""" Container for the result of running the generate quantities (GQ) method """ from collections import Counter from typing import ( Any, Dict, Generic, Hashable, List, MutableMapping, NoReturn, Optional, Tuple, TypeVar, Union, overload, ) import numpy as np import pandas as pd try: import xarray as xr XARRAY_INSTALLED = True except ImportError: XARRAY_INSTALLED = False from cmdstanpy.cmdstan_args import Method from cmdstanpy.utils import build_xarray_data, flatten_chains, get_logger from cmdstanpy.utils.stancsv import scan_generic_csv from .mcmc import CmdStanMCMC from .metadata import InferenceMetadata from .mle import CmdStanMLE from .runset import RunSet from .vb import CmdStanVB Fit = TypeVar('Fit', CmdStanMCMC, CmdStanMLE, CmdStanVB) class CmdStanGQ(Generic[Fit]): """ Container for outputs from CmdStan generate_quantities run. Created by :meth:`CmdStanModel.generate_quantities`. """ def __init__( self, runset: RunSet, previous_fit: Fit, ) -> None: """Initialize object.""" if not runset.method == Method.GENERATE_QUANTITIES: raise ValueError( 'Wrong runset method, expecting generate_quantities runset, ' 'found method {}'.format(runset.method) ) self.runset = runset self.previous_fit: Fit = previous_fit self._draws: np.ndarray = np.array(()) config = self._validate_csv_files() self._metadata = InferenceMetadata(config) def __repr__(self) -> str: repr = 'CmdStanGQ: model={} chains={}{}'.format( self.runset.model, self.chains, self.runset._args.method_args.compose(0, cmd=[]), ) repr = '{}\n csv_files:\n\t{}\n output_files:\n\t{}'.format( repr, '\n\t'.join(self.runset.csv_files), '\n\t'.join(self.runset.stdout_files), ) return repr def __getattr__(self, attr: str) -> np.ndarray: """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 __getstate__(self) -> dict: # This function returns the mapping of objects to serialize with pickle. # See https://docs.python.org/3/library/pickle.html#object.__getstate__ # for details. We call _assemble_generated_quantities to ensure # the data are loaded prior to serialization. self._assemble_generated_quantities() return self.__dict__ def _validate_csv_files(self) -> Dict[str, Any]: """ Checks that Stan CSV output files for all chains are consistent and returns dict containing config and column names. Raises exception when inconsistencies detected. """ dzero = {} for i in range(self.chains): if i == 0: dzero = scan_generic_csv( path=self.runset.csv_files[i], ) else: drest = scan_generic_csv( path=self.runset.csv_files[i], ) for key in dzero: if ( key not in [ 'id', 'fitted_params', 'diagnostic_file', 'metric_file', 'profile_file', 'init', 'seed', 'start_datetime', ] and dzero[key] != drest[key] ): raise ValueError( 'CmdStan config mismatch in Stan CSV file {}: ' 'arg {} is {}, expected {}'.format( self.runset.csv_files[i], key, dzero[key], drest[key], ) ) return dzero @property def chains(self) -> int: """Number of chains.""" return self.runset.chains @property def chain_ids(self) -> List[int]: """Chain ids.""" return self.runset.chain_ids @property def column_names(self) -> Tuple[str, ...]: """ Names of generated quantities of interest. """ return self._metadata.cmdstan_config['column_names'] # type: ignore @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 def draws( self, *, inc_warmup: bool = False, inc_iterations: bool = False, concat_chains: bool = False, inc_sample: bool = False, ) -> np.ndarray: """ Returns a numpy.ndarray over the generated quantities draws from all chains which is stored column major so that the values for a parameter are contiguous in memory, likewise all draws from a chain are contiguous. By default, returns a 3D array arranged (draws, chains, columns); parameter ``concat_chains=True`` will return a 2D array where all chains are flattened into a single column, preserving chain order, so that given M chains of N draws, the first N draws are from chain 1, ..., and the the last N draws are from chain M. :param inc_warmup: When ``True`` and the warmup draws are present in the output, i.e., the sampler was run with ``save_warmup=True``, then the warmup draws are included. Default value is ``False``. :param concat_chains: When ``True`` return a 2D array flattening all all draws from all chains. Default value is ``False``. :param inc_sample: When ``True`` include all columns in the previous_fit draws array