Source code for rsmtool.configuration_parser

"""
Configuration parser.

Classes related to parsing configuration files
and creating configuration objects.

:author: Jeremy Biggs (jbiggs@ets.org)
:author: Anastassia Loukina (aloukina@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)

:organization: ETS
"""

import json
import logging
import re
import warnings
from collections import Counter
from copy import copy, deepcopy
from os import getcwd
from os.path import abspath
from pathlib import Path

from skll.learner import Learner
from skll.metrics import SCORERS

from . import HAS_RSMEXTRA
from .utils.constants import BOOLEAN_FIELDS, CHECK_FIELDS, DEFAULTS, ID_FIELDS, LIST_FIELDS
from .utils.files import parse_json_with_comments
from .utils.models import is_skll_model

if HAS_RSMEXTRA:
    from rsmextra.settings import (default_feature_subset_file, # noqa
                                   default_feature_sign)


[docs]def configure(context, config_file_or_obj_or_dict): """ Create a Configuration object. Get the configuration for ``context`` from the input ``config_file_or_obj_or_dict``. Parameters ---------- context : str The context that is being configured. Must be one of "rsmtool", "rsmeval", "rsmcompare", "rsmsummarize", "rsmpredict", or "rsmxval". config_file_or_obj_or_dict : str or pathlib.Path or dict or Configuration Path to the experiment configuration file either a a string or as a ``pathlib.Path`` object. Users can also pass a ``Configuration`` object that is in memory or a Python dictionary with keys corresponding to fields in the configuration file. Given a configuration file, any relative paths in the configuration file will be interpreted relative to the location of the file. Given a ``Configuration`` object, relative paths will be interpreted relative to the ``configdir`` attribute, that _must_ be set. Given a dictionary, the reference path is set to the current directory. Returns ------- configuration : Configuration The Configuration object for the tool. Raises ------ AttributeError If the ``configdir`` attribute for the Configuration input is not set. ValueError If ``config_file_or_obj_or_dict`` contains anything except a string, a path, a dictionary, or a ``Configuration`` object. """ # check what sort of input we got # if we got a string we consider this to be path to config file if isinstance(config_file_or_obj_or_dict, (str, Path)): # Instantiate configuration parser object parser = ConfigurationParser(config_file_or_obj_or_dict) configuration = parser.parse(context=context) elif isinstance(config_file_or_obj_or_dict, dict): # directly instantiate the Configuration from the dictionary configuration = Configuration(config_file_or_obj_or_dict, context=context) elif isinstance(config_file_or_obj_or_dict, Configuration): # raise an error if we are passed a Configuration object # without a configdir attribute. This can only # happen if the object was constructed using an earlier version # of RSMTool and stored try: assert config_file_or_obj_or_dict.configdir is not None except AssertionError: raise AttributeError("Configuration object must have configdir attribute.") else: configuration = config_file_or_obj_or_dict else: raise ValueError("The input to run_experiment must be " "a path to the file (str), a dictionary, " "or a configuration object. You passed " "{}.".format(type(config_file_or_obj_or_dict))) return configuration
[docs]class Configuration: """ Configuration class. Encapsulates all of the configuration parameters and methods to access these parameters. """ def __init__(self, configdict, *, configdir=None, context='rsmtool', logger=None): """ Create an object of the `Configuration` class. This method can be used to directly instantiate a Configuration object. Parameters ---------- configdict : dict A dictionary of configuration parameters. The dictionary must be a valid configuration dictionary with default values filled as necessary. configdir : str, optional, keyword-only The reference path used to resolve any relative paths in the configuration object. When ``None``, will be set during initialization to the current working directory. Defaults to ``None``, context : str The context of the tool. One of {"rsmtool", "rsmeval", "rsmcompare", "rsmpredict", "rsmsummarize"}. Defaults to "rsmtool". logger : logging object, optional A logging object. If ``None`` is passed, get logger from ``__name__``. Defaults to ``None``. """ if not isinstance(configdict, dict): raise TypeError('The input must be a dictionary.') # create a logger instance self.logger = logger if logger else logging.getLogger(__name__) # process and validate the configuration dictionary configdict = ConfigurationParser.process_config(configdict) configdict = ConfigurationParser.validate_config(configdict, context=context) # set configdir to `cwd` if not given and let the user know if configdir is None: configdir = Path(getcwd()) self.logger.info("Configuration directory will be set to {}".format(configdir)) else: configdir = Path(configdir).resolve() self._config = configdict self._configdir = configdir self._context = context def __contains__(self, key): """ Check if the configuration object contains a given key. Parameters ---------- key : str Key to check in the Configuration object. Returns ------- key_check : bool ``True`` if key is in configuration object, else ``False``. """ return key in self._config def __getitem__(self, key): """ Get configuration value for the given key. Parameters ---------- key : str Key to check in the configuration object Returns ------- value The value in the configuration object dictionary. """ return self._config[key] def __setitem__(self, key, value): """ Set configuration value for the given key. Parameters ---------- key : str Key to set in the configuration object. value The value to be set for the key. """ self._config[key] = value def __len__(self): """ Return the size of the configuration dictionary. Returns ------- size : int The number of elements in the configuration dictionary. """ return len(self._config) def __str__(self): """ Return string representation of the configuration dictionary. Returns ------- config_string : str A string representation of the underlying configuration dictionary as encoded by ``json.dumps()``. It only includes the configuration options that can be set by the user. """ expected_fields = (CHECK_FIELDS[self._context]['required'] + CHECK_FIELDS[self._context]['optional']) output_config = {k: v for k, v in self._config.items() if k in expected_fields} return json.dumps(output_config, indent=4, separators=(',', ': ')) def __iter__(self): """ Iterate through configuration object keys. Yields ------ key : str A key in the config dictionary """ for key in self.keys(): yield key @property def configdir(self): """ Get the path to configuration directory. Get the path to the configuration reference directory that will be used to resolve any relative paths in the configuration. Returns ------- configdir : str The path to the configuration reference directory. """ return str(self._configdir) @configdir.setter def configdir(self, new_path): """ Set a new configuration reference directory. Parameters ---------- new_path : str Path to the new configuration reference directory used to resolve any relative paths in the configuration object. """ if new_path is None: raise ValueError("The `configdir` attribute cannot be set to `None` ") # TODO: replace `Path(abspath(new_path))` with `Path(new_path).resolve() # once this Windows bug is fixed: https://bugs.python.org/issue38671 self._configdir = Path(abspath(new_path)) @property def context(self): """Get the context.""" return self._context @context.setter def context(self, new_context): """ Set a new context. Parameters ---------- new_context : str A new context for the configuration object. """ self._context = new_context
[docs] def get(self, key, default=None): """ Get value or default for the given key. Parameters ---------- key : str Key to check in the Configuration object. default, optional The default value to return, if no key exists. Defaults to ``None``. Returns ------- value The value in the configuration object dictionary. """ return self._config.get(key, default)
[docs] def to_dict(self): """ Get a dictionary representation of the configuration object. Returns ------- config : dict The configuration dictionary. """ return self._config
[docs] def keys(self): """ Return keys as a list. Returns ------- keys : list of str A list of keys in the configuration object. """ return [k for k in self._config.keys()]
[docs] def values(self): """ Return configuration values as a list. Returns ------- values : list A list of values in the configuration object. """ return [v for v in self._config.values()]
[docs] def items(self): """ Return configuration items as a list of tuples. Returns ------- items : list of tuples A list of (key, value) tuples in the configuration object. """ return [(k, v) for k, v in self._config.items()]
[docs] def pop(self, key, default=None): """ Remove and return an element from the object having the given key. Parameters ---------- key : str Key to pop in the configuration object. default, optional The default value to return, if no key exists. Defaults to ``None``. Returns ------- value The value removed from the object. """ return self._config.pop(key, default)
[docs] def copy(self, deep=True): """ Return a copy of the object. Parameters ---------- deep : bool, optional Whether to perform a deep copy. Defaults to ``True``. Returns ------- copy : Configuration A new configuration object. """ if deep: return deepcopy(self) return copy(self)
[docs] def save(self, output_dir=None): """ Save the configuration file to the output directory specified. Parameters ---------- output_dir : str The path to the output directory. If ``None``, the current directory is used. Defaults to ``None``. """ # save a copy of the main config into the output directory if output_dir is None: output_dir = Path(getcwd()) # Create output directory, if it does not exist output_dir = Path(output_dir).resolve() / 'output' output_dir.mkdir(parents=True, exist_ok=True) id_field = ID_FIELDS[self._context] experiment_id = self._config[id_field] context = self._context outjson = output_dir / f"{experiment_id}_{context}.json" with outjson.open(mode='w') as outfile: outfile.write(str(self))
