mirror of
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109 lines
5.8 KiB
Python
109 lines
5.8 KiB
Python
#!/usr/bin/env python3
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import os
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import sys
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from math import sqrt, nan
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from random import randrange
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from scipy import stats
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import pandas as pd
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import numpy as np
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CURDIR = os.path.dirname(os.path.realpath(__file__))
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sys.path.insert(0, os.path.join(CURDIR, 'helpers'))
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from pure_http_client import ClickHouseClient
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# unpooled variance z-test for proportions of two samples
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def twosample_proportion_ztest(s1, s2, t1, t2, alpha):
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if s1 == 0 or s2 == 0 or s1 > t1 or s2 > t2 or t1 + t2 == 0:
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return nan, nan, nan, nan
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p1 = s1 / t1
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p2 = s2 / t2
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se = sqrt(p1 * (1 - p1) / t1 + p2 * (1 - p2) / t2)
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if se == 0:
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return nan, nan, nan, nan
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z_stat = (p1 - p2) / se
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one_side = 1 - stats.norm.cdf(abs(z_stat))
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p_value = one_side * 2
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z = stats.norm.ppf(1 - 0.5 * alpha)
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ci_lower = (p1 - p2) - z * se
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ci_upper = (p1 - p2) + z * se
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return z_stat, p_value, ci_lower, ci_upper
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def test_and_check(name, z_stat, p_value, ci_lower, ci_upper, precision=1e-2):
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client = ClickHouseClient()
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real = client.query_return_df(
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"SELECT roundBankers({}.1, 16) as z_stat, ".format(name) +
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"roundBankers({}.2, 16) as p_value, ".format(name) +
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"roundBankers({}.3, 16) as ci_lower, ".format(name) +
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"roundBankers({}.4, 16) as ci_upper ".format(name) +
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"FORMAT TabSeparatedWithNames;")
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real_z_stat = real['z_stat'][0]
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real_p_value = real['p_value'][0]
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real_ci_lower = real['ci_lower'][0]
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real_ci_upper = real['ci_upper'][0]
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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)
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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)
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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)
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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)
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def test_mean_ztest():
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counts = [0, 0]
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nobs = [0, 0]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(10, 10, 10, 10, 0.05)
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counts = [10, 10]
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nobs = [10, 10]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(10, 10, 10, 10, 0.05)
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counts = [16, 16]
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nobs = [16, 18]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [10, 20]
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nobs = [30, 40]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [20, 10]
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nobs = [40, 30]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [randrange(10,20), randrange(10,20)]
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nobs = [randrange(counts[0] + 1, counts[0] * 2), randrange(counts[1], counts[1] * 2)]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [randrange(1,100), randrange(1,200)]
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nobs = [randrange(counts[0], counts[0] * 2), randrange(counts[1], counts[1] * 3)]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [randrange(1,200), randrange(1,100)]
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nobs = [randrange(counts[0], counts[0] * 3), randrange(counts[1], counts[1] * 2)]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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counts = [randrange(1,1000), randrange(1,1000)]
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nobs = [randrange(counts[0], counts[0] * 2), randrange(counts[1], counts[1] * 2)]
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z_stat, p_value, ci_lower, ci_upper = twosample_proportion_ztest(counts[0], counts[1], nobs[0], nobs[1], 0.05)
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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)
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if __name__ == "__main__":
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test_mean_ztest()
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print("Ok.")
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