"""
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)
:date: 10/25/2017
:organization: ETS
"""
import json
import logging
import re
import warnings
from collections import Counter
from configparser import ConfigParser
from os import getcwd, makedirs
from os.path import (basename,
join,
splitext)
from ruamel import yaml
from rsmtool import HAS_RSMEXTRA
from rsmtool.utils import parse_json_with_comments
from rsmtool.utils import (DEFAULTS,
CHECK_FIELDS,
LIST_FIELDS,
BOOLEAN_FIELDS,
MODEL_NAME_MAPPING,
FIELD_NAME_MAPPING,
is_skll_model)
from skll import Learner
from skll.metrics import SCORERS
if HAS_RSMEXTRA:
from rsmextra.settings import (default_feature_subset_file,
default_feature_sign)
[docs]class Configuration:
"""
Configuration class, which encapsulates all of the
configuration parameters and methods to access these
parameters.
"""
def __init__(self, config_dict, filepath=None, context='rsmtool'):
"""
Create an object of the `Configuration` class.
Parameters
----------
config_dict : dict
A dictionary of configuration parameters.
filepath : str, optional
The path to the configuration file.
Defaults to None.
context : {'rsmtool', 'rsmeval', 'rsmcompare',
'rsmpredict', 'rsmsummarize'}
The context of the tool.
Defaults to 'rsmtool'.
"""
self._config = config_dict
self._filepath = filepath
self._context = context
def __contains__(self, key):
"""
Check if configuration object
contains a given key.
Parameters
----------
key : str
Key to check in the Configuration object.
Returns
-------
key_check : bool
True if key in Configuration object, else False
"""
return key in self._config
def __getitem__(self, key):
"""
Get value, 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 value, given key.
Parameters
----------
key : str
Key to check in the Configuration object.
value
A value to be set on the key.
"""
self._config[key] = value
def __len__(self):
"""
Return the length of the Configuration dictionary.
Returns
-------
length : int
The length of the config dictionary (i.e. number of elements)
"""
return len(self._config)
def __str__(self):
"""
Return string representation of the object keys
as comma-separated list.
Returns
-------
config_names : str
A comma-separated list of names from the config dictionary.
"""
return ', '.join(self._config)
def __iter__(self):
"""
Iterate through configuration object keys.
Yields
------
key
A key in the config dictionary
"""
for key in self.keys():
yield key
@property
def filepath(self):
"""
Get file path.
Returns
-------
filepath : str
The path for the config file.
"""
return self._filepath
@filepath.setter
def filepath(self, new_path):
"""
Set a new file path
Parameters
----------
new_path : str
A new file path for the Configuration object.
"""
self._filepath = new_path
@property
def context(self):
"""
Get the context.
"""
return self._context
[docs] def get(self, key, default=None):
"""
Get value or default, 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 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 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 save(self, output_dir=None):
"""
Save the configuration file to the output directory specified.
Parameters
----------
output_dir : str
The path to the output directory.
"""
# save a copy of the main config into the output directory
if output_dir is None:
output_dir = getcwd()
# Create output directory, if it does not exist
output_dir = join(output_dir, 'output')
makedirs(output_dir, exist_ok=True)
outjson = join(output_dir,
'{}_{}.json'.format(self._config['experiment_id'],
self._context))
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}
with open(outjson, 'w') as outfile:
json.dump(output_config, outfile, indent=4, separators=(',', ': '))
[docs] def check_exclude_listwise(self):
"""
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'):
"""
Make sure the `flag_column` field is in the correct format. Get
flag columns and values for filtering if any and convert single
values to lists. Raises an exception if `flag_column` is not
correctly specified.
Returns
-------
new_filtering_dict : dict
Properly formatted `flag_column` dictionary.
flag_column : {'flag_column', 'flag_column_test'}
The flag column to check.
Defaults to 'flag_column'.
Raises
------
ValueError
If the `flag_column` is not a dictionary
"""
config = self._config
new_filter_dict = {}
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]]
logging.warning("The filtering condition {}"
" for column {} was converted "
"to list. Only responses where "
"{} == {} will be used for "
"training and/or evaluating 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])
)
else:
new_filter_dict[column] = original_filter_dict[column]
model_eval = ', '.join(map(str,
original_filter_dict[column]))
logging.info("Only responses where "
"{} equals one of the following values "
"will be used for training and/or "
"evaluating the model: "
"{}.".format(column,
model_eval))
return new_filter_dict
[docs] def get_trim_min_max(self):
"""
Get the specified trim min and max, 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
"""
config = self._config
spec_trim_min = config.get('trim_min', None)
spec_trim_max = config.get('trim_max', None)
if spec_trim_min:
spec_trim_min = float(spec_trim_min)
if spec_trim_max:
spec_trim_max = float(spec_trim_max)
return (spec_trim_min, spec_trim_max)
[docs] def get_default_converter(self):
"""
Get the default converter dictionary for 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 a list of values, 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:
"""
A `ConfigurationParser` class to create a
`Configuration` object.
