8678 lines
352 KiB
Python
8678 lines
352 KiB
Python
""" Test functions for stats module
|
||
|
||
WRITTEN BY LOUIS LUANGKESORN <lluang@yahoo.com> FOR THE STATS MODULE
|
||
BASED ON WILKINSON'S STATISTICS QUIZ
|
||
https://www.stanford.edu/~clint/bench/wilk.txt
|
||
|
||
Additional tests by a host of SciPy developers.
|
||
"""
|
||
import os
|
||
import re
|
||
import warnings
|
||
from collections import namedtuple
|
||
from itertools import product
|
||
import hypothesis.extra.numpy as npst
|
||
import hypothesis
|
||
import contextlib
|
||
|
||
from numpy.testing import (assert_, assert_equal,
|
||
assert_almost_equal, assert_array_almost_equal,
|
||
assert_array_equal, assert_approx_equal,
|
||
assert_allclose, assert_warns, suppress_warnings,
|
||
assert_array_less)
|
||
import pytest
|
||
from pytest import raises as assert_raises
|
||
import numpy.ma.testutils as mat
|
||
from numpy import array, arange, float32, float64, power
|
||
import numpy as np
|
||
|
||
import scipy.stats as stats
|
||
import scipy.stats.mstats as mstats
|
||
import scipy.stats._mstats_basic as mstats_basic
|
||
from scipy.stats._ksstats import kolmogn
|
||
from scipy.special._testutils import FuncData
|
||
from scipy.special import binom
|
||
from scipy import optimize
|
||
from .common_tests import check_named_results
|
||
from scipy.spatial.distance import cdist
|
||
from scipy.stats._axis_nan_policy import _broadcast_concatenate
|
||
from scipy.stats._stats_py import _permutation_distribution_t
|
||
from scipy._lib._util import AxisError
|
||
|
||
|
||
""" Numbers in docstrings beginning with 'W' refer to the section numbers
|
||
and headings found in the STATISTICS QUIZ of Leland Wilkinson. These are
|
||
considered to be essential functionality. True testing and
|
||
evaluation of a statistics package requires use of the
|
||
NIST Statistical test data. See McCoullough(1999) Assessing The Reliability
|
||
of Statistical Software for a test methodology and its
|
||
implementation in testing SAS, SPSS, and S-Plus
|
||
"""
|
||
|
||
# Datasets
|
||
# These data sets are from the nasty.dat sets used by Wilkinson
|
||
# For completeness, I should write the relevant tests and count them as failures
|
||
# Somewhat acceptable, since this is still beta software. It would count as a
|
||
# good target for 1.0 status
|
||
X = array([1,2,3,4,5,6,7,8,9], float)
|
||
ZERO = array([0,0,0,0,0,0,0,0,0], float)
|
||
BIG = array([99999991,99999992,99999993,99999994,99999995,99999996,99999997,
|
||
99999998,99999999], float)
|
||
LITTLE = array([0.99999991,0.99999992,0.99999993,0.99999994,0.99999995,0.99999996,
|
||
0.99999997,0.99999998,0.99999999], float)
|
||
HUGE = array([1e+12,2e+12,3e+12,4e+12,5e+12,6e+12,7e+12,8e+12,9e+12], float)
|
||
TINY = array([1e-12,2e-12,3e-12,4e-12,5e-12,6e-12,7e-12,8e-12,9e-12], float)
|
||
ROUND = array([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5], float)
|
||
|
||
|
||
class TestTrimmedStats:
|
||
# TODO: write these tests to handle missing values properly
|
||
dprec = np.finfo(np.float64).precision
|
||
|
||
def test_tmean(self):
|
||
y = stats.tmean(X, (2, 8), (True, True))
|
||
assert_approx_equal(y, 5.0, significant=self.dprec)
|
||
|
||
y1 = stats.tmean(X, limits=(2, 8), inclusive=(False, False))
|
||
y2 = stats.tmean(X, limits=None)
|
||
assert_approx_equal(y1, y2, significant=self.dprec)
|
||
|
||
x_2d = arange(63, dtype=float64).reshape(9, 7)
|
||
y = stats.tmean(x_2d, axis=None)
|
||
assert_approx_equal(y, x_2d.mean(), significant=self.dprec)
|
||
|
||
y = stats.tmean(x_2d, axis=0)
|
||
assert_array_almost_equal(y, x_2d.mean(axis=0), decimal=8)
|
||
|
||
y = stats.tmean(x_2d, axis=1)
|
||
assert_array_almost_equal(y, x_2d.mean(axis=1), decimal=8)
|
||
|
||
y = stats.tmean(x_2d, limits=(2, 61), axis=None)
|
||
assert_approx_equal(y, 31.5, significant=self.dprec)
|
||
|
||
y = stats.tmean(x_2d, limits=(2, 21), axis=0)
|
||
y_true = [14, 11.5, 9, 10, 11, 12, 13]
|
||
assert_array_almost_equal(y, y_true, decimal=8)
|
||
|
||
y = stats.tmean(x_2d, limits=(2, 21), inclusive=(True, False), axis=0)
|
||
y_true = [10.5, 11.5, 9, 10, 11, 12, 13]
|
||
assert_array_almost_equal(y, y_true, decimal=8)
|
||
|
||
x_2d_with_nan = np.array(x_2d)
|
||
x_2d_with_nan[-1, -3:] = np.nan
|
||
y = stats.tmean(x_2d_with_nan, limits=(1, 13), axis=0)
|
||
y_true = [7, 4.5, 5.5, 6.5, np.nan, np.nan, np.nan]
|
||
assert_array_almost_equal(y, y_true, decimal=8)
|
||
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "Mean of empty slice")
|
||
|
||
y = stats.tmean(x_2d, limits=(2, 21), axis=1)
|
||
y_true = [4, 10, 17, 21, np.nan, np.nan, np.nan, np.nan, np.nan]
|
||
assert_array_almost_equal(y, y_true, decimal=8)
|
||
|
||
y = stats.tmean(x_2d, limits=(2, 21),
|
||
inclusive=(False, True), axis=1)
|
||
y_true = [4.5, 10, 17, 21, np.nan, np.nan, np.nan, np.nan, np.nan]
|
||
assert_array_almost_equal(y, y_true, decimal=8)
|
||
|
||
def test_tvar(self):
|
||
y = stats.tvar(X, limits=(2, 8), inclusive=(True, True))
|
||
assert_approx_equal(y, 4.6666666666666661, significant=self.dprec)
|
||
|
||
y = stats.tvar(X, limits=None)
|
||
assert_approx_equal(y, X.var(ddof=1), significant=self.dprec)
|
||
|
||
x_2d = arange(63, dtype=float64).reshape((9, 7))
|
||
y = stats.tvar(x_2d, axis=None)
|
||
assert_approx_equal(y, x_2d.var(ddof=1), significant=self.dprec)
|
||
|
||
y = stats.tvar(x_2d, axis=0)
|
||
assert_array_almost_equal(y[0], np.full((1, 7), 367.50000000), decimal=8)
|
||
|
||
y = stats.tvar(x_2d, axis=1)
|
||
assert_array_almost_equal(y[0], np.full((1, 9), 4.66666667), decimal=8)
|
||
|
||
y = stats.tvar(x_2d[3, :])
|
||
assert_approx_equal(y, 4.666666666666667, significant=self.dprec)
|
||
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "Degrees of freedom <= 0 for slice.")
|
||
|
||
# Limiting some values along one axis
|
||
y = stats.tvar(x_2d, limits=(1, 5), axis=1, inclusive=(True, True))
|
||
assert_approx_equal(y[0], 2.5, significant=self.dprec)
|
||
|
||
# Limiting all values along one axis
|
||
y = stats.tvar(x_2d, limits=(0, 6), axis=1, inclusive=(True, True))
|
||
assert_approx_equal(y[0], 4.666666666666667, significant=self.dprec)
|
||
assert_equal(y[1], np.nan)
|
||
|
||
def test_tstd(self):
|
||
y = stats.tstd(X, (2, 8), (True, True))
|
||
assert_approx_equal(y, 2.1602468994692865, significant=self.dprec)
|
||
|
||
y = stats.tstd(X, limits=None)
|
||
assert_approx_equal(y, X.std(ddof=1), significant=self.dprec)
|
||
|
||
def test_tmin(self):
|
||
assert_equal(stats.tmin(4), 4)
|
||
|
||
x = np.arange(10)
|
||
assert_equal(stats.tmin(x), 0)
|
||
assert_equal(stats.tmin(x, lowerlimit=0), 0)
|
||
assert_equal(stats.tmin(x, lowerlimit=0, inclusive=False), 1)
|
||
|
||
x = x.reshape((5, 2))
|
||
assert_equal(stats.tmin(x, lowerlimit=0, inclusive=False), [2, 1])
|
||
assert_equal(stats.tmin(x, axis=1), [0, 2, 4, 6, 8])
|
||
assert_equal(stats.tmin(x, axis=None), 0)
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "invalid value*")
|
||
assert_equal(stats.tmin(x), np.nan)
|
||
assert_equal(stats.tmin(x, nan_policy='omit'), 0.)
|
||
assert_raises(ValueError, stats.tmin, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.tmin, x, nan_policy='foobar')
|
||
msg = "'propagate', 'raise', 'omit'"
|
||
with assert_raises(ValueError, match=msg):
|
||
stats.tmin(x, nan_policy='foo')
|
||
|
||
# check that that if a full slice is masked, the output returns a
|
||
# nan instead of a garbage value.
|
||
with suppress_warnings() as sup:
|
||
sup.filter(RuntimeWarning, "All-NaN slice encountered")
|
||
x = np.arange(16).reshape(4, 4)
|
||
res = stats.tmin(x, lowerlimit=4, axis=1)
|
||
assert_equal(res, [np.nan, 4, 8, 12])
|
||
|
||
def test_tmax(self):
|
||
assert_equal(stats.tmax(4), 4)
|
||
|
||
x = np.arange(10)
|
||
assert_equal(stats.tmax(x), 9)
|
||
assert_equal(stats.tmax(x, upperlimit=9), 9)
|
||
assert_equal(stats.tmax(x, upperlimit=9, inclusive=False), 8)
|
||
|
||
x = x.reshape((5, 2))
|
||
assert_equal(stats.tmax(x, upperlimit=9, inclusive=False), [8, 7])
|
||
assert_equal(stats.tmax(x, axis=1), [1, 3, 5, 7, 9])
|
||
assert_equal(stats.tmax(x, axis=None), 9)
|
||
|
||
x = np.arange(10.)
|
||
x[6] = np.nan
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "invalid value*")
|
||
assert_equal(stats.tmax(x), np.nan)
|
||
assert_equal(stats.tmax(x, nan_policy='omit'), 9.)
|
||
assert_raises(ValueError, stats.tmax, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.tmax, x, nan_policy='foobar')
|
||
|
||
# check that that if a full slice is masked, the output returns a
|
||
# nan instead of a garbage value.
|
||
with suppress_warnings() as sup:
|
||
sup.filter(RuntimeWarning, "All-NaN slice encountered")
|
||
x = np.arange(16).reshape(4, 4)
|
||
res = stats.tmax(x, upperlimit=11, axis=1)
|
||
assert_equal(res, [3, 7, 11, np.nan])
|
||
|
||
def test_tsem(self):
|
||
y = stats.tsem(X, limits=(3, 8), inclusive=(False, True))
|
||
y_ref = np.array([4, 5, 6, 7, 8])
|
||
assert_approx_equal(y, y_ref.std(ddof=1) / np.sqrt(y_ref.size),
|
||
significant=self.dprec)
|
||
|
||
assert_approx_equal(stats.tsem(X, limits=[-1, 10]),
|
||
stats.tsem(X, limits=None),
|
||
significant=self.dprec)
|
||
|
||
|
||
class TestCorrPearsonr:
|
||
""" W.II.D. Compute a correlation matrix on all the variables.
|
||
|
||
All the correlations, except for ZERO and MISS, should be exactly 1.
|
||
ZERO and MISS should have undefined or missing correlations with the
|
||
other variables. The same should go for SPEARMAN correlations, if
|
||
your program has them.
|
||
"""
|
||
|
||
def test_pXX(self):
|
||
y = stats.pearsonr(X,X)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pXBIG(self):
|
||
y = stats.pearsonr(X,BIG)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pXLITTLE(self):
|
||
y = stats.pearsonr(X,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pXHUGE(self):
|
||
y = stats.pearsonr(X,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pXTINY(self):
|
||
y = stats.pearsonr(X,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pXROUND(self):
|
||
y = stats.pearsonr(X,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pBIGBIG(self):
|
||
y = stats.pearsonr(BIG,BIG)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pBIGLITTLE(self):
|
||
y = stats.pearsonr(BIG,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pBIGHUGE(self):
|
||
y = stats.pearsonr(BIG,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pBIGTINY(self):
|
||
y = stats.pearsonr(BIG,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pBIGROUND(self):
|
||
y = stats.pearsonr(BIG,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pLITTLELITTLE(self):
|
||
y = stats.pearsonr(LITTLE,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pLITTLEHUGE(self):
|
||
y = stats.pearsonr(LITTLE,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pLITTLETINY(self):
|
||
y = stats.pearsonr(LITTLE,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pLITTLEROUND(self):
|
||
y = stats.pearsonr(LITTLE,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pHUGEHUGE(self):
|
||
y = stats.pearsonr(HUGE,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pHUGETINY(self):
|
||
y = stats.pearsonr(HUGE,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pHUGEROUND(self):
|
||
y = stats.pearsonr(HUGE,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pTINYTINY(self):
|
||
y = stats.pearsonr(TINY,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pTINYROUND(self):
|
||
y = stats.pearsonr(TINY,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pROUNDROUND(self):
|
||
y = stats.pearsonr(ROUND,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_pearsonr_result_attributes(self):
|
||
res = stats.pearsonr(X, X)
|
||
attributes = ('correlation', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
assert_equal(res.correlation, res.statistic)
|
||
|
||
def test_r_almost_exactly_pos1(self):
|
||
a = arange(3.0)
|
||
r, prob = stats.pearsonr(a, a)
|
||
|
||
assert_allclose(r, 1.0, atol=1e-15)
|
||
# With n = len(a) = 3, the error in prob grows like the
|
||
# square root of the error in r.
|
||
assert_allclose(prob, 0.0, atol=np.sqrt(2*np.spacing(1.0)))
|
||
|
||
def test_r_almost_exactly_neg1(self):
|
||
a = arange(3.0)
|
||
r, prob = stats.pearsonr(a, -a)
|
||
|
||
assert_allclose(r, -1.0, atol=1e-15)
|
||
# With n = len(a) = 3, the error in prob grows like the
|
||
# square root of the error in r.
|
||
assert_allclose(prob, 0.0, atol=np.sqrt(2*np.spacing(1.0)))
|
||
|
||
def test_basic(self):
|
||
# A basic test, with a correlation coefficient
|
||
# that is not 1 or -1.
|
||
a = array([-1, 0, 1])
|
||
b = array([0, 0, 3])
|
||
r, prob = stats.pearsonr(a, b)
|
||
assert_approx_equal(r, np.sqrt(3)/2)
|
||
assert_approx_equal(prob, 1/3)
|
||
|
||
def test_constant_input(self):
|
||
# Zero variance input
|
||
# See https://github.com/scipy/scipy/issues/3728
|
||
msg = "An input array is constant"
|
||
with pytest.warns(stats.ConstantInputWarning, match=msg):
|
||
r, p = stats.pearsonr([0.667, 0.667, 0.667], [0.123, 0.456, 0.789])
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
|
||
def test_near_constant_input(self):
|
||
# Near constant input (but not constant):
|
||
x = [2, 2, 2 + np.spacing(2)]
|
||
y = [3, 3, 3 + 6*np.spacing(3)]
|
||
msg = "An input array is nearly constant; the computed"
|
||
with pytest.warns(stats.NearConstantInputWarning, match=msg):
|
||
# r and p are garbage, so don't bother checking them in this case.
|
||
# (The exact value of r would be 1.)
|
||
r, p = stats.pearsonr(x, y)
|
||
|
||
def test_very_small_input_values(self):
|
||
# Very small values in an input. A naive implementation will
|
||
# suffer from underflow.
|
||
# See https://github.com/scipy/scipy/issues/9353
|
||
x = [0.004434375, 0.004756007, 0.003911996, 0.0038005, 0.003409971]
|
||
y = [2.48e-188, 7.41e-181, 4.09e-208, 2.08e-223, 2.66e-245]
|
||
r, p = stats.pearsonr(x,y)
|
||
|
||
# The expected values were computed using mpmath with 80 digits
|
||
# of precision.
|
||
assert_allclose(r, 0.7272930540750450)
|
||
assert_allclose(p, 0.1637805429533202)
|
||
|
||
def test_very_large_input_values(self):
|
||
# Very large values in an input. A naive implementation will
|
||
# suffer from overflow.
|
||
# See https://github.com/scipy/scipy/issues/8980
|
||
x = 1e90*np.array([0, 0, 0, 1, 1, 1, 1])
|
||
y = 1e90*np.arange(7)
|
||
|
||
r, p = stats.pearsonr(x, y)
|
||
|
||
# The expected values were computed using mpmath with 80 digits
|
||
# of precision.
|
||
assert_allclose(r, 0.8660254037844386)
|
||
assert_allclose(p, 0.011724811003954638)
|
||
|
||
def test_extremely_large_input_values(self):
|
||
# Extremely large values in x and y. These values would cause the
|
||
# product sigma_x * sigma_y to overflow if the two factors were
|
||
# computed independently.
|
||
x = np.array([2.3e200, 4.5e200, 6.7e200, 8e200])
|
||
y = np.array([1.2e199, 5.5e200, 3.3e201, 1.0e200])
|
||
r, p = stats.pearsonr(x, y)
|
||
|
||
# The expected values were computed using mpmath with 80 digits
|
||
# of precision.
|
||
assert_allclose(r, 0.351312332103289)
|
||
assert_allclose(p, 0.648687667896711)
|
||
|
||
def test_length_two_pos1(self):
|
||
# Inputs with length 2.
|
||
# See https://github.com/scipy/scipy/issues/7730
|
||
res = stats.pearsonr([1, 2], [3, 5])
|
||
r, p = res
|
||
assert_equal(r, 1)
|
||
assert_equal(p, 1)
|
||
assert_equal(res.confidence_interval(), (-1, 1))
|
||
|
||
def test_length_two_neg2(self):
|
||
# Inputs with length 2.
|
||
# See https://github.com/scipy/scipy/issues/7730
|
||
r, p = stats.pearsonr([2, 1], [3, 5])
|
||
assert_equal(r, -1)
|
||
assert_equal(p, 1)
|
||
|
||
# Expected values computed with R 3.6.2 cor.test, e.g.
|
||
# options(digits=16)
|
||
# x <- c(1, 2, 3, 4)
|
||
# y <- c(0, 1, 0.5, 1)
|
||
# cor.test(x, y, method = "pearson", alternative = "g")
|
||
# correlation coefficient and p-value for alternative='two-sided'
|
||
# calculated with mpmath agree to 16 digits.
|
||
@pytest.mark.parametrize('alternative, pval, rlow, rhigh, sign',
|
||
[('two-sided', 0.325800137536, -0.814938968841, 0.99230697523, 1),
|
||
('less', 0.8370999312316, -1, 0.985600937290653, 1),
|
||
('greater', 0.1629000687684, -0.6785654158217636, 1, 1),
|
||
('two-sided', 0.325800137536, -0.992306975236, 0.81493896884, -1),
|
||
('less', 0.1629000687684, -1.0, 0.6785654158217636, -1),
|
||
('greater', 0.8370999312316, -0.985600937290653, 1.0, -1)])
|
||
def test_basic_example(self, alternative, pval, rlow, rhigh, sign):
|
||
x = [1, 2, 3, 4]
|
||
y = np.array([0, 1, 0.5, 1]) * sign
|
||
result = stats.pearsonr(x, y, alternative=alternative)
|
||
assert_allclose(result.statistic, 0.6741998624632421*sign, rtol=1e-12)
|
||
assert_allclose(result.pvalue, pval, rtol=1e-6)
|
||
ci = result.confidence_interval()
|
||
assert_allclose(ci, (rlow, rhigh), rtol=1e-6)
|
||
|
||
def test_negative_correlation_pvalue_gh17795(self):
|
||
x = np.arange(10)
|
||
y = -x
|
||
test_greater = stats.pearsonr(x, y, alternative='greater')
|
||
test_less = stats.pearsonr(x, y, alternative='less')
|
||
assert_allclose(test_greater.pvalue, 1)
|
||
assert_allclose(test_less.pvalue, 0, atol=1e-20)
|
||
|
||
def test_length3_r_exactly_negative_one(self):
|
||
x = [1, 2, 3]
|
||
y = [5, -4, -13]
|
||
res = stats.pearsonr(x, y)
|
||
|
||
# The expected r and p are exact.
|
||
r, p = res
|
||
assert_allclose(r, -1.0)
|
||
assert_allclose(p, 0.0, atol=1e-7)
|
||
|
||
assert_equal(res.confidence_interval(), (-1, 1))
|
||
|
||
def test_unequal_lengths(self):
|
||
x = [1, 2, 3]
|
||
y = [4, 5]
|
||
assert_raises(ValueError, stats.pearsonr, x, y)
|
||
|
||
def test_len1(self):
|
||
x = [1]
|
||
y = [2]
|
||
assert_raises(ValueError, stats.pearsonr, x, y)
|
||
|
||
def test_complex_data(self):
|
||
x = [-1j, -2j, -3.0j]
|
||
y = [-1j, -2j, -3.0j]
|
||
message = 'This function does not support complex data'
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.pearsonr(x, y)
|
||
|
||
@pytest.mark.xslow
|
||
@pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
|
||
@pytest.mark.parametrize('method', ('permutation', 'monte_carlo'))
|
||
def test_resampling_pvalue(self, method, alternative):
|
||
rng = np.random.default_rng(24623935790378923)
|
||
size = 100 if method == 'permutation' else 1000
|
||
x = rng.normal(size=size)
|
||
y = rng.normal(size=size)
|
||
methods = {'permutation': stats.PermutationMethod(random_state=rng),
|
||
'monte_carlo': stats.MonteCarloMethod(rvs=(rng.normal,)*2)}
|
||
method = methods[method]
|
||
res = stats.pearsonr(x, y, alternative=alternative, method=method)
|
||
ref = stats.pearsonr(x, y, alternative=alternative)
|
||
assert_allclose(res.statistic, ref.statistic, rtol=1e-15)
|
||
assert_allclose(res.pvalue, ref.pvalue, rtol=1e-2, atol=1e-3)
|
||
|
||
@pytest.mark.xslow
|
||
@pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
|
||
def test_bootstrap_ci(self, alternative):
|
||
rng = np.random.default_rng(24623935790378923)
|
||
x = rng.normal(size=100)
|
||
y = rng.normal(size=100)
|
||
res = stats.pearsonr(x, y, alternative=alternative)
|
||
|
||
method = stats.BootstrapMethod(random_state=rng)
|
||
res_ci = res.confidence_interval(method=method)
|
||
ref_ci = res.confidence_interval()
|
||
|
||
assert_allclose(res_ci, ref_ci, atol=1e-2)
|
||
|
||
def test_invalid_method(self):
|
||
message = "`method` must be an instance of..."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.pearsonr([1, 2], [3, 4], method="asymptotic")
|
||
|
||
res = stats.pearsonr([1, 2], [3, 4])
|
||
with pytest.raises(ValueError, match=message):
|
||
res.confidence_interval(method="exact")
|
||
|
||
|
||
class TestFisherExact:
|
||
"""Some tests to show that fisher_exact() works correctly.
|
||
|
||
Note that in SciPy 0.9.0 this was not working well for large numbers due to
|
||
inaccuracy of the hypergeom distribution (see #1218). Fixed now.
|
||
|
||
Also note that R and SciPy have different argument formats for their
|
||
hypergeometric distribution functions.
|
||
|
||
R:
|
||
> phyper(18999, 99000, 110000, 39000, lower.tail = FALSE)
|
||
[1] 1.701815e-09
|
||
"""
|
||
|
||
def test_basic(self):
|
||
fisher_exact = stats.fisher_exact
|
||
|
||
res = fisher_exact([[14500, 20000], [30000, 40000]])[1]
|
||
assert_approx_equal(res, 0.01106, significant=4)
|
||
res = fisher_exact([[100, 2], [1000, 5]])[1]
|
||
assert_approx_equal(res, 0.1301, significant=4)
|
||
res = fisher_exact([[2, 7], [8, 2]])[1]
|
||
assert_approx_equal(res, 0.0230141, significant=6)
|
||
res = fisher_exact([[5, 1], [10, 10]])[1]
|
||
assert_approx_equal(res, 0.1973244, significant=6)
|
||
res = fisher_exact([[5, 15], [20, 20]])[1]
|
||
assert_approx_equal(res, 0.0958044, significant=6)
|
||
res = fisher_exact([[5, 16], [20, 25]])[1]
|
||
assert_approx_equal(res, 0.1725862, significant=6)
|
||
res = fisher_exact([[10, 5], [10, 1]])[1]
|
||
assert_approx_equal(res, 0.1973244, significant=6)
|
||
res = fisher_exact([[5, 0], [1, 4]])[1]
|
||
assert_approx_equal(res, 0.04761904, significant=6)
|
||
res = fisher_exact([[0, 1], [3, 2]])[1]
|
||
assert_approx_equal(res, 1.0)
|
||
res = fisher_exact([[0, 2], [6, 4]])[1]
|
||
assert_approx_equal(res, 0.4545454545)
|
||
res = fisher_exact([[2, 7], [8, 2]])
|
||
assert_approx_equal(res[1], 0.0230141, significant=6)
|
||
assert_approx_equal(res[0], 4.0 / 56)
|
||
|
||
def test_precise(self):
|
||
# results from R
|
||
#
|
||
# R defines oddsratio differently (see Notes section of fisher_exact
|
||
# docstring), so those will not match. We leave them in anyway, in
|
||
# case they will be useful later on. We test only the p-value.
|
||
tablist = [
|
||
([[100, 2], [1000, 5]], (2.505583993422285e-001, 1.300759363430016e-001)),
|
||
([[2, 7], [8, 2]], (8.586235135736206e-002, 2.301413756522114e-002)),
|
||
([[5, 1], [10, 10]], (4.725646047336584e+000, 1.973244147157190e-001)),
|
||
([[5, 15], [20, 20]], (3.394396617440852e-001, 9.580440012477637e-002)),
|
||
([[5, 16], [20, 25]], (3.960558326183334e-001, 1.725864953812994e-001)),
|
||
([[10, 5], [10, 1]], (2.116112781158483e-001, 1.973244147157190e-001)),
|
||
([[10, 5], [10, 0]], (0.000000000000000e+000, 6.126482213438734e-002)),
|
||
([[5, 0], [1, 4]], (np.inf, 4.761904761904762e-002)),
|
||
([[0, 5], [1, 4]], (0.000000000000000e+000, 1.000000000000000e+000)),
|
||
([[5, 1], [0, 4]], (np.inf, 4.761904761904758e-002)),
|
||
([[0, 1], [3, 2]], (0.000000000000000e+000, 1.000000000000000e+000))
|
||
]
|
||
for table, res_r in tablist:
|
||
res = stats.fisher_exact(np.asarray(table))
|
||
np.testing.assert_almost_equal(res[1], res_r[1], decimal=11,
|
||
verbose=True)
|
||
|
||
def test_gh4130(self):
|
||
# Previously, a fudge factor used to distinguish between theoeretically
|
||
# and numerically different probability masses was 1e-4; it has been
|
||
# tightened to fix gh4130. Accuracy checked against R fisher.test.
|
||
# options(digits=16)
|
||
# table <- matrix(c(6, 108, 37, 200), nrow = 2)
|
||
# fisher.test(table, alternative = "t")
|
||
x = [[6, 37], [108, 200]]
|
||
res = stats.fisher_exact(x)
|
||
assert_allclose(res[1], 0.005092697748126)
|
||
|
||
# case from https://github.com/brentp/fishers_exact_test/issues/27
|
||
# That package has an (absolute?) fudge factor of 1e-6; too big
|
||
x = [[22, 0], [0, 102]]
|
||
res = stats.fisher_exact(x)
|
||
assert_allclose(res[1], 7.175066786244549e-25)
|
||
|
||
# case from https://github.com/brentp/fishers_exact_test/issues/1
|
||
x = [[94, 48], [3577, 16988]]
|
||
res = stats.fisher_exact(x)
|
||
assert_allclose(res[1], 2.069356340993818e-37)
|
||
|
||
def test_gh9231(self):
|
||
# Previously, fisher_exact was extremely slow for this table
|
||
# As reported in gh-9231, the p-value should be very nearly zero
|
||
x = [[5829225, 5692693], [5760959, 5760959]]
|
||
res = stats.fisher_exact(x)
|
||
assert_allclose(res[1], 0, atol=1e-170)
|
||
|
||
@pytest.mark.slow
|
||
def test_large_numbers(self):
|
||
# Test with some large numbers. Regression test for #1401
|
||
pvals = [5.56e-11, 2.666e-11, 1.363e-11] # from R
|
||
for pval, num in zip(pvals, [75, 76, 77]):
|
||
res = stats.fisher_exact([[17704, 496], [1065, num]])[1]
|
||
assert_approx_equal(res, pval, significant=4)
|
||
|
||
res = stats.fisher_exact([[18000, 80000], [20000, 90000]])[1]
|
||
assert_approx_equal(res, 0.2751, significant=4)
|
||
|
||
def test_raises(self):
|
||
# test we raise an error for wrong shape of input.
|
||
assert_raises(ValueError, stats.fisher_exact,
|
||
np.arange(6).reshape(2, 3))
|
||
|
||
def test_row_or_col_zero(self):
|
||
tables = ([[0, 0], [5, 10]],
|
||
[[5, 10], [0, 0]],
|
||
[[0, 5], [0, 10]],
|
||
[[5, 0], [10, 0]])
|
||
for table in tables:
|
||
oddsratio, pval = stats.fisher_exact(table)
|
||
assert_equal(pval, 1.0)
|
||
assert_equal(oddsratio, np.nan)
|
||
|
||
def test_less_greater(self):
|
||
tables = (
|
||
# Some tables to compare with R:
|
||
[[2, 7], [8, 2]],
|
||
[[200, 7], [8, 300]],
|
||
[[28, 21], [6, 1957]],
|
||
[[190, 800], [200, 900]],
|
||
# Some tables with simple exact values
|
||
# (includes regression test for ticket #1568):
|
||
[[0, 2], [3, 0]],
|
||
[[1, 1], [2, 1]],
|
||
[[2, 0], [1, 2]],
|
||
[[0, 1], [2, 3]],
|
||
[[1, 0], [1, 4]],
|
||
)
|
||
pvals = (
|
||
# from R:
|
||
[0.018521725952066501, 0.9990149169715733],
|
||
[1.0, 2.0056578803889148e-122],
|
||
[1.0, 5.7284374608319831e-44],
|
||
[0.7416227, 0.2959826],
|
||
# Exact:
|
||
[0.1, 1.0],
|
||
[0.7, 0.9],
|
||
[1.0, 0.3],
|
||
[2./3, 1.0],
|
||
[1.0, 1./3],
|
||
)
|
||
for table, pval in zip(tables, pvals):
|
||
res = []
|
||
res.append(stats.fisher_exact(table, alternative="less")[1])
|
||
res.append(stats.fisher_exact(table, alternative="greater")[1])
|
||
assert_allclose(res, pval, atol=0, rtol=1e-7)
|
||
|
||
def test_gh3014(self):
|
||
# check if issue #3014 has been fixed.
|
||
# before, this would have risen a ValueError
|
||
odds, pvalue = stats.fisher_exact([[1, 2], [9, 84419233]])
|
||
|
||
@pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
|
||
def test_result(self, alternative):
|
||
table = np.array([[14500, 20000], [30000, 40000]])
|
||
res = stats.fisher_exact(table, alternative=alternative)
|
||
assert_equal((res.statistic, res.pvalue), res)
|
||
|
||
|
||
class TestCorrSpearmanr:
|
||
""" W.II.D. Compute a correlation matrix on all the variables.
|
||
|
||
All the correlations, except for ZERO and MISS, should be exactly 1.
|
||
ZERO and MISS should have undefined or missing correlations with the
|
||
other variables. The same should go for SPEARMAN correlations, if
|
||
your program has them.
|
||
"""
|
||
|
||
def test_scalar(self):
|
||
y = stats.spearmanr(4., 2.)
|
||
assert_(np.isnan(y).all())
|
||
|
||
def test_uneven_lengths(self):
|
||
assert_raises(ValueError, stats.spearmanr, [1, 2, 1], [8, 9])
|
||
assert_raises(ValueError, stats.spearmanr, [1, 2, 1], 8)
|
||
|
||
def test_uneven_2d_shapes(self):
|
||
# Different number of columns should work - those just get concatenated.
|
||
np.random.seed(232324)
|
||
x = np.random.randn(4, 3)
|
||
y = np.random.randn(4, 2)
|
||
assert stats.spearmanr(x, y).statistic.shape == (5, 5)
|
||
assert stats.spearmanr(x.T, y.T, axis=1).pvalue.shape == (5, 5)
|
||
|
||
assert_raises(ValueError, stats.spearmanr, x, y, axis=1)
|
||
assert_raises(ValueError, stats.spearmanr, x.T, y.T)
|
||
|
||
def test_ndim_too_high(self):
|
||
np.random.seed(232324)
|
||
x = np.random.randn(4, 3, 2)
|
||
assert_raises(ValueError, stats.spearmanr, x)
|
||
assert_raises(ValueError, stats.spearmanr, x, x)
|
||
assert_raises(ValueError, stats.spearmanr, x, None, None)
|
||
# But should work with axis=None (raveling axes) for two input arrays
|
||
assert_allclose(stats.spearmanr(x, x, axis=None),
|
||
stats.spearmanr(x.flatten(), x.flatten(), axis=0))
|
||
|
||
def test_nan_policy(self):
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_array_equal(stats.spearmanr(x, x), (np.nan, np.nan))
|
||
assert_array_equal(stats.spearmanr(x, x, nan_policy='omit'),
|
||
(1.0, 0.0))
|
||
assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='foobar')
|
||
|
||
def test_nan_policy_bug_12458(self):
|
||
np.random.seed(5)
|
||
x = np.random.rand(5, 10)
|
||
k = 6
|
||
x[:, k] = np.nan
|
||
y = np.delete(x, k, axis=1)
|
||
corx, px = stats.spearmanr(x, nan_policy='omit')
|
||
cory, py = stats.spearmanr(y)
|
||
corx = np.delete(np.delete(corx, k, axis=1), k, axis=0)
|
||
px = np.delete(np.delete(px, k, axis=1), k, axis=0)
|
||
assert_allclose(corx, cory, atol=1e-14)
|
||
assert_allclose(px, py, atol=1e-14)
|
||
|
||
def test_nan_policy_bug_12411(self):
|
||
np.random.seed(5)
|
||
m = 5
|
||
n = 10
|
||
x = np.random.randn(m, n)
|
||
x[1, 0] = np.nan
|
||
x[3, -1] = np.nan
|
||
corr, pvalue = stats.spearmanr(x, axis=1, nan_policy="propagate")
|
||
res = [[stats.spearmanr(x[i, :], x[j, :]).statistic for i in range(m)]
|
||
for j in range(m)]
|
||
assert_allclose(corr, res)
|
||
|
||
def test_sXX(self):
|
||
y = stats.spearmanr(X,X)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sXBIG(self):
|
||
y = stats.spearmanr(X,BIG)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sXLITTLE(self):
|
||
y = stats.spearmanr(X,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sXHUGE(self):
|
||
y = stats.spearmanr(X,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sXTINY(self):
|
||
y = stats.spearmanr(X,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sXROUND(self):
|
||
y = stats.spearmanr(X,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sBIGBIG(self):
|
||
y = stats.spearmanr(BIG,BIG)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sBIGLITTLE(self):
|
||
y = stats.spearmanr(BIG,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sBIGHUGE(self):
|
||
y = stats.spearmanr(BIG,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sBIGTINY(self):
|
||
y = stats.spearmanr(BIG,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sBIGROUND(self):
|
||
y = stats.spearmanr(BIG,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sLITTLELITTLE(self):
|
||
y = stats.spearmanr(LITTLE,LITTLE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sLITTLEHUGE(self):
|
||
y = stats.spearmanr(LITTLE,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sLITTLETINY(self):
|
||
y = stats.spearmanr(LITTLE,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sLITTLEROUND(self):
|
||
y = stats.spearmanr(LITTLE,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sHUGEHUGE(self):
|
||
y = stats.spearmanr(HUGE,HUGE)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sHUGETINY(self):
|
||
y = stats.spearmanr(HUGE,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sHUGEROUND(self):
|
||
y = stats.spearmanr(HUGE,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sTINYTINY(self):
|
||
y = stats.spearmanr(TINY,TINY)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sTINYROUND(self):
|
||
y = stats.spearmanr(TINY,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_sROUNDROUND(self):
|
||
y = stats.spearmanr(ROUND,ROUND)
|
||
r = y[0]
|
||
assert_approx_equal(r,1.0)
|
||
|
||
def test_spearmanr_result_attributes(self):
|
||
res = stats.spearmanr(X, X)
|
||
attributes = ('correlation', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
assert_equal(res.correlation, res.statistic)
|
||
|
||
def test_1d_vs_2d(self):
|
||
x1 = [1, 2, 3, 4, 5, 6]
|
||
x2 = [1, 2, 3, 4, 6, 5]
|
||
res1 = stats.spearmanr(x1, x2)
|
||
res2 = stats.spearmanr(np.asarray([x1, x2]).T)
|
||
assert_allclose(res1, res2)
|
||
|
||
def test_1d_vs_2d_nans(self):
|
||
# Now the same with NaNs present. Regression test for gh-9103.
|
||
for nan_policy in ['propagate', 'omit']:
|
||
x1 = [1, np.nan, 3, 4, 5, 6]
|
||
x2 = [1, 2, 3, 4, 6, np.nan]
|
||
res1 = stats.spearmanr(x1, x2, nan_policy=nan_policy)
|
||
res2 = stats.spearmanr(np.asarray([x1, x2]).T, nan_policy=nan_policy)
|
||
assert_allclose(res1, res2)
|
||
|
||
def test_3cols(self):
|
||
x1 = np.arange(6)
|
||
x2 = -x1
|
||
x3 = np.array([0, 1, 2, 3, 5, 4])
|
||
x = np.asarray([x1, x2, x3]).T
|
||
actual = stats.spearmanr(x)
|
||
expected_corr = np.array([[1, -1, 0.94285714],
|
||
[-1, 1, -0.94285714],
|
||
[0.94285714, -0.94285714, 1]])
|
||
expected_pvalue = np.zeros((3, 3), dtype=float)
|
||
expected_pvalue[2, 0:2] = 0.00480466472
|
||
expected_pvalue[0:2, 2] = 0.00480466472
|
||
|
||
assert_allclose(actual.statistic, expected_corr)
|
||
assert_allclose(actual.pvalue, expected_pvalue)
|
||
|
||
def test_gh_9103(self):
|
||
# Regression test for gh-9103.
|
||
x = np.array([[np.nan, 3.0, 4.0, 5.0, 5.1, 6.0, 9.2],
|
||
[5.0, np.nan, 4.1, 4.8, 4.9, 5.0, 4.1],
|
||
[0.5, 4.0, 7.1, 3.8, 8.0, 5.1, 7.6]]).T
|
||
corr = np.array([[np.nan, np.nan, np.nan],
|
||
[np.nan, np.nan, np.nan],
|
||
[np.nan, np.nan, 1.]])
|
||
assert_allclose(stats.spearmanr(x, nan_policy='propagate').statistic,
|
||
corr)
|
||
|
||
res = stats.spearmanr(x, nan_policy='omit').statistic
|
||
assert_allclose((res[0][1], res[0][2], res[1][2]),
|
||
(0.2051957, 0.4857143, -0.4707919), rtol=1e-6)
|
||
|
||
def test_gh_8111(self):
|
||
# Regression test for gh-8111 (different result for float/int/bool).
|
||
n = 100
|
||
np.random.seed(234568)
|
||
x = np.random.rand(n)
|
||
m = np.random.rand(n) > 0.7
|
||
|
||
# bool against float, no nans
|
||
a = (x > .5)
|
||
b = np.array(x)
|
||
res1 = stats.spearmanr(a, b, nan_policy='omit').statistic
|
||
|
||
# bool against float with NaNs
|
||
b[m] = np.nan
|
||
res2 = stats.spearmanr(a, b, nan_policy='omit').statistic
|
||
|
||
# int against float with NaNs
|
||
a = a.astype(np.int32)
|
||
res3 = stats.spearmanr(a, b, nan_policy='omit').statistic
|
||
|
||
expected = [0.865895477, 0.866100381, 0.866100381]
|
||
assert_allclose([res1, res2, res3], expected)
|
||
|
||
|
||
class TestCorrSpearmanr2:
|
||
"""Some further tests of the spearmanr function."""
|
||
|
||
def test_spearmanr_vs_r(self):
|
||
# Cross-check with R:
|
||
# cor.test(c(1,2,3,4,5),c(5,6,7,8,7),method="spearmanr")
|
||
x1 = [1, 2, 3, 4, 5]
|
||
x2 = [5, 6, 7, 8, 7]
|
||
expected = (0.82078268166812329, 0.088587005313543798)
|
||
res = stats.spearmanr(x1, x2)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
def test_empty_arrays(self):
|
||
assert_equal(stats.spearmanr([], []), (np.nan, np.nan))
|
||
|
||
def test_normal_draws(self):
|
||
np.random.seed(7546)
|
||
x = np.array([np.random.normal(loc=1, scale=1, size=500),
|
||
np.random.normal(loc=1, scale=1, size=500)])
|
||
corr = [[1.0, 0.3],
|
||
[0.3, 1.0]]
|
||
x = np.dot(np.linalg.cholesky(corr), x)
|
||
expected = (0.28659685838743354, 6.579862219051161e-11)
|
||
res = stats.spearmanr(x[0], x[1])
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
def test_corr_1(self):
|
||
assert_approx_equal(stats.spearmanr([1, 1, 2], [1, 1, 2])[0], 1.0)
|
||
|
||
def test_nan_policies(self):
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_array_equal(stats.spearmanr(x, x), (np.nan, np.nan))
|
||
assert_allclose(stats.spearmanr(x, x, nan_policy='omit'),
|
||
(1.0, 0))
|
||
assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='foobar')
|
||
|
||
def test_unequal_lengths(self):
|
||
x = np.arange(10.)
|
||
y = np.arange(20.)
|
||
assert_raises(ValueError, stats.spearmanr, x, y)
|
||
|
||
def test_omit_paired_value(self):
|
||
x1 = [1, 2, 3, 4]
|
||
x2 = [8, 7, 6, np.nan]
|
||
res1 = stats.spearmanr(x1, x2, nan_policy='omit')
|
||
res2 = stats.spearmanr(x1[:3], x2[:3], nan_policy='omit')
|
||
assert_equal(res1, res2)
|
||
|
||
def test_gh_issue_6061_windows_overflow(self):
|
||
x = list(range(2000))
|
||
y = list(range(2000))
|
||
y[0], y[9] = y[9], y[0]
|
||
y[10], y[434] = y[434], y[10]
|
||
y[435], y[1509] = y[1509], y[435]
|
||
# rho = 1 - 6 * (2 * (9^2 + 424^2 + 1074^2))/(2000 * (2000^2 - 1))
|
||
# = 1 - (1 / 500)
|
||
# = 0.998
|
||
x.append(np.nan)
|
||
y.append(3.0)
|
||
assert_almost_equal(stats.spearmanr(x, y, nan_policy='omit')[0], 0.998)
|
||
|
||
def test_tie0(self):
|
||
# with only ties in one or both inputs
|
||
warn_msg = "An input array is constant"
|
||
with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
|
||
r, p = stats.spearmanr([2, 2, 2], [2, 2, 2])
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
r, p = stats.spearmanr([2, 0, 2], [2, 2, 2])
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
r, p = stats.spearmanr([2, 2, 2], [2, 0, 2])
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
|
||
def test_tie1(self):
|
||
# Data
|
||
x = [1.0, 2.0, 3.0, 4.0]
|
||
y = [1.0, 2.0, 2.0, 3.0]
|
||
# Ranks of the data, with tie-handling.
|
||
xr = [1.0, 2.0, 3.0, 4.0]
|
||
yr = [1.0, 2.5, 2.5, 4.0]
|
||
# Result of spearmanr should be the same as applying
|
||
# pearsonr to the ranks.
|
||
sr = stats.spearmanr(x, y)
|
||
pr = stats.pearsonr(xr, yr)
|
||
assert_almost_equal(sr, pr)
|
||
|
||
def test_tie2(self):
|
||
# Test tie-handling if inputs contain nan's
|
||
# Data without nan's
|
||
x1 = [1, 2, 2.5, 2]
|
||
y1 = [1, 3, 2.5, 4]
|
||
# Same data with nan's
|
||
x2 = [1, 2, 2.5, 2, np.nan]
|
||
y2 = [1, 3, 2.5, 4, np.nan]
|
||
|
||
# Results for two data sets should be the same if nan's are ignored
|
||
sr1 = stats.spearmanr(x1, y1)
|
||
sr2 = stats.spearmanr(x2, y2, nan_policy='omit')
|
||
assert_almost_equal(sr1, sr2)
|
||
|
||
def test_ties_axis_1(self):
|
||
z1 = np.array([[1, 1, 1, 1], [1, 2, 3, 4]])
|
||
z2 = np.array([[1, 2, 3, 4], [1, 1, 1, 1]])
|
||
z3 = np.array([[1, 1, 1, 1], [1, 1, 1, 1]])
|
||
warn_msg = "An input array is constant"
|
||
with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
|
||
r, p = stats.spearmanr(z1, axis=1)
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
r, p = stats.spearmanr(z2, axis=1)
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
r, p = stats.spearmanr(z3, axis=1)
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
|
||
def test_gh_11111(self):
|
||
x = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||
y = np.array([0, 0.009783728115345005, 0, 0, 0.0019759230121848587,
|
||
0.0007535430349118562, 0.0002661781514710257, 0, 0,
|
||
0.0007835762419683435])
|
||
warn_msg = "An input array is constant"
|
||
with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
|
||
r, p = stats.spearmanr(x, y)
|
||
assert_equal(r, np.nan)
|
||
assert_equal(p, np.nan)
|
||
|
||
def test_index_error(self):
|
||
x = np.array([1.0, 7.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||
y = np.array([0, 0.009783728115345005, 0, 0, 0.0019759230121848587,
|
||
0.0007535430349118562, 0.0002661781514710257, 0, 0,
|
||
0.0007835762419683435])
|
||
assert_raises(ValueError, stats.spearmanr, x, y, axis=2)
|
||
|
||
def test_alternative(self):
|
||
# Test alternative parameter
|
||
|
||
# Simple test - Based on the above ``test_spearmanr_vs_r``
|
||
x1 = [1, 2, 3, 4, 5]
|
||
x2 = [5, 6, 7, 8, 7]
|
||
|
||
# strong positive correlation
|
||
expected = (0.82078268166812329, 0.088587005313543798)
|
||
|
||
# correlation > 0 -> large "less" p-value
|
||
res = stats.spearmanr(x1, x2, alternative="less")
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], 1 - (expected[1] / 2))
|
||
|
||
# correlation > 0 -> small "less" p-value
|
||
res = stats.spearmanr(x1, x2, alternative="greater")
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1] / 2)
|
||
|
||
with pytest.raises(ValueError, match="`alternative` must be 'less'..."):
|
||
stats.spearmanr(x1, x2, alternative="ekki-ekki")
|
||
|
||
@pytest.mark.parametrize("alternative", ('two-sided', 'less', 'greater'))
|
||
def test_alternative_nan_policy(self, alternative):
|
||
# Test nan policies
|
||
x1 = [1, 2, 3, 4, 5]
|
||
x2 = [5, 6, 7, 8, 7]
|
||
x1nan = x1 + [np.nan]
|
||
x2nan = x2 + [np.nan]
|
||
|
||
# test nan_policy="propagate"
|
||
assert_array_equal(stats.spearmanr(x1nan, x2nan), (np.nan, np.nan))
|
||
|
||
# test nan_policy="omit"
|
||
res_actual = stats.spearmanr(x1nan, x2nan, nan_policy='omit',
|
||
alternative=alternative)
|
||
res_expected = stats.spearmanr(x1, x2, alternative=alternative)
|
||
assert_allclose(res_actual, res_expected)
|
||
|
||
# test nan_policy="raise"
|
||
message = 'The input contains nan values'
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.spearmanr(x1nan, x2nan, nan_policy='raise',
|
||
alternative=alternative)
|
||
|
||
# test invalid nan_policy
|
||
message = "nan_policy must be one of..."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.spearmanr(x1nan, x2nan, nan_policy='ekki-ekki',
|
||
alternative=alternative)
|
||
|
||
|
||
# W.II.E. Tabulate X against X, using BIG as a case weight. The values
|
||
# should appear on the diagonal and the total should be 899999955.
|
||
# If the table cannot hold these values, forget about working with
|
||
# census data. You can also tabulate HUGE against TINY. There is no
|
||
# reason a tabulation program should not be able to distinguish
|
||
# different values regardless of their magnitude.
|
||
|
||
# I need to figure out how to do this one.
|
||
|
||
|
||
def test_kendalltau():
|
||
# For the cases without ties, both variants should give the same
|
||
# result.
|
||
variants = ('b', 'c')
|
||
|
||
# case without ties, con-dis equal zero
|
||
x = [5, 2, 1, 3, 6, 4, 7, 8]
|
||
y = [5, 2, 6, 3, 1, 8, 7, 4]
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (0.0, 1.0)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# case without ties, con-dis equal zero
|
||
x = [0, 5, 2, 1, 3, 6, 4, 7, 8]
|
||
y = [5, 2, 0, 6, 3, 1, 8, 7, 4]
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (0.0, 1.0)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# case without ties, con-dis close to zero
|
||
x = [5, 2, 1, 3, 6, 4, 7]
|
||
y = [5, 2, 6, 3, 1, 7, 4]
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (-0.14285714286, 0.77261904762)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# case without ties, con-dis close to zero
|
||
x = [2, 1, 3, 6, 4, 7, 8]
|
||
y = [2, 6, 3, 1, 8, 7, 4]
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (0.047619047619, 1.0)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# simple case without ties
|
||
x = np.arange(10)
|
||
y = np.arange(10)
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (1.0, 5.511463844797e-07)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# swap a couple of values
|
||
b = y[1]
|
||
y[1] = y[2]
|
||
y[2] = b
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (0.9555555555555556, 5.511463844797e-06)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# swap a couple more
|
||
b = y[5]
|
||
y[5] = y[6]
|
||
y[6] = b
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (0.9111111111111111, 2.976190476190e-05)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# same in opposite direction
|
||
x = np.arange(10)
|
||
y = np.arange(10)[::-1]
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (-1.0, 5.511463844797e-07)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# swap a couple of values
|
||
b = y[1]
|
||
y[1] = y[2]
|
||
y[2] = b
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (-0.9555555555555556, 5.511463844797e-06)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# swap a couple more
|
||
b = y[5]
|
||
y[5] = y[6]
|
||
y[6] = b
|
||
# Cross-check with exact result from R:
|
||
# cor.test(x,y,method="kendall",exact=1)
|
||
expected = (-0.9111111111111111, 2.976190476190e-05)
|
||
for taux in variants:
|
||
res = stats.kendalltau(x, y, variant=taux)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# Check a case where variants are different
|
||
# Example values found from Kendall (1970).
|
||
# P-value is the same for the both variants
|
||
x = array([1, 2, 2, 4, 4, 6, 6, 8, 9, 9])
|
||
y = array([1, 2, 4, 4, 4, 4, 8, 8, 8, 10])
|
||
expected = 0.85895569
|
||
assert_approx_equal(stats.kendalltau(x, y, variant='b')[0], expected)
|
||
expected = 0.825
|
||
assert_approx_equal(stats.kendalltau(x, y, variant='c')[0], expected)
|
||
|
||
# check exception in case of ties and method='exact' requested
|
||
y[2] = y[1]
|
||
assert_raises(ValueError, stats.kendalltau, x, y, method='exact')
|
||
|
||
# check exception in case of invalid method keyword
|
||
assert_raises(ValueError, stats.kendalltau, x, y, method='banana')
|
||
|
||
# check exception in case of invalid variant keyword
|
||
assert_raises(ValueError, stats.kendalltau, x, y, variant='rms')
|
||
|
||
# tau-b with some ties
|
||
# Cross-check with R:
|
||
# cor.test(c(12,2,1,12,2),c(1,4,7,1,0),method="kendall",exact=FALSE)
|
||
x1 = [12, 2, 1, 12, 2]
|
||
x2 = [1, 4, 7, 1, 0]
|
||
expected = (-0.47140452079103173, 0.28274545993277478)
|
||
res = stats.kendalltau(x1, x2)
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('correlation', 'pvalue')
|
||
for taux in variants:
|
||
res = stats.kendalltau(x1, x2, variant=taux)
|
||
check_named_results(res, attributes)
|
||
assert_equal(res.correlation, res.statistic)
|
||
|
||
# with only ties in one or both inputs in tau-b or tau-c
|
||
for taux in variants:
|
||
assert_equal(stats.kendalltau([2, 2, 2], [2, 2, 2], variant=taux),
|
||
(np.nan, np.nan))
|
||
assert_equal(stats.kendalltau([2, 0, 2], [2, 2, 2], variant=taux),
|
||
(np.nan, np.nan))
|
||
assert_equal(stats.kendalltau([2, 2, 2], [2, 0, 2], variant=taux),
|
||
(np.nan, np.nan))
|
||
|
||
# empty arrays provided as input
|
||
assert_equal(stats.kendalltau([], []), (np.nan, np.nan))
|
||
|
||
# check with larger arrays
|
||
np.random.seed(7546)
|
||
x = np.array([np.random.normal(loc=1, scale=1, size=500),
|
||
np.random.normal(loc=1, scale=1, size=500)])
|
||
corr = [[1.0, 0.3],
|
||
[0.3, 1.0]]
|
||
x = np.dot(np.linalg.cholesky(corr), x)
|
||
expected = (0.19291382765531062, 1.1337095377742629e-10)
|
||
res = stats.kendalltau(x[0], x[1])
|
||
assert_approx_equal(res[0], expected[0])
|
||
assert_approx_equal(res[1], expected[1])
|
||
|
||
# this should result in 1 for taub but not tau-c
|
||
assert_approx_equal(stats.kendalltau([1, 1, 2], [1, 1, 2], variant='b')[0],
|
||
1.0)
|
||
assert_approx_equal(stats.kendalltau([1, 1, 2], [1, 1, 2], variant='c')[0],
|
||
0.88888888)
|
||
|
||
# test nan_policy
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_array_equal(stats.kendalltau(x, x), (np.nan, np.nan))
|
||
assert_allclose(stats.kendalltau(x, x, nan_policy='omit'),
|
||
(1.0, 5.5114638e-6), rtol=1e-06)
|
||
assert_allclose(stats.kendalltau(x, x, nan_policy='omit', method='asymptotic'),
|
||
(1.0, 0.00017455009626808976), rtol=1e-06)
|
||
assert_raises(ValueError, stats.kendalltau, x, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.kendalltau, x, x, nan_policy='foobar')
|
||
|
||
# test unequal length inputs
|
||
x = np.arange(10.)
