Source code for cmdstanpy.utils

Utility functions
import contextlib
import functools
import json
import logging
import math
import os
import platform
import re
import shutil
import subprocess
import sys
import tempfile
from collections import OrderedDict
from import Collection
from enum import Enum, auto
from typing import (

import numpy as np
import pandas as pd
import ujson
from import tqdm

from cmdstanpy import (

from . import progress as progbar

EXTENSION = '.exe' if platform.system() == 'Windows' else ''

class BaseType(Enum):
    """Stan langauge base type"""

    COMPLEX = auto()
    PRIM = auto()  # future: int / real

    def __repr__(self) -> str:
        return '<%s.%s>' % (self.__class__.__name__,

def get_logger() -> logging.Logger:
    """cmdstanpy logger"""
    logger = logging.getLogger('cmdstanpy')
    if len(logger.handlers) == 0:
        # send all messages to handlers
        # add a default handler to the logger to INFO and higher
        handler = logging.StreamHandler()
                '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    return logger

def validate_dir(install_dir: str) -> None:
    """Check that specified install directory exists, can write."""
    if not os.path.exists(install_dir):
        except (IOError, OSError, PermissionError) as e:
            raise ValueError(
                'Cannot create directory: {}'.format(install_dir)
            ) from e
        if not os.path.isdir(install_dir):
            raise ValueError(
                'File exists, should be a directory: {}'.format(install_dir)
            with open('tmp_test_w', 'w'):
            os.remove('tmp_test_w')  # cleanup
        except OSError as e:
            raise ValueError(
                'Cannot write files to directory {}'.format(install_dir)
            ) from e

def get_latest_cmdstan(cmdstan_dir: str) -> Optional[str]:
    Given a valid directory path, find all installed CmdStan versions
    and return highest (i.e., latest) version number.

    Assumes directory consists of CmdStan releases, created by
    function `install_cmdstan`, and therefore dirnames have format
    "cmdstan-<maj>.<min>.<patch>" or "cmdstan-<maj>.<min>.<patch>-rc<num>",
    which is CmdStan release practice as of v 2.24.
    versions = [
        for name in os.listdir(cmdstan_dir)
        if os.path.isdir(os.path.join(cmdstan_dir, name))
        and name.startswith('cmdstan-')
        and name[8].isdigit()
        and len(name[8:].split('.')) == 3
    if len(versions) == 0:
        return None
    # munge rc for sort, e.g. 2.25.0-rc1 -> 2.25.-99
    for i in range(len(versions)):  # # pylint: disable=C0200
        if '-rc' in versions[i]:
            comps = versions[i].split('-rc')
            mmp = comps[0].split('.')
            rc_num = comps[1]
            patch = str(int(rc_num) - 100)
            versions[i] = '.'.join([mmp[0], mmp[1], patch])

    versions.sort(key=lambda s: list(map(int, s.split('.'))))
    latest = versions[len(versions) - 1]

    # unmunge as needed
    mmp = latest.split('.')
    if int(mmp[2]) < 0:
        rc_num = str(int(mmp[2]) + 100)
        mmp[2] = "0-rc" + rc_num
        latest = '.'.join(mmp)

    return 'cmdstan-' + latest

def validate_cmdstan_path(path: str) -> None:
    Validate that CmdStan directory exists and binaries have been built.
    Throws exception if specified path is invalid.
    if not os.path.isdir(path):
        raise ValueError(f'No CmdStan directory, path {path} does not exist.')
    if not os.path.exists(os.path.join(path, 'bin', 'stanc' + EXTENSION)):
        raise ValueError(
            f'CmdStan installataion missing binaries in {path}/bin. '
            'Re-install cmdstan by running command "install_cmdstan '
            '--overwrite", or Python code "import cmdstanpy; '

