saad
/
ai-content-maker
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
ai-content-maker/.venv/Lib/site-packages/scipy/stats/tests/test_morestats.py

2971 lines
125 KiB
Python
Raw Blame History

# Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
import warnings
import sys
from functools import partial
import numpy as np
from numpy.random import RandomState
from numpy.testing import (assert_array_equal, assert_almost_equal,
assert_array_less, assert_array_almost_equal,
assert_, assert_allclose, assert_equal,
suppress_warnings)
import pytest
from pytest import raises as assert_raises
import re
from scipy import optimize, stats, special
from scipy.stats._morestats import _abw_state, _get_As_weibull, _Avals_weibull
from .common_tests import check_named_results
from .._hypotests import _get_wilcoxon_distr, _get_wilcoxon_distr2
from scipy.stats._binomtest import _binary_search_for_binom_tst
from scipy.stats._distr_params import distcont
distcont = dict(distcont) # type: ignore
# Matplotlib is not a scipy dependency but is optionally used in probplot, so
# check if it's available
try:
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
import matplotlib.pyplot as plt
have_matplotlib = True
except Exception:
have_matplotlib = False
# test data gear.dat from NIST for Levene and Bartlett test
# https://www.itl.nist.gov/div898/handbook/eda/section3/eda3581.htm
g1 = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000]
g2 = [0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988]
g3 = [0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996]
g4 = [1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996]
g5 = [0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996]
g6 = [1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996]
g7 = [0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002]
g8 = [0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006]
g9 = [1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991]
g10 = [0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997]
# The loggamma RVS stream is changing due to gh-13349; this version
# preserves the old stream so that tests don't change.
def _old_loggamma_rvs(*args, **kwargs):
return np.log(stats.gamma.rvs(*args, **kwargs))
class TestBayes_mvs:
def test_basic(self):
# Expected values in this test simply taken from the function. For
# some checks regarding correctness of implementation, see review in
# gh-674
data = [6, 9, 12, 7, 8, 8, 13]
mean, var, std = stats.bayes_mvs(data)
assert_almost_equal(mean.statistic, 9.0)
assert_allclose(mean.minmax, (7.103650222492964, 10.896349777507034),
rtol=1e-6)
assert_almost_equal(var.statistic, 10.0)
assert_allclose(var.minmax, (3.1767242068607087, 24.45910381334018),
rtol=1e-09)
assert_almost_equal(std.statistic, 2.9724954732045084, decimal=14)
assert_allclose(std.minmax, (1.7823367265645145, 4.9456146050146312),
rtol=1e-14)
def test_empty_input(self):
assert_raises(ValueError, stats.bayes_mvs, [])
def test_result_attributes(self):
x = np.arange(15)
attributes = ('statistic', 'minmax')
res = stats.bayes_mvs(x)
for i in res:
check_named_results(i, attributes)
class TestMvsdist:
def test_basic(self):
data = [6, 9, 12, 7, 8, 8, 13]
mean, var, std = stats.mvsdist(data)
assert_almost_equal(mean.mean(), 9.0)
assert_allclose(mean.interval(0.9), (7.103650222492964,
10.896349777507034), rtol=1e-14)
assert_almost_equal(var.mean(), 10.0)
assert_allclose(var.interval(0.9), (3.1767242068607087,
24.45910381334018), rtol=1e-09)
assert_almost_equal(std.mean(), 2.9724954732045084, decimal=14)
assert_allclose(std.interval(0.9), (1.7823367265645145,
4.9456146050146312), rtol=1e-14)
def test_empty_input(self):
assert_raises(ValueError, stats.mvsdist, [])
def test_bad_arg(self):
# Raise ValueError if fewer than two data points are given.
data = [1]
assert_raises(ValueError, stats.mvsdist, data)
def test_warns(self):
# regression test for gh-5270
# make sure there are no spurious divide-by-zero warnings
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
[x.mean() for x in stats.mvsdist([1, 2, 3])]
[x.mean() for x in stats.mvsdist([1, 2, 3, 4, 5])]
class TestShapiro:
def test_basic(self):
x1 = [0.11, 7.87, 4.61, 10.14, 7.95, 3.14, 0.46,
4.43, 0.21, 4.75, 0.71, 1.52, 3.24,
0.93, 0.42, 4.97, 9.53, 4.55, 0.47, 6.66]
w, pw = stats.shapiro(x1)
shapiro_test = stats.shapiro(x1)
assert_almost_equal(w, 0.90047299861907959, decimal=6)
assert_almost_equal(shapiro_test.statistic, 0.90047299861907959, decimal=6)
assert_almost_equal(pw, 0.042089745402336121, decimal=6)
assert_almost_equal(shapiro_test.pvalue, 0.042089745402336121, decimal=6)
x2 = [1.36, 1.14, 2.92, 2.55, 1.46, 1.06, 5.27, -1.11,
3.48, 1.10, 0.88, -0.51, 1.46, 0.52, 6.20, 1.69,
0.08, 3.67, 2.81, 3.49]
w, pw = stats.shapiro(x2)
shapiro_test = stats.shapiro(x2)
assert_almost_equal(w, 0.9590270, decimal=6)
assert_almost_equal(shapiro_test.statistic, 0.9590270, decimal=6)
assert_almost_equal(pw, 0.52460, decimal=3)
assert_almost_equal(shapiro_test.pvalue, 0.52460, decimal=3)
# Verified against R
x3 = stats.norm.rvs(loc=5, scale=3, size=100, random_state=12345678)
w, pw = stats.shapiro(x3)
shapiro_test = stats.shapiro(x3)
assert_almost_equal(w, 0.9772805571556091, decimal=6)
assert_almost_equal(shapiro_test.statistic, 0.9772805571556091, decimal=6)
assert_almost_equal(pw, 0.08144091814756393, decimal=3)
assert_almost_equal(shapiro_test.pvalue, 0.08144091814756393, decimal=3)
# Extracted from original paper
x4 = [0.139, 0.157, 0.175, 0.256, 0.344, 0.413, 0.503, 0.577, 0.614,
0.655, 0.954, 1.392, 1.557, 1.648, 1.690, 1.994, 2.174, 2.206,
3.245, 3.510, 3.571, 4.354, 4.980, 6.084, 8.351]
W_expected = 0.83467
p_expected = 0.000914
w, pw = stats.shapiro(x4)
shapiro_test = stats.shapiro(x4)
assert_almost_equal(w, W_expected, decimal=4)
assert_almost_equal(shapiro_test.statistic, W_expected, decimal=4)
assert_almost_equal(pw, p_expected, decimal=5)
assert_almost_equal(shapiro_test.pvalue, p_expected, decimal=5)
def test_2d(self):
x1 = [[0.11, 7.87, 4.61, 10.14, 7.95, 3.14, 0.46,
4.43, 0.21, 4.75], [0.71, 1.52, 3.24,
0.93, 0.42, 4.97, 9.53, 4.55, 0.47, 6.66]]
w, pw = stats.shapiro(x1)
shapiro_test = stats.shapiro(x1)
assert_almost_equal(w, 0.90047299861907959, decimal=6)
assert_almost_equal(shapiro_test.statistic, 0.90047299861907959, decimal=6)
assert_almost_equal(pw, 0.042089745402336121, decimal=6)
assert_almost_equal(shapiro_test.pvalue, 0.042089745402336121, decimal=6)
x2 = [[1.36, 1.14, 2.92, 2.55, 1.46, 1.06, 5.27, -1.11,
3.48, 1.10], [0.88, -0.51, 1.46, 0.52, 6.20, 1.69,
0.08, 3.67, 2.81, 3.49]]
w, pw = stats.shapiro(x2)
shapiro_test = stats.shapiro(x2)
assert_almost_equal(w, 0.9590270, decimal=6)
assert_almost_equal(shapiro_test.statistic, 0.9590270, decimal=6)
assert_almost_equal(pw, 0.52460, decimal=3)
assert_almost_equal(shapiro_test.pvalue, 0.52460, decimal=3)
def test_empty_input(self):
assert_raises(ValueError, stats.shapiro, [])
assert_raises(ValueError, stats.shapiro, [[], [], []])
def test_not_enough_values(self):
assert_raises(ValueError, stats.shapiro, [1, 2])
assert_raises(ValueError, stats.shapiro, np.array([[], [2]], dtype=object))
def test_bad_arg(self):
# Length of x is less than 3.
x = [1]
assert_raises(ValueError, stats.shapiro, x)
def test_nan_input(self):
x = np.arange(10.)
x[9] = np.nan
w, pw = stats.shapiro(x)
shapiro_test = stats.shapiro(x)
assert_equal(w, np.nan)
assert_equal(shapiro_test.statistic, np.nan)
# Originally, shapiro returned a p-value of 1 in this case,
# but there is no way to produce a numerical p-value if the
# statistic is not a number. NaN is more appropriate.
assert_almost_equal(pw, np.nan)
assert_almost_equal(shapiro_test.pvalue, np.nan)
def test_gh14462(self):
# shapiro is theoretically location-invariant, but when the magnitude
# of the values is much greater than the variance, there can be
# numerical issues. Fixed by subtracting median from the data.
# See gh-14462.
trans_val, maxlog = stats.boxcox([122500, 474400, 110400])
res = stats.shapiro(trans_val)
# Reference from R:
# options(digits=16)
# x = c(0.00000000e+00, 3.39996924e-08, -6.35166875e-09)
# shapiro.test(x)
ref = (0.86468431705371, 0.2805581751566)
assert_allclose(res, ref, rtol=1e-5)
def test_length_3_gh18322(self):
# gh-18322 reported that the p-value could be negative for input of
# length 3. Check that this is resolved.
res = stats.shapiro([0.6931471805599453, 0.0, 0.0])
assert res.pvalue >= 0
# R `shapiro.test` doesn't produce an accurate p-value in the case
# above. Check that the formula used in `stats.shapiro` is not wrong.
# options(digits=16)
# x = c(-0.7746653110021126, -0.4344432067942129, 1.8157053280290931)
# shapiro.test(x)
x = [-0.7746653110021126, -0.4344432067942129, 1.8157053280290931]
res = stats.shapiro(x)
assert_allclose(res.statistic, 0.84658770645509)
assert_allclose(res.pvalue, 0.2313666489882, rtol=1e-6)
class TestAnderson:
def test_normal(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A, crit, sig = stats.anderson(x1)
assert_array_less(crit[:-1], A)
A, crit, sig = stats.anderson(x2)
assert_array_less(A, crit[-2:])
v = np.ones(10)
v[0] = 0
A, crit, sig = stats.anderson(v)
# The expected statistic 3.208057 was computed independently of scipy.
# For example, in R:
# > library(nortest)
# > v <- rep(1, 10)
# > v[1] <- 0
# > result <- ad.test(v)
# > result$statistic
# A
# 3.208057
assert_allclose(A, 3.208057)
def test_expon(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A, crit, sig = stats.anderson(x1, 'expon')
assert_array_less(A, crit[-2:])
with np.errstate(all='ignore'):
A, crit, sig = stats.anderson(x2, 'expon')
assert_(A > crit[-1])
def test_gumbel(self):
# Regression test for gh-6306. Before that issue was fixed,
# this case would return a2=inf.
v = np.ones(100)
v[0] = 0.0
a2, crit, sig = stats.anderson(v, 'gumbel')
# A brief reimplementation of the calculation of the statistic.
n = len(v)
xbar, s = stats.gumbel_l.fit(v)
logcdf = stats.gumbel_l.logcdf(v, xbar, s)
logsf = stats.gumbel_l.logsf(v, xbar, s)
i = np.arange(1, n+1)
expected_a2 = -n - np.mean((2*i - 1) * (logcdf + logsf[::-1]))
assert_allclose(a2, expected_a2)
def test_bad_arg(self):
assert_raises(ValueError, stats.anderson, [1], dist='plate_of_shrimp')
def test_result_attributes(self):
rs = RandomState(1234567890)
x = rs.standard_exponential(size=50)
res = stats.anderson(x)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
def test_gumbel_l(self):
# gh-2592, gh-6337
# Adds support to 'gumbel_r' and 'gumbel_l' as valid inputs for dist.
rs = RandomState(1234567890)
x = rs.gumbel(size=100)
A1, crit1, sig1 = stats.anderson(x, 'gumbel')
A2, crit2, sig2 = stats.anderson(x, 'gumbel_l')
assert_allclose(A2, A1)
def test_gumbel_r(self):
# gh-2592, gh-6337
# Adds support to 'gumbel_r' and 'gumbel_l' as valid inputs for dist.
rs = RandomState(1234567890)
x1 = rs.gumbel(size=100)
x2 = np.ones(100)
# A constant array is a degenerate case and breaks gumbel_r.fit, so
# change one value in x2.
x2[0] = 0.996
A1, crit1, sig1 = stats.anderson(x1, 'gumbel_r')
A2, crit2, sig2 = stats.anderson(x2, 'gumbel_r')
assert_array_less(A1, crit1[-2:])
assert_(A2 > crit2[-1])
def test_weibull_min_case_A(self):
# data and reference values from `anderson` reference [7]
x = np.array([225, 171, 198, 189, 189, 135, 162, 135, 117, 162])
res = stats.anderson(x, 'weibull_min')
m, loc, scale = res.fit_result.params
assert_allclose((m, loc, scale), (2.38, 99.02, 78.23), rtol=2e-3)
assert_allclose(res.statistic, 0.260, rtol=1e-3)
assert res.statistic < res.critical_values[0]
c = 1 / m # ~0.42
assert_allclose(c, 1/2.38, rtol=2e-3)
# interpolate between rows for c=0.4 and c=0.45, indices -3 and -2
As40 = _Avals_weibull[-3]
As45 = _Avals_weibull[-2]
As_ref = As40 + (c - 0.4)/(0.45 - 0.4) * (As45 - As40)
# atol=1e-3 because results are rounded up to the next third decimal
assert np.all(res.critical_values > As_ref)
assert_allclose(res.critical_values, As_ref, atol=1e-3)
def test_weibull_min_case_B(self):
# From `anderson` reference [7]
x = np.array([74, 57, 48, 29, 502, 12, 70, 21,
29, 386, 59, 27, 153, 26, 326])
message = "Maximum likelihood estimation has converged to "
with pytest.raises(ValueError, match=message):
stats.anderson(x, 'weibull_min')
def test_weibull_warning_error(self):
# Check for warning message when there are too few observations
# This is also an example in which an error occurs during fitting
x = -np.array([225, 75, 57, 168, 107, 12, 61, 43, 29])
wmessage = "Critical values of the test statistic are given for the..."
