Source code for cmdstanpy.stanfit.vb

"""Container for the results of running autodiff variational inference"""

from collections import OrderedDict
from typing import Dict, Optional, Tuple, Union

import numpy as np
import pandas as pd

from cmdstanpy.cmdstan_args import Method
from cmdstanpy.utils import BaseType, scan_variational_csv

from .metadata import InferenceMetadata
from .runset import RunSet

[docs]class CmdStanVB: """ Container for outputs from CmdStan variational run. Created by :meth:`CmdStanModel.variational`. """ def __init__(self, runset: RunSet) -> None: """Initialize object.""" if not runset.method == Method.VARIATIONAL: raise ValueError( 'Wrong runset method, expecting variational inference, ' 'found method {}'.format(runset.method) ) self.runset = runset self._set_variational_attrs(runset.csv_files[0]) def __repr__(self) -> str: repr = 'CmdStanVB: model={}{}'.format( self.runset.model, self.runset._args.method_args.compose(0, cmd=[]) ) repr = '{}\n csv_file:\n\t{}\n output_file:\n\t{}'.format( repr, '\n\t'.join(self.runset.csv_files), '\n\t'.join(self.runset.stdout_files), ) # TODO - diagnostic, profiling files return repr def __getattr__(self, attr: str) -> Union[np.ndarray, float]: """Synonymous with ``fit.stan_variable(attr)""" if attr.startswith("_"): raise AttributeError(f"Unknown variable name {attr}") try: return self.stan_variable(attr) except ValueError as e: # pylint: disable=raise-missing-from raise AttributeError(*e.args) def _set_variational_attrs(self, sample_csv_0: str) -> None: meta = scan_variational_csv(sample_csv_0) self._metadata = InferenceMetadata(meta) # these three assignments don't grant type information self._column_names: Tuple[str, ...] = meta['column_names'] self._eta: float = meta['eta'] self._variational_mean: np.ndarray = meta['variational_mean'] self._variational_sample: np.ndarray = meta['variational_sample'] @property def columns(self) -> int: """ Total number of information items returned by sampler. Includes approximation information and names of model parameters and computed quantities. """ return len(self._column_names) @property def column_names(self) -> Tuple[str, ...]: """ Names of information items returned by sampler for each draw. Includes approximation information and names of model parameters and computed quantities. """ return self._column_names @property def eta(self) -> float: """ Step size scaling parameter 'eta' """ return self._eta @property def variational_params_np(self) -> np.ndarray: """ Returns inferred parameter means as numpy array. """ return self._variational_mean @property def variational_params_pd(self) -> pd.DataFrame: """ Returns inferred parameter means as pandas DataFrame. """ return pd.DataFrame([self._variational_mean], columns=self.column_names) @property def variational_params_dict(self) -> Dict[str, np.ndarray]: """Returns inferred parameter means as Dict.""" return OrderedDict(zip(self.column_names, self._variational_mean)) @property def metadata(self) -> InferenceMetadata: """ Returns object which contains CmdStan configuration as well as information about the names and structure of the inference method and model output variables. """ return self._metadata
[docs] def stan_variable(self, var: str) -> Union[np.ndarray, float]: """ Return a numpy.ndarray which contains the estimates for the for the named Stan program variable where the dimensions of the numpy.ndarray match the shape of the Stan program variable. This functionaltiy is also available via a shortcut using ``.`` - writing ``fit.a`` is a synonym for ``fit.stan_variable("a")`` :param var: variable name See Also -------- CmdStanVB.stan_variables CmdStanMCMC.stan_variable CmdStanMLE.stan_variable CmdStanGQ.stan_variable """ if var is None: raise ValueError('No variable name specified.') if var not in self._metadata.stan_vars_dims: raise ValueError( f'Unknown variable name: {var}\n' 'Available variables are ' + ", ".join(self._metadata.stan_vars_dims) ) col_idxs = list(self._metadata.stan_vars_cols[var]) shape: Tuple[int, ...] = () if len(col_idxs) > 1: shape = self._metadata.stan_vars_dims[var] result: np.ndarray = np.asarray(self._variational_mean)[ col_idxs ].reshape(shape, order="F") if self._metadata.stan_vars_types[var] == BaseType.COMPLEX: result = result[..., 0] + 1j * result[..., 1] return result else: return float(self._variational_mean[col_idxs[0]])
[docs] def stan_variables(self) -> Dict[str, Union[np.ndarray, float]]: """ Return a dictionary mapping Stan program variables names to the corresponding numpy.ndarray containing the inferred values. See Also -------- CmdStanVB.stan_variable CmdStanMCMC.stan_variables CmdStanMLE.stan_variables CmdStanGQ.stan_variables """ result = {} for name in self._metadata.stan_vars_dims.keys(): result[name] = self.stan_variable(name) return result
@property def variational_sample(self) -> np.ndarray: """Returns the set of approximate posterior output draws.""" return self._variational_sample
[docs] def save_csvfiles(self, dir: Optional[str] = None) -> None: """ Move output CSV files to specified directory. If files were written to the temporary session directory, clean filename. E.g., save 'bernoulli-201912081451-1-5nm6as7u.csv' as 'bernoulli-201912081451-1.csv'. :param dir: directory path See Also -------- stanfit.RunSet.save_csvfiles cmdstanpy.from_csv """ self.runset.save_csvfiles(dir)