|
||
y = np.arange(20.)
|
||
assert_raises(ValueError, stats.kendalltau, x, y)
|
||
|
||
# test all ties
|
||
tau, p_value = stats.kendalltau([], [])
|
||
assert_equal(np.nan, tau)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.kendalltau([0], [0])
|
||
assert_equal(np.nan, tau)
|
||
assert_equal(np.nan, p_value)
|
||
|
||
# Regression test for GitHub issue #6061 - Overflow on Windows
|
||
x = np.arange(2000, dtype=float)
|
||
x = np.ma.masked_greater(x, 1995)
|
||
y = np.arange(2000, dtype=float)
|
||
y = np.concatenate((y[1000:], y[:1000]))
|
||
assert_(np.isfinite(stats.kendalltau(x,y)[1]))
|
||
|
||
|
||
def test_kendalltau_vs_mstats_basic():
|
||
np.random.seed(42)
|
||
for s in range(2,10):
|
||
a = []
|
||
# Generate rankings with ties
|
||
for i in range(s):
|
||
a += [i]*i
|
||
b = list(a)
|
||
np.random.shuffle(a)
|
||
np.random.shuffle(b)
|
||
expected = mstats_basic.kendalltau(a, b)
|
||
actual = stats.kendalltau(a, b)
|
||
assert_approx_equal(actual[0], expected[0])
|
||
assert_approx_equal(actual[1], expected[1])
|
||
|
||
|
||
def test_kendalltau_nan_2nd_arg():
|
||
# regression test for gh-6134: nans in the second arg were not handled
|
||
x = [1., 2., 3., 4.]
|
||
y = [np.nan, 2.4, 3.4, 3.4]
|
||
|
||
r1 = stats.kendalltau(x, y, nan_policy='omit')
|
||
r2 = stats.kendalltau(x[1:], y[1:])
|
||
assert_allclose(r1.statistic, r2.statistic, atol=1e-15)
|
||
|
||
|
||
def test_kendalltau_deprecations():
|
||
msg_dep = "keyword argument 'initial_lexsort'"
|
||
with pytest.deprecated_call(match=msg_dep):
|
||
stats.kendalltau([], [], initial_lexsort=True)
|
||
with pytest.deprecated_call(match=f"use keyword arguments|{msg_dep}"):
|
||
stats.kendalltau([], [], True)
|
||
|
||
|
||
def test_kendalltau_gh18139_overflow():
|
||
# gh-18139 reported an overflow in `kendalltau` that appeared after
|
||
# SciPy 0.15.1. Check that this particular overflow does not occur.
|
||
# (Test would fail if warning were emitted.)
|
||
import random
|
||
random.seed(6272161)
|
||
classes = [1, 2, 3, 4, 5, 6, 7]
|
||
n_samples = 2 * 10 ** 5
|
||
x = random.choices(classes, k=n_samples)
|
||
y = random.choices(classes, k=n_samples)
|
||
res = stats.kendalltau(x, y)
|
||
# Reference value from SciPy 0.15.1
|
||
assert_allclose(res.statistic, 0.0011816493905730343)
|
||
# Reference p-value from `permutation_test` w/ n_resamples=9999 (default).
|
||
# Expected to be accurate to at least two digits.
|
||
assert_allclose(res.pvalue, 0.4894, atol=2e-3)
|
||
|
||
|
||
class TestKendallTauAlternative:
|
||
def test_kendalltau_alternative_asymptotic(self):
|
||
# Test alternative parameter, asymptotic method (due to tie)
|
||
|
||
# Based on TestCorrSpearman2::test_alternative
|
||
x1 = [1, 2, 3, 4, 5]
|
||
x2 = [5, 6, 7, 8, 7]
|
||
|
||
# strong positive correlation
|
||
expected = stats.kendalltau(x1, x2, alternative="two-sided")
|
||
assert expected[0] > 0
|
||
|
||
# rank correlation > 0 -> large "less" p-value
|
||
res = stats.kendalltau(x1, x2, alternative="less")
|
||
assert_equal(res[0], expected[0])
|
||
assert_allclose(res[1], 1 - (expected[1] / 2))
|
||
|
||
# rank correlation > 0 -> small "greater" p-value
|
||
res = stats.kendalltau(x1, x2, alternative="greater")
|
||
assert_equal(res[0], expected[0])
|
||
assert_allclose(res[1], expected[1] / 2)
|
||
|
||
# reverse the direction of rank correlation
|
||
x2.reverse()
|
||
|
||
# strong negative correlation
|
||
expected = stats.kendalltau(x1, x2, alternative="two-sided")
|
||
assert expected[0] < 0
|
||
|
||
# rank correlation < 0 -> large "greater" p-value
|
||
res = stats.kendalltau(x1, x2, alternative="greater")
|
||
assert_equal(res[0], expected[0])
|
||
assert_allclose(res[1], 1 - (expected[1] / 2))
|
||
|
||
# rank correlation < 0 -> small "less" p-value
|
||
res = stats.kendalltau(x1, x2, alternative="less")
|
||
assert_equal(res[0], expected[0])
|
||
assert_allclose(res[1], expected[1] / 2)
|
||
|
||
with pytest.raises(ValueError, match="`alternative` must be 'less'..."):
|
||
stats.kendalltau(x1, x2, alternative="ekki-ekki")
|
||
|
||
# There are a lot of special cases considered in the calculation of the
|
||
# exact p-value, so we test each separately. We also need to test
|
||
# separately when the observed statistic is in the left tail vs the right
|
||
# tail because the code leverages symmetry of the null distribution; to
|
||
# do that we use the same test case but negate one of the samples.
|
||
# Reference values computed using R cor.test, e.g.
|
||
# options(digits=16)
|
||
# x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
|
||
# y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
|
||
# cor.test(x, y, method = "kendall", alternative = "g")
|
||
|
||
alternatives = ('less', 'two-sided', 'greater')
|
||
p_n1 = [np.nan, np.nan, np.nan]
|
||
p_n2 = [1, 1, 0.5]
|
||
p_c0 = [1, 0.3333333333333, 0.1666666666667]
|
||
p_c1 = [0.9583333333333, 0.3333333333333, 0.1666666666667]
|
||
p_no_correlation = [0.5916666666667, 1, 0.5916666666667]
|
||
p_no_correlationb = [0.5475694444444, 1, 0.5475694444444]
|
||
p_n_lt_171 = [0.9624118165785, 0.1194389329806, 0.0597194664903]
|
||
p_n_lt_171b = [0.246236925303, 0.4924738506059, 0.755634083327]
|
||
p_n_lt_171c = [0.9847475308925, 0.03071385306533, 0.01535692653267]
|
||
|
||
def exact_test(self, x, y, alternative, rev, stat_expected, p_expected):
|
||
if rev:
|
||
y = -np.asarray(y)
|
||
stat_expected *= -1
|
||
res = stats.kendalltau(x, y, method='exact', alternative=alternative)
|
||
res_expected = stat_expected, p_expected
|
||
assert_allclose(res, res_expected)
|
||
|
||
case_R_n1 = (list(zip(alternatives, p_n1, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_n1), [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_n1)
|
||
def test_against_R_n1(self, alternative, p_expected, rev):
|
||
x, y = [1], [2]
|
||
stat_expected = np.nan
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_n2 = (list(zip(alternatives, p_n2, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_n2), [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_n2)
|
||
def test_against_R_n2(self, alternative, p_expected, rev):
|
||
x, y = [1, 2], [3, 4]
|
||
stat_expected = 0.9999999999999998
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_c0 = (list(zip(alternatives, p_c0, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_c0), [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_c0)
|
||
def test_against_R_c0(self, alternative, p_expected, rev):
|
||
x, y = [1, 2, 3], [1, 2, 3]
|
||
stat_expected = 1
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_c1 = (list(zip(alternatives, p_c1, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_c1), [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_c1)
|
||
def test_against_R_c1(self, alternative, p_expected, rev):
|
||
x, y = [1, 2, 3, 4], [1, 2, 4, 3]
|
||
stat_expected = 0.6666666666666667
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_no_corr = (list(zip(alternatives, p_no_correlation, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_no_correlation),
|
||
[True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_no_corr)
|
||
def test_against_R_no_correlation(self, alternative, p_expected, rev):
|
||
x, y = [1, 2, 3, 4, 5], [1, 5, 4, 2, 3]
|
||
stat_expected = 0
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_no_cor_b = (list(zip(alternatives, p_no_correlationb, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_no_correlationb),
|
||
[True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_no_cor_b)
|
||
def test_against_R_no_correlationb(self, alternative, p_expected, rev):
|
||
x, y = [1, 2, 3, 4, 5, 6, 7, 8], [8, 6, 1, 3, 2, 5, 4, 7]
|
||
stat_expected = 0
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_lt_171 = (list(zip(alternatives, p_n_lt_171, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_n_lt_171), [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171)
|
||
def test_against_R_lt_171(self, alternative, p_expected, rev):
|
||
# Data from Hollander & Wolfe (1973), p. 187f.
|
||
# Used from https://rdrr.io/r/stats/cor.test.html
|
||
x = [44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1]
|
||
y = [2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8]
|
||
stat_expected = 0.4444444444444445
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_lt_171b = (list(zip(alternatives, p_n_lt_171b, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_n_lt_171b),
|
||
[True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171b)
|
||
def test_against_R_lt_171b(self, alternative, p_expected, rev):
|
||
np.random.seed(0)
|
||
x = np.random.rand(100)
|
||
y = np.random.rand(100)
|
||
stat_expected = -0.04686868686868687
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_R_lt_171c = (list(zip(alternatives, p_n_lt_171c, [False]*3))
|
||
+ list(zip(alternatives, reversed(p_n_lt_171c),
|
||
[True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171c)
|
||
def test_against_R_lt_171c(self, alternative, p_expected, rev):
|
||
np.random.seed(0)
|
||
x = np.random.rand(170)
|
||
y = np.random.rand(170)
|
||
stat_expected = 0.1115906717716673
|
||
self.exact_test(x, y, alternative, rev, stat_expected, p_expected)
|
||
|
||
case_gt_171 = (list(zip(alternatives, [False]*3)) +
|
||
list(zip(alternatives, [True]*3)))
|
||
|
||
@pytest.mark.parametrize("alternative, rev", case_gt_171)
|
||
def test_gt_171(self, alternative, rev):
|
||
np.random.seed(0)
|
||
x = np.random.rand(400)
|
||
y = np.random.rand(400)
|
||
res0 = stats.kendalltau(x, y, method='exact',
|
||
alternative=alternative)
|
||
res1 = stats.kendalltau(x, y, method='asymptotic',
|
||
alternative=alternative)
|
||
assert_equal(res0[0], res1[0])
|
||
assert_allclose(res0[1], res1[1], rtol=1e-3)
|
||
|
||
@pytest.mark.parametrize("method", ('exact', 'asymptotic'))
|
||
@pytest.mark.parametrize("alternative", ('two-sided', 'less', 'greater'))
|
||
def test_nan_policy(self, method, alternative):
|
||
# Test nan policies
|
||
x1 = [1, 2, 3, 4, 5]
|
||
x2 = [5, 6, 7, 8, 9]
|
||
x1nan = x1 + [np.nan]
|
||
x2nan = x2 + [np.nan]
|
||
|
||
# test nan_policy="propagate"
|
||
res_actual = stats.kendalltau(x1nan, x2nan,
|
||
method=method, alternative=alternative)
|
||
res_expected = (np.nan, np.nan)
|
||
assert_allclose(res_actual, res_expected)
|
||
|
||
# test nan_policy="omit"
|
||
res_actual = stats.kendalltau(x1nan, x2nan, nan_policy='omit',
|
||
method=method, alternative=alternative)
|
||
res_expected = stats.kendalltau(x1, x2, method=method,
|
||
alternative=alternative)
|
||
assert_allclose(res_actual, res_expected)
|
||
|
||
# test nan_policy="raise"
|
||
message = 'The input contains nan values'
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.kendalltau(x1nan, x2nan, nan_policy='raise',
|
||
method=method, alternative=alternative)
|
||
|
||
# test invalid nan_policy
|
||
message = "nan_policy must be one of..."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.kendalltau(x1nan, x2nan, nan_policy='ekki-ekki',
|
||
method=method, alternative=alternative)
|
||
|
||
|
||
def test_weightedtau():
|
||
x = [12, 2, 1, 12, 2]
|
||
y = [1, 4, 7, 1, 0]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(x, y, additive=False)
|
||
assert_approx_equal(tau, -0.62205716951801038)
|
||
assert_equal(np.nan, p_value)
|
||
# This must be exactly Kendall's tau
|
||
tau, p_value = stats.weightedtau(x, y, weigher=lambda x: 1)
|
||
assert_approx_equal(tau, -0.47140452079103173)
|
||
assert_equal(np.nan, p_value)
|
||
|
||
# test for namedtuple attribute results
|
||
res = stats.weightedtau(x, y)
|
||
attributes = ('correlation', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
assert_equal(res.correlation, res.statistic)
|
||
|
||
# Asymmetric, ranked version
|
||
tau, p_value = stats.weightedtau(x, y, rank=None)
|
||
assert_approx_equal(tau, -0.4157652301037516)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(y, x, rank=None)
|
||
assert_approx_equal(tau, -0.7181341329699029)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(x, y, rank=None, additive=False)
|
||
assert_approx_equal(tau, -0.40644850966246893)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(y, x, rank=None, additive=False)
|
||
assert_approx_equal(tau, -0.83766582937355172)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(x, y, rank=False)
|
||
assert_approx_equal(tau, -0.51604397940261848)
|
||
assert_equal(np.nan, p_value)
|
||
# This must be exactly Kendall's tau
|
||
tau, p_value = stats.weightedtau(x, y, rank=True, weigher=lambda x: 1)
|
||
assert_approx_equal(tau, -0.47140452079103173)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau(y, x, rank=True, weigher=lambda x: 1)
|
||
assert_approx_equal(tau, -0.47140452079103173)
|
||
assert_equal(np.nan, p_value)
|
||
# Test argument conversion
|
||
tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.float64), y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.int16), y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.float64),
|
||
np.asarray(y, dtype=np.float64))
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
# All ties
|
||
tau, p_value = stats.weightedtau([], [])
|
||
assert_equal(np.nan, tau)
|
||
assert_equal(np.nan, p_value)
|
||
tau, p_value = stats.weightedtau([0], [0])
|
||
assert_equal(np.nan, tau)
|
||
assert_equal(np.nan, p_value)
|
||
# Size mismatches
|
||
assert_raises(ValueError, stats.weightedtau, [0, 1], [0, 1, 2])
|
||
assert_raises(ValueError, stats.weightedtau, [0, 1], [0, 1], [0])
|
||
# NaNs
|
||
x = [12, 2, 1, 12, 2]
|
||
y = [1, 4, 7, 1, np.nan]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
x = [12, 2, np.nan, 12, 2]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
# NaNs when the dtype of x and y are all np.float64
|
||
x = [12.0, 2.0, 1.0, 12.0, 2.0]
|
||
y = [1.0, 4.0, 7.0, 1.0, np.nan]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
x = [12.0, 2.0, np.nan, 12.0, 2.0]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.56694968153682723)
|
||
# NaNs when there are more than one NaN in x or y
|
||
x = [12.0, 2.0, 1.0, 12.0, 1.0]
|
||
y = [1.0, 4.0, 7.0, 1.0, 1.0]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.6615242347139803)
|
||
x = [12.0, 2.0, np.nan, 12.0, np.nan]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.6615242347139803)
|
||
y = [np.nan, 4.0, 7.0, np.nan, np.nan]
|
||
tau, p_value = stats.weightedtau(x, y)
|
||
assert_approx_equal(tau, -0.6615242347139803)
|
||
|
||
|
||
def test_segfault_issue_9710():
|
||
# https://github.com/scipy/scipy/issues/9710
|
||
# This test was created to check segfault
|
||
# In issue SEGFAULT only repros in optimized builds after calling the function twice
|
||
stats.weightedtau([1], [1.0])
|
||
stats.weightedtau([1], [1.0])
|
||
# The code below also caused SEGFAULT
|
||
stats.weightedtau([np.nan], [52])
|
||
|
||
|
||
def test_kendall_tau_large():
|
||
n = 172
|
||
# Test omit policy
|
||
x = np.arange(n + 1).astype(float)
|
||
y = np.arange(n + 1).astype(float)
|
||
y[-1] = np.nan
|
||
_, pval = stats.kendalltau(x, y, method='exact', nan_policy='omit')
|
||
assert_equal(pval, 0.0)
|
||
|
||
|
||
def test_weightedtau_vs_quadratic():
|
||
# Trivial quadratic implementation, all parameters mandatory
|
||
def wkq(x, y, rank, weigher, add):
|
||
tot = conc = disc = u = v = 0
|
||
for (i, j) in product(range(len(x)), range(len(x))):
|
||
w = weigher(rank[i]) + weigher(rank[j]) if add \
|
||
else weigher(rank[i]) * weigher(rank[j])
|
||
tot += w
|
||
if x[i] == x[j]:
|
||
u += w
|
||
if y[i] == y[j]:
|
||
v += w
|
||
if x[i] < x[j] and y[i] < y[j] or x[i] > x[j] and y[i] > y[j]:
|
||
conc += w
|
||
elif x[i] < x[j] and y[i] > y[j] or x[i] > x[j] and y[i] < y[j]:
|
||
disc += w
|
||
return (conc - disc) / np.sqrt(tot - u) / np.sqrt(tot - v)
|
||
|
||
def weigher(x):
|
||
return 1. / (x + 1)
|
||
|
||
np.random.seed(42)
|
||
for s in range(3,10):
|
||
a = []
|
||
# Generate rankings with ties
|
||
for i in range(s):
|
||
a += [i]*i
|
||
b = list(a)
|
||
np.random.shuffle(a)
|
||
np.random.shuffle(b)
|
||
# First pass: use element indices as ranks
|
||
rank = np.arange(len(a), dtype=np.intp)
|
||
for _ in range(2):
|
||
for add in [True, False]:
|
||
expected = wkq(a, b, rank, weigher, add)
|
||
actual = stats.weightedtau(a, b, rank, weigher, add).statistic
|
||
assert_approx_equal(expected, actual)
|
||
# Second pass: use a random rank
|
||
np.random.shuffle(rank)
|
||
|
||
|
||
class TestFindRepeats:
|
||
|
||
def test_basic(self):
|
||
a = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 5]
|
||
res, nums = stats.find_repeats(a)
|
||
assert_array_equal(res, [1, 2, 3, 4])
|
||
assert_array_equal(nums, [3, 3, 2, 2])
|
||
|
||
def test_empty_result(self):
|
||
# Check that empty arrays are returned when there are no repeats.
|
||
for a in [[10, 20, 50, 30, 40], []]:
|
||
repeated, counts = stats.find_repeats(a)
|
||
assert_array_equal(repeated, [])
|
||
assert_array_equal(counts, [])
|
||
|
||
|
||
class TestRegression:
|
||
|
||
def test_linregressBIGX(self):
|
||
# W.II.F. Regress BIG on X.
|
||
result = stats.linregress(X, BIG)
|
||
assert_almost_equal(result.intercept, 99999990)
|
||
assert_almost_equal(result.rvalue, 1.0)
|
||
# The uncertainty ought to be almost zero
|
||
# since all points lie on a line
|
||
assert_almost_equal(result.stderr, 0.0)
|
||
assert_almost_equal(result.intercept_stderr, 0.0)
|
||
|
||
def test_regressXX(self):
|
||
# W.IV.B. Regress X on X.
|
||
# The constant should be exactly 0 and the regression coefficient
|
||
# should be 1. This is a perfectly valid regression and the
|
||
# program should not complain.
|
||
result = stats.linregress(X, X)
|
||
assert_almost_equal(result.intercept, 0.0)
|
||
assert_almost_equal(result.rvalue, 1.0)
|
||
# The uncertainly on regression through two points ought to be 0
|
||
assert_almost_equal(result.stderr, 0.0)
|
||
assert_almost_equal(result.intercept_stderr, 0.0)
|
||
|
||
# W.IV.C. Regress X on BIG and LITTLE (two predictors). The program
|
||
# should tell you that this model is "singular" because BIG and
|
||
# LITTLE are linear combinations of each other. Cryptic error
|
||
# messages are unacceptable here. Singularity is the most
|
||
# fundamental regression error.
|
||
#
|
||
# Need to figure out how to handle multiple linear regression.
|
||
# This is not obvious
|
||
|
||
def test_regressZEROX(self):
|
||
# W.IV.D. Regress ZERO on X.
|
||
# The program should inform you that ZERO has no variance or it should
|
||
# go ahead and compute the regression and report a correlation and
|
||
# total sum of squares of exactly 0.
|
||
result = stats.linregress(X, ZERO)
|
||
assert_almost_equal(result.intercept, 0.0)
|
||
assert_almost_equal(result.rvalue, 0.0)
|
||
|
||
def test_regress_simple(self):
|
||
# Regress a line with sinusoidal noise.
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
|
||
result = stats.linregress(x, y)
|
||
lr = stats._stats_mstats_common.LinregressResult
|
||
assert_(isinstance(result, lr))
|
||
assert_almost_equal(result.stderr, 2.3957814497838803e-3)
|
||
|
||
def test_regress_alternative(self):
|
||
# test alternative parameter
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10 # slope is greater than zero
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
|
||
with pytest.raises(ValueError, match="alternative must be 'less'..."):
|
||
stats.linregress(x, y, alternative="ekki-ekki")
|
||
|
||
res1 = stats.linregress(x, y, alternative="two-sided")
|
||
|
||
# slope is greater than zero, so "less" p-value should be large
|
||
res2 = stats.linregress(x, y, alternative="less")
|
||
assert_allclose(res2.pvalue, 1 - (res1.pvalue / 2))
|
||
|
||
# slope is greater than zero, so "greater" p-value should be small
|
||
res3 = stats.linregress(x, y, alternative="greater")
|
||
assert_allclose(res3.pvalue, res1.pvalue / 2)
|
||
|
||
assert res1.rvalue == res2.rvalue == res3.rvalue
|
||
|
||
def test_regress_against_R(self):
|
||
# test against R `lm`
|
||
# options(digits=16)
|
||
# x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)
|
||
# y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)
|
||
# relation <- lm(y~x)
|
||
# print(summary(relation))
|
||
|
||
x = [151, 174, 138, 186, 128, 136, 179, 163, 152, 131]
|
||
y = [63, 81, 56, 91, 47, 57, 76, 72, 62, 48]
|
||
res = stats.linregress(x, y, alternative="two-sided")
|
||
# expected values from R's `lm` above
|
||
assert_allclose(res.slope, 0.6746104491292)
|
||
assert_allclose(res.intercept, -38.4550870760770)
|
||
assert_allclose(res.rvalue, np.sqrt(0.95478224775))
|
||
assert_allclose(res.pvalue, 1.16440531074e-06)
|
||
assert_allclose(res.stderr, 0.0519051424731)
|
||
assert_allclose(res.intercept_stderr, 8.0490133029927)
|
||
|
||
def test_regress_simple_onearg_rows(self):
|
||
# Regress a line w sinusoidal noise,
|
||
# with a single input of shape (2, N)
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
rows = np.vstack((x, y))
|
||
|
||
result = stats.linregress(rows)
|
||
assert_almost_equal(result.stderr, 2.3957814497838803e-3)
|
||
assert_almost_equal(result.intercept_stderr, 1.3866936078570702e-1)
|
||
|
||
def test_regress_simple_onearg_cols(self):
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
columns = np.hstack((np.expand_dims(x, 1), np.expand_dims(y, 1)))
|
||
|
||
result = stats.linregress(columns)
|
||
assert_almost_equal(result.stderr, 2.3957814497838803e-3)
|
||
assert_almost_equal(result.intercept_stderr, 1.3866936078570702e-1)
|
||
|
||
def test_regress_shape_error(self):
|
||
# Check that a single input argument to linregress with wrong shape
|
||
# results in a ValueError.
|
||
assert_raises(ValueError, stats.linregress, np.ones((3, 3)))
|
||
|
||
def test_linregress(self):
|
||
# compared with multivariate ols with pinv
|
||
x = np.arange(11)
|
||
y = np.arange(5, 16)
|
||
y[[(1), (-2)]] -= 1
|
||
y[[(0), (-1)]] += 1
|
||
|
||
result = stats.linregress(x, y)
|
||
|
||
# This test used to use 'assert_array_almost_equal' but its
|
||
# formualtion got confusing since LinregressResult became
|
||
# _lib._bunch._make_tuple_bunch instead of namedtuple
|
||
# (for backwards compatibility, see PR #12983)
|
||
def assert_ae(x, y):
|
||
return assert_almost_equal(x, y, decimal=14)
|
||
assert_ae(result.slope, 1.0)
|
||
assert_ae(result.intercept, 5.0)
|
||
assert_ae(result.rvalue, 0.98229948625750)
|
||
assert_ae(result.pvalue, 7.45259691e-008)
|
||
assert_ae(result.stderr, 0.063564172616372733)
|
||
assert_ae(result.intercept_stderr, 0.37605071654517686)
|
||
|
||
def test_regress_simple_negative_cor(self):
|
||
# If the slope of the regression is negative the factor R tend
|
||
# to -1 not 1. Sometimes rounding errors makes it < -1
|
||
# leading to stderr being NaN.
|
||
a, n = 1e-71, 100000
|
||
x = np.linspace(a, 2 * a, n)
|
||
y = np.linspace(2 * a, a, n)
|
||
result = stats.linregress(x, y)
|
||
|
||
# Make sure propagated numerical errors
|
||
# did not bring rvalue below -1 (or were coersced)
|
||
assert_(result.rvalue >= -1)
|
||
assert_almost_equal(result.rvalue, -1)
|
||
|
||
# slope and intercept stderror should stay numeric
|
||
assert_(not np.isnan(result.stderr))
|
||
assert_(not np.isnan(result.intercept_stderr))
|
||
|
||
def test_linregress_result_attributes(self):
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
result = stats.linregress(x, y)
|
||
|
||
# Result is of a correct class
|
||
lr = stats._stats_mstats_common.LinregressResult
|
||
assert_(isinstance(result, lr))
|
||
|
||
# LinregressResult elements have correct names
|
||
attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
|
||
check_named_results(result, attributes)
|
||
# Also check that the extra attribute (intercept_stderr) is present
|
||
assert 'intercept_stderr' in dir(result)
|
||
|
||
def test_regress_two_inputs(self):
|
||
# Regress a simple line formed by two points.
|
||
x = np.arange(2)
|
||
y = np.arange(3, 5)
|
||
result = stats.linregress(x, y)
|
||
|
||
# Non-horizontal line
|
||
assert_almost_equal(result.pvalue, 0.0)
|
||
|
||
# Zero error through two points
|
||
assert_almost_equal(result.stderr, 0.0)
|
||
assert_almost_equal(result.intercept_stderr, 0.0)
|
||
|
||
def test_regress_two_inputs_horizontal_line(self):
|
||
# Regress a horizontal line formed by two points.
|
||
x = np.arange(2)
|
||
y = np.ones(2)
|
||
result = stats.linregress(x, y)
|
||
|
||
# Horizontal line
|
||
assert_almost_equal(result.pvalue, 1.0)
|
||
|
||
# Zero error through two points
|
||
assert_almost_equal(result.stderr, 0.0)
|
||
assert_almost_equal(result.intercept_stderr, 0.0)
|
||
|
||
def test_nist_norris(self):
|
||
x = [0.2, 337.4, 118.2, 884.6, 10.1, 226.5, 666.3, 996.3, 448.6, 777.0,
|
||
558.2, 0.4, 0.6, 775.5, 666.9, 338.0, 447.5, 11.6, 556.0, 228.1,
|
||
995.8, 887.6, 120.2, 0.3, 0.3, 556.8, 339.1, 887.2, 999.0, 779.0,
|
||
11.1, 118.3, 229.2, 669.1, 448.9, 0.5]
|
||
|
||
y = [0.1, 338.8, 118.1, 888.0, 9.2, 228.1, 668.5, 998.5, 449.1, 778.9,
|
||
559.2, 0.3, 0.1, 778.1, 668.8, 339.3, 448.9, 10.8, 557.7, 228.3,
|
||
998.0, 888.8, 119.6, 0.3, 0.6, 557.6, 339.3, 888.0, 998.5, 778.9,
|
||
10.2, 117.6, 228.9, 668.4, 449.2, 0.2]
|
||
|
||
result = stats.linregress(x, y)
|
||
|
||
assert_almost_equal(result.slope, 1.00211681802045)
|
||
assert_almost_equal(result.intercept, -0.262323073774029)
|
||
assert_almost_equal(result.rvalue**2, 0.999993745883712)
|
||
assert_almost_equal(result.pvalue, 0.0)
|
||
assert_almost_equal(result.stderr, 0.00042979684820)
|
||
assert_almost_equal(result.intercept_stderr, 0.23281823430153)
|
||
|
||
def test_compare_to_polyfit(self):
|
||
x = np.linspace(0, 100, 100)
|
||
y = 0.2 * np.linspace(0, 100, 100) + 10
|
||
y += np.sin(np.linspace(0, 20, 100))
|
||
result = stats.linregress(x, y)
|
||
poly = np.polyfit(x, y, 1) # Fit 1st degree polynomial
|
||
|
||
# Make sure linear regression slope and intercept
|
||
# match with results from numpy polyfit
|
||
assert_almost_equal(result.slope, poly[0])
|
||
assert_almost_equal(result.intercept, poly[1])
|
||
|
||
def test_empty_input(self):
|
||
assert_raises(ValueError, stats.linregress, [], [])
|
||
|
||
def test_nan_input(self):
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
|
||
with np.errstate(invalid="ignore"):
|
||
result = stats.linregress(x, x)
|
||
|
||
# Make sure the result still comes back as `LinregressResult`
|
||
lr = stats._stats_mstats_common.LinregressResult
|
||
assert_(isinstance(result, lr))
|
||
assert_array_equal(result, (np.nan,)*5)
|
||
assert_equal(result.intercept_stderr, np.nan)
|
||
|
||
def test_identical_x(self):
|
||
x = np.zeros(10)
|
||
y = np.random.random(10)
|
||
msg = "Cannot calculate a linear regression"
|
||
with assert_raises(ValueError, match=msg):
|
||
stats.linregress(x, y)
|
||
|
||
|
||
def test_theilslopes():
|
||
# Basic slope test.
|
||
slope, intercept, lower, upper = stats.theilslopes([0,1,1])
|
||
assert_almost_equal(slope, 0.5)
|
||
assert_almost_equal(intercept, 0.5)
|
||
|
||
msg = ("method must be either 'joint' or 'separate'."
|
||
"'joint_separate' is invalid.")
|
||
with pytest.raises(ValueError, match=msg):
|
||
stats.theilslopes([0, 1, 1], method='joint_separate')
|
||
|
||
slope, intercept, lower, upper = stats.theilslopes([0, 1, 1],
|
||
method='joint')
|
||
assert_almost_equal(slope, 0.5)
|
||
assert_almost_equal(intercept, 0.0)
|
||
|
||
# Test of confidence intervals.
|
||
x = [1, 2, 3, 4, 10, 12, 18]
|
||
y = [9, 15, 19, 20, 45, 55, 78]
|
||
slope, intercept, lower, upper = stats.theilslopes(y, x, 0.07,
|
||
method='separate')
|
||
assert_almost_equal(slope, 4)
|
||
assert_almost_equal(intercept, 4.0)
|
||
assert_almost_equal(upper, 4.38, decimal=2)
|
||
assert_almost_equal(lower, 3.71, decimal=2)
|
||
|
||
slope, intercept, lower, upper = stats.theilslopes(y, x, 0.07,
|
||
method='joint')
|
||
assert_almost_equal(slope, 4)
|
||
assert_almost_equal(intercept, 6.0)
|
||
assert_almost_equal(upper, 4.38, decimal=2)
|
||
assert_almost_equal(lower, 3.71, decimal=2)
|
||
|
||
|
||
def test_cumfreq():
|
||
x = [1, 4, 2, 1, 3, 1]
|
||
cumfreqs, lowlim, binsize, extrapoints = stats.cumfreq(x, numbins=4)
|
||
assert_array_almost_equal(cumfreqs, np.array([3., 4., 5., 6.]))
|
||
cumfreqs, lowlim, binsize, extrapoints = stats.cumfreq(
|
||
x, numbins=4, defaultreallimits=(1.5, 5))
|
||
assert_(extrapoints == 3)
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('cumcount', 'lowerlimit', 'binsize', 'extrapoints')
|
||
res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
|
||
check_named_results(res, attributes)
|
||
|
||
|
||
def test_relfreq():
|
||
a = np.array([1, 4, 2, 1, 3, 1])
|
||
relfreqs, lowlim, binsize, extrapoints = stats.relfreq(a, numbins=4)
|
||
assert_array_almost_equal(relfreqs,
|
||
array([0.5, 0.16666667, 0.16666667, 0.16666667]))
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('frequency', 'lowerlimit', 'binsize', 'extrapoints')
|
||
res = stats.relfreq(a, numbins=4)
|
||
check_named_results(res, attributes)
|
||
|
||
# check array_like input is accepted
|
||
relfreqs2, lowlim, binsize, extrapoints = stats.relfreq([1, 4, 2, 1, 3, 1],
|
||
numbins=4)
|
||
assert_array_almost_equal(relfreqs, relfreqs2)
|
||
|
||
|
||
class TestScoreatpercentile:
|
||
def setup_method(self):
|
||
self.a1 = [3, 4, 5, 10, -3, -5, 6]
|
||
self.a2 = [3, -6, -2, 8, 7, 4, 2, 1]
|
||
self.a3 = [3., 4, 5, 10, -3, -5, -6, 7.0]
|
||
|
||
def test_basic(self):
|
||
x = arange(8) * 0.5
|
||
assert_equal(stats.scoreatpercentile(x, 0), 0.)
|
||
assert_equal(stats.scoreatpercentile(x, 100), 3.5)
|
||
assert_equal(stats.scoreatpercentile(x, 50), 1.75)
|
||
|
||
def test_fraction(self):
|
||
scoreatperc = stats.scoreatpercentile
|
||
|
||
# Test defaults
|
||
assert_equal(scoreatperc(list(range(10)), 50), 4.5)
|
||
assert_equal(scoreatperc(list(range(10)), 50, (2,7)), 4.5)
|
||
assert_equal(scoreatperc(list(range(100)), 50, limit=(1, 8)), 4.5)
|
||
assert_equal(scoreatperc(np.array([1, 10,100]), 50, (10,100)), 55)
|
||
assert_equal(scoreatperc(np.array([1, 10,100]), 50, (1,10)), 5.5)
|
||
|
||
# explicitly specify interpolation_method 'fraction' (the default)
|
||
assert_equal(scoreatperc(list(range(10)), 50, interpolation_method='fraction'),
|
||
4.5)
|
||
assert_equal(scoreatperc(list(range(10)), 50, limit=(2, 7),
|
||
interpolation_method='fraction'),
|
||
4.5)
|
||
assert_equal(scoreatperc(list(range(100)), 50, limit=(1, 8),
|
||
interpolation_method='fraction'),
|
||
4.5)
|
||
assert_equal(scoreatperc(np.array([1, 10,100]), 50, (10, 100),
|
||
interpolation_method='fraction'),
|
||
55)
|
||
assert_equal(scoreatperc(np.array([1, 10,100]), 50, (1,10),
|
||
interpolation_method='fraction'),
|
||
5.5)
|
||
|
||
def test_lower_higher(self):
|
||
scoreatperc = stats.scoreatpercentile
|
||
|
||
# interpolation_method 'lower'/'higher'
|
||
assert_equal(scoreatperc(list(range(10)), 50,
|
||
interpolation_method='lower'), 4)
|
||
assert_equal(scoreatperc(list(range(10)), 50,
|
||
interpolation_method='higher'), 5)
|
||
assert_equal(scoreatperc(list(range(10)), 50, (2,7),
|
||
interpolation_method='lower'), 4)
|
||
assert_equal(scoreatperc(list(range(10)), 50, limit=(2,7),
|
||
interpolation_method='higher'), 5)
|
||
assert_equal(scoreatperc(list(range(100)), 50, (1,8),
|
||
interpolation_method='lower'), 4)
|
||
assert_equal(scoreatperc(list(range(100)), 50, (1,8),
|
||
interpolation_method='higher'), 5)
|
||
assert_equal(scoreatperc(np.array([1, 10, 100]), 50, (10, 100),
|
||
interpolation_method='lower'), 10)
|
||
assert_equal(scoreatperc(np.array([1, 10, 100]), 50, limit=(10, 100),
|
||
interpolation_method='higher'), 100)
|
||
assert_equal(scoreatperc(np.array([1, 10, 100]), 50, (1, 10),
|
||
interpolation_method='lower'), 1)
|
||
assert_equal(scoreatperc(np.array([1, 10, 100]), 50, limit=(1, 10),
|
||
interpolation_method='higher'), 10)
|
||
|
||
def test_sequence_per(self):
|
||
x = arange(8) * 0.5
|
||
expected = np.array([0, 3.5, 1.75])
|
||
res = stats.scoreatpercentile(x, [0, 100, 50])
|
||
assert_allclose(res, expected)
|
||
assert_(isinstance(res, np.ndarray))
|
||
# Test with ndarray. Regression test for gh-2861
|
||
assert_allclose(stats.scoreatpercentile(x, np.array([0, 100, 50])),
|
||
expected)
|
||
# Also test combination of 2-D array, axis not None and array-like per
|
||
res2 = stats.scoreatpercentile(np.arange(12).reshape((3,4)),
|
||
np.array([0, 1, 100, 100]), axis=1)
|
||
expected2 = array([[0, 4, 8],
|
||
[0.03, 4.03, 8.03],
|
||
[3, 7, 11],
|
||
[3, 7, 11]])
|
||
assert_allclose(res2, expected2)
|
||
|
||
def test_axis(self):
|
||
scoreatperc = stats.scoreatpercentile
|
||
x = arange(12).reshape(3, 4)
|
||
|
||
assert_equal(scoreatperc(x, (25, 50, 100)), [2.75, 5.5, 11.0])
|
||
|
||
r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
|
||
assert_equal(scoreatperc(x, (25, 50, 100), axis=0), r0)
|
||
|
||
r1 = [[0.75, 4.75, 8.75], [1.5, 5.5, 9.5], [3, 7, 11]]
|
||
assert_equal(scoreatperc(x, (25, 50, 100), axis=1), r1)
|
||
|
||
x = array([[1, 1, 1],
|
||
[1, 1, 1],
|
||
[4, 4, 3],
|
||
[1, 1, 1],
|
||
[1, 1, 1]])
|
||
score = stats.scoreatpercentile(x, 50)
|
||
assert_equal(score.shape, ())
|
||
assert_equal(score, 1.0)
|
||
score = stats.scoreatpercentile(x, 50, axis=0)
|
||
assert_equal(score.shape, (3,))
|
||
assert_equal(score, [1, 1, 1])
|
||
|
||
def test_exception(self):
|
||
assert_raises(ValueError, stats.scoreatpercentile, [1, 2], 56,
|
||
interpolation_method='foobar')
|
||
assert_raises(ValueError, stats.scoreatpercentile, [1], 101)
|
||
assert_raises(ValueError, stats.scoreatpercentile, [1], -1)
|
||
|
||
def test_empty(self):
|
||
assert_equal(stats.scoreatpercentile([], 50), np.nan)
|
||
assert_equal(stats.scoreatpercentile(np.array([[], []]), 50), np.nan)
|
||
assert_equal(stats.scoreatpercentile([], [50, 99]), [np.nan, np.nan])
|
||
|
||
|
||
@pytest.mark.filterwarnings('ignore::FutureWarning')
|
||
class TestMode:
|
||
|
||
def test_empty(self):
|
||
vals, counts = stats.mode([])
|
||
assert_equal(vals, np.array([]))
|
||
assert_equal(counts, np.array([]))
|
||
|
||
def test_scalar(self):
|
||
vals, counts = stats.mode(4.)
|
||
assert_equal(vals, np.array([4.]))
|
||
assert_equal(counts, np.array([1]))
|
||
|
||
def test_basic(self):
|
||
data1 = [3, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6]
|
||
vals = stats.mode(data1)
|
||
assert_equal(vals[0], 6)
|
||
assert_equal(vals[1], 3)
|
||
|
||
def test_axes(self):
|
||
data1 = [10, 10, 30, 40]
|
||
data2 = [10, 10, 10, 10]
|
||
data3 = [20, 10, 20, 20]
|
||
data4 = [30, 30, 30, 30]
|
||
data5 = [40, 30, 30, 30]
|
||
arr = np.array([data1, data2, data3, data4, data5])
|
||
|
||
vals = stats.mode(arr, axis=None, keepdims=True)
|
||
assert_equal(vals[0], np.array([[30]]))
|
||
assert_equal(vals[1], np.array([[8]]))
|
||
|
||
vals = stats.mode(arr, axis=0, keepdims=True)
|
||
assert_equal(vals[0], np.array([[10, 10, 30, 30]]))
|
||
assert_equal(vals[1], np.array([[2, 3, 3, 2]]))
|
||
|
||
vals = stats.mode(arr, axis=1, keepdims=True)
|
||
assert_equal(vals[0], np.array([[10], [10], [20], [30], [30]]))
|
||
assert_equal(vals[1], np.array([[2], [4], [3], [4], [3]]))
|
||
|
||
@pytest.mark.parametrize('axis', np.arange(-4, 0))
|
||
def test_negative_axes_gh_15375(self, axis):
|
||
np.random.seed(984213899)
|
||
a = np.random.rand(10, 11, 12, 13)
|
||
res0 = stats.mode(a, axis=a.ndim+axis)
|
||
res1 = stats.mode(a, axis=axis)
|
||
np.testing.assert_array_equal(res0, res1)
|
||
|
||
def test_mode_result_attributes(self):
|
||
data1 = [3, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6]
|
||
data2 = []
|
||
actual = stats.mode(data1)
|
||
attributes = ('mode', 'count')
|
||
check_named_results(actual, attributes)
|
||
actual2 = stats.mode(data2)
|
||
check_named_results(actual2, attributes)
|
||
|
||
def test_mode_nan(self):
|
||
data1 = [3, np.nan, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6]
|
||
actual = stats.mode(data1)
|
||
assert_equal(actual, (6, 3))
|
||
|
||
actual = stats.mode(data1, nan_policy='omit')
|
||
assert_equal(actual, (6, 3))
|
||
assert_raises(ValueError, stats.mode, data1, nan_policy='raise')
|
||
assert_raises(ValueError, stats.mode, data1, nan_policy='foobar')
|
||
|
||
@pytest.mark.parametrize("data", [
|
||
[3, 5, 1, 1, 3],
|
||
[3, np.nan, 5, 1, 1, 3],
|
||
[3, 5, 1],
|
||
[3, np.nan, 5, 1],
|
||
])
|
||
@pytest.mark.parametrize('keepdims', [False, True])
|
||
def test_smallest_equal(self, data, keepdims):
|
||
result = stats.mode(data, nan_policy='omit', keepdims=keepdims)
|
||
if keepdims:
|
||
assert_equal(result[0][0], 1)
|
||
else:
|
||
assert_equal(result[0], 1)
|
||
|
||
@pytest.mark.parametrize('axis', np.arange(-3, 3))
|
||
def test_mode_shape_gh_9955(self, axis, dtype=np.float64):
|
||
rng = np.random.default_rng(984213899)
|
||
a = rng.uniform(size=(3, 4, 5)).astype(dtype)
|
||
res = stats.mode(a, axis=axis, keepdims=False)
|
||
reference_shape = list(a.shape)
|
||
reference_shape.pop(axis)
|
||
np.testing.assert_array_equal(res.mode.shape, reference_shape)
|
||
np.testing.assert_array_equal(res.count.shape, reference_shape)
|
||
|
||
def test_nan_policy_propagate_gh_9815(self):
|
||
# mode should treat np.nan as it would any other object when
|
||
# nan_policy='propagate'
|
||
a = [2, np.nan, 1, np.nan]
|
||
res = stats.mode(a)
|
||
assert np.isnan(res.mode) and res.count == 2
|
||
|
||
def test_keepdims(self):
|
||
# test empty arrays (handled by `np.mean`)
|
||
a = np.zeros((1, 2, 3, 0))
|
||
|
||
res = stats.mode(a, axis=1, keepdims=False)
|
||
assert res.mode.shape == res.count.shape == (1, 3, 0)
|
||
|
||
res = stats.mode(a, axis=1, keepdims=True)
|
||
assert res.mode.shape == res.count.shape == (1, 1, 3, 0)
|
||
|
||
# test nan_policy='propagate'
|
||
a = [[1, 3, 3, np.nan], [1, 1, np.nan, 1]]
|
||
|
||
res = stats.mode(a, axis=1, keepdims=False)
|
||
assert_array_equal(res.mode, [3, 1])
|
||
assert_array_equal(res.count, [2, 3])
|
||
|
||
res = stats.mode(a, axis=1, keepdims=True)
|
||
assert_array_equal(res.mode, [[3], [1]])
|
||
assert_array_equal(res.count, [[2], [3]])
|
||
|
||
a = np.array(a)
|
||
res = stats.mode(a, axis=None, keepdims=False)
|
||
ref = stats.mode(a.ravel(), keepdims=False)
|
||
assert_array_equal(res, ref)
|
||
assert res.mode.shape == ref.mode.shape == ()
|
||
|
||
res = stats.mode(a, axis=None, keepdims=True)
|
||
ref = stats.mode(a.ravel(), keepdims=True)
|
||
assert_equal(res.mode.ravel(), ref.mode.ravel())
|
||
assert res.mode.shape == (1, 1)
|
||
assert_equal(res.count.ravel(), ref.count.ravel())
|
||
assert res.count.shape == (1, 1)
|
||
|
||
# test nan_policy='omit'
|
||
a = [[1, np.nan, np.nan, np.nan, 1],
|
||
[np.nan, np.nan, np.nan, np.nan, 2],
|
||
[1, 2, np.nan, 5, 5]]
|
||
|
||
res = stats.mode(a, axis=1, keepdims=False, nan_policy='omit')
|
||
assert_array_equal(res.mode, [1, 2, 5])
|
||
assert_array_equal(res.count, [2, 1, 2])
|
||
|
||
res = stats.mode(a, axis=1, keepdims=True, nan_policy='omit')
|
||
assert_array_equal(res.mode, [[1], [2], [5]])
|
||
assert_array_equal(res.count, [[2], [1], [2]])
|
||
|
||
a = np.array(a)
|
||
res = stats.mode(a, axis=None, keepdims=False, nan_policy='omit')
|
||
ref = stats.mode(a.ravel(), keepdims=False, nan_policy='omit')
|
||
assert_array_equal(res, ref)
|
||
assert res.mode.shape == ref.mode.shape == ()
|
||
|
||
res = stats.mode(a, axis=None, keepdims=True, nan_policy='omit')
|
||
ref = stats.mode(a.ravel(), keepdims=True, nan_policy='omit')
|
||
assert_equal(res.mode.ravel(), ref.mode.ravel())
|
||
assert res.mode.shape == (1, 1)
|
||
assert_equal(res.count.ravel(), ref.count.ravel())
|
||
assert res.count.shape == (1, 1)
|
||
|
||
@pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
|
||
def test_gh16955(self, nan_policy):
|
||
# Check that bug reported in gh-16955 is resolved
|
||
shape = (4, 3)
|
||
data = np.ones(shape)
|
||
data[0, 0] = np.nan
|
||
res = stats.mode(a=data, axis=1, keepdims=False, nan_policy=nan_policy)
|
||
assert_array_equal(res.mode, [1, 1, 1, 1])
|
||
assert_array_equal(res.count, [2, 3, 3, 3])
|
||
|
||
# Test with input from gh-16595. Support for non-numeric input
|
||
# was deprecated, so check for the appropriate error.
|
||
my_dtype = np.dtype([('asdf', np.uint8), ('qwer', np.float64, (3,))])
|
||
test = np.zeros(10, dtype=my_dtype)
|
||
with pytest.raises(TypeError, match="Argument `a` is not..."):
|
||
stats.mode(test, nan_policy=nan_policy)
|
||
|
||
def test_gh9955(self):
|
||
# The behavior of mode with empty slices (whether the input was empty
|
||
# or all elements were omitted) was inconsistent. Test that this is
|
||
# resolved: the mode of an empty slice is NaN and the count is zero.
|
||
res = stats.mode([])
|
||
ref = (np.nan, 0)
|
||
assert_equal(res, ref)
|
||
|
||
res = stats.mode([np.nan], nan_policy='omit')
|
||
assert_equal(res, ref)
|
||
|
||
a = [[10., 20., 20.], [np.nan, np.nan, np.nan]]
|
||
res = stats.mode(a, axis=1, nan_policy='omit')
|
||
ref = ([20, np.nan], [2, 0])
|
||
assert_equal(res, ref)
|
||
|
||
res = stats.mode(a, axis=1, nan_policy='propagate')
|
||
ref = ([20, np.nan], [2, 3])
|
||
assert_equal(res, ref)
|
||
|
||
z = np.array([[], []])
|
||
res = stats.mode(z, axis=1)
|
||
ref = ([np.nan, np.nan], [0, 0])
|
||
assert_equal(res, ref)
|
||
|
||
@pytest.mark.filterwarnings('ignore::RuntimeWarning') # np.mean warns
|
||
@pytest.mark.parametrize('z', [np.empty((0, 1, 2)), np.empty((1, 1, 2))])
|
||
def test_gh17214(self, z):
|
||
res = stats.mode(z, axis=None, keepdims=True)
|
||
ref = np.mean(z, axis=None, keepdims=True)
|
||
assert res[0].shape == res[1].shape == ref.shape == (1, 1, 1)
|
||
|
||
def test_raise_non_numeric_gh18254(self):
|
||
message = "Argument `a` is not recognized as numeric."
