some new features
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ilgazca
269
.venv/lib/python3.12/site-packages/cmdstanpy/stanfit/__init__.py
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269
.venv/lib/python3.12/site-packages/cmdstanpy/stanfit/__init__.py
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"""Container objects for results of CmdStan run(s)."""
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import glob
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import os
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from typing import Any, Dict, List, Optional, Union
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from cmdstanpy.cmdstan_args import (
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CmdStanArgs,
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LaplaceArgs,
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OptimizeArgs,
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PathfinderArgs,
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SamplerArgs,
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VariationalArgs,
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)
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from cmdstanpy.utils import check_sampler_csv, get_logger, scan_config
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from .gq import CmdStanGQ
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from .laplace import CmdStanLaplace
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from .mcmc import CmdStanMCMC
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from .metadata import InferenceMetadata
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from .mle import CmdStanMLE
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from .pathfinder import CmdStanPathfinder
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from .runset import RunSet
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from .vb import CmdStanVB
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__all__ = [
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"RunSet",
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"InferenceMetadata",
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"CmdStanMCMC",
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"CmdStanMLE",
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"CmdStanVB",
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"CmdStanGQ",
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"CmdStanLaplace",
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"CmdStanPathfinder",
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]
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def from_csv(
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path: Union[str, List[str], os.PathLike, None] = None,
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method: Optional[str] = None,
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) -> Union[
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CmdStanMCMC, CmdStanMLE, CmdStanVB, CmdStanPathfinder, CmdStanLaplace, None
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]:
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"""
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Instantiate a CmdStan object from a the Stan CSV files from a CmdStan run.
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CSV files are specified from either a list of Stan CSV files or a single
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filepath which can be either a directory name, a Stan CSV filename, or
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a pathname pattern (i.e., a Python glob). The optional argument 'method'
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checks that the CSV files were produced by that method.
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Stan CSV files from CmdStan methods 'sample', 'optimize', and 'variational'
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result in objects of class CmdStanMCMC, CmdStanMLE, and CmdStanVB,
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respectively.
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:param path: directory path
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:param method: method name (optional)
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:return: either a CmdStanMCMC, CmdStanMLE, or CmdStanVB object
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"""
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if path is None:
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raise ValueError('Must specify path to Stan CSV files.')
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if method is not None and method not in [
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'sample',
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'optimize',
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'variational',
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'laplace',
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'pathfinder',
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]:
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raise ValueError(
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'Bad method argument {}, must be one of: '
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'"sample", "optimize", "variational"'.format(method)
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)
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csvfiles = []
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if isinstance(path, list):
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csvfiles = path
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elif isinstance(path, str) and '*' in path:
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splits = os.path.split(path)
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if splits[0] is not None:
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if not (os.path.exists(splits[0]) and os.path.isdir(splits[0])):
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raise ValueError(
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'Invalid path specification, {} '
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' unknown directory: {}'.format(path, splits[0])
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)
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csvfiles = glob.glob(path)
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elif isinstance(path, (str, os.PathLike)):
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if os.path.exists(path) and os.path.isdir(path):
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for file in os.listdir(path):
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if os.path.splitext(file)[1] == ".csv":
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csvfiles.append(os.path.join(path, file))
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elif os.path.exists(path):
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csvfiles.append(str(path))
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else:
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raise ValueError('Invalid path specification: {}'.format(path))
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else:
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raise ValueError('Invalid path specification: {}'.format(path))
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if len(csvfiles) == 0:
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raise ValueError('No CSV files found in directory {}'.format(path))
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for file in csvfiles:
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if not (os.path.exists(file) and os.path.splitext(file)[1] == ".csv"):
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raise ValueError(
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'Bad CSV file path spec,'
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' includes non-csv file: {}'.format(file)
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)
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config_dict: Dict[str, Any] = {}
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try:
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with open(csvfiles[0], 'r') as fd:
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scan_config(fd, config_dict, 0)
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except (IOError, OSError, PermissionError) as e:
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raise ValueError('Cannot read CSV file: {}'.format(csvfiles[0])) from e
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if 'model' not in config_dict or 'method' not in config_dict:
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raise ValueError("File {} is not a Stan CSV file.".format(csvfiles[0]))
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if method is not None and method != config_dict['method']:
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raise ValueError(
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'Expecting Stan CSV output files from method {}, '
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' found outputs from method {}'.format(
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method, config_dict['method']
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)
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)
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try:
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if config_dict['method'] == 'sample':
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chains = len(csvfiles)
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sampler_args = SamplerArgs(
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iter_sampling=config_dict['num_samples'],
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iter_warmup=config_dict['num_warmup'],
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thin=config_dict['thin'],
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save_warmup=config_dict['save_warmup'],
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)
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# bugfix 425, check for fixed_params output
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try:
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check_sampler_csv(
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csvfiles[0],
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iter_sampling=config_dict['num_samples'],
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iter_warmup=config_dict['num_warmup'],
