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test_moments.py
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import nose
import sys
import functools
import warnings
from datetime import datetime
from numpy.random import randn
from numpy.testing.decorators import slow
import numpy as np
from distutils.version import LooseVersion
from pandas import Series, DataFrame, Panel, bdate_range, isnull, notnull, concat
from pandas.util.testing import (
assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_index_equal
)
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
from pandas.compat import range, zip, PY3, StringIO
N, K = 100, 10
class Base(tm.TestCase):
_multiprocess_can_split_ = True
_nan_locs = np.arange(20, 40)
_inf_locs = np.array([])
def _create_data(self):
arr = randn(N)
arr[self._nan_locs] = np.NaN
self.arr = arr
self.rng = bdate_range(datetime(2009, 1, 1), periods=N)
self.series = Series(arr.copy(), index=self.rng)
self.frame = DataFrame(randn(N, K), index=self.rng,
columns=np.arange(K))
class TestMoments(Base):
def setUp(self):
self._create_data()
warnings.simplefilter("ignore", category=FutureWarning)
def test_centered_axis_validation(self):
# ok
mom.rolling_mean(Series(np.ones(10)),3,center=True ,axis=0)
# bad axis
self.assertRaises(ValueError, mom.rolling_mean,Series(np.ones(10)),3,center=True ,axis=1)
# ok ok
mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=0)
mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=1)
# bad axis
self.assertRaises(ValueError, mom.rolling_mean,DataFrame(np.ones((10,10))),3,center=True ,axis=2)
def test_rolling_sum(self):
self._check_moment_func(mom.rolling_sum, np.sum)
def test_rolling_count(self):
counter = lambda x: np.isfinite(x).astype(float).sum()
self._check_moment_func(mom.rolling_count, counter,
has_min_periods=False,
preserve_nan=False,
fill_value=0)
def test_rolling_mean(self):
self._check_moment_func(mom.rolling_mean, np.mean)
def test_cmov_mean(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49,
16.68, 9.48, 10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
rs = mom.rolling_mean(vals, 5, center=True)
assert_almost_equal(xp, rs)
xp = Series(rs)
rs = mom.rolling_mean(Series(vals), 5, center=True)
assert_series_equal(xp, rs)
def test_cmov_window(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert_almost_equal(xp, rs)
xp = Series(rs)
rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
assert_series_equal(xp, rs)
def test_cmov_window_corner(self):
# GH 8238
tm._skip_if_no_scipy()
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assertTrue(np.isnan(rs).all())
# empty
vals = np.array([])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assertEqual(len(rs), 0)
# shorter than window
vals = np.random.randn(5)
rs = mom.rolling_window(vals, 10, 'boxcar')
self.assertTrue(np.isnan(rs).all())
self.assertEqual(len(rs), 5)
def test_cmov_window_frame(self):
# Gh 8238
tm._skip_if_no_scipy()
vals = np.array([[ 12.18, 3.64],
[ 10.18, 9.16],
[ 13.24, 14.61],
[ 4.51, 8.11],
[ 6.15, 11.44],
[ 9.14, 6.21],
[ 11.31, 10.67],
[ 2.94, 6.51],
[ 9.42, 8.39],
[ 12.44, 7.34 ]])
xp = np.array([[ np.nan, np.nan],
[ np.nan, np.nan],
[ 9.252, 9.392],
[ 8.644, 9.906],
[ 8.87 , 10.208],
[ 6.81 , 8.588],
[ 7.792, 8.644],
[ 9.05 , 7.824],
[ np.nan, np.nan],
[ np.nan, np.nan]])