|
||
|
||
class ArrLike:
|
||
def __init__(self, x):
|
||
self._x = x
|
||
|
||
def __array__(self, dtype=None, copy=None):
|
||
return self._x.astype(object)
|
||
|
||
with pytest.raises(TypeError, match=message):
|
||
stats.mode(ArrLike(np.arange(3)))
|
||
with pytest.raises(TypeError, match=message):
|
||
stats.mode(np.arange(3, dtype=object))
|
||
|
||
class TestSEM:
|
||
|
||
testcase = [1, 2, 3, 4]
|
||
scalar_testcase = 4.
|
||
|
||
def test_sem(self):
|
||
# This is not in R, so used:
|
||
# sqrt(var(testcase)*3/4)/sqrt(3)
|
||
|
||
# y = stats.sem(self.shoes[0])
|
||
# assert_approx_equal(y,0.775177399)
|
||
with suppress_warnings() as sup, np.errstate(invalid="ignore"):
|
||
sup.filter(RuntimeWarning, "Degrees of freedom <= 0 for slice")
|
||
y = stats.sem(self.scalar_testcase)
|
||
assert_(np.isnan(y))
|
||
|
||
y = stats.sem(self.testcase)
|
||
assert_approx_equal(y, 0.6454972244)
|
||
n = len(self.testcase)
|
||
assert_allclose(stats.sem(self.testcase, ddof=0) * np.sqrt(n/(n-2)),
|
||
stats.sem(self.testcase, ddof=2))
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_equal(stats.sem(x), np.nan)
|
||
assert_equal(stats.sem(x, nan_policy='omit'), 0.9128709291752769)
|
||
assert_raises(ValueError, stats.sem, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.sem, x, nan_policy='foobar')
|
||
|
||
|
||
class TestZmapZscore:
|
||
|
||
@pytest.mark.parametrize(
|
||
'x, y',
|
||
[([1, 2, 3, 4], [1, 2, 3, 4]),
|
||
([1, 2, 3], [0, 1, 2, 3, 4])]
|
||
)
|
||
def test_zmap(self, x, y):
|
||
z = stats.zmap(x, y)
|
||
# For these simple cases, calculate the expected result directly
|
||
# by using the formula for the z-score.
|
||
expected = (x - np.mean(y))/np.std(y)
|
||
assert_allclose(z, expected, rtol=1e-12)
|
||
|
||
def test_zmap_axis(self):
|
||
# Test use of 'axis' keyword in zmap.
|
||
x = np.array([[0.0, 0.0, 1.0, 1.0],
|
||
[1.0, 1.0, 1.0, 2.0],
|
||
[2.0, 0.0, 2.0, 0.0]])
|
||
|
||
t1 = 1.0/np.sqrt(2.0/3)
|
||
t2 = np.sqrt(3.)/3
|
||
t3 = np.sqrt(2.)
|
||
|
||
z0 = stats.zmap(x, x, axis=0)
|
||
z1 = stats.zmap(x, x, axis=1)
|
||
|
||
z0_expected = [[-t1, -t3/2, -t3/2, 0.0],
|
||
[0.0, t3, -t3/2, t1],
|
||
[t1, -t3/2, t3, -t1]]
|
||
z1_expected = [[-1.0, -1.0, 1.0, 1.0],
|
||
[-t2, -t2, -t2, np.sqrt(3.)],
|
||
[1.0, -1.0, 1.0, -1.0]]
|
||
|
||
assert_array_almost_equal(z0, z0_expected)
|
||
assert_array_almost_equal(z1, z1_expected)
|
||
|
||
def test_zmap_ddof(self):
|
||
# Test use of 'ddof' keyword in zmap.
|
||
x = np.array([[0.0, 0.0, 1.0, 1.0],
|
||
[0.0, 1.0, 2.0, 3.0]])
|
||
|
||
z = stats.zmap(x, x, axis=1, ddof=1)
|
||
|
||
z0_expected = np.array([-0.5, -0.5, 0.5, 0.5])/(1.0/np.sqrt(3))
|
||
z1_expected = np.array([-1.5, -0.5, 0.5, 1.5])/(np.sqrt(5./3))
|
||
assert_array_almost_equal(z[0], z0_expected)
|
||
assert_array_almost_equal(z[1], z1_expected)
|
||
|
||
@pytest.mark.parametrize('ddof', [0, 2])
|
||
def test_zmap_nan_policy_omit(self, ddof):
|
||
# nans in `scores` are propagated, regardless of `nan_policy`.
|
||
# `nan_policy` only affects how nans in `compare` are handled.
|
||
scores = np.array([-3, -1, 2, np.nan])
|
||
compare = np.array([-8, -3, 2, 7, 12, np.nan])
|
||
z = stats.zmap(scores, compare, ddof=ddof, nan_policy='omit')
|
||
assert_allclose(z, stats.zmap(scores, compare[~np.isnan(compare)],
|
||
ddof=ddof))
|
||
|
||
@pytest.mark.parametrize('ddof', [0, 2])
|
||
def test_zmap_nan_policy_omit_with_axis(self, ddof):
|
||
scores = np.arange(-5.0, 9.0).reshape(2, -1)
|
||
compare = np.linspace(-8, 6, 24).reshape(2, -1)
|
||
compare[0, 4] = np.nan
|
||
compare[0, 6] = np.nan
|
||
compare[1, 1] = np.nan
|
||
z = stats.zmap(scores, compare, nan_policy='omit', axis=1, ddof=ddof)
|
||
expected = np.array([stats.zmap(scores[0],
|
||
compare[0][~np.isnan(compare[0])],
|
||
ddof=ddof),
|
||
stats.zmap(scores[1],
|
||
compare[1][~np.isnan(compare[1])],
|
||
ddof=ddof)])
|
||
assert_allclose(z, expected, rtol=1e-14)
|
||
|
||
def test_zmap_nan_policy_raise(self):
|
||
scores = np.array([1, 2, 3])
|
||
compare = np.array([-8, -3, 2, 7, 12, np.nan])
|
||
with pytest.raises(ValueError, match='input contains nan'):
|
||
stats.zmap(scores, compare, nan_policy='raise')
|
||
|
||
def test_zscore(self):
|
||
# not in R, so tested by using:
|
||
# (testcase[i] - mean(testcase, axis=0)) / sqrt(var(testcase) * 3/4)
|
||
y = stats.zscore([1, 2, 3, 4])
|
||
desired = ([-1.3416407864999, -0.44721359549996, 0.44721359549996,
|
||
1.3416407864999])
|
||
assert_array_almost_equal(desired, y, decimal=12)
|
||
|
||
def test_zscore_axis(self):
|
||
# Test use of 'axis' keyword in zscore.
|
||
x = np.array([[0.0, 0.0, 1.0, 1.0],
|
||
[1.0, 1.0, 1.0, 2.0],
|
||
[2.0, 0.0, 2.0, 0.0]])
|
||
|
||
t1 = 1.0/np.sqrt(2.0/3)
|
||
t2 = np.sqrt(3.)/3
|
||
t3 = np.sqrt(2.)
|
||
|
||
z0 = stats.zscore(x, axis=0)
|
||
z1 = stats.zscore(x, axis=1)
|
||
|
||
z0_expected = [[-t1, -t3/2, -t3/2, 0.0],
|
||
[0.0, t3, -t3/2, t1],
|
||
[t1, -t3/2, t3, -t1]]
|
||
z1_expected = [[-1.0, -1.0, 1.0, 1.0],
|
||
[-t2, -t2, -t2, np.sqrt(3.)],
|
||
[1.0, -1.0, 1.0, -1.0]]
|
||
|
||
assert_array_almost_equal(z0, z0_expected)
|
||
assert_array_almost_equal(z1, z1_expected)
|
||
|
||
def test_zscore_ddof(self):
|
||
# Test use of 'ddof' keyword in zscore.
|
||
x = np.array([[0.0, 0.0, 1.0, 1.0],
|
||
[0.0, 1.0, 2.0, 3.0]])
|
||
|
||
z = stats.zscore(x, axis=1, ddof=1)
|
||
|
||
z0_expected = np.array([-0.5, -0.5, 0.5, 0.5])/(1.0/np.sqrt(3))
|
||
z1_expected = np.array([-1.5, -0.5, 0.5, 1.5])/(np.sqrt(5./3))
|
||
assert_array_almost_equal(z[0], z0_expected)
|
||
assert_array_almost_equal(z[1], z1_expected)
|
||
|
||
def test_zscore_nan_propagate(self):
|
||
x = np.array([1, 2, np.nan, 4, 5])
|
||
z = stats.zscore(x, nan_policy='propagate')
|
||
assert all(np.isnan(z))
|
||
|
||
def test_zscore_nan_omit(self):
|
||
x = np.array([1, 2, np.nan, 4, 5])
|
||
|
||
z = stats.zscore(x, nan_policy='omit')
|
||
|
||
expected = np.array([-1.2649110640673518,
|
||
-0.6324555320336759,
|
||
np.nan,
|
||
0.6324555320336759,
|
||
1.2649110640673518
|
||
])
|
||
assert_array_almost_equal(z, expected)
|
||
|
||
def test_zscore_nan_omit_with_ddof(self):
|
||
x = np.array([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0])
|
||
z = stats.zscore(x, ddof=1, nan_policy='omit')
|
||
expected = np.r_[np.nan, stats.zscore(x[1:], ddof=1)]
|
||
assert_allclose(z, expected, rtol=1e-13)
|
||
|
||
def test_zscore_nan_raise(self):
|
||
x = np.array([1, 2, np.nan, 4, 5])
|
||
|
||
assert_raises(ValueError, stats.zscore, x, nan_policy='raise')
|
||
|
||
def test_zscore_constant_input_1d(self):
|
||
x = [-0.087] * 3
|
||
z = stats.zscore(x)
|
||
assert_equal(z, np.full(len(x), np.nan))
|
||
|
||
def test_zscore_constant_input_2d(self):
|
||
x = np.array([[10.0, 10.0, 10.0, 10.0],
|
||
[10.0, 11.0, 12.0, 13.0]])
|
||
z0 = stats.zscore(x, axis=0)
|
||
assert_equal(z0, np.array([[np.nan, -1.0, -1.0, -1.0],
|
||
[np.nan, 1.0, 1.0, 1.0]]))
|
||
z1 = stats.zscore(x, axis=1)
|
||
assert_equal(z1, np.array([[np.nan, np.nan, np.nan, np.nan],
|
||
stats.zscore(x[1])]))
|
||
z = stats.zscore(x, axis=None)
|
||
assert_equal(z, stats.zscore(x.ravel()).reshape(x.shape))
|
||
|
||
y = np.ones((3, 6))
|
||
z = stats.zscore(y, axis=None)
|
||
assert_equal(z, np.full(y.shape, np.nan))
|
||
|
||
def test_zscore_constant_input_2d_nan_policy_omit(self):
|
||
x = np.array([[10.0, 10.0, 10.0, 10.0],
|
||
[10.0, 11.0, 12.0, np.nan],
|
||
[10.0, 12.0, np.nan, 10.0]])
|
||
z0 = stats.zscore(x, nan_policy='omit', axis=0)
|
||
s = np.sqrt(3/2)
|
||
s2 = np.sqrt(2)
|
||
assert_allclose(z0, np.array([[np.nan, -s, -1.0, np.nan],
|
||
[np.nan, 0, 1.0, np.nan],
|
||
[np.nan, s, np.nan, np.nan]]))
|
||
z1 = stats.zscore(x, nan_policy='omit', axis=1)
|
||
assert_allclose(z1, np.array([[np.nan, np.nan, np.nan, np.nan],
|
||
[-s, 0, s, np.nan],
|
||
[-s2/2, s2, np.nan, -s2/2]]))
|
||
|
||
def test_zscore_2d_all_nan_row(self):
|
||
# A row is all nan, and we use axis=1.
|
||
x = np.array([[np.nan, np.nan, np.nan, np.nan],
|
||
[10.0, 10.0, 12.0, 12.0]])
|
||
z = stats.zscore(x, nan_policy='omit', axis=1)
|
||
assert_equal(z, np.array([[np.nan, np.nan, np.nan, np.nan],
|
||
[-1.0, -1.0, 1.0, 1.0]]))
|
||
|
||
def test_zscore_2d_all_nan(self):
|
||
# The entire 2d array is nan, and we use axis=None.
|
||
y = np.full((2, 3), np.nan)
|
||
z = stats.zscore(y, nan_policy='omit', axis=None)
|
||
assert_equal(z, y)
|
||
|
||
@pytest.mark.parametrize('x', [np.array([]), np.zeros((3, 0, 5))])
|
||
def test_zscore_empty_input(self, x):
|
||
z = stats.zscore(x)
|
||
assert_equal(z, x)
|
||
|
||
def test_gzscore_normal_array(self):
|
||
x = np.array([1, 2, 3, 4])
|
||
z = stats.gzscore(x)
|
||
desired = np.log(x / stats.gmean(x)) / np.log(stats.gstd(x, ddof=0))
|
||
assert_allclose(desired, z)
|
||
|
||
def test_gzscore_masked_array(self):
|
||
x = np.array([1, 2, -1, 3, 4])
|
||
mx = np.ma.masked_array(x, mask=[0, 0, 1, 0, 0])
|
||
z = stats.gzscore(mx)
|
||
desired = ([-1.526072095151, -0.194700599824, np.inf, 0.584101799472,
|
||
1.136670895503])
|
||
assert_allclose(desired, z)
|
||
|
||
def test_zscore_masked_element_0_gh19039(self):
|
||
# zscore returned all NaNs when 0th element was masked. See gh-19039.
|
||
rng = np.random.default_rng(8675309)
|
||
x = rng.standard_normal(10)
|
||
mask = np.zeros_like(x)
|
||
y = np.ma.masked_array(x, mask)
|
||
y.mask[0] = True
|
||
|
||
ref = stats.zscore(x[1:]) # compute reference from non-masked elements
|
||
assert not np.any(np.isnan(ref))
|
||
res = stats.zscore(y)
|
||
assert_allclose(res[1:], ref)
|
||
res = stats.zscore(y, axis=None)
|
||
assert_allclose(res[1:], ref)
|
||
|
||
y[1:] = y[1] # when non-masked elements are identical, result is nan
|
||
res = stats.zscore(y)
|
||
assert_equal(res[1:], np.nan)
|
||
res = stats.zscore(y, axis=None)
|
||
assert_equal(res[1:], np.nan)
|
||
|
||
class TestMedianAbsDeviation:
|
||
def setup_class(self):
|
||
self.dat_nan = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9,
|
||
3.03, 3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5,
|
||
3.6, 3.7, 3.7, 3.7, 3.7, 3.77, 5.28, np.nan])
|
||
self.dat = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03,
|
||
3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7,
|
||
3.7, 3.7, 3.7, 3.77, 5.28, 28.95])
|
||
|
||
def test_median_abs_deviation(self):
|
||
assert_almost_equal(stats.median_abs_deviation(self.dat, axis=None),
|
||
0.355)
|
||
dat = self.dat.reshape(6, 4)
|
||
mad = stats.median_abs_deviation(dat, axis=0)
|
||
mad_expected = np.asarray([0.435, 0.5, 0.45, 0.4])
|
||
assert_array_almost_equal(mad, mad_expected)
|
||
|
||
def test_mad_nan_omit(self):
|
||
mad = stats.median_abs_deviation(self.dat_nan, nan_policy='omit')
|
||
assert_almost_equal(mad, 0.34)
|
||
|
||
def test_axis_and_nan(self):
|
||
x = np.array([[1.0, 2.0, 3.0, 4.0, np.nan],
|
||
[1.0, 4.0, 5.0, 8.0, 9.0]])
|
||
mad = stats.median_abs_deviation(x, axis=1)
|
||
assert_equal(mad, np.array([np.nan, 3.0]))
|
||
|
||
def test_nan_policy_omit_with_inf(self):
|
||
z = np.array([1, 3, 4, 6, 99, np.nan, np.inf])
|
||
mad = stats.median_abs_deviation(z, nan_policy='omit')
|
||
assert_equal(mad, 3.0)
|
||
|
||
@pytest.mark.parametrize('axis', [0, 1, 2, None])
|
||
def test_size_zero_with_axis(self, axis):
|
||
x = np.zeros((3, 0, 4))
|
||
mad = stats.median_abs_deviation(x, axis=axis)
|
||
assert_equal(mad, np.full_like(x.sum(axis=axis), fill_value=np.nan))
|
||
|
||
@pytest.mark.parametrize('nan_policy, expected',
|
||
[('omit', np.array([np.nan, 1.5, 1.5])),
|
||
('propagate', np.array([np.nan, np.nan, 1.5]))])
|
||
def test_nan_policy_with_axis(self, nan_policy, expected):
|
||
x = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
|
||
[1, 5, 3, 6, np.nan, np.nan],
|
||
[5, 6, 7, 9, 9, 10]])
|
||
mad = stats.median_abs_deviation(x, nan_policy=nan_policy, axis=1)
|
||
assert_equal(mad, expected)
|
||
|
||
@pytest.mark.parametrize('axis, expected',
|
||
[(1, [2.5, 2.0, 12.0]), (None, 4.5)])
|
||
def test_center_mean_with_nan(self, axis, expected):
|
||
x = np.array([[1, 2, 4, 9, np.nan],
|
||
[0, 1, 1, 1, 12],
|
||
[-10, -10, -10, 20, 20]])
|
||
mad = stats.median_abs_deviation(x, center=np.mean, nan_policy='omit',
|
||
axis=axis)
|
||
assert_allclose(mad, expected, rtol=1e-15, atol=1e-15)
|
||
|
||
def test_center_not_callable(self):
|
||
with pytest.raises(TypeError, match='callable'):
|
||
stats.median_abs_deviation([1, 2, 3, 5], center=99)
|
||
|
||
|
||
def _check_warnings(warn_list, expected_type, expected_len):
|
||
"""
|
||
Checks that all of the warnings from a list returned by
|
||
`warnings.catch_all(record=True)` are of the required type and that the list
|
||
contains expected number of warnings.
|
||
"""
|
||
assert_equal(len(warn_list), expected_len, "number of warnings")
|
||
for warn_ in warn_list:
|
||
assert_(warn_.category is expected_type)
|
||
|
||
|
||
class TestIQR:
|
||
|
||
def test_basic(self):
|
||
x = np.arange(8) * 0.5
|
||
np.random.shuffle(x)
|
||
assert_equal(stats.iqr(x), 1.75)
|
||
|
||
def test_api(self):
|
||
d = np.ones((5, 5))
|
||
stats.iqr(d)
|
||
stats.iqr(d, None)
|
||
stats.iqr(d, 1)
|
||
stats.iqr(d, (0, 1))
|
||
stats.iqr(d, None, (10, 90))
|
||
stats.iqr(d, None, (30, 20), 1.0)
|
||
stats.iqr(d, None, (25, 75), 1.5, 'propagate')
|
||
stats.iqr(d, None, (50, 50), 'normal', 'raise', 'linear')
|
||
stats.iqr(d, None, (25, 75), -0.4, 'omit', 'lower', True)
|
||
|
||
def test_empty(self):
|
||
assert_equal(stats.iqr([]), np.nan)
|
||
assert_equal(stats.iqr(np.arange(0)), np.nan)
|
||
|
||
def test_constant(self):
|
||
# Constant array always gives 0
|
||
x = np.ones((7, 4))
|
||
assert_equal(stats.iqr(x), 0.0)
|
||
assert_array_equal(stats.iqr(x, axis=0), np.zeros(4))
|
||
assert_array_equal(stats.iqr(x, axis=1), np.zeros(7))
|
||
assert_equal(stats.iqr(x, interpolation='linear'), 0.0)
|
||
assert_equal(stats.iqr(x, interpolation='midpoint'), 0.0)
|
||
assert_equal(stats.iqr(x, interpolation='nearest'), 0.0)
|
||
assert_equal(stats.iqr(x, interpolation='lower'), 0.0)
|
||
assert_equal(stats.iqr(x, interpolation='higher'), 0.0)
|
||
|
||
# 0 only along constant dimensions
|
||
# This also tests much of `axis`
|
||
y = np.ones((4, 5, 6)) * np.arange(6)
|
||
assert_array_equal(stats.iqr(y, axis=0), np.zeros((5, 6)))
|
||
assert_array_equal(stats.iqr(y, axis=1), np.zeros((4, 6)))
|
||
assert_array_equal(stats.iqr(y, axis=2), np.full((4, 5), 2.5))
|
||
assert_array_equal(stats.iqr(y, axis=(0, 1)), np.zeros(6))
|
||
assert_array_equal(stats.iqr(y, axis=(0, 2)), np.full(5, 3.))
|
||
assert_array_equal(stats.iqr(y, axis=(1, 2)), np.full(4, 3.))
|
||
|
||
def test_scalarlike(self):
|
||
x = np.arange(1) + 7.0
|
||
assert_equal(stats.iqr(x[0]), 0.0)
|
||
assert_equal(stats.iqr(x), 0.0)
|
||
assert_array_equal(stats.iqr(x, keepdims=True), [0.0])
|
||
|
||
def test_2D(self):
|
||
x = np.arange(15).reshape((3, 5))
|
||
assert_equal(stats.iqr(x), 7.0)
|
||
assert_array_equal(stats.iqr(x, axis=0), np.full(5, 5.))
|
||
assert_array_equal(stats.iqr(x, axis=1), np.full(3, 2.))
|
||
assert_array_equal(stats.iqr(x, axis=(0, 1)), 7.0)
|
||
assert_array_equal(stats.iqr(x, axis=(1, 0)), 7.0)
|
||
|
||
def test_axis(self):
|
||
# The `axis` keyword is also put through its paces in `test_keepdims`.
|
||
o = np.random.normal(size=(71, 23))
|
||
x = np.dstack([o] * 10) # x.shape = (71, 23, 10)
|
||
q = stats.iqr(o)
|
||
|
||
assert_equal(stats.iqr(x, axis=(0, 1)), q)
|
||
x = np.moveaxis(x, -1, 0) # x.shape = (10, 71, 23)
|
||
assert_equal(stats.iqr(x, axis=(2, 1)), q)
|
||
x = x.swapaxes(0, 1) # x.shape = (71, 10, 23)
|
||
assert_equal(stats.iqr(x, axis=(0, 2)), q)
|
||
x = x.swapaxes(0, 1) # x.shape = (10, 71, 23)
|
||
|
||
assert_equal(stats.iqr(x, axis=(0, 1, 2)),
|
||
stats.iqr(x, axis=None))
|
||
assert_equal(stats.iqr(x, axis=(0,)),
|
||
stats.iqr(x, axis=0))
|
||
|
||
d = np.arange(3 * 5 * 7 * 11)
|
||
# Older versions of numpy only shuffle along axis=0.
|
||
# Not sure about newer, don't care.
|
||
np.random.shuffle(d)
|
||
d = d.reshape((3, 5, 7, 11))
|
||
assert_equal(stats.iqr(d, axis=(0, 1, 2))[0],
|
||
stats.iqr(d[:,:,:, 0].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(0, 1, 3))[1],
|
||
stats.iqr(d[:,:, 1,:].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(3, 1, -4))[2],
|
||
stats.iqr(d[:,:, 2,:].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(3, 1, 2))[2],
|
||
stats.iqr(d[2,:,:,:].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(3, 2))[2, 1],
|
||
stats.iqr(d[2, 1,:,:].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(1, -2))[2, 1],
|
||
stats.iqr(d[2, :, :, 1].ravel()))
|
||
assert_equal(stats.iqr(d, axis=(1, 3))[2, 2],
|
||
stats.iqr(d[2, :, 2,:].ravel()))
|
||
|
||
assert_raises(AxisError, stats.iqr, d, axis=4)
|
||
assert_raises(ValueError, stats.iqr, d, axis=(0, 0))
|
||
|
||
def test_rng(self):
|
||
x = np.arange(5)
|
||
assert_equal(stats.iqr(x), 2)
|
||
assert_equal(stats.iqr(x, rng=(25, 87.5)), 2.5)
|
||
assert_equal(stats.iqr(x, rng=(12.5, 75)), 2.5)
|
||
assert_almost_equal(stats.iqr(x, rng=(10, 50)), 1.6) # 3-1.4
|
||
|
||
assert_raises(ValueError, stats.iqr, x, rng=(0, 101))
|
||
assert_raises(ValueError, stats.iqr, x, rng=(np.nan, 25))
|
||
assert_raises(TypeError, stats.iqr, x, rng=(0, 50, 60))
|
||
|
||
def test_interpolation(self):
|
||
x = np.arange(5)
|
||
y = np.arange(4)
|
||
# Default
|
||
assert_equal(stats.iqr(x), 2)
|
||
assert_equal(stats.iqr(y), 1.5)
|
||
# Linear
|
||
assert_equal(stats.iqr(x, interpolation='linear'), 2)
|
||
assert_equal(stats.iqr(y, interpolation='linear'), 1.5)
|
||
# Higher
|
||
assert_equal(stats.iqr(x, interpolation='higher'), 2)
|
||
assert_equal(stats.iqr(x, rng=(25, 80), interpolation='higher'), 3)
|
||
assert_equal(stats.iqr(y, interpolation='higher'), 2)
|
||
# Lower (will generally, but not always be the same as higher)
|
||
assert_equal(stats.iqr(x, interpolation='lower'), 2)
|
||
assert_equal(stats.iqr(x, rng=(25, 80), interpolation='lower'), 2)
|
||
assert_equal(stats.iqr(y, interpolation='lower'), 2)
|
||
# Nearest
|
||
assert_equal(stats.iqr(x, interpolation='nearest'), 2)
|
||
assert_equal(stats.iqr(y, interpolation='nearest'), 1)
|
||
# Midpoint
|
||
assert_equal(stats.iqr(x, interpolation='midpoint'), 2)
|
||
assert_equal(stats.iqr(x, rng=(25, 80), interpolation='midpoint'), 2.5)
|
||
assert_equal(stats.iqr(y, interpolation='midpoint'), 2)
|
||
|
||
# Check all method= values new in numpy 1.22.0 are accepted
|
||
for method in ('inverted_cdf', 'averaged_inverted_cdf',
|
||
'closest_observation', 'interpolated_inverted_cdf',
|
||
'hazen', 'weibull', 'median_unbiased',
|
||
'normal_unbiased'):
|
||
stats.iqr(y, interpolation=method)
|
||
|
||
assert_raises(ValueError, stats.iqr, x, interpolation='foobar')
|
||
|
||
def test_keepdims(self):
|
||
# Also tests most of `axis`
|
||
x = np.ones((3, 5, 7, 11))
|
||
assert_equal(stats.iqr(x, axis=None, keepdims=False).shape, ())
|
||
assert_equal(stats.iqr(x, axis=2, keepdims=False).shape, (3, 5, 11))
|
||
assert_equal(stats.iqr(x, axis=(0, 1), keepdims=False).shape, (7, 11))
|
||
assert_equal(stats.iqr(x, axis=(0, 3), keepdims=False).shape, (5, 7))
|
||
assert_equal(stats.iqr(x, axis=(1,), keepdims=False).shape, (3, 7, 11))
|
||
assert_equal(stats.iqr(x, (0, 1, 2, 3), keepdims=False).shape, ())
|
||
assert_equal(stats.iqr(x, axis=(0, 1, 3), keepdims=False).shape, (7,))
|
||
|
||
assert_equal(stats.iqr(x, axis=None, keepdims=True).shape, (1, 1, 1, 1))
|
||
assert_equal(stats.iqr(x, axis=2, keepdims=True).shape, (3, 5, 1, 11))
|
||
assert_equal(stats.iqr(x, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11))
|
||
assert_equal(stats.iqr(x, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1))
|
||
assert_equal(stats.iqr(x, axis=(1,), keepdims=True).shape, (3, 1, 7, 11))
|
||
assert_equal(stats.iqr(x, (0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1))
|
||
assert_equal(stats.iqr(x, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1))
|
||
|
||
def test_nanpolicy(self):
|
||
x = np.arange(15.0).reshape((3, 5))
|
||
|
||
# No NaNs
|
||
assert_equal(stats.iqr(x, nan_policy='propagate'), 7)
|
||
assert_equal(stats.iqr(x, nan_policy='omit'), 7)
|
||
assert_equal(stats.iqr(x, nan_policy='raise'), 7)
|
||
|
||
# Yes NaNs
|
||
x[1, 2] = np.nan
|
||
with warnings.catch_warnings(record=True):
|
||
warnings.simplefilter("always")
|
||
assert_equal(stats.iqr(x, nan_policy='propagate'),
|
||
np.nan)
|
||
assert_equal(stats.iqr(x, axis=0, nan_policy='propagate'),
|
||
[5, 5, np.nan, 5, 5])
|
||
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'),
|
||
[2, np.nan, 2])
|
||
|
||
with warnings.catch_warnings(record=True):
|
||
warnings.simplefilter("always")
|
||
assert_equal(stats.iqr(x, nan_policy='omit'), 7.5)
|
||
assert_equal(stats.iqr(x, axis=0, nan_policy='omit'), np.full(5, 5))
|
||
assert_equal(stats.iqr(x, axis=1, nan_policy='omit'), [2, 2.5, 2])
|
||
|
||
assert_raises(ValueError, stats.iqr, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.iqr, x, axis=0, nan_policy='raise')
|
||
assert_raises(ValueError, stats.iqr, x, axis=1, nan_policy='raise')
|
||
|
||
# Bad policy
|
||
assert_raises(ValueError, stats.iqr, x, nan_policy='barfood')
|
||
|
||
def test_scale(self):
|
||
x = np.arange(15.0).reshape((3, 5))
|
||
|
||
# No NaNs
|
||
assert_equal(stats.iqr(x, scale=1.0), 7)
|
||
assert_almost_equal(stats.iqr(x, scale='normal'), 7 / 1.3489795)
|
||
assert_equal(stats.iqr(x, scale=2.0), 3.5)
|
||
|
||
# Yes NaNs
|
||
x[1, 2] = np.nan
|
||
with warnings.catch_warnings(record=True):
|
||
warnings.simplefilter("always")
|
||
assert_equal(stats.iqr(x, scale=1.0, nan_policy='propagate'), np.nan)
|
||
assert_equal(stats.iqr(x, scale='normal', nan_policy='propagate'), np.nan)
|
||
assert_equal(stats.iqr(x, scale=2.0, nan_policy='propagate'), np.nan)
|
||
# axis=1 chosen to show behavior with both nans and without
|
||
assert_equal(stats.iqr(x, axis=1, scale=1.0,
|
||
nan_policy='propagate'), [2, np.nan, 2])
|
||
assert_almost_equal(stats.iqr(x, axis=1, scale='normal',
|
||
nan_policy='propagate'),
|
||
np.array([2, np.nan, 2]) / 1.3489795)
|
||
assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'),
|
||
[1, np.nan, 1])
|
||
# Since NumPy 1.17.0.dev, warnings are no longer emitted by
|
||
# np.percentile with nans, so we don't check the number of
|
||
# warnings here. See https://github.com/numpy/numpy/pull/12679.
|
||
|
||
assert_equal(stats.iqr(x, scale=1.0, nan_policy='omit'), 7.5)
|
||
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='omit'),
|
||
7.5 / 1.3489795)
|
||
assert_equal(stats.iqr(x, scale=2.0, nan_policy='omit'), 3.75)
|
||
|
||
# Bad scale
|
||
assert_raises(ValueError, stats.iqr, x, scale='foobar')
|
||
|
||
|
||
class TestMoments:
|
||
"""
|
||
Comparison numbers are found using R v.1.5.1
|
||
note that length(testcase) = 4
|
||
testmathworks comes from documentation for the
|
||
Statistics Toolbox for Matlab and can be found at both
|
||
https://www.mathworks.com/help/stats/kurtosis.html
|
||
https://www.mathworks.com/help/stats/skewness.html
|
||
Note that both test cases came from here.
|
||
"""
|
||
testcase = [1,2,3,4]
|
||
scalar_testcase = 4.
|
||
np.random.seed(1234)
|
||
testcase_moment_accuracy = np.random.rand(42)
|
||
testmathworks = [1.165, 0.6268, 0.0751, 0.3516, -0.6965]
|
||
|
||
def _assert_equal(self, actual, expect, *, shape=None, dtype=None):
|
||
expect = np.asarray(expect)
|
||
if shape is not None:
|
||
expect = np.broadcast_to(expect, shape)
|
||
assert_array_equal(actual, expect)
|
||
if dtype is None:
|
||
dtype = expect.dtype
|
||
assert actual.dtype == dtype
|
||
|
||
@pytest.mark.parametrize('size', [10, (10, 2)])
|
||
@pytest.mark.parametrize('m, c', product((0, 1, 2, 3), (None, 0, 1)))
|
||
def test_moment_center_scalar_moment(self, size, m, c):
|
||
rng = np.random.default_rng(6581432544381372042)
|
||
x = rng.random(size=size)
|
||
res = stats.moment(x, m, center=c)
|
||
c = np.mean(x, axis=0) if c is None else c
|
||
ref = np.sum((x - c)**m, axis=0)/len(x)
|
||
assert_allclose(res, ref, atol=1e-16)
|
||
|
||
@pytest.mark.parametrize('size', [10, (10, 2)])
|
||
@pytest.mark.parametrize('c', (None, 0, 1))
|
||
def test_moment_center_array_moment(self, size, c):
|
||
rng = np.random.default_rng(1706828300224046506)
|
||
x = rng.random(size=size)
|
||
m = [0, 1, 2, 3]
|
||
res = stats.moment(x, m, center=c)
|
||
ref = [stats.moment(x, i, center=c) for i in m]
|
||
assert_equal(res, ref)
|
||
|
||
def test_moment(self):
|
||
# mean((testcase-mean(testcase))**power,axis=0),axis=0))**power))
|
||
y = stats.moment(self.scalar_testcase)
|
||
assert_approx_equal(y, 0.0)
|
||
y = stats.moment(self.testcase, 0)
|
||
assert_approx_equal(y, 1.0)
|
||
y = stats.moment(self.testcase, 1)
|
||
assert_approx_equal(y, 0.0, 10)
|
||
y = stats.moment(self.testcase, 2)
|
||
assert_approx_equal(y, 1.25)
|
||
y = stats.moment(self.testcase, 3)
|
||
assert_approx_equal(y, 0.0)
|
||
y = stats.moment(self.testcase, 4)
|
||
assert_approx_equal(y, 2.5625)
|
||
|
||
# check array_like input for moment
|
||
y = stats.moment(self.testcase, [1, 2, 3, 4])
|
||
assert_allclose(y, [0, 1.25, 0, 2.5625])
|
||
|
||
# check moment input consists only of integers
|
||
y = stats.moment(self.testcase, 0.0)
|
||
assert_approx_equal(y, 1.0)
|
||
assert_raises(ValueError, stats.moment, self.testcase, 1.2)
|
||
y = stats.moment(self.testcase, [1.0, 2, 3, 4.0])
|
||
assert_allclose(y, [0, 1.25, 0, 2.5625])
|
||
|
||
# test empty input
|
||
message = r"Mean of empty slice\.|invalid value encountered.*"
|
||
with pytest.warns(RuntimeWarning, match=message):
|
||
y = stats.moment([])
|
||
self._assert_equal(y, np.nan, dtype=np.float64)
|
||
y = stats.moment(np.array([], dtype=np.float32))
|
||
self._assert_equal(y, np.nan, dtype=np.float32)
|
||
y = stats.moment(np.zeros((1, 0)), axis=0)
|
||
self._assert_equal(y, [], shape=(0,), dtype=np.float64)
|
||
y = stats.moment([[]], axis=1)
|
||
self._assert_equal(y, np.nan, shape=(1,), dtype=np.float64)
|
||
y = stats.moment([[]], order=[0, 1], axis=0)
|
||
self._assert_equal(y, [], shape=(2, 0))
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_equal(stats.moment(x, 2), np.nan)
|
||
assert_almost_equal(stats.moment(x, nan_policy='omit'), 0.0)
|
||
assert_raises(ValueError, stats.moment, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.moment, x, nan_policy='foobar')
|
||
|
||
@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.complex128])
|
||
@pytest.mark.parametrize('expect, order', [(0, 1), (1, 0)])
|
||
def test_constant_moments(self, dtype, expect, order):
|
||
x = np.random.rand(5).astype(dtype)
|
||
y = stats.moment(x, order=order)
|
||
self._assert_equal(y, expect, dtype=dtype)
|
||
|
||
y = stats.moment(np.broadcast_to(x, (6, 5)), axis=0, order=order)
|
||
self._assert_equal(y, expect, shape=(5,), dtype=dtype)
|
||
|
||
y = stats.moment(np.broadcast_to(x, (1, 2, 3, 4, 5)), axis=2,
|
||
order=order)
|
||
self._assert_equal(y, expect, shape=(1, 2, 4, 5), dtype=dtype)
|
||
|
||
y = stats.moment(np.broadcast_to(x, (1, 2, 3, 4, 5)), axis=None,
|
||
order=order)
|
||
self._assert_equal(y, expect, shape=(), dtype=dtype)
|
||
|
||
def test_moment_propagate_nan(self):
|
||
# Check that the shape of the result is the same for inputs
|
||
# with and without nans, cf gh-5817
|
||
a = np.arange(8).reshape(2, -1).astype(float)
|
||
a[1, 0] = np.nan
|
||
mm = stats.moment(a, 2, axis=1, nan_policy="propagate")
|
||
np.testing.assert_allclose(mm, [1.25, np.nan], atol=1e-15)
|
||
|
||
def test_moment_empty_order(self):
|
||
# tests moment with empty `order` list
|
||
with pytest.raises(ValueError, match=r"'order' must be a scalar or a"
|
||
r" non-empty 1D list/array."):
|
||
stats.moment([1, 2, 3, 4], order=[])
|
||
|
||
def test_rename_moment_order(self):
|
||
# Parameter 'order' was formerly known as 'moment'. The old name
|
||
# has not been deprecated, so it must continue to work.
|
||
x = np.arange(10)
|
||
res = stats.moment(x, moment=3)
|
||
ref = stats.moment(x, order=3)
|
||
np.testing.assert_equal(res, ref)
|
||
|
||
def test_skewness(self):
|
||
# Scalar test case
|
||
y = stats.skew(self.scalar_testcase)
|
||
assert np.isnan(y)
|
||
# sum((testmathworks-mean(testmathworks,axis=0))**3,axis=0) /
|
||
# ((sqrt(var(testmathworks)*4/5))**3)/5
|
||
y = stats.skew(self.testmathworks)
|
||
assert_approx_equal(y, -0.29322304336607, 10)
|
||
y = stats.skew(self.testmathworks, bias=0)
|
||
assert_approx_equal(y, -0.437111105023940, 10)
|
||
y = stats.skew(self.testcase)
|
||
assert_approx_equal(y, 0.0, 10)
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
with np.errstate(invalid='ignore'):
|
||
assert_equal(stats.skew(x), np.nan)
|
||
assert_equal(stats.skew(x, nan_policy='omit'), 0.)
|
||
assert_raises(ValueError, stats.skew, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.skew, x, nan_policy='foobar')
|
||
|
||
def test_skewness_scalar(self):
|
||
# `skew` must return a scalar for 1-dim input
|
||
assert_equal(stats.skew(arange(10)), 0.0)
|
||
|
||
def test_skew_propagate_nan(self):
|
||
# Check that the shape of the result is the same for inputs
|
||
# with and without nans, cf gh-5817
|
||
a = np.arange(8).reshape(2, -1).astype(float)
|
||
a[1, 0] = np.nan
|
||
with np.errstate(invalid='ignore'):
|
||
s = stats.skew(a, axis=1, nan_policy="propagate")
|
||
np.testing.assert_allclose(s, [0, np.nan], atol=1e-15)
|
||
|
||
def test_skew_constant_value(self):
|
||
# Skewness of a constant input should be zero even when the mean is not
|
||
# exact (gh-13245)
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
a = np.repeat(-0.27829495, 10)
|
||
assert np.isnan(stats.skew(a))
|
||
assert np.isnan(stats.skew(a * float(2**50)))
|
||
assert np.isnan(stats.skew(a / float(2**50)))
|
||
assert np.isnan(stats.skew(a, bias=False))
|
||
|
||
# similarly, from gh-11086:
|
||
assert np.isnan(stats.skew([14.3]*7))
|
||
assert np.isnan(stats.skew(1 + np.arange(-3, 4)*1e-16))
|
||
|
||
def test_kurtosis(self):
|
||
# Scalar test case
|
||
y = stats.kurtosis(self.scalar_testcase)
|
||
assert np.isnan(y)
|
||
|
||
# sum((testcase-mean(testcase,axis=0))**4,axis=0)
|
||
# / ((sqrt(var(testcase)*3/4))**4)
|
||
# / 4
|
||
#
|
||
# sum((test2-mean(testmathworks,axis=0))**4,axis=0)
|
||
# / ((sqrt(var(testmathworks)*4/5))**4)
|
||
# / 5
|
||
#
|
||
# Set flags for axis = 0 and
|
||
# fisher=0 (Pearson's defn of kurtosis for compatibility with Matlab)
|
||
y = stats.kurtosis(self.testmathworks, 0, fisher=0, bias=1)
|
||
assert_approx_equal(y, 2.1658856802973, 10)
|
||
|
||
# Note that MATLAB has confusing docs for the following case
|
||
# kurtosis(x,0) gives an unbiased estimate of Pearson's skewness
|
||
# kurtosis(x) gives a biased estimate of Fisher's skewness (Pearson-3)
|
||
# The MATLAB docs imply that both should give Fisher's
|
||
y = stats.kurtosis(self.testmathworks, fisher=0, bias=0)
|
||
assert_approx_equal(y, 3.663542721189047, 10)
|
||
y = stats.kurtosis(self.testcase, 0, 0)
|
||
assert_approx_equal(y, 1.64)
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_equal(stats.kurtosis(x), np.nan)
|
||
assert_almost_equal(stats.kurtosis(x, nan_policy='omit'), -1.230000)
|
||
assert_raises(ValueError, stats.kurtosis, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.kurtosis, x, nan_policy='foobar')
|
||
|
||
def test_kurtosis_array_scalar(self):
|
||
assert_equal(type(stats.kurtosis([1, 2, 3])), np.float64)
|
||
|
||
def test_kurtosis_propagate_nan(self):
|
||
# Check that the shape of the result is the same for inputs
|
||
# with and without nans, cf gh-5817
|
||
a = np.arange(8).reshape(2, -1).astype(float)
|
||
a[1, 0] = np.nan
|
||
k = stats.kurtosis(a, axis=1, nan_policy="propagate")
|
||
np.testing.assert_allclose(k, [-1.36, np.nan], atol=1e-15)
|
||
|
||
def test_kurtosis_constant_value(self):
|
||
# Kurtosis of a constant input should be zero, even when the mean is not
|
||
# exact (gh-13245)
|
||
a = np.repeat(-0.27829495, 10)
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
assert np.isnan(stats.kurtosis(a, fisher=False))
|
||
assert np.isnan(stats.kurtosis(a * float(2**50), fisher=False))
|
||
assert np.isnan(stats.kurtosis(a / float(2**50), fisher=False))
|
||
assert np.isnan(stats.kurtosis(a, fisher=False, bias=False))
|
||
|
||
def test_moment_accuracy(self):
|
||
# 'moment' must have a small enough error compared to the slower
|
||
# but very accurate numpy.power() implementation.
|
||
tc_no_mean = self.testcase_moment_accuracy - \
|
||
np.mean(self.testcase_moment_accuracy)
|
||
assert_allclose(np.power(tc_no_mean, 42).mean(),
|
||
stats.moment(self.testcase_moment_accuracy, 42))
|
||
|
||
def test_precision_loss_gh15554(self):
|
||
# gh-15554 was one of several issues that have reported problems with
|
||
# constant or near-constant input. We can't always fix these, but
|
||
# make sure there's a warning.
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
rng = np.random.default_rng(34095309370)
|
||
a = rng.random(size=(100, 10))
|
||
a[:, 0] = 1.01
|
||
stats.skew(a)[0]
|
||
|
||
def test_empty_1d(self):
|
||
message = r"Mean of empty slice\.|invalid value encountered.*"
|
||
with pytest.warns(RuntimeWarning, match=message):
|
||
stats.skew([])
|
||
with pytest.warns(RuntimeWarning, match=message):
|
||
stats.kurtosis([])
|
||
|
||
|
||
@hypothesis.strategies.composite
|
||
def ttest_data_axis_strategy(draw):
|
||
# draw an array under shape and value constraints
|
||
dtype = npst.floating_dtypes()
|
||
elements = dict(allow_nan=False, allow_infinity=False)
|
||
shape = npst.array_shapes(min_dims=1, min_side=2)
|
||
data = draw(npst.arrays(dtype=dtype, elements=elements, shape=shape))
|
||
|
||
# determine axes over which nonzero variance can be computed accurately
|
||
ok_axes = []
|
||
# Locally, I don't need catch_warnings or simplefilter, and I can just
|
||
# suppress RuntimeWarning. I include all that in hope of getting the same
|
||
# behavior on CI.
|
||
with warnings.catch_warnings():
|
||
warnings.simplefilter("error")
|
||
for axis in range(len(data.shape)):
|
||
with contextlib.suppress(Exception):
|
||
var = stats.moment(data, order=2, axis=axis)
|
||
if np.all(var > 0) and np.all(np.isfinite(var)):
|
||
ok_axes.append(axis)
|
||
# if there are no valid axes, tell hypothesis to try a different example
|
||
hypothesis.assume(ok_axes)
|
||
|
||
# draw one of the valid axes
|
||
axis = draw(hypothesis.strategies.sampled_from(ok_axes))
|
||
|
||
return data, axis
|
||
|
||
|
||
class TestStudentTest:
|
||
X1 = np.array([-1, 0, 1])
|
||
X2 = np.array([0, 1, 2])
|
||
T1_0 = 0
|
||
P1_0 = 1
|
||
T1_1 = -1.7320508075
|
||
P1_1 = 0.22540333075
|
||
T1_2 = -3.464102
|
||
P1_2 = 0.0741799
|
||
T2_0 = 1.732051
|
||
P2_0 = 0.2254033
|
||
P1_1_l = P1_1 / 2
|
||
P1_1_g = 1 - (P1_1 / 2)
|
||
|
||
def test_onesample(self):
|
||
with suppress_warnings() as sup, \
|
||
np.errstate(invalid="ignore", divide="ignore"):
|
||
sup.filter(RuntimeWarning, "Degrees of freedom <= 0 for slice")
|
||
t, p = stats.ttest_1samp(4., 3.)
