def ilp_rsd(input_dict):
rsd = RSD()
settings = input_dict.get('settings',None)
pos = input_dict.get('pos', None)
neg = input_dict.get('neg', None)
examples = input_dict.get('examples', None)
b = input_dict['b']
subgroups = input_dict['subgroups'] == 'true'
# Parse settings
if settings:
rsd.settingsAsFacts(settings)
# Parse settings provided as parameters (these have higher priority)
for setting, def_val in RSD.ESSENTIAL_PARAMS.items():
rsd.set(setting, input_dict.get(setting, def_val))
# Check for illegal predicates
for pl_script in [b, pos, neg, examples]:
check_input(pl_script)
# Run rsd
features, arff, rules = rsd.induce(b, examples=examples, pos=pos, neg=neg, cn2sd=subgroups)
return {'features' : features, 'arff' : arff, 'rules' : rules}
def ilp_rsd(input_dict):
rsd = RSD()
settings = input_dict.get('settings',None)
pos = input_dict.get('pos', None)
neg = input_dict.get('neg', None)
examples = input_dict.get('examples', None)
b = input_dict['b']
subgroups = input_dict['subgroups'] == 'true'
# Parse settings
if settings:
rsd.settingsAsFacts(settings)
# Parse settings provided as parameters (these have higher priority)
for setting, def_val in RSD.ESSENTIAL_PARAMS.items():
rsd.set(setting, input_dict.get(setting, def_val))
# Check for illegal predicates
for pl_script in [b, pos, neg, examples]:
check_input(pl_script)
# Run rsd
features, arff, rules = rsd.induce(b, examples=examples, pos=pos, neg=neg, cn2sd=subgroups)
return {'features' : features, 'arff' : arff, 'rules' : rules}
def rsd_runner(
examples, background_knowledge, pos_examples=None, neg_examples=None, settings=None, subgroups=False,
clauselength=RSD.ESSENTIAL_PARAMS['clauselength'],
depth=RSD.ESSENTIAL_PARAMS['depth'],
negation=RSD.ESSENTIAL_PARAMS['negation'],
min_coverage=RSD.ESSENTIAL_PARAMS['min_coverage'],
filtering=RSD.ESSENTIAL_PARAMS['filtering']
):
rsd = RSD()
if settings:
rsd.settingsAsFacts(settings)
rsd.set('clauselength', clauselength)
rsd.set('depth', depth)
rsd.set('negation', negation)
rsd.set('min_coverage', min_coverage)
rsd.set('filtering', filtering)
features, arff, rules = rsd.induce(background_knowledge, examples=examples, pos=pos_examples, neg=neg_examples, cn2sd=subgroups)
return {'arff' : arff, 'rules' : rules, 'features' : features}