emessage = "An error occurred while fitting the Weibull distribution..."
wcontext = pytest.warns(UserWarning, match=wmessage)
econtext = pytest.raises(ValueError, match=emessage)
with wcontext, econtext:
stats.anderson(x, 'weibull_min')
@pytest.mark.parametrize('distname',
['norm', 'expon', 'gumbel_l', 'extreme1',
'gumbel', 'gumbel_r', 'logistic', 'weibull_min'])
def test_anderson_fit_params(self, distname):
# check that anderson now returns a FitResult
rng = np.random.default_rng(330691555377792039)
real_distname = ('gumbel_l' if distname in {'extreme1', 'gumbel'}
else distname)
dist = getattr(stats, real_distname)
params = distcont[real_distname]
x = dist.rvs(*params, size=1000, random_state=rng)
res = stats.anderson(x, distname)
assert res.fit_result.success
def test_anderson_weibull_As(self):
m = 1 # "when mi < 2, so that c > 0.5, the last line...should be used"
assert_equal(_get_As_weibull(1/m), _Avals_weibull[-1])
m = np.inf
assert_equal(_get_As_weibull(1/m), _Avals_weibull[0])
class TestAndersonKSamp:
def test_example1a(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
assert_almost_equal(Tk, 4.449, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm[0:5], 4)
assert_allclose(p, 0.0021, atol=0.00025)
def test_example1b(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass arrays
t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0])
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True)
assert_almost_equal(Tk, 4.480, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm[0:5], 4)
assert_allclose(p, 0.0020, atol=0.00025)
@pytest.mark.slow
def test_example2a(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
# Pass lists instead of arrays
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
samples = (t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14)
Tk, tm, p = stats.anderson_ksamp(samples, midrank=False)
assert_almost_equal(Tk, 3.288, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm[0:5], 4)
assert_allclose(p, 0.0041, atol=0.00025)
rng = np.random.default_rng(6989860141921615054)
method = stats.PermutationMethod(n_resamples=9999, random_state=rng)
res = stats.anderson_ksamp(samples, midrank=False, method=method)
assert_array_equal(res.statistic, Tk)
assert_array_equal(res.critical_values, tm)
assert_allclose(res.pvalue, p, atol=6e-4)
def test_example2b(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
t9, t10, t11, t12, t13, t14),
midrank=True)
assert_almost_equal(Tk, 3.294, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm[0:5], 4)
assert_allclose(p, 0.0041, atol=0.00025)
def test_R_kSamples(self):
# test values generates with R package kSamples
# package version 1.2-6 (2017-06-14)
# r1 = 1:100
# continuous case (no ties) --> version 1
# res <- kSamples::ad.test(r1, r1 + 40.5)
# res$ad[1, "T.AD"] # 41.105
# res$ad[1, " asympt. P-value"] # 5.8399e-18
#
# discrete case (ties allowed) --> version 2 (here: midrank=True)
# res$ad[2, "T.AD"] # 41.235
#
# res <- kSamples::ad.test(r1, r1 + .5)
# res$ad[1, "T.AD"] # -1.2824
# res$ad[1, " asympt. P-value"] # 1
# res$ad[2, "T.AD"] # -1.2944
#
# res <- kSamples::ad.test(r1, r1 + 7.5)
# res$ad[1, "T.AD"] # 1.4923
# res$ad[1, " asympt. P-value"] # 0.077501
#
# res <- kSamples::ad.test(r1, r1 + 6)
# res$ad[2, "T.AD"] # 0.63892
# res$ad[2, " asympt. P-value"] # 0.17981
#
# res <- kSamples::ad.test(r1, r1 + 11.5)
# res$ad[1, "T.AD"] # 4.5042
# res$ad[1, " asympt. P-value"] # 0.00545
#
# res <- kSamples::ad.test(r1, r1 + 13.5)
# res$ad[1, "T.AD"] # 6.2982
# res$ad[1, " asympt. P-value"] # 0.00118
x1 = np.linspace(1, 100, 100)
# test case: different distributions;p-value floored at 0.001
# test case for issue #5493 / #8536
with suppress_warnings() as sup:
sup.filter(UserWarning, message='p-value floored')
s, _, p = stats.anderson_ksamp([x1, x1 + 40.5], midrank=False)
assert_almost_equal(s, 41.105, 3)
assert_equal(p, 0.001)
with suppress_warnings() as sup:
sup.filter(UserWarning, message='p-value floored')
s, _, p = stats.anderson_ksamp([x1, x1 + 40.5])
assert_almost_equal(s, 41.235, 3)
assert_equal(p, 0.001)
# test case: similar distributions --> p-value capped at 0.25
with suppress_warnings() as sup:
sup.filter(UserWarning, message='p-value capped')
s, _, p = stats.anderson_ksamp([x1, x1 + .5], midrank=False)
assert_almost_equal(s, -1.2824, 4)
assert_equal(p, 0.25)
with suppress_warnings() as sup:
sup.filter(UserWarning, message='p-value capped')
s, _, p = stats.anderson_ksamp([x1, x1 + .5])
assert_almost_equal(s, -1.2944, 4)
assert_equal(p, 0.25)
# test case: check interpolated p-value in [0.01, 0.25] (no ties)
s, _, p = stats.anderson_ksamp([x1, x1 + 7.5], midrank=False)
assert_almost_equal(s, 1.4923, 4)
assert_allclose(p, 0.0775, atol=0.005, rtol=0)
# test case: check interpolated p-value in [0.01, 0.25] (w/ ties)
s, _, p = stats.anderson_ksamp([x1, x1 + 6])
assert_almost_equal(s, 0.6389, 4)
assert_allclose(p, 0.1798, atol=0.005, rtol=0)
# test extended critical values for p=0.001 and p=0.005
s, _, p = stats.anderson_ksamp([x1, x1 + 11.5], midrank=False)
assert_almost_equal(s, 4.5042, 4)
assert_allclose(p, 0.00545, atol=0.0005, rtol=0)
s, _, p = stats.anderson_ksamp([x1, x1 + 13.5], midrank=False)
assert_almost_equal(s, 6.2982, 4)
assert_allclose(p, 0.00118, atol=0.0001, rtol=0)
def test_not_enough_samples(self):
assert_raises(ValueError, stats.anderson_ksamp, np.ones(5))
def test_no_distinct_observations(self):
assert_raises(ValueError, stats.anderson_ksamp,
(np.ones(5), np.ones(5)))
def test_empty_sample(self):
assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), []))
def test_result_attributes(self):
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
res = stats.anderson_ksamp((t1, t2), midrank=False)
attributes = ('statistic', 'critical_values', 'significance_level')
check_named_results(res, attributes)
assert_equal(res.significance_level, res.pvalue)
class TestAnsari:
def test_small(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
with suppress_warnings() as sup:
sup.filter(UserWarning, "Ties preclude use of exact statistic.")
W, pval = stats.ansari(x, y)
assert_almost_equal(W, 23.5, 11)
assert_almost_equal(pval, 0.13499256881897437, 11)
def test_approx(self):
ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98))
parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108,
106, 99))
with suppress_warnings() as sup:
sup.filter(UserWarning, "Ties preclude use of exact statistic.")
W, pval = stats.ansari(ramsay, parekh)
assert_almost_equal(W, 185.5, 11)
assert_almost_equal(pval, 0.18145819972867083, 11)
def test_exact(self):
W, pval = stats.ansari([1, 2, 3, 4], [15, 5, 20, 8, 10, 12])
assert_almost_equal(W, 10.0, 11)
assert_almost_equal(pval, 0.533333333333333333, 7)
def test_bad_arg(self):
assert_raises(ValueError, stats.ansari, [], [1])
assert_raises(ValueError, stats.ansari, [1], [])
def test_result_attributes(self):
x = [1, 2, 3, 3, 4]
y = [3, 2, 6, 1, 6, 1, 4, 1]
with suppress_warnings() as sup:
sup.filter(UserWarning, "Ties preclude use of exact statistic.")
res = stats.ansari(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_bad_alternative(self):
# invalid value for alternative must raise a ValueError
x1 = [1, 2, 3, 4]
x2 = [5, 6, 7, 8]
match = "'alternative' must be 'two-sided'"
with assert_raises(ValueError, match=match):
stats.ansari(x1, x2, alternative='foo')
def test_alternative_exact(self):
x1 = [-5, 1, 5, 10, 15, 20, 25] # high scale, loc=10
x2 = [7.5, 8.5, 9.5, 10.5, 11.5, 12.5] # low scale, loc=10
# ratio of scales is greater than 1. So, the
# p-value must be high when `alternative='less'`
# and low when `alternative='greater'`.
statistic, pval = stats.ansari(x1, x2)
pval_l = stats.ansari(x1, x2, alternative='less').pvalue
pval_g = stats.ansari(x1, x2, alternative='greater').pvalue
assert pval_l > 0.95
assert pval_g < 0.05 # level of significance.
# also check if the p-values sum up to 1 plus the probability
# mass under the calculated statistic.
prob = _abw_state.pmf(statistic, len(x1), len(x2))
assert_allclose(pval_g + pval_l, 1 + prob, atol=1e-12)
# also check if one of the one-sided p-value equals half the
# two-sided p-value and the other one-sided p-value is its
# compliment.
assert_allclose(pval_g, pval/2, atol=1e-12)
assert_allclose(pval_l, 1+prob-pval/2, atol=1e-12)
# sanity check. The result should flip if
# we exchange x and y.
pval_l_reverse = stats.ansari(x2, x1, alternative='less').pvalue
pval_g_reverse = stats.ansari(x2, x1, alternative='greater').pvalue
assert pval_l_reverse < 0.05
assert pval_g_reverse > 0.95
@pytest.mark.parametrize(
'x, y, alternative, expected',
# the tests are designed in such a way that the
# if else statement in ansari test for exact
# mode is covered.
[([1, 2, 3, 4], [5, 6, 7, 8], 'less', 0.6285714285714),
([1, 2, 3, 4], [5, 6, 7, 8], 'greater', 0.6285714285714),
([1, 2, 3], [4, 5, 6, 7, 8], 'less', 0.8928571428571),
([1, 2, 3], [4, 5, 6, 7, 8], 'greater', 0.2857142857143),
([1, 2, 3, 4, 5], [6, 7, 8], 'less', 0.2857142857143),
([1, 2, 3, 4, 5], [6, 7, 8], 'greater', 0.8928571428571)]
)
def test_alternative_exact_with_R(self, x, y, alternative, expected):
# testing with R on arbitrary data
# Sample R code used for the third test case above:
# ```R
# > options(digits=16)
# > x <- c(1,2,3)
# > y <- c(4,5,6,7,8)
# > ansari.test(x, y, alternative='less', exact=TRUE)
#
# Ansari-Bradley test
#
# data: x and y
# AB = 6, p-value = 0.8928571428571
# alternative hypothesis: true ratio of scales is less than 1
#
# ```
pval = stats.ansari(x, y, alternative=alternative).pvalue
assert_allclose(pval, expected, atol=1e-12)
def test_alternative_approx(self):
# intuitive tests for approximation
x1 = stats.norm.rvs(0, 5, size=100, random_state=123)
x2 = stats.norm.rvs(0, 2, size=100, random_state=123)
# for m > 55 or n > 55, the test should automatically
# switch to approximation.
pval_l = stats.ansari(x1, x2, alternative='less').pvalue
pval_g = stats.ansari(x1, x2, alternative='greater').pvalue
assert_allclose(pval_l, 1.0, atol=1e-12)
assert_allclose(pval_g, 0.0, atol=1e-12)
# also check if one of the one-sided p-value equals half the
# two-sided p-value and the other one-sided p-value is its
# compliment.
x1 = stats.norm.rvs(0, 2, size=60, random_state=123)
x2 = stats.norm.rvs(0, 1.5, size=60, random_state=123)
pval = stats.ansari(x1, x2).pvalue
pval_l = stats.ansari(x1, x2, alternative='less').pvalue
pval_g = stats.ansari(x1, x2, alternative='greater').pvalue
assert_allclose(pval_g, pval/2, atol=1e-12)
assert_allclose(pval_l, 1-pval/2, atol=1e-12)
class TestBartlett:
def test_data(self):
# https://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
T, pval = stats.bartlett(*args)
assert_almost_equal(T, 20.78587342806484, 7)
assert_almost_equal(pval, 0.0136358632781, 7)
def test_bad_arg(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.bartlett, [1])
def test_result_attributes(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
res = stats.bartlett(*args)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_empty_arg(self):
args = (g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, [])
assert_equal((np.nan, np.nan), stats.bartlett(*args))
# temporary fix for issue #9252: only accept 1d input
def test_1d_input(self):
x = np.array([[1, 2], [3, 4]])
assert_raises(ValueError, stats.bartlett, g1, x)
class TestLevene:
def test_data(self):
# https://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
W, pval = stats.levene(*args)
assert_almost_equal(W, 1.7059176930008939, 7)
assert_almost_equal(pval, 0.0990829755522, 7)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
W1, pval1 = stats.levene(g1, g2, g3, center='mean')
W2, pval2 = stats.levene(g1, g2, g3, center='trimmed',
proportiontocut=0.0)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
np.random.seed(1234)
x2 = np.random.permutation(x)
# Use center='trimmed'
W0, pval0 = stats.levene(x, y, center='trimmed',
proportiontocut=0.125)
W1, pval1 = stats.levene(x2, y, center='trimmed',
proportiontocut=0.125)
# Trim the data here, and use center='mean'
W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(W0, W2)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_equal_mean_median(self):
x = np.linspace(-1, 1, 21)
np.random.seed(1234)
x2 = np.random.permutation(x)
y = x**3
W1, pval1 = stats.levene(x, y, center='mean')
W2, pval2 = stats.levene(x2, y, center='median')
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1, 1, 21)
assert_raises(TypeError, stats.levene, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1, 1, 21)
assert_raises(ValueError, stats.levene, x, x, center='trim')
def test_too_few_args(self):
assert_raises(ValueError, stats.levene, [1])
def test_result_attributes(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
res = stats.levene(*args)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
# temporary fix for issue #9252: only accept 1d input
def test_1d_input(self):
x = np.array([[1, 2], [3, 4]])
assert_raises(ValueError, stats.levene, g1, x)
class TestBinomTest:
"""Tests for stats.binomtest."""
