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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/scipy/stats/tests/test_stats.py

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Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
""" 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 warnings
from collections import namedtuple
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)
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 .common_tests import check_named_results
from scipy.sparse.sputils import matrix
from scipy.spatial.distance import cdist
""" 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(object):
# 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)
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')
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')
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(object):
""" 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_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
with assert_warns(stats.PearsonRConstantInputWarning):
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)]
with assert_warns(stats.PearsonRNearConstantInputWarning):
# 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
r, p = stats.pearsonr([1, 2], [3, 5])
assert_equal(r, 1)
assert_equal(p, 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)
def test_more_basic_examples(self):
x = [1, 2, 3, 4]
y = [0, 1, 0.5, 1]
r, p = stats.pearsonr(x, y)
# The expected values were computed using mpmath with 80 digits
# of precision.
assert_allclose(r, 0.674199862463242)
assert_allclose(p, 0.325800137536758)
x = [1, 2, 3]
y = [5, -4, -13]
r, p = stats.pearsonr(x, y)
# The expected r and p are exact.
assert_allclose(r, -1.0)
assert_allclose(p, 0.0, atol=1e-7)
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)
class TestFisherExact(object):
"""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)
@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]])
class TestCorrSpearmanr(object):
""" 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 corelations, 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).correlation.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_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)
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.correlation, 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').correlation,
corr)
res = stats.spearmanr(x, nan_policy='omit').correlation
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').correlation
# bool against float with NaNs
b[m] = np.nan
res2 = stats.spearmanr(a, b, nan_policy='omit').correlation
# int against float with NaNs
a = a.astype(np.int32)
res3 = stats.spearmanr(a, b, nan_policy='omit').correlation
expected = [0.865895477, 0.866100381, 0.866100381]
assert_allclose([res1, res2, res3], expected)
class TestCorrSpearmanr2(object):
"""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
with assert_warns(stats.SpearmanRConstantInputWarning):
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]])
with assert_warns(stats.SpearmanRConstantInputWarning):
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])
with assert_warns(stats.SpearmanRConstantInputWarning):
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)
# 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():
# 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)
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)
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)
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)
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)
res = stats.kendalltau(x, y)
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)
res = stats.kendalltau(x, y)
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)
res = stats.kendalltau(x, y)
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)
res = stats.kendalltau(x, y)
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)
res = stats.kendalltau(x, y)
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)
res = stats.kendalltau(x, y)
assert_approx_equal(res[0], expected[0])
assert_approx_equal(res[1], expected[1])
# check exception in case of ties
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')
# 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')
res = stats.kendalltau(x1, x2)
check_named_results(res, attributes)
# with only ties in one or both inputs
assert_equal(stats.kendalltau([2,2,2], [2,2,2]), (np.nan, np.nan))
assert_equal(stats.kendalltau([2,0,2], [2,2,2]), (np.nan, np.nan))
assert_equal(stats.kendalltau([2,2,2], [2,0,2]), (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])
# and do we get a tau of 1 for identical inputs?
assert_approx_equal(stats.kendalltau([1,1,2], [1,1,2])[0], 1.0)
# 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.correlation, r2.correlation, atol=1e-15)
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)
# 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)
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.
x = np.arange(n)
y = np.arange(n)
_, pval = stats.kendalltau(x, y, method='exact')
assert_equal(pval, 0.0)
y[-1], y[-2] = y[-2], y[-1]
_, pval = stats.kendalltau(x, y, method='exact')
assert_equal(pval, 0.0)
y[-3], y[-4] = y[-4], y[-3]
_, pval = stats.kendalltau(x, y, method='exact')
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 in range(len(x)):
for j in 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)
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, lambda x: 1./(x+1), add)
actual = stats.weightedtau(a, b, rank, lambda x: 1./(x+1), add).correlation
assert_approx_equal(expected, actual)
# Second pass: use a random rank
np.random.shuffle(rank)
class TestFindRepeats(object):
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(object):
def test_linregressBIGX(self):
# W.II.F. Regress BIG on X.
# The constant should be 99999990 and the regression coefficient should be 1.
y = stats.linregress(X,BIG)
intercept = y[1]
r = y[2]
assert_almost_equal(intercept,99999990)
assert_almost_equal(r,1.0)
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. The program should not complain.