|
||
assert_(np.isnan(t))
|
||
assert_(np.isnan(p))
|
||
|
||
t, p = stats.ttest_1samp(self.X1, 0)
|
||
|
||
assert_array_almost_equal(t, self.T1_0)
|
||
assert_array_almost_equal(p, self.P1_0)
|
||
|
||
res = stats.ttest_1samp(self.X1, 0)
|
||
attributes = ('statistic', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
|
||
t, p = stats.ttest_1samp(self.X2, 0)
|
||
|
||
assert_array_almost_equal(t, self.T2_0)
|
||
assert_array_almost_equal(p, self.P2_0)
|
||
|
||
t, p = stats.ttest_1samp(self.X1, 1)
|
||
|
||
assert_array_almost_equal(t, self.T1_1)
|
||
assert_array_almost_equal(p, self.P1_1)
|
||
|
||
t, p = stats.ttest_1samp(self.X1, 2)
|
||
|
||
assert_array_almost_equal(t, self.T1_2)
|
||
assert_array_almost_equal(p, self.P1_2)
|
||
|
||
# check nan policy
|
||
x = stats.norm.rvs(loc=5, scale=10, size=51, random_state=7654567)
|
||
x[50] = np.nan
|
||
with np.errstate(invalid="ignore"):
|
||
assert_array_equal(stats.ttest_1samp(x, 5.0), (np.nan, np.nan))
|
||
|
||
assert_array_almost_equal(stats.ttest_1samp(x, 5.0, nan_policy='omit'),
|
||
(-1.6412624074367159, 0.107147027334048005))
|
||
assert_raises(ValueError, stats.ttest_1samp, x, 5.0, nan_policy='raise')
|
||
assert_raises(ValueError, stats.ttest_1samp, x, 5.0,
|
||
nan_policy='foobar')
|
||
|
||
def test_1samp_alternative(self):
|
||
assert_raises(ValueError, stats.ttest_1samp, self.X1, 0,
|
||
alternative="error")
|
||
|
||
t, p = stats.ttest_1samp(self.X1, 1, alternative="less")
|
||
assert_allclose(p, self.P1_1_l)
|
||
assert_allclose(t, self.T1_1)
|
||
|
||
t, p = stats.ttest_1samp(self.X1, 1, alternative="greater")
|
||
assert_allclose(p, self.P1_1_g)
|
||
assert_allclose(t, self.T1_1)
|
||
|
||
@pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
|
||
def test_1samp_ci_1d(self, alternative):
|
||
# test confidence interval method against reference values
|
||
rng = np.random.default_rng(8066178009154342972)
|
||
n = 10
|
||
x = rng.normal(size=n, loc=1.5, scale=2)
|
||
popmean = rng.normal() # this shouldn't affect confidence interval
|
||
# Reference values generated with R t.test:
|
||
# options(digits=16)
|
||
# x = c(2.75532884, 0.93892217, 0.94835861, 1.49489446, -0.62396595,
|
||
# -1.88019867, -1.55684465, 4.88777104, 5.15310979, 4.34656348)
|
||
# t.test(x, conf.level=0.85, alternative='l')
|
||
|
||
ref = {'two-sided': [0.3594423211709136, 2.9333455028290860],
|
||
'greater': [0.7470806207371626, np.inf],
|
||
'less': [-np.inf, 2.545707203262837]}
|
||
res = stats.ttest_1samp(x, popmean=popmean, alternative=alternative)
|
||
ci = res.confidence_interval(confidence_level=0.85)
|
||
assert_allclose(ci, ref[alternative])
|
||
assert_equal(res.df, n-1)
|
||
|
||
def test_1samp_ci_iv(self):
|
||
# test `confidence_interval` method input validation
|
||
res = stats.ttest_1samp(np.arange(10), 0)
|
||
message = '`confidence_level` must be a number between 0 and 1.'
|
||
with pytest.raises(ValueError, match=message):
|
||
res.confidence_interval(confidence_level=10)
|
||
|
||
@pytest.mark.xslow
|
||
@hypothesis.given(alpha=hypothesis.strategies.floats(1e-15, 1-1e-15),
|
||
data_axis=ttest_data_axis_strategy())
|
||
@pytest.mark.parametrize('alternative', ['less', 'greater'])
|
||
def test_pvalue_ci(self, alpha, data_axis, alternative):
|
||
# test relationship between one-sided p-values and confidence intervals
|
||
data, axis = data_axis
|
||
res = stats.ttest_1samp(data, 0,
|
||
alternative=alternative, axis=axis)
|
||
l, u = res.confidence_interval(confidence_level=alpha)
|
||
popmean = l if alternative == 'greater' else u
|
||
popmean = np.expand_dims(popmean, axis=axis)
|
||
res = stats.ttest_1samp(data, popmean,
|
||
alternative=alternative, axis=axis)
|
||
np.testing.assert_allclose(res.pvalue, 1-alpha)
|
||
|
||
|
||
class TestPercentileOfScore:
|
||
|
||
def f(self, *args, **kwargs):
|
||
return stats.percentileofscore(*args, **kwargs)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 40),
|
||
("mean", 35),
|
||
("strict", 30),
|
||
("weak", 40)])
|
||
def test_unique(self, kind, result):
|
||
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||
assert_equal(self.f(a, 4, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 45),
|
||
("mean", 40),
|
||
("strict", 30),
|
||
("weak", 50)])
|
||
def test_multiple2(self, kind, result):
|
||
a = [1, 2, 3, 4, 4, 5, 6, 7, 8, 9]
|
||
assert_equal(self.f(a, 4, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 50),
|
||
("mean", 45),
|
||
("strict", 30),
|
||
("weak", 60)])
|
||
def test_multiple3(self, kind, result):
|
||
a = [1, 2, 3, 4, 4, 4, 5, 6, 7, 8]
|
||
assert_equal(self.f(a, 4, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 30),
|
||
("mean", 30),
|
||
("strict", 30),
|
||
("weak", 30)])
|
||
def test_missing(self, kind, result):
|
||
a = [1, 2, 3, 5, 6, 7, 8, 9, 10, 11]
|
||
assert_equal(self.f(a, 4, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 40),
|
||
("mean", 35),
|
||
("strict", 30),
|
||
("weak", 40)])
|
||
def test_large_numbers(self, kind, result):
|
||
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
|
||
assert_equal(self.f(a, 40, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 50),
|
||
("mean", 45),
|
||
("strict", 30),
|
||
("weak", 60)])
|
||
def test_large_numbers_multiple3(self, kind, result):
|
||
a = [10, 20, 30, 40, 40, 40, 50, 60, 70, 80]
|
||
assert_equal(self.f(a, 40, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", 30),
|
||
("mean", 30),
|
||
("strict", 30),
|
||
("weak", 30)])
|
||
def test_large_numbers_missing(self, kind, result):
|
||
a = [10, 20, 30, 50, 60, 70, 80, 90, 100, 110]
|
||
assert_equal(self.f(a, 40, kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", [0, 10, 100, 100]),
|
||
("mean", [0, 5, 95, 100]),
|
||
("strict", [0, 0, 90, 100]),
|
||
("weak", [0, 10, 100, 100])])
|
||
def test_boundaries(self, kind, result):
|
||
a = [10, 20, 30, 50, 60, 70, 80, 90, 100, 110]
|
||
assert_equal(self.f(a, [0, 10, 110, 200], kind=kind), result)
|
||
|
||
@pytest.mark.parametrize("kind, result", [("rank", [0, 10, 100]),
|
||
("mean", [0, 5, 95]),
|
||
("strict", [0, 0, 90]),
|
||
("weak", [0, 10, 100])])
|
||
def test_inf(self, kind, result):
|
||
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, +np.inf]
|
||
assert_equal(self.f(a, [-np.inf, 1, +np.inf], kind=kind), result)
|
||
|
||
cases = [("propagate", [], 1, np.nan),
|
||
("propagate", [np.nan], 1, np.nan),
|
||
("propagate", [np.nan], [0, 1, 2], [np.nan, np.nan, np.nan]),
|
||
("propagate", [1, 2], [1, 2, np.nan], [50, 100, np.nan]),
|
||
("omit", [1, 2, np.nan], [0, 1, 2], [0, 50, 100]),
|
||
("omit", [1, 2], [0, 1, np.nan], [0, 50, np.nan]),
|
||
("omit", [np.nan, np.nan], [0, 1, 2], [np.nan, np.nan, np.nan])]
|
||
|
||
@pytest.mark.parametrize("policy, a, score, result", cases)
|
||
def test_nans_ok(self, policy, a, score, result):
|
||
assert_equal(self.f(a, score, nan_policy=policy), result)
|
||
|
||
cases = [
|
||
("raise", [1, 2, 3, np.nan], [1, 2, 3],
|
||
"The input contains nan values"),
|
||
("raise", [1, 2, 3], [1, 2, 3, np.nan],
|
||
"The input contains nan values"),
|
||
]
|
||
|
||
@pytest.mark.parametrize("policy, a, score, message", cases)
|
||
def test_nans_fail(self, policy, a, score, message):
|
||
with assert_raises(ValueError, match=message):
|
||
self.f(a, score, nan_policy=policy)
|
||
|
||
@pytest.mark.parametrize("shape", [
|
||
(6, ),
|
||
(2, 3),
|
||
(2, 1, 3),
|
||
(2, 1, 1, 3),
|
||
])
|
||
def test_nd(self, shape):
|
||
a = np.array([0, 1, 2, 3, 4, 5])
|
||
scores = a.reshape(shape)
|
||
results = scores*10
|
||
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||
assert_equal(self.f(a, scores, kind="rank"), results)
|
||
|
||
|
||
PowerDivCase = namedtuple('Case', # type: ignore[name-match]
|
||
['f_obs', 'f_exp', 'ddof', 'axis',
|
||
'chi2', # Pearson's
|
||
'log', # G-test (log-likelihood)
|
||
'mod_log', # Modified log-likelihood
|
||
'cr', # Cressie-Read (lambda=2/3)
|
||
])
|
||
|
||
# The details of the first two elements in power_div_1d_cases are used
|
||
# in a test in TestPowerDivergence. Check that code before making
|
||
# any changes here.
|
||
power_div_1d_cases = [
|
||
# Use the default f_exp.
|
||
PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=None, ddof=0, axis=None,
|
||
chi2=4,
|
||
log=2*(4*np.log(4/8) + 12*np.log(12/8)),
|
||
mod_log=2*(8*np.log(8/4) + 8*np.log(8/12)),
|
||
cr=(4*((4/8)**(2/3) - 1) + 12*((12/8)**(2/3) - 1))/(5/9)),
|
||
# Give a non-uniform f_exp.
|
||
PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=[2, 16, 12, 2], ddof=0, axis=None,
|
||
chi2=24,
|
||
log=2*(4*np.log(4/2) + 8*np.log(8/16) + 8*np.log(8/2)),
|
||
mod_log=2*(2*np.log(2/4) + 16*np.log(16/8) + 2*np.log(2/8)),
|
||
cr=(4*((4/2)**(2/3) - 1) + 8*((8/16)**(2/3) - 1) +
|
||
8*((8/2)**(2/3) - 1))/(5/9)),
|
||
# f_exp is a scalar.
|
||
PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=8, ddof=0, axis=None,
|
||
chi2=4,
|
||
log=2*(4*np.log(4/8) + 12*np.log(12/8)),
|
||
mod_log=2*(8*np.log(8/4) + 8*np.log(8/12)),
|
||
cr=(4*((4/8)**(2/3) - 1) + 12*((12/8)**(2/3) - 1))/(5/9)),
|
||
# f_exp equal to f_obs.
|
||
PowerDivCase(f_obs=[3, 5, 7, 9], f_exp=[3, 5, 7, 9], ddof=0, axis=0,
|
||
chi2=0, log=0, mod_log=0, cr=0),
|
||
]
|
||
|
||
|
||
power_div_empty_cases = [
|
||
# Shape is (0,)--a data set with length 0. The computed
|
||
# test statistic should be 0.
|
||
PowerDivCase(f_obs=[],
|
||
f_exp=None, ddof=0, axis=0,
|
||
chi2=0, log=0, mod_log=0, cr=0),
|
||
# Shape is (0, 3). This is 3 data sets, but each data set has
|
||
# length 0, so the computed test statistic should be [0, 0, 0].
|
||
PowerDivCase(f_obs=np.array([[],[],[]]).T,
|
||
f_exp=None, ddof=0, axis=0,
|
||
chi2=[0, 0, 0],
|
||
log=[0, 0, 0],
|
||
mod_log=[0, 0, 0],
|
||
cr=[0, 0, 0]),
|
||
# Shape is (3, 0). This represents an empty collection of
|
||
# data sets in which each data set has length 3. The test
|
||
# statistic should be an empty array.
|
||
PowerDivCase(f_obs=np.array([[],[],[]]),
|
||
f_exp=None, ddof=0, axis=0,
|
||
chi2=[],
|
||
log=[],
|
||
mod_log=[],
|
||
cr=[]),
|
||
]
|
||
|
||
|
||
class TestPowerDivergence:
|
||
|
||
def check_power_divergence(self, f_obs, f_exp, ddof, axis, lambda_,
|
||
expected_stat):
|
||
f_obs = np.asarray(f_obs)
|
||
if axis is None:
|
||
num_obs = f_obs.size
|
||
else:
|
||
b = np.broadcast(f_obs, f_exp)
|
||
num_obs = b.shape[axis]
|
||
|
||
with suppress_warnings() as sup:
|
||
sup.filter(RuntimeWarning, "Mean of empty slice")
|
||
stat, p = stats.power_divergence(
|
||
f_obs=f_obs, f_exp=f_exp, ddof=ddof,
|
||
axis=axis, lambda_=lambda_)
|
||
assert_allclose(stat, expected_stat)
|
||
|
||
if lambda_ == 1 or lambda_ == "pearson":
|
||
# Also test stats.chisquare.
|
||
stat, p = stats.chisquare(f_obs=f_obs, f_exp=f_exp, ddof=ddof,
|
||
axis=axis)
|
||
assert_allclose(stat, expected_stat)
|
||
|
||
ddof = np.asarray(ddof)
|
||
expected_p = stats.distributions.chi2.sf(expected_stat,
|
||
num_obs - 1 - ddof)
|
||
assert_allclose(p, expected_p)
|
||
|
||
def test_basic(self):
|
||
for case in power_div_1d_cases:
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
None, case.chi2)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"pearson", case.chi2)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
1, case.chi2)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"log-likelihood", case.log)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"mod-log-likelihood", case.mod_log)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"cressie-read", case.cr)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
2/3, case.cr)
|
||
|
||
def test_basic_masked(self):
|
||
for case in power_div_1d_cases:
|
||
mobs = np.ma.array(case.f_obs)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
None, case.chi2)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
"pearson", case.chi2)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
1, case.chi2)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
"log-likelihood", case.log)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
"mod-log-likelihood", case.mod_log)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
"cressie-read", case.cr)
|
||
self.check_power_divergence(
|
||
mobs, case.f_exp, case.ddof, case.axis,
|
||
2/3, case.cr)
|
||
|
||
def test_axis(self):
|
||
case0 = power_div_1d_cases[0]
|
||
case1 = power_div_1d_cases[1]
|
||
f_obs = np.vstack((case0.f_obs, case1.f_obs))
|
||
f_exp = np.vstack((np.ones_like(case0.f_obs)*np.mean(case0.f_obs),
|
||
case1.f_exp))
|
||
# Check the four computational code paths in power_divergence
|
||
# using a 2D array with axis=1.
|
||
self.check_power_divergence(
|
||
f_obs, f_exp, 0, 1,
|
||
"pearson", [case0.chi2, case1.chi2])
|
||
self.check_power_divergence(
|
||
f_obs, f_exp, 0, 1,
|
||
"log-likelihood", [case0.log, case1.log])
|
||
self.check_power_divergence(
|
||
f_obs, f_exp, 0, 1,
|
||
"mod-log-likelihood", [case0.mod_log, case1.mod_log])
|
||
self.check_power_divergence(
|
||
f_obs, f_exp, 0, 1,
|
||
"cressie-read", [case0.cr, case1.cr])
|
||
# Reshape case0.f_obs to shape (2,2), and use axis=None.
|
||
# The result should be the same.
|
||
self.check_power_divergence(
|
||
np.array(case0.f_obs).reshape(2, 2), None, 0, None,
|
||
"pearson", case0.chi2)
|
||
|
||
def test_ddof_broadcasting(self):
|
||
# Test that ddof broadcasts correctly.
|
||
# ddof does not affect the test statistic. It is broadcast
|
||
# with the computed test statistic for the computation of
|
||
# the p value.
|
||
|
||
case0 = power_div_1d_cases[0]
|
||
case1 = power_div_1d_cases[1]
|
||
# Create 4x2 arrays of observed and expected frequencies.
|
||
f_obs = np.vstack((case0.f_obs, case1.f_obs)).T
|
||
f_exp = np.vstack((np.ones_like(case0.f_obs)*np.mean(case0.f_obs),
|
||
case1.f_exp)).T
|
||
|
||
expected_chi2 = [case0.chi2, case1.chi2]
|
||
|
||
# ddof has shape (2, 1). This is broadcast with the computed
|
||
# statistic, so p will have shape (2,2).
|
||
ddof = np.array([[0], [1]])
|
||
|
||
stat, p = stats.power_divergence(f_obs, f_exp, ddof=ddof)
|
||
assert_allclose(stat, expected_chi2)
|
||
|
||
# Compute the p values separately, passing in scalars for ddof.
|
||
stat0, p0 = stats.power_divergence(f_obs, f_exp, ddof=ddof[0,0])
|
||
stat1, p1 = stats.power_divergence(f_obs, f_exp, ddof=ddof[1,0])
|
||
|
||
assert_array_equal(p, np.vstack((p0, p1)))
|
||
|
||
def test_empty_cases(self):
|
||
with warnings.catch_warnings():
|
||
for case in power_div_empty_cases:
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"pearson", case.chi2)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"log-likelihood", case.log)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"mod-log-likelihood", case.mod_log)
|
||
self.check_power_divergence(
|
||
case.f_obs, case.f_exp, case.ddof, case.axis,
|
||
"cressie-read", case.cr)
|
||
|
||
def test_power_divergence_result_attributes(self):
|
||
f_obs = power_div_1d_cases[0].f_obs
|
||
f_exp = power_div_1d_cases[0].f_exp
|
||
ddof = power_div_1d_cases[0].ddof
|
||
axis = power_div_1d_cases[0].axis
|
||
|
||
res = stats.power_divergence(f_obs=f_obs, f_exp=f_exp, ddof=ddof,
|
||
axis=axis, lambda_="pearson")
|
||
attributes = ('statistic', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
|
||
def test_power_divergence_gh_12282(self):
|
||
# The sums of observed and expected frequencies must match
|
||
f_obs = np.array([[10, 20], [30, 20]])
|
||
f_exp = np.array([[5, 15], [35, 25]])
|
||
with assert_raises(ValueError, match='For each axis slice...'):
|
||
stats.power_divergence(f_obs=[10, 20], f_exp=[30, 60])
|
||
with assert_raises(ValueError, match='For each axis slice...'):
|
||
stats.power_divergence(f_obs=f_obs, f_exp=f_exp, axis=1)
|
||
stat, pval = stats.power_divergence(f_obs=f_obs, f_exp=f_exp)
|
||
assert_allclose(stat, [5.71428571, 2.66666667])
|
||
assert_allclose(pval, [0.01682741, 0.10247043])
|
||
|
||
|
||
def test_gh_chisquare_12282():
|
||
# Currently `chisquare` is implemented via power_divergence
|
||
# in case that ever changes, perform a basic test like
|
||
# test_power_divergence_gh_12282
|
||
with assert_raises(ValueError, match='For each axis slice...'):
|
||
stats.chisquare(f_obs=[10, 20], f_exp=[30, 60])
|
||
|
||
|
||
@pytest.mark.parametrize("n, dtype", [(200, np.uint8), (1000000, np.int32)])
|
||
def test_chiquare_data_types_attributes(n, dtype):
|
||
# Regression test for gh-10159 and gh-18368
|
||
obs = np.array([n, 0], dtype=dtype)
|
||
exp = np.array([n // 2, n // 2], dtype=dtype)
|
||
res = stats.chisquare(obs, exp)
|
||
stat, p = res
|
||
assert_allclose(stat, n, rtol=1e-13)
|
||
# check that attributes are identical to unpacked outputs - see gh-18368
|
||
assert_equal(res.statistic, stat)
|
||
assert_equal(res.pvalue, p)
|
||
|
||
|
||
def test_chisquare_masked_arrays():
|
||
# Test masked arrays.
|
||
obs = np.array([[8, 8, 16, 32, -1], [-1, -1, 3, 4, 5]]).T
|
||
mask = np.array([[0, 0, 0, 0, 1], [1, 1, 0, 0, 0]]).T
|
||
mobs = np.ma.masked_array(obs, mask)
|
||
expected_chisq = np.array([24.0, 0.5])
|
||
expected_g = np.array([2*(2*8*np.log(0.5) + 32*np.log(2.0)),
|
||
2*(3*np.log(0.75) + 5*np.log(1.25))])
|
||
|
||
chi2 = stats.distributions.chi2
|
||
|
||
chisq, p = stats.chisquare(mobs)
|
||
mat.assert_array_equal(chisq, expected_chisq)
|
||
mat.assert_array_almost_equal(p, chi2.sf(expected_chisq,
|
||
mobs.count(axis=0) - 1))
|
||
|
||
g, p = stats.power_divergence(mobs, lambda_='log-likelihood')
|
||
mat.assert_array_almost_equal(g, expected_g, decimal=15)
|
||
mat.assert_array_almost_equal(p, chi2.sf(expected_g,
|
||
mobs.count(axis=0) - 1))
|
||
|
||
chisq, p = stats.chisquare(mobs.T, axis=1)
|
||
mat.assert_array_equal(chisq, expected_chisq)
|
||
mat.assert_array_almost_equal(p, chi2.sf(expected_chisq,
|
||
mobs.T.count(axis=1) - 1))
|
||
g, p = stats.power_divergence(mobs.T, axis=1, lambda_="log-likelihood")
|
||
mat.assert_array_almost_equal(g, expected_g, decimal=15)
|
||
mat.assert_array_almost_equal(p, chi2.sf(expected_g,
|
||
mobs.count(axis=0) - 1))
|
||
|
||
obs1 = np.ma.array([3, 5, 6, 99, 10], mask=[0, 0, 0, 1, 0])
|
||
exp1 = np.ma.array([2, 4, 8, 10, 99], mask=[0, 0, 0, 0, 1])
|
||
chi2, p = stats.chisquare(obs1, f_exp=exp1)
|
||
# Because of the mask at index 3 of obs1 and at index 4 of exp1,
|
||
# only the first three elements are included in the calculation
|
||
# of the statistic.
|
||
mat.assert_array_equal(chi2, 1/2 + 1/4 + 4/8)
|
||
|
||
# When axis=None, the two values should have type np.float64.
|
||
chisq, p = stats.chisquare(np.ma.array([1,2,3]), axis=None)
|
||
assert_(isinstance(chisq, np.float64))
|
||
assert_(isinstance(p, np.float64))
|
||
assert_equal(chisq, 1.0)
|
||
assert_almost_equal(p, stats.distributions.chi2.sf(1.0, 2))
|
||
|
||
# Empty arrays:
|
||
# A data set with length 0 returns a masked scalar.
|
||
with np.errstate(invalid='ignore'):
|
||
with suppress_warnings() as sup:
|
||
sup.filter(RuntimeWarning, "Mean of empty slice")
|
||
chisq, p = stats.chisquare(np.ma.array([]))
|
||
assert_(isinstance(chisq, np.ma.MaskedArray))
|
||
assert_equal(chisq.shape, ())
|
||
assert_(chisq.mask)
|
||
|
||
empty3 = np.ma.array([[],[],[]])
|
||
|
||
# empty3 is a collection of 0 data sets (whose lengths would be 3, if
|
||
# there were any), so the return value is an array with length 0.
|
||
chisq, p = stats.chisquare(empty3)
|
||
assert_(isinstance(chisq, np.ma.MaskedArray))
|
||
mat.assert_array_equal(chisq, [])
|
||
|
||
# empty3.T is an array containing 3 data sets, each with length 0,
|
||
# so an array of size (3,) is returned, with all values masked.
|
||
with np.errstate(invalid='ignore'):
|
||
with suppress_warnings() as sup:
|
||
sup.filter(RuntimeWarning, "Mean of empty slice")
|
||
chisq, p = stats.chisquare(empty3.T)
|
||
|
||
assert_(isinstance(chisq, np.ma.MaskedArray))
|
||
assert_equal(chisq.shape, (3,))
|
||
assert_(np.all(chisq.mask))
|
||
|
||
|
||
def test_power_divergence_against_cressie_read_data():
|
||
# Test stats.power_divergence against tables 4 and 5 from
|
||
# Cressie and Read, "Multimonial Goodness-of-Fit Tests",
|
||
# J. R. Statist. Soc. B (1984), Vol 46, No. 3, pp. 440-464.
|
||
# This tests the calculation for several values of lambda.
|
||
|
||
# Table 4 data recalculated for greater precision according to:
|
||
# Shelby J. Haberman, Analysis of Qualitative Data: Volume 1
|
||
# Introductory Topics, Academic Press, New York, USA (1978).
|
||
obs = np.array([15, 11, 14, 17, 5, 11, 10, 4, 8,
|
||
10, 7, 9, 11, 3, 6, 1, 1, 4])
|
||
beta = -0.083769 # Haberman (1978), p. 15
|
||
i = np.arange(1, len(obs) + 1)
|
||
alpha = np.log(obs.sum() / np.exp(beta*i).sum())
|
||
expected_counts = np.exp(alpha + beta*i)
|
||
|
||
# `table4` holds just the second and third columns from Table 4.
|
||
table4 = np.vstack((obs, expected_counts)).T
|
||
|
||
table5 = np.array([
|
||
# lambda, statistic
|
||
-10.0, 72.2e3,
|
||
-5.0, 28.9e1,
|
||
-3.0, 65.6,
|
||
-2.0, 40.6,
|
||
-1.5, 34.0,
|
||
-1.0, 29.5,
|
||
-0.5, 26.5,
|
||
0.0, 24.6,
|
||
0.5, 23.4,
|
||
0.67, 23.1,
|
||
1.0, 22.7,
|
||
1.5, 22.6,
|
||
2.0, 22.9,
|
||
3.0, 24.8,
|
||
5.0, 35.5,
|
||
10.0, 21.4e1,
|
||
]).reshape(-1, 2)
|
||
|
||
for lambda_, expected_stat in table5:
|
||
stat, p = stats.power_divergence(table4[:,0], table4[:,1],
|
||
lambda_=lambda_)
|
||
assert_allclose(stat, expected_stat, rtol=5e-3)
|
||
|
||
|
||
def test_friedmanchisquare():
|
||
# see ticket:113
|
||
# verified with matlab and R
|
||
# From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
|
||
# 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
|
||
x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661, 0.583,
|
||
0.775, 1.0, 0.94, 0.619, 0.972, 0.957]),
|
||
array([0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668, 0.583,
|
||
0.838, 1.0, 0.962, 0.666, 0.981, 0.978]),
|
||
array([0.771, 0.590, 0.968, 0.654, 0.886, 0.916, 0.609, 0.563,
|
||
0.866, 1.0, 0.965, 0.614, 0.9751, 0.946]),
|
||
array([0.798, 0.569, 0.967, 0.657, 0.898, 0.931, 0.685, 0.625,
|
||
0.875, 1.0, 0.962, 0.669, 0.975, 0.970])]
|
||
|
||
# From "Bioestadistica para las ciencias de la salud" Xf=18.95 p<0.001:
|
||
x2 = [array([4,3,5,3,5,3,2,5,4,4,4,3]),
|
||
array([2,2,1,2,3,1,2,3,2,1,1,3]),
|
||
array([2,4,3,3,4,3,3,4,4,1,2,1]),
|
||
array([3,5,4,3,4,4,3,3,3,4,4,4])]
|
||
|
||
# From Jerrorl H. Zar, "Biostatistical Analysis"(example 12.6),
|
||
# Xf=10.68, 0.005 < p < 0.01:
|
||
# Probability from this example is inexact
|
||
# using Chisquare approximation of Friedman Chisquare.
|
||
x3 = [array([7.0,9.9,8.5,5.1,10.3]),
|
||
array([5.3,5.7,4.7,3.5,7.7]),
|
||
array([4.9,7.6,5.5,2.8,8.4]),
|
||
array([8.8,8.9,8.1,3.3,9.1])]
|
||
|
||
assert_array_almost_equal(stats.friedmanchisquare(x1[0],x1[1],x1[2],x1[3]),
|
||
(10.2283464566929, 0.0167215803284414))
|
||
assert_array_almost_equal(stats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
|
||
(18.9428571428571, 0.000280938375189499))
|
||
assert_array_almost_equal(stats.friedmanchisquare(x3[0],x3[1],x3[2],x3[3]),
|
||
(10.68, 0.0135882729582176))
|
||
assert_raises(ValueError, stats.friedmanchisquare,x3[0],x3[1])
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.friedmanchisquare(*x1)
|
||
check_named_results(res, attributes)
|
||
|
||
# test using mstats
|
||
assert_array_almost_equal(mstats.friedmanchisquare(x1[0], x1[1],
|
||
x1[2], x1[3]),
|
||
(10.2283464566929, 0.0167215803284414))
|
||
# the following fails
|
||
# assert_array_almost_equal(mstats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
|
||
# (18.9428571428571, 0.000280938375189499))
|
||
assert_array_almost_equal(mstats.friedmanchisquare(x3[0], x3[1],
|
||
x3[2], x3[3]),
|
||
(10.68, 0.0135882729582176))
|
||
assert_raises(ValueError, mstats.friedmanchisquare,x3[0],x3[1])
|
||
|
||
|
||
class TestKSTest:
|
||
"""Tests kstest and ks_1samp agree with K-S various sizes, alternatives, modes."""
|
||
|
||
def _testOne(self, x, alternative, expected_statistic, expected_prob,
|
||
mode='auto', decimal=14):
|
||
result = stats.kstest(x, 'norm', alternative=alternative, mode=mode)
|
||
expected = np.array([expected_statistic, expected_prob])
|
||
assert_array_almost_equal(np.array(result), expected, decimal=decimal)
|
||
|
||
def _test_kstest_and_ks1samp(self, x, alternative, mode='auto', decimal=14):
|
||
result = stats.kstest(x, 'norm', alternative=alternative, mode=mode)
|
||
result_1samp = stats.ks_1samp(x, stats.norm.cdf,
|
||
alternative=alternative, mode=mode)
|
||
assert_array_almost_equal(np.array(result), result_1samp, decimal=decimal)
|
||
|
||
def test_namedtuple_attributes(self):
|
||
x = np.linspace(-1, 1, 9)
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.kstest(x, 'norm')
|
||
check_named_results(res, attributes)
|
||
|
||
def test_agree_with_ks_1samp(self):
|
||
x = np.linspace(-1, 1, 9)
|
||
self._test_kstest_and_ks1samp(x, 'two-sided')
|
||
|
||
x = np.linspace(-15, 15, 9)
|
||
self._test_kstest_and_ks1samp(x, 'two-sided')
|
||
|
||
x = [-1.23, 0.06, -0.60, 0.17, 0.66, -0.17, -0.08, 0.27, -0.98, -0.99]
|
||
self._test_kstest_and_ks1samp(x, 'two-sided')
|
||
self._test_kstest_and_ks1samp(x, 'greater', mode='exact')
|
||
self._test_kstest_and_ks1samp(x, 'less', mode='exact')
|
||
|
||
# missing: no test that uses *args
|
||
|
||
|
||
class TestKSOneSample:
|
||
"""
|
||
Tests kstest and ks_samp 1-samples with K-S various sizes, alternatives, modes.
|
||
"""
|
||
|
||
def _testOne(self, x, alternative, expected_statistic, expected_prob,
|
||
mode='auto', decimal=14):
|
||
result = stats.ks_1samp(x, stats.norm.cdf, alternative=alternative, mode=mode)
|
||
expected = np.array([expected_statistic, expected_prob])
|
||
assert_array_almost_equal(np.array(result), expected, decimal=decimal)
|
||
|
||
def test_namedtuple_attributes(self):
|
||
x = np.linspace(-1, 1, 9)
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.ks_1samp(x, stats.norm.cdf)
|
||
check_named_results(res, attributes)
|
||
|
||
def test_agree_with_r(self):
|
||
# comparing with some values from R
|
||
x = np.linspace(-1, 1, 9)
|
||
self._testOne(x, 'two-sided', 0.15865525393145705, 0.95164069201518386)
|
||
|
||
x = np.linspace(-15, 15, 9)
|
||
self._testOne(x, 'two-sided', 0.44435602715924361, 0.038850140086788665)
|
||
|
||
x = [-1.23, 0.06, -0.60, 0.17, 0.66, -0.17, -0.08, 0.27, -0.98, -0.99]
|
||
self._testOne(x, 'two-sided', 0.293580126801961, 0.293408463684361)
|
||
self._testOne(x, 'greater', 0.293580126801961, 0.146988835042376, mode='exact')
|
||
self._testOne(x, 'less', 0.109348552425692, 0.732768892470675, mode='exact')
|
||
|
||
def test_known_examples(self):
|
||
# the following tests rely on deterministically replicated rvs
|
||
x = stats.norm.rvs(loc=0.2, size=100, random_state=987654321)
|
||
self._testOne(x, 'two-sided', 0.12464329735846891, 0.089444888711820769,
|
||
mode='asymp')
|
||
self._testOne(x, 'less', 0.12464329735846891, 0.040989164077641749)
|
||
self._testOne(x, 'greater', 0.0072115233216310994, 0.98531158590396228)
|
||
|
||
def test_ks1samp_allpaths(self):
|
||
# Check NaN input, output.
|
||
assert_(np.isnan(kolmogn(np.nan, 1, True)))
|
||
with assert_raises(ValueError, match='n is not integral: 1.5'):
|
||
kolmogn(1.5, 1, True)
|
||
assert_(np.isnan(kolmogn(-1, 1, True)))
|
||
|
||
dataset = np.asarray([
|
||
# Check x out of range
|
||
(101, 1, True, 1.0),
|
||
(101, 1.1, True, 1.0),
|
||
(101, 0, True, 0.0),
|
||
(101, -0.1, True, 0.0),
|
||
|
||
(32, 1.0 / 64, True, 0.0), # Ruben-Gambino
|
||
(32, 1.0 / 64, False, 1.0), # Ruben-Gambino
|
||
|
||
# Miller
|
||
(32, 0.5, True, 0.9999999363163307),
|
||
# Miller 2 * special.smirnov(32, 0.5)
|
||
(32, 0.5, False, 6.368366937916623e-08),
|
||
|
||
# Check some other paths
|
||
(32, 1.0 / 8, True, 0.34624229979775223),
|
||
(32, 1.0 / 4, True, 0.9699508336558085),
|
||
(1600, 0.49, False, 0.0),
|
||
# 2 * special.smirnov(1600, 1/16.0)
|
||
(1600, 1 / 16.0, False, 7.0837876229702195e-06),
|
||
# _kolmogn_DMTW
|
||
(1600, 14 / 1600, False, 0.99962357317602),
|
||
# _kolmogn_PelzGood
|
||
(1600, 1 / 32, False, 0.08603386296651416),
|
||
])
|
||
FuncData(kolmogn, dataset, (0, 1, 2), 3).check(dtypes=[int, float, bool])
|
||
|
||
@pytest.mark.parametrize("ksfunc", [stats.kstest, stats.ks_1samp])
|
||
@pytest.mark.parametrize("alternative, x6val, ref_location, ref_sign",
|
||
[('greater', 6, 6, +1),
|
||
('less', 7, 7, -1),
|
||
('two-sided', 6, 6, +1),
|
||
('two-sided', 7, 7, -1)])
|
||
def test_location_sign(self, ksfunc, alternative,
|
||
x6val, ref_location, ref_sign):
|
||
# Test that location and sign corresponding with statistic are as
|
||
# expected. (Test is designed to be easy to predict.)
|
||
x = np.arange(10) + 0.5
|
||
x[6] = x6val
|
||
cdf = stats.uniform(scale=10).cdf
|
||
res = ksfunc(x, cdf, alternative=alternative)
|
||
assert_allclose(res.statistic, 0.1, rtol=1e-15)
|
||
assert res.statistic_location == ref_location
|
||
assert res.statistic_sign == ref_sign
|
||
|
||
# missing: no test that uses *args
|
||
|
||
|
||
class TestKSTwoSamples:
|
||
"""Tests 2-samples with K-S various sizes, alternatives, modes."""
|
||
|
||
def _testOne(self, x1, x2, alternative, expected_statistic, expected_prob,
|
||
mode='auto'):
|
||
result = stats.ks_2samp(x1, x2, alternative, mode=mode)
|
||
expected = np.array([expected_statistic, expected_prob])
|
||
assert_array_almost_equal(np.array(result), expected)
|
||
|
||
def testSmall(self):
|
||
self._testOne([0], [1], 'two-sided', 1.0/1, 1.0)
|
||
self._testOne([0], [1], 'greater', 1.0/1, 0.5)
|
||
self._testOne([0], [1], 'less', 0.0/1, 1.0)
|
||
self._testOne([1], [0], 'two-sided', 1.0/1, 1.0)
|
||
self._testOne([1], [0], 'greater', 0.0/1, 1.0)
|
||
self._testOne([1], [0], 'less', 1.0/1, 0.5)
|
||
|
||
def testTwoVsThree(self):
|
||
data1 = np.array([1.0, 2.0])
|
||
data1p = data1 + 0.01
|
||
data1m = data1 - 0.01
|
||
data2 = np.array([1.0, 2.0, 3.0])
|
||
self._testOne(data1p, data2, 'two-sided', 1.0 / 3, 1.0)
|
||
self._testOne(data1p, data2, 'greater', 1.0 / 3, 0.7)
|
||
self._testOne(data1p, data2, 'less', 1.0 / 3, 0.7)
|
||
self._testOne(data1m, data2, 'two-sided', 2.0 / 3, 0.6)
|
||
self._testOne(data1m, data2, 'greater', 2.0 / 3, 0.3)
|
||
self._testOne(data1m, data2, 'less', 0, 1.0)
|
||
|
||
def testTwoVsFour(self):
|
||
data1 = np.array([1.0, 2.0])
|
||
data1p = data1 + 0.01
|
||
data1m = data1 - 0.01
|
||
data2 = np.array([1.0, 2.0, 3.0, 4.0])
|
||
self._testOne(data1p, data2, 'two-sided', 2.0 / 4, 14.0/15)
|
||
self._testOne(data1p, data2, 'greater', 2.0 / 4, 8.0/15)
|
||
self._testOne(data1p, data2, 'less', 1.0 / 4, 12.0/15)
|
||
|
||
self._testOne(data1m, data2, 'two-sided', 3.0 / 4, 6.0/15)
|
||
self._testOne(data1m, data2, 'greater', 3.0 / 4, 3.0/15)
|
||
self._testOne(data1m, data2, 'less', 0, 1.0)
|
||
|
||
def test100_100(self):
|
||
x100 = np.linspace(1, 100, 100)
|
||
x100_2_p1 = x100 + 2 + 0.1
|
||
x100_2_m1 = x100 + 2 - 0.1
|
||
self._testOne(x100, x100_2_p1, 'two-sided', 3.0 / 100, 0.9999999999962055)
|
||
self._testOne(x100, x100_2_p1, 'greater', 3.0 / 100, 0.9143290114276248)
|
||
self._testOne(x100, x100_2_p1, 'less', 0, 1.0)
|
||
self._testOne(x100, x100_2_m1, 'two-sided', 2.0 / 100, 1.0)
|
||
self._testOne(x100, x100_2_m1, 'greater', 2.0 / 100, 0.960978450786184)
|
||
self._testOne(x100, x100_2_m1, 'less', 0, 1.0)
|
||
|
||
def test100_110(self):
|
||
x100 = np.linspace(1, 100, 100)
|
||
x110 = np.linspace(1, 100, 110)
|
||
x110_20_p1 = x110 + 20 + 0.1
|
||
x110_20_m1 = x110 + 20 - 0.1
|
||
# 100, 110
|
||
self._testOne(x100, x110_20_p1, 'two-sided', 232.0 / 1100, 0.015739183865607353)
|
||
self._testOne(x100, x110_20_p1, 'greater', 232.0 / 1100, 0.007869594319053203)
|
||
self._testOne(x100, x110_20_p1, 'less', 0, 1)
|
||
self._testOne(x100, x110_20_m1, 'two-sided', 229.0 / 1100, 0.017803803861026313)
|
||
self._testOne(x100, x110_20_m1, 'greater', 229.0 / 1100, 0.008901905958245056)
|
||
self._testOne(x100, x110_20_m1, 'less', 0.0, 1.0)
|
||
|
||
def testRepeatedValues(self):
|
||
x2233 = np.array([2] * 3 + [3] * 4 + [5] * 5 + [6] * 4, dtype=int)
|
||
x3344 = x2233 + 1
|
||
x2356 = np.array([2] * 3 + [3] * 4 + [5] * 10 + [6] * 4, dtype=int)
|
||
x3467 = np.array([3] * 10 + [4] * 2 + [6] * 10 + [7] * 4, dtype=int)
|
||
self._testOne(x2233, x3344, 'two-sided', 5.0/16, 0.4262934613454952)
|
||
self._testOne(x2233, x3344, 'greater', 5.0/16, 0.21465428276573786)
|
||
self._testOne(x2233, x3344, 'less', 0.0/16, 1.0)
|
||
self._testOne(x2356, x3467, 'two-sided', 190.0/21/26, 0.0919245790168125)
|
||
self._testOne(x2356, x3467, 'greater', 190.0/21/26, 0.0459633806858544)
|
||
self._testOne(x2356, x3467, 'less', 70.0/21/26, 0.6121593130022775)
|
||
|
||
def testEqualSizes(self):
|
||
data2 = np.array([1.0, 2.0, 3.0])
|
||
self._testOne(data2, data2+1, 'two-sided', 1.0/3, 1.0)
|
||
self._testOne(data2, data2+1, 'greater', 1.0/3, 0.75)
|
||
self._testOne(data2, data2+1, 'less', 0.0/3, 1.)
|
||
self._testOne(data2, data2+0.5, 'two-sided', 1.0/3, 1.0)
|
||
self._testOne(data2, data2+0.5, 'greater', 1.0/3, 0.75)
|
||
self._testOne(data2, data2+0.5, 'less', 0.0/3, 1.)
|
||
self._testOne(data2, data2-0.5, 'two-sided', 1.0/3, 1.0)
|
||
self._testOne(data2, data2-0.5, 'greater', 0.0/3, 1.0)
|
||
self._testOne(data2, data2-0.5, 'less', 1.0/3, 0.75)
|
||
|
||
@pytest.mark.slow
|
||
def testMiddlingBoth(self):
|
||
# 500, 600
|
||
n1, n2 = 500, 600
|
||
delta = 1.0/n1/n2/2/2
|
||
x = np.linspace(1, 200, n1) - delta
|
||
y = np.linspace(2, 200, n2)
|
||
self._testOne(x, y, 'two-sided', 2000.0 / n1 / n2, 1.0,
|
||
mode='auto')
|
||
self._testOne(x, y, 'two-sided', 2000.0 / n1 / n2, 1.0,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'greater', 2000.0 / n1 / n2, 0.9697596024683929,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
|
||
mode='asymp')
|
||
with suppress_warnings() as sup:
|
||
message = "ks_2samp: Exact calculation unsuccessful."
|
||
sup.filter(RuntimeWarning, message)
|
||
self._testOne(x, y, 'greater', 2000.0 / n1 / n2, 0.9697596024683929,
|
||
mode='exact')
|
||
self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
|
||
mode='exact')
|
||
with warnings.catch_warnings(record=True) as w:
|
||
warnings.simplefilter("always")
|
||
self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
|
||
mode='exact')
|
||
_check_warnings(w, RuntimeWarning, 1)
|
||
|
||
@pytest.mark.slow
|
||
def testMediumBoth(self):
|
||
# 1000, 1100
|
||
n1, n2 = 1000, 1100
|
||
delta = 1.0/n1/n2/2/2
|
||
x = np.linspace(1, 200, n1) - delta
|
||
y = np.linspace(2, 200, n2)
|
||
self._testOne(x, y, 'two-sided', 6600.0 / n1 / n2, 1.0,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'two-sided', 6600.0 / n1 / n2, 1.0,
|
||
mode='auto')
|
||
self._testOne(x, y, 'greater', 6600.0 / n1 / n2, 0.9573185808092622,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
|
||
mode='asymp')
|
||
|
||
with suppress_warnings() as sup:
|
||
message = "ks_2samp: Exact calculation unsuccessful."
|
||
sup.filter(RuntimeWarning, message)
|
||
self._testOne(x, y, 'greater', 6600.0 / n1 / n2, 0.9573185808092622,
|
||
mode='exact')
|
||
self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
|
||
mode='exact')
|
||
with warnings.catch_warnings(record=True) as w:
|
||
warnings.simplefilter("always")
|
||
self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
|
||
mode='exact')
|
||
_check_warnings(w, RuntimeWarning, 1)
|
||
|
||
def testLarge(self):
|
||
# 10000, 110
|
||
n1, n2 = 10000, 110
|
||
lcm = n1*11.0
|
||
delta = 1.0/n1/n2/2/2
|
||
x = np.linspace(1, 200, n1) - delta
|
||
y = np.linspace(2, 100, n2)
|
||
self._testOne(x, y, 'two-sided', 55275.0 / lcm, 4.2188474935755949e-15)
|
||
self._testOne(x, y, 'greater', 561.0 / lcm, 0.99115454582047591)
|
||
self._testOne(x, y, 'less', 55275.0 / lcm, 3.1317328311518713e-26)
|
||
|
||
def test_gh11184(self):
|
||
# 3000, 3001, exact two-sided
|
||
np.random.seed(123456)
|
||
x = np.random.normal(size=3000)
|
||
y = np.random.normal(size=3001) * 1.5
|
||
self._testOne(x, y, 'two-sided', 0.11292880151060758, 2.7755575615628914e-15,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'two-sided', 0.11292880151060758, 2.7755575615628914e-15,
|
||
mode='exact')
|
||
|
||
@pytest.mark.xslow
|
||
def test_gh11184_bigger(self):
|
||
# 10000, 10001, exact two-sided
|
||
np.random.seed(123456)
|
||
x = np.random.normal(size=10000)
|
||
y = np.random.normal(size=10001) * 1.5
|
||
self._testOne(x, y, 'two-sided', 0.10597913208679133, 3.3149311398483503e-49,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'two-sided', 0.10597913208679133, 2.7755575615628914e-15,
|
||
mode='exact')
|
||
self._testOne(x, y, 'greater', 0.10597913208679133, 2.7947433906389253e-41,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'less', 0.09658002199780022, 2.7947433906389253e-41,
|
||
mode='asymp')
|
||
|
||
@pytest.mark.xslow
|
||
def test_gh12999(self):
|
||
np.random.seed(123456)
|
||
for x in range(1000, 12000, 1000):
|
||
vals1 = np.random.normal(size=(x))
|
||
vals2 = np.random.normal(size=(x + 10), loc=0.5)
|
||
exact = stats.ks_2samp(vals1, vals2, mode='exact').pvalue
|
||
asymp = stats.ks_2samp(vals1, vals2, mode='asymp').pvalue
|
||
# these two p-values should be in line with each other
|
||
assert_array_less(exact, 3 * asymp)
|
||
assert_array_less(asymp, 3 * exact)
|
||
|
||
@pytest.mark.slow
|
||
def testLargeBoth(self):
|
||
# 10000, 11000
|
||
n1, n2 = 10000, 11000
|
||
lcm = n1*11.0
|
||
delta = 1.0/n1/n2/2/2
|
||
x = np.linspace(1, 200, n1) - delta
|
||
y = np.linspace(2, 200, n2)
|
||
self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990660108966576,
|
||
mode='asymp')
|
||
self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990456491488628,
|
||
mode='exact')
|
||
self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990660108966576,
|
||
mode='auto')
|
||
self._testOne(x, y, 'greater', 563.0 / lcm, 0.7561851877420673)
|
||
self._testOne(x, y, 'less', 10.0 / lcm, 0.9998239693191724)
|
||
with suppress_warnings() as sup:
|
||
message = "ks_2samp: Exact calculation unsuccessful."
|
||
sup.filter(RuntimeWarning, message)
|
||
self._testOne(x, y, 'greater', 563.0 / lcm, 0.7561851877420673,
|
||
mode='exact')
|
||
self._testOne(x, y, 'less', 10.0 / lcm, 0.9998239693191724,
|
||
mode='exact')
|
||
|
||
def testNamedAttributes(self):
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.ks_2samp([1, 2], [3])
|
||
check_named_results(res, attributes)
|
||
|
||
@pytest.mark.slow
|
||
def test_some_code_paths(self):
|
||
# Check that some code paths are executed
|
||
from scipy.stats._stats_py import (
|
||
_count_paths_outside_method,
|
||
_compute_outer_prob_inside_method
|
||
)
|
||
|
||
_compute_outer_prob_inside_method(1, 1, 1, 1)
|
||
_count_paths_outside_method(1000, 1, 1, 1001)
|
||
|
||
with np.errstate(invalid='raise'):
|
||
assert_raises(FloatingPointError, _count_paths_outside_method,
|
||
1100, 1099, 1, 1)
|
||
assert_raises(FloatingPointError, _count_paths_outside_method,
|
||
2000, 1000, 1, 1)
|
||
|
||
def test_argument_checking(self):
|
||
# Check that an empty array causes a ValueError
|
||
assert_raises(ValueError, stats.ks_2samp, [], [1])
|
||
assert_raises(ValueError, stats.ks_2samp, [1], [])
|
||
assert_raises(ValueError, stats.ks_2samp, [], [])
|
||
|
||
@pytest.mark.slow
|
||
def test_gh12218(self):
|
||
"""Ensure gh-12218 is fixed."""
|
||
# gh-1228 triggered a TypeError calculating sqrt(n1*n2*(n1+n2)).
|
||
# n1, n2 both large integers, the product exceeded 2^64
|
||
np.random.seed(12345678)
|
||
n1 = 2097152 # 2*^21
|
||
rvs1 = stats.uniform.rvs(size=n1, loc=0., scale=1)
|
||
rvs2 = rvs1 + 1 # Exact value of rvs2 doesn't matter.
|
||
stats.ks_2samp(rvs1, rvs2, alternative='greater', mode='asymp')
|
||
stats.ks_2samp(rvs1, rvs2, alternative='less', mode='asymp')
|
||
stats.ks_2samp(rvs1, rvs2, alternative='two-sided', mode='asymp')
|
||
|
||
def test_warnings_gh_14019(self):
|
||
# Check that RuntimeWarning is raised when method='auto' and exact
|
||
# p-value calculation fails. See gh-14019.
|
||
rng = np.random.RandomState(seed=23493549)
|
||
# random samples of the same size as in the issue
|
||
data1 = rng.random(size=881) + 0.5
|
||
data2 = rng.random(size=369)
|
||
message = "ks_2samp: Exact calculation unsuccessful"
|
||
with pytest.warns(RuntimeWarning, match=message):
|
||
res = stats.ks_2samp(data1, data2, alternative='less')
|
||
assert_allclose(res.pvalue, 0, atol=1e-14)
|
||
|
||
@pytest.mark.parametrize("ksfunc", [stats.kstest, stats.ks_2samp])
|
||
@pytest.mark.parametrize("alternative, x6val, ref_location, ref_sign",
|
||
[('greater', 5.9, 5.9, +1),
|
||
('less', 6.1, 6.0, -1),
|
||
('two-sided', 5.9, 5.9, +1),
|
||
('two-sided', 6.1, 6.0, -1)])
|
||
def test_location_sign(self, ksfunc, alternative,
|
||
x6val, ref_location, ref_sign):
|
||
# Test that location and sign corresponding with statistic are as
|
||
# expected. (Test is designed to be easy to predict.)
|
||
x = np.arange(10, dtype=np.float64)
|
||
y = x.copy()
|
||
x[6] = x6val
|
||
res = stats.ks_2samp(x, y, alternative=alternative)
|
||
assert res.statistic == 0.1
|
||
assert res.statistic_location == ref_location
|
||
assert res.statistic_sign == ref_sign
|
||
|
||
|
||
def test_ttest_rel():
|
||
# regression test
|
||
tr,pr = 0.81248591389165692, 0.41846234511362157
|
||
tpr = ([tr,-tr],[pr,pr])
|
||
|
||
rvs1 = np.linspace(1,100,100)
|
||
rvs2 = np.linspace(1.01,99.989,100)
|
||
rvs1_2D = np.array([np.linspace(1,100,100), np.linspace(1.01,99.989,100)])
|
||
rvs2_2D = np.array([np.linspace(1.01,99.989,100), np.linspace(1,100,100)])
|
||
|
||
t,p = stats.ttest_rel(rvs1, rvs2, axis=0)
|
||
assert_array_almost_equal([t,p],(tr,pr))
|
||
t,p = stats.ttest_rel(rvs1_2D.T, rvs2_2D.T, axis=0)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
t,p = stats.ttest_rel(rvs1_2D, rvs2_2D, axis=1)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
|
||
# test scalars
|
||
with suppress_warnings() as sup, \
|
||
np.errstate(invalid="ignore", divide="ignore"):
|
||
sup.filter(RuntimeWarning, "Degrees of freedom <= 0 for slice")
|
||
t, p = stats.ttest_rel(4., 3.)