# Expected results here are from R binom.test, e.g.
# options(digits=16)
# binom.test(484, 967, p=0.48)
#
def test_two_sided_pvalues1(self):
# `tol` could be stricter on most architectures, but the value
# here is limited by accuracy of `binom.cdf` for large inputs on
# Linux_Python_37_32bit_full and aarch64
rtol = 1e-10 # aarch64 observed rtol: 1.5e-11
res = stats.binomtest(10079999, 21000000, 0.48)
assert_allclose(res.pvalue, 1.0, rtol=rtol)
res = stats.binomtest(10079990, 21000000, 0.48)
assert_allclose(res.pvalue, 0.9966892187965, rtol=rtol)
res = stats.binomtest(10080009, 21000000, 0.48)
assert_allclose(res.pvalue, 0.9970377203856, rtol=rtol)
res = stats.binomtest(10080017, 21000000, 0.48)
assert_allclose(res.pvalue, 0.9940754817328, rtol=1e-9)
def test_two_sided_pvalues2(self):
rtol = 1e-10 # no aarch64 failure with 1e-15, preemptive bump
res = stats.binomtest(9, n=21, p=0.48)
assert_allclose(res.pvalue, 0.6689672431939, rtol=rtol)
res = stats.binomtest(4, 21, 0.48)
assert_allclose(res.pvalue, 0.008139563452106, rtol=rtol)
res = stats.binomtest(11, 21, 0.48)
assert_allclose(res.pvalue, 0.8278629664608, rtol=rtol)
res = stats.binomtest(7, 21, 0.48)
assert_allclose(res.pvalue, 0.1966772901718, rtol=rtol)
res = stats.binomtest(3, 10, .5)
assert_allclose(res.pvalue, 0.34375, rtol=rtol)
res = stats.binomtest(2, 2, .4)
assert_allclose(res.pvalue, 0.16, rtol=rtol)
res = stats.binomtest(2, 4, .3)
assert_allclose(res.pvalue, 0.5884, rtol=rtol)
def test_edge_cases(self):
rtol = 1e-10 # aarch64 observed rtol: 1.33e-15
res = stats.binomtest(484, 967, 0.5)
assert_allclose(res.pvalue, 1, rtol=rtol)
res = stats.binomtest(3, 47, 3/47)
assert_allclose(res.pvalue, 1, rtol=rtol)
res = stats.binomtest(13, 46, 13/46)
assert_allclose(res.pvalue, 1, rtol=rtol)
res = stats.binomtest(15, 44, 15/44)
assert_allclose(res.pvalue, 1, rtol=rtol)
res = stats.binomtest(7, 13, 0.5)
assert_allclose(res.pvalue, 1, rtol=rtol)
res = stats.binomtest(6, 11, 0.5)
assert_allclose(res.pvalue, 1, rtol=rtol)
def test_binary_srch_for_binom_tst(self):
# Test that old behavior of binomtest is maintained
# by the new binary search method in cases where d
# exactly equals the input on one side.
n = 10
p = 0.5
k = 3
# First test for the case where k > mode of PMF
i = np.arange(np.ceil(p * n), n+1)
d = stats.binom.pmf(k, n, p)
# Old way of calculating y, probably consistent with R.
y1 = np.sum(stats.binom.pmf(i, n, p) <= d, axis=0)
# New way with binary search.
ix = _binary_search_for_binom_tst(lambda x1:
-stats.binom.pmf(x1, n, p),
-d, np.ceil(p * n), n)
y2 = n - ix + int(d == stats.binom.pmf(ix, n, p))
assert_allclose(y1, y2, rtol=1e-9)
# Now test for the other side.
k = 7
i = np.arange(np.floor(p * n) + 1)
d = stats.binom.pmf(k, n, p)
# Old way of calculating y.
y1 = np.sum(stats.binom.pmf(i, n, p) <= d, axis=0)
# New way with binary search.
ix = _binary_search_for_binom_tst(lambda x1:
stats.binom.pmf(x1, n, p),
d, 0, np.floor(p * n))
y2 = ix + 1
assert_allclose(y1, y2, rtol=1e-9)
# Expected results here are from R 3.6.2 binom.test
@pytest.mark.parametrize('alternative, pval, ci_low, ci_high',
[('less', 0.148831050443,
0.0, 0.2772002496709138),
('greater', 0.9004695898947,
0.1366613252458672, 1.0),
('two-sided', 0.2983720970096,
0.1266555521019559, 0.2918426890886281)])
def test_confidence_intervals1(self, alternative, pval, ci_low, ci_high):
res = stats.binomtest(20, n=100, p=0.25, alternative=alternative)
assert_allclose(res.pvalue, pval, rtol=1e-12)
assert_equal(res.statistic, 0.2)
ci = res.proportion_ci(confidence_level=0.95)
assert_allclose((ci.low, ci.high), (ci_low, ci_high), rtol=1e-12)
# Expected results here are from R 3.6.2 binom.test.
@pytest.mark.parametrize('alternative, pval, ci_low, ci_high',
[('less',
0.005656361, 0.0, 0.1872093),
('greater',
0.9987146, 0.008860761, 1.0),
('two-sided',
0.01191714, 0.006872485, 0.202706269)])
def test_confidence_intervals2(self, alternative, pval, ci_low, ci_high):
res = stats.binomtest(3, n=50, p=0.2, alternative=alternative)
assert_allclose(res.pvalue, pval, rtol=1e-6)
assert_equal(res.statistic, 0.06)
ci = res.proportion_ci(confidence_level=0.99)
assert_allclose((ci.low, ci.high), (ci_low, ci_high), rtol=1e-6)
# Expected results here are from R 3.6.2 binom.test.
@pytest.mark.parametrize('alternative, pval, ci_high',
[('less', 0.05631351, 0.2588656),
('greater', 1.0, 1.0),
('two-sided', 0.07604122, 0.3084971)])
def test_confidence_interval_exact_k0(self, alternative, pval, ci_high):
# Test with k=0, n = 10.
res = stats.binomtest(0, 10, p=0.25, alternative=alternative)
assert_allclose(res.pvalue, pval, rtol=1e-6)
ci = res.proportion_ci(confidence_level=0.95)
assert_equal(ci.low, 0.0)
assert_allclose(ci.high, ci_high, rtol=1e-6)
# Expected results here are from R 3.6.2 binom.test.
@pytest.mark.parametrize('alternative, pval, ci_low',
[('less', 1.0, 0.0),
('greater', 9.536743e-07, 0.7411344),
('two-sided', 9.536743e-07, 0.6915029)])
def test_confidence_interval_exact_k_is_n(self, alternative, pval, ci_low):
# Test with k = n = 10.
res = stats.binomtest(10, 10, p=0.25, alternative=alternative)
assert_allclose(res.pvalue, pval, rtol=1e-6)
ci = res.proportion_ci(confidence_level=0.95)
assert_equal(ci.high, 1.0)
assert_allclose(ci.low, ci_low, rtol=1e-6)
# Expected results are from the prop.test function in R 3.6.2.
@pytest.mark.parametrize(
'k, alternative, corr, conf, ci_low, ci_high',
[[3, 'two-sided', True, 0.95, 0.08094782, 0.64632928],
[3, 'two-sided', True, 0.99, 0.0586329, 0.7169416],
[3, 'two-sided', False, 0.95, 0.1077913, 0.6032219],
[3, 'two-sided', False, 0.99, 0.07956632, 0.6799753],
[3, 'less', True, 0.95, 0.0, 0.6043476],
[3, 'less', True, 0.99, 0.0, 0.6901811],
[3, 'less', False, 0.95, 0.0, 0.5583002],
[3, 'less', False, 0.99, 0.0, 0.6507187],
[3, 'greater', True, 0.95, 0.09644904, 1.0],
[3, 'greater', True, 0.99, 0.06659141, 1.0],
[3, 'greater', False, 0.95, 0.1268766, 1.0],
[3, 'greater', False, 0.99, 0.08974147, 1.0],
[0, 'two-sided', True, 0.95, 0.0, 0.3445372],
[0, 'two-sided', False, 0.95, 0.0, 0.2775328],
[0, 'less', True, 0.95, 0.0, 0.2847374],
[0, 'less', False, 0.95, 0.0, 0.212942],
[0, 'greater', True, 0.95, 0.0, 1.0],
[0, 'greater', False, 0.95, 0.0, 1.0],
[10, 'two-sided', True, 0.95, 0.6554628, 1.0],
[10, 'two-sided', False, 0.95, 0.7224672, 1.0],
[10, 'less', True, 0.95, 0.0, 1.0],
[10, 'less', False, 0.95, 0.0, 1.0],
[10, 'greater', True, 0.95, 0.7152626, 1.0],
[10, 'greater', False, 0.95, 0.787058, 1.0]]
)
def test_ci_wilson_method(self, k, alternative, corr, conf,
ci_low, ci_high):
res = stats.binomtest(k, n=10, p=0.1, alternative=alternative)
if corr:
method = 'wilsoncc'
else:
method = 'wilson'
ci = res.proportion_ci(confidence_level=conf, method=method)
assert_allclose((ci.low, ci.high), (ci_low, ci_high), rtol=1e-6)
def test_estimate_equals_hypothesized_prop(self):
# Test the special case where the estimated proportion equals
# the hypothesized proportion. When alternative is 'two-sided',
# the p-value is 1.
res = stats.binomtest(4, 16, 0.25)
assert_equal(res.statistic, 0.25)
assert_equal(res.pvalue, 1.0)
@pytest.mark.parametrize('k, n', [(0, 0), (-1, 2)])
def test_invalid_k_n(self, k, n):
with pytest.raises(ValueError,
match="must be an integer not less than"):
stats.binomtest(k, n)
def test_invalid_k_too_big(self):
with pytest.raises(ValueError,
match=r"k \(11\) must not be greater than n \(10\)."):
stats.binomtest(11, 10, 0.25)
def test_invalid_k_wrong_type(self):
with pytest.raises(TypeError,
match="k must be an integer."):
stats.binomtest([10, 11], 21, 0.25)
def test_invalid_p_range(self):
message = r'p \(-0.5\) must be in range...'
with pytest.raises(ValueError, match=message):
stats.binomtest(50, 150, p=-0.5)
message = r'p \(1.5\) must be in range...'
with pytest.raises(ValueError, match=message):
stats.binomtest(50, 150, p=1.5)
def test_invalid_confidence_level(self):
res = stats.binomtest(3, n=10, p=0.1)
message = r"confidence_level \(-1\) must be in the interval"
with pytest.raises(ValueError, match=message):
res.proportion_ci(confidence_level=-1)
def test_invalid_ci_method(self):
res = stats.binomtest(3, n=10, p=0.1)
with pytest.raises(ValueError, match=r"method \('plate of shrimp'\) must be"):
res.proportion_ci(method="plate of shrimp")
def test_invalid_alternative(self):
with pytest.raises(ValueError, match=r"alternative \('ekki'\) not..."):
stats.binomtest(3, n=10, p=0.1, alternative='ekki')
def test_alias(self):
res = stats.binomtest(3, n=10, p=0.1)
assert_equal(res.proportion_estimate, res.statistic)
@pytest.mark.skipif(sys.maxsize <= 2**32, reason="32-bit does not overflow")
def test_boost_overflow_raises(self):
# Boost.Math error policy should raise exceptions in Python
with pytest.raises(OverflowError, match='Error in function...'):
stats.binomtest(5, 6, p=sys.float_info.min)
class TestFligner:
def test_data(self):
# numbers from R: fligner.test in package stats
x1 = np.arange(5)
assert_array_almost_equal(stats.fligner(x1, x1**2),
(3.2282229927203536, 0.072379187848207877),
11)
def test_trimmed1(self):
# Perturb input to break ties in the transformed data
# See https://github.com/scipy/scipy/pull/8042 for more details
rs = np.random.RandomState(123)
def _perturb(g):
return (np.asarray(g) + 1e-10 * rs.randn(len(g))).tolist()
g1_ = _perturb(g1)
g2_ = _perturb(g2)
g3_ = _perturb(g3)
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
Xsq1, pval1 = stats.fligner(g1_, g2_, g3_, center='mean')
Xsq2, pval2 = stats.fligner(g1_, g2_, g3_, center='trimmed',
proportiontocut=0.0)
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
# Use center='trimmed'
Xsq1, pval1 = stats.fligner(x, y, center='trimmed',
proportiontocut=0.125)
# Trim the data here, and use center='mean'
Xsq2, pval2 = stats.fligner(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