y = stats.linregress(X,X)
intercept = y[1]
r = y[2]
assert_almost_equal(intercept,0.0)
assert_almost_equal(r,1.0)
# 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. 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.
y = stats.linregress(X,ZERO)
intercept = y[1]
r = y[2]
assert_almost_equal(intercept,0.0)
assert_almost_equal(r,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))
res = stats.linregress(x, y)
assert_almost_equal(res[4], 2.3957814497838803e-3)
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))
res = stats.linregress(rows)
assert_almost_equal(res[4], 2.3957814497838803e-3)
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))
cols = np.hstack((np.expand_dims(x, 1), np.expand_dims(y, 1)))
res = stats.linregress(cols)
assert_almost_equal(res[4], 2.3957814497838803e-3)
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
res = (1.0, 5.0, 0.98229948625750, 7.45259691e-008, 0.063564172616372733)
assert_array_almost_equal(stats.linregress(x,y),res,decimal=14)
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)
stats.linregress(x, y)
res = stats.linregress(x, y)
assert_(res[2] >= -1) # propagated numerical errors were not corrected
assert_almost_equal(res[2], -1) # perfect negative correlation case
assert_(not np.isnan(res[4])) # stderr should stay finite
def test_linregress_result_attributes(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))
res = stats.linregress(x, y)
attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
check_named_results(res, attributes)
def test_regress_two_inputs(self):
# Regress a simple line formed by two points.
x = np.arange(2)
y = np.arange(3, 5)
res = stats.linregress(x, y)
assert_almost_equal(res[3], 0.0) # non-horizontal line
assert_almost_equal(res[4], 0.0) # zero stderr
def test_regress_two_inputs_horizontal_line(self):
# Regress a horizontal line formed by two points.
x = np.arange(2)
y = np.ones(2)
res = stats.linregress(x, y)
assert_almost_equal(res[3], 1.0) # horizontal line
assert_almost_equal(res[4], 0.0) # zero stderr
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]
# Expected values
exp_slope = 1.00211681802045
exp_intercept = -0.262323073774029
exp_rsquared = 0.999993745883712
actual = stats.linregress(x, y)
assert_almost_equal(actual.slope, exp_slope)
assert_almost_equal(actual.intercept, exp_intercept)
assert_almost_equal(actual.rvalue**2, exp_rsquared)
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"):
assert_array_equal(stats.linregress(x, x),
(np.nan, np.nan, np.nan, np.nan, np.nan))
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)
# 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)
assert_almost_equal(slope, 4)
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(object):
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])
class TestItemfreq(object):
a = [5, 7, 1, 2, 1, 5, 7] * 10
b = [1, 2, 5, 7]
def test_numeric_types(self):
# Check itemfreq works for all dtypes (adapted from np.unique tests)
def _check_itemfreq(dt):
a = np.array(self.a, dt)
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
v = stats.itemfreq(a)
assert_array_equal(v[:, 0], [1, 2, 5, 7])
assert_array_equal(v[:, 1], np.array([20, 10, 20, 20], dtype=dt))
dtypes = [np.int32, np.int64, np.float32, np.float64,
np.complex64, np.complex128]
for dt in dtypes:
_check_itemfreq(dt)
def test_object_arrays(self):
a, b = self.a, self.b
dt = 'O'
aa = np.empty(len(a), dt)
aa[:] = a
bb = np.empty(len(b), dt)
bb[:] = b
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
v = stats.itemfreq(aa)
assert_array_equal(v[:, 0], bb)
def test_structured_arrays(self):
a, b = self.a, self.b
dt = [('', 'i'), ('', 'i')]
aa = np.array(list(zip(a, a)), dt)
bb = np.array(list(zip(b, b)), dt)
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
v = stats.itemfreq(aa)
# Arrays don't compare equal because v[:,0] is object array
assert_equal(tuple(v[2, 0]), tuple(bb[2]))
class TestMode(object):
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][0], 6)
assert_equal(vals[1][0], 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)
assert_equal(vals[0], np.array([30]))
assert_equal(vals[1], np.array([8]))
vals = stats.mode(arr, axis=0)
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)
assert_equal(vals[0], np.array([[10], [10], [20], [30], [30]]))
assert_equal(vals[1], np.array([[2], [4], [3], [4], [3]]))
def test_strings(self):
data1 = ['rain', 'showers', 'showers']
vals = stats.mode(data1)
assert_equal(vals[0][0], 'showers')
assert_equal(vals[1][0], 2)
def test_mixed_objects(self):
objects = [10, True, np.nan, 'hello', 10]
arr = np.empty((5,), dtype=object)
arr[:] = objects
vals = stats.mode(arr)
assert_equal(vals[0][0], 10)
assert_equal(vals[1][0], 2)
def test_objects(self):
