some new features
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ilgazca
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"""
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Container for the result of running Pathfinder.
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"""
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from typing import Dict, List, Optional, Tuple, Union
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import numpy as np
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from cmdstanpy.cmdstan_args import Method
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from cmdstanpy.stanfit.metadata import InferenceMetadata
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from cmdstanpy.stanfit.runset import RunSet
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from cmdstanpy.utils.stancsv import scan_generic_csv
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class CmdStanPathfinder:
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"""
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Container for outputs from the Pathfinder algorithm.
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Created by :meth:`CmdStanModel.pathfinder()`.
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"""
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def __init__(self, runset: RunSet):
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"""Initialize object."""
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if not runset.method == Method.PATHFINDER:
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raise ValueError(
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'Wrong runset method, expecting Pathfinder runset, '
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'found method {}'.format(runset.method)
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)
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self._runset = runset
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self._draws: np.ndarray = np.array(())
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config = scan_generic_csv(runset.csv_files[0])
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self._metadata = InferenceMetadata(config)
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def create_inits(
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self, seed: Optional[int] = None, chains: int = 4
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) -> Union[List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
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"""
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Create initial values for the parameters of the model
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by randomly selecting draws from the Pathfinder approximation.
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:param seed: Used for random selection, defaults to None
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:param chains: Number of initial values to return, defaults to 4
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:return: The initial values for the parameters of the model.
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If ``chains`` is 1, a dictionary is returned, otherwise a list
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of dictionaries is returned, in the format expected for the
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``inits`` argument. of :meth:`CmdStanModel.sample`.
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"""
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self._assemble_draws()
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rng = np.random.default_rng(seed)
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idxs = rng.choice(self._draws.shape[0], size=chains, replace=False)
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if chains == 1:
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draw = self._draws[idxs[0]]
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return {
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name: var.extract_reshape(draw)
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for name, var in self._metadata.stan_vars.items()
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}
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else:
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return [
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{
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name: var.extract_reshape(self._draws[idx])
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for name, var in self._metadata.stan_vars.items()
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}
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for idx in idxs
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]
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def __repr__(self) -> str:
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rep = 'CmdStanPathfinder: model={}{}'.format(
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self._runset.model,
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self._runset._args.method_args.compose(0, cmd=[]),
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)
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rep = '{}\n csv_files:\n\t{}\n output_files:\n\t{}'.format(
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rep,
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'\n\t'.join(self._runset.csv_files),
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'\n\t'.join(self._runset.stdout_files),
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)
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return rep
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# below this is identical to same functions in Laplace
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def _assemble_draws(self) -> None:
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if self._draws.shape != (0,):
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return
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with open(self._runset.csv_files[0], 'r') as fd:
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while (fd.readline()).startswith("#"):
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pass
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self._draws = np.loadtxt(
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fd,
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dtype=float,
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ndmin=2,
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delimiter=',',
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comments="#",
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)
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def stan_variable(self, var: str) -> np.ndarray:
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"""
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Return a numpy.ndarray which contains the estimates for the
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for the named Stan program variable where the dimensions of the
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numpy.ndarray match the shape of the Stan program variable.
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This functionaltiy is also available via a shortcut using ``.`` -
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writing ``fit.a`` is a synonym for ``fit.stan_variable("a")``
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:param var: variable name
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See Also
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--------
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CmdStanPathfinder.stan_variables
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CmdStanMLE.stan_variable
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CmdStanMCMC.stan_variable
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CmdStanVB.stan_variable
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CmdStanGQ.stan_variable
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CmdStanLaplace.stan_variable
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"""
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self._assemble_draws()
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try:
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out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
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self._draws
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)
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return out
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except KeyError:
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# pylint: disable=raise-missing-from
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raise ValueError(
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f'Unknown variable name: {var}\n'
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'Available variables are '
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+ ", ".join(self._metadata.stan_vars.keys())
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)
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def stan_variables(self) -> Dict[str, np.ndarray]:
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"""
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Return a dictionary mapping Stan program variables names
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to the corresponding numpy.ndarray containing the inferred values.
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See Also
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--------
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CmdStanPathfinder.stan_variable
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CmdStanMCMC.stan_variables
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CmdStanMLE.stan_variables
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CmdStanVB.stan_variables
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CmdStanGQ.stan_variables
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CmdStanLaplace.stan_variables
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"""
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result = {}
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for name in self._metadata.stan_vars:
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result[name] = self.stan_variable(name)
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return result
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def method_variables(self) -> Dict[str, np.ndarray]:
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"""
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Returns a dictionary of all sampler variables, i.e., all
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output column names ending in `__`. Assumes that all variables
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are scalar variables where column name is variable name.
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Maps each column name to a numpy.ndarray (draws x chains x 1)
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containing per-draw diagnostic values.
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"""
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self._assemble_draws()
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return {
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name: var.extract_reshape(self._draws)
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for name, var in self._metadata.method_vars.items()
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}
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def draws(self) -> np.ndarray:
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"""
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Return a numpy.ndarray containing the draws from the
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approximate posterior distribution. This is a 2-D array
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of shape (draws, parameters).
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"""
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self._assemble_draws()
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return self._draws
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def __getattr__(self, attr: str) -> np.ndarray:
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"""Synonymous with ``fit.stan_variable(attr)"""
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if attr.startswith("_"):
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raise AttributeError(f"Unknown variable name {attr}")
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try:
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return self.stan_variable(attr)
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except ValueError as e:
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# pylint: disable=raise-missing-from
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raise AttributeError(*e.args)
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def __getstate__(self) -> dict:
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# This function returns the mapping of objects to serialize with pickle.
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# See https://docs.python.org/3/library/pickle.html#object.__getstate__
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# for details. We call _assemble_draws to ensure posterior samples have
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# been loaded prior to serialization.
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self._assemble_draws()
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return self.__dict__
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@property
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def metadata(self) -> InferenceMetadata:
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"""
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Returns object which contains CmdStan configuration as well as
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information about the names and structure of the inference method
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and model output variables.
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"""
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return self._metadata
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@property
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def column_names(self) -> Tuple[str, ...]:
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"""
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Names of all outputs from the sampler, comprising sampler parameters
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and all components of all model parameters, transformed parameters,
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and quantities of interest. Corresponds to Stan CSV file header row,
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with names munged to array notation, e.g. `beta[1]` not `beta.1`.
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"""
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return self._metadata.cmdstan_config['column_names'] # type: ignore
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@property
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def is_resampled(self) -> bool:
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"""
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Returns True if the draws were resampled from several Pathfinder
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approximations, False otherwise.
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"""
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return ( # type: ignore
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self._metadata.cmdstan_config.get("num_paths", 4) > 1
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and self._metadata.cmdstan_config.get('psis_resample', 1)
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in (1, 'true')
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and self._metadata.cmdstan_config.get('calculate_lp', 1)
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in (1, 'true')
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)
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def save_csvfiles(self, dir: Optional[str] = None) -> None:
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"""
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Move output CSV files to specified directory. If files were
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written to the temporary session directory, clean filename.
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E.g., save 'bernoulli-201912081451-1-5nm6as7u.csv' as
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'bernoulli-201912081451-1.csv'.
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:param dir: directory path
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See Also
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--------
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stanfit.RunSet.save_csvfiles
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cmdstanpy.from_csv
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"""
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self._runset.save_csvfiles(dir)
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