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thin=config_dict['thin'],
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save_warmup=config_dict['save_warmup'],
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)
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except ValueError:
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try:
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check_sampler_csv(
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csvfiles[0],
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is_fixed_param=True,
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iter_sampling=config_dict['num_samples'],
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iter_warmup=config_dict['num_warmup'],
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thin=config_dict['thin'],
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save_warmup=config_dict['save_warmup'],
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)
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sampler_args = SamplerArgs(
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iter_sampling=config_dict['num_samples'],
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iter_warmup=config_dict['num_warmup'],
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thin=config_dict['thin'],
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save_warmup=config_dict['save_warmup'],
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fixed_param=True,
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)
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except ValueError as e:
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raise ValueError(
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'Invalid or corrupt Stan CSV output file, '
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) from e
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cmdstan_args = CmdStanArgs(
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model_name=config_dict['model'],
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model_exe=config_dict['model'],
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chain_ids=[x + 1 for x in range(chains)],
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method_args=sampler_args,
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)
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runset = RunSet(args=cmdstan_args, chains=chains)
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runset._csv_files = csvfiles
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for i in range(len(runset._retcodes)):
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runset._set_retcode(i, 0)
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fit = CmdStanMCMC(runset)
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fit.draws()
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return fit
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elif config_dict['method'] == 'optimize':
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if 'algorithm' not in config_dict:
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raise ValueError(
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"Cannot find optimization algorithm"
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" in file {}.".format(csvfiles[0])
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)
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optimize_args = OptimizeArgs(
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algorithm=config_dict['algorithm'],
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save_iterations=config_dict['save_iterations'],
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jacobian=config_dict.get('jacobian', 0),
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)
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cmdstan_args = CmdStanArgs(
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model_name=config_dict['model'],
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model_exe=config_dict['model'],
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chain_ids=None,
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method_args=optimize_args,
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)
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runset = RunSet(args=cmdstan_args)
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runset._csv_files = csvfiles
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for i in range(len(runset._retcodes)):
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runset._set_retcode(i, 0)
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return CmdStanMLE(runset)
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elif config_dict['method'] == 'variational':
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if 'algorithm' not in config_dict:
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raise ValueError(
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"Cannot find variational algorithm"
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" in file {}.".format(csvfiles[0])
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)
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variational_args = VariationalArgs(
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algorithm=config_dict['algorithm'],
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iter=config_dict['iter'],
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grad_samples=config_dict['grad_samples'],
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elbo_samples=config_dict['elbo_samples'],
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eta=config_dict['eta'],
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tol_rel_obj=config_dict['tol_rel_obj'],
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eval_elbo=config_dict['eval_elbo'],
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output_samples=config_dict['output_samples'],
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)
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cmdstan_args = CmdStanArgs(
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model_name=config_dict['model'],
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model_exe=config_dict['model'],
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chain_ids=None,
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method_args=variational_args,
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)
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runset = RunSet(args=cmdstan_args)
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runset._csv_files = csvfiles
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for i in range(len(runset._retcodes)):
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runset._set_retcode(i, 0)
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return CmdStanVB(runset)
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elif config_dict['method'] == 'laplace':
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laplace_args = LaplaceArgs(
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mode=config_dict['mode'],
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draws=config_dict['draws'],
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jacobian=config_dict['jacobian'],
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)
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cmdstan_args = CmdStanArgs(
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model_name=config_dict['model'],
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model_exe=config_dict['model'],
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chain_ids=None,
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method_args=laplace_args,
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)
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runset = RunSet(args=cmdstan_args)
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runset._csv_files = csvfiles
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for i in range(len(runset._retcodes)):
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runset._set_retcode(i, 0)
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mode: CmdStanMLE = from_csv(
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config_dict['mode'],
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method='optimize',
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) # type: ignore
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return CmdStanLaplace(runset, mode=mode)
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elif config_dict['method'] == 'pathfinder':
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pathfinder_args = PathfinderArgs(
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num_draws=config_dict['num_draws'],
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num_paths=config_dict['num_paths'],
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)
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cmdstan_args = CmdStanArgs(
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model_name=config_dict['model'],
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model_exe=config_dict['model'],
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chain_ids=None,
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method_args=pathfinder_args,
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)
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runset = RunSet(args=cmdstan_args)
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runset._csv_files = csvfiles
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for i in range(len(runset._retcodes)):
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runset._set_retcode(i, 0)
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return CmdStanPathfinder(runset)
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else:
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get_logger().info(
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'Unable to process CSV output files from method %s.',
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(config_dict['method']),
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)
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return None
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except (IOError, OSError, PermissionError) as e:
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raise ValueError(
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'An error occurred processing the CSV files:\n\t{}'.format(str(e))
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) from e
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