[docs] def check_exclude_listwise(self): """ Check for candidate exclusion. Check if we are excluding candidates based on number of responses, and add this to the configuration file. Returns ------- exclude_listwise : bool Whether to exclude list-wise. """ exclude_listwise = False if self._config.get('min_items_per_candidate'): exclude_listwise = True return exclude_listwise
[docs] def check_flag_column(self, flag_column='flag_column', partition='unknown'): """ Make sure the column name in ``flag_column`` is correctly specified. Get flag columns and values for filtering, if any, and convert single values to lists. Raises an exception if the column name in ``flag_column`` is not correctly specified in the configuration file. Parameters ---------- flag_column : str The flag column name to check. Currently used names are "flag_column" or "flag_column_test". Defaults to "flag_column". partition: str The data partition which is filtered based on the flag column name. One of {"train", "test", "both", "unknown"}. Defaults to "both". Returns ------- new_filtering_dict : dict Properly formatted dictionary for the column name in ``flag_column``. Raises ------ ValueError If the specified value of the column name in ``flag_column`` is not a dictionary. ValueError If the value of ``partition`` is not in the expected list. ValueError If the value of ``partition`` does not match the ``flag_column``. """ config = self._config new_filter_dict = {} flag_message = {"train": "training", "test": "evaluating", "both": "training and evaluating", "unknown": "training and/or evaluating"} if partition not in flag_message: raise ValueError("Unknown value for partition: {} " "This must be one of the following: {}." "".format(partition, ','.join(flag_message.keys()))) if flag_column == "flag_column_test": if partition in ["both", "train"]: raise ValueError("Conditions specified in 'flag_column_test' " "can only be applied to the evaluation partition.") if config.get(flag_column): original_filter_dict = config[flag_column] # first check that the value is a dictionary if not isinstance(original_filter_dict, dict): raise ValueError("`flag_column` must be a dictionary. " "Please refer to the documentation for " "further information.") for column in original_filter_dict: # if we were given a single value, convert it to list if not isinstance(original_filter_dict[column], list): new_filter_dict[column] = [original_filter_dict[column]] self.logger.warning("The filtering condition {}" " for column {} was converted " "to list. Only responses where " "{} == {} will be used for " "{} the " "model. You can ignore this " "warning if this is the correct " "interpretation of your " "configuration settings" ".".format(original_filter_dict[column], column, column, original_filter_dict[column], flag_message[partition]) ) else: new_filter_dict[column] = original_filter_dict[column] model_eval = ', '.join(map(str, original_filter_dict[column])) self.logger.info("Only responses where " "{} equals one of the following values " "will be used for {} the model: " "{}.".format(column, flag_message[partition], model_eval)) return new_filter_dict
[docs] def get_trim_min_max_tolerance(self): """ Get trim min, trim max, and tolerance values. Get the specified trim min and max, and trim_tolerance if any, and make sure they are numeric. Returns ------- spec_trim_min : float Specified trim min value. spec_trim_max : float Specified trim max value. spec_trim_tolerance: float Specified trim tolerance value. """ config = self._config spec_trim_min = config.get('trim_min', None) spec_trim_max = config.get('trim_max', None) spec_trim_tolerance = config.get('trim_tolerance', None) if spec_trim_min: spec_trim_min = float(spec_trim_min) if spec_trim_max: spec_trim_max = float(spec_trim_max) if spec_trim_tolerance: spec_trim_tolerance = float(spec_trim_tolerance) return (spec_trim_min, spec_trim_max, spec_trim_tolerance)
[docs] def get_rater_error_variance(self): """ Get specified rater error variance, if any, and make sure it's numeric. Returns ------- rater_error_variance : float Specified rater error variance. """ config = self._config rater_error_variance = config.get('rater_error_variance', None) if rater_error_variance: rater_error_variance = float(rater_error_variance) return rater_error_variance
[docs] def get_default_converter(self): """ Get default converter dictionary for data reader. Returns ------- default_converter : dict The default converter for a train or test file. """ string_columns = [self._config['id_column']] candidate = self._config.get('candidate_column') if candidate is not None: string_columns.append(candidate) subgroups = self._config.get('subgroups') if subgroups: string_columns.extend(subgroups) return dict([(column, str) for column in string_columns if column])