# The following test looks reasonable at first, but fligner() uses the
# function stats.rankdata(), and in one of the cases in this test,
# there are ties, while in the other (because of normal rounding
# errors) there are not. This difference leads to differences in the
# third significant digit of W.
#
#def test_equal_mean_median(self):
# x = np.linspace(-1,1,21)
# y = x**3
# W1, pval1 = stats.fligner(x, y, center='mean')
# W2, pval2 = stats.fligner(x, y, center='median')
# assert_almost_equal(W1, W2)
# assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1, 1, 21)
assert_raises(TypeError, stats.fligner, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1, 1, 21)
assert_raises(ValueError, stats.fligner, x, x, center='trim')
def test_bad_num_args(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.fligner, [1])
def test_empty_arg(self):
x = np.arange(5)
assert_equal((np.nan, np.nan), stats.fligner(x, x**2, []))
def mood_cases_with_ties():
# Generate random `x` and `y` arrays with ties both between and within the
# samples. Expected results are (statistic, pvalue) from SAS.
expected_results = [(-1.76658511464992, .0386488678399305),
(-.694031428192304, .2438312498647250),
(-1.15093525352151, .1248794365836150)]
seeds = [23453254, 1298352315, 987234597]
for si, seed in enumerate(seeds):
rng = np.random.default_rng(seed)
xy = rng.random(100)
# Generate random indices to make ties
tie_ind = rng.integers(low=0, high=99, size=5)
# Generate a random number of ties for each index.
num_ties_per_ind = rng.integers(low=1, high=5, size=5)
# At each `tie_ind`, mark the next `n` indices equal to that value.
for i, n in zip(tie_ind, num_ties_per_ind):
for j in range(i + 1, i + n):
xy[j] = xy[i]
# scramble order of xy before splitting into `x, y`
rng.shuffle(xy)
x, y = np.split(xy, 2)
yield x, y, 'less', *expected_results[si]
class TestMood:
@pytest.mark.parametrize("x,y,alternative,stat_expect,p_expect",
mood_cases_with_ties())
def test_against_SAS(self, x, y, alternative, stat_expect, p_expect):
"""
Example code used to generate SAS output:
DATA myData;
INPUT X Y;
CARDS;
1 0
1 1
1 2
1 3
1 4
2 0
2 1
2 4
2 9
2 16
ods graphics on;
proc npar1way mood data=myData ;
class X;
ods output MoodTest=mt;
proc contents data=mt;
proc print data=mt;
format Prob1 17.16 Prob2 17.16 Statistic 17.16 Z 17.16 ;
title "Mood Two-Sample Test";
proc print data=myData;
title "Data for above results";
run;
"""
statistic, pvalue = stats.mood(x, y, alternative=alternative)
assert_allclose(stat_expect, statistic, atol=1e-16)
assert_allclose(p_expect, pvalue, atol=1e-16)
@pytest.mark.parametrize("alternative, expected",
[('two-sided', (1.019938533549930,
.3077576129778760)),
('less', (1.019938533549930,
1 - .1538788064889380)),
('greater', (1.019938533549930,
.1538788064889380))])
def test_against_SAS_2(self, alternative, expected):
# Code to run in SAS in above function
x = [111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98]
y = [107, 108, 106, 98, 105, 103, 110, 105, 104, 100,
96, 108, 103, 104, 114, 114, 113, 108, 106, 99]
res = stats.mood(x, y, alternative=alternative)
assert_allclose(res, expected)
def test_mood_order_of_args(self):
# z should change sign when the order of arguments changes, pvalue
# should not change
np.random.seed(1234)
x1 = np.random.randn(10, 1)
x2 = np.random.randn(15, 1)
z1, p1 = stats.mood(x1, x2)
z2, p2 = stats.mood(x2, x1)
assert_array_almost_equal([z1, p1], [-z2, p2])
def test_mood_with_axis_none(self):
# Test with axis = None, compare with results from R
x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047,
1.59528080213779, 0.329507771815361, -0.820468384118015,
0.487429052428485, 0.738324705129217, 0.575781351653492,
-0.305388387156356, 1.51178116845085, 0.389843236411431,
-0.621240580541804, -2.2146998871775, 1.12493091814311,
-0.0449336090152309, -0.0161902630989461, 0.943836210685299,
0.821221195098089, 0.593901321217509]
x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882,
-1.13037567424629, -0.0802517565509893, 0.132420284381094,
0.707954729271733, -0.23969802417184, 1.98447393665293,
-0.138787012119665, 0.417650750792556, 0.981752777463662,
-0.392695355503813, -1.03966897694891, 1.78222896030858,
-2.31106908460517, 0.878604580921265, 0.035806718015226,
1.01282869212708, 0.432265154539617, 2.09081920524915,
-1.19992581964387, 1.58963820029007, 1.95465164222325,
0.00493777682814261, -2.45170638784613, 0.477237302613617,
-0.596558168631403, 0.792203270299649, 0.289636710177348]
x1 = np.array(x1)
x2 = np.array(x2)
x1.shape = (10, 2)
x2.shape = (15, 2)
assert_array_almost_equal(stats.mood(x1, x2, axis=None),
[-1.31716607555, 0.18778296257])
def test_mood_2d(self):
# Test if the results of mood test in 2-D case are consistent with the
# R result for the same inputs. Numbers from R mood.test().
ny = 5
np.random.seed(1234)
x1 = np.random.randn(10, ny)
x2 = np.random.randn(15, ny)
z_vectest, pval_vectest = stats.mood(x1, x2)
for j in range(ny):
assert_array_almost_equal([z_vectest[j], pval_vectest[j]],
stats.mood(x1[:, j], x2[:, j]))
# inverse order of dimensions
x1 = x1.transpose()
x2 = x2.transpose()
z_vectest, pval_vectest = stats.mood(x1, x2, axis=1)
for i in range(ny):
# check axis handling is self consistent
assert_array_almost_equal([z_vectest[i], pval_vectest[i]],
stats.mood(x1[i, :], x2[i, :]))
def test_mood_3d(self):
shape = (10, 5, 6)
np.random.seed(1234)
x1 = np.random.randn(*shape)
x2 = np.random.randn(*shape)
for axis in range(3):
z_vectest, pval_vectest = stats.mood(x1, x2, axis=axis)
# Tests that result for 3-D arrays is equal to that for the
# same calculation on a set of 1-D arrays taken from the
# 3-D array
axes_idx = ([1, 2], [0, 2], [0, 1]) # the two axes != axis
for i in range(shape[axes_idx[axis][0]]):
for j in range(shape[axes_idx[axis][1]]):
if axis == 0:
slice1 = x1[:, i, j]
slice2 = x2[:, i, j]
elif axis == 1:
slice1 = x1[i, :, j]
slice2 = x2[i, :, j]
else:
slice1 = x1[i, j, :]
slice2 = x2[i, j, :]
assert_array_almost_equal([z_vectest[i, j],
pval_vectest[i, j]],
stats.mood(slice1, slice2))
def test_mood_bad_arg(self):
# Raise ValueError when the sum of the lengths of the args is
# less than 3
assert_raises(ValueError, stats.mood, [1], [])
def test_mood_alternative(self):
np.random.seed(0)
x = stats.norm.rvs(scale=0.75, size=100)
y = stats.norm.rvs(scale=1.25, size=100)
stat1, p1 = stats.mood(x, y, alternative='two-sided')
stat2, p2 = stats.mood(x, y, alternative='less')
stat3, p3 = stats.mood(x, y, alternative='greater')
assert stat1 == stat2 == stat3
assert_allclose(p1, 0, atol=1e-7)
assert_allclose(p2, p1/2)
assert_allclose(p3, 1 - p1/2)
with pytest.raises(ValueError, match="`alternative` must be..."):
stats.mood(x, y, alternative='ekki-ekki')
@pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
def test_result(self, alternative):
rng = np.random.default_rng(265827767938813079281100964083953437622)
x1 = rng.standard_normal((10, 1))
x2 = rng.standard_normal((15, 1))
res = stats.mood(x1, x2, alternative=alternative)
assert_equal((res.statistic, res.pvalue), res)
class TestProbplot:
def test_basic(self):
x = stats.norm.rvs(size=20, random_state=12345)
osm, osr = stats.probplot(x, fit=False)
osm_expected = [-1.8241636, -1.38768012, -1.11829229, -0.91222575,
-0.73908135, -0.5857176, -0.44506467, -0.31273668,
-0.18568928, -0.06158146, 0.06158146, 0.18568928,
0.31273668, 0.44506467, 0.5857176, 0.73908135,
0.91222575, 1.11829229, 1.38768012, 1.8241636]
assert_allclose(osr, np.sort(x))
assert_allclose(osm, osm_expected)
res, res_fit = stats.probplot(x, fit=True)
res_fit_expected = [1.05361841, 0.31297795, 0.98741609]
assert_allclose(res_fit, res_fit_expected)
def test_sparams_keyword(self):
x = stats.norm.rvs(size=100, random_state=123456)
# Check that None, () and 0 (loc=0, for normal distribution) all work
# and give the same results
osm1, osr1 = stats.probplot(x, sparams=None, fit=False)
osm2, osr2 = stats.probplot(x, sparams=0, fit=False)
osm3, osr3 = stats.probplot(x, sparams=(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osm1, osm3)
assert_allclose(osr1, osr2)
assert_allclose(osr1, osr3)
# Check giving (loc, scale) params for normal distribution
osm, osr = stats.probplot(x, sparams=(), fit=False)
def test_dist_keyword(self):
x = stats.norm.rvs(size=20, random_state=12345)
osm1, osr1 = stats.probplot(x, fit=False, dist='t', sparams=(3,))
osm2, osr2 = stats.probplot(x, fit=False, dist=stats.t, sparams=(3,))
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
assert_raises(ValueError, stats.probplot, x, dist='wrong-dist-name')
assert_raises(AttributeError, stats.probplot, x, dist=[])
class custom_dist:
"""Some class that looks just enough like a distribution."""
def ppf(self, q):
return stats.norm.ppf(q, loc=2)
osm1, osr1 = stats.probplot(x, sparams=(2,), fit=False)
osm2, osr2 = stats.probplot(x, dist=custom_dist(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_plot_kwarg(self):
fig = plt.figure()
fig.add_subplot(111)
x = stats.t.rvs(3, size=100, random_state=7654321)
res1, fitres1 = stats.probplot(x, plot=plt)
plt.close()
res2, fitres2 = stats.probplot(x, plot=None)
res3 = stats.probplot(x, fit=False, plot=plt)
plt.close()
res4 = stats.probplot(x, fit=False, plot=None)
# Check that results are consistent between combinations of `fit` and
# `plot` keywords.
assert_(len(res1) == len(res2) == len(res3) == len(res4) == 2)
assert_allclose(res1, res2)
assert_allclose(res1, res3)
assert_allclose(res1, res4)
assert_allclose(fitres1, fitres2)
# Check that a Matplotlib Axes object is accepted
fig = plt.figure()
ax = fig.add_subplot(111)
stats.probplot(x, fit=False, plot=ax)
plt.close()
def test_probplot_bad_args(self):
# Raise ValueError when given an invalid distribution.
assert_raises(ValueError, stats.probplot, [1], dist="plate_of_shrimp")
def test_empty(self):
assert_equal(stats.probplot([], fit=False),
(np.array([]), np.array([])))
assert_equal(stats.probplot([], fit=True),
((np.array([]), np.array([])),
(np.nan, np.nan, 0.0)))
def test_array_of_size_one(self):
with np.errstate(invalid='ignore'):
assert_equal(stats.probplot([1], fit=True),
((np.array([0.]), np.array([1])),
(np.nan, np.nan, 0.0)))
class TestWilcoxon:
def test_wilcoxon_bad_arg(self):
# Raise ValueError when two args of different lengths are given or
# zero_method is unknown.
assert_raises(ValueError, stats.wilcoxon, [1], [1, 2])
assert_raises(ValueError, stats.wilcoxon, [1, 2], [1, 2], "dummy")
assert_raises(ValueError, stats.wilcoxon, [1, 2], [1, 2],
alternative="dummy")
assert_raises(ValueError, stats.wilcoxon, [1]*10, mode="xyz")
def test_zero_diff(self):
x = np.arange(20)
# pratt and wilcox do not work if x - y == 0
assert_raises(ValueError, stats.wilcoxon, x, x, "wilcox",
mode="approx")
assert_raises(ValueError, stats.wilcoxon, x, x, "pratt",
mode="approx")
# ranksum is n*(n+1)/2, split in half if zero_method == "zsplit"
assert_equal(stats.wilcoxon(x, x, "zsplit", mode="approx"),
(20*21/4, 1.0))
def test_pratt(self):
# regression test for gh-6805: p-value matches value from R package
# coin (wilcoxsign_test) reported in the issue
x = [1, 2, 3, 4]
y = [1, 2, 3, 5]
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Sample size too small")
res = stats.wilcoxon(x, y, zero_method="pratt", mode="approx")
assert_allclose(res, (0.0, 0.31731050786291415))
def test_wilcoxon_arg_type(self):
# Should be able to accept list as arguments.
# Address issue 6070.
arr = [1, 2, 3, 0, -1, 3, 1, 2, 1, 1, 2]
_ = stats.wilcoxon(arr, zero_method="pratt", mode="approx")
_ = stats.wilcoxon(arr, zero_method="zsplit", mode="approx")
_ = stats.wilcoxon(arr, zero_method="wilcox", mode="approx")
def test_accuracy_wilcoxon(self):
freq = [1, 4, 16, 15, 8, 4, 5, 1, 2]
nums = range(-4, 5)
x = np.concatenate([[u] * v for u, v in zip(nums, freq)])
y = np.zeros(x.size)
T, p = stats.wilcoxon(x, y, "pratt", mode="approx")
assert_allclose(T, 423)
assert_allclose(p, 0.0031724568006762576)
T, p = stats.wilcoxon(x, y, "zsplit", mode="approx")
assert_allclose(T, 441)
assert_allclose(p, 0.0032145343172473055)
T, p = stats.wilcoxon(x, y, "wilcox", mode="approx")
assert_allclose(T, 327)
assert_allclose(p, 0.00641346115861)
# Test the 'correction' option, using values computed in R with:
# > wilcox.test(x, y, paired=TRUE, exact=FALSE, correct={FALSE,TRUE})
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
T, p = stats.wilcoxon(x, y, correction=False, mode="approx")
assert_equal(T, 34)
assert_allclose(p, 0.6948866, rtol=1e-6)
T, p = stats.wilcoxon(x, y, correction=True, mode="approx")
assert_equal(T, 34)
assert_allclose(p, 0.7240817, rtol=1e-6)
def test_wilcoxon_result_attributes(self):
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
res = stats.wilcoxon(x, y, correction=False, mode="approx")
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
def test_wilcoxon_has_zstatistic(self):
rng = np.random.default_rng(89426135444)
x, y = rng.random(15), rng.random(15)
res = stats.wilcoxon(x, y, mode="approx")
ref = stats.norm.ppf(res.pvalue/2)
assert_allclose(res.zstatistic, ref)
res = stats.wilcoxon(x, y, mode="exact")
assert not hasattr(res, 'zstatistic')
res = stats.wilcoxon(x, y)
assert not hasattr(res, 'zstatistic')
def test_wilcoxon_tie(self):