[docs]def set_cmdstan_path(path: str) -> None: """ Validate, then set CmdStan directory path. """ validate_cmdstan_path(path) os.environ['CMDSTAN'] = path
[docs]def set_make_env(make: str) -> None: """ set MAKE environmental variable. """ os.environ['MAKE'] = make
[docs]def cmdstan_path() -> str: """ Validate, then return CmdStan directory path. """ cmdstan = '' if 'CMDSTAN' in os.environ and len(os.environ['CMDSTAN']) > 0: cmdstan = os.environ['CMDSTAN'] else: cmdstan_dir = os.path.expanduser(os.path.join('~', _DOT_CMDSTAN)) if not os.path.exists(cmdstan_dir): raise ValueError( 'No CmdStan installation found, run command "install_cmdstan"' 'or (re)activate your conda environment!' ) latest_cmdstan = get_latest_cmdstan(cmdstan_dir) if latest_cmdstan is None: raise ValueError( 'No CmdStan installation found, run command "install_cmdstan"' 'or (re)activate your conda environment!' ) cmdstan = os.path.join(cmdstan_dir, latest_cmdstan) os.environ['CMDSTAN'] = cmdstan validate_cmdstan_path(cmdstan) return os.path.normpath(cmdstan)
[docs]def cmdstan_version() -> Optional[Tuple[int, ...]]: """ Parses version string out of CmdStan makefile variable CMDSTAN_VERSION, returns Tuple(Major, minor). If CmdStan installation is not found or cannot parse version from makefile logs warning and returns None. Lenient behavoir required for CI tests, per comment: """ try: makefile = os.path.join(cmdstan_path(), 'makefile') except ValueError as e: get_logger().info('No CmdStan installation found.') get_logger().debug("%s", e) return None if not os.path.exists(makefile): get_logger().info( 'CmdStan installation %s missing makefile, cannot get version.', cmdstan_path(), ) return None with open(makefile, 'r') as fd: contents = start_idx = contents.find('CMDSTAN_VERSION := ') if start_idx < 0: get_logger().info( 'Cannot parse version from makefile: %s.', makefile, ) return None start_idx += len('CMDSTAN_VERSION := ') end_idx = contents.find('\n', start_idx) version = contents[start_idx:end_idx] splits = version.split('.') if len(splits) != 3: get_logger().info( 'Cannot parse version, expected "<major>.<minor>.<patch>", ' 'found: "%s".', version, ) return None return tuple(int(x) for x in splits[0:2])
def cmdstan_version_before( major: int, minor: int, info: Optional[Dict[str, str]] = None ) -> bool: """ Check that CmdStan version is less than Major.minor version. :param major: Major version number :param minor: Minor version number :return: True if version at or above major.minor, else False. """ cur_version = None if info is None or 'stan_version_major' not in info: cur_version = cmdstan_version() else: cur_version = ( int(info['stan_version_major']), int(info['stan_version_minor']), ) if cur_version is None: get_logger().info( 'Cannot determine whether version is before %d.%d.', major, minor ) return False if cur_version[0] < major or ( cur_version[0] == major and cur_version[1] < minor ): return True return False def cxx_toolchain_path( version: Optional[str] = None, install_dir: Optional[str] = None ) -> Tuple[str, ...]: """ Validate, then activate C++ toolchain directory path. """ if platform.system() != 'Windows': raise RuntimeError( 'Functionality is currently only supported on Windows' ) if version is not None and not isinstance(version, str): raise TypeError('Format version number as a string') logger = get_logger() if 'CMDSTAN_TOOLCHAIN' in os.environ: toolchain_root = os.environ['CMDSTAN_TOOLCHAIN'] if os.path.exists(os.path.join(toolchain_root, 'mingw64')): compiler_path = os.path.join( toolchain_root, 'mingw64' if (sys.maxsize > 2 ** 32) else 'mingw32', 'bin', ) if os.path.exists(compiler_path): tool_path = os.path.join(toolchain_root, 'usr', 'bin') if not os.path.exists(tool_path): tool_path = '' compiler_path = '' logger.warning( 'Found invalid installion for RTools40 on %s', toolchain_root, ) toolchain_root = '' else: compiler_path = '' logger.warning( 'Found invalid installion for RTools40 on %s', toolchain_root, ) toolchain_root = '' elif os.path.exists(os.path.join(toolchain_root, 'mingw_64')): compiler_path = os.path.join( toolchain_root, 'mingw_64' if (sys.maxsize > 2 ** 32) else 'mingw_32', 'bin', ) if os.path.exists(compiler_path): tool_path = os.path.join(toolchain_root, 'bin') if not os.path.exists(tool_path): tool_path = '' compiler_path = '' logger.warning( 'Found invalid installion for RTools35 on %s', toolchain_root, ) toolchain_root = '' else: compiler_path = '' logger.warning( 'Found invalid installion for RTools35 on %s', toolchain_root, ) toolchain_root = '' else: rtools40_home = os.environ.get('RTOOLS40_HOME') cmdstan_dir = os.path.expanduser(os.path.join('~', _DOT_CMDSTAN)) for toolchain_root in ( ([rtools40_home] if rtools40_home is not None else []) + ( [ os.path.join(install_dir, 'RTools40'), os.path.join(install_dir, 'RTools35'), os.path.join(install_dir, 'RTools30'), os.path.join(install_dir, 'RTools'), ] if install_dir is not None else [] ) + [ os.path.join(cmdstan_dir, 