# Python objects must be sortable (le + eq) and have ne defined
# for np.unique to work. hash is for set.
class Point(object):
def __init__(self, x):
self.x = x
def __eq__(self, other):
return self.x == other.x
def __ne__(self, other):
return self.x != other.x
def __lt__(self, other):
return self.x < other.x
def __hash__(self):
return hash(self.x)
points = [Point(x) for x in [1, 2, 3, 4, 3, 2, 2, 2]]
arr = np.empty((8,), dtype=object)
arr[:] = points
assert_(len(set(points)) == 4)
assert_equal(np.unique(arr).shape, (4,))
vals = stats.mode(arr)
assert_equal(vals[0][0], Point(2))
assert_equal(vals[1][0], 4)
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],
])
def test_smallest_equal(self, data):
result = stats.mode(data, nan_policy='omit')
assert_equal(result[0][0], 1)
def test_obj_arrays_ndim(self):
# regression test for gh-9645: `mode` fails for object arrays w/ndim > 1
data = [['Oxidation'], ['Oxidation'], ['Polymerization'], ['Reduction']]
ar = np.array(data, dtype=object)
m = stats.mode(ar, axis=0)
assert np.all(m.mode == 'Oxidation') and m.mode.shape == (1, 1)
assert np.all(m.count == 2) and m.count.shape == (1, 1)
data1 = data + [[np.nan]]
ar1 = np.array(data1, dtype=object)
m = stats.mode(ar1, axis=0)
assert np.all(m.mode == 'Oxidation') and m.mode.shape == (1, 1)
assert np.all(m.count == 2) and m.count.shape == (1, 1)
class TestVariability(object):
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')
def test_zmap(self):
# not in R, so tested by using:
# (testcase[i] - mean(testcase, axis=0)) / sqrt(var(testcase) * 3/4)
y = stats.zmap(self.testcase,self.testcase)
desired = ([-1.3416407864999, -0.44721359549996, 0.44721359549996, 1.3416407864999])
assert_array_almost_equal(desired,y,decimal=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)
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(self.testcase)
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_raise(self):
x = np.array([1, 2, np.nan, 4, 5])
assert_raises(ValueError, stats.zscore, x, nan_policy='raise')
class TestMedianAbsDeviation(object):
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(sef):
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)
class TestMedianAbsoluteDeviation(object):
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_mad_empty(self):
dat = []
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad = stats.median_absolute_deviation(dat)
assert_equal(mad, np.nan)
def test_mad_nan_shape1(self):
z = np.ones((3, 0))
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad_axis0 = stats.median_absolute_deviation(z, axis=0)
mad_axis1 = stats.median_absolute_deviation(z, axis=1)
assert_equal(mad_axis0, np.nan)
assert_equal(mad_axis1, np.array([np.nan, np.nan, np.nan]))
assert_equal(mad_axis1.shape, (3,))
def test_mad_nan_shape2(self):
z = np.ones((3, 0, 2))
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad_axis0 = stats.median_absolute_deviation(z, axis=0)
mad_axis1 = stats.median_absolute_deviation(z, axis=1)
mad_axis2 = stats.median_absolute_deviation(z, axis=2)
assert_equal(mad_axis0, np.nan)
assert_equal(mad_axis1, np.array([[np.nan, np.nan],
[np.nan, np.nan],
[np.nan, np.nan]]))
assert_equal(mad_axis1.shape, (3, 2))
assert_equal(mad_axis2, np.nan)
def test_mad_nan_propagate(self):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad = stats.median_absolute_deviation(self.dat_nan,
nan_policy='propagate')
assert_equal(mad, np.nan)
def test_mad_nan_raise(self):
with assert_raises(ValueError):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
stats.median_absolute_deviation(self.dat_nan,
nan_policy='raise')
def test_mad_scale_default(self):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad = stats.median_absolute_deviation(self.dat, scale=1.0)
mad_float = stats.median_absolute_deviation(self.dat, scale=1.0)
assert_almost_equal(mad, 0.355)
assert_almost_equal(mad, mad_float)
def test_mad_scale_normal(self):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
mad = stats.median_absolute_deviation(self.dat, scale="normal")
scale = 1.4826022185056018
mad_float = stats.median_absolute_deviation(self.dat, scale=scale)
assert_almost_equal(mad, 0.526323787)
assert_almost_equal(mad, mad_float)
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(object):
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.rollaxis(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(np.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)
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(object):
"""
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 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
y = stats.moment([])
assert_equal(y, np.nan)
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')
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_variation(self):
# variation = samplestd / mean
y = stats.variation(self.scalar_testcase)
assert_approx_equal(y, 0.0)
y = stats.variation(self.testcase)
assert_approx_equal(y, 0.44721359549996, 10)
x = np.arange(10.)