# DataFrame
rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_na_min_periods(self):
tm._skip_if_no_scipy()
# min_periods
vals = Series(np.random.randn(10))
vals[4] = np.nan
vals[8] = np.nan
xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xps = {
'hamming': [np.nan, np.nan, 8.71384, 9.56348, 12.38009,
14.03687, 13.8567, 11.81473, np.nan, np.nan],
'triang': [np.nan, np.nan, 9.28667, 10.34667, 12.00556,
13.33889, 13.38, 12.33667, np.nan, np.nan],
'barthann': [np.nan, np.nan, 8.4425, 9.1925, 12.5575,
14.3675, 14.0825, 11.5675, np.nan, np.nan],
'bohman': [np.nan, np.nan, 7.61599, 9.1764, 12.83559,
14.17267, 14.65923, 11.10401, np.nan, np.nan],
'blackmanharris': [np.nan, np.nan, 6.97691, 9.16438, 13.05052,
14.02156, 15.10512, 10.74574, np.nan, np.nan],
'nuttall': [np.nan, np.nan, 7.04618, 9.16786, 13.02671,
14.03559, 15.05657, 10.78514, np.nan, np.nan],
'blackman': [np.nan, np.nan, 7.73345, 9.17869, 12.79607,
14.20036, 14.57726, 11.16988, np.nan, np.nan],
'bartlett': [np.nan, np.nan, 8.4425, 9.1925, 12.5575,
14.3675, 14.0825, 11.5675, np.nan, np.nan]}
for wt in win_types:
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt in win_types:
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular_missing_data(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, np.nan, 10.63, 14.48])
xps = {
'bartlett': [np.nan, np.nan, 9.70333, 10.5225, 8.4425,
9.1925, 12.5575, 14.3675, 15.61667, 13.655],
'blackman': [np.nan, np.nan, 9.04582, 11.41536, 7.73345,
9.17869, 12.79607, 14.20036, 15.8706, 13.655],
'barthann': [np.nan, np.nan, 9.70333, 10.5225, 8.4425,
9.1925, 12.5575, 14.3675, 15.61667, 13.655],
'bohman': [np.nan, np.nan, 8.9444, 11.56327, 7.61599,
9.1764, 12.83559, 14.17267, 15.90976, 13.655],
'hamming': [np.nan, np.nan, 9.59321, 10.29694, 8.71384,
9.56348, 12.38009, 14.20565, 15.24694, 13.69758],
'nuttall': [np.nan, np.nan, 8.47693, 12.2821, 7.04618,
9.16786, 13.02671, 14.03673, 16.08759, 13.65553],
'triang': [np.nan, np.nan, 9.33167, 9.76125, 9.28667,
10.34667, 12.00556, 13.82125, 14.49429, 13.765],
'blackmanharris': [np.nan, np.nan, 8.42526, 12.36824, 6.97691,
9.16438, 13.05052, 14.02175, 16.1098,
13.65509]
}
for wt in win_types:
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, min_periods=3)
assert_series_equal(xp, rs)
def test_cmov_window_special(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2., 'width': 2.},
{'width': 0.5}]
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xps = {
'gaussian': [np.nan, np.nan, 8.97297, 9.76077, 12.24763,
13.89053, 13.65671, 12.01002, np.nan, np.nan],
'general_gaussian': [np.nan, np.nan, 9.85011, 10.71589,
11.73161, 13.08516, 12.95111, 12.74577,
np.nan, np.nan],
'slepian': [np.nan, np.nan, 9.81073, 10.89359, 11.70284,
12.88331, 12.96079, 12.77008, np.nan, np.nan],
'kaiser': [np.nan, np.nan, 9.86851, 11.02969, 11.65161,
12.75129, 12.90702, 12.83757, np.nan, np.nan]
}
for wt, k in zip(win_types, kwds):
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, center=True,
**k)
assert_series_equal(xp, rs)
def test_cmov_window_special_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2., 'width': 2.},
{'width': 0.5}]
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt, k in zip(win_types, kwds):
rs = mom.rolling_window(Series(vals), 5, wt, center=True,
**k)
assert_series_equal(xp, rs)
def test_rolling_median(self):
self._check_moment_func(mom.rolling_median, np.median)