# Regression test for gh-2391.
# Corresponding R code is:
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=FALSE)
# > result$p.value
# [1] 0.001565402
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=TRUE)
# > result$p.value
# [1] 0.001904195
stat, p = stats.wilcoxon([0.1] * 10, mode="approx")
expected_p = 0.001565402
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
stat, p = stats.wilcoxon([0.1] * 10, correction=True, mode="approx")
expected_p = 0.001904195
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
def test_onesided(self):
# tested against "R version 3.4.1 (2017-06-30)"
# x <- c(125, 115, 130, 140, 140, 115, 140, 125, 140, 135)
# y <- c(110, 122, 125, 120, 140, 124, 123, 137, 135, 145)
# cfg <- list(x = x, y = y, paired = TRUE, exact = FALSE)
# do.call(wilcox.test, c(cfg, list(alternative = "less", correct = FALSE)))
# do.call(wilcox.test, c(cfg, list(alternative = "less", correct = TRUE)))
# do.call(wilcox.test, c(cfg, list(alternative = "greater", correct = FALSE)))
# do.call(wilcox.test, c(cfg, list(alternative = "greater", correct = TRUE)))
x = [125, 115, 130, 140, 140, 115, 140, 125, 140, 135]
y = [110, 122, 125, 120, 140, 124, 123, 137, 135, 145]
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Sample size too small")
w, p = stats.wilcoxon(x, y, alternative="less", mode="approx")
assert_equal(w, 27)
assert_almost_equal(p, 0.7031847, decimal=6)
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Sample size too small")
w, p = stats.wilcoxon(x, y, alternative="less", correction=True,
mode="approx")
assert_equal(w, 27)
assert_almost_equal(p, 0.7233656, decimal=6)
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Sample size too small")
w, p = stats.wilcoxon(x, y, alternative="greater", mode="approx")
assert_equal(w, 27)
assert_almost_equal(p, 0.2968153, decimal=6)
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Sample size too small")
w, p = stats.wilcoxon(x, y, alternative="greater", correction=True,
mode="approx")
assert_equal(w, 27)
assert_almost_equal(p, 0.3176447, decimal=6)
def test_exact_basic(self):
for n in range(1, 51):
pmf1 = _get_wilcoxon_distr(n)
pmf2 = _get_wilcoxon_distr2(n)
assert_equal(n*(n+1)/2 + 1, len(pmf1))
assert_equal(sum(pmf1), 1)
assert_array_almost_equal(pmf1, pmf2)
def test_exact_pval(self):
# expected values computed with "R version 3.4.1 (2017-06-30)"
x = np.array([1.81, 0.82, 1.56, -0.48, 0.81, 1.28, -1.04, 0.23,
-0.75, 0.14])
y = np.array([0.71, 0.65, -0.2, 0.85, -1.1, -0.45, -0.84, -0.24,
-0.68, -0.76])
_, p = stats.wilcoxon(x, y, alternative="two-sided", mode="exact")
assert_almost_equal(p, 0.1054688, decimal=6)
_, p = stats.wilcoxon(x, y, alternative="less", mode="exact")
assert_almost_equal(p, 0.9580078, decimal=6)
_, p = stats.wilcoxon(x, y, alternative="greater", mode="exact")
assert_almost_equal(p, 0.05273438, decimal=6)
x = np.arange(0, 20) + 0.5
y = np.arange(20, 0, -1)
_, p = stats.wilcoxon(x, y, alternative="two-sided", mode="exact")
assert_almost_equal(p, 0.8694878, decimal=6)
_, p = stats.wilcoxon(x, y, alternative="less", mode="exact")
assert_almost_equal(p, 0.4347439, decimal=6)
_, p = stats.wilcoxon(x, y, alternative="greater", mode="exact")
assert_almost_equal(p, 0.5795889, decimal=6)
# These inputs were chosen to give a W statistic that is either the
# center of the distribution (when the length of the support is odd), or
# the value to the left of the center (when the length of the support is
# even). Also, the numbers are chosen so that the W statistic is the
# sum of the positive values.
@pytest.mark.parametrize('x', [[-1, -2, 3],
[-1, 2, -3, -4, 5],
[-1, -2, 3, -4, -5, -6, 7, 8]])
def test_exact_p_1(self, x):
w, p = stats.wilcoxon(x)
x = np.array(x)
wtrue = x[x > 0].sum()
assert_equal(w, wtrue)
assert_equal(p, 1)
def test_auto(self):
# auto default to exact if there are no ties and n<= 25
x = np.arange(0, 25) + 0.5
y = np.arange(25, 0, -1)
assert_equal(stats.wilcoxon(x, y),
stats.wilcoxon(x, y, mode="exact"))
# if there are ties (i.e. zeros in d = x-y), then switch to approx
d = np.arange(0, 13)
with suppress_warnings() as sup:
sup.filter(UserWarning, message="Exact p-value calculation")
w, p = stats.wilcoxon(d)
assert_equal(stats.wilcoxon(d, mode="approx"), (w, p))
# use approximation for samples > 25
d = np.arange(1, 52)
assert_equal(stats.wilcoxon(d), stats.wilcoxon(d, mode="approx"))
@pytest.mark.parametrize('size', [3, 5, 10])
def test_permutation_method(self, size):
rng = np.random.default_rng(92348034828501345)
x = rng.random(size=size)
res = stats.wilcoxon(x, method=stats.PermutationMethod())
ref = stats.wilcoxon(x, method='exact')
assert_equal(res.statistic, ref.statistic)
assert_equal(res.pvalue, ref.pvalue)
x = rng.random(size=size*10)
rng = np.random.default_rng(59234803482850134)
pm = stats.PermutationMethod(n_resamples=99, random_state=rng)
ref = stats.wilcoxon(x, method=pm)
rng = np.random.default_rng(59234803482850134)
pm = stats.PermutationMethod(n_resamples=99, random_state=rng)
res = stats.wilcoxon(x, method=pm)
assert_equal(np.round(res.pvalue, 2), res.pvalue) # n_resamples used
assert_equal(res.pvalue, ref.pvalue) # random_state used
class TestKstat:
def test_moments_normal_distribution(self):
np.random.seed(32149)
data = np.random.randn(12345)
moments = [stats.kstat(data, n) for n in [1, 2, 3, 4]]
expected = [0.011315, 1.017931, 0.05811052, 0.0754134]
assert_allclose(moments, expected, rtol=1e-4)
# test equivalence with `stats.moment`
m1 = stats.moment(data, order=1)
m2 = stats.moment(data, order=2)
m3 = stats.moment(data, order=3)
assert_allclose((m1, m2, m3), expected[:-1], atol=0.02, rtol=1e-2)
def test_empty_input(self):
assert_raises(ValueError, stats.kstat, [])
def test_nan_input(self):
data = np.arange(10.)
data[6] = np.nan
assert_equal(stats.kstat(data), np.nan)
def test_kstat_bad_arg(self):
# Raise ValueError if n > 4 or n < 1.
data = np.arange(10)
for n in [0, 4.001]:
assert_raises(ValueError, stats.kstat, data, n=n)
class TestKstatVar:
def test_empty_input(self):
assert_raises(ValueError, stats.kstatvar, [])
def test_nan_input(self):
data = np.arange(10.)
data[6] = np.nan
assert_equal(stats.kstat(data), np.nan)
def test_bad_arg(self):
# Raise ValueError is n is not 1 or 2.
data = [1]
n = 10
assert_raises(ValueError, stats.kstatvar, data, n=n)
class TestPpccPlot:
def setup_method(self):
self.x = _old_loggamma_rvs(5, size=500, random_state=7654321) + 5
def test_basic(self):
N = 5
svals, ppcc = stats.ppcc_plot(self.x, -10, 10, N=N)
ppcc_expected = [0.21139644, 0.21384059, 0.98766719, 0.97980182,
0.93519298]
assert_allclose(svals, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
def test_dist(self):
# Test that we can specify distributions both by name and as objects.
svals1, ppcc1 = stats.ppcc_plot(self.x, -10, 10, dist='tukeylambda')
svals2, ppcc2 = stats.ppcc_plot(self.x, -10, 10,
dist=stats.tukeylambda)
assert_allclose(svals1, svals2, rtol=1e-20)
assert_allclose(ppcc1, ppcc2, rtol=1e-20)
# Test that 'tukeylambda' is the default dist
svals3, ppcc3 = stats.ppcc_plot(self.x, -10, 10)
assert_allclose(svals1, svals3, rtol=1e-20)
assert_allclose(ppcc1, ppcc3, rtol=1e-20)
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
ax = fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=plt)
fig.delaxes(ax)
# Check that a Matplotlib Axes object is accepted
ax = fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=ax)
plt.close()
def test_invalid_inputs(self):
# `b` has to be larger than `a`
assert_raises(ValueError, stats.ppcc_plot, self.x, 1, 0)
# Raise ValueError when given an invalid distribution.
assert_raises(ValueError, stats.ppcc_plot, [1, 2, 3], 0, 1,
dist="plate_of_shrimp")
def test_empty(self):
# For consistency with probplot return for one empty array,
# ppcc contains all zeros and svals is the same as for normal array
# input.
svals, ppcc = stats.ppcc_plot([], 0, 1)
assert_allclose(svals, np.linspace(0, 1, num=80))
assert_allclose(ppcc, np.zeros(80, dtype=float))
class TestPpccMax:
def test_ppcc_max_bad_arg(self):
# Raise ValueError when given an invalid distribution.
data = [1]
assert_raises(ValueError, stats.ppcc_max, data, dist="plate_of_shrimp")
def test_ppcc_max_basic(self):
x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
random_state=1234567) + 1e4
assert_almost_equal(stats.ppcc_max(x), -0.71215366521264145, decimal=7)
def test_dist(self):
x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
random_state=1234567) + 1e4
# Test that we can specify distributions both by name and as objects.
max1 = stats.ppcc_max(x, dist='tukeylambda')
max2 = stats.ppcc_max(x, dist=stats.tukeylambda)
assert_almost_equal(max1, -0.71215366521264145, decimal=5)
assert_almost_equal(max2, -0.71215366521264145, decimal=5)
# Test that 'tukeylambda' is the default dist
max3 = stats.ppcc_max(x)
assert_almost_equal(max3, -0.71215366521264145, decimal=5)
def test_brack(self):
x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
random_state=1234567) + 1e4
assert_raises(ValueError, stats.ppcc_max, x, brack=(0.0, 1.0, 0.5))
assert_almost_equal(stats.ppcc_max(x, brack=(0, 1)),
-0.71215366521264145, decimal=7)
assert_almost_equal(stats.ppcc_max(x, brack=(-2, 2)),
-0.71215366521264145, decimal=7)
class TestBoxcox_llf:
def test_basic(self):
x = stats.norm.rvs(size=10000, loc=10, random_state=54321)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf_expected = -x.size / 2. * np.log(np.sum(x.std()**2))
assert_allclose(llf, llf_expected)
def test_array_like(self):
x = stats.norm.rvs(size=100, loc=10, random_state=54321)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, list(x))
assert_allclose(llf, llf2, rtol=1e-12)
def test_2d_input(self):
# Note: boxcox_llf() was already working with 2-D input (sort of), so
# keep it like that. boxcox() doesn't work with 2-D input though, due
# to brent() returning a scalar.
x = stats.norm.rvs(size=100, loc=10, random_state=54321)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T)
assert_allclose([llf, llf], llf2, rtol=1e-12)
def test_empty(self):
assert_(np.isnan(stats.boxcox_llf(1, [])))
def test_gh_6873(self):
# Regression test for gh-6873.
# This example was taken from gh-7534, a duplicate of gh-6873.
data = [198.0, 233.0, 233.0, 392.0]
llf = stats.boxcox_llf(-8, data)
# The expected value was computed with mpmath.
assert_allclose(llf, -17.93934208579061)
def test_instability_gh20021(self):
data = [2003, 1950, 1997, 2000, 2009]
llf = stats.boxcox_llf(1e-8, data)
# The expected value was computed with mpsci, set mpmath.mp.dps=100
assert_allclose(llf, -15.32401272869016598)