|
||
assert_(np.isnan(t))
|
||
assert_(np.isnan(p))
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.ttest_rel(rvs1, rvs2, axis=0)
|
||
check_named_results(res, attributes)
|
||
|
||
# test on 3 dimensions
|
||
rvs1_3D = np.dstack([rvs1_2D,rvs1_2D,rvs1_2D])
|
||
rvs2_3D = np.dstack([rvs2_2D,rvs2_2D,rvs2_2D])
|
||
t,p = stats.ttest_rel(rvs1_3D, rvs2_3D, axis=1)
|
||
assert_array_almost_equal(np.abs(t), tr)
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (2, 3))
|
||
|
||
t, p = stats.ttest_rel(np.moveaxis(rvs1_3D, 2, 0),
|
||
np.moveaxis(rvs2_3D, 2, 0),
|
||
axis=2)
|
||
assert_array_almost_equal(np.abs(t), tr)
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (3, 2))
|
||
|
||
# test alternative parameter
|
||
assert_raises(ValueError, stats.ttest_rel, rvs1, rvs2, alternative="error")
|
||
|
||
t, p = stats.ttest_rel(rvs1, rvs2, axis=0, alternative="less")
|
||
assert_allclose(p, 1 - pr/2)
|
||
assert_allclose(t, tr)
|
||
|
||
t, p = stats.ttest_rel(rvs1, rvs2, axis=0, alternative="greater")
|
||
assert_allclose(p, pr/2)
|
||
assert_allclose(t, tr)
|
||
|
||
# check nan policy
|
||
rng = np.random.RandomState(12345678)
|
||
x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
|
||
x[500] = np.nan
|
||
y = (stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng) +
|
||
stats.norm.rvs(scale=0.2, size=501, random_state=rng))
|
||
y[500] = np.nan
|
||
|
||
with np.errstate(invalid="ignore"):
|
||
assert_array_equal(stats.ttest_rel(x, x), (np.nan, np.nan))
|
||
|
||
assert_array_almost_equal(stats.ttest_rel(x, y, nan_policy='omit'),
|
||
(0.25299925303978066, 0.8003729814201519))
|
||
assert_raises(ValueError, stats.ttest_rel, x, y, nan_policy='raise')
|
||
assert_raises(ValueError, stats.ttest_rel, x, y, nan_policy='foobar')
|
||
|
||
# test zero division problem
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
t, p = stats.ttest_rel([0, 0, 0], [1, 1, 1])
|
||
assert_equal((np.abs(t), p), (np.inf, 0))
|
||
with np.errstate(invalid="ignore"):
|
||
assert_equal(stats.ttest_rel([0, 0, 0], [0, 0, 0]), (np.nan, np.nan))
|
||
|
||
# check that nan in input array result in nan output
|
||
anan = np.array([[1, np.nan], [-1, 1]])
|
||
assert_equal(stats.ttest_rel(anan, np.zeros((2, 2))),
|
||
([0, np.nan], [1, np.nan]))
|
||
|
||
# test incorrect input shape raise an error
|
||
x = np.arange(24)
|
||
assert_raises(ValueError, stats.ttest_rel, x.reshape((8, 3)),
|
||
x.reshape((2, 3, 4)))
|
||
|
||
# Convert from two-sided p-values to one sided using T result data.
|
||
def convert(t, p, alt):
|
||
if (t < 0 and alt == "less") or (t > 0 and alt == "greater"):
|
||
return p / 2
|
||
return 1 - (p / 2)
|
||
converter = np.vectorize(convert)
|
||
|
||
rvs1_2D[:, 20:30] = np.nan
|
||
rvs2_2D[:, 15:25] = np.nan
|
||
|
||
tr, pr = stats.ttest_rel(rvs1_2D, rvs2_2D, 0, nan_policy='omit')
|
||
|
||
t, p = stats.ttest_rel(rvs1_2D, rvs2_2D, 0, nan_policy='omit',
|
||
alternative='less')
|
||
assert_allclose(t, tr, rtol=1e-14)
|
||
with np.errstate(invalid='ignore'):
|
||
assert_allclose(p, converter(tr, pr, 'less'), rtol=1e-14)
|
||
|
||
t, p = stats.ttest_rel(rvs1_2D, rvs2_2D, 0, nan_policy='omit',
|
||
alternative='greater')
|
||
assert_allclose(t, tr, rtol=1e-14)
|
||
with np.errstate(invalid='ignore'):
|
||
assert_allclose(p, converter(tr, pr, 'greater'), rtol=1e-14)
|
||
|
||
|
||
def test_ttest_rel_nan_2nd_arg():
|
||
# regression test for gh-6134: nans in the second arg were not handled
|
||
x = [np.nan, 2.0, 3.0, 4.0]
|
||
y = [1.0, 2.0, 1.0, 2.0]
|
||
|
||
r1 = stats.ttest_rel(x, y, nan_policy='omit')
|
||
r2 = stats.ttest_rel(y, x, nan_policy='omit')
|
||
assert_allclose(r2.statistic, -r1.statistic, atol=1e-15)
|
||
assert_allclose(r2.pvalue, r1.pvalue, atol=1e-15)
|
||
|
||
# NB: arguments are paired when NaNs are dropped
|
||
r3 = stats.ttest_rel(y[1:], x[1:])
|
||
assert_allclose(r2, r3, atol=1e-15)
|
||
|
||
# .. and this is consistent with R. R code:
|
||
# x = c(NA, 2.0, 3.0, 4.0)
|
||
# y = c(1.0, 2.0, 1.0, 2.0)
|
||
# t.test(x, y, paired=TRUE)
|
||
assert_allclose(r2, (-2, 0.1835), atol=1e-4)
|
||
|
||
|
||
def test_ttest_rel_empty_1d_returns_nan():
|
||
# Two empty inputs should return a TtestResult containing nan
|
||
# for both values.
|
||
result = stats.ttest_rel([], [])
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
assert_equal(result, (np.nan, np.nan))
|
||
|
||
|
||
@pytest.mark.parametrize('b, expected_shape',
|
||
[(np.empty((1, 5, 0)), (3, 5)),
|
||
(np.empty((1, 0, 0)), (3, 0))])
|
||
def test_ttest_rel_axis_size_zero(b, expected_shape):
|
||
# In this test, the length of the axis dimension is zero.
|
||
# The results should be arrays containing nan with shape
|
||
# given by the broadcast nonaxis dimensions.
|
||
a = np.empty((3, 1, 0))
|
||
result = stats.ttest_rel(a, b, axis=-1)
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
expected_value = np.full(expected_shape, fill_value=np.nan)
|
||
assert_equal(result.statistic, expected_value)
|
||
assert_equal(result.pvalue, expected_value)
|
||
|
||
|
||
def test_ttest_rel_nonaxis_size_zero():
|
||
# In this test, the length of the axis dimension is nonzero,
|
||
# but one of the nonaxis dimensions has length 0. Check that
|
||
# we still get the correctly broadcast shape, which is (5, 0)
|
||
# in this case.
|
||
a = np.empty((1, 8, 0))
|
||
b = np.empty((5, 8, 1))
|
||
result = stats.ttest_rel(a, b, axis=1)
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
assert_equal(result.statistic.shape, (5, 0))
|
||
assert_equal(result.pvalue.shape, (5, 0))
|
||
|
||
|
||
@pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
|
||
def test_ttest_rel_ci_1d(alternative):
|
||
# test confidence interval method against reference values
|
||
rng = np.random.default_rng(3749065329432213059)
|
||
n = 10
|
||
x = rng.normal(size=n, loc=1.5, scale=2)
|
||
y = rng.normal(size=n, loc=2, scale=2)
|
||
# Reference values generated with R t.test:
|
||
# options(digits=16)
|
||
# x = c(1.22825792, 1.63950485, 4.39025641, 0.68609437, 2.03813481,
|
||
# -1.20040109, 1.81997937, 1.86854636, 2.94694282, 3.94291373)
|
||
# y = c(3.49961496, 1.53192536, 5.53620083, 2.91687718, 0.04858043,
|
||
# 3.78505943, 3.3077496 , 2.30468892, 3.42168074, 0.56797592)
|
||
# t.test(x, y, paired=TRUE, conf.level=0.85, alternative='l')
|
||
|
||
ref = {'two-sided': [-1.912194489914035, 0.400169725914035],
|
||
'greater': [-1.563944820311475, np.inf],
|
||
'less': [-np.inf, 0.05192005631147523]}
|
||
res = stats.ttest_rel(x, y, alternative=alternative)
|
||
ci = res.confidence_interval(confidence_level=0.85)
|
||
assert_allclose(ci, ref[alternative])
|
||
assert_equal(res.df, n-1)
|
||
|
||
|
||
@pytest.mark.parametrize("test_fun, args",
|
||
[(stats.ttest_1samp, (np.arange(10), 0)),
|
||
(stats.ttest_rel, (np.arange(10), np.arange(10)))])
|
||
def test_ttest_ci_iv(test_fun, args):
|
||
# test `confidence_interval` method input validation
|
||
res = test_fun(*args)
|
||
message = '`confidence_level` must be a number between 0 and 1.'
|
||
with pytest.raises(ValueError, match=message):
|
||
res.confidence_interval(confidence_level=10)
|
||
|
||
|
||
def _desc_stats(x1, x2, axis=0):
|
||
def _stats(x, axis=0):
|
||
x = np.asarray(x)
|
||
mu = np.mean(x, axis=axis)
|
||
std = np.std(x, axis=axis, ddof=1)
|
||
nobs = x.shape[axis]
|
||
return mu, std, nobs
|
||
return _stats(x1, axis) + _stats(x2, axis)
|
||
|
||
|
||
def test_ttest_ind():
|
||
# regression test
|
||
tr = 1.0912746897927283
|
||
pr = 0.27647818616351882
|
||
tpr = ([tr,-tr],[pr,pr])
|
||
|
||
rvs2 = np.linspace(1,100,100)
|
||
rvs1 = np.linspace(5,105,100)
|
||
rvs1_2D = np.array([rvs1, rvs2])
|
||
rvs2_2D = np.array([rvs2, rvs1])
|
||
|
||
t,p = stats.ttest_ind(rvs1, rvs2, axis=0)
|
||
assert_array_almost_equal([t,p],(tr,pr))
|
||
# test from_stats API
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(rvs1,
|
||
rvs2)),
|
||
[t, p])
|
||
t,p = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*args),
|
||
[t, p])
|
||
t,p = stats.ttest_ind(rvs1_2D, rvs2_2D, axis=1)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
args = _desc_stats(rvs1_2D, rvs2_2D, axis=1)
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*args),
|
||
[t, p])
|
||
|
||
# test scalars
|
||
with suppress_warnings() as sup, np.errstate(invalid="ignore"):
|
||
sup.filter(RuntimeWarning, "Degrees of freedom <= 0 for slice")
|
||
t, p = stats.ttest_ind(4., 3.)
|
||
assert_(np.isnan(t))
|
||
assert_(np.isnan(p))
|
||
|
||
# test on 3 dimensions
|
||
rvs1_3D = np.dstack([rvs1_2D,rvs1_2D,rvs1_2D])
|
||
rvs2_3D = np.dstack([rvs2_2D,rvs2_2D,rvs2_2D])
|
||
t,p = stats.ttest_ind(rvs1_3D, rvs2_3D, axis=1)
|
||
assert_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (2, 3))
|
||
|
||
t, p = stats.ttest_ind(np.moveaxis(rvs1_3D, 2, 0),
|
||
np.moveaxis(rvs2_3D, 2, 0),
|
||
axis=2)
|
||
assert_array_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (3, 2))
|
||
|
||
# test alternative parameter
|
||
assert_raises(ValueError, stats.ttest_ind, rvs1, rvs2, alternative="error")
|
||
assert_raises(ValueError, stats.ttest_ind_from_stats,
|
||
*_desc_stats(rvs1_2D.T, rvs2_2D.T), alternative="error")
|
||
|
||
t, p = stats.ttest_ind(rvs1, rvs2, alternative="less")
|
||
assert_allclose(p, 1 - (pr/2))
|
||
assert_allclose(t, tr)
|
||
|
||
t, p = stats.ttest_ind(rvs1, rvs2, alternative="greater")
|
||
assert_allclose(p, pr/2)
|
||
assert_allclose(t, tr)
|
||
|
||
# Below makes sure ttest_ind_from_stats p-val functions identically to
|
||
# ttest_ind
|
||
t, p = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, alternative="less")
|
||
args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
|
||
assert_allclose(
|
||
stats.ttest_ind_from_stats(*args, alternative="less"), [t, p])
|
||
|
||
t, p = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, alternative="greater")
|
||
args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
|
||
assert_allclose(
|
||
stats.ttest_ind_from_stats(*args, alternative="greater"), [t, p])
|
||
|
||
# check nan policy
|
||
rng = np.random.RandomState(12345678)
|
||
x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
|
||
x[500] = np.nan
|
||
y = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
|
||
|
||
with np.errstate(invalid="ignore"):
|
||
assert_array_equal(stats.ttest_ind(x, y), (np.nan, np.nan))
|
||
|
||
assert_array_almost_equal(stats.ttest_ind(x, y, nan_policy='omit'),
|
||
(0.24779670949091914, 0.80434267337517906))
|
||
assert_raises(ValueError, stats.ttest_ind, x, y, nan_policy='raise')
|
||
assert_raises(ValueError, stats.ttest_ind, x, y, nan_policy='foobar')
|
||
|
||
# test zero division problem
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
t, p = stats.ttest_ind([0, 0, 0], [1, 1, 1])
|
||
assert_equal((np.abs(t), p), (np.inf, 0))
|
||
|
||
with np.errstate(invalid="ignore"):
|
||
assert_equal(stats.ttest_ind([0, 0, 0], [0, 0, 0]), (np.nan, np.nan))
|
||
|
||
# check that nan in input array result in nan output
|
||
anan = np.array([[1, np.nan], [-1, 1]])
|
||
assert_equal(stats.ttest_ind(anan, np.zeros((2, 2))),
|
||
([0, np.nan], [1, np.nan]))
|
||
|
||
rvs1_3D[:, :, 10:15] = np.nan
|
||
rvs2_3D[:, :, 6:12] = np.nan
|
||
|
||
# Convert from two-sided p-values to one sided using T result data.
|
||
def convert(t, p, alt):
|
||
if (t < 0 and alt == "less") or (t > 0 and alt == "greater"):
|
||
return p / 2
|
||
return 1 - (p / 2)
|
||
converter = np.vectorize(convert)
|
||
|
||
tr, pr = stats.ttest_ind(rvs1_3D, rvs2_3D, 0, nan_policy='omit')
|
||
|
||
t, p = stats.ttest_ind(rvs1_3D, rvs2_3D, 0, nan_policy='omit',
|
||
alternative='less')
|
||
assert_allclose(t, tr, rtol=1e-14)
|
||
assert_allclose(p, converter(tr, pr, 'less'), rtol=1e-14)
|
||
|
||
t, p = stats.ttest_ind(rvs1_3D, rvs2_3D, 0, nan_policy='omit',
|
||
alternative='greater')
|
||
assert_allclose(t, tr, rtol=1e-14)
|
||
assert_allclose(p, converter(tr, pr, 'greater'), rtol=1e-14)
|
||
|
||
|
||
class Test_ttest_ind_permutations:
|
||
N = 20
|
||
|
||
# data for most tests
|
||
np.random.seed(0)
|
||
a = np.vstack((np.arange(3*N//4), np.random.random(3*N//4)))
|
||
b = np.vstack((np.arange(N//4) + 100, np.random.random(N//4)))
|
||
|
||
# data for equal variance tests
|
||
a2 = np.arange(10)
|
||
b2 = np.arange(10) + 100
|
||
|
||
# data for exact test
|
||
a3 = [1, 2]
|
||
b3 = [3, 4]
|
||
|
||
# data for bigger test
|
||
np.random.seed(0)
|
||
rvs1 = stats.norm.rvs(loc=5, scale=10, # type: ignore
|
||
size=500).reshape(100, 5).T
|
||
rvs2 = stats.norm.rvs(loc=8, scale=20, size=100) # type: ignore
|
||
|
||
p_d = [1/1001, (676+1)/1001] # desired pvalues
|
||
p_d_gen = [1/1001, (672 + 1)/1001] # desired pvalues for Generator seed
|
||
p_d_big = [(993+1)/1001, (685+1)/1001, (840+1)/1001,
|
||
(955+1)/1001, (255+1)/1001]
|
||
|
||
params = [
|
||
(a, b, {"axis": 1}, p_d), # basic test
|
||
(a.T, b.T, {'axis': 0}, p_d), # along axis 0
|
||
(a[0, :], b[0, :], {'axis': None}, p_d[0]), # 1d data
|
||
(a[0, :].tolist(), b[0, :].tolist(), {'axis': None}, p_d[0]),
|
||
# different seeds
|
||
(a, b, {'random_state': 0, "axis": 1}, p_d),
|
||
(a, b, {'random_state': np.random.RandomState(0), "axis": 1}, p_d),
|
||
(a2, b2, {'equal_var': True}, 1/1001), # equal variances
|
||
(rvs1, rvs2, {'axis': -1, 'random_state': 0}, p_d_big), # bigger test
|
||
(a3, b3, {}, 1/3), # exact test
|
||
(a, b, {'random_state': np.random.default_rng(0), "axis": 1}, p_d_gen),
|
||
]
|
||
|
||
@pytest.mark.parametrize("a,b,update,p_d", params)
|
||
def test_ttest_ind_permutations(self, a, b, update, p_d):
|
||
options_a = {'axis': None, 'equal_var': False}
|
||
options_p = {'axis': None, 'equal_var': False,
|
||
'permutations': 1000, 'random_state': 0}
|
||
options_a.update(update)
|
||
options_p.update(update)
|
||
|
||
stat_a, _ = stats.ttest_ind(a, b, **options_a)
|
||
stat_p, pvalue = stats.ttest_ind(a, b, **options_p)
|
||
assert_array_almost_equal(stat_a, stat_p, 5)
|
||
assert_array_almost_equal(pvalue, p_d)
|
||
|
||
def test_ttest_ind_exact_alternative(self):
|
||
np.random.seed(0)
|
||
N = 3
|
||
a = np.random.rand(2, N, 2)
|
||
b = np.random.rand(2, N, 2)
|
||
|
||
options_p = {'axis': 1, 'permutations': 1000}
|
||
|
||
options_p.update(alternative="greater")
|
||
res_g_ab = stats.ttest_ind(a, b, **options_p)
|
||
res_g_ba = stats.ttest_ind(b, a, **options_p)
|
||
|
||
options_p.update(alternative="less")
|
||
res_l_ab = stats.ttest_ind(a, b, **options_p)
|
||
res_l_ba = stats.ttest_ind(b, a, **options_p)
|
||
|
||
options_p.update(alternative="two-sided")
|
||
res_2_ab = stats.ttest_ind(a, b, **options_p)
|
||
res_2_ba = stats.ttest_ind(b, a, **options_p)
|
||
|
||
# Alternative doesn't affect the statistic
|
||
assert_equal(res_g_ab.statistic, res_l_ab.statistic)
|
||
assert_equal(res_g_ab.statistic, res_2_ab.statistic)
|
||
|
||
# Reversing order of inputs negates statistic
|
||
assert_equal(res_g_ab.statistic, -res_g_ba.statistic)
|
||
assert_equal(res_l_ab.statistic, -res_l_ba.statistic)
|
||
assert_equal(res_2_ab.statistic, -res_2_ba.statistic)
|
||
|
||
# Reversing order of inputs does not affect p-value of 2-sided test
|
||
assert_equal(res_2_ab.pvalue, res_2_ba.pvalue)
|
||
|
||
# In exact test, distribution is perfectly symmetric, so these
|
||
# identities are exactly satisfied.
|
||
assert_equal(res_g_ab.pvalue, res_l_ba.pvalue)
|
||
assert_equal(res_l_ab.pvalue, res_g_ba.pvalue)
|
||
mask = res_g_ab.pvalue <= 0.5
|
||
assert_equal(res_g_ab.pvalue[mask] + res_l_ba.pvalue[mask],
|
||
res_2_ab.pvalue[mask])
|
||
assert_equal(res_l_ab.pvalue[~mask] + res_g_ba.pvalue[~mask],
|
||
res_2_ab.pvalue[~mask])
|
||
|
||
def test_ttest_ind_exact_selection(self):
|
||
# test the various ways of activating the exact test
|
||
np.random.seed(0)
|
||
N = 3
|
||
a = np.random.rand(N)
|
||
b = np.random.rand(N)
|
||
res0 = stats.ttest_ind(a, b)
|
||
res1 = stats.ttest_ind(a, b, permutations=1000)
|
||
res2 = stats.ttest_ind(a, b, permutations=0)
|
||
res3 = stats.ttest_ind(a, b, permutations=np.inf)
|
||
assert res1.pvalue != res0.pvalue
|
||
assert res2.pvalue == res0.pvalue
|
||
assert res3.pvalue == res1.pvalue
|
||
|
||
def test_ttest_ind_exact_distribution(self):
|
||
# the exact distribution of the test statistic should have
|
||
# binom(na + nb, na) elements, all unique. This was not always true
|
||
# in gh-4824; fixed by gh-13661.
|
||
np.random.seed(0)
|
||
a = np.random.rand(3)
|
||
b = np.random.rand(4)
|
||
|
||
data = np.concatenate((a, b))
|
||
na, nb = len(a), len(b)
|
||
|
||
permutations = 100000
|
||
t_stat, _, _ = _permutation_distribution_t(data, permutations, na,
|
||
True)
|
||
|
||
n_unique = len(set(t_stat))
|
||
assert n_unique == binom(na + nb, na)
|
||
assert len(t_stat) == n_unique
|
||
|
||
def test_ttest_ind_randperm_alternative(self):
|
||
np.random.seed(0)
|
||
N = 50
|
||
a = np.random.rand(2, 3, N)
|
||
b = np.random.rand(3, N)
|
||
options_p = {'axis': -1, 'permutations': 1000, "random_state": 0}
|
||
|
||
options_p.update(alternative="greater")
|
||
res_g_ab = stats.ttest_ind(a, b, **options_p)
|
||
res_g_ba = stats.ttest_ind(b, a, **options_p)
|
||
|
||
options_p.update(alternative="less")
|
||
res_l_ab = stats.ttest_ind(a, b, **options_p)
|
||
res_l_ba = stats.ttest_ind(b, a, **options_p)
|
||
|
||
# Alternative doesn't affect the statistic
|
||
assert_equal(res_g_ab.statistic, res_l_ab.statistic)
|
||
|
||
# Reversing order of inputs negates statistic
|
||
assert_equal(res_g_ab.statistic, -res_g_ba.statistic)
|
||
assert_equal(res_l_ab.statistic, -res_l_ba.statistic)
|
||
|
||
# For random permutations, the chance of ties between the observed
|
||
# test statistic and the population is small, so:
|
||
assert_equal(res_g_ab.pvalue + res_l_ab.pvalue,
|
||
1 + 1/(options_p['permutations'] + 1))
|
||
assert_equal(res_g_ba.pvalue + res_l_ba.pvalue,
|
||
1 + 1/(options_p['permutations'] + 1))
|
||
|
||
@pytest.mark.slow()
|
||
def test_ttest_ind_randperm_alternative2(self):
|
||
np.random.seed(0)
|
||
N = 50
|
||
a = np.random.rand(N, 4)
|
||
b = np.random.rand(N, 4)
|
||
options_p = {'permutations': 20000, "random_state": 0}
|
||
|
||
options_p.update(alternative="greater")
|
||
res_g_ab = stats.ttest_ind(a, b, **options_p)
|
||
|
||
options_p.update(alternative="less")
|
||
res_l_ab = stats.ttest_ind(a, b, **options_p)
|
||
|
||
options_p.update(alternative="two-sided")
|
||
res_2_ab = stats.ttest_ind(a, b, **options_p)
|
||
|
||
# For random permutations, the chance of ties between the observed
|
||
# test statistic and the population is small, so:
|
||
assert_equal(res_g_ab.pvalue + res_l_ab.pvalue,
|
||
1 + 1/(options_p['permutations'] + 1))
|
||
|
||
# For for large sample sizes, the distribution should be approximately
|
||
# symmetric, so these identities should be approximately satisfied
|
||
mask = res_g_ab.pvalue <= 0.5
|
||
assert_allclose(2 * res_g_ab.pvalue[mask],
|
||
res_2_ab.pvalue[mask], atol=2e-2)
|
||
assert_allclose(2 * (1-res_g_ab.pvalue[~mask]),
|
||
res_2_ab.pvalue[~mask], atol=2e-2)
|
||
assert_allclose(2 * res_l_ab.pvalue[~mask],
|
||
res_2_ab.pvalue[~mask], atol=2e-2)
|
||
assert_allclose(2 * (1-res_l_ab.pvalue[mask]),
|
||
res_2_ab.pvalue[mask], atol=2e-2)
|
||
|
||
def test_ttest_ind_permutation_nanpolicy(self):
|
||
np.random.seed(0)
|
||
N = 50
|
||
a = np.random.rand(N, 5)
|
||
b = np.random.rand(N, 5)
|
||
a[5, 1] = np.nan
|
||
b[8, 2] = np.nan
|
||
a[9, 3] = np.nan
|
||
b[9, 3] = np.nan
|
||
options_p = {'permutations': 1000, "random_state": 0}
|
||
|
||
# Raise
|
||
options_p.update(nan_policy="raise")
|
||
with assert_raises(ValueError, match="The input contains nan values"):
|
||
res = stats.ttest_ind(a, b, **options_p)
|
||
|
||
# Propagate
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "invalid value*")
|
||
options_p.update(nan_policy="propagate")
|
||
res = stats.ttest_ind(a, b, **options_p)
|
||
|
||
mask = np.isnan(a).any(axis=0) | np.isnan(b).any(axis=0)
|
||
res2 = stats.ttest_ind(a[:, ~mask], b[:, ~mask], **options_p)
|
||
|
||
assert_equal(res.pvalue[mask], np.nan)
|
||
assert_equal(res.statistic[mask], np.nan)
|
||
|
||
assert_allclose(res.pvalue[~mask], res2.pvalue)
|
||
assert_allclose(res.statistic[~mask], res2.statistic)
|
||
|
||
# Propagate 1d
|
||
res = stats.ttest_ind(a.ravel(), b.ravel(), **options_p)
|
||
assert np.isnan(res.pvalue) # assert makes sure it's a scalar
|
||
assert np.isnan(res.statistic)
|
||
|
||
def test_ttest_ind_permutation_check_inputs(self):
|
||
with assert_raises(ValueError, match="Permutations must be"):
|
||
stats.ttest_ind(self.a2, self.b2, permutations=-3)
|
||
with assert_raises(ValueError, match="Permutations must be"):
|
||
stats.ttest_ind(self.a2, self.b2, permutations=1.5)
|
||
with assert_raises(ValueError, match="'hello' cannot be used"):
|
||
stats.ttest_ind(self.a, self.b, permutations=1,
|
||
random_state='hello', axis=1)
|
||
|
||
def test_ttest_ind_permutation_check_p_values(self):
|
||
# p-values should never be exactly zero
|
||
N = 10
|
||
a = np.random.rand(N, 20)
|
||
b = np.random.rand(N, 20)
|
||
p_values = stats.ttest_ind(a, b, permutations=1).pvalue
|
||
print(0.0 not in p_values)
|
||
assert 0.0 not in p_values
|
||
|
||
|
||
class Test_ttest_ind_common:
|
||
# for tests that are performed on variations of the t-test such as
|
||
# permutations and trimming
|
||
@pytest.mark.slow()
|
||
@pytest.mark.parametrize("kwds", [{'permutations': 200, 'random_state': 0},
|
||
{'trim': .2}, {}],
|
||
ids=["permutations", "trim", "basic"])
|
||
@pytest.mark.parametrize('equal_var', [True, False],
|
||
ids=['equal_var', 'unequal_var'])
|
||
def test_ttest_many_dims(self, kwds, equal_var):
|
||
# Test that test works on many-dimensional arrays
|
||
np.random.seed(0)
|
||
a = np.random.rand(5, 4, 4, 7, 1, 6)
|
||
b = np.random.rand(4, 1, 8, 2, 6)
|
||
res = stats.ttest_ind(a, b, axis=-3, **kwds)
|
||
|
||
# compare fully-vectorized t-test against t-test on smaller slice
|
||
i, j, k = 2, 3, 1
|
||
a2 = a[i, :, j, :, 0, :]
|
||
b2 = b[:, 0, :, k, :]
|
||
res2 = stats.ttest_ind(a2, b2, axis=-2, **kwds)
|
||
assert_equal(res.statistic[i, :, j, k, :],
|
||
res2.statistic)
|
||
assert_equal(res.pvalue[i, :, j, k, :],
|
||
res2.pvalue)
|
||
|
||
# compare against t-test on one axis-slice at a time
|
||
|
||
# manually broadcast with tile; move axis to end to simplify
|
||
x = np.moveaxis(np.tile(a, (1, 1, 1, 1, 2, 1)), -3, -1)
|
||
y = np.moveaxis(np.tile(b, (5, 1, 4, 1, 1, 1)), -3, -1)
|
||
shape = x.shape[:-1]
|
||
statistics = np.zeros(shape)
|
||
pvalues = np.zeros(shape)
|
||
for indices in product(*(range(i) for i in shape)):
|
||
xi = x[indices] # use tuple to index single axis slice
|
||
yi = y[indices]
|
||
res3 = stats.ttest_ind(xi, yi, axis=-1, **kwds)
|
||
statistics[indices] = res3.statistic
|
||
pvalues[indices] = res3.pvalue
|
||
|
||
assert_allclose(statistics, res.statistic)
|
||
assert_allclose(pvalues, res.pvalue)
|
||
|
||
@pytest.mark.parametrize("kwds", [{'permutations': 200, 'random_state': 0},
|
||
{'trim': .2}, {}],
|
||
ids=["trim", "permutations", "basic"])
|
||
@pytest.mark.parametrize("axis", [-1, 0])
|
||
def test_nans_on_axis(self, kwds, axis):
|
||
# confirm that with `nan_policy='propagate'`, NaN results are returned
|
||
# on the correct location
|
||
a = np.random.randint(10, size=(5, 3, 10)).astype('float')
|
||
b = np.random.randint(10, size=(5, 3, 10)).astype('float')
|
||
# set some indices in `a` and `b` to be `np.nan`.
|
||
a[0][2][3] = np.nan
|
||
b[2][0][6] = np.nan
|
||
|
||
# arbitrarily use `np.sum` as a baseline for which indices should be
|
||
# NaNs
|
||
expected = np.isnan(np.sum(a + b, axis=axis))
|
||
# multidimensional inputs to `t.sf(np.abs(t), df)` with NaNs on some
|
||
# indices throws an warning. See issue gh-13844
|
||
with suppress_warnings() as sup, np.errstate(invalid="ignore"):
|
||
sup.filter(RuntimeWarning,
|
||
"invalid value encountered in less_equal")
|
||
sup.filter(RuntimeWarning, "Precision loss occurred")
|
||
res = stats.ttest_ind(a, b, axis=axis, **kwds)
|
||
p_nans = np.isnan(res.pvalue)
|
||
assert_array_equal(p_nans, expected)
|
||
statistic_nans = np.isnan(res.statistic)
|
||
assert_array_equal(statistic_nans, expected)
|
||
|
||
|
||
class Test_ttest_trim:
|
||
params = [
|
||
[[1, 2, 3], [1.1, 2.9, 4.2], 0.53619490753126731, -0.6864951273557258,
|
||
.2],
|
||
[[56, 128.6, 12, 123.8, 64.34, 78, 763.3], [1.1, 2.9, 4.2],
|
||
0.00998909252078421, 4.591598691181999, .2],
|
||
[[56, 128.6, 12, 123.8, 64.34, 78, 763.3], [1.1, 2.9, 4.2],
|
||
0.10512380092302633, 2.832256715395378, .32],
|
||
[[2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9],
|
||
[6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1],
|
||
0.002878909511344, -4.2461168970325, .2],
|
||
[[-0.84504783, 0.13366078, 3.53601757, -0.62908581, 0.54119466,
|
||
-1.16511574, -0.08836614, 1.18495416, 2.48028757, -1.58925028,
|
||
-1.6706357, 0.3090472, -2.12258305, 0.3697304, -1.0415207,
|
||
-0.57783497, -0.90997008, 1.09850192, 0.41270579, -1.4927376],
|
||
[1.2725522, 1.1657899, 2.7509041, 1.2389013, -0.9490494, -1.0752459,
|
||
1.1038576, 2.9912821, 3.5349111, 0.4171922, 1.0168959, -0.7625041,
|
||
-0.4300008, 3.0431921, 1.6035947, 0.5285634, -0.7649405, 1.5575896,
|
||
1.3670797, 1.1726023], 0.005293305834235, -3.0983317739483, .2]]
|
||
|
||
@pytest.mark.parametrize("a,b,pr,tr,trim", params)
|
||
def test_ttest_compare_r(self, a, b, pr, tr, trim):
|
||
'''
|
||
Using PairedData's yuen.t.test method. Something to note is that there
|
||
are at least 3 R packages that come with a trimmed t-test method, and
|
||
comparisons were made between them. It was found that PairedData's
|
||
method's results match this method, SAS, and one of the other R
|
||
methods. A notable discrepancy was the DescTools implementation of the
|
||
function, which only sometimes agreed with SAS, WRS2, PairedData and
|
||
this implementation. For this reason, most comparisons in R are made
|
||
against PairedData's method.
|
||
|
||
Rather than providing the input and output for all evaluations, here is
|
||
a representative example:
|
||
> library(PairedData)
|
||
> a <- c(1, 2, 3)
|
||
> b <- c(1.1, 2.9, 4.2)
|
||
> options(digits=16)
|
||
> yuen.t.test(a, b, tr=.2)
|
||
|
||
Two-sample Yuen test, trim=0.2
|
||
|
||
data: x and y
|
||
t = -0.68649512735573, df = 3.4104431643464, p-value = 0.5361949075313
|
||
alternative hypothesis: true difference in trimmed means is not equal
|
||
to 0
|
||
95 percent confidence interval:
|
||
-3.912777195645217 2.446110528978550
|
||
sample estimates:
|
||
trimmed mean of x trimmed mean of y
|
||
2.000000000000000 2.73333333333333
|
||
'''
|
||
statistic, pvalue = stats.ttest_ind(a, b, trim=trim, equal_var=False)
|
||
assert_allclose(statistic, tr, atol=1e-15)
|
||
assert_allclose(pvalue, pr, atol=1e-15)
|
||
|
||
def test_compare_SAS(self):
|
||
# Source of the data used in this test:
|
||
# https://support.sas.com/resources/papers/proceedings14/1660-2014.pdf
|
||
a = [12, 14, 18, 25, 32, 44, 12, 14, 18, 25, 32, 44]
|
||
b = [17, 22, 14, 12, 30, 29, 19, 17, 22, 14, 12, 30, 29, 19]
|
||
# In this paper, a trimming percentage of 5% is used. However,
|
||
# in their implementation, the number of values trimmed is rounded to
|
||
# the nearest whole number. However, consistent with
|
||
# `scipy.stats.trimmed_mean`, this test truncates to the lower
|
||
# whole number. In this example, the paper notes that 1 value is
|
||
# trimmed off of each side. 9% replicates this amount of trimming.
|
||
statistic, pvalue = stats.ttest_ind(a, b, trim=.09, equal_var=False)
|
||
assert_allclose(pvalue, 0.514522, atol=1e-6)
|
||
assert_allclose(statistic, 0.669169, atol=1e-6)
|
||
|
||
def test_equal_var(self):
|
||
'''
|
||
The PairedData library only supports unequal variances. To compare
|
||
samples with equal variances, the multicon library is used.
|
||
> library(multicon)
|
||
> a <- c(2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9)
|
||
> b <- c(6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1)
|
||
> dv = c(a,b)
|
||
> iv = c(rep('a', length(a)), rep('b', length(b)))
|
||
> yuenContrast(dv~ iv, EQVAR = TRUE)
|
||
$Ms
|
||
N M wgt
|
||
a 11 2.442857142857143 1
|
||
b 11 5.385714285714286 -1
|
||
|
||
$test
|
||
stat df crit p
|
||
results -4.246116897032513 12 2.178812829667228 0.00113508833897713
|
||
'''
|
||
a = [2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9]
|
||
b = [6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1]
|
||
# `equal_var=True` is default
|
||
statistic, pvalue = stats.ttest_ind(a, b, trim=.2)
|
||
assert_allclose(pvalue, 0.00113508833897713, atol=1e-10)
|
||
assert_allclose(statistic, -4.246116897032513, atol=1e-10)
|
||
|
||
@pytest.mark.parametrize('alt,pr,tr',
|
||
(('greater', 0.9985605452443, -4.2461168970325),
|
||
('less', 0.001439454755672, -4.2461168970325),),
|
||
)
|
||
def test_alternatives(self, alt, pr, tr):
|
||
'''
|
||
> library(PairedData)
|
||
> a <- c(2.7,2.7,1.1,3.0,1.9,3.0,3.8,3.8,0.3,1.9,1.9)
|
||
> b <- c(6.5,5.4,8.1,3.5,0.5,3.8,6.8,4.9,9.5,6.2,4.1)
|
||
> options(digits=16)
|
||
> yuen.t.test(a, b, alternative = 'greater')
|
||
'''
|
||
a = [2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9]
|
||
b = [6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1]
|
||
|
||
statistic, pvalue = stats.ttest_ind(a, b, trim=.2, equal_var=False,
|
||
alternative=alt)
|
||
assert_allclose(pvalue, pr, atol=1e-10)
|
||
assert_allclose(statistic, tr, atol=1e-10)
|
||
|
||
def test_errors_unsupported(self):
|
||
# confirm that attempting to trim with NaNs or permutations raises an
|
||
# error
|
||
match = "Permutations are currently not supported with trimming."
|
||
with assert_raises(ValueError, match=match):
|
||
stats.ttest_ind([1, 2], [2, 3], trim=.2, permutations=2)
|
||
|
||
@pytest.mark.parametrize("trim", [-.2, .5, 1])
|
||
def test_trim_bounds_error(self, trim):
|
||
match = "Trimming percentage should be 0 <= `trim` < .5."
|
||
with assert_raises(ValueError, match=match):
|
||
stats.ttest_ind([1, 2], [2, 1], trim=trim)
|
||
|
||
|
||
class Test_ttest_CI:
|
||
# indices in order [alternative={two-sided, less, greater},
|
||
# equal_var={False, True}, trim={0, 0.2}]
|
||
# reference values in order `statistic, df, pvalue, low, high`
|
||
# equal_var=False reference values computed with R PairedData yuen.t.test:
|
||
#
|
||
# library(PairedData)
|
||
# options(digits=16)
|
||
# a < - c(0.88236329, 0.97318744, 0.4549262, 0.97893335, 0.0606677,
|
||
# 0.44013366, 0.55806018, 0.40151434, 0.14453315, 0.25860601,
|
||
# 0.20202162)
|
||
# b < - c(0.93455277, 0.42680603, 0.49751939, 0.14152846, 0.711435,
|
||
# 0.77669667, 0.20507578, 0.78702772, 0.94691855, 0.32464958,
|
||
# 0.3873582, 0.35187468, 0.21731811)
|
||
# yuen.t.test(a, b, tr=0, conf.level = 0.9, alternative = 'l')
|
||
#
|
||
# equal_var=True reference values computed with R multicon yuenContrast:
|
||
#
|
||
# library(multicon)
|
||
# options(digits=16)
|
||
# a < - c(0.88236329, 0.97318744, 0.4549262, 0.97893335, 0.0606677,
|
||
# 0.44013366, 0.55806018, 0.40151434, 0.14453315, 0.25860601,
|
||
# 0.20202162)
|
||
# b < - c(0.93455277, 0.42680603, 0.49751939, 0.14152846, 0.711435,
|
||
# 0.77669667, 0.20507578, 0.78702772, 0.94691855, 0.32464958,
|
||
# 0.3873582, 0.35187468, 0.21731811)
|
||
# dv = c(a, b)
|
||
# iv = c(rep('a', length(a)), rep('b', length(b)))
|
||
# yuenContrast(dv~iv, EQVAR = FALSE, alternative = 'unequal', tr = 0.2)
|
||
r = np.empty(shape=(3, 2, 2, 5))
|
||
r[0, 0, 0] = [-0.2314607, 19.894435, 0.8193209, -0.247220294, 0.188729943]
|
||
r[1, 0, 0] = [-0.2314607, 19.894435, 0.40966045, -np.inf, 0.1382426469]
|
||
r[2, 0, 0] = [-0.2314607, 19.894435, 0.5903395, -0.1967329982, np.inf]
|
||
r[0, 0, 1] = [-0.2452886, 11.427896, 0.8105823, -0.34057446, 0.25847383]
|
||
r[1, 0, 1] = [-0.2452886, 11.427896, 0.40529115, -np.inf, 0.1865829074]
|
||
r[2, 0, 1] = [-0.2452886, 11.427896, 0.5947089, -0.268683541, np.inf]
|
||
# confidence interval not available for equal_var=True
|
||
r[0, 1, 0] = [-0.2345625322555006, 22, 0.8167175905643815, None, None]
|
||
r[1, 1, 0] = [-0.2345625322555006, 22, 0.4083587952821908, None, None]
|
||
r[2, 1, 0] = [-0.2345625322555006, 22, 0.5916412047178092, None, None]
|
||
r[0, 1, 1] = [-0.2505369406507428, 14, 0.8058115135702835, None, None]
|
||
r[1, 1, 1] = [-0.2505369406507428, 14, 0.4029057567851417, None, None]
|
||
r[2, 1, 1] = [-0.2505369406507428, 14, 0.5970942432148583, None, None]
|
||
@pytest.mark.parametrize('alternative', ['two-sided', 'less', 'greater'])
|
||
@pytest.mark.parametrize('equal_var', [False, True])
|
||
@pytest.mark.parametrize('trim', [0, 0.2])
|
||
def test_confidence_interval(self, alternative, equal_var, trim):
|
||
if equal_var and trim:
|
||
pytest.xfail('Discrepancy in `main`; needs further investigation.')
|
||
|
||
rng = np.random.default_rng(3810954496107292580)
|
||
x = rng.random(11)
|
||
y = rng.random(13)
|
||
|
||
res = stats.ttest_ind(x, y, alternative=alternative,
|
||
equal_var=equal_var, trim=trim)
|
||
|
||
alternatives = {'two-sided': 0, 'less': 1, 'greater': 2}
|
||
ref = self.r[alternatives[alternative], int(equal_var), int(np.ceil(trim))]
|
||
statistic, df, pvalue, low, high = ref
|
||
assert_allclose(res.statistic, statistic)
|
||
assert_allclose(res.df, df)
|
||
assert_allclose(res.pvalue, pvalue)
|
||
if not equal_var: # CI not available when `equal_var is True`
|
||
ci = res.confidence_interval(0.9)
|
||
assert_allclose(ci.low, low)
|
||
assert_allclose(ci.high, high)
|
||
|
||
|
||
def test__broadcast_concatenate():
|
||
# test that _broadcast_concatenate properly broadcasts arrays along all
|
||
# axes except `axis`, then concatenates along axis
|
||
np.random.seed(0)
|
||
a = np.random.rand(5, 4, 4, 3, 1, 6)
|
||
b = np.random.rand(4, 1, 8, 2, 6)
|
||
c = _broadcast_concatenate((a, b), axis=-3)
|
||
# broadcast manually as an independent check
|
||
a = np.tile(a, (1, 1, 1, 1, 2, 1))
|
||
b = np.tile(b[None, ...], (5, 1, 4, 1, 1, 1))
|
||
for index in product(*(range(i) for i in c.shape)):
|
||
i, j, k, l, m, n = index
|
||
if l < a.shape[-3]:
|
||
assert a[i, j, k, l, m, n] == c[i, j, k, l, m, n]
|
||
else:
|
||
assert b[i, j, k, l - a.shape[-3], m, n] == c[i, j, k, l, m, n]
|
||
|
||
|
||
def test_ttest_ind_with_uneq_var():
|
||
# check vs. R
|
||
a = (1, 2, 3)
|
||
b = (1.1, 2.9, 4.2)
|
||
pr = 0.53619490753126731
|
||
tr = -0.68649512735572582
|
||
t, p = stats.ttest_ind(a, b, equal_var=False)
|
||
assert_array_almost_equal([t,p], [tr, pr])
|
||
# test from desc stats API
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(a, b),
|
||
equal_var=False),
|
||
[t, p])
|
||
|
||
a = (1, 2, 3, 4)
|
||
pr = 0.84354139131608286
|
||
tr = -0.2108663315950719
|
||
t, p = stats.ttest_ind(a, b, equal_var=False)
|
||
assert_array_almost_equal([t,p], [tr, pr])
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(a, b),
|
||
equal_var=False),
|
||
[t, p])
|
||
|
||
# regression test
|
||
tr = 1.0912746897927283
|
||
tr_uneq_n = 0.66745638708050492
|
||
pr = 0.27647831993021388
|
||
pr_uneq_n = 0.50873585065616544
|
||
tpr = ([tr,-tr],[pr,pr])
|
||
|
||
rvs3 = np.linspace(1,100, 25)
|
||
rvs2 = np.linspace(1,100,100)
|
||
rvs1 = np.linspace(5,105,100)
|
||
rvs1_2D = np.array([rvs1, rvs2])
|
||
|
||
rvs2_2D = np.array([rvs2, rvs1])
|
||
|
||
t,p = stats.ttest_ind(rvs1, rvs2, axis=0, equal_var=False)
|
||
assert_array_almost_equal([t,p],(tr,pr))
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(rvs1,
|
||
rvs2),
|
||
equal_var=False),
|
||
(t, p))
|
||
|
||
t,p = stats.ttest_ind(rvs1, rvs3, axis=0, equal_var=False)
|
||
assert_array_almost_equal([t,p], (tr_uneq_n, pr_uneq_n))
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*_desc_stats(rvs1,
|
||
rvs3),
|
||
equal_var=False),
|
||
(t, p))
|
||
|
||
t,p = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, equal_var=False)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*args,
|
||
equal_var=False),
|
||
(t, p))
|
||
|
||
t,p = stats.ttest_ind(rvs1_2D, rvs2_2D, axis=1, equal_var=False)
|
||
assert_array_almost_equal([t,p],tpr)
|
||
args = _desc_stats(rvs1_2D, rvs2_2D, axis=1)
|
||
assert_array_almost_equal(stats.ttest_ind_from_stats(*args,
|
||
equal_var=False),
|
||
(t, p))
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.ttest_ind(rvs1, rvs2, axis=0, equal_var=False)
|
||
check_named_results(res, attributes)
|
||
|
||
# test on 3 dimensions
|
||
rvs1_3D = np.dstack([rvs1_2D,rvs1_2D,rvs1_2D])
|
||
rvs2_3D = np.dstack([rvs2_2D,rvs2_2D,rvs2_2D])
|
||
t,p = stats.ttest_ind(rvs1_3D, rvs2_3D, axis=1, equal_var=False)
|
||
assert_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (2, 3))
|
||
args = _desc_stats(rvs1_3D, rvs2_3D, axis=1)
|
||
t, p = stats.ttest_ind_from_stats(*args, equal_var=False)
|
||
assert_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (2, 3))
|
||
|
||
t, p = stats.ttest_ind(np.moveaxis(rvs1_3D, 2, 0),
|
||
np.moveaxis(rvs2_3D, 2, 0),
|
||
axis=2, equal_var=False)
|
||
assert_array_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (3, 2))
|
||
args = _desc_stats(np.moveaxis(rvs1_3D, 2, 0),
|
||
np.moveaxis(rvs2_3D, 2, 0), axis=2)
|
||
t, p = stats.ttest_ind_from_stats(*args, equal_var=False)
|
||
assert_array_almost_equal(np.abs(t), np.abs(tr))
|
||
assert_array_almost_equal(np.abs(p), pr)
|
||
assert_equal(t.shape, (3, 2))
|
||
|
||
# test zero division problem
|
||
with pytest.warns(RuntimeWarning, match="Precision loss occurred"):
|
||
t, p = stats.ttest_ind([0, 0, 0], [1, 1, 1], equal_var=False)
|
||
assert_equal((np.abs(t), p), (np.inf, 0))
|
||
with np.errstate(all='ignore'):
|
||
assert_equal(stats.ttest_ind([0, 0, 0], [0, 0, 0], equal_var=False),
|
||
(np.nan, np.nan))
|
||
|
||
# check that nan in input array result in nan output
|
||
anan = np.array([[1, np.nan], [-1, 1]])
|
||
assert_equal(stats.ttest_ind(anan, np.zeros((2, 2)), equal_var=False),
|
||
([0, np.nan], [1, np.nan]))
|
||
|
||
|
||
def test_ttest_ind_nan_2nd_arg():
|
||
# regression test for gh-6134: nans in the second arg were not handled
|
||
x = [np.nan, 2.0, 3.0, 4.0]
|
||
y = [1.0, 2.0, 1.0, 2.0]
|
||
|
||
r1 = stats.ttest_ind(x, y, nan_policy='omit')
|
||
r2 = stats.ttest_ind(y, x, nan_policy='omit')
|
||
assert_allclose(r2.statistic, -r1.statistic, atol=1e-15)
|
||
assert_allclose(r2.pvalue, r1.pvalue, atol=1e-15)
|
||
|
||
# NB: arguments are not paired when NaNs are dropped
|
||
r3 = stats.ttest_ind(y, x[1:])
|
||
assert_allclose(r2, r3, atol=1e-15)
|
||
|
||
# .. and this is consistent with R. R code:
|
||
# x = c(NA, 2.0, 3.0, 4.0)
|
||
# y = c(1.0, 2.0, 1.0, 2.0)
|
||
# t.test(x, y, var.equal=TRUE)
|
||
assert_allclose(r2, (-2.5354627641855498, 0.052181400457057901),
|
||
atol=1e-15)
|
||
|
||
|
||
def test_ttest_ind_empty_1d_returns_nan():
|
||
# Two empty inputs should return a TtestResult containing nan
|
||
# for both values.
|
||
result = stats.ttest_ind([], [])
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
assert_equal(result, (np.nan, np.nan))
|
||
|
||
|
||
@pytest.mark.parametrize('b, expected_shape',
|
||
[(np.empty((1, 5, 0)), (3, 5)),
|
||
(np.empty((1, 0, 0)), (3, 0))])
|
||
def test_ttest_ind_axis_size_zero(b, expected_shape):