def test_rolling_min(self):
self._check_moment_func(mom.rolling_min, np.min)
a = np.array([1, 2, 3, 4, 5])
b = mom.rolling_min(a, window=100, min_periods=1)
assert_almost_equal(b, np.ones(len(a)))
self.assertRaises(ValueError, mom.rolling_min, np.array([1,
2, 3]), window=3, min_periods=5)
def test_rolling_max(self):
self._check_moment_func(mom.rolling_max, np.max)
a = np.array([1, 2, 3, 4, 5])
b = mom.rolling_max(a, window=100, min_periods=1)
assert_almost_equal(a, b)
self.assertRaises(ValueError, mom.rolling_max, np.array([1,
2, 3]), window=3, min_periods=5)
def test_rolling_quantile(self):
qs = [.1, .5, .9]
def scoreatpercentile(a, per):
values = np.sort(a, axis=0)
idx = per / 1. * (values.shape[0] - 1)
return values[int(idx)]
for q in qs:
def f(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_quantile(x, window, q,
min_periods=min_periods,
freq=freq,
center=center)
def alt(x):
return scoreatpercentile(x, q)
self._check_moment_func(f, alt)
def test_rolling_apply(self):
# suppress warnings about empty slices, as we are deliberately testing with a 0-length Series
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning)
ser = Series([])
assert_series_equal(ser, mom.rolling_apply(ser, 10, lambda x: x.mean()))
def roll_mean(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq,
center=center)
self._check_moment_func(roll_mean, np.mean)
# GH 8080
s = Series([None, None, None])
result = mom.rolling_apply(s, 2, lambda x: len(x), min_periods=0)
expected = Series([1., 2., 2.])
assert_series_equal(result, expected)
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
result = mom.rolling_apply(arr, 10, np.sum)
self.assertTrue(isnull(result).all())
result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
assert_almost_equal(result, result)
def test_rolling_std(self):
self._check_moment_func(mom.rolling_std,
lambda x: np.std(x, ddof=1))
self._check_moment_func(functools.partial(mom.rolling_std, ddof=0),
lambda x: np.std(x, ddof=0))
def test_rolling_std_1obs(self):
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1)
expected = np.array([np.nan] * 5)
assert_almost_equal(result, expected)
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1, ddof=0)
expected = np.zeros(5)
assert_almost_equal(result, expected)
result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]),
3, min_periods=2)
self.assertTrue(np.isnan(result[2]))
def test_rolling_std_neg_sqrt(self):
# unit test from Bottleneck
# Test move_nanstd for neg sqrt.
a = np.array([0.0011448196318903589,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767])
b = mom.rolling_std(a, window=3)
self.assertTrue(np.isfinite(b[2:]).all())
b = mom.ewmstd(a, span=3)
self.assertTrue(np.isfinite(b[2:]).all())
def test_rolling_var(self):
self._check_moment_func(mom.rolling_var,
lambda x: np.var(x, ddof=1),
test_stable=True)
self._check_moment_func(functools.partial(mom.rolling_var, ddof=0),
lambda x: np.var(x, ddof=0))
def test_rolling_skew(self):
try:
from scipy.stats import skew
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_skew,
lambda x: skew(x, bias=False))
def test_rolling_kurt(self):
try:
from scipy.stats import kurtosis
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_kurt,
lambda x: kurtosis(x, bias=False))
def test_fperr_robustness(self):
# TODO: remove this once python 2.5 out of picture
if PY3:
