Python scipy.stats.skewtest() Examples
The following are 13
code examples of scipy.stats.skewtest().
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Example #1
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_nd_input(self):
x = np.array((-2,-1,0,1,2,3)*4)**2
x_2d = np.vstack([x] * 2).T
for func in [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]:
res_1d = func(x)
res_2d = func(x_2d)
assert_allclose(res_2d[0], [res_1d[0]] * 2)
assert_allclose(res_2d[1], [res_1d[1]] * 2)
Example #2
| Source File: test_statstools.py From vnpy_crypto with MIT License | 5 votes |
|
def test_omni_normtest():
#tests against R fBasics
from scipy import stats
st_pv_R = np.array(
[[3.994138321207883, -1.129304302161460, 1.648881473704978],
[0.1357325110375005, 0.2587694866795507, 0.0991719192710234]])
nt = omni_normtest(x)
assert_almost_equal(nt, st_pv_R[:, 0], 14)
st = stats.skewtest(x)
assert_almost_equal(st, st_pv_R[:, 1], 14)
kt = stats.kurtosistest(x)
assert_almost_equal(kt, st_pv_R[:, 2], 11)
st_pv_R = np.array(
[[34.523210399523926, 4.429509162503833, 3.860396220444025],
[3.186985686465249e-08, 9.444780064482572e-06, 1.132033129378485e-04]])
x2 = x**2
#TODO: fix precision in these test with relative tolerance
nt = omni_normtest(x2)
assert_almost_equal(nt, st_pv_R[:, 0], 12)
st = stats.skewtest(x2)
assert_almost_equal(st, st_pv_R[:, 1], 12)
kt = stats.kurtosistest(x2)
assert_almost_equal(kt, st_pv_R[:, 2], 12)
Example #3
| Source File: test_stats.py From Computable with MIT License | 5 votes |
|
def test_normalitytests():
# 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
yield assert_array_almost_equal, stats.normaltest(x), (st_normal, pv_normal)
yield assert_array_almost_equal, stats.skewtest(x), (st_skew, pv_skew)
yield assert_array_almost_equal, stats.kurtosistest(x), (st_kurt, pv_kurt)
Example #4
| Source File: test_stats.py From Computable with MIT License | 5 votes |
|
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)
Example #5
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_vs_nonmasked(self):
x = np.array((-2,-1,0,1,2,3)*4)**2
assert_array_almost_equal(mstats.normaltest(x),
stats.normaltest(x))
assert_array_almost_equal(mstats.skewtest(x),
stats.skewtest(x))
assert_array_almost_equal(mstats.kurtosistest(x),
stats.kurtosistest(x))
funcs = [stats.normaltest, stats.skewtest, stats.kurtosistest]
mfuncs = [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]
x = [1, 2, 3, 4]
for func, mfunc in zip(funcs, mfuncs):
assert_raises(ValueError, func, x)
assert_raises(ValueError, mfunc, x)
Example #6
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_axis_None(self):
# Test axis=None (equal to axis=0 for 1-D input)
x = np.array((-2,-1,0,1,2,3)*4)**2
assert_allclose(mstats.normaltest(x, axis=None), mstats.normaltest(x))
assert_allclose(mstats.skewtest(x, axis=None), mstats.skewtest(x))
assert_allclose(mstats.kurtosistest(x, axis=None),
mstats.kurtosistest(x))
Example #7
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_maskedarray_input(self):
# Add some masked values, test result doesn't change
x = np.array((-2,-1,0,1,2,3)*4)**2
xm = np.ma.array(np.r_[np.inf, x, 10],
mask=np.r_[True, [False] * x.size, True])
assert_allclose(mstats.normaltest(xm), stats.normaltest(x))
assert_allclose(mstats.skewtest(xm), stats.skewtest(x))
assert_allclose(mstats.kurtosistest(xm), stats.kurtosistest(x))
Example #8
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_skewtest(self):
# this test is for 1D data
for n in self.get_n():
if n > 8:
x, y, xm, ym = self.generate_xy_sample(n)
r = stats.skewtest(x)
rm = stats.mstats.skewtest(xm)
assert_allclose(r[0], rm[0], rtol=1e-15)
# TODO this test is not performed as it is a known issue that
# mstats returns a slightly different p-value what is a bit
# strange is that other tests like test_maskedarray_input don't
# fail!
#~ assert_almost_equal(r[1], rm[1])
Example #9
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_skewtest_2D_notmasked(self):
# a normal ndarray is passed to the masked function
x = np.random.random((20, 2)) * 20.