x[9] = np.nan
assert_equal(stats.variation(x), np.nan)
assert_almost_equal(stats.variation(x, nan_policy='omit'),
0.6454972243679028)
assert_raises(ValueError, stats.variation, x, nan_policy='raise')
assert_raises(ValueError, stats.variation, x, nan_policy='foobar')
def test_variation_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
vv = stats.variation(a, axis=1, nan_policy="propagate")
np.testing.assert_allclose(vv, [0.7453559924999299, np.nan], atol=1e-15)
def test_skewness(self):
# Scalar test case
y = stats.skew(self.scalar_testcase)
assert_approx_equal(y, 0.0)
# 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_kurtosis(self):
# Scalar test case
y = stats.kurtosis(self.scalar_testcase)
assert_approx_equal(y, -3.0)
# 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])), float)
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_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))
class TestStudentTest(object):
X1 = np.array([-1, 0, 1])
X2 = np.array([0, 1, 2])
T1_0 = 0
P1_0 = 1
T1_1 = -1.732051
P1_1 = 0.2254033
T1_2 = -3.464102
P1_2 = 0.0741799
T2_0 = 1.732051
P2_0 = 0.2254033
def test_onesample(self):
with suppress_warnings() as sup, np.errstate(invalid="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
np.random.seed(7654567)
x = stats.norm.rvs(loc=5, scale=10, size=51)
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_percentileofscore():
pcos = stats.percentileofscore
assert_equal(pcos([1,2,3,4,5,6,7,8,9,10],4), 40.0)
for (kind, result) in [('mean', 35.0),
('strict', 30.0),
('weak', 40.0)]:
assert_equal(pcos(np.arange(10) + 1, 4, kind=kind), result)
# multiple - 2
for (kind, result) in [('rank', 45.0),
('strict', 30.0),
('weak', 50.0),
('mean', 40.0)]:
assert_equal(pcos([1,2,3,4,4,5,6,7,8,9], 4, kind=kind), result)
# multiple - 3
assert_equal(pcos([1,2,3,4,4,4,5,6,7,8], 4), 50.0)
for (kind, result) in [('rank', 50.0),
('mean', 45.0),
('strict', 30.0),
('weak', 60.0)]:
assert_equal(pcos([1,2,3,4,4,4,5,6,7,8], 4, kind=kind), result)
# missing
for kind in ('rank', 'mean', 'strict', 'weak'):
assert_equal(pcos([1,2,3,5,6,7,8,9,10,11], 4, kind=kind), 30)
# larger numbers
for (kind, result) in [('mean', 35.0),
('strict', 30.0),
('weak', 40.0)]:
assert_equal(
pcos([10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 40,
kind=kind), result)
for (kind, result) in [('mean', 45.0),
('strict', 30.0),
('weak', 60.0)]:
assert_equal(
pcos([10, 20, 30, 40, 40, 40, 50, 60, 70, 80],
40, kind=kind), result)
for kind in ('rank', 'mean', 'strict', 'weak'):
assert_equal(
pcos([10, 20, 30, 50, 60, 70, 80, 90, 100, 110],
40, kind=kind), 30.0)
# boundaries
for (kind, result) in [('rank', 10.0),
('mean', 5.0),
('strict', 0.0),
('weak', 10.0)]:
assert_equal(
pcos([10, 20, 30, 50, 60, 70, 80, 90, 100, 110],
10, kind=kind), result)
for (kind, result) in [('rank', 100.0),
('mean', 95.0),
('strict', 90.0),
('weak', 100.0)]:
assert_equal(
pcos([10, 20, 30, 50, 60, 70, 80, 90, 100, 110],
110, kind=kind), result)
# out of bounds
for (kind, score, result) in [('rank', 200, 100.0),
('mean', 200, 100.0),
('mean', 0, 0.0)]:
assert_equal(
pcos([10, 20, 30, 50, 60, 70, 80, 90, 100, 110],
score, kind=kind), result)
assert_raises(ValueError, pcos, [1, 2, 3, 3, 4], 3, kind='unrecognized')
PowerDivCase = namedtuple('Case', ['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(object):
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)
@pytest.mark.parametrize("n, dtype", [(200, np.uint8), (1000000, np.int32)])
def test_chiquare_data_types(n, dtype):
