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from __future__ import unicode_literals
import json
from snips_inference_agl.common.utils import json_string
from snips_inference_agl.preprocessing import tokenize
from snips_inference_agl.result import unresolved_slot
from snips_inference_agl.slot_filler import SlotFiller
@SlotFiller.register("keyword_slot_filler")
class KeywordSlotFiller(SlotFiller):
def __init__(self, config=None, **shared):
super(KeywordSlotFiller, self).__init__(config, **shared)
self.slots_keywords = None
self.language = None
@property
def fitted(self):
return self.slots_keywords is not None
def fit(self, dataset, intent):
self.language = dataset["language"]
self.slots_keywords = dict()
utterances = dataset["intents"][intent]["utterances"]
for utterance in utterances:
for chunk in utterance["data"]:
if "slot_name" in chunk:
text = chunk["text"]
if self.config.get("lowercase", False):
text = text.lower()
self.slots_keywords[text] = [
chunk["entity"],
chunk["slot_name"]
]
return self
def get_slots(self, text):
tokens = tokenize(text, self.language)
slots = []
for token in tokens:
normalized_value = token.value
if self.config.get("lowercase", False):
normalized_value = normalized_value.lower()
if normalized_value in self.slots_keywords:
entity = self.slots_keywords[normalized_value][0]
slot_name = self.slots_keywords[normalized_value][1]
slot = unresolved_slot((token.start, token.end), token.value,
entity, slot_name)
slots.append(slot)
return slots
def persist(self, path):
model = {
"language": self.language,
"slots_keywords": self.slots_keywords,
"config": self.config.to_dict()
}
with path.open(mode="w", encoding="utf8") as f:
f.write(json_string(model))
@classmethod
def from_path(cls, path, **shared):
with path.open() as f:
model = json.load(f)
slot_filler = cls()
slot_filler.language = model["language"]
slot_filler.slots_keywords = model["slots_keywords"]
slot_filler.config = cls.config_type.from_dict(model["config"])
return slot_filler
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