'RTools40'), os.path.join(os.path.abspath("/"), "RTools40"), os.path.join(cmdstan_dir, 'RTools35'), os.path.join(os.path.abspath("/"), "RTools35"), os.path.join(cmdstan_dir, 'RTools'), os.path.join(os.path.abspath("/"), "RTools"), os.path.join(os.path.abspath("/"), "RBuildTools"), ] ): compiler_path = '' tool_path = '' if os.path.exists(toolchain_root): if version not in ('35', '3.5', '3'): compiler_path = os.path.join( toolchain_root, 'mingw64' if (sys.maxsize > 2 ** 32) else 'mingw32', 'bin', ) if os.path.exists(compiler_path): tool_path = os.path.join(toolchain_root, 'usr', 'bin') if not os.path.exists(tool_path): tool_path = '' compiler_path = '' logger.warning( 'Found invalid installation for RTools40 on %s', toolchain_root, ) toolchain_root = '' else: break else: compiler_path = '' logger.warning( 'Found invalid installation for RTools40 on %s', toolchain_root, ) toolchain_root = '' else: compiler_path = os.path.join( toolchain_root, 'mingw_64' if (sys.maxsize > 2 ** 32) else 'mingw_32', 'bin', ) if os.path.exists(compiler_path): tool_path = os.path.join(toolchain_root, 'bin') if not os.path.exists(tool_path): tool_path = '' compiler_path = '' logger.warning( 'Found invalid installation for RTools35 on %s', toolchain_root, ) toolchain_root = '' else: break else: compiler_path = '' logger.warning( 'Found invalid installation for RTools35 on %s', toolchain_root, ) toolchain_root = '' else: toolchain_root = '' if not toolchain_root: raise ValueError( 'no RTools toolchain installation found, ' 'run command line script ' '"python -m cmdstanpy.install_cxx_toolchain"' )'Add C++ toolchain to $PATH: %s', toolchain_root) os.environ['PATH'] = ';'.join( list( OrderedDict.fromkeys( [compiler_path, tool_path] + os.getenv('PATH', '').split(';') ) ) ) return compiler_path, tool_path def rewrite_inf_nan( data: Union[float, int, List[Any]] ) -> Union[str, int, float, List[Any]]: """Replaces NaN and Infinity with string representations""" if isinstance(data, float): if math.isnan(data): return 'NaN' if math.isinf(data): return ('+' if data > 0 else '-') + 'inf' return data elif isinstance(data, list): return [rewrite_inf_nan(item) for item in data] else: return data def serialize_complex(c: Any) -> List[float]: if isinstance(c, complex): return [c.real, c.imag] else: raise TypeError(f"Unserializable type: {type(c)}")
[docs]def write_stan_json(path: str, data: Mapping[str, Any]) -> None: """ Dump a mapping of strings to data to a JSON file. Values can be any numeric type, a boolean (converted to int), or any collection compatible with :func:`numpy.asarray`, e.g a :class:`pandas.Series`. Produces a file compatible with the `Json Format for Cmdstan <>`__ :param path: File path for the created json. Will be overwritten if already in existence. :param data: A mapping from strings to values. This can be a dictionary or something more exotic like an :class:`xarray.Dataset`. This will be copied before type conversion, not modified """ data_out = {} for key, val in data.items(): handle_nan_inf = False if val is not None: if isinstance(val, (str, bytes)) or ( type(val).__module__ != 'numpy' and not isinstance(val, (Collection, bool, int, float)) ): raise TypeError( f"Invalid type '{type(val)}' provided to " + f"write_stan_json for key '{key}'" ) try: handle_nan_inf = not np.all(np.isfinite(val)) except TypeError: # handles cases like val == ['hello'] # pylint: disable=raise-missing-from raise ValueError( "Invalid type provided to " f"write_stan_json for key '{key}' " f"as part of collection {type(val)}" ) if type(val).__module__ == 'numpy': data_out[key] = val.tolist() elif isinstance(val, Collection): data_out[key] = np.asarray(val).tolist() elif isinstance(val, bool): data_out[key] = int(val) else: data_out[key] = val if handle_nan_inf: data_out[key] = rewrite_inf_nan(data_out[key]) with open(path, 'w') as fd: try: ujson.dump(data_out, fd) except TypeError as e: get_logger().debug(e) json.dump(data_out, fd, default=serialize_complex)
def rload(fname: str) -> Optional[Dict[str, Union[int, float, np.ndarray]]]: """Parse data and parameter variable values from an R dump format file. This parser only supports the subset of R dump data as described in the "Dump Data Format" section of the CmdStan manual, i.e., scalar, vector, matrix, and array data types. """ data_dict = {} with open(fname, 'r') as fd: lines = fd.readlines() # Variable data may span multiple lines, parse accordingly idx = 0 while idx < len(lines) and '<-' not in lines[idx]: idx += 1 if idx == len(lines): return None start_idx = idx idx += 1 while True: while idx < len(lines) and '<-' not in lines[idx]: idx += 1 next_var = idx var_data = ''.join(lines[start_idx:next_var]).replace('\n', '') lhs, rhs = [item.strip() for item in var_data.split('<-')] lhs = lhs.replace('"', '') # strip optional Jags double quotes rhs = rhs.replace('L', '') # strip R long int qualifier data_dict[lhs] = parse_rdump_value(rhs) if idx == len(lines): break start_idx = next_var idx += 1 return data_dict def parse_rdump_value(rhs: str) -> Union[int, float, np.ndarray]: """Process right hand side of Rdump variable assignment statement. Value is either scalar, vector, or multi-dim structure. Use regex to capture structure values, dimensions. """ pat = re.compile( r'structure\(\s*c\((?P<vals>[^)]*)\)' r'(,\s*\.Dim\s*=\s*c\s*\((?P<dims>[^)]*)\s*\))?