FeryET

docs_train, docs_test, y_train, y_test = train_test_split(docs, y, test_size=0.1, shuffle=True)

FeryET

labels = ["rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey"]
raw_docs, y = fetch_20newsgroups(subset='all', return_X_y=True, categories=labels)

FeryET

import logging

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import (RepeatedStratifiedKFold, cross_val_score, )
from sklearn.pipeline import Pipeline

FeryET

processor = SpacyCleaner(chunksize=1000, workers=workers)
docs = processor.transform(raw_docs)

FeryET

def foo(func, *args, **kwargs):
    res = func(*args, **kwargs)
    return res["a key"]["in json"]["response"]

class A:
    def __init__(self, api):
        super().__init__()
        self.api = api

    def get_x(self):