# -*- coding: utf-8 -*-
""" Predicates recommendation service. """
from typing import Any
from orkgnlp.clustering.encoders import TransformerKmeansEncoder
from orkgnlp.clustering.predicates.decoder import PredicatesRecommenderDecoder
from orkgnlp.common.config import orkgnlp_context
from orkgnlp.common.service.base import (
ORKGNLPBaseDecoder,
ORKGNLPBaseEncoder,
ORKGNLPBaseRunner,
ORKGNLPBaseService,
)
from orkgnlp.common.service.runners import ORKGNLPONNXRunner
from orkgnlp.common.util import io
[docs]
class PredicatesRecommender(ORKGNLPBaseService):
"""
The PredicatesRecommender requires a clustering model, vectorizer, training set and predicates.
The required files are downloaded while initiation, if it has not happened before.
You can pass the parameter ``force_download=True`` to remove and re-download the previous downloaded service files.
"""
SERVICE_NAME = "predicates-clustering"
def __init__(self, *args: Any, **kwargs: Any):
super().__init__(self.SERVICE_NAME, *args, **kwargs)
requirements = self._config.requirements
encoder: ORKGNLPBaseEncoder = TransformerKmeansEncoder("allenai/scibert_scivocab_uncased")
runner: ORKGNLPBaseRunner = ORKGNLPONNXRunner(io.read_onnx(requirements["model"]))
decoder: ORKGNLPBaseDecoder = PredicatesRecommenderDecoder(
io.read_df_from_json(requirements["training_data"], key="instances"),
io.read_json(requirements["mapping"]),
)
self._register_pipeline("main", encoder, runner, decoder)
[docs]
def __call__(self, title: str, abstract: str):
"""
Recommends predicates for a research paper.
:param title: Title of the research paper.
:param abstract: Abstract of the research paper.
:return: List of predicates.
"""
return self._run(
raw_input="{} {}".format(title, abstract), output_names=["label", "labels_"]
)
orkgnlp_context.get("SERVICE_MAP")[PredicatesRecommender.SERVICE_NAME] = PredicatesRecommender