# Regression test for gh-10159.
obs = np.array([n, 0], dtype=dtype)
exp = np.array([n // 2, n // 2], dtype=dtype)
stat, p = stats.chisquare(obs, exp)
assert_allclose(stat, n, rtol=1e-13)
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.
# `table4` holds just the second and third columns from Table 4.
table4 = np.array([
# observed, expected,
15, 15.171,
11, 13.952,
14, 12.831,
17, 11.800,
5, 10.852,
11, 9.9796,
10, 9.1777,
4, 8.4402,
8, 7.7620,
10, 7.1383,
7, 6.5647,
9, 6.0371,
11, 5.5520,
3, 5.1059,
6, 4.6956,
1, 4.3183,
1, 3.9713,
4, 3.6522,
]).reshape(-1, 2)
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(object):
"""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(object):
"""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
np.random.seed(987654321)
x = stats.norm.rvs(loc=0.2, size=100)
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
(32, 0.5, True, 0.9999999363163307), # Miller
(32, 0.5, False, 6.368366937916623e-08), # Miller 2 * special.smirnov(32, 0.5)
# Check some other paths
(32, 1.0 / 8, True, 0.34624229979775223),
(32, 1.0 / 4, True, 0.9699508336558085),
(1600, 0.49, False, 0.0),
(1600, 1 / 16.0, False, 7.0837876229702195e-06), # 2 * special.smirnov(1600, 1/16.0)
(1600, 14 / 1600, False, 0.99962357317602), # _kolmogn_DMTW
(1600, 1 / 32, False, 0.08603386296651416), # _kolmogn_PelzGood
])
FuncData(kolmogn, dataset, (0, 1, 2), 3).check(dtypes=[int, float, bool])
# missing: no test that uses *args
class TestKSTwoSamples(object):
"""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:
sup.filter(RuntimeWarning, "ks_2samp: Exact calculation unsuccessful. Switching to mode=asymp.")
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:
sup.filter(RuntimeWarning, "ks_2samp: Exact calculation unsuccessful. Switching to mode=asymp.")
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
print(x[0], x[-1], y[0], y[-1])
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')
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
print(x[0], x[-1], y[0], y[-1])
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.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:
sup.filter(RuntimeWarning, "ks_2samp: Exact calculation unsuccessful. Switching to mode=asymp.")
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 import _count_paths_outside_method, _compute_prob_inside_method
_compute_prob_inside_method(1, 1, 1, 1)
_count_paths_outside_method(1000, 1, 1, 1001)
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, [], [])
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_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"):
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.rollaxis(rvs1_3D,2), np.rollaxis(rvs2_3D,2), axis=2)
assert_array_almost_equal(np.abs(t), tr)
assert_array_almost_equal(np.abs(p), pr)
assert_equal(t.shape, (3, 2))
# check nan policy
np.random.seed(12345678)
x = stats.norm.rvs(loc=5, scale=10, size=501)
x[500] = np.nan
y = (stats.norm.rvs(loc=5, scale=10, size=501) +
stats.norm.rvs(scale=0.2, size=501))
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
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)))
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 Ttest_relResult containing nan
# for both values.
result = stats.ttest_rel([], [])
assert isinstance(result, stats.stats.Ttest_relResult)
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.Ttest_relResult)
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.Ttest_relResult)
assert_equal(result.statistic.shape, (5, 0))
assert_equal(result.pvalue.shape, (5, 0))
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.rollaxis(rvs1_3D,2), np.rollaxis(rvs2_3D,2), 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))
# check nan policy
np.random.seed(12345678)
x = stats.norm.rvs(loc=5, scale=10, size=501)
x[500] = np.nan
y = stats.norm.rvs(loc=5, scale=10, size=500)
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
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]))
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.rollaxis(rvs1_3D,2), np.rollaxis(rvs2_3D,2),
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.rollaxis(rvs1_3D, 2),
np.rollaxis(rvs2_3D, 2), 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
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 Ttest_indResult containing nan
