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