"""
def __init__(self):
# Set configuration object to None
self._config = None
self._filepath = None
def _check_config_is_loaded(self):
"""
Check to make sure a configuration file
or dictionary was loaded; otherwise,
raise ``NameError``.
Raises
------
NameError
If no configuration file or dictionary was loaded.
"""
if self._config is None:
raise NameError('No configuration file was loaded '
'Make sure to load a configuration file '
'from a dict using the `load_config_from_dict()` '
'method or use the `read_config_from_file()` method '
'with the appropriate sub-class object to read from '
'a file. You can use the `get_configparser` class '
'method to instantiate the appropriate sub-class '
'object for reading either `.json` or `.cfg` files.')
[docs] @classmethod
def get_configparser(cls, filepath, *args, **kwargs):
"""
Get the correct `ConfigurationParser` object,
based on the file extension.
Parameters
----------
filepath : str
The path to the configuration file.
Returns
-------
config : ConfigurationParser
The configuration parser object.
Raises
------
ValueError
If config file is not .json or .cfg.
"""
_, extension = splitext(filepath)
if extension.lower() not in CONFIG_TYPE:
raise ValueError('Configuration file must be '
'in either `.json` or `.cfg`'
'format. You specified: {}.'.format(extension))
return CONFIG_TYPE[extension.lower()](*args, **kwargs)
[docs] @staticmethod
def check_id_fields(id_field_values):
"""
Check whether the ID fields in the given dictionary
are properly formatted, i.e., they ::
- do not contain any spaces
- are shorter than 200 characters
Parameters
----------
id_field_values : dict
A dictionary containing the ID fields names
as the keys and the value from the configuration
file as the value.
Raises
------
ValueError
If the values for the ID fields in the given
dictionary are not formatted correctly.
"""
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))
[docs] def load_config_from_dict(self, config_dict):
"""
Load configuration dictionary.
Parameters
----------
config_dict : dict
A dictionary containing the configuration
parameters to parse.
Raises
------
TypeError
If `config_dict` is not a ``dict``
AttributeError
If config has already been assigned.
"""
if not isinstance(config_dict, dict):
raise TypeError('The `config_dict` must be a dictionary.')
if self._config is None:
self._config = config_dict
else:
raise AttributeError('A configuration dictionary has already'
'been assigned. You cannot assign another'
'dictionary.')
[docs] def read_config_from_file(self, filepath):
"""
Read the configuration file.
Parameters
----------
filepath : str
The path to the configuration file.
Raises
------
NotImplementedError
This method must be implemented in subclass.
"""
raise NotImplementedError("The method `read_config_from_file()` "
"is only implemented in the subclasses "
"``CFGConfigurationParser`` and "
"``JSONConfigurationParser``. "
"You can use the class method "
"`get_configparser()` to retrieve "
"the correct configuration parser object "
"for parsing JSON or CFG files.")
[docs] def normalize_config(self, inplace=True):
"""
Normalize the field names in `self._config` or `config` in order to
maintain backwards compatibility with old configuration files.
Parameters
----------
inplace : bool
Maintain the state of the config object produced by
this method.
Defaults to True.
Returns
-------
new_config : Configuration
A normalized configuration object
Raises
------
ValueError
If no JSON configuration object exists, or if value passed for
`use_scaled_predictions` is in the wrong format.
"""
# Check to make sure a configuration file
# or dictionary has been loaded.
self._check_config_is_loaded()
# Get the parameter dictionary
config = self._config
# Create a new JSON object with the normalized field names
new_config = {}
for field_name in config:
if field_name in FIELD_NAME_MAPPING:
norm_field_name = FIELD_NAME_MAPPING[field_name]
warnings.warn("""The field name "{}" is deprecated """
"""and will be removed in a future """
"""release, please use the """
"""new field name "{}" """
"""instead.""".format(field_name,
norm_field_name),
category=DeprecationWarning)
else:
norm_field_name = field_name
new_config[norm_field_name] = config[field_name]
# Convert old values for prediction scaling:
if 'use_scaled_predictions' in new_config:
if new_config['use_scaled_predictions'] in ['scale', True]:
new_config['use_scaled_predictions'] = True
elif new_config['use_scaled_predictions'] in ['raw', False]:
new_config['use_scaled_predictions'] = False
else:
raise ValueError("Please use the new format "
"to specify prediction scaling:\n "
"'use_scaled_predictions': true/false")
# Convert old model names to new ones, if we have them
if 'model' in new_config:
model_name = new_config['model']
if model_name == 'empWtDropNeg':
# If someone is using `empWtDropNeg`, we tell them that it is
# no longer available and they should be using NNLR instead.
logging.error("""The model name "empWtDropNeg" is """
"""no longer available, please use the """
"""equivalent model "NNLR" instead.""")