# for both values.
result = stats.ttest_ind([], [])
assert isinstance(result, stats.stats.Ttest_indResult)
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.Ttest_indResult)
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.Ttest_indResult)
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.Ttest_indResult)
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_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,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)),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))
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]))
class TestDescribe(object):
def test_describe_scalar(self):
with suppress_warnings() as sup, np.errstate(invalid="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_array_almost_equal(sk, 0.0, decimal=13)
assert_array_almost_equal(kurt, -3.0, decimal=13)
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)
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))
check_named_results(stats.skewtest(x), attributes)
assert_array_almost_equal(stats.kurtosistest(x), (st_kurt, pv_kurt))
check_named_results(stats.kurtosistest(x), attributes)
# 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)
with np.errstate(all='ignore'):
assert_raises(ValueError, stats.skewtest, x, nan_policy='raise')
assert_raises(ValueError, stats.skewtest, x, nan_policy='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)
assert_raises(ValueError, stats.kurtosistest, x, nan_policy='raise')
assert_raises(ValueError, stats.kurtosistest, x, nan_policy='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 cleary non-normal but power of
# negtative 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(object):
def test_ranksums_result_attributes(self):
res = stats.ranksums(np.arange(5), np.arange(25))
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
class TestJarqueBera(object):
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)
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_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(object):
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_default(self):
# The default value for alternative is None
with suppress_warnings() as sup:
sup.filter(DeprecationWarning,
"Calling `mannwhitneyu` without .*`alternative`")
u1, p1 = stats.mannwhitneyu(self.X, self.Y)
u2, p2 = stats.mannwhitneyu(self.Y, self.X)
u3, p3 = stats.mannwhitneyu(self.X, self.Y, alternative=None)
assert_equal(p1, p2)
assert_equal(p1, p3)
assert_equal(u1, 102)
assert_equal(u2, 102)
assert_equal(u3, 102)
assert_approx_equal(p1, 4.5941632666275e-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_no_correct_default(self):
# The default value for alternative is None
with suppress_warnings() as sup:
sup.filter(DeprecationWarning,
"Calling `mannwhitneyu` without .*`alternative`")
u1, p1 = stats.mannwhitneyu(self.X, self.Y, False)
u2, p2 = stats.mannwhitneyu(self.Y, self.X, False)
u3, p3 = stats.mannwhitneyu(self.X, self.Y, False,
alternative=None)
assert_equal(p1, p2)
assert_equal(p1, p3)
assert_equal(u1, 102)
assert_equal(u2, 102)
assert_equal(u3, 102)
assert_approx_equal(p1, 4.40940099958089e-05,
significant=self.significant)
def test_mannwhitneyu_ones(self):
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.])
# p-value verified with matlab and R to 5 significant digits
assert_array_almost_equal(stats.stats.mannwhitneyu(x, y,
alternative='less'),
(16980.5, 2.8214327656317373e-005),
decimal=12)
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)
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):
# Note this doesn't test when axis is not specified
x = stats.gmean(array_like, axis=axis, dtype=dtype)
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):
x = stats.hmean(array_like, axis=axis, dtype=dtype)
assert_allclose(x, desired, rtol=rtol)
assert_equal(x.dtype, dtype)
class TestHarMean(object):
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_2d_matrix_axis0(self):
# Test a 2d list with axis=0
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
desired = matrix([[22.88135593, 39.13043478, 52.90076336, 65.45454545]])
check_equal_hmean(matrix(a), desired, axis=0)
def test_2d_matrix_axis1(self):
# Test a 2d list with axis=1
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
desired = matrix([[19.2, 63.03939962, 103.80078637]]).T
check_equal_hmean(matrix(a), desired, axis=1)
class TestGeoMean(object):
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_2d_matrix_axis0(self):
# Test a 2d list with axis=0
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
desired = matrix([[35.56893304, 49.32424149, 61.3579244, 72.68482371]])
check_equal_gmean(matrix(a), desired, axis=0)
a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
desired = matrix([1, 2, 3, 4])
check_equal_gmean(matrix(a), desired, axis=0, rtol=1e-14)
a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
desired = matrix(stats.gmean(a, axis=0))
check_equal_gmean(matrix(a), desired, axis=0, rtol=1e-14)
def test_2d_matrix_axis1(self):
# Test a 2d list with axis=1
a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
desired = matrix([[22.13363839, 64.02171746, 104.40086817]]).T
check_equal_gmean(matrix(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 = matrix([[v], [v], [v]])
check_equal_gmean(matrix(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(all='ignore'):
check_equal_gmean(a, desired)
class TestGeometricStandardDeviation(object):
# 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.binom_test(x, n, p), res,
significant=12, err_msg='fail forp=%f' % p)
assert_approx_equal(stats.binom_test(50,100,0.1), 5.8320387857343647e-024,
significant=12, err_msg='fail forp=%f' % p)
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.binom_test(v, k, 0.5) 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.binom_test(v, v*k, 1./k) 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.binom_test(v+1, v*k, 1./k) for v in range(1, 11)
for k in range(2, 11)]
res4_m1 = [stats.binom_test(v-1, v*k, 1./k) 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(object):
# 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.), [])
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.rollaxis(a, axis), 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(object):
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 TestFOneWay(object):
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, 'r') 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
with assert_warns(stats.F_onewayConstantInputWarning):
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
with assert_warns(stats.F_onewayConstantInputWarning):
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 assert_warns(stats.F_onewayConstantInputWarning):
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.