[docs] def get_names_and_paths(self, keys, names): """ Get a list of values for the given keys. Remove any values that are ``None``. Parameters ------- keys : list A list of keys whose values to retrieve. names : list The default value to use if key cannot be found. Defaults to ``None``. Returns ------- values : list The list of values. Raises ------ ValueError If there are any duplicate keys or names. """ assert len(keys) == len(names) # Make sure keys are not duplicated if not len(set(keys)) == len(keys): raise ValueError('The ``keys`` must be unique. However, the ' 'following duplicate keys were found: {}.' ''.format(', '.join([key for key, val in Counter(keys).items() if val > 1]))) # Make sure names are not duplicated if not len(set(names)) == len(names): raise ValueError('The``names`` must be unique. However, the ' 'following duplicate names were found: {}.' ''.format(', '.join([name for name, val in Counter(names).items() if val > 1]))) existing_names = [] existing_paths = [] for idx, key in enumerate(keys): path = self._config.get(key) # if the `features` field is a list, # do not include it in the container if key == 'features': if isinstance(path, list): continue if path is not None: existing_paths.append(path) existing_names.append(names[idx]) return existing_names, existing_paths
[docs]class ConfigurationParser: """``ConfigurationParser`` class to create ``Configuration`` objects.""" # initialize class logger attribute to None logger = None def __init__(self, pathlike, logger=None): """ Instantiate a ``ConfigurationParser`` for a given config file path. Parameters ---------- pathlike : str or pathlib.Path A string containing the path to the configuration file that is to be parsed. A ``pathlib.Path`` instance is also acceptable. logger : logging.Logger object, optional Custom logger object to use, if not ``None``. Otherwise a new logger is created. Defaults to ``None``. Raises ------ FileNotFoundError If the given path does not exist. OSError If the given path is a directory, not a file. ValueError If the file at the given path does not have a valid extension (".json"). """ # if we passed in a string, convert it to a Path if isinstance(pathlike, str): pathlike = Path(pathlike) # raise an exception if the file does not exist if not pathlike.exists(): raise FileNotFoundError(f"The configuration file {pathlike} " f"was not found.") # raise an exception if the user specified a directory if not pathlike.is_file(): raise OSError(f"The given path {pathlike} should be a " f"file, not a directory.") # make sure we have a file that ends in ".json" extension = pathlike.suffix.lower() if extension != '.json': raise ValueError(f"The configuration file must be in `.json` " f"format. You specified: {extension}.") # set the various attributes to None self._filename = pathlike.name self._configdir = pathlike.resolve().parent # set the `logger` class attribute to the given logger, if provided # or else create a new logger self.__class__.logger = logger if logger else logging.getLogger(__name__) @staticmethod def _fix_json(json_string): """ Fix the configuration JSON. Take a bit of JSON that might have bad quotes or capitalized booleans and fixes that stuff. Parameters ---------- json_string : str A string to be reformatted for JSON parsing. Return ------ json_string : str The updated string. """ json_string = json_string.replace('True', 'true') json_string = json_string.replace('False', 'false') json_string = json_string.replace("'", '"') return json_string def _parse_json_file(self, filepath): """ Parse the configuration JSON. A private method to parse JSON configuration files and return a Python dictionary. Parameters ---------- filepath : pathlib.Path A ``pathlib.Path`` object containing the JSON configuration filepath. Returns ------- configdict : dict A Python dictionary containing the parameters from the JSON configuration file. Raises ------ ValueError If the JSON file could not be parsed. """ try: configdict = parse_json_with_comments(filepath) except ValueError: raise ValueError('The main configuration file `{}` exists but ' 'is formatted incorrectly. Please check that ' 'each line ends with a comma, there is no comma ' 'at the end of the last line, and that all quotes ' 'match.'.format(filepath)) return configdict
[docs] def parse(self, context='rsmtool'): """ Parse configuration file. Parse the configuration file for which this parser was instantiated. Parameters ---------- context : str, optional Context of the tool in which we are validating. One of: {"rsmtool", "rsmeval", "rsmpredict", "rsmcompare", "rsmsummarize", "rsmxval"}. Defaults to "rsmtool". Returns ------- configuration : Configuration A configuration object containing the parameters in the file that we instantiated the parser for. """ filepath = self._configdir / self._filename configdict = self._parse_json_file(filepath) # create a new Configuration object which will automatically # process and validate the configuration # dictionary being passed in return Configuration(configdict, configdir=self._configdir, context=context)
