def test_xii():
# Pandas DataSeries integration with correlation_permutation_test.
import pandas as pd
import numpy as np
from scipy.stats import kendalltau
x = [4.02, 4.52, 4.79, 4.89, 5.27, 5.63, 5.89, 6.08, 6.13, 6.19, 6.47]
y = [4.56, 2.92, 2.71, 3.34, 3.53, 3.47, 3.20, 4.51, 3.76, 3.77, 4.03]
side = "both"
df0 = pd.DataFrame(columns=["X", "Y"], data=zip(x, y))
df = pd.DataFrame(columns=["X", "Y"], data=zip(x, y))
def ktau(x, y):
tau, _ = kendalltau(x, y)
return tau
expected = 0.1646
result = mcpt.correlation_permutation_test(
df["X"], df["Y"], f=ktau, side=side, seed=6919
)
# Check the result is correct.
assert expected >= result.lower
assert expected <= result.upper
# Check the inputs haven't changed as a result of function.
assert df0.equals(df)
def test_x():
# Testing use of a custom function.
from scipy.stats import kendalltau
x0 = [4.02, 4.52, 4.79, 4.89, 5.27, 5.63, 5.89, 6.08, 6.13, 6.19, 6.47]
y0 = [4.56, 2.92, 2.71, 3.34, 3.53, 3.47, 3.20, 4.51, 3.76, 3.77, 4.03]
x = [4.02, 4.52, 4.79, 4.89, 5.27, 5.63, 5.89, 6.08, 6.13, 6.19, 6.47]
y = [4.56, 2.92, 2.71, 3.34, 3.53, 3.47, 3.20, 4.51, 3.76, 3.77, 4.03]
def ktau(x, y):
tau, _ = kendalltau(x, y)
return tau
result = mcpt.correlation_permutation_test(x, y, side="both", f=ktau)
# Check the result is correct.
assert result.lower <= 0.1646
assert result.upper >= 0.1646
# Check the inputs haven't changed as a result of function.
assert x0 == x
assert y0 == y
def test_viii():
# Testing that we obtain the correct result when cores are > 1.
x0 = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y0 = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
expected = 0.025554177689594355
f = "pearsonr"
n = 10000
side = "both"
result = mcpt.correlation_permutation_test(x, y, f, side, n=n, cores=2, seed=4919)
# Check the result is correct.
assert expected >= result.lower
assert expected <= result.upper
# Check the inputs haven't changed as a result of function.
assert y0 == y
assert x0 == x
def test_v():
# Taken from http://biol09.biol.umontreal.ca/PLcourses/Statistical_tests.pdf
# True p-value calculated through exhaustive permutation of y.
x0 = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y0 = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
expected = 0.0144144069664903
f = "pearsonr"
n = 10000
side = "greater"
result = mcpt.correlation_permutation_test(x, y, f, side, n=n, seed=4919)
# Check the result is correct.
assert expected >= result.lower
assert expected <= result.upper
# Check the inputs haven't changed as a result of function.
assert y0 == y
assert x0 == x
def test_ix():
# Test that seeding works.
x0 = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y0 = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
seed = 4919
n = 10000
for side in ["greater", "lower", "both"]:
for cores in [1, 2]:
# Run two tests with the same seed.
result_a = mcpt.permutation_test(
x, y, "mean", side, n=n, cores=cores, seed=seed
)
result_b = mcpt.permutation_test(
x, y, "mean", side, n=n, cores=cores, seed=seed
)
# Check that the seeded results are equivalent.
assert result_a == result_b
# Run up to ten unseeded permutations and ensure that it differs from the seeded.
for _ in range(10):
result_c = mcpt.permutation_test(x, y, "mean", side, n=n, cores=cores)
if result_a != result_c:
break
else:
raise Exception("result_a always identical to result_c")
# Run up to ten unseeded permutation tests and ensure that it differes from
# the previously unseeded result.
for _ in range(10):
result_d = mcpt.permutation_test(x, y, "mean", side, n=n, cores=cores)
if result_c != result_d:
break
else:
raise Exception("result_c always identical to result_d")
# Check the inputs haven't changed as a result of function.
assert x0 == x
assert y0 == y
# Run two tests with the same seed.
result_a = mcpt.correlation_permutation_test(
x, y, "pearsonr", side, n=n, cores=cores, seed=seed
)
result_b = mcpt.correlation_permutation_test(
x, y, "pearsonr", side, n=n, cores=cores, seed=seed
)
# Check that the seeded results are equivalent.
assert result_a == result_b
# Run up to ten unseeded permutations and ensure that it differs from the seeded.
for _ in range(10):
result_c = mcpt.correlation_permutation_test(
x, y, "pearsonr", side, n=n, cores=cores
)
if result_a != result_c:
break
else:
raise Exception("result_a always identical to result_c")
# Run up to ten unseeded permutation tests and ensure that it differes from
# the previously unseeded result.
for _ in range(10):
result_d = mcpt.correlation_permutation_test(
x, y, "pearsonr", side, n=n, cores=cores
)
if result_c != result_d:
break
else:
raise Exception("result_c always identical to result_d")
# Check the inputs haven't changed as a result of function.
assert x0 == x
assert y0 == y