with assert_warns(stats.F_onewayBadInputSizesWarning):
result = stats.f_oneway([1, 2, 3], [], [4, 5, 6, 7])
assert_equal(result, (np.nan, np.nan))
def test_length0_2d_error(self):
with assert_warns(stats.F_onewayBadInputSizesWarning):
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):
with assert_warns(stats.F_onewayBadInputSizesWarning):
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(np.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(object):
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(object):
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, 0.97787, significant=4)
def test_tippett(self):
Z, p = stats.combine_pvalues([.01, .2, .3], method='tippett')
assert_approx_equal(p, 0.970299, significant=4)
def test_mudholkar_george(self):
Z, p = stats.combine_pvalues([.01, .2, .3], method='mudholkar_george')
assert_approx_equal(p, 3.7191571041915e-07, significant=4)
def test_mudholkar_george_equal_fisher_minus_pearson(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')
# 0.5 here is because logistic = log(u) - log(1-u), i.e. no 2 factors
assert_approx_equal(0.5 * (Z_f-Z_p), Z, significant=4)
class TestCdfDistanceValidation(object):
"""
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 TestWassersteinDistance(object):
""" Tests for wasserstein_distance() output values.
"""
def test_simple(self):
# For basic distributions, the value of the Wasserstein distance is
# straightforward.
assert_almost_equal(
stats.wasserstein_distance([0, 1], [0], [1, 1], [1]),
.5)
assert_almost_equal(stats.wasserstein_distance(
[0, 1], [0], [3, 1], [1]),
.25)
assert_almost_equal(stats.wasserstein_distance(
[0, 2], [0], [1, 1], [1]),
1)
assert_almost_equal(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 x.
assert_almost_equal(stats.wasserstein_distance([0], [1]), 1)
assert_almost_equal(stats.wasserstein_distance([-5], [5]), 10)
assert_almost_equal(
stats.wasserstein_distance([1, 2, 3, 4, 5], [11, 12, 13, 14, 15]),
10)
assert_almost_equal(
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_almost_equal(
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_almost_equal(
stats.wasserstein_distance(u, v),
np.mean(np.abs(u)))
u_weights = np.arange(len(u))
v_weights = u_weights[::-1]
assert_almost_equal(
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_almost_equal(
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(object):
""" 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(object):
# 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)
class TestRatioUniforms(object):
""" Tests for rvs_ratio_uniforms.