|
||
# In this test, the length of the axis dimension is zero.
|
||
# The results should be arrays containing nan with shape
|
||
# given by the broadcast nonaxis dimensions.
|
||
a = np.empty((3, 1, 0))
|
||
result = stats.ttest_ind(a, b, axis=-1)
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
expected_value = np.full(expected_shape, fill_value=np.nan)
|
||
assert_equal(result.statistic, expected_value)
|
||
assert_equal(result.pvalue, expected_value)
|
||
|
||
|
||
def test_ttest_ind_nonaxis_size_zero():
|
||
# In this test, the length of the axis dimension is nonzero,
|
||
# but one of the nonaxis dimensions has length 0. Check that
|
||
# we still get the correctly broadcast shape, which is (5, 0)
|
||
# in this case.
|
||
a = np.empty((1, 8, 0))
|
||
b = np.empty((5, 8, 1))
|
||
result = stats.ttest_ind(a, b, axis=1)
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
assert_equal(result.statistic.shape, (5, 0))
|
||
assert_equal(result.pvalue.shape, (5, 0))
|
||
|
||
|
||
def test_ttest_ind_nonaxis_size_zero_different_lengths():
|
||
# In this test, the length of the axis dimension is nonzero,
|
||
# and that size is different in the two inputs,
|
||
# and one of the nonaxis dimensions has length 0. Check that
|
||
# we still get the correctly broadcast shape, which is (5, 0)
|
||
# in this case.
|
||
a = np.empty((1, 7, 0))
|
||
b = np.empty((5, 8, 1))
|
||
result = stats.ttest_ind(a, b, axis=1)
|
||
assert isinstance(result, stats._stats_py.TtestResult)
|
||
assert_equal(result.statistic.shape, (5, 0))
|
||
assert_equal(result.pvalue.shape, (5, 0))
|
||
|
||
|
||
def test_gh5686():
|
||
mean1, mean2 = np.array([1, 2]), np.array([3, 4])
|
||
std1, std2 = np.array([5, 3]), np.array([4, 5])
|
||
nobs1, nobs2 = np.array([130, 140]), np.array([100, 150])
|
||
# This will raise a TypeError unless gh-5686 is fixed.
|
||
stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
|
||
|
||
|
||
def test_ttest_ind_from_stats_inputs_zero():
|
||
# Regression test for gh-6409.
|
||
result = stats.ttest_ind_from_stats(0, 0, 6, 0, 0, 6, equal_var=False)
|
||
assert_equal(result, [np.nan, np.nan])
|
||
|
||
|
||
def test_ttest_single_observation():
|
||
# test that p-values are uniformly distributed under the null hypothesis
|
||
rng = np.random.default_rng(246834602926842)
|
||
x = rng.normal(size=(10000, 2))
|
||
y = rng.normal(size=(10000, 1))
|
||
q = rng.uniform(size=100)
|
||
|
||
res = stats.ttest_ind(x, y, equal_var=True, axis=-1)
|
||
assert stats.ks_1samp(res.pvalue, stats.uniform().cdf).pvalue > 0.1
|
||
assert_allclose(np.percentile(res.pvalue, q*100), q, atol=1e-2)
|
||
|
||
res = stats.ttest_ind(y, x, equal_var=True, axis=-1)
|
||
assert stats.ks_1samp(res.pvalue, stats.uniform().cdf).pvalue > 0.1
|
||
assert_allclose(np.percentile(res.pvalue, q*100), q, atol=1e-2)
|
||
|
||
# reference values from R:
|
||
# options(digits=16)
|
||
# t.test(c(2, 3, 5), c(1.5), var.equal=TRUE)
|
||
res = stats.ttest_ind([2, 3, 5], [1.5], equal_var=True)
|
||
assert_allclose(res, (1.0394023007754, 0.407779907736), rtol=1e-10)
|
||
|
||
|
||
def test_ttest_1samp_new():
|
||
n1, n2, n3 = (10,15,20)
|
||
rvn1 = stats.norm.rvs(loc=5,scale=10,size=(n1,n2,n3))
|
||
|
||
# check multidimensional array and correct axis handling
|
||
# deterministic rvn1 and rvn2 would be better as in test_ttest_rel
|
||
t1,p1 = stats.ttest_1samp(rvn1[:,:,:], np.ones((n2,n3)),axis=0)
|
||
t2,p2 = stats.ttest_1samp(rvn1[:,:,:], 1,axis=0)
|
||
t3,p3 = stats.ttest_1samp(rvn1[:,0,0], 1)
|
||
assert_array_almost_equal(t1,t2, decimal=14)
|
||
assert_almost_equal(t1[0,0],t3, decimal=14)
|
||
assert_equal(t1.shape, (n2,n3))
|
||
|
||
t1,p1 = stats.ttest_1samp(rvn1[:,:,:], np.ones((n1, 1, n3)),axis=1)
|
||
t2,p2 = stats.ttest_1samp(rvn1[:,:,:], 1,axis=1)
|
||
t3,p3 = stats.ttest_1samp(rvn1[0,:,0], 1)
|
||
assert_array_almost_equal(t1,t2, decimal=14)
|
||
assert_almost_equal(t1[0,0],t3, decimal=14)
|
||
assert_equal(t1.shape, (n1,n3))
|
||
|
||
t1,p1 = stats.ttest_1samp(rvn1[:,:,:], np.ones((n1,n2,1)),axis=2)
|
||
t2,p2 = stats.ttest_1samp(rvn1[:,:,:], 1,axis=2)
|
||
t3,p3 = stats.ttest_1samp(rvn1[0,0,:], 1)
|
||
assert_array_almost_equal(t1,t2, decimal=14)
|
||
assert_almost_equal(t1[0,0],t3, decimal=14)
|
||
assert_equal(t1.shape, (n1,n2))
|
||
|
||
# test zero division problem
|
||
t, p = stats.ttest_1samp([0, 0, 0], 1)
|
||
assert_equal((np.abs(t), p), (np.inf, 0))
|
||
|
||
# test alternative parameter
|
||
# Convert from two-sided p-values to one sided using T result data.
|
||
def convert(t, p, alt):
|
||
if (t < 0 and alt == "less") or (t > 0 and alt == "greater"):
|
||
return p / 2
|
||
return 1 - (p / 2)
|
||
converter = np.vectorize(convert)
|
||
tr, pr = stats.ttest_1samp(rvn1[:, :, :], 1)
|
||
|
||
t, p = stats.ttest_1samp(rvn1[:, :, :], 1, alternative="greater")
|
||
pc = converter(tr, pr, "greater")
|
||
assert_allclose(p, pc)
|
||
assert_allclose(t, tr)
|
||
|
||
t, p = stats.ttest_1samp(rvn1[:, :, :], 1, alternative="less")
|
||
pc = converter(tr, pr, "less")
|
||
assert_allclose(p, pc)
|
||
assert_allclose(t, tr)
|
||
|
||
with np.errstate(all='ignore'):
|
||
assert_equal(stats.ttest_1samp([0, 0, 0], 0), (np.nan, np.nan))
|
||
|
||
# check that nan in input array result in nan output
|
||
anan = np.array([[1, np.nan],[-1, 1]])
|
||
assert_equal(stats.ttest_1samp(anan, 0), ([0, np.nan], [1, np.nan]))
|
||
|
||
rvn1[0:2, 1:3, 4:8] = np.nan
|
||
|
||
tr, pr = stats.ttest_1samp(rvn1[:, :, :], 1, nan_policy='omit')
|
||
|
||
t, p = stats.ttest_1samp(rvn1[:, :, :], 1, nan_policy='omit',
|
||
alternative="greater")
|
||
pc = converter(tr, pr, "greater")
|
||
assert_allclose(p, pc)
|
||
assert_allclose(t, tr)
|
||
|
||
t, p = stats.ttest_1samp(rvn1[:, :, :], 1, nan_policy='omit',
|
||
alternative="less")
|
||
pc = converter(tr, pr, "less")
|
||
assert_allclose(p, pc)
|
||
assert_allclose(t, tr)
|
||
|
||
|
||
def test_ttest_1samp_popmean_array():
|
||
# when popmean.shape[axis] != 1, raise an error
|
||
# if the user wants to test multiple null hypotheses simultaneously,
|
||
# use standard broadcasting rules
|
||
rng = np.random.default_rng(2913300596553337193)
|
||
x = rng.random(size=(1, 15, 20))
|
||
|
||
message = r"`popmean.shape\[axis\]` must equal 1."
|
||
popmean = rng.random(size=(5, 2, 20))
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.ttest_1samp(x, popmean=popmean, axis=-2)
|
||
|
||
popmean = rng.random(size=(5, 1, 20))
|
||
res = stats.ttest_1samp(x, popmean=popmean, axis=-2)
|
||
assert res.statistic.shape == (5, 20)
|
||
|
||
ci = np.expand_dims(res.confidence_interval(), axis=-2)
|
||
res = stats.ttest_1samp(x, popmean=ci, axis=-2)
|
||
assert_allclose(res.pvalue, 0.05)
|
||
|
||
|
||
class TestDescribe:
|
||
def test_describe_scalar(self):
|
||
with suppress_warnings() as sup, \
|
||
np.errstate(invalid="ignore", divide="ignore"):
|
||
sup.filter(RuntimeWarning, "Degrees of freedom <= 0 for slice")
|
||
n, mm, m, v, sk, kurt = stats.describe(4.)
|
||
assert_equal(n, 1)
|
||
assert_equal(mm, (4.0, 4.0))
|
||
assert_equal(m, 4.0)
|
||
assert np.isnan(v)
|
||
assert np.isnan(sk)
|
||
assert np.isnan(kurt)
|
||
|
||
def test_describe_numbers(self):
|
||
x = np.vstack((np.ones((3,4)), np.full((2, 4), 2)))
|
||
nc, mmc = (5, ([1., 1., 1., 1.], [2., 2., 2., 2.]))
|
||
mc = np.array([1.4, 1.4, 1.4, 1.4])
|
||
vc = np.array([0.3, 0.3, 0.3, 0.3])
|
||
skc = [0.40824829046386357] * 4
|
||
kurtc = [-1.833333333333333] * 4
|
||
n, mm, m, v, sk, kurt = stats.describe(x)
|
||
assert_equal(n, nc)
|
||
assert_equal(mm, mmc)
|
||
assert_equal(m, mc)
|
||
assert_equal(v, vc)
|
||
assert_array_almost_equal(sk, skc, decimal=13)
|
||
assert_array_almost_equal(kurt, kurtc, decimal=13)
|
||
n, mm, m, v, sk, kurt = stats.describe(x.T, axis=1)
|
||
assert_equal(n, nc)
|
||
assert_equal(mm, mmc)
|
||
assert_equal(m, mc)
|
||
assert_equal(v, vc)
|
||
assert_array_almost_equal(sk, skc, decimal=13)
|
||
assert_array_almost_equal(kurt, kurtc, decimal=13)
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
|
||
nc, mmc = (9, (0.0, 8.0))
|
||
mc = 4.0
|
||
vc = 7.5
|
||
skc = 0.0
|
||
kurtc = -1.2300000000000002
|
||
n, mm, m, v, sk, kurt = stats.describe(x, nan_policy='omit')
|
||
assert_equal(n, nc)
|
||
assert_equal(mm, mmc)
|
||
assert_equal(m, mc)
|
||
assert_equal(v, vc)
|
||
assert_array_almost_equal(sk, skc)
|
||
assert_array_almost_equal(kurt, kurtc, decimal=13)
|
||
|
||
assert_raises(ValueError, stats.describe, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.describe, x, nan_policy='foobar')
|
||
|
||
def test_describe_result_attributes(self):
|
||
actual = stats.describe(np.arange(5))
|
||
attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness',
|
||
'kurtosis')
|
||
check_named_results(actual, attributes)
|
||
|
||
def test_describe_ddof(self):
|
||
x = np.vstack((np.ones((3, 4)), np.full((2, 4), 2)))
|
||
nc, mmc = (5, ([1., 1., 1., 1.], [2., 2., 2., 2.]))
|
||
mc = np.array([1.4, 1.4, 1.4, 1.4])
|
||
vc = np.array([0.24, 0.24, 0.24, 0.24])
|
||
skc = [0.40824829046386357] * 4
|
||
kurtc = [-1.833333333333333] * 4
|
||
n, mm, m, v, sk, kurt = stats.describe(x, ddof=0)
|
||
assert_equal(n, nc)
|
||
assert_allclose(mm, mmc, rtol=1e-15)
|
||
assert_allclose(m, mc, rtol=1e-15)
|
||
assert_allclose(v, vc, rtol=1e-15)
|
||
assert_array_almost_equal(sk, skc, decimal=13)
|
||
assert_array_almost_equal(kurt, kurtc, decimal=13)
|
||
|
||
def test_describe_axis_none(self):
|
||
x = np.vstack((np.ones((3, 4)), np.full((2, 4), 2)))
|
||
|
||
# expected values
|
||
e_nobs, e_minmax = (20, (1.0, 2.0))
|
||
e_mean = 1.3999999999999999
|
||
e_var = 0.25263157894736848
|
||
e_skew = 0.4082482904638634
|
||
e_kurt = -1.8333333333333333
|
||
|
||
# actual values
|
||
a = stats.describe(x, axis=None)
|
||
|
||
assert_equal(a.nobs, e_nobs)
|
||
assert_almost_equal(a.minmax, e_minmax)
|
||
assert_almost_equal(a.mean, e_mean)
|
||
assert_almost_equal(a.variance, e_var)
|
||
assert_array_almost_equal(a.skewness, e_skew, decimal=13)
|
||
assert_array_almost_equal(a.kurtosis, e_kurt, decimal=13)
|
||
|
||
def test_describe_empty(self):
|
||
assert_raises(ValueError, stats.describe, [])
|
||
|
||
|
||
def test_normalitytests():
|
||
assert_raises(ValueError, stats.skewtest, 4.)
|
||
assert_raises(ValueError, stats.kurtosistest, 4.)
|
||
assert_raises(ValueError, stats.normaltest, 4.)
|
||
|
||
# numbers verified with R: dagoTest in package fBasics
|
||
st_normal, st_skew, st_kurt = (3.92371918, 1.98078826, -0.01403734)
|
||
pv_normal, pv_skew, pv_kurt = (0.14059673, 0.04761502, 0.98880019)
|
||
pv_skew_less, pv_kurt_less = 1 - pv_skew / 2, pv_kurt / 2
|
||
pv_skew_greater, pv_kurt_greater = pv_skew / 2, 1 - pv_kurt / 2
|
||
x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
|
||
attributes = ('statistic', 'pvalue')
|
||
|
||
assert_array_almost_equal(stats.normaltest(x), (st_normal, pv_normal))
|
||
check_named_results(stats.normaltest(x), attributes)
|
||
assert_array_almost_equal(stats.skewtest(x), (st_skew, pv_skew))
|
||
assert_array_almost_equal(stats.skewtest(x, alternative='less'),
|
||
(st_skew, pv_skew_less))
|
||
assert_array_almost_equal(stats.skewtest(x, alternative='greater'),
|
||
(st_skew, pv_skew_greater))
|
||
check_named_results(stats.skewtest(x), attributes)
|
||
assert_array_almost_equal(stats.kurtosistest(x), (st_kurt, pv_kurt))
|
||
assert_array_almost_equal(stats.kurtosistest(x, alternative='less'),
|
||
(st_kurt, pv_kurt_less))
|
||
assert_array_almost_equal(stats.kurtosistest(x, alternative='greater'),
|
||
(st_kurt, pv_kurt_greater))
|
||
check_named_results(stats.kurtosistest(x), attributes)
|
||
|
||
# some more intuitive tests for kurtosistest and skewtest.
|
||
# see gh-13549.
|
||
# skew parameter is 1 > 0
|
||
a1 = stats.skewnorm.rvs(a=1, size=10000, random_state=123)
|
||
pval = stats.skewtest(a1, alternative='greater').pvalue
|
||
assert_almost_equal(pval, 0.0, decimal=5)
|
||
# excess kurtosis of laplace is 3 > 0
|
||
a2 = stats.laplace.rvs(size=10000, random_state=123)
|
||
pval = stats.kurtosistest(a2, alternative='greater').pvalue
|
||
assert_almost_equal(pval, 0.0)
|
||
|
||
# Test axis=None (equal to axis=0 for 1-D input)
|
||
assert_array_almost_equal(stats.normaltest(x, axis=None),
|
||
(st_normal, pv_normal))
|
||
assert_array_almost_equal(stats.skewtest(x, axis=None),
|
||
(st_skew, pv_skew))
|
||
assert_array_almost_equal(stats.kurtosistest(x, axis=None),
|
||
(st_kurt, pv_kurt))
|
||
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
with np.errstate(invalid="ignore"):
|
||
assert_array_equal(stats.skewtest(x), (np.nan, np.nan))
|
||
|
||
expected = (1.0184643553962129, 0.30845733195153502)
|
||
assert_array_almost_equal(stats.skewtest(x, nan_policy='omit'), expected)
|
||
|
||
# test alternative with nan_policy='omit'
|
||
a1[10:100] = np.nan
|
||
z, p = stats.skewtest(a1, nan_policy='omit')
|
||
zl, pl = stats.skewtest(a1, nan_policy='omit', alternative='less')
|
||
zg, pg = stats.skewtest(a1, nan_policy='omit', alternative='greater')
|
||
assert_allclose(zl, z, atol=1e-15)
|
||
assert_allclose(zg, z, atol=1e-15)
|
||
assert_allclose(pl, 1 - p/2, atol=1e-15)
|
||
assert_allclose(pg, p/2, atol=1e-15)
|
||
|
||
with np.errstate(all='ignore'):
|
||
assert_raises(ValueError, stats.skewtest, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.skewtest, x, nan_policy='foobar')
|
||
assert_raises(ValueError, stats.skewtest, list(range(8)),
|
||
alternative='foobar')
|
||
|
||
x = np.arange(30.)
|
||
x[29] = np.nan
|
||
with np.errstate(all='ignore'):
|
||
assert_array_equal(stats.kurtosistest(x), (np.nan, np.nan))
|
||
|
||
expected = (-2.2683547379505273, 0.023307594135872967)
|
||
assert_array_almost_equal(stats.kurtosistest(x, nan_policy='omit'),
|
||
expected)
|
||
|
||
# test alternative with nan_policy='omit'
|
||
a2[10:20] = np.nan
|
||
z, p = stats.kurtosistest(a2[:100], nan_policy='omit')
|
||
zl, pl = stats.kurtosistest(a2[:100], nan_policy='omit',
|
||
alternative='less')
|
||
zg, pg = stats.kurtosistest(a2[:100], nan_policy='omit',
|
||
alternative='greater')
|
||
assert_allclose(zl, z, atol=1e-15)
|
||
assert_allclose(zg, z, atol=1e-15)
|
||
assert_allclose(pl, 1 - p/2, atol=1e-15)
|
||
assert_allclose(pg, p/2, atol=1e-15)
|
||
|
||
assert_raises(ValueError, stats.kurtosistest, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.kurtosistest, x, nan_policy='foobar')
|
||
assert_raises(ValueError, stats.kurtosistest, list(range(20)),
|
||
alternative='foobar')
|
||
|
||
with np.errstate(all='ignore'):
|
||
assert_array_equal(stats.normaltest(x), (np.nan, np.nan))
|
||
|
||
expected = (6.2260409514287449, 0.04446644248650191)
|
||
assert_array_almost_equal(stats.normaltest(x, nan_policy='omit'), expected)
|
||
|
||
assert_raises(ValueError, stats.normaltest, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.normaltest, x, nan_policy='foobar')
|
||
|
||
# regression test for issue gh-9033: x clearly non-normal but power of
|
||
# negative denom needs to be handled correctly to reject normality
|
||
counts = [128, 0, 58, 7, 0, 41, 16, 0, 0, 167]
|
||
x = np.hstack([np.full(c, i) for i, c in enumerate(counts)])
|
||
assert_equal(stats.kurtosistest(x)[1] < 0.01, True)
|
||
|
||
|
||
class TestRankSums:
|
||
|
||
np.random.seed(0)
|
||
x, y = np.random.rand(2, 10)
|
||
|
||
@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
|
||
def test_ranksums_result_attributes(self, alternative):
|
||
# ranksums pval = mannwhitneyu pval w/out continuity or tie correction
|
||
res1 = stats.ranksums(self.x, self.y,
|
||
alternative=alternative).pvalue
|
||
res2 = stats.mannwhitneyu(self.x, self.y, use_continuity=False,
|
||
alternative=alternative).pvalue
|
||
assert_allclose(res1, res2)
|
||
|
||
def test_ranksums_named_results(self):
|
||
res = stats.ranksums(self.x, self.y)
|
||
check_named_results(res, ('statistic', 'pvalue'))
|
||
|
||
def test_input_validation(self):
|
||
with assert_raises(ValueError, match="`alternative` must be 'less'"):
|
||
stats.ranksums(self.x, self.y, alternative='foobar')
|
||
|
||
|
||
class TestJarqueBera:
|
||
def test_jarque_bera_stats(self):
|
||
np.random.seed(987654321)
|
||
x = np.random.normal(0, 1, 100000)
|
||
y = np.random.chisquare(10000, 100000)
|
||
z = np.random.rayleigh(1, 100000)
|
||
|
||
assert_equal(stats.jarque_bera(x)[0], stats.jarque_bera(x).statistic)
|
||
assert_equal(stats.jarque_bera(x)[1], stats.jarque_bera(x).pvalue)
|
||
|
||
assert_equal(stats.jarque_bera(y)[0], stats.jarque_bera(y).statistic)
|
||
assert_equal(stats.jarque_bera(y)[1], stats.jarque_bera(y).pvalue)
|
||
|
||
assert_equal(stats.jarque_bera(z)[0], stats.jarque_bera(z).statistic)
|
||
assert_equal(stats.jarque_bera(z)[1], stats.jarque_bera(z).pvalue)
|
||
|
||
assert_(stats.jarque_bera(x)[1] > stats.jarque_bera(y)[1])
|
||
assert_(stats.jarque_bera(x).pvalue > stats.jarque_bera(y).pvalue)
|
||
|
||
assert_(stats.jarque_bera(x)[1] > stats.jarque_bera(z)[1])
|
||
assert_(stats.jarque_bera(x).pvalue > stats.jarque_bera(z).pvalue)
|
||
|
||
assert_(stats.jarque_bera(y)[1] > stats.jarque_bera(z)[1])
|
||
assert_(stats.jarque_bera(y).pvalue > stats.jarque_bera(z).pvalue)
|
||
|
||
def test_jarque_bera_array_like(self):
|
||
np.random.seed(987654321)
|
||
x = np.random.normal(0, 1, 100000)
|
||
|
||
jb_test1 = JB1, p1 = stats.jarque_bera(list(x))
|
||
jb_test2 = JB2, p2 = stats.jarque_bera(tuple(x))
|
||
jb_test3 = JB3, p3 = stats.jarque_bera(x.reshape(2, 50000))
|
||
|
||
assert JB1 == JB2 == JB3 == jb_test1.statistic == jb_test2.statistic == jb_test3.statistic # noqa: E501
|
||
assert p1 == p2 == p3 == jb_test1.pvalue == jb_test2.pvalue == jb_test3.pvalue
|
||
|
||
def test_jarque_bera_size(self):
|
||
assert_raises(ValueError, stats.jarque_bera, [])
|
||
|
||
def test_axis(self):
|
||
rng = np.random.RandomState(seed=122398129)
|
||
x = rng.random(size=(2, 45))
|
||
|
||
assert_equal(stats.jarque_bera(x, axis=None),
|
||
stats.jarque_bera(x.ravel()))
|
||
|
||
res = stats.jarque_bera(x, axis=1)
|
||
s0, p0 = stats.jarque_bera(x[0, :])
|
||
s1, p1 = stats.jarque_bera(x[1, :])
|
||
assert_allclose(res.statistic, [s0, s1])
|
||
assert_allclose(res.pvalue, [p0, p1])
|
||
|
||
resT = stats.jarque_bera(x.T, axis=0)
|
||
assert_allclose(res, resT)
|
||
|
||
|
||
def test_skewtest_too_few_samples():
|
||
# Regression test for ticket #1492.
|
||
# skewtest requires at least 8 samples; 7 should raise a ValueError.
|
||
x = np.arange(7.0)
|
||
assert_raises(ValueError, stats.skewtest, x)
|
||
|
||
|
||
def test_kurtosistest_too_few_samples():
|
||
# Regression test for ticket #1425.
|
||
# kurtosistest requires at least 5 samples; 4 should raise a ValueError.
|
||
x = np.arange(4.0)
|
||
assert_raises(ValueError, stats.kurtosistest, x)
|
||
|
||
|
||
class TestMannWhitneyU:
|
||
X = [19.8958398126694, 19.5452691647182, 19.0577309166425, 21.716543054589,
|
||
20.3269502208702, 20.0009273294025, 19.3440043632957, 20.4216806548105,
|
||
19.0649894736528, 18.7808043120398, 19.3680942943298, 19.4848044069953,
|
||
20.7514611265663, 19.0894948874598, 19.4975522356628, 18.9971170734274,
|
||
20.3239606288208, 20.6921298083835, 19.0724259532507, 18.9825187935021,
|
||
19.5144462609601, 19.8256857844223, 20.5174677102032, 21.1122407995892,
|
||
17.9490854922535, 18.2847521114727, 20.1072217648826, 18.6439891962179,
|
||
20.4970638083542, 19.5567594734914]
|
||
|
||
Y = [19.2790668029091, 16.993808441865, 18.5416338448258, 17.2634018833575,
|
||
19.1577183624616, 18.5119655377495, 18.6068455037221, 18.8358343362655,
|
||
19.0366413269742, 18.1135025515417, 19.2201873866958, 17.8344909022841,
|
||
18.2894380745856, 18.6661374133922, 19.9688601693252, 16.0672254617636,
|
||
19.00596360572, 19.201561539032, 19.0487501090183, 19.0847908674356]
|
||
|
||
significant = 14
|
||
|
||
def test_mannwhitneyu_one_sided(self):
|
||
u1, p1 = stats.mannwhitneyu(self.X, self.Y, alternative='less')
|
||
u2, p2 = stats.mannwhitneyu(self.Y, self.X, alternative='greater')
|
||
u3, p3 = stats.mannwhitneyu(self.X, self.Y, alternative='greater')
|
||
u4, p4 = stats.mannwhitneyu(self.Y, self.X, alternative='less')
|
||
|
||
assert_equal(p1, p2)
|
||
assert_equal(p3, p4)
|
||
assert_(p1 != p3)
|
||
assert_equal(u1, 498)
|
||
assert_equal(u2, 102)
|
||
assert_equal(u3, 498)
|
||
assert_equal(u4, 102)
|
||
assert_approx_equal(p1, 0.999957683256589, significant=self.significant)
|
||
assert_approx_equal(p3, 4.5941632666275e-05, significant=self.significant)
|
||
|
||
def test_mannwhitneyu_two_sided(self):
|
||
u1, p1 = stats.mannwhitneyu(self.X, self.Y, alternative='two-sided')
|
||
u2, p2 = stats.mannwhitneyu(self.Y, self.X, alternative='two-sided')
|
||
|
||
assert_equal(p1, p2)
|
||
assert_equal(u1, 498)
|
||
assert_equal(u2, 102)
|
||
assert_approx_equal(p1, 9.188326533255e-05,
|
||
significant=self.significant)
|
||
|
||
def test_mannwhitneyu_no_correct_one_sided(self):
|
||
u1, p1 = stats.mannwhitneyu(self.X, self.Y, False,
|
||
alternative='less')
|
||
u2, p2 = stats.mannwhitneyu(self.Y, self.X, False,
|
||
alternative='greater')
|
||
u3, p3 = stats.mannwhitneyu(self.X, self.Y, False,
|
||
alternative='greater')
|
||
u4, p4 = stats.mannwhitneyu(self.Y, self.X, False,
|
||
alternative='less')
|
||
|
||
assert_equal(p1, p2)
|
||
assert_equal(p3, p4)
|
||
assert_(p1 != p3)
|
||
assert_equal(u1, 498)
|
||
assert_equal(u2, 102)
|
||
assert_equal(u3, 498)
|
||
assert_equal(u4, 102)
|
||
assert_approx_equal(p1, 0.999955905990004, significant=self.significant)
|
||
assert_approx_equal(p3, 4.40940099958089e-05, significant=self.significant)
|
||
|
||
def test_mannwhitneyu_no_correct_two_sided(self):
|
||
u1, p1 = stats.mannwhitneyu(self.X, self.Y, False,
|
||
alternative='two-sided')
|
||
u2, p2 = stats.mannwhitneyu(self.Y, self.X, False,
|
||
alternative='two-sided')
|
||
|
||
assert_equal(p1, p2)
|
||
assert_equal(u1, 498)
|
||
assert_equal(u2, 102)
|
||
assert_approx_equal(p1, 8.81880199916178e-05,
|
||
significant=self.significant)
|
||
|
||
def test_mannwhitneyu_ones(self):
|
||
# test for gh-1428
|
||
x = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 2.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1.])
|
||
|
||
y = np.array([1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1.,
|
||
2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2., 1., 1., 3.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,
|
||
1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,
|
||
2., 1., 1., 2., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2.,
|
||
2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
1., 2., 1., 1., 1., 1., 1., 2., 2., 2., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
||
2., 1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,
|
||
1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 2., 1., 1.,
|
||
1., 1., 1., 1.])
|
||
|
||
# checked against R wilcox.test
|
||
assert_allclose(stats.mannwhitneyu(x, y, alternative='less'),
|
||
(16980.5, 2.8214327656317373e-005))
|
||
# p-value from R, e.g. wilcox.test(x, y, alternative="g")
|
||
assert_allclose(stats.mannwhitneyu(x, y, alternative='greater'),
|
||
(16980.5, 0.9999719954296))
|
||
assert_allclose(stats.mannwhitneyu(x, y, alternative='two-sided'),
|
||
(16980.5, 5.642865531266e-05))
|
||
|
||
def test_mannwhitneyu_result_attributes(self):
|
||
# test for namedtuple attribute results
|
||
attributes = ('statistic', 'pvalue')
|
||
res = stats.mannwhitneyu(self.X, self.Y, alternative="less")
|
||
check_named_results(res, attributes)
|
||
|
||
|
||
def test_pointbiserial():
|
||
# same as mstats test except for the nan
|
||
# Test data: https://web.archive.org/web/20060504220742/https://support.sas.com/ctx/samples/index.jsp?sid=490&tab=output
|
||
x = [1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,
|
||
0,0,0,0,1]
|
||
y = [14.8,13.8,12.4,10.1,7.1,6.1,5.8,4.6,4.3,3.5,3.3,3.2,3.0,
|
||
2.8,2.8,2.5,2.4,2.3,2.1,1.7,1.7,1.5,1.3,1.3,1.2,1.2,1.1,
|
||
0.8,0.7,0.6,0.5,0.2,0.2,0.1]
|
||
assert_almost_equal(stats.pointbiserialr(x, y)[0], 0.36149, 5)
|
||
|
||
# test for namedtuple attribute results
|
||
attributes = ('correlation', 'pvalue')
|
||
res = stats.pointbiserialr(x, y)
|
||
check_named_results(res, attributes)
|
||
assert_equal(res.correlation, res.statistic)
|
||
|
||
|
||
def test_obrientransform():
|
||
# A couple tests calculated by hand.
|
||
x1 = np.array([0, 2, 4])
|
||
t1 = stats.obrientransform(x1)
|
||
expected = [7, -2, 7]
|
||
assert_allclose(t1[0], expected)
|
||
|
||
x2 = np.array([0, 3, 6, 9])
|
||
t2 = stats.obrientransform(x2)
|
||
expected = np.array([30, 0, 0, 30])
|
||
assert_allclose(t2[0], expected)
|
||
|
||
# Test two arguments.
|
||
a, b = stats.obrientransform(x1, x2)
|
||
assert_equal(a, t1[0])
|
||
assert_equal(b, t2[0])
|
||
|
||
# Test three arguments.
|
||
a, b, c = stats.obrientransform(x1, x2, x1)
|
||
assert_equal(a, t1[0])
|
||
assert_equal(b, t2[0])
|
||
assert_equal(c, t1[0])
|
||
|
||
# This is a regression test to check np.var replacement.
|
||
# The author of this test didn't separately verify the numbers.
|
||
x1 = np.arange(5)
|
||
result = np.array(
|
||
[[5.41666667, 1.04166667, -0.41666667, 1.04166667, 5.41666667],
|
||
[21.66666667, 4.16666667, -1.66666667, 4.16666667, 21.66666667]])
|
||
assert_array_almost_equal(stats.obrientransform(x1, 2*x1), result, decimal=8)
|
||
|
||
# Example from "O'Brien Test for Homogeneity of Variance"
|
||
# by Herve Abdi.
|
||
values = range(5, 11)
|
||
reps = np.array([5, 11, 9, 3, 2, 2])
|
||
data = np.repeat(values, reps)
|
||
transformed_values = np.array([3.1828, 0.5591, 0.0344,
|
||
1.6086, 5.2817, 11.0538])
|
||
expected = np.repeat(transformed_values, reps)
|
||
result = stats.obrientransform(data)
|
||
assert_array_almost_equal(result[0], expected, decimal=4)
|
||
|
||
|
||
def check_equal_gmean(array_like, desired, axis=None, dtype=None, rtol=1e-7,
|
||
weights=None):
|
||
# Note this doesn't test when axis is not specified
|
||
x = stats.gmean(array_like, axis=axis, dtype=dtype, weights=weights)
|
||
assert_allclose(x, desired, rtol=rtol)
|
||
assert_equal(x.dtype, dtype)
|
||
|
||
|
||
def check_equal_hmean(array_like, desired, axis=None, dtype=None, rtol=1e-7,
|
||
weights=None):
|
||
x = stats.hmean(array_like, axis=axis, dtype=dtype, weights=weights)
|
||
assert_allclose(x, desired, rtol=rtol)
|
||
assert_equal(x.dtype, dtype)
|
||
|
||
|
||
def check_equal_pmean(array_like, exp, desired, axis=None, dtype=None,
|
||
rtol=1e-7, weights=None):
|
||
x = stats.pmean(array_like, exp, axis=axis, dtype=dtype, weights=weights)
|
||
assert_allclose(x, desired, rtol=rtol)
|
||
assert_equal(x.dtype, dtype)
|
||
|
||
|
||
class TestHarMean:
|
||
def test_0(self):
|
||
a = [1, 0, 2]
|
||
desired = 0
|
||
check_equal_hmean(a, desired)
|
||
|
||
def test_1d_list(self):
|
||
# Test a 1d list
|
||
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
|
||
desired = 34.1417152147
|
||
check_equal_hmean(a, desired)
|
||
|
||
a = [1, 2, 3, 4]
|
||
desired = 4. / (1. / 1 + 1. / 2 + 1. / 3 + 1. / 4)
|
||
check_equal_hmean(a, desired)
|
||
|
||
def test_1d_array(self):
|
||
# Test a 1d array
|
||
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
|
||
desired = 34.1417152147
|
||
check_equal_hmean(a, desired)
|
||
|
||
def test_1d_array_with_zero(self):
|
||
a = np.array([1, 0])
|
||
desired = 0.0
|
||
assert_equal(stats.hmean(a), desired)
|
||
|
||
def test_1d_array_with_negative_value(self):
|
||
a = np.array([1, 0, -1])
|
||
assert_raises(ValueError, stats.hmean, a)
|
||
|
||
# Note the next tests use axis=None as default, not axis=0
|
||
def test_2d_list(self):
|
||
# Test a 2d list
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = 38.6696271841
|
||
check_equal_hmean(a, desired)
|
||
|
||
def test_2d_array(self):
|
||
# Test a 2d array
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = 38.6696271841
|
||
check_equal_hmean(np.array(a), desired)
|
||
|
||
def test_2d_axis0(self):
|
||
# Test a 2d list with axis=0
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([22.88135593, 39.13043478, 52.90076336, 65.45454545])
|
||
check_equal_hmean(a, desired, axis=0)
|
||
|
||
def test_2d_axis0_with_zero(self):
|
||
a = [[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([22.88135593, 0.0, 52.90076336, 65.45454545])
|
||
assert_allclose(stats.hmean(a, axis=0), desired)
|
||
|
||
def test_2d_axis1(self):
|
||
# Test a 2d list with axis=1
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([19.2, 63.03939962, 103.80078637])
|
||
check_equal_hmean(a, desired, axis=1)
|
||
|
||
def test_2d_axis1_with_zero(self):
|
||
a = [[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([0.0, 63.03939962, 103.80078637])
|
||
assert_allclose(stats.hmean(a, axis=1), desired)
|
||
|
||
def test_weights_1d_list(self):
|
||
# Desired result from:
|
||
# https://www.hackmath.net/en/math-problem/35871
|
||
a = [2, 10, 6]
|
||
weights = [10, 5, 3]
|
||
desired = 3
|
||
check_equal_hmean(a, desired, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_2d_array_axis0(self):
|
||
# Desired result from:
|
||
# https://www.hackmath.net/en/math-problem/35871
|
||
a = np.array([[2, 5], [10, 5], [6, 5]])
|
||
weights = np.array([[10, 1], [5, 1], [3, 1]])
|
||
desired = np.array([3, 5])
|
||
check_equal_hmean(a, desired, axis=0, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_2d_array_axis1(self):
|
||
# Desired result from:
|
||
# https://www.hackmath.net/en/math-problem/35871
|
||
a = np.array([[2, 10, 6], [7, 7, 7]])
|
||
weights = np.array([[10, 5, 3], [1, 1, 1]])
|
||
desired = np.array([3, 7])
|
||
check_equal_hmean(a, desired, axis=1, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_masked_1d_array(self):
|
||
# Desired result from:
|
||
# https://www.hackmath.net/en/math-problem/35871
|
||
a = np.array([2, 10, 6, 42])
|
||
weights = np.ma.array([10, 5, 3, 42], mask=[0, 0, 0, 1])
|
||
desired = 3
|
||
check_equal_hmean(a, desired, weights=weights, rtol=1e-5)
|
||
|
||
|
||
class TestGeoMean:
|
||
def test_0(self):
|
||
a = [1, 0, 2]
|
||
desired = 0
|
||
check_equal_gmean(a, desired)
|
||
|
||
def test_1d_list(self):
|
||
# Test a 1d list
|
||
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
|
||
desired = 45.2872868812
|
||
check_equal_gmean(a, desired)
|
||
|
||
a = [1, 2, 3, 4]
|
||
desired = power(1 * 2 * 3 * 4, 1. / 4.)
|
||
check_equal_gmean(a, desired, rtol=1e-14)
|
||
|
||
def test_1d_array(self):
|
||
# Test a 1d array
|
||
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
|
||
desired = 45.2872868812
|
||
check_equal_gmean(a, desired)
|
||
|
||
a = array([1, 2, 3, 4], float32)
|
||
desired = power(1 * 2 * 3 * 4, 1. / 4.)
|
||
check_equal_gmean(a, desired, dtype=float32)
|
||
|
||
# Note the next tests use axis=None as default, not axis=0
|
||
def test_2d_list(self):
|
||
# Test a 2d list
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = 52.8885199
|
||
check_equal_gmean(a, desired)
|
||
|
||
def test_2d_array(self):
|
||
# Test a 2d array
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = 52.8885199
|
||
check_equal_gmean(array(a), desired)
|
||
|
||
def test_2d_axis0(self):
|
||
# Test a 2d list with axis=0
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([35.56893304, 49.32424149, 61.3579244, 72.68482371])
|
||
check_equal_gmean(a, desired, axis=0)
|
||
|
||
a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
|
||
desired = array([1, 2, 3, 4])
|
||
check_equal_gmean(a, desired, axis=0, rtol=1e-14)
|
||
|
||
def test_2d_axis1(self):
|
||
# Test a 2d list with axis=1
|
||
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
|
||
desired = np.array([22.13363839, 64.02171746, 104.40086817])
|
||
check_equal_gmean(a, desired, axis=1)
|
||
|
||
a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
|
||
v = power(1 * 2 * 3 * 4, 1. / 4.)
|
||
desired = array([v, v, v])
|
||
check_equal_gmean(a, desired, axis=1, rtol=1e-14)
|
||
|
||
def test_large_values(self):
|
||
a = array([1e100, 1e200, 1e300])
|
||
desired = 1e200
|
||
check_equal_gmean(a, desired, rtol=1e-13)
|
||
|
||
def test_1d_list0(self):
|
||
# Test a 1d list with zero element
|
||
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 0]
|
||
desired = 0.0 # due to exp(-inf)=0
|
||
with np.errstate(all='ignore'):
|
||
check_equal_gmean(a, desired)
|
||
|
||
def test_1d_array0(self):
|
||
# Test a 1d array with zero element
|
||
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 0])
|
||
desired = 0.0 # due to exp(-inf)=0
|
||
with np.errstate(divide='ignore'):
|
||
check_equal_gmean(a, desired)
|
||
|
||
def test_1d_list_neg(self):
|
||
# Test a 1d list with negative element
|
||
a = [10, 20, 30, 40, 50, 60, 70, 80, 90, -1]
|
||
desired = np.nan # due to log(-1) = nan
|
||
with np.errstate(invalid='ignore'):
|
||
check_equal_gmean(a, desired)
|
||
|
||
def test_weights_1d_list(self):
|
||
# Desired result from:
|
||
# https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
|
||
a = [1, 2, 3, 4, 5]
|
||
weights = [2, 5, 6, 4, 3]
|
||
desired = 2.77748
|
||
check_equal_gmean(a, desired, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_1d_array(self):
|
||
# Desired result from:
|
||
# https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
|
||
a = np.array([1, 2, 3, 4, 5])
|
||
weights = np.array([2, 5, 6, 4, 3])
|
||
desired = 2.77748
|
||
check_equal_gmean(a, desired, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_masked_1d_array(self):
|
||
# Desired result from:
|
||
# https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
|
||
a = np.array([1, 2, 3, 4, 5, 6])
|
||
weights = np.ma.array([2, 5, 6, 4, 3, 5], mask=[0, 0, 0, 0, 0, 1])
|
||
desired = 2.77748
|
||
check_equal_gmean(a, desired, weights=weights, rtol=1e-5)
|
||
|
||
|
||
class TestPowMean:
|
||
|
||
def pmean_reference(a, p):
|
||
return (np.sum(a**p) / a.size)**(1/p)
|
||
|
||
def wpmean_reference(a, p, weights):
|
||
return (np.sum(weights * a**p) / np.sum(weights))**(1/p)
|
||
|
||
def test_bad_exponent(self):
|
||
with pytest.raises(ValueError, match='Power mean only defined for'):
|
||
stats.pmean([1, 2, 3], [0])
|
||
with pytest.raises(ValueError, match='Power mean only defined for'):
|
||
stats.pmean([1, 2, 3], np.array([0]))
|
||
|
||
def test_1d_list(self):
|
||
a, p = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 3.5
|
||
desired = TestPowMean.pmean_reference(np.array(a), p)
|
||
check_equal_pmean(a, p, desired)
|
||
|
||
a, p = [1, 2, 3, 4], 2
|
||
desired = np.sqrt((1**2 + 2**2 + 3**2 + 4**2) / 4)
|
||
check_equal_pmean(a, p, desired)
|
||
|
||
def test_1d_array(self):
|
||
a, p = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]), -2.5
|
||
desired = TestPowMean.pmean_reference(a, p)
|
||
check_equal_pmean(a, p, desired)
|
||
|
||
def test_1d_array_with_zero(self):
|
||
a, p = np.array([1, 0]), -1
|
||
desired = 0.0
|
||
assert_equal(stats.pmean(a, p), desired)
|
||
|
||
def test_1d_array_with_negative_value(self):
|
||
a, p = np.array([1, 0, -1]), 1.23
|
||
with pytest.raises(ValueError, match='Power mean only defined if all'):
|
||
stats.pmean(a, p)
|
||
|
||
@pytest.mark.parametrize(
|
||
("a", "p"),
|
||
[([[10, 20], [50, 60], [90, 100]], -0.5),
|
||
(np.array([[10, 20], [50, 60], [90, 100]]), 0.5)]
|
||
)
|
||
def test_2d_axisnone(self, a, p):
|
||
desired = TestPowMean.pmean_reference(np.array(a), p)
|
||
check_equal_pmean(a, p, desired)
|
||
|
||
@pytest.mark.parametrize(
|
||
("a", "p"),
|
||
[([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], -0.5),
|
||
([[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], 0.5)]
|
||
)
|
||
def test_2d_list_axis0(self, a, p):
|
||
desired = [
|
||
TestPowMean.pmean_reference(
|
||
np.array([a[i][j] for i in range(len(a))]), p
|
||
)
|
||
for j in range(len(a[0]))
|
||
]
|
||
check_equal_pmean(a, p, desired, axis=0)
|
||
|
||
@pytest.mark.parametrize(
|
||
("a", "p"),
|
||
[([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], -0.5),
|
||
([[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], 0.5)]
|
||
)
|
||
def test_2d_list_axis1(self, a, p):
|
||
desired = [TestPowMean.pmean_reference(np.array(a_), p) for a_ in a]
|
||
check_equal_pmean(a, p, desired, axis=1)
|
||
|
||
def test_weights_1d_list(self):
|
||
a, p = [2, 10, 6], -1.23456789
|
||
weights = [10, 5, 3]
|
||
desired = TestPowMean.wpmean_reference(np.array(a), p, weights)
|
||
check_equal_pmean(a, p, desired, weights=weights, rtol=1e-5)
|
||
|
||
def test_weights_masked_1d_array(self):
|
||
a, p = np.array([2, 10, 6, 42]), 1
|
||
weights = np.ma.array([10, 5, 3, 42], mask=[0, 0, 0, 1])
|
||
desired = np.average(a, weights=weights)
|
||
check_equal_pmean(a, p, desired, weights=weights, rtol=1e-5)
|
||
|
||
@pytest.mark.parametrize(
|
||
("axis", "fun_name", "p"),
|
||
[(None, "wpmean_reference", 9.87654321),
|
||
(0, "gmean", 0),
|
||
(1, "hmean", -1)]
|
||
)
|
||
def test_weights_2d_array(self, axis, fun_name, p):
|
||
if fun_name == 'wpmean_reference':
|
||
def fun(a, axis, weights):
|
||
return TestPowMean.wpmean_reference(a, p, weights)
|
||
else:
|
||
fun = getattr(stats, fun_name)
|
||
a = np.array([[2, 5], [10, 5], [6, 5]])
|
||
weights = np.array([[10, 1], [5, 1], [3, 1]])
|
||
desired = fun(a, axis=axis, weights=weights)
|
||
check_equal_pmean(a, p, desired, axis=axis, weights=weights, rtol=1e-5)
|
||
|
||
|
||
class TestGeometricStandardDeviation:
|
||
# must add 1 as `gstd` is only defined for positive values
|
||
array_1d = np.arange(2 * 3 * 4) + 1
|
||
gstd_array_1d = 2.294407613602
|
||
array_3d = array_1d.reshape(2, 3, 4)
|
||
|
||
def test_1d_array(self):
|
||
gstd_actual = stats.gstd(self.array_1d)
|
||
assert_allclose(gstd_actual, self.gstd_array_1d)
|
||
|
||
def test_1d_numeric_array_like_input(self):
|
||
gstd_actual = stats.gstd(tuple(self.array_1d))
|
||
assert_allclose(gstd_actual, self.gstd_array_1d)
|
||
|
||
def test_raises_value_error_non_array_like_input(self):
|
||
with pytest.raises(ValueError, match='Invalid array input'):
|
||
stats.gstd('This should fail as it can not be cast to an array.')