[docs] @classmethod def validate_config(cls, config, context='rsmtool'): """ Validate configuration file. Ensure that all required fields are specified, add default values values for all unspecified fields, and ensure that all specified fields are valid. Parameters ---------- context : str, optional Context of the tool in which we are validating. One of {"rsmtool", "rsmeval", "rsmpredict", "rsmcompare", "rsmsummarize"}. Defaults to "rsmtool". inplace : bool Maintain the state of the config object produced by this method. Defaults to ``True``. Returns ------- config_obj : Configuration A configuration object Raises ------ ValueError If config does not exist, and no config passed. """ # make a copy of the given parameter dictionary new_config = deepcopy(config) # 1. Check to make sure all required fields are specified required_fields = CHECK_FIELDS[context]['required'] for field in required_fields: if field not in new_config: raise ValueError("The config file must " "specify '{}'".format(field)) # 2. Add default values for unspecified optional fields # for given RSMTool context defaults = DEFAULTS for field in defaults: if field not in new_config: new_config[field] = defaults[field] # 3. Check to make sure no unrecognized fields are specified for field in new_config: if field not in defaults and field not in required_fields: raise ValueError("Unrecognized field '{}'" " in json file".format(field)) # 4. Check to make sure that the ID fields that will be # used as part of filenames are formatted correctly # i.e., they do not contain any spaces and are < 200 characters id_field = ID_FIELDS[context] id_field_values = {id_field: new_config[id_field]} for id_field, id_field_value in id_field_values.items(): if len(id_field_value) > 200: raise ValueError("{} is too long (must be " "<=200 characters)".format(id_field)) if re.search(r'\s', id_field_value): raise ValueError("{} cannot contain any " "spaces".format(id_field)) # 5. Check that the feature file and feature subset/subset file are not # specified together msg = ("You cannot specify BOTH \"features\" and \"{}\". " "Please refer to the \"Selecting Feature Columns\" " "section in the documentation for more details.") if new_config['features'] and new_config['feature_subset_file']: msg = msg.format("feature_subset_file") raise ValueError(msg) if new_config['features'] and new_config['feature_subset']: msg = msg.format("feature_subset") raise ValueError(msg) # 6. Check for fields that require feature_subset_file and try # to use the default feature file if (new_config['feature_subset'] and not new_config['feature_subset_file']): # Check if we have the default subset file from rsmextra if HAS_RSMEXTRA: default_basename = Path(default_feature_subset_file).name new_config['feature_subset_file'] = default_feature_subset_file cls.logger.warning("You requested feature subsets but did not " "specify any feature file. " "The tool will use the default " "feature file {} available via " "rsmextra".format(default_basename)) else: raise ValueError("If you want to use feature subsets, you " "must specify a feature subset file") if new_config['sign'] and not new_config['feature_subset_file']: # Check if we have the default subset file from rsmextra if HAS_RSMEXTRA: default_basename = Path(default_feature_subset_file).name new_config['feature_subset_file'] = default_feature_subset_file cls.logger.warning("You specified the expected sign of " "correlation but did not specify a feature " "subset file. The tool will use " "the default feature subset file {} " "available via " "rsmextra".format(default_basename)) else: raise ValueError("If you want to specify the expected sign of " " correlation for each feature, you must " "specify a feature subset file") # Use the default sign if we are using the default feature file # and sign has not been specified in the config file if HAS_RSMEXTRA: default_feature = default_feature_subset_file if (new_config['feature_subset_file'] == default_feature and not new_config['sign']): new_config['sign'] = default_feature_sign # 7. Check for fields that must be specified together if (new_config['min_items_per_candidate'] and not new_config['candidate_column']): raise ValueError("If you want to filter out candidates with " "responses to less than X items, you need " "to specify the name of the column which " "contains candidate IDs.") # 8. Check that if "skll_objective" is specified, it's # one of the metrics that SKLL allows for AND that it is # specified for a SKLL model and _not_ a built-in # linear regression model if new_config['skll_objective']: if not is_skll_model(new_config['model']): warnings.warn("You specified a custom SKLL objective but also chose a " "non-SKLL model. The objective will be ignored.") else: if new_config['skll_objective'] not in SCORERS: raise ValueError("Invalid SKLL objective. Please refer to the SKLL " "documentation and choose a valid tuning objective.") # 9. Check that if "skll_fixed_parameters" is specified, # it's specified for SKLL model and _not_ a built-in linear # regression model; we cannot check whether the parameters # are valid at parse time but SKLL will raise an error # at run time for any invalid parameters if new_config['skll_fixed_parameters']: if not is_skll_model(new_config['model']): warnings.warn("You specified custom SKLL fixed parameters but " "also chose a non-SKLL model. The parameters will " "be ignored.") # 10. Check that if we are running rsmtool to ask for # expected scores then the SKLL model type must actually # support probabilistic classification. If it's not a SKLL # model at all, we just treat it as a LinearRegression model # which is basically what they all are in the end. if context == 'rsmtool' and new_config['predict_expected_scores']: model_name = new_config['model'] dummy_learner = Learner(model_name) if is_skll_model(model_name) else Learner('LinearRegression') if not hasattr(dummy_learner.model_type, 'predict_proba'): raise ValueError("{} does not support expected scores " "since it is not a probablistic classifier.".format(model_name)) del dummy_learner # 11. Check the fields that requires rsmextra if not HAS_RSMEXTRA: if new_config['special_sections']: raise ValueError("Special sections are only available to ETS" " users by installing the rsmextra package.") # 12. Raise a warning if we are specifiying a feature file but also # telling the system to automatically select transformations if new_config['features'] and new_config['select_transformations']: # Show a warning unless a user passed a list of features. if not isinstance(new_config['features'], list): warnings.warn("You specified a feature file but also set " "`select_transformations` to True. Any " "transformations or signs specified in " "the feature file will be overwritten by " "the automatically selected transformations " "and signs.") # 13. If we have `experiment_names`, check that the length of the list # matches the list of experiment_dirs. if context == 'rsmsummarize' and new_config['experiment_names']: if len(new_config['experiment_names']) != len(new_config['experiment_dirs']): raise ValueError("The number of specified experiment names should be the same" " as the number of specified experiment directories.") # 14. Check that if the user specified min_n_per_group, they also # specified subgroups. If they supplied a dictionary, make # sure the keys match if new_config['min_n_per_group']: # make sure we have subgroups if 'subgroups' not in new_config: raise ValueError("You must specify a list of subgroups in " "in the `subgroups` field if " "you want to use the `min_n_per_group` field") # if we got dictionary, make sure the keys match elif isinstance(new_config['min_n_per_group'], dict): if sorted(new_config['min_n_per_group'].keys()) != sorted(new_config['subgroups']): raise ValueError("The keys in `min_n_per_group` must " "match the subgroups in `subgroups` field") # else convert to dictionary else: new_config['min_n_per_group'] = {group: new_config['min_n_per_group'] for group in new_config['subgroups']} # 15. Clean up config dict to keep only context-specific fields context_relevant_fields = (CHECK_FIELDS[context]['optional'] + CHECK_FIELDS[context]['required']) new_config = {k: v for k, v in new_config.items() if k in context_relevant_fields} return new_config
[docs] @classmethod def process_config(cls, config): """ Process configuration file. Converts fields which are read in as string to the appropriate format. Fields which can take multiple string values are converted to lists if they have not been already formatted as such. Parameters ---------- inplace : bool Maintain the state of the config object produced by this method. Defaults to ``True``. Returns ------- config_obj : Configuration A configuration object Raises ------ NameError If ``config`` does not exist, or ``config`` could not be read. ValueError If boolean configuration fields contain a value other then "true" or "false" (in JSON). """ # Get the parameter dictionary new_config = deepcopy(config) # convert multiple values into lists for field in LIST_FIELDS: if field in new_config and new_config[field] is not None: if not isinstance(new_config[field], list): new_config[field] = new_config[field].split(',') new_config[field] = [prefix.strip() for prefix in new_config[field]] # make sure all boolean values are boolean for field in BOOLEAN_FIELDS: error_message = ('Field {} can only be set to ' 'True or False.'.format(field)) if field in new_config and new_config[field] is not None: if not isinstance(new_config[field], bool): # we first convert the value to string to avoid # attribute errors in case the user supplied an integer. given_value = str(new_config[field]).strip() m = re.match(r'^(true|false)$', given_value, re.I) if not m: raise ValueError(error_message) else: bool_value = json.loads(m.group().lower()) new_config[field] = bool_value return new_config