raise nose.SkipTest("doesn't work on python 3")
# #2114
data = '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1a@\xaa\xaa\xaa\xaa\xaa\xaa\x02@8\x8e\xe38\x8e\xe3\xe8?z\t\xed%\xb4\x97\xd0?\xa2\x0c<\xdd\x9a\x1f\xb6?\x82\xbb\xfa&y\x7f\x9d?\xac\'\xa7\xc4P\xaa\x83?\x90\xdf\xde\xb0k8j?`\xea\xe9u\xf2zQ?*\xe37\x9d\x98N7?\xe2.\xf5&v\x13\x1f?\xec\xc9\xf8\x19\xa4\xb7\x04?\x90b\xf6w\x85\x9f\xeb>\xb5A\xa4\xfaXj\xd2>F\x02\xdb\xf8\xcb\x8d\xb8>.\xac<\xfb\x87^\xa0>\xe8:\xa6\xf9_\xd3\x85>\xfb?\xe2cUU\xfd?\xfc\x7fA\xed8\x8e\xe3?\xa5\xaa\xac\x91\xf6\x12\xca?n\x1cs\xb6\xf9a\xb1?\xe8%D\xf3L-\x97?5\xddZD\x11\xe7~?#>\xe7\x82\x0b\x9ad?\xd9R4Y\x0fxK?;7x;\nP2?N\xf4JO\xb8j\x18?4\xf81\x8a%G\x00?\x9a\xf5\x97\r2\xb4\xe5>\xcd\x9c\xca\xbcB\xf0\xcc>3\x13\x87(\xd7J\xb3>\x99\x19\xb4\xe0\x1e\xb9\x99>ff\xcd\x95\x14&\x81>\x88\x88\xbc\xc7p\xddf>`\x0b\xa6_\x96|N>@\xb2n\xea\x0eS4>U\x98\x938i\x19\x1b>\x8eeb\xd0\xf0\x10\x02>\xbd\xdc-k\x96\x16\xe8=(\x93\x1e\xf2\x0e\x0f\xd0=\xe0n\xd3Bii\xb5=*\xe9\x19Y\x8c\x8c\x9c=\xc6\xf0\xbb\x90]\x08\x83=]\x96\xfa\xc0|`i=>d\xfc\xd5\xfd\xeaP=R0\xfb\xc7\xa7\x8e6=\xc2\x95\xf9_\x8a\x13\x1e=\xd6c\xa6\xea\x06\r\x04=r\xda\xdd8\t\xbc\xea<\xf6\xe6\x93\xd0\xb0\xd2\xd1<\x9d\xdeok\x96\xc3\xb7<&~\xea9s\xaf\x9f<UUUUUU\x13@q\x1c\xc7q\x1c\xc7\xf9?\xf6\x12\xdaKh/\xe1?\xf2\xc3"e\xe0\xe9\xc6?\xed\xaf\x831+\x8d\xae?\xf3\x1f\xad\xcb\x1c^\x94?\x15\x1e\xdd\xbd>\xb8\x02@\xc6\xd2&\xfd\xa8\xf5\xe8?\xd9\xe1\x19\xfe\xc5\xa3\xd0?v\x82"\xa8\xb2/\xb6?\x9dX\x835\xee\x94\x9d?h\x90W\xce\x9e\xb8\x83?\x8a\xc0th~Kj?\\\x80\xf8\x9a\xa9\x87Q?%\xab\xa0\xce\x8c_7?1\xe4\x80\x13\x11*\x1f? \x98\x00\r\xb6\xc6\x04?\x80u\xabf\x9d\xb3\xeb>UNrD\xbew\xd2>\x1c\x13C[\xa8\x9f\xb8>\x12b\xd7<pj\xa0>m-\x1fQ@\xe3\x85>\xe6\x91)l\x00/m>Da\xc6\xf2\xaatS>\x05\xd7]\xee\xe3\xf09>'
arr = np.frombuffer(data, dtype='<f8')
if sys.byteorder != "little":
arr = arr.byteswap().newbyteorder()
result = mom.rolling_sum(arr, 2)
self.assertTrue((result[1:] >= 0).all())
result = mom.rolling_mean(arr, 2)
self.assertTrue((result[1:] >= 0).all())
result = mom.rolling_var(arr, 2)
self.assertTrue((result[1:] >= 0).all())
# #2527, ugh
arr = np.array([0.00012456, 0.0003, 0])
result = mom.rolling_mean(arr, 1)
self.assertTrue(result[-1] >= 0)
result = mom.rolling_mean(-arr, 1)
self.assertTrue(result[-1] <= 0)
def _check_moment_func(self, func, static_comp, window=50,
has_min_periods=True,
has_center=True,
has_time_rule=True,
preserve_nan=True,
fill_value=None,
test_stable=False):
self._check_ndarray(func, static_comp, window=window,
has_min_periods=has_min_periods,
preserve_nan=preserve_nan,
has_center=has_center,
fill_value=fill_value,
test_stable=test_stable)
self._check_structures(func, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
fill_value=fill_value,
has_center=has_center)
def _check_ndarray(self, func, static_comp, window=50,
has_min_periods=True,
preserve_nan=True,
has_center=True,
fill_value=None,
test_stable=False,
test_window=True):
result = func(self.arr, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:]))
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
if has_min_periods:
result = func(arr, 50, min_periods=30)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# min_periods is working correctly
result = func(arr, 20, min_periods=15)
self.assertTrue(np.isnan(result[23]))
self.assertFalse(np.isnan(result[24]))
self.assertFalse(np.isnan(result[-6]))