# This is the data from github user Qukaiyi, given as an example
# of a data set that caused boxcox to fail.
_boxcox_data = [
15957, 112079, 1039553, 711775, 173111, 307382, 183155, 53366, 760875,
207500, 160045, 473714, 40194, 440319, 133261, 265444, 155590, 36660,
904939, 55108, 138391, 339146, 458053, 63324, 1377727, 1342632, 41575,
68685, 172755, 63323, 368161, 199695, 538214, 167760, 388610, 398855,
1001873, 364591, 1320518, 194060, 194324, 2318551, 196114, 64225, 272000,
198668, 123585, 86420, 1925556, 695798, 88664, 46199, 759135, 28051,
345094, 1977752, 51778, 82746, 638126, 2560910, 45830, 140576, 1603787,
57371, 548730, 5343629, 2298913, 998813, 2156812, 423966, 68350, 145237,
131935, 1600305, 342359, 111398, 1409144, 281007, 60314, 242004, 113418,
246211, 61940, 95858, 957805, 40909, 307955, 174159, 124278, 241193,
872614, 304180, 146719, 64361, 87478, 509360, 167169, 933479, 620561,
483333, 97416, 143518, 286905, 597837, 2556043, 89065, 69944, 196858,
88883, 49379, 916265, 1527392, 626954, 54415, 89013, 2883386, 106096,
402697, 45578, 349852, 140379, 34648, 757343, 1305442, 2054757, 121232,
606048, 101492, 51426, 1820833, 83412, 136349, 1379924, 505977, 1303486,
95853, 146451, 285422, 2205423, 259020, 45864, 684547, 182014, 784334,
174793, 563068, 170745, 1195531, 63337, 71833, 199978, 2330904, 227335,
898280, 75294, 2011361, 116771, 157489, 807147, 1321443, 1148635, 2456524,
81839, 1228251, 97488, 1051892, 75397, 3009923, 2732230, 90923, 39735,
132433, 225033, 337555, 1204092, 686588, 1062402, 40362, 1361829, 1497217,
150074, 551459, 2019128, 39581, 45349, 1117187, 87845, 1877288, 164448,
10338362, 24942, 64737, 769946, 2469124, 2366997, 259124, 2667585, 29175,
56250, 74450, 96697, 5920978, 838375, 225914, 119494, 206004, 430907,
244083, 219495, 322239, 407426, 618748, 2087536, 2242124, 4736149, 124624,
406305, 240921, 2675273, 4425340, 821457, 578467, 28040, 348943, 48795,
145531, 52110, 1645730, 1768364, 348363, 85042, 2673847, 81935, 169075,
367733, 135474, 383327, 1207018, 93481, 5934183, 352190, 636533, 145870,
55659, 146215, 73191, 248681, 376907, 1606620, 169381, 81164, 246390,
236093, 885778, 335969, 49266, 381430, 307437, 350077, 34346, 49340,
84715, 527120, 40163, 46898, 4609439, 617038, 2239574, 159905, 118337,
120357, 430778, 3799158, 3516745, 54198, 2970796, 729239, 97848, 6317375,
887345, 58198, 88111, 867595, 210136, 1572103, 1420760, 574046, 845988,
509743, 397927, 1119016, 189955, 3883644, 291051, 126467, 1239907, 2556229,
411058, 657444, 2025234, 1211368, 93151, 577594, 4842264, 1531713, 305084,
479251, 20591, 1466166, 137417, 897756, 594767, 3606337, 32844, 82426,
1294831, 57174, 290167, 322066, 813146, 5671804, 4425684, 895607, 450598,
1048958, 232844, 56871, 46113, 70366, 701618, 97739, 157113, 865047,
194810, 1501615, 1765727, 38125, 2733376, 40642, 437590, 127337, 106310,
4167579, 665303, 809250, 1210317, 45750, 1853687, 348954, 156786, 90793,
1885504, 281501, 3902273, 359546, 797540, 623508, 3672775, 55330, 648221,
266831, 90030, 7118372, 735521, 1009925, 283901, 806005, 2434897, 94321,
309571, 4213597, 2213280, 120339, 64403, 8155209, 1686948, 4327743,
1868312, 135670, 3189615, 1569446, 706058, 58056, 2438625, 520619, 105201,
141961, 179990, 1351440, 3148662, 2804457, 2760144, 70775, 33807, 1926518,
2362142, 186761, 240941, 97860, 1040429, 1431035, 78892, 484039, 57845,
724126, 3166209, 175913, 159211, 1182095, 86734, 1921472, 513546, 326016,
1891609
]
class TestBoxcox:
def test_fixed_lmbda(self):
x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
xt = stats.boxcox(x, lmbda=1)
assert_allclose(xt, x - 1)
xt = stats.boxcox(x, lmbda=-1)
assert_allclose(xt, 1 - 1/x)
xt = stats.boxcox(x, lmbda=0)
assert_allclose(xt, np.log(x))
# Also test that array_like input works
xt = stats.boxcox(list(x), lmbda=0)
assert_allclose(xt, np.log(x))
# test that constant input is accepted; see gh-12225
xt = stats.boxcox(np.ones(10), 2)
assert_equal(xt, np.zeros(10))
def test_lmbda_None(self):
# Start from normal rv's, do inverse transform to check that
# optimization function gets close to the right answer.
lmbda = 2.5
x = stats.norm.rvs(loc=10, size=50000, random_state=1245)
x_inv = (x * lmbda + 1)**(-lmbda)
xt, maxlog = stats.boxcox(x_inv)
assert_almost_equal(maxlog, -1 / lmbda, decimal=2)
def test_alpha(self):
rng = np.random.RandomState(1234)
x = _old_loggamma_rvs(5, size=50, random_state=rng) + 5
# Some regular values for alpha, on a small sample size
_, _, interval = stats.boxcox(x, alpha=0.75)
assert_allclose(interval, [4.004485780226041, 5.138756355035744])
_, _, interval = stats.boxcox(x, alpha=0.05)
assert_allclose(interval, [1.2138178554857557, 8.209033272375663])
# Try some extreme values, see we don't hit the N=500 limit
x = _old_loggamma_rvs(7, size=500, random_state=rng) + 15
_, _, interval = stats.boxcox(x, alpha=0.001)
assert_allclose(interval, [0.3988867, 11.40553131])
_, _, interval = stats.boxcox(x, alpha=0.999)
assert_allclose(interval, [5.83316246, 5.83735292])
def test_boxcox_bad_arg(self):
# Raise ValueError if any data value is negative.
x = np.array([-1, 2])
assert_raises(ValueError, stats.boxcox, x)
# Raise ValueError if data is constant.
assert_raises(ValueError, stats.boxcox, np.array([1]))
# Raise ValueError if data is not 1-dimensional.
assert_raises(ValueError, stats.boxcox, np.array([[1], [2]]))
def test_empty(self):
assert_(stats.boxcox([]).shape == (0,))
def test_gh_6873(self):
# Regression test for gh-6873.
y, lam = stats.boxcox(_boxcox_data)
# The expected value of lam was computed with the function
# powerTransform in the R library 'car'. I trust that value
# to only about five significant digits.
assert_allclose(lam, -0.051654, rtol=1e-5)
@pytest.mark.parametrize("bounds", [(-1, 1), (1.1, 2), (-2, -1.1)])
def test_bounded_optimizer_within_bounds(self, bounds):
# Define custom optimizer with bounds.
def optimizer(fun):
return optimize.minimize_scalar(fun, bounds=bounds,
method="bounded")
_, lmbda = stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
assert bounds[0] < lmbda < bounds[1]
def test_bounded_optimizer_against_unbounded_optimizer(self):
# Test whether setting bounds on optimizer excludes solution from
# unbounded optimizer.
# Get unbounded solution.
_, lmbda = stats.boxcox(_boxcox_data, lmbda=None)
# Set tolerance and bounds around solution.
bounds = (lmbda + 0.1, lmbda + 1)
options = {'xatol': 1e-12}
def optimizer(fun):
return optimize.minimize_scalar(fun, bounds=bounds,
method="bounded", options=options)
# Check bounded solution. Lower bound should be active.
_, lmbda_bounded = stats.boxcox(_boxcox_data, lmbda=None,
optimizer=optimizer)
assert lmbda_bounded != lmbda
assert_allclose(lmbda_bounded, bounds[0])
@pytest.mark.parametrize("optimizer", ["str", (1, 2), 0.1])
def test_bad_optimizer_type_raises_error(self, optimizer):
# Check if error is raised if string, tuple or float is passed
with pytest.raises(ValueError, match="`optimizer` must be a callable"):
stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
def test_bad_optimizer_value_raises_error(self):
# Check if error is raised if `optimizer` function does not return
# `OptimizeResult` object
# Define test function that always returns 1
def optimizer(fun):
return 1
message = "return an object containing the optimal `lmbda`"
with pytest.raises(ValueError, match=message):
stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
@pytest.mark.parametrize(
"bad_x", [np.array([1, -42, 12345.6]), np.array([np.nan, 42, 1])]
)
def test_negative_x_value_raises_error(self, bad_x):
"""Test boxcox_normmax raises ValueError if x contains non-positive values."""
message = "only positive, finite, real numbers"
with pytest.raises(ValueError, match=message):
stats.boxcox_normmax(bad_x)
@pytest.mark.parametrize('x', [
# Attempt to trigger overflow in power expressions.
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
# Attempt to trigger overflow with a large optimal lambda.
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0]),
# Attempt to trigger overflow with large data.
np.array([2003.0e200, 1950.0e200, 1997.0e200, 2000.0e200, 2009.0e200])
])
def test_overflow(self, x):
with pytest.warns(UserWarning, match="The optimal lambda is"):
xt_bc, lam_bc = stats.boxcox(x)
assert np.all(np.isfinite(xt_bc))
class TestBoxcoxNormmax:
def setup_method(self):
self.x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
def test_pearsonr(self):
maxlog = stats.boxcox_normmax(self.x)
assert_allclose(maxlog, 1.804465, rtol=1e-6)
def test_mle(self):
maxlog = stats.boxcox_normmax(self.x, method='mle')
assert_allclose(maxlog, 1.758101, rtol=1e-6)
# Check that boxcox() uses 'mle'
_, maxlog_boxcox = stats.boxcox(self.x)
assert_allclose(maxlog_boxcox, maxlog)
def test_all(self):
maxlog_all = stats.boxcox_normmax(self.x, method='all')
assert_allclose(maxlog_all, [1.804465, 1.758101], rtol=1e-6)
@pytest.mark.parametrize("method", ["mle", "pearsonr", "all"])
@pytest.mark.parametrize("bounds", [(-1, 1), (1.1, 2), (-2, -1.1)])
def test_bounded_optimizer_within_bounds(self, method, bounds):
def optimizer(fun):
return optimize.minimize_scalar(fun, bounds=bounds,
method="bounded")
maxlog = stats.boxcox_normmax(self.x, method=method,
optimizer=optimizer)
assert np.all(bounds[0] < maxlog)
assert np.all(maxlog < bounds[1])
def test_user_defined_optimizer(self):
# tests an optimizer that is not based on scipy.optimize.minimize
lmbda = stats.boxcox_normmax(self.x)
lmbda_rounded = np.round(lmbda, 5)
lmbda_range = np.linspace(lmbda_rounded-0.01, lmbda_rounded+0.01, 1001)
class MyResult:
pass
def optimizer(fun):
# brute force minimum over the range
objs = []
for lmbda in lmbda_range:
objs.append(fun(lmbda))
res = MyResult()
res.x = lmbda_range[np.argmin(objs)]
return res
lmbda2 = stats.boxcox_normmax(self.x, optimizer=optimizer)
assert lmbda2 != lmbda # not identical
assert_allclose(lmbda2, lmbda, 1e-5) # but as close as it should be
def test_user_defined_optimizer_and_brack_raises_error(self):
optimizer = optimize.minimize_scalar
# Using default `brack=None` with user-defined `optimizer` works as
# expected.
stats.boxcox_normmax(self.x, brack=None, optimizer=optimizer)
# Using user-defined `brack` with user-defined `optimizer` is expected
# to throw an error. Instead, users should specify
# optimizer-specific parameters in the optimizer function itself.
with pytest.raises(ValueError, match="`brack` must be None if "
"`optimizer` is given"):
stats.boxcox_normmax(self.x, brack=(-2.0, 2.0),
optimizer=optimizer)
@pytest.mark.parametrize(
'x', ([2003.0, 1950.0, 1997.0, 2000.0, 2009.0],
[0.50000471, 0.50004979, 0.50005902, 0.50009312, 0.50001632]))
def test_overflow(self, x):
message = "The optimal lambda is..."
with pytest.warns(UserWarning, match=message):
lmbda = stats.boxcox_normmax(x, method='mle')
assert np.isfinite(special.boxcox(x, lmbda)).all()
# 10000 is safety factor used in boxcox_normmax
ymax = np.finfo(np.float64).max / 10000
x_treme = np.max(x) if lmbda > 0 else np.min(x)
y_extreme = special.boxcox(x_treme, lmbda)
assert_allclose(y_extreme, ymax * np.sign(lmbda))
def test_negative_ymax(self):
with pytest.raises(ValueError, match="`ymax` must be strictly positive"):
stats.boxcox_normmax(self.x, ymax=-1)
@pytest.mark.parametrize("x", [
# positive overflow in float64
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0],
dtype=np.float64),
# negative overflow in float64
np.array([0.50000471, 0.50004979, 0.50005902, 0.50009312, 0.50001632],
dtype=np.float64),
# positive overflow in float32
np.array([200.3, 195.0, 199.7, 200.0, 200.9],
dtype=np.float32),
# negative overflow in float32
np.array([2e-30, 1e-30, 1e-30, 1e-30, 1e-30, 1e-30],
dtype=np.float32),
])
@pytest.mark.parametrize("ymax", [1e10, 1e30, None])
# TODO: add method "pearsonr" after fix overflow issue
@pytest.mark.parametrize("method", ["mle"])
def test_user_defined_ymax_input_float64_32(self, x, ymax, method):
# Test the maximum of the transformed data close to ymax
with pytest.warns(UserWarning, match="The optimal lambda is"):
kwarg = {'ymax': ymax} if ymax is not None else {}
lmb = stats.boxcox_normmax(x, method=method, **kwarg)
x_treme = [np.min(x), np.max(x)]
ymax_res = max(abs(stats.boxcox(x_treme, lmb)))
if ymax is None:
# 10000 is safety factor used in boxcox_normmax
ymax = np.finfo(x.dtype).max / 10000
assert_allclose(ymax, ymax_res, rtol=1e-5)
@pytest.mark.parametrize("x", [
# positive overflow in float32 but not float64
[200.3, 195.0, 199.7, 200.0, 200.9],
# negative overflow in float32 but not float64
[2e-30, 1e-30, 1e-30, 1e-30, 1e-30, 1e-30],
])
# TODO: add method "pearsonr" after fix overflow issue
@pytest.mark.parametrize("method", ["mle"])
def test_user_defined_ymax_inf(self, x, method):
x_32 = np.asarray(x, dtype=np.float32)
x_64 = np.asarray(x, dtype=np.float64)
# assert overflow with float32 but not float64
with pytest.warns(UserWarning, match="The optimal lambda is"):
stats.boxcox_normmax(x_32, method=method)
stats.boxcox_normmax(x_64, method=method)
# compute the true optimal lambda then compare them
lmb_32 = stats.boxcox_normmax(x_32, ymax=np.inf, method=method)
lmb_64 = stats.boxcox_normmax(x_64, ymax=np.inf, method=method)
assert_allclose(lmb_32, lmb_64, rtol=1e-2)
class TestBoxcoxNormplot:
def setup_method(self):
self.x = _old_loggamma_rvs(5, size=500, random_state=7654321) + 5
def test_basic(self):
N = 5
lmbdas, ppcc = stats.boxcox_normplot(self.x, -10, 10, N=N)
ppcc_expected = [0.57783375, 0.83610988, 0.97524311, 0.99756057,
0.95843297]
assert_allclose(lmbdas, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
ax = fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=plt)
fig.delaxes(ax)
# Check that a Matplotlib Axes object is accepted
ax = fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=ax)
plt.close()
def test_invalid_inputs(self):
# `lb` has to be larger than `la`
assert_raises(ValueError, stats.boxcox_normplot, self.x, 1, 0)
# `x` can not contain negative values
assert_raises(ValueError, stats.boxcox_normplot, [-1, 1], 0, 1)
def test_empty(self):
assert_(stats.boxcox_normplot([], 0, 1).size == 0)
class TestYeojohnson_llf:
def test_array_like(self):
x = stats.norm.rvs(size=100, loc=0, random_state=54321)
lmbda = 1
llf = stats.yeojohnson_llf(lmbda, x)
llf2 = stats.yeojohnson_llf(lmbda, list(x))
assert_allclose(llf, llf2, rtol=1e-12)
def test_2d_input(self):
x = stats.norm.rvs(size=100, loc=10, random_state=54321)
lmbda = 1
llf = stats.yeojohnson_llf(lmbda, x)
llf2 = stats.yeojohnson_llf(lmbda, np.vstack([x, x]).T)
assert_allclose([llf, llf], llf2, rtol=1e-12)
def test_empty(self):
assert_(np.isnan(stats.yeojohnson_llf(1, [])))
class TestYeojohnson:
def test_fixed_lmbda(self):
rng = np.random.RandomState(12345)
# Test positive input
x = _old_loggamma_rvs(5, size=50, random_state=rng) + 5
assert np.all(x > 0)
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt, x)
xt = stats.yeojohnson(x, lmbda=-1)
assert_allclose(xt, 1 - 1 / (x + 1))
xt = stats.yeojohnson(x, lmbda=0)
assert_allclose(xt, np.log(x + 1))
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt, x)
# Test negative input
x = _old_loggamma_rvs(5, size=50, random_state=rng) - 5
assert np.all(x < 0)
xt = stats.yeojohnson(x, lmbda=2)
assert_allclose(xt, -np.log(-x + 1))
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt, x)
xt = stats.yeojohnson(x, lmbda=3)
assert_allclose(xt, 1 / (-x + 1) - 1)
# test both positive and negative input
x = _old_loggamma_rvs(5, size=50, random_state=rng) - 2
assert not np.all(x < 0)
assert not np.all(x >= 0)
pos = x >= 0
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt[pos], x[pos])
xt = stats.yeojohnson(x, lmbda=-1)
assert_allclose(xt[pos], 1 - 1 / (x[pos] + 1))
xt = stats.yeojohnson(x, lmbda=0)
assert_allclose(xt[pos], np.log(x[pos] + 1))
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt[pos], x[pos])
neg = ~pos
xt = stats.yeojohnson(x, lmbda=2)
assert_allclose(xt[neg], -np.log(-x[neg] + 1))
xt = stats.yeojohnson(x, lmbda=1)
assert_allclose(xt[neg], x[neg])
xt = stats.yeojohnson(x, lmbda=3)
assert_allclose(xt[neg], 1 / (-x[neg] + 1) - 1)
@pytest.mark.parametrize('lmbda', [0, .1, .5, 2])
def test_lmbda_None(self, lmbda):