\)' ) val: Union[int, float, np.ndarray] try: if rhs.startswith('structure'): parse = pat.match(rhs) if parse is None or'vals') is None: raise ValueError(rhs) vals = [float(v) for v in'vals').split(',')] val = np.array(vals, order='F') if'dims') is not None: dims = [int(v) for v in'dims').split(',')] val = np.array(vals).reshape(dims, order='F') elif rhs.startswith('c(') and rhs.endswith(')'): val = np.array([float(item) for item in rhs[2:-1].split(',')]) elif '.' in rhs or 'e' in rhs: val = float(rhs) else: val = int(rhs) except TypeError as e: raise ValueError('bad value in Rdump file: {}'.format(rhs)) from e return val def check_sampler_csv( path: str, is_fixed_param: bool = False, iter_sampling: Optional[int] = None, iter_warmup: Optional[int] = None, save_warmup: bool = False, thin: Optional[int] = None, ) -> Dict[str, Any]: """Capture essential config, shape from stan_csv file.""" meta = scan_sampler_csv(path, is_fixed_param) if thin is None: thin = _CMDSTAN_THIN elif thin > _CMDSTAN_THIN: if 'thin' not in meta: raise ValueError( 'bad Stan CSV file {}, ' 'config error, expected thin = {}'.format(path, thin) ) if meta['thin'] != thin: raise ValueError( 'bad Stan CSV file {}, ' 'config error, expected thin = {}, found {}'.format( path, thin, meta['thin'] ) ) draws_sampling = iter_sampling if draws_sampling is None: draws_sampling = _CMDSTAN_SAMPLING draws_warmup = iter_warmup if draws_warmup is None: draws_warmup = _CMDSTAN_WARMUP draws_warmup = int(math.ceil(draws_warmup / thin)) draws_sampling = int(math.ceil(draws_sampling / thin)) if meta['draws_sampling'] != draws_sampling: raise ValueError( 'bad Stan CSV file {}, expected {} draws, found {}'.format( path, draws_sampling, meta['draws_sampling'] ) ) if save_warmup: if not ('save_warmup' in meta and meta['save_warmup'] == 1): raise ValueError( 'bad Stan CSV file {}, ' 'config error, expected save_warmup = 1'.format(path) ) if meta['draws_warmup'] != draws_warmup: raise ValueError( 'bad Stan CSV file {}, ' 'expected {} warmup draws, found {}'.format( path, draws_warmup, meta['draws_warmup'] ) ) return meta def scan_sampler_csv(path: str, is_fixed_param: bool = False) -> Dict[str, Any]: """Process sampler stan_csv output file line by line.""" dict: Dict[str, Any] = {} lineno = 0 with open(path, 'r') as fd: try: lineno = scan_config(fd, dict, lineno) lineno = scan_column_names(fd, dict, lineno) if not is_fixed_param: lineno = scan_warmup_iters(fd, dict, lineno) lineno = scan_hmc_params(fd, dict, lineno) lineno = scan_sampling_iters(fd, dict, lineno, is_fixed_param) except ValueError as e: raise ValueError("Error in reading csv file: " + path) from e return dict def scan_optimize_csv(path: str, save_iters: bool = False) -> Dict[str, Any]: """Process optimizer stan_csv output file line by line.""" dict: Dict[str, Any] = {} lineno = 0 # scan to find config, header, num saved iters with open(path, 'r') as fd: lineno = scan_config(fd, dict, lineno) lineno = scan_column_names(fd, dict, lineno) iters = 0 for line in fd: iters += 1 if save_iters: all_iters: np.ndarray = np.empty( (iters, len(dict['column_names'])), dtype=float, order='F' ) # rescan to capture estimates with open(path, 'r') as fd: for i in range(lineno): fd.readline() for i in range(iters): line = fd.readline().strip() if len(line) < 1: raise ValueError( 'cannot parse CSV file {}, error at line {}'.format( path, lineno + i ) ) xs = line.split(',') if save_iters: all_iters[i, :] = [float(x) for x in xs] if i == iters - 1: mle: np.ndarray = np.array(xs, dtype=float) dict['mle'] = mle if save_iters: dict['all_iters'] = all_iters return dict def scan_generated_quantities_csv(path: str) -> Dict[str, Any]: """ Process standalone generated quantities stan_csv output file line by line. """ dict: Dict[str, Any] = {} lineno = 0 with open(path, 'r') as fd: lineno = scan_config(fd, dict, lineno) lineno = scan_column_names(fd, dict, lineno) return dict def scan_variational_csv(path: str) -> Dict[str, Any]: """Process advi stan_csv output file line by line.""" dict: Dict[str, Any] = {} lineno = 0 with open(path, 'r') as fd: lineno = scan_config(fd, dict, lineno) lineno = scan_column_names(fd, dict, lineno) line = fd.readline().lstrip(' #\t').rstrip() lineno += 1 if line.startswith('Stepsize adaptation complete.'): line = fd.readline().lstrip(' #\t\n') lineno += 1 if not line.startswith('eta'): raise ValueError( 'line {}: expecting eta, found:\n\t "{}"'.format( lineno, line ) ) _, eta = line.split('=') dict['eta'] = float(eta) line = fd.readline().lstrip(' #\t\n') lineno += 1 xs = line.split(',') variational_mean = [float(x) for x in