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https://github.com/hexastack/hexabot
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92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
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from .json_helper import JsonHelper
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"""
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Transform data set from Rasa structure to a compliant one
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How to use:
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from utils.jisf_data_mapper import JisfDataMapper
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mapper = JisfDataMapper()
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#mapper.transform_to_new("train.json")
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mapper.transform_to_new("test.json")
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"""
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class JisfDataMapper(object):
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def transform_to_new(self, filename: str, reverse: bool = False):
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"""this method allows for changing a file's data format."""
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helper=JsonHelper()
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data = helper.read_dataset_json_file(filename)
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copy_file = "copy of "+filename
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# we create a copy of the old data format
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helper.write_dataset_json_file(data, copy_file)
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# alternatively, we could use this method in the opposite direction
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if not reverse:
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data = self.old_to_new(data)
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else:
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data = self.new_to_old(data)
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helper.write_dataset_json_file(data, filename)
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def old_to_new(self,data:dict):
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converted_data=dict()
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converted_data["common_examples"]=[]
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all_intents=set()
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all_slots=dict()
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for k in data.keys():
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common_example=dict()
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#text and intent are the same in both formats
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common_example["text"]=data[k]["text"]
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common_example["intent"]=data[k]["intent"]
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common_example["entities"]=[]
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all_intents.add(common_example["intent"])
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#for every entity, we get its corresponding value as well as the index of its
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#start and finish
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for slot in data[k]["slots"].keys():
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all_slots[slot]=all_slots.get(slot,set())
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entity=dict()
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entity["entity"]=slot
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entity["value"]=data[k]["slots"][slot]
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all_slots[slot].add(entity["value"])
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entity["start"],entity["end"]=tuple(data[k]["positions"][slot])
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common_example["entities"].append(entity)
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converted_data["common_examples"].append(common_example)
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#lookup tables store all the intents as well as all the slot values seen in the dataset
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converted_data["lookup_tables"]=[]
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all_slots["intent"]=all_intents
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for name,value in all_slots.items():
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converted_data["lookup_tables"].append({"name":name,"elements":list(value)})
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#regex features and entity synonyms will remain empty for now
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converted_data["regex_features"]=[]
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converted_data["entity_synonyms"]=[]
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return converted_data
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def new_to_old(self,data:dict):
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old_data=dict()
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dataset=data["common_examples"]
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#for each piece of text, we make a JSON object.
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for i in range(len(dataset)):
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item=dict()
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item["text"]=dataset[i]["text"]
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item["intent"]=dataset[i]["intent"]
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item["slots"]=dict()
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item["positions"]=dict()
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for entity in dataset[i]["entities"]:
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item["slots"][entity["entity"]]=entity["value"]
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item["positions"][entity["entity"]]=[entity["start"],entity["end"]]
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old_data[i]=item
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return old_data
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