mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-12-11 00:42:29 +00:00
209 lines
6.3 KiB
Python
209 lines
6.3 KiB
Python
#!/usr/bin/env python3
|
|
import os
|
|
import sys
|
|
from math import sqrt, nan
|
|
from random import randrange
|
|
from scipy import stats
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
CURDIR = os.path.dirname(os.path.realpath(__file__))
|
|
sys.path.insert(0, os.path.join(CURDIR, "helpers"))
|
|
|
|
from pure_http_client import ClickHouseClient
|
|
|
|
|
|
# unpooled variance z-test for proportions of two samples
|
|
def twosample_proportion_ztest(s1, s2, t1, t2, alpha):
|
|
if s1 == 0 or s2 == 0 or s1 > t1 or s2 > t2 or t1 + t2 == 0:
|
|
return nan, nan, nan, nan
|
|
|
|
p1 = s1 / t1
|
|
p2 = s2 / t2
|
|
se = sqrt(p1 * (1 - p1) / t1 + p2 * (1 - p2) / t2)
|
|
if se == 0:
|
|
return nan, nan, nan, nan
|
|
z_stat = (p1 - p2) / se
|
|
|
|
one_side = 1 - stats.norm.cdf(abs(z_stat))
|
|
p_value = one_side * 2
|
|
|
|
z = stats.norm.ppf(1 - 0.5 * alpha)
|
|
ci_lower = (p1 - p2) - z * se
|
|
ci_upper = (p1 - p2) + z * se
|
|
|
|
return z_stat, p_value, ci_lower, ci_upper
|
|
|
|
|
|
def test_and_check(name, z_stat, p_value, ci_lower, ci_upper, precision=1e-2):
|
|
client = ClickHouseClient()
|
|
real = client.query_return_df(
|
|
"SELECT roundBankers({}.1, 16) as z_stat, ".format(name)
|
|
+ "roundBankers({}.2, 16) as p_value, ".format(name)
|
|
+ "roundBankers({}.3, 16) as ci_lower, ".format(name)
|
|
+ "roundBankers({}.4, 16) as ci_upper ".format(name)
|
|
+ "FORMAT TabSeparatedWithNames;"
|
|
)
|
|
real_z_stat = real["z_stat"][0]
|
|
real_p_value = real["p_value"][0]
|
|
real_ci_lower = real["ci_lower"][0]
|
|
real_ci_upper = real["ci_upper"][0]
|
|
assert (np.isnan(real_z_stat) and np.isnan(z_stat)) or abs(
|
|
real_z_stat - np.float64(z_stat)
|
|
) < precision, "clickhouse_z_stat {}, py_z_stat {}".format(real_z_stat, z_stat)
|
|
assert (np.isnan(real_p_value) and np.isnan(p_value)) or abs(
|
|
real_p_value - np.float64(p_value)
|
|
) < precision, "clickhouse_p_value {}, py_p_value {}".format(real_p_value, p_value)
|
|
assert (np.isnan(real_ci_lower) and np.isnan(ci_lower)) or abs(
|
|
real_ci_lower - np.float64(ci_lower)
|
|
) < precision, "clickhouse_ci_lower {}, py_ci_lower {}".format(
|
|
real_ci_lower, ci_lower
|
|
)
|
|
assert (np.isnan(real_ci_upper) and np.isnan(ci_upper)) or abs(
|
|
real_ci_upper - np.float64(ci_upper)
|
|
) < precision, "clickhouse_ci_upper {}, py_ci_upper {}".format(
|
|
real_ci_upper, ci_upper
|
|
)
|
|
|
|
|
|
def test_mean_ztest():
|
|
counts = [0, 0]
|
|
nobs = [0, 0]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
10, 10, 10, 10, 0.05
|
|
)
|
|
|
|
counts = [10, 10]
|
|
nobs = [10, 10]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
10, 10, 10, 10, 0.05
|
|
)
|
|
|
|
counts = [16, 16]
|
|
nobs = [16, 18]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [10, 20]
|
|
nobs = [30, 40]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [20, 10]
|
|
nobs = [40, 30]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [randrange(10, 20), randrange(10, 20)]
|
|
nobs = [
|
|
randrange(counts[0] + 1, counts[0] * 2),
|
|
randrange(counts[1], counts[1] * 2),
|
|
]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [randrange(1, 100), randrange(1, 200)]
|
|
nobs = [randrange(counts[0], counts[0] * 2), randrange(counts[1], counts[1] * 3)]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [randrange(1, 200), randrange(1, 100)]
|
|
nobs = [randrange(counts[0], counts[0] * 3), randrange(counts[1], counts[1] * 2)]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
counts = [randrange(1, 1000), randrange(1, 1000)]
|
|
nobs = [randrange(counts[0], counts[0] * 2), randrange(counts[1], counts[1] * 2)]
|
|
z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(
|
|
counts[0], counts[1], nobs[0], nobs[1], 0.05
|
|
)
|
|
test_and_check(
|
|
"proportionsZTest(%d, %d, %d, %d, 0.95, 'unpooled')"
|
|
% (counts[0], counts[1], nobs[0], nobs[1]),
|
|
z_stat,
|
|
p_value,
|
|
ci_lower,
|
|
ci_upper,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_mean_ztest()
|
|
print("Ok.")
|