mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-09-20 00:30:49 +00:00
94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
#!/usr/bin/env python3
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import os
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import sys
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from statistics import variance
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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 means of two samples
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def scipy_anova(rvs):
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return stats.f_oneway(*rvs)
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def test_and_check(rvs, n_groups, f_stat, p_value, precision=1e-2):
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client = ClickHouseClient()
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client.query("DROP TABLE IF EXISTS anova;")
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client.query("CREATE TABLE anova (left Float64, right UInt64) ENGINE = Memory;")
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for group in range(n_groups):
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client.query(
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f"""INSERT INTO anova VALUES {", ".join([f'({i},{group})' for i in rvs[group]])};"""
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)
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real = client.query_return_df(
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"""SELECT roundBankers(a.1, 16) as f_stat, roundBankers(a.2, 16) as p_value FROM (SELECT anova(left, right) as a FROM anova) FORMAT TabSeparatedWithNames;"""
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)
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real_f_stat = real["f_stat"][0]
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real_p_value = real["p_value"][0]
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assert (
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abs(real_f_stat - np.float64(f_stat)) < precision
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), f"clickhouse_f_stat {real_f_stat}, py_f_stat {f_stat}"
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assert (
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abs(real_p_value - np.float64(p_value)) < precision
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), f"clickhouse_p_value {real_p_value}, py_p_value {p_value}"
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client.query("DROP TABLE IF EXISTS anova;")
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def test_anova():
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n_groups = 3
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rvs = []
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loc = 0
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scale = 5
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size = 500
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for _ in range(n_groups):
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rvs.append(np.round(stats.norm.rvs(loc=loc, scale=scale, size=size), 2))
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loc += 5
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f_stat, p_value = scipy_anova(rvs)
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test_and_check(rvs, n_groups, f_stat, p_value)
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n_groups = 6
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rvs = []
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loc = 0
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scale = 5
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size = 500
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for _ in range(n_groups):
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rvs.append(np.round(stats.norm.rvs(loc=loc, scale=scale, size=size), 2))
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f_stat, p_value = scipy_anova(rvs)
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test_and_check(rvs, n_groups, f_stat, p_value)
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n_groups = 10
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rvs = []
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loc = 1
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scale = 2
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size = 100
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for _ in range(n_groups):
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rvs.append(np.round(stats.norm.rvs(loc=loc, scale=scale, size=size), 2))
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loc += 1
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scale += 2
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size += 100
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f_stat, p_value = scipy_anova(rvs)
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test_and_check(rvs, n_groups, f_stat, p_value)
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n_groups = 20
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rvs = []
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loc = 0
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scale = 10
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size = 1100
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for _ in range(n_groups):
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rvs.append(np.round(stats.norm.rvs(loc=loc, scale=scale, size=size), 2))
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size -= 50
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f_stat, p_value = scipy_anova(rvs)
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test_and_check(rvs, n_groups, f_stat, p_value)
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if __name__ == "__main__":
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test_anova()
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print("Ok.")
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