MattEding

Namespace(file=None, n_jobs=4, n_neighbors=5, sampling_strategy='auto', trials=3)
1 ecoli
	0.6515465679999999
2 optical_digits
	1.081901593
3 satimage
	0.757048266
4 pen_digits
	0.7772778269999998
5 abalone

MattEding

import argparse
import functools

import numpy as np
import pandas as pd

from imblearn.datasets import fetch_datasets
import imblearn.datasets._zenodo as zenodo
from imblearn.metrics import specificity_score
from imblearn.over_sampling import SMOTE

MattEding

from time import time

import numpy as np
from scipy import sparse

from imblearn.over_sampling import SMOTE


def benchmark(sampler, X, y):
    imb = np.unique(y, return_counts=True)

MattEding

from collections import namedtuple

from bs4 import BeautifulSoup
import requests


Time = namedtuple('Time', 'hr min sec')

def playlist_duration(url, speed=1):
    source = requests.get(url)

MattEding

import collections
import itertools
import json
import os
import random


SEPARATOR = '=' * 25 + '\n'
DEFAULT = collections.defaultdict(str)

MattEding

import os
import re
import PyPDF2


def split_eurika_book(pdf_path, page_list, *, name_format='G{grade} - M{module} L{lesson}.pdf'):
    """Splits a PDF of a Eurika Math book into separate sections based on
    a list of page numbers provided.
    """
    pattern = re.compile(r'[gG](\d+).[mM](\d+)')