"""
def test_rv_generation(self):
# use KS test to check distribution of rvs
# normal distribution
f = stats.norm.pdf
v_bound = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
umax, vmin, vmax = np.sqrt(f(0)), -v_bound, v_bound
rvs = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=2500,
random_state=12345)
assert_equal(stats.kstest(rvs, 'norm')[1] > 0.25, True)
# exponential distribution
rvs = stats.rvs_ratio_uniforms(lambda x: np.exp(-x), umax=1,
vmin=0, vmax=2*np.exp(-1),
size=1000, random_state=12345)
assert_equal(stats.kstest(rvs, 'expon')[1] > 0.25, True)
def test_shape(self):
# test shape of return value depending on size parameter
f = stats.norm.pdf
v_bound = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
umax, vmin, vmax = np.sqrt(f(0)), -v_bound, v_bound
r1 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=3,
random_state=1234)
r2 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(3,),
random_state=1234)
r3 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(3, 1),
random_state=1234)
assert_equal(r1, r2)
assert_equal(r2, r3.flatten())
assert_equal(r1.shape, (3,))
assert_equal(r3.shape, (3, 1))
r4 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(3, 3, 3),
random_state=12)
r5 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=27,
random_state=12)
assert_equal(r4.flatten(), r5)
assert_equal(r4.shape, (3, 3, 3))
r6 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, random_state=1234)
r7 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=1,
random_state=1234)
r8 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(1, ),
random_state=1234)
assert_equal(r6, r7)
assert_equal(r7, r8)
def test_random_state(self):
f = stats.norm.pdf
v_bound = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
umax, vmin, vmax = np.sqrt(f(0)), -v_bound, v_bound
np.random.seed(1234)
r1 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(3, 4))
r2 = stats.rvs_ratio_uniforms(f, umax, vmin, vmax, size=(3, 4),
random_state=1234)
assert_equal(r1, r2)
def test_exceptions(self):
f = stats.norm.pdf
# need vmin < vmax
assert_raises(ValueError,
stats.rvs_ratio_uniforms, pdf=f, umax=1, vmin=3, vmax=1)
assert_raises(ValueError,
stats.rvs_ratio_uniforms, pdf=f, umax=1, vmin=1, vmax=1)
# need umax > 0
assert_raises(ValueError,
stats.rvs_ratio_uniforms, pdf=f, umax=-1, vmin=1, vmax=1)
assert_raises(ValueError,
stats.rvs_ratio_uniforms, pdf=f, umax=0, vmin=1, vmax=1)
class TestEppsSingleton(object):
def test_statistic_1(self):
# first example in Goerg & Kaiser, also in original paper of
# Epps & Singleton. Note: values do not match exactly, the
# value of the interquartile range varies depending on how
# quantiles are computed
x = np.array([-0.35, 2.55, 1.73, 0.73, 0.35, 2.69, 0.46, -0.94, -0.37, 12.07])
y = np.array([-1.15, -0.15, 2.48, 3.25, 3.71, 4.29, 5.00, 7.74, 8.38, 8.60])
w, p = stats.epps_singleton_2samp(x, y)
assert_almost_equal(w, 15.14, decimal=1)
assert_almost_equal(p, 0.00442, decimal=3)
def test_statistic_2(self):
# second example in Goerg & Kaiser, again not a perfect match
x = np.array((0, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 10,
10, 10, 10))
y = np.array((10, 4, 0, 5, 10, 10, 0, 5, 6, 7, 10, 3, 1, 7, 0, 8, 1,
5, 8, 10))
w, p = stats.epps_singleton_2samp(x, y)
assert_allclose(w, 8.900, atol=0.001)
assert_almost_equal(p, 0.06364, decimal=3)
def test_epps_singleton_array_like(self):
np.random.seed(1234)
x, y = np.arange(30), np.arange(28)
w1, p1 = stats.epps_singleton_2samp(list(x), list(y))
w2, p2 = stats.epps_singleton_2samp(tuple(x), tuple(y))
w3, p3 = stats.epps_singleton_2samp(x, y)
assert_(w1 == w2 == w3)
assert_(p1 == p2 == p3)
def test_epps_singleton_size(self):
# raise error if less than 5 elements
x, y = (1, 2, 3, 4), np.arange(10)
assert_raises(ValueError, stats.epps_singleton_2samp, x, y)
def test_epps_singleton_nonfinite(self):
# raise error if there are non-finite values
x, y = (1, 2, 3, 4, 5, np.inf), np.arange(10)
assert_raises(ValueError, stats.epps_singleton_2samp, x, y)
x, y = np.arange(10), (1, 2, 3, 4, 5, np.nan)
assert_raises(ValueError, stats.epps_singleton_2samp, x, y)
def test_epps_singleton_1d_input(self):
x = np.arange(100).reshape(-1, 1)
assert_raises(ValueError, stats.epps_singleton_2samp, x, x)
def test_names(self):
x, y = np.arange(20), np.arange(30)
res = stats.epps_singleton_2samp(x, y)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
class TestMGCErrorWarnings(object):
""" 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(object):
""" 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.slow
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)
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