Bootstrapping hypothesis testing of distribution equality using Efron's alghoritm

bootstrap_compare_dist.py

from typing import Tuple

import numpy as np
import time
import numba
from scipy.stats import mannwhitneyu


@numba.njit(parallel=True, fastmath=True, nogil=True)
def compare_dist(z: np.ndarray, y: np.ndarray, n_samples: int = 10_000) -> Tuple[np.ndarray, float, float]:
    n = z.shape[0]
    m = y.shape[0]
    x = np.concatenate((z, y))

    t_obs = z.mean() - y.mean()
    t = np.zeros(n_samples)
    for i in numba.prange(n_samples):
        x_ = np.random.choice(x, n + m)
        t[i] = x_[:n].mean() - x_[m:].mean()
    return t, t_obs, float(np.sum(np.greater_equal(t, t_obs)) / n_samples)

np.random.seed(42)

size = 100_000

z = np.random.normal(0.00005, 0.5, size)
y = np.random.normal(0, 0.5, size)
n_samples = 10_000

start = time.time()
t = compare_dist(z, y, n_samples)
print(f'p-value: {t[2]}')
end = time.time()
print(end - start)

start = time.time()
t = mannwhitneyu(z, y)
print(f'p-value: {t[1]}')
end = time.time()
print(end - start)

# Numba debug
# bootstrap.compare_dist.parallel_diagnostics(level=4)
# bootstrap.compare_dist.inspect_types()