as well, excepting columns for variables already present in the generated quantities drawset. Default value is ``False``. See Also -------- CmdStanGQ.draws_pd CmdStanGQ.draws_xr CmdStanMCMC.draws """ self._assemble_generated_quantities() inc_warmup |= inc_iterations if inc_warmup: if ( isinstance(self.previous_fit, CmdStanMCMC) and not self.previous_fit._save_warmup ): get_logger().warning( "Sample doesn't contain draws from warmup iterations," ' rerun sampler with "save_warmup=True".' ) elif ( isinstance(self.previous_fit, CmdStanMLE) and not self.previous_fit._save_iterations ): get_logger().warning( "MLE doesn't contain draws from pre-convergence iterations," ' rerun optimization with "save_iterations=True".' ) elif isinstance(self.previous_fit, CmdStanVB): get_logger().warning( "Variational fit doesn't make sense with argument " '"inc_warmup=True"' ) if inc_sample: cols_1 = self.previous_fit.column_names cols_2 = self.column_names dups = [ item for item, count in Counter(cols_1 + cols_2).items() if count > 1 ] drop_cols: List[int] = [] for dup in dups: drop_cols.extend( self.previous_fit._metadata.stan_vars[dup].columns() ) start_idx, _ = self._draws_start(inc_warmup) previous_draws = self._previous_draws(True) if concat_chains and inc_sample: return flatten_chains( np.dstack( ( np.delete(previous_draws, drop_cols, axis=1), self._draws, ) )[start_idx:, :, :] ) if concat_chains: return flatten_chains(self._draws[start_idx:, :, :]) if inc_sample: return np.dstack( ( np.delete(previous_draws, drop_cols, axis=1), self._draws, ) )[start_idx:, :, :] return self._draws[start_idx:, :, :] def draws_pd( self, vars: Union[List[str], str, None] = None, inc_warmup: bool = False, inc_sample: bool = False, ) -> pd.DataFrame: """ Returns the generated quantities draws as a pandas DataFrame. Flattens all chains into single column. Container variables (array, vector, matrix) will span multiple columns, one column per element. E.g. variable 'matrix[2,2] foo' spans 4 columns: 'foo[1,1], ... foo[2,2]'. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the output, i.e., the sampler was run with ``save_warmup=True``, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_xr CmdStanMCMC.draws_pd """ if vars is not None: if isinstance(vars, str): vars_list = [vars] else: vars_list = vars vars_list = list(dict.fromkeys(vars_list)) if inc_warmup: if ( isinstance(self.previous_fit, CmdStanMCMC) and not self.previous_fit._save_warmup ): get_logger().warning( "Sample doesn't contain draws from warmup iterations," ' rerun sampler with "save_warmup=True".' ) elif ( isinstance(self.previous_fit, CmdStanMLE) and not self.previous_fit._save_iterations ): get_logger().warning( "MLE doesn't contain draws from pre-convergence iterations," ' rerun optimization with "save_iterations=True".' ) elif isinstance(self.previous_fit, CmdStanVB): get_logger().warning( "Variational fit doesn't make sense with argument " '"inc_warmup=True"' ) self._assemble_generated_quantities() all_columns = ['chain__', 'iter__', 'draw__'] + list(self.column_names) gq_cols: List[str] = [] mcmc_vars: List[str] = [] if vars is not None: for var in vars_list: if var in self._metadata.stan_vars: info = self._metadata.stan_vars[var] gq_cols.extend( self.column_names[info.start_idx : info.end_idx] ) elif ( inc_sample and var in self.previous_fit._metadata.stan_vars ): info = self.previous_fit._metadata.stan_vars[var] mcmc_vars.extend( self.previous_fit.column_names[ info.start_idx : info.end_idx ] ) elif var in ['chain__', 'iter__', 'draw__']: gq_cols.append(var) else: raise ValueError('Unknown variable: {}'.format(var)) else: gq_cols = all_columns vars_list = gq_cols previous_draws_pd = self._previous_draws_pd(mcmc_vars, inc_warmup) draws = self.draws(inc_warmup=inc_warmup) # add long-form columns for chain, iteration, draw n_draws, n_chains, _ = draws.shape chains_col = ( np.repeat(np.arange(1, n_chains + 1), n_draws) .reshape(1, n_chains, n_draws) .T ) iter_col = ( np.tile(np.arange(1, n_draws + 1), n_chains) .reshape(1, n_chains, n_draws) .T ) draw_col = ( np.arange(1, (n_draws * n_chains) + 1) .reshape(1, n_chains, n_draws) .T ) draws = np.concatenate([chains_col, iter_col, draw_col, draws], axis=2) draws_pd = pd.DataFrame( data=flatten_chains(draws), columns=all_columns, ) if inc_sample and mcmc_vars: if gq_cols: return