def __init__(self, config=None, **kwargs):
super(IntegralGrader, self).__init__(config, **kwargs)
self.true_input_positions = self.validate_input_positions(
self.config['input_positions'])
# The below are copied from FormulaGrader.__init__
# Set up the various lists we use
self.functions, self.random_funcs = construct_functions(
self.config["whitelist"], self.config["blacklist"],
self.config["user_functions"])
self.constants = construct_constants(self.config["user_constants"])
# TODO I would like to move this into construct_constants at some point,
# perhaps giving construct_constants and optional argument specifying additional defaults
if 'infty' not in self.constants:
self.constants['infty'] = float('inf')
# Construct the schema for sample_from
# First, accept all VariableSamplingSets
# Then, accept any list that RealInterval can interpret
# Finally, single numbers or tuples of numbers will be handled by DiscreteSet
schema_sample_from = Schema({
Required(varname, default=RealInterval()):
Any(VariableSamplingSet, All(list,
lambda pair: RealInterval(pair)),
lambda tup: DiscreteSet(tup))
for varname in self.config['variables']
})
self.config['sample_from'] = schema_sample_from(
self.config['sample_from'])
def validate_math_config(self):
"""Performs generic math configuration validation"""
validate_blacklist_whitelist_config(self.default_functions,
self.config['blacklist'],
self.config['whitelist'])
# Make a copy of self.default_variables, so we don't change the base version
self.default_variables = self.default_variables.copy()
# Remove any deleted user constants from self.default_variables
remove_keys = [
key for key in self.config['user_constants']
if self.config['user_constants'][key] is None
]
for entry in remove_keys:
if entry in self.default_variables:
del self.default_variables[entry]
del self.config['user_constants'][entry]
warn_if_override(self.config, 'variables', self.default_variables)
warn_if_override(self.config, 'numbered_vars', self.default_variables)
warn_if_override(self.config, 'user_constants', self.default_variables)
warn_if_override(self.config, 'user_functions', self.default_functions)
validate_no_collisions(self.config,
keys=['variables', 'user_constants'])
self.permitted_functions = get_permitted_functions(
self.default_functions, self.config['whitelist'],
self.config['blacklist'], self.config['user_functions'])
# Set up the various lists we use
self.functions, self.random_funcs = construct_functions(
self.default_functions, self.config["user_functions"])
self.constants = construct_constants(self.default_variables,
self.config["user_constants"])
self.suffixes = construct_suffixes(self.default_suffixes,
self.config["metric_suffixes"])
# Construct the schema for sample_from
# First, accept all VariableSamplingSets
# Then, accept any list that RealInterval can interpret
# Finally, single numbers or tuples of numbers will be handled by DiscreteSet
schema_sample_from = Schema({
Required(varname, default=RealInterval()):
Any(VariableSamplingSet, All(list, Coerce(RealInterval)),
Coerce(DiscreteSet))
for varname in (self.config['variables'] +
self.config['numbered_vars'])
})
self.config['sample_from'] = schema_sample_from(
self.config['sample_from'])
def __init__(self, config=None, **kwargs):
"""
Validate the FormulaGrader's configuration.
First, we allow the ItemGrader initializer to construct the function list.
We then construct the lists of functions, suffixes and constants.
Finally, we refine the sample_from entry.
"""
super(FormulaGrader, self).__init__(config, **kwargs)
# finish validating
validate_blacklist_whitelist_config(self.default_functions,
self.config['blacklist'],
self.config['whitelist'])
validate_no_collisions(self.config,
keys=['variables', 'user_constants'])
warn_if_override(self.config, 'variables', self.default_variables)
warn_if_override(self.config, 'numbered_vars', self.default_variables)
warn_if_override(self.config, 'user_constants', self.default_variables)
warn_if_override(self.config, 'user_functions', self.default_functions)
self.permitted_functions = get_permitted_functions(
self.default_functions, self.config['whitelist'],
self.config['blacklist'], self.config['user_functions'])
# store the comparer utils
self.comparer_utils = self.get_comparer_utils()
# Set up the various lists we use
self.functions, self.random_funcs = construct_functions(
self.default_functions, self.config["user_functions"])
self.constants = construct_constants(self.default_variables,
self.config["user_constants"])
self.suffixes = construct_suffixes(self.default_suffixes,
self.config["metric_suffixes"])
# Construct the schema for sample_from
# First, accept all VariableSamplingSets
# Then, accept any list that RealInterval can interpret
# Finally, single numbers or tuples of numbers will be handled by DiscreteSet
schema_sample_from = Schema({
Required(varname, default=RealInterval()):
Any(VariableSamplingSet, All(list, Coerce(RealInterval)),
Coerce(DiscreteSet))
for varname in (self.config['variables'] +
self.config['numbered_vars'])
})
self.config['sample_from'] = schema_sample_from(
self.config['sample_from'])
def __init__(self, config=None, **kwargs):
super(IntegralGrader, self).__init__(config, **kwargs)
self.true_input_positions = self.validate_input_positions(
self.config['input_positions'])
# The below are copied from FormulaGrader.__init__
validate_blacklist_whitelist_config(self.default_functions,
self.config['blacklist'],
self.config['whitelist'])
validate_no_collisions(self.config,
keys=['variables', 'user_constants'])
warn_if_override(self.config, 'variables', self.default_variables)
warn_if_override(self.config, 'user_constants', self.default_variables)
warn_if_override(self.config, 'user_functions', self.default_functions)
self.permitted_functions = get_permitted_functions(
self.default_functions, self.config['whitelist'],
self.config['blacklist'], self.config['user_functions'])
self.functions, self.random_funcs = construct_functions(
self.default_functions, self.config["user_functions"])
self.constants = construct_constants(self.default_variables,
self.config["user_constants"])
# Construct the schema for sample_from
# First, accept all VariableSamplingSets
# Then, accept any list that RealInterval can interpret
# Finally, single numbers or tuples of numbers will be handled by DiscreteSet
schema_sample_from = Schema({
Required(varname, default=RealInterval()):
Any(VariableSamplingSet, All(list, Coerce(RealInterval)),
Coerce(DiscreteSet))
for varname in self.config['variables']
})
self.config['sample_from'] = schema_sample_from(
self.config['sample_from'])