import spacy
import spacy_ke

# load spacy model
nlp = spacy.load("en_core_web_sm")

# spacy v3.0.x factory.
# if you're using spacy v2.x.x swich to `nlp.add_pipe(spacy_ke.Yake(nlp))`
nlp.add_pipe("yake")

doc = nlp(
    "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence "
    "concerned with the interactions between computers and human language, in particular how to program computers "
    "to process and analyze large amounts of natural language data. "
)

for keyword, score in doc._.extract_keywords(n=3):
    print(keyword, "-", score)

window: int = 2  # default
lemmatize: bool = False  # default
candidate_selection: str = "ngram"  # default, use "chunk" for noun phrase selection.

nlp.add_pipe(
    Yake(
        nlp,
        window=window,  # default
        lemmatize=lemmatize,  # default
        candidate_selection="ngram"  # default, use "chunk" for noun phrase selection
    )
)

from typing import Iterable
from spacy.tokens import Doc
from spacy_ke.util import registry, Candidate


@registry.candidate_selection.register("custom")
def custom_selection(doc: Doc, n=3) -> Iterable[Candidate]:
    ...


nlp.add_pipe(
    Yake(
        nlp,
        candidate_selection="custom"
    )
)