some new features
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ilgazca
284
.venv/lib/python3.12/site-packages/cmdstanpy/stanfit/mle.py
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284
.venv/lib/python3.12/site-packages/cmdstanpy/stanfit/mle.py
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"""Container for the result of running optimization"""
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from collections import OrderedDict
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from typing import Dict, Optional, Tuple, Union
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import numpy as np
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import pandas as pd
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from cmdstanpy.cmdstan_args import Method, OptimizeArgs
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from cmdstanpy.utils import get_logger, scan_optimize_csv
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from .metadata import InferenceMetadata
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from .runset import RunSet
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class CmdStanMLE:
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"""
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Container for outputs from CmdStan optimization.
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Created by :meth:`CmdStanModel.optimize`.
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"""
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def __init__(self, runset: RunSet) -> None:
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"""Initialize object."""
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if not runset.method == Method.OPTIMIZE:
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raise ValueError(
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'Wrong runset method, expecting optimize 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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# info from runset to be exposed
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self.converged = runset._check_retcodes()
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optimize_args = self.runset._args.method_args
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assert isinstance(
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optimize_args, OptimizeArgs
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) # make the typechecker happy
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self._save_iterations: bool = optimize_args.save_iterations
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self._set_mle_attrs(runset.csv_files[0])
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def __repr__(self) -> str:
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repr = 'CmdStanMLE: model={}{}'.format(
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self.runset.model, self.runset._args.method_args.compose(0, cmd=[])
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)
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repr = '{}\n csv_file:\n\t{}\n output_file:\n\t{}'.format(
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repr,
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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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if not self.converged:
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repr = '{}\n Warning: invalid estimate, '.format(repr)
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repr = '{} optimization failed to converge.'.format(repr)
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return repr
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def __getattr__(self, attr: str) -> Union[np.ndarray, float]:
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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 _set_mle_attrs(self, sample_csv_0: str) -> None:
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meta = scan_optimize_csv(sample_csv_0, self._save_iterations)
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self._metadata = InferenceMetadata(meta)
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self._column_names: Tuple[str, ...] = meta['column_names']
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self._mle: np.ndarray = meta['mle']
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if self._save_iterations:
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self._all_iters: np.ndarray = meta['all_iters']
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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 estimated quantities, includes joint log probability,
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and all parameters, transformed parameters, and generated quantities.
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"""
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return self._column_names
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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 optimized_params_np(self) -> np.ndarray:
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"""
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Returns all final estimates from the optimizer as a numpy.ndarray
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which contains all optimizer outputs, i.e., the value for `lp__`
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as well as all Stan program variables.
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"""
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if not self.converged:
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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return self._mle
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@property
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def optimized_iterations_np(self) -> Optional[np.ndarray]:
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"""
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Returns all saved iterations from the optimizer and final estimate
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as a numpy.ndarray which contains all optimizer outputs, i.e.,
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the value for `lp__` as well as all Stan program variables.
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"""
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if not self._save_iterations:
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get_logger().warning(
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'Intermediate iterations not saved to CSV output file. '
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'Rerun the optimize method with "save_iterations=True".'
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)
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return None
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if not self.converged:
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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return self._all_iters
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@property
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def optimized_params_pd(self) -> pd.DataFrame:
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"""
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Returns all final estimates from the optimizer as a pandas.DataFrame
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which contains all optimizer outputs, i.e., the value for `lp__`
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as well as all Stan program variables.
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"""
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if not self.runset._check_retcodes():
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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return pd.DataFrame([self._mle], columns=self.column_names)
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@property
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def optimized_iterations_pd(self) -> Optional[pd.DataFrame]:
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"""
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Returns all saved iterations from the optimizer and final estimate
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as a pandas.DataFrame which contains all optimizer outputs, i.e.,
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the value for `lp__` as well as all Stan program variables.
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"""
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if not self._save_iterations:
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get_logger().warning(
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'Intermediate iterations not saved to CSV output file. '
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'Rerun the optimize method with "save_iterations=True".'
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)
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return None
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if not self.converged:
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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return pd.DataFrame(self._all_iters, columns=self.column_names)
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@property
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def optimized_params_dict(self) -> Dict[str, np.float64]:
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"""
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Returns all estimates from the optimizer, including `lp__` as a
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Python Dict. Only returns estimate from final iteration.
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"""
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if not self.runset._check_retcodes():
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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return OrderedDict(zip(self.column_names, self._mle))
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def stan_variable(
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self,
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var: str,
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*,
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inc_iterations: bool = False,
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warn: bool = True,
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) -> Union[np.ndarray, float]:
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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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:param inc_iterations: When ``True`` and the intermediate estimates
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are included in the output, i.e., the optimizer was run with
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``save_iterations=True``, then intermediate estimates are included.
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Default value is ``False``.
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See Also
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--------
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CmdStanMLE.stan_variables
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CmdStanMCMC.stan_variable
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CmdStanPathfinder.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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if var not in self._metadata.stan_vars:
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raise ValueError(
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f'Unknown variable name: {var}\n'
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'Available variables are ' + ", ".join(self._metadata.stan_vars)
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)
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if warn and inc_iterations and not self._save_iterations:
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get_logger().warning(
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'Intermediate iterations not saved to CSV output file. '
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'Rerun the optimize method with "save_iterations=True".'
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)
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if warn and not self.runset._check_retcodes():
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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)
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if inc_iterations and self._save_iterations:
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data = self._all_iters
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else:
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data = self._mle
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try:
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out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
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data
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)
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# TODO(2.0) remove
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if out.shape == () or out.shape == (1,):
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get_logger().warning(
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"The default behavior of CmdStanMLE.stan_variable() "
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"will change in a future release to always return a "
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"numpy.ndarray, even for scalar variables."
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)
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return out.item() # type: ignore
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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(
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self, inc_iterations: bool = False
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) -> Dict[str, Union[np.ndarray, float]]:
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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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:param inc_iterations: When ``True`` and the intermediate estimates
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are included in the output, i.e., the optimizer was run with
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``save_iterations=True``, then intermediate estimates are included.
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Default value is ``False``.
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See Also
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--------
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CmdStanMLE.stan_variable
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CmdStanMCMC.stan_variables
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CmdStanPathfinder.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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if not self.runset._check_retcodes():
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get_logger().warning(
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'Invalid estimate, optimization failed to converge.'
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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(
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name, inc_iterations=inc_iterations, warn=False
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)
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return result
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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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