xs] dict['variational_mean'] = np.array(variational_mean) dict['variational_sample'] = pd.read_csv( path, comment='#', skiprows=lineno, header=None, float_precision='high', ) return dict def scan_config(fd: TextIO, config_dict: Dict[str, Any], lineno: int) -> int: """ Scan initial stan_csv file comments lines and save non-default configuration information to config_dict. """ cur_pos = fd.tell() line = fd.readline().strip() while len(line) > 0 and line.startswith('#'): lineno += 1 if line.endswith('(Default)'): line = line.replace('(Default)', '') line = line.lstrip(' #\t') key_val = line.split('=') if len(key_val) == 2: if key_val[0].strip() == 'file' and not key_val[1].endswith('csv'): config_dict['data_file'] = key_val[1].strip() elif key_val[0].strip() != 'file': raw_val = key_val[1].strip() val: Union[int, float, str] try: val = int(raw_val) except ValueError: try: val = float(raw_val) except ValueError: val = raw_val config_dict[key_val[0].strip()] = val cur_pos = fd.tell() line = fd.readline().strip() return lineno def scan_warmup_iters( fd: TextIO, config_dict: Dict[str, Any], lineno: int ) -> int: """ Check warmup iterations, if any. """ if 'save_warmup' not in config_dict: return lineno cur_pos = fd.tell() line = fd.readline().strip() draws_found = 0 while len(line) > 0 and not line.startswith('#'): lineno += 1 draws_found += 1 cur_pos = fd.tell() line = fd.readline().strip() config_dict['draws_warmup'] = draws_found return lineno def scan_column_names( fd: TextIO, config_dict: MutableMapping[str, Any], lineno: int ) -> int: """ Process columns header, add to config_dict as 'column_names' """ line = fd.readline().strip() lineno += 1 names = line.split(',') config_dict['column_names_raw'] = tuple(names) config_dict['column_names'] = tuple(munge_varnames(names)) return lineno def munge_varnames(names: List[str]) -> List[str]: """ Change formatting for indices of container var elements from use of dot separator to array-like notation, e.g., rewrite label ``y_forecast.2.4`` to ``y_forecast[2,4]``. """ if names is None: raise ValueError('missing argument "names"') result = [] for name in names: if '.' not in name: result.append(name) else: head, *rest = name.split('.') result.append(''.join([head, '[', ','.join(rest), ']'])) return result def parse_method_vars(names: Tuple[str, ...]) -> Dict[str, Tuple[int, ...]]: """ Parses out names ending in `__` from list of CSV file column names. Return a dict mapping sampler variable name to Stan CSV file column, using zero-based column indexing. Currently, (Stan 2.X) all CmdStan inference method vars are scalar, the map entries are tuples of int to allow for structured variables. """ if names is None: raise ValueError('missing argument "names"') # note: method vars are currently all scalar so not checking for structure return {v: (k,) for (k, v) in enumerate(names) if v.endswith('__')} def parse_stan_vars( names: Tuple[str, ...] ) -> Tuple[ Dict[str, Tuple[int, ...]], Dict[str, Tuple[int, ...]], Dict[str, BaseType] ]: """ Parses out Stan variable names (i.e., names not ending in `__`) from list of CSV file column names. Returns three dicts which map variable names to base type, dimensions and CSV file columns, respectively, using zero-based column indexing. Note: assumes: (a) munged varnames and (b) container vars are non-ragged and dense; no checks on size, indices. """ if names is None: raise ValueError('missing argument "names"') dims_map: Dict[str, Tuple[int, ...]] = {} cols_map: Dict[str, Tuple[int, ...]] = {} types_map: Dict[str, BaseType] = {} idxs = [] dims: Union[List[str], List[int]] for (idx, name) in enumerate(names): if name.endswith('real]') or name.endswith('imag]'): basetype = BaseType.COMPLEX else: basetype = BaseType.PRIM idxs.append(idx) var, *dims = name.split('[') if var.endswith('__'): idxs = [] elif len(dims) == 0: dims_map[var] = () cols_map[var] = tuple(idxs) types_map[var] = basetype idxs = [] else: if idx < len(names) - 1 and names[idx + 1].split('[')[0] == var: continue coords = dims[0][:-1].split(',') if coords[-1] == 'imag': dims = [int(x) for x in coords[:-1]] dims.append(2) else: dims = [int(x) for x in coords] dims_map[var] = tuple(dims) cols_map[var] = tuple(idxs) types_map[var] = basetype idxs = [] return (dims_map, cols_map, types_map) def scan_hmc_params( fd: TextIO, config_dict: Dict[str, Any], lineno: int ) -> int: """ Scan step size, metric from stan_csv file comment lines. """ metric = config_dict['metric'] line = fd.readline().strip() lineno += 1 if not line == '# Adaptation terminated': raise ValueError( 'line {}: expecting metric, found:\n\t "{}"'.format(lineno, line) ) line = fd.readline().strip() lineno += 1 label, step_size = line.split('=') if not label.startswith('# Step size'): raise ValueError( 'line {}: expecting step size, ' 'found:\n\t "{}"'.format(lineno, line) ) try: float(step_size.strip()) except ValueError as e: raise ValueError( 'line {}: invalid step size: {}'.format(lineno, step_size) ) from e if metric == 'unit_e': return lineno line = fd.readline().strip() lineno += 1 if not ( ( metric == 'diag_e' and line == '# Diagonal elements of inverse mass matrix:' ) or ( metric == 'dense_e' and line == '# Elements of inverse mass matrix:' ) ): raise ValueError( 'line {}: invalid or missing mass matrix ' 'specification'.format(lineno) ) line = fd.readline().lstrip(' #\t') lineno += 1 num_unconstrained_params = len(line.split(',')) if metric == 'diag_e': return lineno else: for _ in range(1, num_unconstrained_params): line = fd.readline().lstrip(' #\t') lineno += 1 if len(line.split(',')) != num_unconstrained_params: raise ValueError( 'line {}: invalid or missing mass matrix ' 'specification'.format(lineno) ) return lineno def scan_sampling_iters( fd: TextIO, config_dict: Dict[str, Any], lineno: int, is_fixed_param: bool ) -> int: """ Parse sampling iteration, save number of iterations to config_dict. Also save number of divergences, max_treedepth hits """ draws_found = 0 num_cols = len(config_dict['column_names']) if not is_fixed_param: idx_divergent = config_dict['column_names'].index('divergent__') idx_treedepth = config_dict['column_names'].index('treedepth__') max_treedepth = config_dict['max_depth'] ct_divergences = 0 ct_max_treedepth = 0 cur_pos = fd.tell() line = fd.readline().strip() while len(line) > 0 and not line.startswith('#'): lineno += 1 draws_found += 1 data = line.split(',') if len(data) != num_cols: raise ValueError( 'line {}: bad draw, expecting {} items, found {}\n'.format( lineno, num_cols, len(line.split(',')) ) + 'This error could be caused by running out of disk space.\n' 'Try clearing up TEMP or setting output_dir to a path' ' on another drive.', ) cur_pos = fd.tell() line = fd.readline().strip() if not is_fixed_param: ct_divergences += int(data[idx_divergent]) # type: ignore if int(data[idx_treedepth]) == max_treedepth: # type: ignore ct_max_treedepth += 1 config_dict['draws_sampling'] = draws_found if not is_fixed_param: config_dict['ct_divergences'] = ct_divergences config_dict['ct_max_treedepth'] = ct_max_treedepth return lineno def read_metric(path: str) -> List[int]: """ Read metric file in JSON or Rdump format. Return dimensions of entry "inv_metric". """ if path.endswith('.json'): with open(path, 'r') as fd: metric_dict = ujson.load(fd) if 'inv_metric' in metric_dict: dims_np: np.ndarray = np.asarray(metric_dict['inv_metric']) return list(dims_np.shape) else: raise ValueError( 'metric file {}, bad or missing' ' entry "inv_metric"'.format(path) ) else: dims = list(read_rdump_metric(path)) if dims is None: raise ValueError( 'metric file {}, bad or missing' ' entry "inv_metric"'.format(path) ) return dims def read_rdump_metric(path: str) -> List[int]: """ Find dimensions of variable named 'inv_metric' in Rdump data file. """ metric_dict = rload(path) if metric_dict is None or not ( 'inv_metric' in metric_dict and isinstance(metric_dict['inv_metric'], np.ndarray) ): raise ValueError( 'metric file {}, bad or missing entry "inv_metric"'.format(path) ) return list(metric_dict['inv_metric'].shape) def do_command( cmd: List[str], cwd: Optional[str] = None, *, fd_out: Optional[TextIO] = sys.stdout, pbar: Optional[Callable[[str], None]] = None, ) -> None: """ Run command as subprocess, polls process output pipes and either streams outputs to supplied output stream or sends each line (stripped) to the supplied progress bar callback hook. Raises ``RuntimeError`` on non-zero return code or execption ``OSError``. :param cmd: command and args. :param cwd: directory in which to run command, if unspecified, run command in the current working directory. :param fd_out: when supplied, streams to this output stream, else writes to sys.stdout. :param pbar: optional callback hook to tqdm, which takes single ``str`` arguent, see: """ get_logger().debug('cmd: %s\ncwd: %s', ' '.join(cmd), cwd) try: # NB: Using this rather than cwd arg to Popen due to windows behavior with pushd(cwd if cwd is not None else '.'): # TODO: replace with in later Python versions? proc = subprocess.Popen( cmd, bufsize=1, stdin=subprocess.DEVNULL, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, # avoid buffer overflow env=os.environ, universal_newlines=True, ) while proc.poll() is None: if proc.stdout is not None: line = proc.stdout.readline() if fd_out is not None: fd_out.write(line) if pbar is not None: pbar(line.strip()) stdout, _ = proc.communicate() if stdout: if len(stdout) > 0: if fd_out is not None: fd_out.write(stdout) if pbar is not None: pbar(stdout.strip()) if proc.returncode != 0: # throw RuntimeError + msg serror = '' try: serror = os.strerror(proc.returncode) except (ArithmeticError, ValueError): pass msg = 'Command {}\n\t{} {}'.format( cmd, returncode_msg(proc.returncode), serror ) raise RuntimeError(msg) except OSError as e: msg = 'Command: {}\nfailed with error {}\n'.format(cmd, str(e)) raise RuntimeError(msg) from e def returncode_msg(retcode: int) -> str: """interpret retcode""" if retcode < 0: sig = -1 * retcode return f'terminated by signal {sig}' if retcode <= 125: return 'error during processing' if retcode == 126: # shouldn't happen return '' if retcode == 127: return 'program not found' sig = retcode - 128 return f'terminated by signal {sig}' def windows_short_path(path: str) -> str: """ Gets the short path name of a given long path. On non-Windows platforms, returns the path If (base)path does not exist, function raises RuntimeError """ if platform.system() != 'Windows': return path if os.path.isfile(path) or ( not os.path.isdir(path) and os.path.splitext(path)[1] != '' ): base_path, file_name = os.path.split(path) else: base_path, file_name = path, '' if not os.path.exists(base_path): raise RuntimeError( 'Windows short path function needs a valid directory. ' 'Base directory does not exist: "{}"'.format(base_path) ) import ctypes from ctypes import wintypes # pylint: disable=invalid-name _GetShortPathNameW = ( ctypes.windll.kernel32.GetShortPathNameW # type: ignore ) _GetShortPathNameW.argtypes = [ wintypes.LPCWSTR, wintypes.LPWSTR, wintypes.DWORD, ] _GetShortPathNameW.restype = wintypes.DWORD output_buf_size = 0 while True: output_buf = ctypes.create_unicode_buffer(output_buf_size) needed = _GetShortPathNameW(base_path, output_buf, output_buf_size) if output_buf_size >= needed: short_base_path = output_buf.value break else: output_buf_size = needed short_path = ( os.path.join(short_base_path, file_name) if file_name else short_base_path ) return short_path def create_named_text_file( dir: str, prefix: str, suffix: str, name_only: bool = False ) -> str: """ Create a named unique file, return filename. Flag 'name_only' will create then delete the tmp file; this lets us create filename args for commands which disallow overwriting existing files (e.g., 'stansummary'). """ fd = tempfile.NamedTemporaryFile( mode='w+', prefix=prefix, suffix=suffix, dir=dir, delete=name_only ) path = fd.close() return path
[docs]def show_versions(output: bool = True) -> str: """Prints out system and dependency information for debugging""" import importlib import locale import struct deps_info = [] try: (sysname, _, release, _, machine, processor) = platform.uname() deps_info.extend( [ ("python", sys.version), ("python-bits", struct.calcsize("P") * 8), ("OS", f"{sysname}"), ("OS-release", f"{release}"), ("machine", f"{machine}"), ("processor", f"{processor}"), ("byteorder", f"{sys.byteorder}"), ("LC_ALL", f'{os.environ.get("LC_ALL", "None")}'), ("LANG", f'{os.environ.get("LANG", "None")}'), ("LOCALE", f"{locale.getlocale()}"), ] ) # pylint: disable=broad-except except Exception: pass try: deps_info.append(('cmdstan_folder', cmdstan_path())) deps_info.append(('cmdstan', str(cmdstan_version()))) # pylint: disable=broad-except except Exception: deps_info.append(('cmdstan', 'NOT FOUND')) deps = ['cmdstanpy', 'pandas', 'xarray', 'tdqm', 'numpy', 'ujson'] for module in deps: try: if module in sys.modules: mod = sys.modules[module] else: mod = importlib.import_module(module) # pylint: disable=broad-except except Exception: deps_info.append((module, None)) else: try: ver = mod.__version__ # type: ignore deps_info.append((module, ver)) # pylint: disable=broad-except except Exception: deps_info.append((module, "installed")) out = 'INSTALLED VERSIONS\n---------------------\n' for k, info in deps_info: out += f'{k}: {info}\n' if output: print(out) return " " else: return out
[docs]def install_cmdstan( version: Optional[str] = None, dir: Optional[str] = None, overwrite: bool = False, compiler: bool = False, progress: bool = False, verbose: bool = False, cores: int = 1, *, interactive: bool = False, ) -> bool: """ Download and install a CmdStan release from GitHub. Downloads the release tar.gz file to temporary storage. Retries GitHub requests in order to allow for transient network outages. Builds CmdStan executables and tests the compiler by building example model ``bernoulli.stan``. :param version: CmdStan version string, e.g. "2.29.2". Defaults to latest CmdStan release. :param dir: Path to install directory. Defaults to hidden directory ``$HOME/.cmdstan``. If no directory is specified and the above directory does not exist, directory ``$HOME/.cmdstan`` will be created and populated. :param overwrite: Boolean value; when ``True``, will overwrite and rebuild an existing CmdStan installation. Default is ``False``. :param compiler: Boolean value; when ``True`` on WINDOWS ONLY, use the C++ compiler from the ``install_cxx_toolchain`` command or install one if none is found. :param progress: Boolean value; when ``True``, show a progress bar for downloading and unpacking CmdStan. Default is ``False``. :param verbose: Boolean value; when ``True``, show console output from all intallation steps, i.e., download, build, and test CmdStan release. Default is ``False``. :param cores: Integer, number of cores to use in the ``make`` command. Default is 1 core. :param interactive: Boolean value; if true, ignore all other arguments to this function and run in an interactive mode, prompting the user to provide the other information manually through the standard input. This flag should only be used in interactive environments, e.g. on the command line. :return: Boolean value; ``True`` for success. """ logger = get_logger() try: from .install_cmdstan import ( InstallationSettings, InteractiveSettings, run_install, ) args: Union[InstallationSettings, InteractiveSettings] if interactive: if any( [ version, dir, overwrite, compiler, progress, verbose, cores != 1, ] ): logger.warning( "Interactive installation requested but other arguments" " were used.