# Start from normal rv's, do inverse transform to check that
# optimization function gets close to the right answer.
def _inverse_transform(x, lmbda):
x_inv = np.zeros(x.shape, dtype=x.dtype)
pos = x >= 0
# when x >= 0
if abs(lmbda) < np.spacing(1.):
x_inv[pos] = np.exp(x[pos]) - 1
else: # lmbda != 0
x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1
# when x < 0
if abs(lmbda - 2) > np.spacing(1.):
x_inv[~pos] = 1 - np.power(-(2 - lmbda) * x[~pos] + 1,
1 / (2 - lmbda))
else: # lmbda == 2
x_inv[~pos] = 1 - np.exp(-x[~pos])
return x_inv
n_samples = 20000
np.random.seed(1234567)
x = np.random.normal(loc=0, scale=1, size=(n_samples))
x_inv = _inverse_transform(x, lmbda)
xt, maxlog = stats.yeojohnson(x_inv)
assert_allclose(maxlog, lmbda, atol=1e-2)
assert_almost_equal(0, np.linalg.norm(x - xt) / n_samples, decimal=2)
assert_almost_equal(0, xt.mean(), decimal=1)
assert_almost_equal(1, xt.std(), decimal=1)
def test_empty(self):
assert_(stats.yeojohnson([]).shape == (0,))
def test_array_like(self):
x = stats.norm.rvs(size=100, loc=0, random_state=54321)
xt1, _ = stats.yeojohnson(x)
xt2, _ = stats.yeojohnson(list(x))
assert_allclose(xt1, xt2, rtol=1e-12)
@pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
def test_input_dtype_complex(self, dtype):
x = np.arange(6, dtype=dtype)
err_msg = ('Yeo-Johnson transformation is not defined for complex '
'numbers.')
with pytest.raises(ValueError, match=err_msg):
stats.yeojohnson(x)
@pytest.mark.parametrize('dtype', [np.int8, np.uint8, np.int16, np.int32])
def test_input_dtype_integer(self, dtype):
x_int = np.arange(8, dtype=dtype)
x_float = np.arange(8, dtype=np.float64)
xt_int, lmbda_int = stats.yeojohnson(x_int)
xt_float, lmbda_float = stats.yeojohnson(x_float)
assert_allclose(xt_int, xt_float, rtol=1e-7)
assert_allclose(lmbda_int, lmbda_float, rtol=1e-7)
def test_input_high_variance(self):
# non-regression test for gh-10821
x = np.array([3251637.22, 620695.44, 11642969.00, 2223468.22,
85307500.00, 16494389.89, 917215.88, 11642969.00,
2145773.87, 4962000.00, 620695.44, 651234.50,
1907876.71, 4053297.88, 3251637.22, 3259103.08,
9547969.00, 20631286.23, 12807072.08, 2383819.84,
90114500.00, 17209575.46, 12852969.00, 2414609.99,
2170368.23])
xt_yeo, lam_yeo = stats.yeojohnson(x)
xt_box, lam_box = stats.boxcox(x + 1)
assert_allclose(xt_yeo, xt_box, rtol=1e-6)
assert_allclose(lam_yeo, lam_box, rtol=1e-6)
@pytest.mark.parametrize('x', [
np.array([1.0, float("nan"), 2.0]),
np.array([1.0, float("inf"), 2.0]),
np.array([1.0, -float("inf"), 2.0]),
np.array([-1.0, float("nan"), float("inf"), -float("inf"), 1.0])
])
def test_nonfinite_input(self, x):
with pytest.raises(ValueError, match='Yeo-Johnson input must be finite'):
xt_yeo, lam_yeo = stats.yeojohnson(x)
@pytest.mark.parametrize('x', [
# Attempt to trigger overflow in power expressions.
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
# Attempt to trigger overflow with a large optimal lambda.
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0]),
# Attempt to trigger overflow with large data.
np.array([2003.0e200, 1950.0e200, 1997.0e200, 2000.0e200, 2009.0e200])
])
def test_overflow(self, x):
# non-regression test for gh-18389
def optimizer(fun, lam_yeo):
out = optimize.fminbound(fun, -lam_yeo, lam_yeo, xtol=1.48e-08)
result = optimize.OptimizeResult()
result.x = out
return result
with np.errstate(all="raise"):
xt_yeo, lam_yeo = stats.yeojohnson(x)
xt_box, lam_box = stats.boxcox(
x + 1, optimizer=partial(optimizer, lam_yeo=lam_yeo))
assert np.isfinite(np.var(xt_yeo))
assert np.isfinite(np.var(xt_box))
assert_allclose(lam_yeo, lam_box, rtol=1e-6)
assert_allclose(xt_yeo, xt_box, rtol=1e-4)
@pytest.mark.parametrize('x', [
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0])
])
@pytest.mark.parametrize('scale', [1, 1e-12, 1e-32, 1e-150, 1e32, 1e200])
@pytest.mark.parametrize('sign', [1, -1])
def test_overflow_underflow_signed_data(self, x, scale, sign):
# non-regression test for gh-18389
with np.errstate(all="raise"):
xt_yeo, lam_yeo = stats.yeojohnson(sign * x * scale)
assert np.all(np.sign(sign * x) == np.sign(xt_yeo))
assert np.isfinite(lam_yeo)
assert np.isfinite(np.var(xt_yeo))
@pytest.mark.parametrize('x', [
np.array([0, 1, 2, 3]),
np.array([0, -1, 2, -3]),
np.array([0, 0, 0])
])
@pytest.mark.parametrize('sign', [1, -1])
@pytest.mark.parametrize('brack', [None, (-2, 2)])
def test_integer_signed_data(self, x, sign, brack):
with np.errstate(all="raise"):
x_int = sign * x
x_float = x_int.astype(np.float64)
lam_yeo_int = stats.yeojohnson_normmax(x_int, brack=brack)
xt_yeo_int = stats.yeojohnson(x_int, lmbda=lam_yeo_int)
lam_yeo_float = stats.yeojohnson_normmax(x_float, brack=brack)
xt_yeo_float = stats.yeojohnson(x_float, lmbda=lam_yeo_float)
assert np.all(np.sign(x_int) == np.sign(xt_yeo_int))
assert np.isfinite(lam_yeo_int)
assert np.isfinite(np.var(xt_yeo_int))
assert lam_yeo_int == lam_yeo_float
assert np.all(xt_yeo_int == xt_yeo_float)
class TestYeojohnsonNormmax:
def setup_method(self):
self.x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
def test_mle(self):
maxlog = stats.yeojohnson_normmax(self.x)
assert_allclose(maxlog, 1.876393, rtol=1e-6)
def test_darwin_example(self):
# test from original paper "A new family of power transformations to
# improve normality or symmetry" by Yeo and Johnson.
x = [6.1, -8.4, 1.0, 2.0, 0.7, 2.9, 3.5, 5.1, 1.8, 3.6, 7.0, 3.0, 9.3,
7.5, -6.0]
lmbda = stats.yeojohnson_normmax(x)
assert np.allclose(lmbda, 1.305, atol=1e-3)
class TestCircFuncs:
# In gh-5747, the R package `circular` was used to calculate reference
# values for the circular variance, e.g.:
# library(circular)
# options(digits=16)
# x = c(0, 2*pi/3, 5*pi/3)
# var.circular(x)
@pytest.mark.parametrize("test_func,expected",
[(stats.circmean, 0.167690146),
(stats.circvar, 0.006455174270186603),
(stats.circstd, 6.520702116)])
def test_circfuncs(self, test_func, expected):
x = np.array([355, 5, 2, 359, 10, 350])
assert_allclose(test_func(x, high=360), expected, rtol=1e-7)
def test_circfuncs_small(self):
x = np.array([20, 21, 22, 18, 19, 20.5, 19.2])
M1 = x.mean()
M2 = stats.circmean(x, high=360)
assert_allclose(M2, M1, rtol=1e-5)
V1 = (x*np.pi/180).var()
# for small variations, circvar is approximately half the
# linear variance
V1 = V1 / 2.