# Otherwise, just raise a deprecation warning if they are using
# an old model name
elif model_name in MODEL_NAME_MAPPING:
norm_model_name = MODEL_NAME_MAPPING[model_name]
warnings.warn("""The model name "{}" is deprecated """
"""and will be removed in a future """
"""release, please use the new model """
"""name "{}" instead.""".format(model_name,
norm_model_name),
category=DeprecationWarning)
new_config['model'] = norm_model_name
if inplace:
self._config = new_config
return Configuration(self._config, self._filepath)
[docs] def validate_config(self, context='rsmtool', inplace=True):
"""
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.
Possible values are ::
{'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.
"""
# Check to make sure a configuration file
# or dictionary has been loaded.
self._check_config_is_loaded()
# Get the parameter dictionary
new_config = self._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 formatted correctly
id_fields = ['comparison_id',
'experiment_id',
'summary_id']
id_field_values = {field: new_config[field] for field in new_config
if field in id_fields}
# we do not need to validate any IDs for `rsmpredict`
self.check_id_fields(id_field_values)
# 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 = basename(default_feature_subset_file)
new_config['feature_subset_file'] = default_feature_subset_file
logging.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 = basename(default_feature_subset_file)
new_config['feature_subset_file'] = default_feature_subset_file
logging.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']):
logging.warning("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 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
# 10. 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.")
# 11. 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']:
logging.warning("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.")
# 12. 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}
if inplace:
self._config = new_config
return Configuration(self._config, self._filepath)
[docs] def process_config(self, inplace=True):
"""
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 no config read.
"""
# Check to make sure a configuration file
# or dictionary has been loaded.
self._check_config_is_loaded()
# Get the parameter dictionary
new_config = self._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
if inplace:
self._config = new_config
return Configuration(self._config, self._filepath)
[docs] def normalize_validate_and_process_config(self, context='rsmtool'):
"""
Normalize, validate, and process data from a config file.
Parameters
----------
context : str, optional
Context of the tool in which we are validating.
Possible values are ::
{'rsmtool', 'rsmeval',
'rsmpredict', 'rsmcompare',
'rsmsummarize'}
Defaults to 'rsmtool'.
Returns
-------
config_obj : Configuration
A configuration object
Raises
-------
NameError
If config does not exist, or no config read.
"""
# Check to make sure a configuration file
# or dictionary has been loaded.
self._check_config_is_loaded()
self.normalize_config()
self.validate_config(context=context)
self.process_config()
return Configuration(self._config, self._filepath, context=context)
[docs] def read_normalize_validate_and_process_config(self,
filepath,
context='rsmtool'):
"""
Read, normalize, validate, and process data from a config file.
Parameters
----------
filepath : str
The path to the configuration file.
context : str, optional
Context of the tool in which we are validating.
Possible values are ::
{'rsmtool', 'rsmeval',
'rsmpredict', 'rsmcompare', 'rsmsummarize'}
Defaults to 'rsmtool'.
Returns
-------
config_obj : Configuration
A configuration object
"""
logging.info('Reading and preprocessing configuration file: {}'.format(filepath))
self.read_config_from_file(filepath)
return self.normalize_validate_and_process_config(context=context)
[docs]class JSONConfigurationParser(ConfigurationParser):
"""
A subclass of `ConfigurationParser` for parsing
JSON-style config files.
"""
def __init__(self):
super().__init__()
[docs] def read_config_from_file(self, filepath):
"""
Read the configuration file.
Parameters
----------
filepath : str
The path to the configuration file.
Raises
------
ValueError
If main configuration file is improperly formatted.
"""
try:
config = 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))
self._config = config
self._filepath = filepath
[docs]class CFGConfigurationParser(ConfigurationParser):
"""
A subclass of `ConfiguraitonParser` for parsing
Microsoft INI-style config files.
"""
def __init__(self):
super().__init__()
@staticmethod
def _fix_json(json_string):
"""
Takes 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
[docs] def read_config_from_file(self, filepath):
"""
Read the configuration file.
Parameters
----------
filepath : str
The path to the configuration file.
Raises
------
ValueError
If main configuration file is improperly formatted.
"""
# Get the `ConfigParser` object
py_config_parser = ConfigParser()
# Try to read main configuration file.
try:
py_config_parser.read(filepath)
except Exception as error:
raise ValueError('Main configuration file '
'could not be read: {}'.format(error))
config = {}
# Loop through all sections of the ConfigParser
# object and add items to the dictionary
for section in py_config_parser.sections():
for name, value in py_config_parser.items(section):
# Check if the key already exists in the config dictionary.
# If it does, skip it and log a warning.
if name in config:
logging.warning('There are duplicate keys for `{}`'
'in the configuration file. Only '
'the first instance will be '
'included.'.format(name))
continue
# Otherwise, safe convert the value
# and add it to the dictionary.
else:
value = self._fix_json(value)
value = yaml.safe_load(value)
config[name] = value
self._config = config
self._filepath = filepath
# Global config types
CONFIG_TYPE = {'.cfg': CFGConfigurationParser,
'.json': JSONConfigurationParser}