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run_for_subject.py
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run_for_subject.py
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import time
from rq import get_current_job
from quasimodo.data_structures.inputs import Inputs
from quasimodo.data_structures.module_reference_interface import ModuleReferenceInterface
from quasimodo.data_structures.subject import Subject
from quasimodo.default_submodule_factory import DefaultSubmoduleFactory
def run_for_subject(subject):
job = get_current_job()
factory = DefaultSubmoduleFactory()
submodule_generation_names = [
"google-autocomplete",
"bing-autocomplete",
"yahoo-questions",
"answerscom-questions",
"quora-questions",
"reddit-questions",
"fact-combinor",
]
submodule_normalization_names = [
"lower-case",
"tbc-cleaner",
"only-subject",
"filter-object",
"no-personal",
"singular-subject",
"cleaning-predicate",
"basic-modality",
"present-continuous",
"are-transformation",
"can-transformation",
"be-normalization",
"identical-subj-obj",
"present-conjugate"
]
submodule_normalization_global_names = [
"similar-object-remover",
"fact-combinor"
]
submodule_validation_names = [
"google-book",
"flickr-clusters",
"imagetag",
"wikipedia-cooccurrence",
"simple-wikipedia-cooccurrence",
"conceptual-captions",
"what-questions"
]
empty_input = Inputs()
empty_input = empty_input.add_subjects({Subject(subject.lower())})
module_reference = ModuleReferenceInterface("")
pattern_submodule = factory.get_submodule("manual-patterns-google", module_reference)
empty_input = pattern_submodule.process(empty_input)
result = []
result.append(dict())
result[-1]["step name"] = "Assertion Generation"
result[-1]["steps"] = []
job.meta = result
job.save_meta()
generated_facts = []
for submodule_name in submodule_generation_names:
submodule = factory.get_submodule(submodule_name, module_reference)
begin_time = time.time()
input_temp = submodule.process(empty_input)
generated_facts += input_temp.get_generated_facts()
step_info = dict()
step_info["name"] = submodule.get_name()
step_info["facts"] = [x.to_dict() for x in input_temp.get_generated_facts()]
step_info["time"] = time.time() - begin_time
result[-1]["steps"].append(step_info)
job.meta = result
job.save_meta()
new_input = empty_input.add_generated_facts(generated_facts)
result.append(dict())
result[-1]["step name"] = "Assertion Normalization"
result[-1]["steps"] = []
for submodule_name in submodule_normalization_names:
submodule = factory.get_submodule(submodule_name, module_reference)
step_info = dict()
begin_time = time.time()
step_info["name"] = submodule.get_name()
step_info["modifications"] = []
for generated_fact in new_input.get_generated_facts():
input_temp = empty_input.add_generated_facts([generated_fact])
input_temp = submodule.process(input_temp)
if len(input_temp.get_generated_facts()) != 1 or input_temp.get_generated_facts()[0] != generated_fact:
modification = {
"from": generated_fact.to_dict(),
"to": [x.to_dict() for x in input_temp.get_generated_facts()]
}
step_info["modifications"].append(modification)
step_info["time"] = time.time() - begin_time
result[-1]["steps"].append(step_info)
job.meta = result
job.save_meta()
new_input = submodule.process(new_input)
result.append(dict())
result[-1]["step name"] = "Assertion Normalization Global"
result[-1]["steps"] = []
for submodule_name in submodule_normalization_global_names:
submodule = factory.get_submodule(submodule_name, module_reference)
begin_time = time.time()
new_input = submodule.process(new_input)
step_info = dict()
step_info["name"] = submodule.get_name()
step_info["facts"] = [x.to_dict() for x in new_input.get_generated_facts()]
step_info["time"] = time.time() - begin_time
result[-1]["steps"].append(step_info)
job.meta = result
job.save_meta()
result.append(dict())
result[-1]["step name"] = "Assertion Validation"
result[-1]["steps"] = []
begin_time = time.time()
for submodule_name in submodule_validation_names:
submodule = factory.get_submodule(submodule_name, module_reference)
new_input = submodule.process(new_input)
step_info = dict()
step_info["name"] = "All validations"
step_info["facts"] = [x.to_dict() for x in new_input.get_generated_facts()]
step_info["time"] = time.time() - begin_time
result[-1]["steps"].append(step_info)
job.meta = result
job.save_meta()