|
||
|
||
def test_raises_value_error_zero_entry(self):
|
||
with pytest.raises(ValueError, match='Non positive value'):
|
||
stats.gstd(np.append(self.array_1d, [0]))
|
||
|
||
def test_raises_value_error_negative_entry(self):
|
||
with pytest.raises(ValueError, match='Non positive value'):
|
||
stats.gstd(np.append(self.array_1d, [-1]))
|
||
|
||
def test_raises_value_error_inf_entry(self):
|
||
with pytest.raises(ValueError, match='Infinite value'):
|
||
stats.gstd(np.append(self.array_1d, [np.inf]))
|
||
|
||
def test_propagates_nan_values(self):
|
||
a = array([[1, 1, 1, 16], [np.nan, 1, 2, 3]])
|
||
gstd_actual = stats.gstd(a, axis=1)
|
||
assert_allclose(gstd_actual, np.array([4, np.nan]))
|
||
|
||
def test_ddof_equal_to_number_of_observations(self):
|
||
with pytest.raises(ValueError, match='Degrees of freedom <= 0'):
|
||
stats.gstd(self.array_1d, ddof=self.array_1d.size)
|
||
|
||
def test_3d_array(self):
|
||
gstd_actual = stats.gstd(self.array_3d, axis=None)
|
||
assert_allclose(gstd_actual, self.gstd_array_1d)
|
||
|
||
def test_3d_array_axis_type_tuple(self):
|
||
gstd_actual = stats.gstd(self.array_3d, axis=(1,2))
|
||
assert_allclose(gstd_actual, [2.12939215, 1.22120169])
|
||
|
||
def test_3d_array_axis_0(self):
|
||
gstd_actual = stats.gstd(self.array_3d, axis=0)
|
||
gstd_desired = np.array([
|
||
[6.1330555493918, 3.958900210120, 3.1206598248344, 2.6651441426902],
|
||
[2.3758135028411, 2.174581428192, 2.0260062829505, 1.9115518327308],
|
||
[1.8205343606803, 1.746342404566, 1.6846557065742, 1.6325269194382]
|
||
])
|
||
assert_allclose(gstd_actual, gstd_desired)
|
||
|
||
def test_3d_array_axis_1(self):
|
||
gstd_actual = stats.gstd(self.array_3d, axis=1)
|
||
gstd_desired = np.array([
|
||
[3.118993630946, 2.275985934063, 1.933995977619, 1.742896469724],
|
||
[1.271693593916, 1.254158641801, 1.238774141609, 1.225164057869]
|
||
])
|
||
assert_allclose(gstd_actual, gstd_desired)
|
||
|
||
def test_3d_array_axis_2(self):
|
||
gstd_actual = stats.gstd(self.array_3d, axis=2)
|
||
gstd_desired = np.array([
|
||
[1.8242475707664, 1.2243686572447, 1.1318311657788],
|
||
[1.0934830582351, 1.0724479791887, 1.0591498540749]
|
||
])
|
||
assert_allclose(gstd_actual, gstd_desired)
|
||
|
||
def test_masked_3d_array(self):
|
||
ma = np.ma.masked_where(self.array_3d > 16, self.array_3d)
|
||
gstd_actual = stats.gstd(ma, axis=2)
|
||
gstd_desired = stats.gstd(self.array_3d, axis=2)
|
||
mask = [[0, 0, 0], [0, 1, 1]]
|
||
assert_allclose(gstd_actual, gstd_desired)
|
||
assert_equal(gstd_actual.mask, mask)
|
||
|
||
|
||
def test_binomtest():
|
||
# precision tests compared to R for ticket:986
|
||
pp = np.concatenate((np.linspace(0.1, 0.2, 5),
|
||
np.linspace(0.45, 0.65, 5),
|
||
np.linspace(0.85, 0.95, 5)))
|
||
n = 501
|
||
x = 450
|
||
results = [0.0, 0.0, 1.0159969301994141e-304,
|
||
2.9752418572150531e-275, 7.7668382922535275e-250,
|
||
2.3381250925167094e-099, 7.8284591587323951e-081,
|
||
9.9155947819961383e-065, 2.8729390725176308e-050,
|
||
1.7175066298388421e-037, 0.0021070691951093692,
|
||
0.12044570587262322, 0.88154763174802508, 0.027120993063129286,
|
||
2.6102587134694721e-006]
|
||
|
||
for p, res in zip(pp, results):
|
||
assert_approx_equal(stats.binomtest(x, n, p).pvalue, res,
|
||
significant=12, err_msg='fail forp=%f' % p)
|
||
assert_approx_equal(stats.binomtest(50, 100, 0.1).pvalue,
|
||
5.8320387857343647e-024,
|
||
significant=12)
|
||
|
||
|
||
def test_binomtest2():
|
||
# test added for issue #2384
|
||
res2 = [
|
||
[1.0, 1.0],
|
||
[0.5, 1.0, 0.5],
|
||
[0.25, 1.00, 1.00, 0.25],
|
||
[0.125, 0.625, 1.000, 0.625, 0.125],
|
||
[0.0625, 0.3750, 1.0000, 1.0000, 0.3750, 0.0625],
|
||
[0.03125, 0.21875, 0.68750, 1.00000, 0.68750, 0.21875, 0.03125],
|
||
[0.015625, 0.125000, 0.453125, 1.000000, 1.000000, 0.453125, 0.125000,
|
||
0.015625],
|
||
[0.0078125, 0.0703125, 0.2890625, 0.7265625, 1.0000000, 0.7265625,
|
||
0.2890625, 0.0703125, 0.0078125],
|
||
[0.00390625, 0.03906250, 0.17968750, 0.50781250, 1.00000000,
|
||
1.00000000, 0.50781250, 0.17968750, 0.03906250, 0.00390625],
|
||
[0.001953125, 0.021484375, 0.109375000, 0.343750000, 0.753906250,
|
||
1.000000000, 0.753906250, 0.343750000, 0.109375000, 0.021484375,
|
||
0.001953125]
|
||
]
|
||
for k in range(1, 11):
|
||
res1 = [stats.binomtest(v, k, 0.5).pvalue for v in range(k + 1)]
|
||
assert_almost_equal(res1, res2[k-1], decimal=10)
|
||
|
||
|
||
def test_binomtest3():
|
||
# test added for issue #2384
|
||
# test when x == n*p and neighbors
|
||
res3 = [stats.binomtest(v, v*k, 1./k).pvalue
|
||
for v in range(1, 11) for k in range(2, 11)]
|
||
assert_equal(res3, np.ones(len(res3), int))
|
||
|
||
# > bt=c()
|
||
# > for(i in as.single(1:10)) {
|
||
# + for(k in as.single(2:10)) {
|
||
# + bt = c(bt, binom.test(i-1, k*i,(1/k))$p.value);
|
||
# + print(c(i+1, k*i,(1/k)))
|
||
# + }
|
||
# + }
|
||
binom_testm1 = np.array([
|
||
0.5, 0.5555555555555556, 0.578125, 0.5904000000000003,
|
||
0.5981224279835393, 0.603430543396034, 0.607304096221924,
|
||
0.610255656871054, 0.612579511000001, 0.625, 0.670781893004115,
|
||
0.68853759765625, 0.6980101120000006, 0.703906431368616,
|
||
0.70793209416498, 0.7108561134173507, 0.713076544331419,
|
||
0.714820192935702, 0.6875, 0.7268709038256367, 0.7418963909149174,
|
||
0.74986110468096, 0.7548015520398076, 0.7581671424768577,
|
||
0.760607984787832, 0.762459425024199, 0.7639120677676575, 0.7265625,
|
||
0.761553963657302, 0.774800934828818, 0.7818005980538996,
|
||
0.78613491480358, 0.789084353140195, 0.7912217659828884,
|
||
0.79284214559524, 0.794112956558801, 0.75390625, 0.7856929451142176,
|
||
0.7976688481430754, 0.8039848974727624, 0.807891868948366,
|
||
0.8105487660137676, 0.812473307174702, 0.8139318233591120,
|
||
0.815075399104785, 0.7744140625, 0.8037322594985427,
|
||
0.814742863657656, 0.8205425178645808, 0.8241275984172285,
|
||
0.8265645374416, 0.8283292196088257, 0.829666291102775,
|
||
0.8307144686362666, 0.7905273437499996, 0.8178712053954738,
|
||
0.828116983756619, 0.833508948940494, 0.8368403871552892,
|
||
0.839104213210105, 0.840743186196171, 0.84198481438049,
|
||
0.8429580531563676, 0.803619384765625, 0.829338573944648,
|
||
0.8389591907548646, 0.84401876783902, 0.84714369697889,
|
||
0.8492667010581667, 0.850803474598719, 0.851967542858308,
|
||
0.8528799045949524, 0.8145294189453126, 0.838881732845347,
|
||
0.847979024541911, 0.852760894015685, 0.8557134656773457,
|
||
0.8577190131799202, 0.85917058278431, 0.860270010472127,
|
||
0.861131648404582, 0.823802947998047, 0.846984756807511,
|
||
0.855635653643743, 0.860180994825685, 0.86298688573253,
|
||
0.864892525675245, 0.866271647085603, 0.867316125625004,
|
||
0.8681346531755114
|
||
])
|
||
|
||
# > bt=c()
|
||
# > for(i in as.single(1:10)) {
|
||
# + for(k in as.single(2:10)) {
|
||
# + bt = c(bt, binom.test(i+1, k*i,(1/k))$p.value);
|
||
# + print(c(i+1, k*i,(1/k)))
|
||
# + }
|
||
# + }
|
||
|
||
binom_testp1 = np.array([
|
||
0.5, 0.259259259259259, 0.26171875, 0.26272, 0.2632244513031551,
|
||
0.2635138663069203, 0.2636951804161073, 0.2638162407564354,
|
||
0.2639010709000002, 0.625, 0.4074074074074074, 0.42156982421875,
|
||
0.4295746560000003, 0.43473045988554, 0.4383309503172684,
|
||
0.4409884859402103, 0.4430309389962837, 0.444649849401104, 0.6875,
|
||
0.4927602499618962, 0.5096031427383425, 0.5189636628480,
|
||
0.5249280070771274, 0.5290623300865124, 0.5320974248125793,
|
||
0.5344204730474308, 0.536255847400756, 0.7265625, 0.5496019313526808,
|
||
0.5669248746708034, 0.576436455045805, 0.5824538812831795,
|
||
0.5866053321547824, 0.589642781414643, 0.5919618019300193,
|
||
0.593790427805202, 0.75390625, 0.590868349763505, 0.607983393277209,
|
||
0.617303847446822, 0.623172512167948, 0.627208862156123,
|
||
0.6301556891501057, 0.632401894928977, 0.6341708982290303,
|
||
0.7744140625, 0.622562037497196, 0.639236102912278, 0.648263335014579,
|
||
0.65392850011132, 0.657816519817211, 0.660650782947676,
|
||
0.662808780346311, 0.6645068560246006, 0.7905273437499996,
|
||
0.6478843304312477, 0.6640468318879372, 0.6727589686071775,
|
||
0.6782129857784873, 0.681950188903695, 0.684671508668418,
|
||
0.686741824999918, 0.688369886732168, 0.803619384765625,
|
||
0.668716055304315, 0.684360013879534, 0.6927642396829181,
|
||
0.6980155964704895, 0.701609591890657, 0.7042244320992127,
|
||
0.7062125081341817, 0.707775152962577, 0.8145294189453126,
|
||
0.686243374488305, 0.7013873696358975, 0.709501223328243,
|
||
0.714563595144314, 0.718024953392931, 0.7205416252126137,
|
||
0.722454130389843, 0.723956813292035, 0.823802947998047,
|
||
0.701255953767043, 0.715928221686075, 0.723772209289768,
|
||
0.7286603031173616, 0.7319999279787631, 0.7344267920995765,
|
||
0.736270323773157, 0.737718376096348
|
||
])
|
||
|
||
res4_p1 = [stats.binomtest(v+1, v*k, 1./k).pvalue
|
||
for v in range(1, 11) for k in range(2, 11)]
|
||
res4_m1 = [stats.binomtest(v-1, v*k, 1./k).pvalue
|
||
for v in range(1, 11) for k in range(2, 11)]
|
||
|
||
assert_almost_equal(res4_p1, binom_testp1, decimal=13)
|
||
assert_almost_equal(res4_m1, binom_testm1, decimal=13)
|
||
|
||
|
||
class TestTrim:
|
||
# test trim functions
|
||
def test_trim1(self):
|
||
a = np.arange(11)
|
||
assert_equal(np.sort(stats.trim1(a, 0.1)), np.arange(10))
|
||
assert_equal(np.sort(stats.trim1(a, 0.2)), np.arange(9))
|
||
assert_equal(np.sort(stats.trim1(a, 0.2, tail='left')),
|
||
np.arange(2, 11))
|
||
assert_equal(np.sort(stats.trim1(a, 3/11., tail='left')),
|
||
np.arange(3, 11))
|
||
assert_equal(stats.trim1(a, 1.0), [])
|
||
assert_equal(stats.trim1(a, 1.0, tail='left'), [])
|
||
|
||
# empty input
|
||
assert_equal(stats.trim1([], 0.1), [])
|
||
assert_equal(stats.trim1([], 3/11., tail='left'), [])
|
||
assert_equal(stats.trim1([], 4/6.), [])
|
||
|
||
# test axis
|
||
a = np.arange(24).reshape(6, 4)
|
||
ref = np.arange(4, 24).reshape(5, 4) # first row trimmed
|
||
|
||
axis = 0
|
||
trimmed = stats.trim1(a, 0.2, tail='left', axis=axis)
|
||
assert_equal(np.sort(trimmed, axis=axis), ref)
|
||
|
||
axis = 1
|
||
trimmed = stats.trim1(a.T, 0.2, tail='left', axis=axis)
|
||
assert_equal(np.sort(trimmed, axis=axis), ref.T)
|
||
|
||
def test_trimboth(self):
|
||
a = np.arange(11)
|
||
assert_equal(np.sort(stats.trimboth(a, 3/11.)), np.arange(3, 8))
|
||
assert_equal(np.sort(stats.trimboth(a, 0.2)),
|
||
np.array([2, 3, 4, 5, 6, 7, 8]))
|
||
assert_equal(np.sort(stats.trimboth(np.arange(24).reshape(6, 4), 0.2)),
|
||
np.arange(4, 20).reshape(4, 4))
|
||
assert_equal(np.sort(stats.trimboth(np.arange(24).reshape(4, 6).T,
|
||
2/6.)),
|
||
np.array([[2, 8, 14, 20], [3, 9, 15, 21]]))
|
||
assert_raises(ValueError, stats.trimboth,
|
||
np.arange(24).reshape(4, 6).T, 4/6.)
|
||
|
||
# empty input
|
||
assert_equal(stats.trimboth([], 0.1), [])
|
||
assert_equal(stats.trimboth([], 3/11.), [])
|
||
assert_equal(stats.trimboth([], 4/6.), [])
|
||
|
||
def test_trim_mean(self):
|
||
# don't use pre-sorted arrays
|
||
a = np.array([4, 8, 2, 0, 9, 5, 10, 1, 7, 3, 6])
|
||
idx = np.array([3, 5, 0, 1, 2, 4])
|
||
a2 = np.arange(24).reshape(6, 4)[idx, :]
|
||
a3 = np.arange(24).reshape(6, 4, order='F')[idx, :]
|
||
assert_equal(stats.trim_mean(a3, 2/6.),
|
||
np.array([2.5, 8.5, 14.5, 20.5]))
|
||
assert_equal(stats.trim_mean(a2, 2/6.),
|
||
np.array([10., 11., 12., 13.]))
|
||
idx4 = np.array([1, 0, 3, 2])
|
||
a4 = np.arange(24).reshape(4, 6)[idx4, :]
|
||
assert_equal(stats.trim_mean(a4, 2/6.),
|
||
np.array([9., 10., 11., 12., 13., 14.]))
|
||
# shuffled arange(24) as array_like
|
||
a = [7, 11, 12, 21, 16, 6, 22, 1, 5, 0, 18, 10, 17, 9, 19, 15, 23,
|
||
20, 2, 14, 4, 13, 8, 3]
|
||
assert_equal(stats.trim_mean(a, 2/6.), 11.5)
|
||
assert_equal(stats.trim_mean([5,4,3,1,2,0], 2/6.), 2.5)
|
||
|
||
# check axis argument
|
||
np.random.seed(1234)
|
||
a = np.random.randint(20, size=(5, 6, 4, 7))
|
||
for axis in [0, 1, 2, 3, -1]:
|
||
res1 = stats.trim_mean(a, 2/6., axis=axis)
|
||
res2 = stats.trim_mean(np.moveaxis(a, axis, 0), 2/6.)
|
||
assert_equal(res1, res2)
|
||
|
||
res1 = stats.trim_mean(a, 2/6., axis=None)
|
||
res2 = stats.trim_mean(a.ravel(), 2/6.)
|
||
assert_equal(res1, res2)
|
||
|
||
assert_raises(ValueError, stats.trim_mean, a, 0.6)
|
||
|
||
# empty input
|
||
assert_equal(stats.trim_mean([], 0.0), np.nan)
|
||
assert_equal(stats.trim_mean([], 0.6), np.nan)
|
||
|
||
|
||
class TestSigmaClip:
|
||
def test_sigmaclip1(self):
|
||
a = np.concatenate((np.linspace(9.5, 10.5, 31), np.linspace(0, 20, 5)))
|
||
fact = 4 # default
|
||
c, low, upp = stats.sigmaclip(a)
|
||
assert_(c.min() > low)
|
||
assert_(c.max() < upp)
|
||
assert_equal(low, c.mean() - fact*c.std())
|
||
assert_equal(upp, c.mean() + fact*c.std())
|
||
assert_equal(c.size, a.size)
|
||
|
||
def test_sigmaclip2(self):
|
||
a = np.concatenate((np.linspace(9.5, 10.5, 31), np.linspace(0, 20, 5)))
|
||
fact = 1.5
|
||
c, low, upp = stats.sigmaclip(a, fact, fact)
|
||
assert_(c.min() > low)
|
||
assert_(c.max() < upp)
|
||
assert_equal(low, c.mean() - fact*c.std())
|
||
assert_equal(upp, c.mean() + fact*c.std())
|
||
assert_equal(c.size, 4)
|
||
assert_equal(a.size, 36) # check original array unchanged
|
||
|
||
def test_sigmaclip3(self):
|
||
a = np.concatenate((np.linspace(9.5, 10.5, 11),
|
||
np.linspace(-100, -50, 3)))
|
||
fact = 1.8
|
||
c, low, upp = stats.sigmaclip(a, fact, fact)
|
||
assert_(c.min() > low)
|
||
assert_(c.max() < upp)
|
||
assert_equal(low, c.mean() - fact*c.std())
|
||
assert_equal(upp, c.mean() + fact*c.std())
|
||
assert_equal(c, np.linspace(9.5, 10.5, 11))
|
||
|
||
def test_sigmaclip_result_attributes(self):
|
||
a = np.concatenate((np.linspace(9.5, 10.5, 11),
|
||
np.linspace(-100, -50, 3)))
|
||
fact = 1.8
|
||
res = stats.sigmaclip(a, fact, fact)
|
||
attributes = ('clipped', 'lower', 'upper')
|
||
check_named_results(res, attributes)
|
||
|
||
def test_std_zero(self):
|
||
# regression test #8632
|
||
x = np.ones(10)
|
||
assert_equal(stats.sigmaclip(x)[0], x)
|
||
|
||
|
||
class TestAlexanderGovern:
|
||
def test_compare_dtypes(self):
|
||
args = [[13, 13, 13, 13, 13, 13, 13, 12, 12],
|
||
[14, 13, 12, 12, 12, 12, 12, 11, 11],
|
||
[14, 14, 13, 13, 13, 13, 13, 12, 12],
|
||
[15, 14, 13, 13, 13, 12, 12, 12, 11]]
|
||
args_int16 = np.array(args, dtype=np.int16)
|
||
args_int32 = np.array(args, dtype=np.int32)
|
||
args_uint8 = np.array(args, dtype=np.uint8)
|
||
args_float64 = np.array(args, dtype=np.float64)
|
||
|
||
res_int16 = stats.alexandergovern(*args_int16)
|
||
res_int32 = stats.alexandergovern(*args_int32)
|
||
res_unit8 = stats.alexandergovern(*args_uint8)
|
||
res_float64 = stats.alexandergovern(*args_float64)
|
||
|
||
assert (res_int16.pvalue == res_int32.pvalue ==
|
||
res_unit8.pvalue == res_float64.pvalue)
|
||
assert (res_int16.statistic == res_int32.statistic ==
|
||
res_unit8.statistic == res_float64.statistic)
|
||
|
||
def test_bad_inputs(self):
|
||
# input array is of size zero
|
||
with assert_raises(ValueError, match="Input sample size must be"
|
||
" greater than one."):
|
||
stats.alexandergovern([1, 2], [])
|
||
# input is a singular non list element
|
||
with assert_raises(ValueError, match="Input sample size must be"
|
||
" greater than one."):
|
||
stats.alexandergovern([1, 2], 2)
|
||
# input list is of size 1
|
||
with assert_raises(ValueError, match="Input sample size must be"
|
||
" greater than one."):
|
||
stats.alexandergovern([1, 2], [2])
|
||
# inputs are not finite (infinity)
|
||
with assert_raises(ValueError, match="Input samples must be finite."):
|
||
stats.alexandergovern([1, 2], [np.inf, np.inf])
|
||
|
||
def test_compare_r(self):
|
||
'''
|
||
Data generated in R with
|
||
> set.seed(1)
|
||
> library("onewaytests")
|
||
> library("tibble")
|
||
> y <- c(rnorm(40, sd=10),
|
||
+ rnorm(30, sd=15),
|
||
+ rnorm(20, sd=20))
|
||
> x <- c(rep("one", times=40),
|
||
+ rep("two", times=30),
|
||
+ rep("eight", times=20))
|
||
> x <- factor(x)
|
||
> ag.test(y ~ x, tibble(y,x))
|
||
|
||
Alexander-Govern Test (alpha = 0.05)
|
||
-------------------------------------------------------------
|
||
data : y and x
|
||
|
||
statistic : 1.359941
|
||
parameter : 2
|
||
p.value : 0.5066321
|
||
|
||
Result : Difference is not statistically significant.
|
||
-------------------------------------------------------------
|
||
Example adapted from:
|
||
https://eval-serv2.metpsy.uni-jena.de/wiki-metheval-hp/index.php/R_FUN_Alexander-Govern
|
||
|
||
'''
|
||
one = [-6.264538107423324, 1.8364332422208225, -8.356286124100471,
|
||
15.952808021377916, 3.295077718153605, -8.204683841180152,
|
||
4.874290524284853, 7.383247051292173, 5.757813516534923,
|
||
-3.0538838715635603, 15.11781168450848, 3.898432364114311,
|
||
-6.2124058054180376, -22.146998871774997, 11.249309181431082,
|
||
-0.4493360901523085, -0.16190263098946087, 9.438362106852992,
|
||
8.212211950980885, 5.939013212175088, 9.189773716082183,
|
||
7.821363007310671, 0.745649833651906, -19.89351695863373,
|
||
6.198257478947102, -0.5612873952900078, -1.557955067053293,
|
||
-14.707523838992744, -4.781500551086204, 4.179415601997024,
|
||
13.58679551529044, -1.0278772734299553, 3.876716115593691,
|
||
-0.5380504058290512, -13.770595568286065, -4.149945632996798,
|
||
-3.942899537103493, -0.5931339671118566, 11.000253719838831,
|
||
7.631757484575442]
|
||
|
||
two = [-2.4678539438038034, -3.8004252020476135, 10.454450631071062,
|
||
8.34994798010486, -10.331335418242798, -10.612427354431794,
|
||
5.468729432052455, 11.527993867731237, -1.6851931822534207,
|
||
13.216615896813222, 5.971588205506021, -9.180395898761569,
|
||
5.116795371366372, -16.94044644121189, 21.495355525515556,
|
||
29.7059984775879, -5.508322146997636, -15.662019394747961,
|
||
8.545794411636193, -2.0258190582123654, 36.024266407571645,
|
||
-0.5886000409975387, 10.346090436761651, 0.4200323817099909,
|
||
-11.14909813323608, 2.8318844927151434, -27.074379433365568,
|
||
21.98332292344329, 2.2988000731784655, 32.58917505543229]
|
||
|
||
eight = [9.510190577993251, -14.198928618436291, 12.214527069781099,
|
||
-18.68195263288503, -25.07266800478204, 5.828924710349257,
|
||
-8.86583746436866, 0.02210703263248262, 1.4868264830332811,
|
||
-11.79041892376144, -11.37337465637004, -2.7035723024766414,
|
||
23.56173993146409, -30.47133600859524, 11.878923752568431,
|
||
6.659007424270365, 21.261996745527256, -6.083678472686013,
|
||
7.400376198325763, 5.341975815444621]
|
||
soln = stats.alexandergovern(one, two, eight)
|
||
assert_allclose(soln.statistic, 1.3599405447999450836)
|
||
assert_allclose(soln.pvalue, 0.50663205309676440091)
|
||
|
||
def test_compare_scholar(self):
|
||
'''
|
||
Data taken from 'The Modification and Evaluation of the
|
||
Alexander-Govern Test in Terms of Power' by Kingsley Ochuko, T.,
|
||
Abdullah, S., Binti Zain, Z., & Soaad Syed Yahaya, S. (2015).
|
||
'''
|
||
young = [482.43, 484.36, 488.84, 495.15, 495.24, 502.69, 504.62,
|
||
518.29, 519.1, 524.1, 524.12, 531.18, 548.42, 572.1, 584.68,
|
||
609.09, 609.53, 666.63, 676.4]
|
||
middle = [335.59, 338.43, 353.54, 404.27, 437.5, 469.01, 485.85,
|
||
487.3, 493.08, 494.31, 499.1, 886.41]
|
||
old = [519.01, 528.5, 530.23, 536.03, 538.56, 538.83, 557.24, 558.61,
|
||
558.95, 565.43, 586.39, 594.69, 629.22, 645.69, 691.84]
|
||
soln = stats.alexandergovern(young, middle, old)
|
||
assert_allclose(soln.statistic, 5.3237, atol=1e-3)
|
||
assert_allclose(soln.pvalue, 0.06982, atol=1e-4)
|
||
|
||
# verify with ag.test in r
|
||
'''
|
||
> library("onewaytests")
|
||
> library("tibble")
|
||
> young <- c(482.43, 484.36, 488.84, 495.15, 495.24, 502.69, 504.62,
|
||
+ 518.29, 519.1, 524.1, 524.12, 531.18, 548.42, 572.1,
|
||
+ 584.68, 609.09, 609.53, 666.63, 676.4)
|
||
> middle <- c(335.59, 338.43, 353.54, 404.27, 437.5, 469.01, 485.85,
|
||
+ 487.3, 493.08, 494.31, 499.1, 886.41)
|
||
> old <- c(519.01, 528.5, 530.23, 536.03, 538.56, 538.83, 557.24,
|
||
+ 558.61, 558.95, 565.43, 586.39, 594.69, 629.22,
|
||
+ 645.69, 691.84)
|
||
> young_fct <- c(rep("young", times=19))
|
||
> middle_fct <-c(rep("middle", times=12))
|
||
> old_fct <- c(rep("old", times=15))
|
||
> ag.test(a ~ b, tibble(a=c(young, middle, old), b=factor(c(young_fct,
|
||
+ middle_fct, old_fct))))
|
||
|
||
Alexander-Govern Test (alpha = 0.05)
|
||
-------------------------------------------------------------
|
||
data : a and b
|
||
|
||
statistic : 5.324629
|
||
parameter : 2
|
||
p.value : 0.06978651
|
||
|
||
Result : Difference is not statistically significant.
|
||
-------------------------------------------------------------
|
||
|
||
'''
|
||
assert_allclose(soln.statistic, 5.324629)
|
||
assert_allclose(soln.pvalue, 0.06978651)
|
||
|
||
def test_compare_scholar3(self):
|
||
'''
|
||
Data taken from 'Robustness And Comparative Power Of WelchAspin,
|
||
Alexander-Govern And Yuen Tests Under Non-Normality And Variance
|
||
Heteroscedasticity', by Ayed A. Almoied. 2017. Page 34-37.
|
||
https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=2775&context=oa_dissertations
|
||
'''
|
||
x1 = [-1.77559, -1.4113, -0.69457, -0.54148, -0.18808, -0.07152,
|
||
0.04696, 0.051183, 0.148695, 0.168052, 0.422561, 0.458555,
|
||
0.616123, 0.709968, 0.839956, 0.857226, 0.929159, 0.981442,
|
||
0.999554, 1.642958]
|
||
x2 = [-1.47973, -1.2722, -0.91914, -0.80916, -0.75977, -0.72253,
|
||
-0.3601, -0.33273, -0.28859, -0.09637, -0.08969, -0.01824,
|
||
0.260131, 0.289278, 0.518254, 0.683003, 0.877618, 1.172475,
|
||
1.33964, 1.576766]
|
||
soln = stats.alexandergovern(x1, x2)
|
||
assert_allclose(soln.statistic, 0.713526, atol=1e-5)
|
||
assert_allclose(soln.pvalue, 0.398276, atol=1e-5)
|
||
|
||
'''
|
||
tested in ag.test in R:
|
||
> library("onewaytests")
|
||
> library("tibble")
|
||
> x1 <- c(-1.77559, -1.4113, -0.69457, -0.54148, -0.18808, -0.07152,
|
||
+ 0.04696, 0.051183, 0.148695, 0.168052, 0.422561, 0.458555,
|
||
+ 0.616123, 0.709968, 0.839956, 0.857226, 0.929159, 0.981442,
|
||
+ 0.999554, 1.642958)
|
||
> x2 <- c(-1.47973, -1.2722, -0.91914, -0.80916, -0.75977, -0.72253,
|
||
+ -0.3601, -0.33273, -0.28859, -0.09637, -0.08969, -0.01824,
|
||
+ 0.260131, 0.289278, 0.518254, 0.683003, 0.877618, 1.172475,
|
||
+ 1.33964, 1.576766)
|
||
> x1_fact <- c(rep("x1", times=20))
|
||
> x2_fact <- c(rep("x2", times=20))
|
||
> a <- c(x1, x2)
|
||
> b <- factor(c(x1_fact, x2_fact))
|
||
> ag.test(a ~ b, tibble(a, b))
|
||
Alexander-Govern Test (alpha = 0.05)
|
||
-------------------------------------------------------------
|
||
data : a and b
|
||
|
||
statistic : 0.7135182
|
||
parameter : 1
|
||
p.value : 0.3982783
|
||
|
||
Result : Difference is not statistically significant.
|
||
-------------------------------------------------------------
|
||
'''
|
||
assert_allclose(soln.statistic, 0.7135182)
|
||
assert_allclose(soln.pvalue, 0.3982783)
|
||
|
||
def test_nan_policy_propogate(self):
|
||
args = [[1, 2, 3, 4], [1, np.nan]]
|
||
# default nan_policy is 'propagate'
|
||
res = stats.alexandergovern(*args)
|
||
assert_equal(res.pvalue, np.nan)
|
||
assert_equal(res.statistic, np.nan)
|
||
|
||
def test_nan_policy_raise(self):
|
||
args = [[1, 2, 3, 4], [1, np.nan]]
|
||
with assert_raises(ValueError, match="The input contains nan values"):
|
||
stats.alexandergovern(*args, nan_policy='raise')
|
||
|
||
def test_nan_policy_omit(self):
|
||
args_nan = [[1, 2, 3, np.nan, 4], [1, np.nan, 19, 25]]
|
||
args_no_nan = [[1, 2, 3, 4], [1, 19, 25]]
|
||
res_nan = stats.alexandergovern(*args_nan, nan_policy='omit')
|
||
res_no_nan = stats.alexandergovern(*args_no_nan)
|
||
assert_equal(res_nan.pvalue, res_no_nan.pvalue)
|
||
assert_equal(res_nan.statistic, res_no_nan.statistic)
|
||
|
||
def test_constant_input(self):
|
||
# Zero variance input, consistent with `stats.pearsonr`
|
||
msg = "An input array is constant; the statistic is not defined."
|
||
with pytest.warns(stats.ConstantInputWarning, match=msg):
|
||
res = stats.alexandergovern([0.667, 0.667, 0.667],
|
||
[0.123, 0.456, 0.789])
|
||
assert_equal(res.statistic, np.nan)
|
||
assert_equal(res.pvalue, np.nan)
|
||
|
||
|
||
class TestFOneWay:
|
||
|
||
def test_trivial(self):
|
||
# A trivial test of stats.f_oneway, with F=0.
|
||
F, p = stats.f_oneway([0, 2], [0, 2])
|
||
assert_equal(F, 0.0)
|
||
assert_equal(p, 1.0)
|
||
|
||
def test_basic(self):
|
||
# Despite being a floating point calculation, this data should
|
||
# result in F being exactly 2.0.
|
||
F, p = stats.f_oneway([0, 2], [2, 4])
|
||
assert_equal(F, 2.0)
|
||
assert_allclose(p, 1 - np.sqrt(0.5), rtol=1e-14)
|
||
|
||
def test_known_exact(self):
|
||
# Another trivial dataset for which the exact F and p can be
|
||
# calculated.
|
||
F, p = stats.f_oneway([2], [2], [2, 3, 4])
|
||
# The use of assert_equal might be too optimistic, but the calculation
|
||
# in this case is trivial enough that it is likely to go through with
|
||
# no loss of precision.
|
||
assert_equal(F, 3/5)
|
||
assert_equal(p, 5/8)
|
||
|
||
def test_large_integer_array(self):
|
||
a = np.array([655, 788], dtype=np.uint16)
|
||
b = np.array([789, 772], dtype=np.uint16)
|
||
F, p = stats.f_oneway(a, b)
|
||
# The expected value was verified by computing it with mpmath with
|
||
# 40 digits of precision.
|
||
assert_allclose(F, 0.77450216931805540, rtol=1e-14)
|
||
|
||
def test_result_attributes(self):
|
||
a = np.array([655, 788], dtype=np.uint16)
|
||
b = np.array([789, 772], dtype=np.uint16)
|
||
res = stats.f_oneway(a, b)
|
||
attributes = ('statistic', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
|
||
def test_nist(self):
|
||
# These are the nist ANOVA files. They can be found at:
|
||
# https://www.itl.nist.gov/div898/strd/anova/anova.html
|
||
filenames = ['SiRstv.dat', 'SmLs01.dat', 'SmLs02.dat', 'SmLs03.dat',
|
||
'AtmWtAg.dat', 'SmLs04.dat', 'SmLs05.dat', 'SmLs06.dat',
|
||
'SmLs07.dat', 'SmLs08.dat', 'SmLs09.dat']
|
||
|
||
for test_case in filenames:
|
||
rtol = 1e-7
|
||
fname = os.path.abspath(os.path.join(os.path.dirname(__file__),
|
||
'data/nist_anova', test_case))
|
||
with open(fname) as f:
|
||
content = f.read().split('\n')
|
||
certified = [line.split() for line in content[40:48]
|
||
if line.strip()]
|
||
dataf = np.loadtxt(fname, skiprows=60)
|
||
y, x = dataf.T
|
||
y = y.astype(int)
|
||
caty = np.unique(y)
|
||
f = float(certified[0][-1])
|
||
|
||
xlist = [x[y == i] for i in caty]
|
||
res = stats.f_oneway(*xlist)
|
||
|
||
# With the hard test cases we relax the tolerance a bit.
|
||
hard_tc = ('SmLs07.dat', 'SmLs08.dat', 'SmLs09.dat')
|
||
if test_case in hard_tc:
|
||
rtol = 1e-4
|
||
|
||
assert_allclose(res[0], f, rtol=rtol,
|
||
err_msg='Failing testcase: %s' % test_case)
|
||
|
||
@pytest.mark.parametrize("a, b, expected", [
|
||
(np.array([42, 42, 42]), np.array([7, 7, 7]), (np.inf, 0)),
|
||
(np.array([42, 42, 42]), np.array([42, 42, 42]), (np.nan, np.nan))
|
||
])
|
||
def test_constant_input(self, a, b, expected):
|
||
# For more details, look on https://github.com/scipy/scipy/issues/11669
|
||
msg = "Each of the input arrays is constant;"
|
||
with pytest.warns(stats.ConstantInputWarning, match=msg):
|
||
f, p = stats.f_oneway(a, b)
|
||
assert f, p == expected
|
||
|
||
@pytest.mark.parametrize('axis', [-2, -1, 0, 1])
|
||
def test_2d_inputs(self, axis):
|
||
a = np.array([[1, 4, 3, 3],
|
||
[2, 5, 3, 3],
|
||
[3, 6, 3, 3],
|
||
[2, 3, 3, 3],
|
||
[1, 4, 3, 3]])
|
||
b = np.array([[3, 1, 5, 3],
|
||
[4, 6, 5, 3],
|
||
[4, 3, 5, 3],
|
||
[1, 5, 5, 3],
|
||
[5, 5, 5, 3],
|
||
[2, 3, 5, 3],
|
||
[8, 2, 5, 3],
|
||
[2, 2, 5, 3]])
|
||
c = np.array([[4, 3, 4, 3],
|
||
[4, 2, 4, 3],
|
||
[5, 4, 4, 3],
|
||
[5, 4, 4, 3]])
|
||
|
||
if axis in [-1, 1]:
|
||
a = a.T
|
||
b = b.T
|
||
c = c.T
|
||
take_axis = 0
|
||
else:
|
||
take_axis = 1
|
||
|
||
warn_msg = "Each of the input arrays is constant;"
|
||
with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
|
||
f, p = stats.f_oneway(a, b, c, axis=axis)
|
||
|
||
# Verify that the result computed with the 2d arrays matches
|
||
# the result of calling f_oneway individually on each slice.
|
||
for j in [0, 1]:
|
||
fj, pj = stats.f_oneway(np.take(a, j, take_axis),
|
||
np.take(b, j, take_axis),
|
||
np.take(c, j, take_axis))
|
||
assert_allclose(f[j], fj, rtol=1e-14)
|
||
assert_allclose(p[j], pj, rtol=1e-14)
|
||
for j in [2, 3]:
|
||
with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
|
||
fj, pj = stats.f_oneway(np.take(a, j, take_axis),
|
||
np.take(b, j, take_axis),
|
||
np.take(c, j, take_axis))
|
||
assert_equal(f[j], fj)
|
||
assert_equal(p[j], pj)
|
||
|
||
def test_3d_inputs(self):
|
||
# Some 3-d arrays. (There is nothing special about the values.)
|
||
a = 1/np.arange(1.0, 4*5*7 + 1).reshape(4, 5, 7)
|
||
b = 2/np.arange(1.0, 4*8*7 + 1).reshape(4, 8, 7)
|
||
c = np.cos(1/np.arange(1.0, 4*4*7 + 1).reshape(4, 4, 7))
|
||
|
||
f, p = stats.f_oneway(a, b, c, axis=1)
|
||
|
||
assert f.shape == (4, 7)
|
||
assert p.shape == (4, 7)
|
||
|
||
for i in range(a.shape[0]):
|
||
for j in range(a.shape[2]):
|
||
fij, pij = stats.f_oneway(a[i, :, j], b[i, :, j], c[i, :, j])
|
||
assert_allclose(fij, f[i, j])
|
||
assert_allclose(pij, p[i, j])
|
||
|
||
def test_length0_1d_error(self):
|
||
# Require at least one value in each group.
|
||
msg = 'at least one input has length 0'
|
||
with pytest.warns(stats.DegenerateDataWarning, match=msg):
|
||
result = stats.f_oneway([1, 2, 3], [], [4, 5, 6, 7])
|
||
assert_equal(result, (np.nan, np.nan))
|
||
|
||
def test_length0_2d_error(self):
|
||
msg = 'at least one input has length 0'
|
||
with pytest.warns(stats.DegenerateDataWarning, match=msg):
|
||
ncols = 3
|
||
a = np.ones((4, ncols))
|
||
b = np.ones((0, ncols))
|
||
c = np.ones((5, ncols))
|
||
f, p = stats.f_oneway(a, b, c)
|
||
nans = np.full((ncols,), fill_value=np.nan)
|
||
assert_equal(f, nans)
|
||
assert_equal(p, nans)
|
||
|
||
def test_all_length_one(self):
|
||
msg = 'all input arrays have length 1.'
|
||
with pytest.warns(stats.DegenerateDataWarning, match=msg):
|
||
result = stats.f_oneway([10], [11], [12], [13])
|
||
assert_equal(result, (np.nan, np.nan))
|
||
|
||
@pytest.mark.parametrize('args', [(), ([1, 2, 3],)])
|
||
def test_too_few_inputs(self, args):
|
||
with assert_raises(TypeError):
|
||
stats.f_oneway(*args)
|
||
|
||
def test_axis_error(self):
|
||
a = np.ones((3, 4))
|
||
b = np.ones((5, 4))
|
||
with assert_raises(AxisError):
|
||
stats.f_oneway(a, b, axis=2)
|
||
|
||
def test_bad_shapes(self):
|
||
a = np.ones((3, 4))
|
||
b = np.ones((5, 4))
|
||
with assert_raises(ValueError):
|
||
stats.f_oneway(a, b, axis=1)
|
||
|
||
|
||
class TestKruskal:
|
||
def test_simple(self):
|
||
x = [1]
|
||
y = [2]
|
||
h, p = stats.kruskal(x, y)
|
||
assert_equal(h, 1.0)
|
||
assert_approx_equal(p, stats.distributions.chi2.sf(h, 1))
|
||
h, p = stats.kruskal(np.array(x), np.array(y))
|
||
assert_equal(h, 1.0)
|
||
assert_approx_equal(p, stats.distributions.chi2.sf(h, 1))
|
||
|
||
def test_basic(self):
|
||
x = [1, 3, 5, 7, 9]
|
||
y = [2, 4, 6, 8, 10]
|
||
h, p = stats.kruskal(x, y)
|
||
assert_approx_equal(h, 3./11, significant=10)
|
||
assert_approx_equal(p, stats.distributions.chi2.sf(3./11, 1))
|
||
h, p = stats.kruskal(np.array(x), np.array(y))
|
||
assert_approx_equal(h, 3./11, significant=10)
|
||
assert_approx_equal(p, stats.distributions.chi2.sf(3./11, 1))
|
||
|
||
def test_simple_tie(self):
|
||
x = [1]
|
||
y = [1, 2]
|
||
h_uncorr = 1.5**2 + 2*2.25**2 - 12
|
||
corr = 0.75
|
||
expected = h_uncorr / corr # 0.5
|
||
h, p = stats.kruskal(x, y)
|
||
# Since the expression is simple and the exact answer is 0.5, it
|
||
# should be safe to use assert_equal().
|
||
assert_equal(h, expected)
|
||
|
||
def test_another_tie(self):
|
||
x = [1, 1, 1, 2]
|
||
y = [2, 2, 2, 2]
|
||
h_uncorr = (12. / 8. / 9.) * 4 * (3**2 + 6**2) - 3 * 9
|
||
corr = 1 - float(3**3 - 3 + 5**3 - 5) / (8**3 - 8)
|
||
expected = h_uncorr / corr
|
||
h, p = stats.kruskal(x, y)
|
||
assert_approx_equal(h, expected)
|
||
|
||
def test_three_groups(self):
|
||
# A test of stats.kruskal with three groups, with ties.
|
||
x = [1, 1, 1]
|
||
y = [2, 2, 2]
|
||
z = [2, 2]
|
||
h_uncorr = (12. / 8. / 9.) * (3*2**2 + 3*6**2 + 2*6**2) - 3 * 9 # 5.0
|
||
corr = 1 - float(3**3 - 3 + 5**3 - 5) / (8**3 - 8)
|
||
expected = h_uncorr / corr # 7.0
|
||
h, p = stats.kruskal(x, y, z)
|
||
assert_approx_equal(h, expected)
|
||
assert_approx_equal(p, stats.distributions.chi2.sf(h, 2))
|
||
|
||
def test_empty(self):
|
||
# A test of stats.kruskal with three groups, with ties.
|
||
x = [1, 1, 1]
|
||
y = [2, 2, 2]
|
||
z = []
|
||
assert_equal(stats.kruskal(x, y, z), (np.nan, np.nan))
|
||
|
||
def test_kruskal_result_attributes(self):
|
||
x = [1, 3, 5, 7, 9]
|
||
y = [2, 4, 6, 8, 10]
|
||
res = stats.kruskal(x, y)
|
||
attributes = ('statistic', 'pvalue')
|
||
check_named_results(res, attributes)
|
||
|
||
def test_nan_policy(self):
|
||
x = np.arange(10.)
|
||
x[9] = np.nan
|
||
assert_equal(stats.kruskal(x, x), (np.nan, np.nan))
|
||
assert_almost_equal(stats.kruskal(x, x, nan_policy='omit'), (0.0, 1.0))
|
||
assert_raises(ValueError, stats.kruskal, x, x, nan_policy='raise')
|
||
assert_raises(ValueError, stats.kruskal, x, x, nan_policy='foobar')
|
||
|
||
def test_large_no_samples(self):
|
||
# Test to see if large samples are handled correctly.
|
||
n = 50000
|
||
x = np.random.randn(n)
|
||
y = np.random.randn(n) + 50
|
||
h, p = stats.kruskal(x, y)
|
||
expected = 0
|
||
assert_approx_equal(p, expected)
|
||
|
||
|
||
class TestCombinePvalues:
|
||
|
||
def test_fisher(self):
|
||
# Example taken from https://en.wikipedia.org/wiki/Fisher%27s_exact_test#Example
|
||
xsq, p = stats.combine_pvalues([.01, .2, .3], method='fisher')
|
||
assert_approx_equal(p, 0.02156, significant=4)
|
||
|
||
def test_stouffer(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='stouffer')
|
||
assert_approx_equal(p, 0.01651, significant=4)
|
||
|
||
def test_stouffer2(self):
|
||
Z, p = stats.combine_pvalues([.5, .5, .5], method='stouffer')
|
||
assert_approx_equal(p, 0.5, significant=4)
|
||
|
||
def test_weighted_stouffer(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='stouffer',
|
||
weights=np.ones(3))
|
||
assert_approx_equal(p, 0.01651, significant=4)
|
||
|
||
def test_weighted_stouffer2(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='stouffer',
|
||
weights=np.array((1, 4, 9)))
|
||
assert_approx_equal(p, 0.1464, significant=4)
|
||
|
||
def test_pearson(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='pearson')
|
||
assert_approx_equal(p, 0.02213, significant=4)
|
||
|
||
def test_tippett(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='tippett')
|
||
assert_approx_equal(p, 0.0297, significant=4)
|
||
|
||
def test_mudholkar_george(self):
|
||
Z, p = stats.combine_pvalues([.1, .1, .1], method='mudholkar_george')
|
||
assert_approx_equal(p, 0.019462, significant=4)
|
||
|
||
def test_mudholkar_george_equal_fisher_pearson_average(self):
|
||
Z, p = stats.combine_pvalues([.01, .2, .3], method='mudholkar_george')
|
||
Z_f, p_f = stats.combine_pvalues([.01, .2, .3], method='fisher')
|
||
Z_p, p_p = stats.combine_pvalues([.01, .2, .3], method='pearson')
|
||
assert_approx_equal(0.5 * (Z_f+Z_p), Z, significant=4)
|
||
|
||
methods = ["fisher", "pearson", "tippett", "stouffer", "mudholkar_george"]
|
||
|
||
@pytest.mark.parametrize("variant", ["single", "all", "random"])
|
||
@pytest.mark.parametrize("method", methods)
|
||
def test_monotonicity(self, variant, method):
|
||
# Test that result increases monotonically with respect to input.
|
||
m, n = 10, 7
|
||
rng = np.random.default_rng(278448169958891062669391462690811630763)
|
||
|
||
# `pvaluess` is an m × n array of p values. Each row corresponds to
|
||
# a set of p values to be combined with p values increasing
|
||
# monotonically down one column (single), simultaneously down each
|
||
# column (all), or independently down each column (random).
|
||
if variant == "single":
|
||
pvaluess = np.full((m, n), rng.random(n))
|
||
pvaluess[:, 0] = np.linspace(0.1, 0.9, m)
|
||
elif variant == "all":
|
||
pvaluess = np.full((n, m), np.linspace(0.1, 0.9, m)).T
|
||
elif variant == "random":
|
||
pvaluess = np.sort(rng.uniform(0, 1, size=(m, n)), axis=0)
|
||
|
||
combined_pvalues = [
|
||
stats.combine_pvalues(pvalues, method=method)[1]
|
||
for pvalues in pvaluess
|
||
]
|
||
assert np.all(np.diff(combined_pvalues) >= 0)
|
||
|
||
@pytest.mark.parametrize("method", methods)
|
||
def test_result(self, method):
|
||
res = stats.combine_pvalues([.01, .2, .3], method=method)
|
||
assert_equal((res.statistic, res.pvalue), res)
|
||
|
||
|
||
class TestCdfDistanceValidation:
|
||
"""
|
||
Test that _cdf_distance() (via wasserstein_distance()) raises ValueErrors
|
||
for bad inputs.
|
||
"""
|
||
|
||
def test_distinct_value_and_weight_lengths(self):
|
||
# When the number of weights does not match the number of values,
|
||
# a ValueError should be raised.
|
||
assert_raises(ValueError, stats.wasserstein_distance,
|
||
[1], [2], [4], [3, 1])
|
||
assert_raises(ValueError, stats.wasserstein_distance, [1], [2], [1, 0])
|
||
|
||
def test_zero_weight(self):
|
||
# When a distribution is given zero weight, a ValueError should be
|
||
# raised.
|
||
assert_raises(ValueError, stats.wasserstein_distance,
|
||
[0, 1], [2], [0, 0])
|
||
assert_raises(ValueError, stats.wasserstein_distance,
|
||
[0, 1], [2], [3, 1], [0])
|
||
|
||
def test_negative_weights(self):
|
||
# A ValueError should be raised if there are any negative weights.
|
||
assert_raises(ValueError, stats.wasserstein_distance,
|
||
[0, 1], [2, 2], [1, 1], [3, -1])
|
||
|
||
def test_empty_distribution(self):
|
||
# A ValueError should be raised when trying to measure the distance
|
||
# between something and nothing.
|
||
assert_raises(ValueError, stats.wasserstein_distance, [], [2, 2])
|
||
assert_raises(ValueError, stats.wasserstein_distance, [1], [])
|
||
|
||
def test_inf_weight(self):
|
||
# An inf weight is not valid.
|
||
assert_raises(ValueError, stats.wasserstein_distance,
|
||
[1, 2, 1], [1, 1], [1, np.inf, 1], [1, 1])
|
||
|
||
|
||
class TestWassersteinDistanceND:
|
||
""" Tests for wasserstein_distance_nd() output values.
|
||
"""
|
||
|
||
def test_published_values(self):
|
||
# Compare against published values and manually computed results.
|
||
# The values and computed result are posted at James D. McCaffrey's blog,
|
||
# https://jamesmccaffrey.wordpress.com/2018/03/05/earth-mover-distance
|
||
# -wasserstein-metric-example-calculation/
|
||
u = [(1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1),
|
||
(4,2), (6,1), (6,1)]
|
||
v = [(2,1), (2,1), (3,2), (3,2), (3,2), (5,1), (5,1), (5,1), (5,1), (5,1),
|
||
(5,1), (5,1), (7,1)]
|
||
|
||
res = stats.wasserstein_distance_nd(u, v)
|
||
# In original post, the author kept two decimal places for ease of calculation.
|
||
# This test uses the more precise value of distance to get the precise results.
|
||
# For comparison, please see the table and figure in the original blog post.
|
||
flow = np.array([2., 3., 5., 1., 1., 1.])