pd.concat( [ previous_draws_pd, draws_pd[gq_cols], ], axis='columns', )[vars_list] else: return previous_draws_pd elif inc_sample and vars is None: cols_1 = list(previous_draws_pd.columns) cols_2 = list(draws_pd.columns) dups = [ item for item, count in Counter(cols_1 + cols_2).items() if count > 1 ] return pd.concat( [ previous_draws_pd.drop(columns=dups).reset_index(drop=True), draws_pd, ], axis=1, ) elif gq_cols: return draws_pd[gq_cols] return draws_pd @overload def draws_xr( self: Union["CmdStanGQ[CmdStanMLE]", "CmdStanGQ[CmdStanVB]"], vars: Union[str, List[str], None] = None, inc_warmup: bool = False, inc_sample: bool = False, ) -> NoReturn: ... @overload def draws_xr( self: "CmdStanGQ[CmdStanMCMC]", vars: Union[str, List[str], None] = None, inc_warmup: bool = False, inc_sample: bool = False, ) -> "xr.Dataset": ... def draws_xr( self, vars: Union[str, List[str], None] = None, inc_warmup: bool = False, inc_sample: bool = False, ) -> "xr.Dataset": """ Returns the generated quantities draws as a xarray Dataset. This method can only be called when the underlying fit was made through sampling, it cannot be used on MLE or VB outputs. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the MCMC sample, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_pd CmdStanMCMC.draws_xr """ if not XARRAY_INSTALLED: raise RuntimeError( 'Package "xarray" is not installed, cannot produce draws array.' ) if not isinstance(self.previous_fit, CmdStanMCMC): raise RuntimeError( 'Method "draws_xr" is only available when ' 'original fit is done via Sampling.' ) mcmc_vars_list = [] dup_vars = [] if vars is not None: if isinstance(vars, str): vars_list = [vars] else: vars_list = vars for var in vars_list: if var not in self._metadata.stan_vars: if inc_sample and ( var in self.previous_fit._metadata.stan_vars ): mcmc_vars_list.append(var) dup_vars.append(var) else: raise ValueError('Unknown variable: {}'.format(var)) else: vars_list = list(self._metadata.stan_vars.keys()) if inc_sample: for var in self.previous_fit._metadata.stan_vars.keys(): if var not in vars_list and var not in mcmc_vars_list: mcmc_vars_list.append(var) for var in dup_vars: vars_list.remove(var) self._assemble_generated_quantities() num_draws = self.previous_fit.num_draws_sampling sample_config = self.previous_fit._metadata.cmdstan_config attrs: MutableMapping[Hashable, Any] = { "stan_version": f"{sample_config['stan_version_major']}." f"{sample_config['stan_version_minor']}." f"{sample_config['stan_version_patch']}", "model": sample_config["model"], "num_draws_sampling": num_draws, } if inc_warmup and sample_config['save_warmup']: num_draws += self.previous_fit.num_draws_warmup attrs["num_draws_warmup"] = self.previous_fit.num_draws_warmup data: MutableMapping[Hashable, Any] = {} coordinates: MutableMapping[Hashable, Any] = { "chain": self.chain_ids, "draw": np.arange(num_draws), } for var in vars_list: build_xarray_data( data, self._metadata.stan_vars[var], self.draws(inc_warmup=inc_warmup), ) if inc_sample: for var in mcmc_vars_list: build_xarray_data( data, self.previous_fit._metadata.stan_vars[var], self.previous_fit.draws(inc_warmup=inc_warmup), ) return xr.Dataset(data, coords=coordinates, attrs=attrs).transpose( 'chain', 'draw', ... ) def stan_variable(self, var: str, **kwargs: bool) -> np.ndarray: """ Return a numpy.ndarray which contains the set of draws for the named Stan program variable. Flattens the chains, leaving the draws in chain order. The first array dimension, corresponds to number of draws in the sample. The remaining dimensions correspond to the shape of the Stan program variable. Underlyingly draws are in chain order, i.e., for a sample with N chains of M draws each, the first M array elements are from chain 1, the next M are from chain 2, and the last M elements are from chain N. * If the variable is a scalar variable, the return array has shape ( draws * chains, 1). * If the variable is a vector, the return array has shape ( draws * chains, len(vector)) * If the variable is a matrix, the return array has shape ( draws * chains, size(dim 1), size(dim 2) ) * If the variable is an array with N dimensions, the return array has shape ( draws * chains, size(dim 1), ..., size(dim N)) For example, if the Stan program variable ``theta`` is a 3x3 matrix, and the sample consists of 4 chains with 1000 post-warmup draws, this function will return a numpy.ndarray with shape (4000,3,3). 