\n\tThese values will be ignored!" ) args = InteractiveSettings() else: args = InstallationSettings( version=version, overwrite=overwrite, verbose=verbose, compiler=compiler, progress=progress, dir=dir, cores=cores, ) run_install(args) # pylint: disable=broad-except except Exception as e: logger.warning('CmdStan installation failed.\n%s', str(e)) return False set_cmdstan_path(os.path.join(args.dir, f"cmdstan-{args.version}")) return True
@progbar.wrap_callback def wrap_url_progress_hook() -> Optional[Callable[[int, int, int], None]]: """Sets up tqdm callback for url downloads.""" pbar: tqdm = tqdm( unit='B', unit_scale=True, unit_divisor=1024, colour='blue', leave=False, ) def download_progress_hook( count: int, block_size: int, total_size: int ) -> None: if is None: = total_size pbar.reset() downloaded_size = count * block_size pbar.update(downloaded_size - pbar.n) if pbar.n >= total_size: pbar.close() return download_progress_hook def flatten_chains(draws_array: np.ndarray) -> np.ndarray: """ Flatten a 3D array of draws X chains X variable into 2D array where all chains are concatenated into a single column. :param draws_array: 3D array of draws """ if len(draws_array.shape) != 3: raise ValueError( 'Expecting 3D array, found array with {} dims'.format( len(draws_array.shape) ) ) num_rows = draws_array.shape[0] * draws_array.shape[1] num_cols = draws_array.shape[2] return draws_array.reshape((num_rows, num_cols), order='F') @contextlib.contextmanager def pushd(new_dir: str) -> Iterator[None]: """Acts like pushd/popd.""" previous_dir = os.getcwd() os.chdir(new_dir) try: yield finally: os.chdir(previous_dir) def report_signal(sig: int) -> None: """Provide info for processes terminated by a system signal.""" print('terminated by signal: {}'.format(sig)) class MaybeDictToFilePath: """Context manager for json files.""" def __init__( self, *objs: Union[ str, Mapping[str, Any], List[Any], int, float, os.PathLike, None ], ): self._unlink = [False] * len(objs) self._paths: List[Any] = [''] * len(objs) i = 0 # pylint: disable=isinstance-second-argument-not-valid-type for obj in objs: if isinstance(obj, Mapping): data_file = create_named_text_file( dir=_TMPDIR, prefix='', suffix='.json' ) get_logger().debug('input tempfile: %s', data_file) write_stan_json(data_file, obj) self._paths[i] = data_file self._unlink[i] = True elif isinstance(obj, (str, os.PathLike)): if not os.path.exists(obj): raise ValueError("File doesn't exist {}".format(obj)) self._paths[i] = obj elif isinstance(obj, list): err_msgs = [] missing_obj_items = [] for j, obj_item in enumerate(obj): if not isinstance(obj_item, str): err_msgs.append( ( 'List element {} must be a filename string,' ' found {}' ).format(j, obj_item) ) elif not os.path.exists(obj_item): missing_obj_items.append( "File doesn't exist: {}".format(obj_item) ) if err_msgs: raise ValueError('\n'.join(err_msgs)) if missing_obj_items: raise ValueError('\n'.join(missing_obj_items)) self._paths[i] = obj elif obj is None: self._paths[i] = None elif i == 1 and isinstance(obj, (int, float)): self._paths[i] = obj else: raise ValueError('data must be string or dict') i += 1 def __enter__(self) -> List[str]: return self._paths def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore for can_unlink, path in zip(self._unlink, self._paths): if can_unlink and path: try: os.remove(path) except PermissionError: pass class SanitizedOrTmpFilePath: """Context manager for tmpfiles, handles spaces in filepath.""" def __init__(self, file_path: str): self._tmpdir = None if ' ' in os.path.abspath(file_path) and platform.system() == 'Windows': base_path, file_name = os.path.split(os.path.abspath(file_path)) os.makedirs(base_path, exist_ok=True) try: short_base_path = windows_short_path(base_path) if os.path.exists(short_base_path): file_path = os.path.join(short_base_path, file_name) except RuntimeError: pass if ' ' in os.path.abspath(file_path): tmpdir = tempfile.mkdtemp() if ' ' in tmpdir: raise RuntimeError( 'Unable to generate temporary path without spaces! \n' + 'Please move your stan file to location without spaces.' ) _, path = tempfile.mkstemp(suffix='.stan', dir=tmpdir) shutil.copy(file_path, path) self._path = path self._tmpdir = tmpdir else: self._path = file_path def __enter__(self) -> Tuple[str, bool]: return self._path, self._tmpdir is not None def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore if self._tmpdir: shutil.rmtree(self._tmpdir, ignore_errors=True)