bench_label_encoder_noop.py

from __future__ import print_function
import random
from timeit import timeit
from functools import partial
import numpy as np
from sklearn.preprocessing import LabelEncoder

N_TRIALS = 3
N_SAMPLES = 10000
MULTILABEL = True


for N_CLASSES in range(2, 50):
    if MULTILABEL:
        classes = np.arange(N_CLASSES)
        y_trivial = [random.sample(classes, random.randint(0, N_CLASSES))
                     for i in range(N_SAMPLES)]
        y_lookup = y_trivial[:]
        y_lookup[0] = [-1]
    else:
        y_trivial = np.random.randint(N_CLASSES, size=N_SAMPLES)
        y_lookup = y_trivial.copy()
        y_lookup[0] = -1
    print(N_CLASSES,
          *[timeit(partial(LabelEncoder().fit(y).transform, y),
                   number=N_TRIALS) / N_TRIALS
            for y in [y_trivial, y_lookup]], sep='\t')