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 kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variables CmdStanMCMC.stan_variable CmdStanMLE.stan_variable CmdStanPathfinder.stan_variable CmdStanVB.stan_variable CmdStanLaplace.stan_variable """ model_var_names = self.previous_fit._metadata.stan_vars.keys() gq_var_names = self._metadata.stan_vars.keys() if not (var in model_var_names or var in gq_var_names): raise ValueError( f'Unknown variable name: {var}\n' 'Available variables are ' + ", ".join(model_var_names | gq_var_names) ) if var not in gq_var_names: # TODO(2.0) atleast1d may not be needed return np.atleast_1d( # type: ignore self.previous_fit.stan_variable(var, **kwargs) ) # is gq variable self._assemble_generated_quantities() draw1, _ = self._draws_start( inc_warmup=kwargs.get('inc_warmup', False) or kwargs.get('inc_iterations', False) ) draws = flatten_chains(self._draws[draw1:]) out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(draws) return out def stan_variables(self, **kwargs: bool) -> Dict[str, np.ndarray]: """ Return a dictionary mapping Stan program variables names to the corresponding numpy.ndarray containing the inferred values. :param kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variable CmdStanMCMC.stan_variables CmdStanMLE.stan_variables CmdStanPathfinder.stan_variables CmdStanVB.stan_variables CmdStanLaplace.stan_variables """ result = {} sample_var_names = self.previous_fit._metadata.stan_vars.keys() gq_var_names = self._metadata.stan_vars.keys() for name in gq_var_names: result[name] = self.stan_variable(name, **kwargs) for name in sample_var_names: if name not in gq_var_names: result[name] = self.stan_variable(name, **kwargs) return result def _assemble_generated_quantities(self) -> None: if self._draws.shape != (0,): return # use numpy loadtxt _, num_draws = self._draws_start(inc_warmup=True) gq_sample: np.ndarray = np.empty( (num_draws, self.chains, len(self.column_names)), dtype=float, order='F', ) for chain in range(self.chains): with open(self.runset.csv_files[chain], 'r') as fd: lines = (line for line in fd if not line.startswith('#')) gq_sample[:, chain, :] = np.loadtxt( lines, dtype=np.ndarray, ndmin=2, skiprows=1, delimiter=',' ) self._draws = gq_sample def _draws_start(self, inc_warmup: bool) -> Tuple[int, int]: draw1 = 0 p_fit = self.previous_fit if isinstance(p_fit, CmdStanMCMC): num_draws = p_fit.num_draws_sampling if p_fit._save_warmup: if inc_warmup: num_draws += p_fit.num_draws_warmup else: draw1 = p_fit.num_draws_warmup elif isinstance(p_fit, CmdStanMLE): num_draws = 1 if p_fit._save_iterations: opt_iters = len(p_fit.optimized_iterations_np) # type: ignore if inc_warmup: num_draws = opt_iters else: draw1 = opt_iters - 1 else: # CmdStanVB: draw1 = 1 # skip mean num_draws = p_fit.variational_sample.shape[0] if inc_warmup: num_draws += 1 return draw1, num_draws def _previous_draws(self, inc_warmup: bool) -> np.ndarray: """ Extract the draws from self.previous_fit. Return is always 3-d """ p_fit = self.previous_fit if isinstance(p_fit, CmdStanMCMC): return p_fit.draws(inc_warmup=inc_warmup) elif isinstance(p_fit, CmdStanMLE): if inc_warmup and p_fit._save_iterations: return p_fit.optimized_iterations_np[:, None] # type: ignore return np.atleast_2d( # type: ignore p_fit.optimized_params_np, )[:, None] else: # CmdStanVB: if inc_warmup: return np.vstack( [p_fit.variational_params_np, p_fit.variational_sample] )[:, None] return p_fit.variational_sample[:, None] def _previous_draws_pd( self, vars: List[str], inc_warmup: bool ) -> pd.DataFrame: if vars: sel: Union[List[str], slice] = vars else: sel = slice(None, None) p_fit = self.previous_fit if isinstance(p_fit, CmdStanMCMC): return p_fit.draws_pd(vars or None, inc_warmup=inc_warmup) elif isinstance(p_fit, CmdStanMLE): if inc_warmup and p_fit._save_iterations: return p_fit.optimized_iterations_pd[sel] # type: ignore else: return p_fit.optimized_params_pd[sel] else: # CmdStanVB: return p_fit.variational_sample_pd[sel] 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) # TODO(2.0): remove @property def mcmc_sample(self) -> Union[CmdStanMCMC, CmdStanMLE, CmdStanVB]: get_logger().warning( "Property `mcmc_sample` is deprecated, use `previous_fit` instead" ) return self.previous_fit