|
||
dist = np.array([1.00, 5**0.5, 4.00, 2**0.5, 1.00, 1.00])
|
||
ref = np.sum(flow * dist)/np.sum(flow)
|
||
assert_allclose(res, ref)
|
||
|
||
@pytest.mark.parametrize('n_value', (4, 15, 35))
|
||
@pytest.mark.parametrize('ndim', (3, 4, 7))
|
||
@pytest.mark.parametrize('max_repeats', (5, 10))
|
||
def test_same_distribution_nD(self, ndim, n_value, max_repeats):
|
||
# Any distribution moved to itself should have a Wasserstein distance
|
||
# of zero.
|
||
rng = np.random.default_rng(363836384995579937222333)
|
||
repeats = rng.integers(1, max_repeats, size=n_value, dtype=int)
|
||
|
||
u_values = rng.random(size=(n_value, ndim))
|
||
v_values = np.repeat(u_values, repeats, axis=0)
|
||
v_weights = rng.random(np.sum(repeats))
|
||
range_repeat = np.repeat(np.arange(len(repeats)), repeats)
|
||
u_weights = np.bincount(range_repeat, weights=v_weights)
|
||
index = rng.permutation(len(v_weights))
|
||
v_values, v_weights = v_values[index], v_weights[index]
|
||
|
||
res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
assert_allclose(res, 0, atol=1e-15)
|
||
|
||
@pytest.mark.parametrize('nu', (8, 9, 38))
|
||
@pytest.mark.parametrize('nv', (8, 12, 17))
|
||
@pytest.mark.parametrize('ndim', (3, 5, 23))
|
||
def test_collapse_nD(self, nu, nv, ndim):
|
||
# test collapse for n dimensional values
|
||
# Collapsing a n-D distribution to a point distribution at zero
|
||
# is equivalent to taking the average of the norm of data.
|
||
rng = np.random.default_rng(38573488467338826109)
|
||
u_values = rng.random(size=(nu, ndim))
|
||
v_values = np.zeros((nv, ndim))
|
||
u_weights = rng.random(size=nu)
|
||
v_weights = rng.random(size=nv)
|
||
ref = np.average(np.linalg.norm(u_values, axis=1), weights=u_weights)
|
||
res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
assert_allclose(res, ref)
|
||
|
||
@pytest.mark.parametrize('nu', (8, 16, 32))
|
||
@pytest.mark.parametrize('nv', (8, 16, 32))
|
||
@pytest.mark.parametrize('ndim', (1, 2, 6))
|
||
def test_zero_weight_nD(self, nu, nv, ndim):
|
||
# Values with zero weight have no impact on the Wasserstein distance.
|
||
rng = np.random.default_rng(38573488467338826109)
|
||
u_values = rng.random(size=(nu, ndim))
|
||
v_values = rng.random(size=(nv, ndim))
|
||
u_weights = rng.random(size=nu)
|
||
v_weights = rng.random(size=nv)
|
||
ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
|
||
add_row, nrows = rng.integers(0, nu, size=2)
|
||
add_value = rng.random(size=(nrows, ndim))
|
||
u_values = np.insert(u_values, add_row, add_value, axis=0)
|
||
u_weights = np.insert(u_weights, add_row, np.zeros(nrows), axis=0)
|
||
res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
assert_allclose(res, ref)
|
||
|
||
def test_inf_values(self):
|
||
# Inf values can lead to an inf distance or trigger a RuntimeWarning
|
||
# (and return NaN) if the distance is undefined.
|
||
uv, vv, uw = [[1, 1], [2, 1]], [[np.inf, -np.inf]], [1, 1]
|
||
distance = stats.wasserstein_distance_nd(uv, vv, uw)
|
||
assert_equal(distance, np.inf)
|
||
with np.errstate(invalid='ignore'):
|
||
uv, vv = [[np.inf, np.inf]], [[np.inf, -np.inf]]
|
||
distance = stats.wasserstein_distance_nd(uv, vv)
|
||
assert_equal(distance, np.nan)
|
||
|
||
@pytest.mark.parametrize('nu', (10, 15, 20))
|
||
@pytest.mark.parametrize('nv', (10, 15, 20))
|
||
@pytest.mark.parametrize('ndim', (1, 3, 5))
|
||
def test_multi_dim_nD(self, nu, nv, ndim):
|
||
# Adding dimension on distributions do not affect the result
|
||
rng = np.random.default_rng(2736495738494849509)
|
||
u_values = rng.random(size=(nu, ndim))
|
||
v_values = rng.random(size=(nv, ndim))
|
||
u_weights = rng.random(size=nu)
|
||
v_weights = rng.random(size=nv)
|
||
ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
|
||
add_dim = rng.integers(0, ndim)
|
||
add_value = rng.random()
|
||
|
||
u_values = np.insert(u_values, add_dim, add_value, axis=1)
|
||
v_values = np.insert(v_values, add_dim, add_value, axis=1)
|
||
res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
assert_allclose(res, ref)
|
||
|
||
@pytest.mark.parametrize('nu', (7, 13, 19))
|
||
@pytest.mark.parametrize('nv', (7, 13, 19))
|
||
@pytest.mark.parametrize('ndim', (2, 4, 7))
|
||
def test_orthogonal_nD(self, nu, nv, ndim):
|
||
# orthogonal transformations do not affect the result of the
|
||
# wasserstein_distance
|
||
rng = np.random.default_rng(34746837464536)
|
||
u_values = rng.random(size=(nu, ndim))
|
||
v_values = rng.random(size=(nv, ndim))
|
||
u_weights = rng.random(size=nu)
|
||
v_weights = rng.random(size=nv)
|
||
ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
|
||
|
||
dist = stats.ortho_group(ndim)
|
||
transform = dist.rvs(random_state=rng)
|
||
shift = rng.random(size=ndim)
|
||
res = stats.wasserstein_distance_nd(u_values @ transform + shift,
|
||
v_values @ transform + shift,
|
||
u_weights, v_weights)
|
||
assert_allclose(res, ref)
|
||
|
||
def test_error_code(self):
|
||
rng = np.random.default_rng(52473644737485644836320101)
|
||
with pytest.raises(ValueError, match='Invalid input values. The inputs'):
|
||
u_values = rng.random(size=(4, 10, 15))
|
||
v_values = rng.random(size=(6, 2, 7))
|
||
_ = stats.wasserstein_distance_nd(u_values, v_values)
|
||
with pytest.raises(ValueError, match='Invalid input values. Dimensions'):
|
||
u_values = rng.random(size=(15,))
|
||
v_values = rng.random(size=(3, 15))
|
||
_ = stats.wasserstein_distance_nd(u_values, v_values)
|
||
with pytest.raises(ValueError,
|
||
match='Invalid input values. If two-dimensional'):
|
||
u_values = rng.random(size=(2, 10))
|
||
v_values = rng.random(size=(2, 2))
|
||
_ = stats.wasserstein_distance_nd(u_values, v_values)
|
||
|
||
@pytest.mark.parametrize('u_size', [1, 10, 300])
|
||
@pytest.mark.parametrize('v_size', [1, 10, 300])
|
||
def test_optimization_vs_analytical(self, u_size, v_size):
|
||
rng = np.random.default_rng(45634745675)
|
||
# Test when u_weights = None, v_weights = None
|
||
u_values = rng.random(size=(u_size, 1))
|
||
v_values = rng.random(size=(v_size, 1))
|
||
u_values_flat = u_values.ravel()
|
||
v_values_flat = v_values.ravel()
|
||
# These three calculations are done using different backends
|
||
# but they must be equal
|
||
d1 = stats.wasserstein_distance(u_values_flat, v_values_flat)
|
||
d2 = stats.wasserstein_distance_nd(u_values, v_values)
|
||
d3 = stats.wasserstein_distance_nd(u_values_flat, v_values_flat)
|
||
assert_allclose(d2, d1)
|
||
assert_allclose(d3, d1)
|
||
# Test with u_weights and v_weights specified.
|
||
u_weights = rng.random(size=u_size)
|
||
v_weights = rng.random(size=v_size)
|
||
d1 = stats.wasserstein_distance(u_values_flat, v_values_flat,
|
||
u_weights, v_weights)
|
||
d2 = stats.wasserstein_distance_nd(u_values, v_values,
|
||
u_weights, v_weights)
|
||
d3 = stats.wasserstein_distance_nd(u_values_flat, v_values_flat,
|
||
u_weights, v_weights)
|
||
assert_allclose(d2, d1)
|
||
assert_allclose(d3, d1)
|
||
|
||
|
||
class TestWassersteinDistance:
|
||
""" Tests for wasserstein_distance() output values.
|
||
"""
|
||
|
||
def test_simple(self):
|
||
# For basic distributions, the value of the Wasserstein distance is
|
||
# straightforward.
|
||
assert_allclose(
|
||
stats.wasserstein_distance([0, 1], [0], [1, 1], [1]),
|
||
.5)
|
||
assert_allclose(stats.wasserstein_distance(
|
||
[0, 1], [0], [3, 1], [1]),
|
||
.25)
|
||
assert_allclose(stats.wasserstein_distance(
|
||
[0, 2], [0], [1, 1], [1]),
|
||
1)
|
||
assert_allclose(stats.wasserstein_distance(
|
||
[0, 1, 2], [1, 2, 3]),
|
||
1)
|
||
|
||
def test_same_distribution(self):
|
||
# Any distribution moved to itself should have a Wasserstein distance
|
||
# of zero.
|
||
assert_equal(stats.wasserstein_distance([1, 2, 3], [2, 1, 3]), 0)
|
||
assert_equal(
|
||
stats.wasserstein_distance([1, 1, 1, 4], [4, 1],
|
||
[1, 1, 1, 1], [1, 3]),
|
||
0)
|
||
|
||
def test_shift(self):
|
||
# If the whole distribution is shifted by x, then the Wasserstein
|
||
# distance should be the norm of x.
|
||
assert_allclose(stats.wasserstein_distance([0], [1]), 1)
|
||
assert_allclose(stats.wasserstein_distance([-5], [5]), 10)
|
||
assert_allclose(
|
||
stats.wasserstein_distance([1, 2, 3, 4, 5], [11, 12, 13, 14, 15]),
|
||
10)
|
||
assert_allclose(
|
||
stats.wasserstein_distance([4.5, 6.7, 2.1], [4.6, 7, 9.2],
|
||
[3, 1, 1], [1, 3, 1]),
|
||
2.5)
|
||
|
||
def test_combine_weights(self):
|
||
# Assigning a weight w to a value is equivalent to including that value
|
||
# w times in the value array with weight of 1.
|
||
assert_allclose(
|
||
stats.wasserstein_distance(
|
||
[0, 0, 1, 1, 1, 1, 5], [0, 3, 3, 3, 3, 4, 4],
|
||
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),
|
||
stats.wasserstein_distance([5, 0, 1], [0, 4, 3],
|
||
[1, 2, 4], [1, 2, 4]))
|
||
|
||
def test_collapse(self):
|
||
# Collapsing a distribution to a point distribution at zero is
|
||
# equivalent to taking the average of the absolute values of the
|
||
# values.
|
||
u = np.arange(-10, 30, 0.3)
|
||
v = np.zeros_like(u)
|
||
assert_allclose(
|
||
stats.wasserstein_distance(u, v),
|
||
np.mean(np.abs(u)))
|
||
|
||
u_weights = np.arange(len(u))
|
||
v_weights = u_weights[::-1]
|
||
assert_allclose(
|
||
stats.wasserstein_distance(u, v, u_weights, v_weights),
|
||
np.average(np.abs(u), weights=u_weights))
|
||
|
||
def test_zero_weight(self):
|
||
# Values with zero weight have no impact on the Wasserstein distance.
|
||
assert_allclose(
|
||
stats.wasserstein_distance([1, 2, 100000], [1, 1],
|
||
[1, 1, 0], [1, 1]),
|
||
stats.wasserstein_distance([1, 2], [1, 1], [1, 1], [1, 1]))
|
||
|
||
def test_inf_values(self):
|
||
# Inf values can lead to an inf distance or trigger a RuntimeWarning
|
||
# (and return NaN) if the distance is undefined.
|
||
assert_equal(
|
||
stats.wasserstein_distance([1, 2, np.inf], [1, 1]),
|
||
np.inf)
|
||
assert_equal(
|
||
stats.wasserstein_distance([1, 2, np.inf], [-np.inf, 1]),
|
||
np.inf)
|
||
assert_equal(
|
||
stats.wasserstein_distance([1, -np.inf, np.inf], [1, 1]),
|
||
np.inf)
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "invalid value*")
|
||
assert_equal(
|
||
stats.wasserstein_distance([1, 2, np.inf], [np.inf, 1]),
|
||
np.nan)
|
||
|
||
|
||
class TestEnergyDistance:
|
||
""" Tests for energy_distance() output values.
|
||
"""
|
||
|
||
def test_simple(self):
|
||
# For basic distributions, the value of the energy distance is
|
||
# straightforward.
|
||
assert_almost_equal(
|
||
stats.energy_distance([0, 1], [0], [1, 1], [1]),
|
||
np.sqrt(2) * .5)
|
||
assert_almost_equal(stats.energy_distance(
|
||
[0, 1], [0], [3, 1], [1]),
|
||
np.sqrt(2) * .25)
|
||
assert_almost_equal(stats.energy_distance(
|
||
[0, 2], [0], [1, 1], [1]),
|
||
2 * .5)
|
||
assert_almost_equal(
|
||
stats.energy_distance([0, 1, 2], [1, 2, 3]),
|
||
np.sqrt(2) * (3*(1./3**2))**.5)
|
||
|
||
def test_same_distribution(self):
|
||
# Any distribution moved to itself should have a energy distance of
|
||
# zero.
|
||
assert_equal(stats.energy_distance([1, 2, 3], [2, 1, 3]), 0)
|
||
assert_equal(
|
||
stats.energy_distance([1, 1, 1, 4], [4, 1], [1, 1, 1, 1], [1, 3]),
|
||
0)
|
||
|
||
def test_shift(self):
|
||
# If a single-point distribution is shifted by x, then the energy
|
||
# distance should be sqrt(2) * sqrt(x).
|
||
assert_almost_equal(stats.energy_distance([0], [1]), np.sqrt(2))
|
||
assert_almost_equal(
|
||
stats.energy_distance([-5], [5]),
|
||
np.sqrt(2) * 10**.5)
|
||
|
||
def test_combine_weights(self):
|
||
# Assigning a weight w to a value is equivalent to including that value
|
||
# w times in the value array with weight of 1.
|
||
assert_almost_equal(
|
||
stats.energy_distance([0, 0, 1, 1, 1, 1, 5], [0, 3, 3, 3, 3, 4, 4],
|
||
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),
|
||
stats.energy_distance([5, 0, 1], [0, 4, 3], [1, 2, 4], [1, 2, 4]))
|
||
|
||
def test_zero_weight(self):
|
||
# Values with zero weight have no impact on the energy distance.
|
||
assert_almost_equal(
|
||
stats.energy_distance([1, 2, 100000], [1, 1], [1, 1, 0], [1, 1]),
|
||
stats.energy_distance([1, 2], [1, 1], [1, 1], [1, 1]))
|
||
|
||
def test_inf_values(self):
|
||
# Inf values can lead to an inf distance or trigger a RuntimeWarning
|
||
# (and return NaN) if the distance is undefined.
|
||
assert_equal(stats.energy_distance([1, 2, np.inf], [1, 1]), np.inf)
|
||
assert_equal(
|
||
stats.energy_distance([1, 2, np.inf], [-np.inf, 1]),
|
||
np.inf)
|
||
assert_equal(
|
||
stats.energy_distance([1, -np.inf, np.inf], [1, 1]),
|
||
np.inf)
|
||
with suppress_warnings() as sup:
|
||
sup.record(RuntimeWarning, "invalid value*")
|
||
assert_equal(
|
||
stats.energy_distance([1, 2, np.inf], [np.inf, 1]),
|
||
np.nan)
|
||
|
||
|
||
class TestBrunnerMunzel:
|
||
# Data from (Lumley, 1996)
|
||
X = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
|
||
Y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
significant = 13
|
||
|
||
def test_brunnermunzel_one_sided(self):
|
||
# Results are compared with R's lawstat package.
|
||
u1, p1 = stats.brunnermunzel(self.X, self.Y, alternative='less')
|
||
u2, p2 = stats.brunnermunzel(self.Y, self.X, alternative='greater')
|
||
u3, p3 = stats.brunnermunzel(self.X, self.Y, alternative='greater')
|
||
u4, p4 = stats.brunnermunzel(self.Y, self.X, alternative='less')
|
||
|
||
assert_approx_equal(p1, p2, significant=self.significant)
|
||
assert_approx_equal(p3, p4, significant=self.significant)
|
||
assert_(p1 != p3)
|
||
assert_approx_equal(u1, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u2, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u3, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u4, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(p1, 0.0028931043330757342,
|
||
significant=self.significant)
|
||
assert_approx_equal(p3, 0.99710689566692423,
|
||
significant=self.significant)
|
||
|
||
def test_brunnermunzel_two_sided(self):
|
||
# Results are compared with R's lawstat package.
|
||
u1, p1 = stats.brunnermunzel(self.X, self.Y, alternative='two-sided')
|
||
u2, p2 = stats.brunnermunzel(self.Y, self.X, alternative='two-sided')
|
||
|
||
assert_approx_equal(p1, p2, significant=self.significant)
|
||
assert_approx_equal(u1, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u2, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(p1, 0.0057862086661515377,
|
||
significant=self.significant)
|
||
|
||
def test_brunnermunzel_default(self):
|
||
# The default value for alternative is two-sided
|
||
u1, p1 = stats.brunnermunzel(self.X, self.Y)
|
||
u2, p2 = stats.brunnermunzel(self.Y, self.X)
|
||
|
||
assert_approx_equal(p1, p2, significant=self.significant)
|
||
assert_approx_equal(u1, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u2, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(p1, 0.0057862086661515377,
|
||
significant=self.significant)
|
||
|
||
def test_brunnermunzel_alternative_error(self):
|
||
alternative = "error"
|
||
distribution = "t"
|
||
nan_policy = "propagate"
|
||
assert_(alternative not in ["two-sided", "greater", "less"])
|
||
assert_raises(ValueError,
|
||
stats.brunnermunzel,
|
||
self.X,
|
||
self.Y,
|
||
alternative,
|
||
distribution,
|
||
nan_policy)
|
||
|
||
def test_brunnermunzel_distribution_norm(self):
|
||
u1, p1 = stats.brunnermunzel(self.X, self.Y, distribution="normal")
|
||
u2, p2 = stats.brunnermunzel(self.Y, self.X, distribution="normal")
|
||
assert_approx_equal(p1, p2, significant=self.significant)
|
||
assert_approx_equal(u1, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u2, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(p1, 0.0017041417600383024,
|
||
significant=self.significant)
|
||
|
||
def test_brunnermunzel_distribution_error(self):
|
||
alternative = "two-sided"
|
||
distribution = "error"
|
||
nan_policy = "propagate"
|
||
assert_(alternative not in ["t", "normal"])
|
||
assert_raises(ValueError,
|
||
stats.brunnermunzel,
|
||
self.X,
|
||
self.Y,
|
||
alternative,
|
||
distribution,
|
||
nan_policy)
|
||
|
||
def test_brunnermunzel_empty_imput(self):
|
||
u1, p1 = stats.brunnermunzel(self.X, [])
|
||
u2, p2 = stats.brunnermunzel([], self.Y)
|
||
u3, p3 = stats.brunnermunzel([], [])
|
||
|
||
assert_equal(u1, np.nan)
|
||
assert_equal(p1, np.nan)
|
||
assert_equal(u2, np.nan)
|
||
assert_equal(p2, np.nan)
|
||
assert_equal(u3, np.nan)
|
||
assert_equal(p3, np.nan)
|
||
|
||
def test_brunnermunzel_nan_input_propagate(self):
|
||
X = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, np.nan]
|
||
Y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
u1, p1 = stats.brunnermunzel(X, Y, nan_policy="propagate")
|
||
u2, p2 = stats.brunnermunzel(Y, X, nan_policy="propagate")
|
||
|
||
assert_equal(u1, np.nan)
|
||
assert_equal(p1, np.nan)
|
||
assert_equal(u2, np.nan)
|
||
assert_equal(p2, np.nan)
|
||
|
||
def test_brunnermunzel_nan_input_raise(self):
|
||
X = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, np.nan]
|
||
Y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
alternative = "two-sided"
|
||
distribution = "t"
|
||
nan_policy = "raise"
|
||
|
||
assert_raises(ValueError,
|
||
stats.brunnermunzel,
|
||
X,
|
||
Y,
|
||
alternative,
|
||
distribution,
|
||
nan_policy)
|
||
assert_raises(ValueError,
|
||
stats.brunnermunzel,
|
||
Y,
|
||
X,
|
||
alternative,
|
||
distribution,
|
||
nan_policy)
|
||
|
||
def test_brunnermunzel_nan_input_omit(self):
|
||
X = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, np.nan]
|
||
Y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
u1, p1 = stats.brunnermunzel(X, Y, nan_policy="omit")
|
||
u2, p2 = stats.brunnermunzel(Y, X, nan_policy="omit")
|
||
|
||
assert_approx_equal(p1, p2, significant=self.significant)
|
||
assert_approx_equal(u1, 3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(u2, -3.1374674823029505,
|
||
significant=self.significant)
|
||
assert_approx_equal(p1, 0.0057862086661515377,
|
||
significant=self.significant)
|
||
|
||
def test_brunnermunzel_return_nan(self):
|
||
""" tests that a warning is emitted when p is nan
|
||
p-value with t-distributions can be nan (0/0) (see gh-15843)
|
||
"""
|
||
x = [1, 2, 3]
|
||
y = [5, 6, 7, 8, 9]
|
||
|
||
msg = "p-value cannot be estimated|divide by zero|invalid value encountered"
|
||
with pytest.warns(RuntimeWarning, match=msg):
|
||
stats.brunnermunzel(x, y, distribution="t")
|
||
|
||
def test_brunnermunzel_normal_dist(self):
|
||
""" tests that a p is 0 for datasets that cause p->nan
|
||
when t-distribution is used (see gh-15843)
|
||
"""
|
||
x = [1, 2, 3]
|
||
y = [5, 6, 7, 8, 9]
|
||
|
||
with pytest.warns(RuntimeWarning, match='divide by zero'):
|
||
_, p = stats.brunnermunzel(x, y, distribution="normal")
|
||
assert_equal(p, 0)
|
||
|
||
|
||
class TestRatioUniforms:
|
||
""" Tests for rvs_ratio_uniforms are in test_sampling.py,
|
||
as rvs_ratio_uniforms is deprecated and moved to stats.sampling """
|
||
def test_consistency(self):
|
||
f = stats.norm.pdf
|
||
v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
|
||
umax = np.sqrt(f(0))
|
||
gen = stats.sampling.RatioUniforms(f, umax=umax, vmin=-v, vmax=v,
|
||
random_state=12345)
|
||
r1 = gen.rvs(10)
|
||
deprecation_msg = ("Please use `RatioUniforms` from the "
|
||
"`scipy.stats.sampling` namespace.")
|
||
with pytest.warns(DeprecationWarning, match=deprecation_msg):
|
||
r2 = stats.rvs_ratio_uniforms(f, umax, -v, v, size=10,
|
||
random_state=12345)
|
||
assert_equal(r1, r2)
|
||
|
||
|
||
class TestMGCErrorWarnings:
|
||
""" Tests errors and warnings derived from MGC.
|
||
"""
|
||
def test_error_notndarray(self):
|
||
# raises error if x or y is not a ndarray
|
||
x = np.arange(20)
|
||
y = [5] * 20
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, y, x)
|
||
|
||
def test_error_shape(self):
|
||
# raises error if number of samples different (n)
|
||
x = np.arange(100).reshape(25, 4)
|
||
y = x.reshape(10, 10)
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
|
||
|
||
def test_error_lowsamples(self):
|
||
# raises error if samples are low (< 3)
|
||
x = np.arange(3)
|
||
y = np.arange(3)
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
|
||
|
||
def test_error_nans(self):
|
||
# raises error if inputs contain NaNs
|
||
x = np.arange(20, dtype=float)
|
||
x[0] = np.nan
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, x)
|
||
|
||
y = np.arange(20)
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
|
||
|
||
def test_error_wrongdisttype(self):
|
||
# raises error if metric is not a function
|
||
x = np.arange(20)
|
||
compute_distance = 0
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, x,
|
||
compute_distance=compute_distance)
|
||
|
||
@pytest.mark.parametrize("reps", [
|
||
-1, # reps is negative
|
||
'1', # reps is not integer
|
||
])
|
||
def test_error_reps(self, reps):
|
||
# raises error if reps is negative
|
||
x = np.arange(20)
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, x, reps=reps)
|
||
|
||
def test_warns_reps(self):
|
||
# raises warning when reps is less than 1000
|
||
x = np.arange(20)
|
||
reps = 100
|
||
assert_warns(RuntimeWarning, stats.multiscale_graphcorr, x, x, reps=reps)
|
||
|
||
def test_error_infty(self):
|
||
# raises error if input contains infinities
|
||
x = np.arange(20)
|
||
y = np.ones(20) * np.inf
|
||
assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
|
||
|
||
|
||
class TestMGCStat:
|
||
""" Test validity of MGC test statistic
|
||
"""
|
||
def _simulations(self, samps=100, dims=1, sim_type=""):
|
||
# linear simulation
|
||
if sim_type == "linear":
|
||
x = np.random.uniform(-1, 1, size=(samps, 1))
|
||
y = x + 0.3 * np.random.random_sample(size=(x.size, 1))
|
||
|
||
# spiral simulation
|
||
elif sim_type == "nonlinear":
|
||
unif = np.array(np.random.uniform(0, 5, size=(samps, 1)))
|
||
x = unif * np.cos(np.pi * unif)
|
||
y = (unif * np.sin(np.pi * unif) +
|
||
0.4*np.random.random_sample(size=(x.size, 1)))
|
||
|
||
# independence (tests type I simulation)
|
||
elif sim_type == "independence":
|
||
u = np.random.normal(0, 1, size=(samps, 1))
|
||
v = np.random.normal(0, 1, size=(samps, 1))
|
||
u_2 = np.random.binomial(1, p=0.5, size=(samps, 1))
|
||
v_2 = np.random.binomial(1, p=0.5, size=(samps, 1))
|
||
x = u/3 + 2*u_2 - 1
|
||
y = v/3 + 2*v_2 - 1
|
||
|
||
# raises error if not approved sim_type
|
||
else:
|
||
raise ValueError("sim_type must be linear, nonlinear, or "
|
||
"independence")
|
||
|
||
# add dimensions of noise for higher dimensions
|
||
if dims > 1:
|
||
dims_noise = np.random.normal(0, 1, size=(samps, dims-1))
|
||
x = np.concatenate((x, dims_noise), axis=1)
|
||
|
||
return x, y
|
||
|
||
@pytest.mark.slow
|
||
@pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
|
||
("linear", 0.97, 1/1000), # test linear simulation
|
||
("nonlinear", 0.163, 1/1000), # test spiral simulation
|
||
("independence", -0.0094, 0.78) # test independence simulation
|
||
])
|
||
def test_oned(self, sim_type, obs_stat, obs_pvalue):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type=sim_type)
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
|
||
assert_approx_equal(stat, obs_stat, significant=1)
|
||
assert_approx_equal(pvalue, obs_pvalue, significant=1)
|
||
|
||
@pytest.mark.slow
|
||
@pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
|
||
("linear", 0.184, 1/1000), # test linear simulation
|
||
("nonlinear", 0.0190, 0.117), # test spiral simulation
|
||
])
|
||
def test_fived(self, sim_type, obs_stat, obs_pvalue):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=5, sim_type=sim_type)
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
|
||
assert_approx_equal(stat, obs_stat, significant=1)
|
||
assert_approx_equal(pvalue, obs_pvalue, significant=1)
|
||
|
||
@pytest.mark.xslow
|
||
def test_twosamp(self):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x = np.random.binomial(100, 0.5, size=(100, 5))
|
||
y = np.random.normal(0, 1, size=(80, 5))
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
|
||
assert_approx_equal(stat, 1.0, significant=1)
|
||
assert_approx_equal(pvalue, 0.001, significant=1)
|
||
|
||
# generate x and y
|
||
y = np.random.normal(0, 1, size=(100, 5))
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y, is_twosamp=True)
|
||
assert_approx_equal(stat, 1.0, significant=1)
|
||
assert_approx_equal(pvalue, 0.001, significant=1)
|
||
|
||
@pytest.mark.slow
|
||
def test_workers(self):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type="linear")
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y, workers=2)
|
||
assert_approx_equal(stat, 0.97, significant=1)
|
||
assert_approx_equal(pvalue, 0.001, significant=1)
|
||
|
||
@pytest.mark.slow
|
||
def test_random_state(self):
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type="linear")
|
||
|
||
# test stat and pvalue
|
||
stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
|
||
assert_approx_equal(stat, 0.97, significant=1)
|
||
assert_approx_equal(pvalue, 0.001, significant=1)
|
||
|
||
@pytest.mark.slow
|
||
def test_dist_perm(self):
|
||
np.random.seed(12345678)
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type="nonlinear")
|
||
distx = cdist(x, x, metric="euclidean")
|
||
disty = cdist(y, y, metric="euclidean")
|
||
|
||
stat_dist, pvalue_dist, _ = stats.multiscale_graphcorr(distx, disty,
|
||
compute_distance=None,
|
||
random_state=1)
|
||
assert_approx_equal(stat_dist, 0.163, significant=1)
|
||
assert_approx_equal(pvalue_dist, 0.001, significant=1)
|
||
|
||
@pytest.mark.slow
|
||
def test_pvalue_literature(self):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type="linear")
|
||
|
||
# test stat and pvalue
|
||
_, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
|
||
assert_allclose(pvalue, 1/1001)
|
||
|
||
@pytest.mark.slow
|
||
def test_alias(self):
|
||
np.random.seed(12345678)
|
||
|
||
# generate x and y
|
||
x, y = self._simulations(samps=100, dims=1, sim_type="linear")
|
||
|
||
res = stats.multiscale_graphcorr(x, y, random_state=1)
|
||
assert_equal(res.stat, res.statistic)
|
||
|
||
|
||
class TestQuantileTest:
|
||
r""" Test the non-parametric quantile test,
|
||
including the computation of confidence intervals
|
||
"""
|
||
|
||
def test_quantile_test_iv(self):
|
||
x = [1, 2, 3]
|
||
|
||
message = "`x` must be a one-dimensional array of numbers."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test([x])
|
||
|
||
message = "`q` must be a scalar."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x, q=[1, 2])
|
||
|
||
message = "`p` must be a float strictly between 0 and 1."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x, p=[0.5, 0.75])
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x, p=2)
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x, p=-0.5)
|
||
|
||
message = "`alternative` must be one of..."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x, alternative='one-sided')
|
||
|
||
message = "`confidence_level` must be a number between 0 and 1."
|
||
with pytest.raises(ValueError, match=message):
|
||
stats.quantile_test(x).confidence_interval(1)
|
||
|
||
@pytest.mark.parametrize(
|
||
'p, alpha, lb, ub, alternative',
|
||
[[0.3, 0.95, 1.221402758160170, 1.476980793882643, 'two-sided'],
|
||
[0.5, 0.9, 1.506817785112854, 1.803988415397857, 'two-sided'],
|
||
[0.25, 0.95, -np.inf, 1.39096812846378, 'less'],
|
||
[0.8, 0.9, 2.117000016612675, np.inf, 'greater']]
|
||
)
|
||
def test_R_ci_quantile(self, p, alpha, lb, ub, alternative):
|
||
# Test against R library `confintr` function `ci_quantile`, e.g.
|
||
# library(confintr)
|
||
# options(digits=16)
|
||
# x <- exp(seq(0, 1, by = 0.01))
|
||
# ci_quantile(x, q = 0.3)$interval
|
||
# ci_quantile(x, q = 0.5, probs = c(0.05, 0.95))$interval
|
||
# ci_quantile(x, q = 0.25, probs = c(0, 0.95))$interval
|
||
# ci_quantile(x, q = 0.8, probs = c(0.1, 1))$interval
|
||
x = np.exp(np.arange(0, 1.01, 0.01))
|
||
res = stats.quantile_test(x, p=p, alternative=alternative)
|
||
assert_allclose(res.confidence_interval(alpha), [lb, ub], rtol=1e-15)
|
||
|
||
@pytest.mark.parametrize(
|
||
'q, p, alternative, ref',
|
||
[[1.2, 0.3, 'two-sided', 0.01515567517648],
|
||
[1.8, 0.5, 'two-sided', 0.1109183496606]]
|
||
)
|
||
def test_R_pvalue(self, q, p, alternative, ref):
|
||
# Test against R library `snpar` function `quant.test`, e.g.
|
||
# library(snpar)
|
||
# options(digits=16)
|
||
# x < - exp(seq(0, 1, by=0.01))
|
||
# quant.test(x, q=1.2, p=0.3, exact=TRUE, alternative='t')
|
||
x = np.exp(np.arange(0, 1.01, 0.01))
|
||
res = stats.quantile_test(x, q=q, p=p, alternative=alternative)
|
||
assert_allclose(res.pvalue, ref, rtol=1e-12)
|
||
|
||
@pytest.mark.parametrize('case', ['continuous', 'discrete'])
|
||
@pytest.mark.parametrize('alternative', ['less', 'greater'])
|
||
@pytest.mark.parametrize('alpha', [0.9, 0.95])
|
||
def test_pval_ci_match(self, case, alternative, alpha):
|
||
# Verify that the following statement holds:
|
||
|
||
# The 95% confidence interval corresponding with alternative='less'
|
||
# has -inf as its lower bound, and the upper bound `xu` is the greatest
|
||
# element from the sample `x` such that:
|
||
# `stats.quantile_test(x, q=xu, p=p, alternative='less').pvalue``
|
||
# will be greater than 5%.
|
||
|
||
# And the corresponding statement for the alternative='greater' case.
|
||
|
||
seed = int((7**len(case) + len(alternative))*alpha)
|
||
rng = np.random.default_rng(seed)
|
||
if case == 'continuous':
|
||
p, q = rng.random(size=2)
|
||
rvs = rng.random(size=100)
|
||
else:
|
||
rvs = rng.integers(1, 11, size=100)
|
||
p = rng.random()
|
||
q = rng.integers(1, 11)
|
||
|
||
res = stats.quantile_test(rvs, q=q, p=p, alternative=alternative)
|
||
ci = res.confidence_interval(confidence_level=alpha)
|
||
|
||
# select elements inside the confidence interval based on alternative
|
||
if alternative == 'less':
|
||
i_inside = rvs <= ci.high
|
||
else:
|
||
i_inside = rvs >= ci.low
|
||
|
||
for x in rvs[i_inside]:
|
||
res = stats.quantile_test(rvs, q=x, p=p, alternative=alternative)
|
||
assert res.pvalue > 1 - alpha
|
||
|
||
for x in rvs[~i_inside]:
|
||
res = stats.quantile_test(rvs, q=x, p=p, alternative=alternative)
|
||
assert res.pvalue < 1 - alpha
|
||
|
||
def test_match_conover_examples(self):
|
||
# Test against the examples in [1] (Conover Practical Nonparametric
|
||
# Statistics Third Edition) pg 139
|
||
|
||
# Example 1
|
||
# Data is [189, 233, 195, 160, 212, 176, 231, 185, 199, 213, 202, 193,
|
||
# 174, 166, 248]
|
||
# Two-sided test of whether the upper quartile (p=0.75) equals 193
|
||
# (q=193). Conover shows that 7 of the observations are less than or
|
||
# equal to 193, and "for the binomial random variable Y, P(Y<=7) =
|
||
# 0.0173", so the two-sided p-value is twice that, 0.0346.
|
||
x = [189, 233, 195, 160, 212, 176, 231, 185, 199, 213, 202, 193,
|
||
174, 166, 248]
|
||
pvalue_expected = 0.0346
|
||
res = stats.quantile_test(x, q=193, p=0.75, alternative='two-sided')
|
||
assert_allclose(res.pvalue, pvalue_expected, rtol=1e-5)
|
||
|
||
# Example 2
|
||
# Conover doesn't give explicit data, just that 8 out of 112
|
||
# observations are 60 or less. The test is whether the median time is
|
||
# equal to 60 against the alternative that the median is greater than
|
||
# 60. The p-value is calculated as P(Y<=8), where Y is again a binomial
|
||
# distributed random variable, now with p=0.5 and n=112. Conover uses a
|
||
# normal approximation, but we can easily calculate the CDF of the
|
||
# binomial distribution.
|
||
x = [59]*8 + [61]*(112-8)
|
||
pvalue_expected = stats.binom(p=0.5, n=112).pmf(k=8)
|
||
res = stats.quantile_test(x, q=60, p=0.5, alternative='greater')
|
||
assert_allclose(res.pvalue, pvalue_expected, atol=1e-10)
|
||
|
||
|
||
class TestPageTrendTest:
|
||
# expected statistic and p-values generated using R at
|
||
# https://rdrr.io/cran/cultevo/, e.g.
|
||
# library(cultevo)
|
||
# data = rbind(c(72, 47, 73, 35, 47, 96, 30, 59, 41, 36, 56, 49, 81, 43,
|
||
# 70, 47, 28, 28, 62, 20, 61, 20, 80, 24, 50),
|
||
# c(68, 52, 60, 34, 44, 20, 65, 88, 21, 81, 48, 31, 31, 67,
|
||
# 69, 94, 30, 24, 40, 87, 70, 43, 50, 96, 43),
|
||
# c(81, 13, 85, 35, 79, 12, 92, 86, 21, 64, 16, 64, 68, 17,
|
||
# 16, 89, 71, 43, 43, 36, 54, 13, 66, 51, 55))
|
||
# result = page.test(data, verbose=FALSE)
|
||
# Most test cases generated to achieve common critical p-values so that
|
||
# results could be checked (to limited precision) against tables in
|
||
# scipy.stats.page_trend_test reference [1]
|
||
|
||
np.random.seed(0)
|
||
data_3_25 = np.random.rand(3, 25)
|
||
data_10_26 = np.random.rand(10, 26)
|
||
|
||
ts = [
|
||
(12805, 0.3886487053947608, False, 'asymptotic', data_3_25),
|
||
(49140, 0.02888978556179862, False, 'asymptotic', data_10_26),
|
||
(12332, 0.7722477197436702, False, 'asymptotic',
|
||
[[72, 47, 73, 35, 47, 96, 30, 59, 41, 36, 56, 49, 81,
|
||
43, 70, 47, 28, 28, 62, 20, 61, 20, 80, 24, 50],
|
||
[68, 52, 60, 34, 44, 20, 65, 88, 21, 81, 48, 31, 31,
|
||
67, 69, 94, 30, 24, 40, 87, 70, 43, 50, 96, 43],
|
||
[81, 13, 85, 35, 79, 12, 92, 86, 21, 64, 16, 64, 68,
|
||
17, 16, 89, 71, 43, 43, 36, 54, 13, 66, 51, 55]]),
|
||
(266, 4.121656378600823e-05, False, 'exact',
|
||
[[1.5, 4., 8.3, 5, 19, 11],
|
||
[5, 4, 3.5, 10, 20, 21],
|
||
[8.4, 3.2, 10, 12, 14, 15]]),
|
||
(332, 0.9566400920502488, True, 'exact',
|
||
[[4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1],
|
||
[4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1],
|
||
[3, 4, 1, 2], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4],
|
||
[1, 2, 3, 4], [1, 2, 3, 4]]),
|
||
(241, 0.9622210164861476, True, 'exact',
|
||
[[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
|
||
[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
|
||
[3, 2, 1], [2, 1, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3],
|
||
[1, 2, 3], [1, 2, 3], [1, 2, 3]]),
|
||
(197, 0.9619432897162209, True, 'exact',
|
||
[[6, 5, 4, 3, 2, 1], [6, 5, 4, 3, 2, 1], [1, 3, 4, 5, 2, 6]]),
|
||
(423, 0.9590458306880073, True, 'exact',
|
||
[[5, 4, 3, 2, 1], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1],
|
||
[5, 4, 3, 2, 1], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1],
|
||
[4, 1, 3, 2, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5],
|
||
[1, 2, 3, 4, 5]]),
|
||
(217, 0.9693058575034678, True, 'exact',
|
||
[[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
|
||
[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
|
||
[2, 1, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3],
|
||
[1, 2, 3]]),
|
||
(395, 0.991530289351305, True, 'exact',
|
||
[[7, 6, 5, 4, 3, 2, 1], [7, 6, 5, 4, 3, 2, 1],
|
||
[6, 5, 7, 4, 3, 2, 1], [1, 2, 3, 4, 5, 6, 7]]),
|
||
(117, 0.9997817843373017, True, 'exact',
|
||
[[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
|
||
[3, 2, 1], [3, 2, 1], [3, 2, 1], [2, 1, 3], [1, 2, 3]]),
|
||
]
|
||
|
||
@pytest.mark.parametrize("L, p, ranked, method, data", ts)
|
||
def test_accuracy(self, L, p, ranked, method, data):
|
||
np.random.seed(42)
|
||
res = stats.page_trend_test(data, ranked=ranked, method=method)
|
||
assert_equal(L, res.statistic)
|
||
assert_allclose(p, res.pvalue)
|
||
assert_equal(method, res.method)
|
||
|
||
ts2 = [
|
||
(542, 0.9481266260876332, True, 'exact',
|
||
[[10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
|
||
[1, 8, 4, 7, 6, 5, 9, 3, 2, 10]]),
|
||
(1322, 0.9993113928199309, True, 'exact',
|
||
[[10, 9, 8, 7, 6, 5, 4, 3, 2, 1], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
|
||
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1], [9, 2, 8, 7, 6, 5, 4, 3, 10, 1],
|
||
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]),
|
||
(2286, 0.9908688345484833, True, 'exact',
|
||
[[8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
|
||
[8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
|
||
[8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
|
||
[8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
|
||
[8, 7, 6, 5, 4, 3, 2, 1], [1, 3, 5, 6, 4, 7, 2, 8],
|
||
[1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8],
|
||
[1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8],
|
||
[1, 2, 3, 4, 5, 6, 7, 8]]),
|
||
]
|
||
|
||
# only the first of these appears slow because intermediate data are
|
||
# cached and used on the rest
|
||
@pytest.mark.parametrize("L, p, ranked, method, data", ts)
|
||
@pytest.mark.slow()
|
||
def test_accuracy2(self, L, p, ranked, method, data):
|
||
np.random.seed(42)
|
||
res = stats.page_trend_test(data, ranked=ranked, method=method)
|
||
assert_equal(L, res.statistic)
|
||
assert_allclose(p, res.pvalue)
|
||
assert_equal(method, res.method)
|
||
|
||
def test_options(self):
|
||
np.random.seed(42)
|
||
m, n = 10, 20
|
||
predicted_ranks = np.arange(1, n+1)
|
||
perm = np.random.permutation(np.arange(n))
|
||
data = np.random.rand(m, n)
|
||
ranks = stats.rankdata(data, axis=1)
|
||
res1 = stats.page_trend_test(ranks)
|
||
res2 = stats.page_trend_test(ranks, ranked=True)
|
||
res3 = stats.page_trend_test(data, ranked=False)
|
||
res4 = stats.page_trend_test(ranks, predicted_ranks=predicted_ranks)
|
||
res5 = stats.page_trend_test(ranks[:, perm],
|
||
predicted_ranks=predicted_ranks[perm])
|
||
assert_equal(res1.statistic, res2.statistic)
|
||
assert_equal(res1.statistic, res3.statistic)
|
||
assert_equal(res1.statistic, res4.statistic)
|
||
assert_equal(res1.statistic, res5.statistic)
|
||
|
||
def test_Ames_assay(self):
|
||
# test from _page_trend_test.py [2] page 151; data on page 144
|
||
np.random.seed(42)
|
||
|
||
data = [[101, 117, 111], [91, 90, 107], [103, 133, 121],
|
||
[136, 140, 144], [190, 161, 201], [146, 120, 116]]
|
||
data = np.array(data).T
|
||
predicted_ranks = np.arange(1, 7)
|
||
|
||
res = stats.page_trend_test(data, ranked=False,
|
||
predicted_ranks=predicted_ranks,
|
||
method="asymptotic")
|
||
assert_equal(res.statistic, 257)
|
||
assert_almost_equal(res.pvalue, 0.0035, decimal=4)
|
||
|
||
res = stats.page_trend_test(data, ranked=False,
|
||
predicted_ranks=predicted_ranks,
|
||
method="exact")
|
||
assert_equal(res.statistic, 257)
|
||
assert_almost_equal(res.pvalue, 0.0023, decimal=4)
|
||
|
||
def test_input_validation(self):
|
||
# test data not a 2d array
|
||
with assert_raises(ValueError, match="`data` must be a 2d array."):
|
||
stats.page_trend_test(None)
|
||
with assert_raises(ValueError, match="`data` must be a 2d array."):
|
||
stats.page_trend_test([])
|
||
with assert_raises(ValueError, match="`data` must be a 2d array."):
|
||
stats.page_trend_test([1, 2])
|
||
with assert_raises(ValueError, match="`data` must be a 2d array."):
|
||
stats.page_trend_test([[[1]]])
|
||
|
||
# test invalid dimensions
|
||
with assert_raises(ValueError, match="Page's L is only appropriate"):
|
||
stats.page_trend_test(np.random.rand(1, 3))
|
||
with assert_raises(ValueError, match="Page's L is only appropriate"):
|
||
stats.page_trend_test(np.random.rand(2, 2))
|
||
|
||
# predicted ranks must include each integer [1, 2, 3] exactly once
|
||
message = "`predicted_ranks` must include each integer"
|
||
with assert_raises(ValueError, match=message):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
predicted_ranks=[0, 1, 2])
|
||
with assert_raises(ValueError, match=message):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
predicted_ranks=[1.1, 2, 3])
|
||
with assert_raises(ValueError, match=message):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
predicted_ranks=[1, 2, 3, 3])
|
||
with assert_raises(ValueError, match=message):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
predicted_ranks="invalid")
|
||
|
||
# test improperly ranked data
|
||
with assert_raises(ValueError, match="`data` is not properly ranked"):
|
||
stats.page_trend_test([[0, 2, 3], [1, 2, 3]], True)
|
||
with assert_raises(ValueError, match="`data` is not properly ranked"):
|
||
stats.page_trend_test([[1, 2, 3], [1, 2, 4]], True)
|
||
|
||
# various
|
||
with assert_raises(ValueError, match="`data` contains NaNs"):
|
||
stats.page_trend_test([[1, 2, 3], [1, 2, np.nan]],
|
||
ranked=False)
|
||
with assert_raises(ValueError, match="`method` must be in"):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
method="ekki")
|
||
with assert_raises(TypeError, match="`ranked` must be boolean."):
|
||
stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
|
||
ranked="ekki")
|
||
|
||
|
||
rng = np.random.default_rng(902340982)
|
||
x = rng.random(10)
|
||
y = rng.random(10)
|
||
|
||
|
||
@pytest.mark.parametrize("fun, args",
|
||
[(stats.wilcoxon, (x,)),
|
||
(stats.ks_1samp, (x, stats.norm.cdf)), # type: ignore[attr-defined] # noqa: E501
|
||
(stats.ks_2samp, (x, y)),
|
||
(stats.kstest, (x, y)),
|
||
])
|
||
def test_rename_mode_method(fun, args):
|
||
|
||
res = fun(*args, method='exact')
|
||
res2 = fun(*args, mode='exact')
|
||
assert_equal(res, res2)
|
||
|
||
err = rf"{fun.__name__}() got multiple values for argument"
|
||
with pytest.raises(TypeError, match=re.escape(err)):
|
||
fun(*args, method='exact', mode='exact')
|
||
|
||
|
||
class TestExpectile:
|
||
def test_same_as_mean(self):
|
||
rng = np.random.default_rng(42)
|
||
x = rng.random(size=20)
|
||
assert_allclose(stats.expectile(x, alpha=0.5), np.mean(x))
|
||
|
||
def test_minimum(self):
|
||
rng = np.random.default_rng(42)
|
||
x = rng.random(size=20)
|
||
assert_allclose(stats.expectile(x, alpha=0), np.amin(x))
|
||
|
||
def test_maximum(self):
|
||
rng = np.random.default_rng(42)
|
||
x = rng.random(size=20)
|
||
assert_allclose(stats.expectile(x, alpha=1), np.amax(x))
|
||
|
||
def test_weights(self):
|
||
# expectile should minimize `fun` defined below; see
|
||
# F. Sobotka and T. Kneib, "Geoadditive expectile regression",
|
||
# Computational Statistics and Data Analysis 56 (2012) 755-767
|
||
# :doi:`10.1016/j.csda.2010.11.015`
|
||
rng = np.random.default_rng(1856392524598679138)
|
||
|
||
def fun(u, a, alpha, weights):
|
||
w = np.full_like(a, fill_value=alpha)
|
||
w[a <= u] = 1 - alpha
|
||
return np.sum(w * weights * (a - u)**2)
|
||
|
||
def expectile2(a, alpha, weights):
|
||
bracket = np.min(a), np.max(a)
|
||
return optimize.minimize_scalar(fun, bracket=bracket,
|
||
args=(a, alpha, weights)).x
|
||
|
||
n = 10
|
||
a = rng.random(n)
|
||
alpha = rng.random()
|
||
weights = rng.random(n)
|
||
|
||
res = stats.expectile(a, alpha, weights=weights)
|
||
ref = expectile2(a, alpha, weights)
|
||
assert_allclose(res, ref)
|
||
|
||
@pytest.mark.parametrize(
|
||
"alpha", [0.2, 0.5 - 1e-12, 0.5, 0.5 + 1e-12, 0.8]
|
||
)
|
||
@pytest.mark.parametrize("n", [20, 2000])
|
||
def test_expectile_properties(self, alpha, n):
|
||
"""
|
||
See Section 6 of
|
||
I. Steinwart, C. Pasin, R.C. Williamson & S. Zhang (2014).
|
||
"Elicitation and Identification of Properties". COLT.
|
||
http://proceedings.mlr.press/v35/steinwart14.html
|
||
|
||
and
|
||
|
||
Propositions 5, 6, 7 of
|
||
F. Bellini, B. Klar, and A. Müller and E. Rosazza Gianin (2013).
|
||
"Generalized Quantiles as Risk Measures"
|
||
http://doi.org/10.2139/ssrn.2225751
|
||
"""
|
||
rng = np.random.default_rng(42)
|
||
x = rng.normal(size=n)
|
||
|
||
# 0. definite / constancy
|
||
# Let T(X) denote the expectile of rv X ~ F.
|
||
# T(c) = c for constant c
|
||
for c in [-5, 0, 0.5]:
|
||
assert_allclose(
|
||
stats.expectile(np.full(shape=n, fill_value=c), alpha=alpha),
|
||
c
|
||
)
|
||
|
||
# 1. translation equivariance
|
||
# T(X + c) = T(X) + c
|
||
c = rng.exponential()
|
||
assert_allclose(
|
||
stats.expectile(x + c, alpha=alpha),
|
||
stats.expectile(x, alpha=alpha) + c,
|
||
)
|
||
assert_allclose(
|
||
stats.expectile(x - c, alpha=alpha),
|
||
stats.expectile(x, alpha=alpha) - c,
|
||
)
|
||
|
||
# 2. positively homogeneity
|
||
# T(cX) = c * T(X) for c > 0
|
||
assert_allclose(
|
||
stats.expectile(c * x, alpha=alpha),
|
||
c * stats.expectile(x, alpha=alpha),
|
||
)
|
||
|
||
# 3. subadditivity
|
||
# Note that subadditivity holds for alpha >= 0.5.
|
||
# T(X + Y) <= T(X) + T(Y)
|
||
# For alpha = 0.5, i.e. the mean, strict equality holds.
|
||
# For alpha < 0.5, one can use property 6. to show
|
||
# T(X + Y) >= T(X) + T(Y)
|
||
y = rng.logistic(size=n, loc=10) # different distribution than x
|
||
if alpha == 0.5:
|
||
def assert_op(a, b):
|
||
assert_allclose(a, b)
|
||
|
||
elif alpha > 0.5:
|
||
def assert_op(a, b):
|
||
assert a < b
|
||
|
||
else:
|
||
def assert_op(a, b):
|
||
assert a > b
|
||
|
||
assert_op(
|
||
stats.expectile(np.r_[x + y], alpha=alpha),
|
||
stats.expectile(x, alpha=alpha)
|
||
+ stats.expectile(y, alpha=alpha)
|
||
)
|
||
|
||
# 4. monotonicity
|
||
# This holds for first order stochastic dominance X:
|
||
# X >= Y whenever P(X <= x) < P(Y <= x)
|
||
# T(X) <= T(Y) whenever X <= Y
|
||
y = rng.normal(size=n, loc=5)
|
||
assert (
|
||
stats.expectile(x, alpha=alpha) <= stats.expectile(y, alpha=alpha)
|
||
)
|
||
|
||
# 5. convexity for alpha > 0.5, concavity for alpha < 0.5
|
||
# convexity is
|
||
# T((1 - c) X + c Y) <= (1 - c) T(X) + c T(Y) for 0 <= c <= 1
|
||
y = rng.logistic(size=n, loc=10)
|
||
for c in [0.1, 0.5, 0.8]:
|
||
assert_op(
|
||
stats.expectile((1-c)*x + c*y, alpha=alpha),
|
||
(1-c) * stats.expectile(x, alpha=alpha) +
|
||
c * stats.expectile(y, alpha=alpha)
|
||
)
|
||
|
||
# 6. negative argument
|
||
# T_{alpha}(-X) = -T_{1-alpha}(X)
|
||
assert_allclose(
|
||
stats.expectile(-x, alpha=alpha),
|
||
-stats.expectile(x, alpha=1-alpha),
|
||
)
|
||
|
||
@pytest.mark.parametrize("n", [20, 2000])
|
||
def test_monotonicity_in_alpha(self, n):
|
||
rng = np.random.default_rng(42)
|
||
x = rng.pareto(a=2, size=n)
|
||
e_list = []
|
||
alpha_seq = np.logspace(-15, np.log10(0.5), 100)
|
||
# sorted list of unique alpha values in interval (0, 1)
|
||
for alpha in np.r_[0, alpha_seq, 1 - alpha_seq[:-1:-1], 1]:
|
||
e_list.append(stats.expectile(x, alpha=alpha))
|
||
assert np.all(np.diff(e_list) > 0)
|