def from_config(cls, cp, **kwargs): """Initializes an instance of this class from the given config file. Parameters ---------- cp : WorkflowConfigParser Config file parser to read. \**kwargs : All additional keyword arguments are passed to the class. Any provided keyword will over ride what is in the config file. """ args = cls._init_args_from_config(cp) # get any other keyword arguments provided in the model section args.update(cls.extra_args_from_config(cp, "model", skip_args=['name'])) args.update(kwargs) return cls(**args)
[docs] def write_metadata(self, fp): """Writes metadata to the given file handler. Parameters ---------- fp : instance The inference file to write to. """ fp.attrs['model'] = fp.attrs['variable_params'] = list(self.variable_params) fp.attrs['sampling_params'] = list(self.sampling_params) fp.write_kwargs_to_attrs(fp.attrs, static_params=self.static_params)