r = stats.skewtest(x)
rm = stats.mstats.skewtest(x)
assert_allclose(np.asarray(r), np.asarray(rm))
Example #10
| Source File: test_mstats_basic.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_skewtest_2D_WithMask(self):
nx = 2
for n in self.get_n():
if n > 8:
x, y, xm, ym = self.generate_xy_sample2D(n, nx)
r = stats.skewtest(x)
rm = stats.mstats.skewtest(xm)
assert_equal(r[0][0],rm[0][0])
assert_equal(r[0][1],rm[0][1])
Example #11
| Source File: test_stats.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
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)
Example #12
| Source File: test_stats.py From GraphicDesignPatternByPython with MIT License | 4 votes |
|
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')
Example #13
| Source File: ABuStatsUtil.py From abu with GNU General Public License v3.0 | 4 votes |
|
def normality_stats(arr):
"""
统计信息偏度，峰度，正态分布检测，p-value
eg:
input:
2014-07-25 223.57
2014-07-28 224.82
2014-07-29 225.01
...
2016-07-22 222.27
2016-07-25 230.01
2016-07-26 225.93
output:
array skew = -0.282635248604699
array skew p-value = 0.009884539532576725
array kurt = 0.009313464006726946
array kurt p-value = 0.8403947352953821
array norm = NormaltestResult(statistic=6.6961445106692237, pvalue=0.035152053009441256)
array norm p-value = 0.035152053009441256
input:
tsla bidu noah sfun goog vips aapl
2014-07-25 223.57 226.50 15.32 12.110 589.02 21.349 97.67
2014-07-28 224.82 225.80 16.13 12.450 590.60 21.548 99.02
2014-07-29 225.01 220.00 16.75 12.220 585.61 21.190 98.38
... ... ... ... ... ... ... ...
2016-07-22 222.27 160.88 25.50 4.850 742.74 13.510 98.66
2016-07-25 230.01 160.25 25.57 4.790 739.77 13.390 97.34
2016-07-26 225.93 163.09 24.75 4.945 740.92 13.655 97.76
output:
array skew = [-0.2826 -0.2544 0.1456 1.0322 0.2095 0.095 0.1719]
array skew p-value = [ 0.0099 0.0198 0.1779 0. 0.0539 0.3781 0.1124]
array kurt = [ 0.0093 -0.8414 -0.4205 0.4802 -1.547 -0.9203 -1.2104]
array kurt p-value = [ 0.8404 0. 0.0201 0.0461 1. 0. 0. ]
array norm = NormaltestResult(statistic=array([ 6.6961, 52.85 , 7.2163, 69.0119, 3.7161,
69.3468, 347.229 ]), pvalue=array([ 0.0352, 0. , 0.0271, 0. , 0.156 , 0. , 0. ]))
array norm p-value = [ 0.0352 0. 0.0271 0. 0.156 0. 0. ]
:param arr: pd.DataFrame or pd.Series or Iterable
"""
log_func = logging.info if ABuEnv.g_is_ipython else print
log_func('array skew = {}'.format(scs.skew(arr)))
log_func('array skew p-value = {}'.format(scs.skewtest(arr)[1]))
log_func('array kurt = {}'.format(scs.kurtosis(arr)))
log_func('array kurt p-value = {}'.format(scs.kurtosistest(arr)[1]))
log_func('array norm = {}'.format(scs.normaltest(arr)))
log_func('array norm p-value = {}'.format(scs.normaltest(arr)[1]))