V2 = stats.circvar(x, high=360)
assert_allclose(V2, V1, rtol=1e-4)
S1 = x.std()
S2 = stats.circstd(x, high=360)
assert_allclose(S2, S1, rtol=1e-4)
@pytest.mark.parametrize("test_func, numpy_func",
[(stats.circmean, np.mean),
(stats.circvar, np.var),
(stats.circstd, np.std)])
def test_circfuncs_close(self, test_func, numpy_func):
# circfuncs should handle very similar inputs (gh-12740)
x = np.array([0.12675364631578953] * 10 + [0.12675365920187928] * 100)
circstat = test_func(x)
normal = numpy_func(x)
assert_allclose(circstat, normal, atol=2e-8)
def test_circmean_axis(self):
x = np.array([[355, 5, 2, 359, 10, 350],
[351, 7, 4, 352, 9, 349],
[357, 9, 8, 358, 4, 356]])
M1 = stats.circmean(x, high=360)
M2 = stats.circmean(x.ravel(), high=360)
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=1)
M2 = [stats.circmean(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=0)
M2 = [stats.circmean(x[:, i], high=360) for i in range(x.shape[1])]
assert_allclose(M1, M2, rtol=1e-14)
def test_circvar_axis(self):
x = np.array([[355, 5, 2, 359, 10, 350],
[351, 7, 4, 352, 9, 349],
[357, 9, 8, 358, 4, 356]])
V1 = stats.circvar(x, high=360)
V2 = stats.circvar(x.ravel(), high=360)
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=1)
V2 = [stats.circvar(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=0)
V2 = [stats.circvar(x[:, i], high=360) for i in range(x.shape[1])]
assert_allclose(V1, V2, rtol=1e-11)
def test_circstd_axis(self):
x = np.array([[355, 5, 2, 359, 10, 350],
[351, 7, 4, 352, 9, 349],
[357, 9, 8, 358, 4, 356]])
S1 = stats.circstd(x, high=360)
S2 = stats.circstd(x.ravel(), high=360)
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=1)
S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=0)
S2 = [stats.circstd(x[:, i], high=360) for i in range(x.shape[1])]
assert_allclose(S1, S2, rtol=1e-11)
@pytest.mark.parametrize("test_func,expected",
[(stats.circmean, 0.167690146),
(stats.circvar, 0.006455174270186603),
(stats.circstd, 6.520702116)])
def test_circfuncs_array_like(self, test_func, expected):
x = [355, 5, 2, 359, 10, 350]
assert_allclose(test_func(x, high=360), expected, rtol=1e-7)
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_empty(self, test_func):
assert_(np.isnan(test_func([])))
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_nan_propagate(self, test_func):
x = [355, 5, 2, 359, 10, 350, np.nan]
assert_(np.isnan(test_func(x, high=360)))
@pytest.mark.parametrize("test_func,expected",
[(stats.circmean,
{None: np.nan, 0: 355.66582264, 1: 0.28725053}),
(stats.circvar,
{None: np.nan,
0: 0.002570671054089924,
1: 0.005545914017677123}),
(stats.circstd,
{None: np.nan, 0: 4.11093193, 1: 6.04265394})])
def test_nan_propagate_array(self, test_func, expected):
x = np.array([[355, 5, 2, 359, 10, 350, 1],
[351, 7, 4, 352, 9, 349, np.nan],
[1, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
for axis in expected.keys():
out = test_func(x, high=360, axis=axis)
if axis is None:
assert_(np.isnan(out))
else:
assert_allclose(out[0], expected[axis], rtol=1e-7)
assert_(np.isnan(out[1:]).all())
@pytest.mark.parametrize("test_func,expected",
[(stats.circmean,
{None: 359.4178026893944,
0: np.array([353.0, 6.0, 3.0, 355.5, 9.5,
349.5]),
1: np.array([0.16769015, 358.66510252])}),
(stats.circvar,
{None: 0.008396678483192477,
0: np.array([1.9997969, 0.4999873, 0.4999873,
6.1230956, 0.1249992, 0.1249992]
)*(np.pi/180)**2,
1: np.array([0.006455174270186603,
0.01016767581393285])}),
(stats.circstd,
{None: 7.440570778057074,
0: np.array([2.00020313, 1.00002539, 1.00002539,
3.50108929, 0.50000317,
0.50000317]),
1: np.array([6.52070212, 8.19138093])})])
def test_nan_omit_array(self, test_func, expected):
x = np.array([[355, 5, 2, 359, 10, 350, np.nan],
[351, 7, 4, 352, 9, 349, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
for axis in expected.keys():
out = test_func(x, high=360, nan_policy='omit', axis=axis)
if axis is None:
assert_allclose(out, expected[axis], rtol=1e-7)
else:
assert_allclose(out[:-1], expected[axis], rtol=1e-7)
assert_(np.isnan(out[-1]))
@pytest.mark.parametrize("test_func,expected",
[(stats.circmean, 0.167690146),
(stats.circvar, 0.006455174270186603),
(stats.circstd, 6.520702116)])
def test_nan_omit(self, test_func, expected):
x = [355, 5, 2, 359, 10, 350, np.nan]
assert_allclose(test_func(x, high=360, nan_policy='omit'),
expected, rtol=1e-7)
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_nan_omit_all(self, test_func):
x = [np.nan, np.nan, np.nan, np.nan, np.nan]
assert_(np.isnan(test_func(x, nan_policy='omit')))
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_nan_omit_all_axis(self, test_func):
x = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan]])
out = test_func(x, nan_policy='omit', axis=1)
assert_(np.isnan(out).all())
assert_(len(out) == 2)
@pytest.mark.parametrize("x",
[[355, 5, 2, 359, 10, 350, np.nan],
np.array([[355, 5, 2, 359, 10, 350, np.nan],
[351, 7, 4, 352, np.nan, 9, 349]])])
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_nan_raise(self, test_func, x):
assert_raises(ValueError, test_func, x, high=360, nan_policy='raise')
@pytest.mark.parametrize("x",
[[355, 5, 2, 359, 10, 350, np.nan],
np.array([[355, 5, 2, 359, 10, 350, np.nan],
[351, 7, 4, 352, np.nan, 9, 349]])])
@pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
stats.circstd])
def test_bad_nan_policy(self, test_func, x):
assert_raises(ValueError, test_func, x, high=360, nan_policy='foobar')
def test_circmean_scalar(self):
x = 1.
M1 = x
M2 = stats.circmean(x)
assert_allclose(M2, M1, rtol=1e-5)
def test_circmean_range(self):
# regression test for gh-6420: circmean(..., high, low) must be
# between `high` and `low`
m = stats.circmean(np.arange(0, 2, 0.1), np.pi, -np.pi)
assert_(m < np.pi)
assert_(m > -np.pi)
def test_circfuncs_uint8(self):
# regression test for gh-7255: overflow when working with
# numpy uint8 data type
x = np.array([150, 10], dtype='uint8')
assert_equal(stats.circmean(x, high=180), 170.0)
assert_allclose(stats.circvar(x, high=180), 0.2339555554617, rtol=1e-7)
assert_allclose(stats.circstd(x, high=180), 20.91551378, rtol=1e-7)
class TestMedianTest:
def test_bad_n_samples(self):
# median_test requires at least two samples.
assert_raises(ValueError, stats.median_test, [1, 2, 3])
def test_empty_sample(self):
# Each sample must contain at least one value.
assert_raises(ValueError, stats.median_test, [], [1, 2, 3])
def test_empty_when_ties_ignored(self):
# The grand median is 1, and all values in the first argument are
# equal to the grand median. With ties="ignore", those values are
# ignored, which results in the first sample being (in effect) empty.
# This should raise a ValueError.
assert_raises(ValueError, stats.median_test,
[1, 1, 1, 1], [2, 0, 1], [2, 0], ties="ignore")
def test_empty_contingency_row(self):
# The grand median is 1, and with the default ties="below", all the
# values in the samples are counted as being below the grand median.
# This would result a row of zeros in the contingency table, which is
# an error.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1])
# With ties="above", all the values are counted as above the
# grand median.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1],
ties="above")
def test_bad_ties(self):
assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5],
ties="foo")
def test_bad_nan_policy(self):
assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5],
nan_policy='foobar')
def test_bad_keyword(self):
assert_raises(TypeError, stats.median_test, [1, 2, 3], [4, 5],
foo="foo")
def test_simple(self):
x = [1, 2, 3]
y = [1, 2, 3]
stat, p, med, tbl = stats.median_test(x, y)
# The median is floating point, but this equality test should be safe.
assert_equal(med, 2.0)
assert_array_equal(tbl, [[1, 1], [2, 2]])
# The expected values of the contingency table equal the contingency
# table, so the statistic should be 0 and the p-value should be 1.
assert_equal(stat, 0)
assert_equal(p, 1)
def test_ties_options(self):
# Test the contingency table calculation.
x = [1, 2, 3, 4]
y = [5, 6]
z = [7, 8, 9]
# grand median is 5.
# Default 'ties' option is "below".
stat, p, m, tbl = stats.median_test(x, y, z)
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 1, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="ignore")
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 0, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="above")
assert_equal(m, 5)
assert_equal(tbl, [[0, 2, 3], [4, 0, 0]])
def test_nan_policy_options(self):
x = [1, 2, np.nan]
y = [4, 5, 6]
mt1 = stats.median_test(x, y, nan_policy='propagate')
s, p, m, t = stats.median_test(x, y, nan_policy='omit')
assert_equal(mt1, (np.nan, np.nan, np.nan, None))
assert_allclose(s, 0.31250000000000006)
assert_allclose(p, 0.57615012203057869)
assert_equal(m, 4.0)
assert_equal(t, np.array([[0, 2], [2, 1]]))
assert_raises(ValueError, stats.median_test, x, y, nan_policy='raise')
def test_basic(self):
# median_test calls chi2_contingency to compute the test statistic
# and p-value. Make sure it hasn't screwed up the call...
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8]
stat, p, m, tbl = stats.median_test(x, y)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, lambda_=0)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, lambda_=0)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, correction=False)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, correction=False)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
@pytest.mark.parametrize("correction", [False, True])
def test_result(self, correction):
x = [1, 2, 3]
y = [1, 2, 3]
res = stats.median_test(x, y, correction=correction)
assert_equal((res.statistic, res.pvalue, res.median, res.table), res)
class TestDirectionalStats:
# Reference implementations are not available
def test_directional_stats_correctness(self):
# Data from Fisher: Dispersion on a sphere, 1953 and
# Mardia and Jupp, Directional Statistics.
decl = -np.deg2rad(np.array([343.2, 62., 36.9, 27., 359.,
5.7, 50.4, 357.6, 44.]))
incl = -np.deg2rad(np.array([66.1, 68.7, 70.1, 82.1, 79.5,
73., 69.3, 58.8, 51.4]))
data = np.stack((np.cos(incl) * np.cos(decl),
np.cos(incl) * np.sin(decl),
np.sin(incl)),
axis=1)
dirstats = stats.directional_stats(data)
directional_mean = dirstats.mean_direction
mean_rounded = np.round(directional_mean, 4)
reference_mean = np.array([0.2984, -0.1346, -0.9449])
assert_allclose(mean_rounded, reference_mean)
@pytest.mark.parametrize('angles, ref', [
([-np.pi/2, np.pi/2], 1.),
([0, 2*np.pi], 0.)
])
def test_directional_stats_2d_special_cases(self, angles, ref):
if callable(ref):
ref = ref(angles)
data = np.stack([np.cos(angles), np.sin(angles)], axis=1)
res = 1 - stats.directional_stats(data).mean_resultant_length
assert_allclose(res, ref)
def test_directional_stats_2d(self):
# Test that for circular data directional_stats
# yields the same result as circmean/circvar
rng = np.random.default_rng(0xec9a6899d5a2830e0d1af479dbe1fd0c)
testdata = 2 * np.pi * rng.random((1000, ))
testdata_vector = np.stack((np.cos(testdata),
np.sin(testdata)),
axis=1)
dirstats = stats.directional_stats(testdata_vector)
directional_mean = dirstats.mean_direction
directional_mean_angle = np.arctan2(directional_mean[1],
directional_mean[0])
directional_mean_angle = directional_mean_angle % (2*np.pi)
circmean = stats.circmean(testdata)
assert_allclose(circmean, directional_mean_angle)
directional_var = 1 - dirstats.mean_resultant_length
circular_var = stats.circvar(testdata)
assert_allclose(directional_var, circular_var)
def test_directional_mean_higher_dim(self):
# test that directional_stats works for higher dimensions
# here a 4D array is reduced over axis = 2
data = np.array([[0.8660254, 0.5, 0.],
[0.8660254, -0.5, 0.]])
full_array = np.tile(data, (2, 2, 2, 1))
expected = np.array([[[1., 0., 0.],
[1., 0., 0.]],
[[1., 0., 0.],
[1., 0., 0.]]])
dirstats = stats.directional_stats(full_array, axis=2)
assert_allclose(expected, dirstats.mean_direction)
def test_directional_stats_list_ndarray_input(self):
# test that list and numpy array inputs yield same results
data = [[0.8660254, 0.5, 0.], [0.8660254, -0.5, 0]]
data_array = np.asarray(data)
res = stats.directional_stats(data)
ref = stats.directional_stats(data_array)
assert_allclose(res.mean_direction, ref.mean_direction)
assert_allclose(res.mean_resultant_length,
res.mean_resultant_length)
def test_directional_stats_1d_error(self):
# test that one-dimensional data raises ValueError
data = np.ones((5, ))
message = (r"samples must at least be two-dimensional. "
r"Instead samples has shape: (5,)")
with pytest.raises(ValueError, match=re.escape(message)):
stats.directional_stats(data)
def test_directional_stats_normalize(self):
# test that directional stats calculations yield same results
# for unnormalized input with normalize=True and normalized
# input with normalize=False
data = np.array([[0.8660254, 0.5, 0.],
[1.7320508, -1., 0.]])
res = stats.directional_stats(data, normalize=True)
normalized_data = data / np.linalg.norm(data, axis=-1,
keepdims=True)
ref = stats.directional_stats(normalized_data,
normalize=False)
assert_allclose(res.mean_direction, ref.mean_direction)
assert_allclose(res.mean_resultant_length,
ref.mean_resultant_length)
class TestFDRControl:
def test_input_validation(self):
message = "`ps` must include only numbers between 0 and 1"
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([-1, 0.5, 0.7])
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([0.5, 0.7, 2])
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([0.5, 0.7, np.nan])
message = "Unrecognized `method` 'YAK'"
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([0.5, 0.7, 0.9], method='YAK')
message = "`axis` must be an integer or `None`"
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([0.5, 0.7, 0.9], axis=1.5)
with pytest.raises(ValueError, match=message):
stats.false_discovery_control([0.5, 0.7, 0.9], axis=(1, 2))
def test_against_TileStats(self):
# See reference [3] of false_discovery_control
ps = [0.005, 0.009, 0.019, 0.022, 0.051, 0.101, 0.361, 0.387]
res = stats.false_discovery_control(ps)
ref = [0.036, 0.036, 0.044, 0.044, 0.082, 0.135, 0.387, 0.387]
assert_allclose(res, ref, atol=1e-3)
@pytest.mark.parametrize("case",
[([0.24617028, 0.01140030, 0.05652047, 0.06841983,
0.07989886, 0.01841490, 0.17540784, 0.06841983,
0.06841983, 0.25464082], 'bh'),
([0.72102493, 0.03339112, 0.16554665, 0.20039952,
0.23402122, 0.05393666, 0.51376399, 0.20039952,
0.20039952, 0.74583488], 'by')])
def test_against_R(self, case):
# Test against p.adjust, e.g.
# p = c(0.22155325, 0.00114003,..., 0.0364813 , 0.25464082)
# p.adjust(p, "BY")
ref, method = case
rng = np.random.default_rng(6134137338861652935)
ps = stats.loguniform.rvs(1e-3, 0.5, size=10, random_state=rng)
ps[3] = ps[7] # force a tie
res = stats.false_discovery_control(ps, method=method)
assert_allclose(res, ref, atol=1e-6)
def test_axis_None(self):
rng = np.random.default_rng(6134137338861652935)
ps = stats.loguniform.rvs(1e-3, 0.5, size=(3, 4, 5), random_state=rng)
res = stats.false_discovery_control(ps, axis=None)
ref = stats.false_discovery_control(ps.ravel())
assert_equal(res, ref)
@pytest.mark.parametrize("axis", [0, 1, -1])
def test_axis(self, axis):
rng = np.random.default_rng(6134137338861652935)
ps = stats.loguniform.rvs(1e-3, 0.5, size=(3, 4, 5), random_state=rng)
res = stats.false_discovery_control(ps, axis=axis)
ref = np.apply_along_axis(stats.false_discovery_control, axis, ps)
assert_equal(res, ref)
def test_edge_cases(self):
assert_array_equal(stats.false_discovery_control([0.25]), [0.25])
assert_array_equal(stats.false_discovery_control(0.25), 0.25)
assert_array_equal(stats.false_discovery_control([]), [])
Reference in New Issue View Git Blame Copy Permalink