self.assertTrue(np.isnan(result[-5]))
arr2 = randn(20)
result = func(arr2, 10, min_periods=5)
self.assertTrue(isnull(result[3]))
self.assertTrue(notnull(result[4]))
# min_periods=0
result0 = func(arr, 20, min_periods=0)
result1 = func(arr, 20, min_periods=1)
assert_almost_equal(result0, result1)
else:
result = func(arr, 50)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# GH 7925
if has_center:
if has_min_periods:
result = func(arr, 20, min_periods=15, center=True)
expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20, min_periods=15)[9:]
else:
result = func(arr, 20, center=True)
expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20)[9:]
self.assert_numpy_array_equal(result, expected)
if test_stable:
result = func(self.arr + 1e9, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:] + 1e9))
# Test window larger than array, #7297
if test_window:
if has_min_periods:
for minp in (0, len(self.arr)-1, len(self.arr)):
result = func(self.arr, len(self.arr)+1, min_periods=minp)
expected = func(self.arr, len(self.arr), min_periods=minp)
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask,
np.isnan(expected)))
nan_mask = ~nan_mask
assert_almost_equal(result[nan_mask], expected[nan_mask])
else:
result = func(self.arr, len(self.arr)+1)
expected = func(self.arr, len(self.arr))
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask, np.isnan(expected)))
nan_mask = ~nan_mask
assert_almost_equal(result[nan_mask], expected[nan_mask])
def _check_structures(self, func, static_comp,
has_min_periods=True, has_time_rule=True,
has_center=True,
fill_value=None):
series_result = func(self.series, 50)
tm.assertIsInstance(series_result, Series)
frame_result = func(self.frame, 50)
self.assertEqual(type(frame_result), DataFrame)
# check time_rule works
if has_time_rule:
win = 25
minp = 10
if has_min_periods:
series_result = func(self.series[::2], win, min_periods=minp,
freq='B')
frame_result = func(self.frame[::2], win, min_periods=minp,
freq='B')
else:
series_result = func(self.series[::2], win, freq='B')
frame_result = func(self.frame[::2], win, freq='B')
last_date = series_result.index[-1]
prev_date = last_date - 24 * datetools.bday
trunc_series = self.series[::2].truncate(prev_date, last_date)
trunc_frame = self.frame[::2].truncate(prev_date, last_date)
assert_almost_equal(series_result[-1], static_comp(trunc_series))
assert_almost_equal(frame_result.xs(last_date),
trunc_frame.apply(static_comp))
# GH 7925
if has_center:
if has_min_periods:
minp = 10
series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.series.index)
frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.frame.index)
series_rs = func(self.series, 25, min_periods=minp,
center=True)
frame_rs = func(self.frame, 25, min_periods=minp,
center=True)
else:
series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.series.index)
frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.frame.index)
series_rs = func(self.series, 25, center=True)
frame_rs = func(self.frame, 25, center=True)
if fill_value is not None:
series_xp = series_xp.fillna(fill_value)
frame_xp = frame_xp.fillna(fill_value)
assert_series_equal(series_xp, series_rs)
assert_frame_equal(frame_xp, frame_rs)
def test_ewma(self):
self._check_ew(mom.ewma)
arr = np.zeros(1000)
arr[5] = 1
result = mom.ewma(arr, span=100, adjust=False).sum()
self.assertTrue(np.abs(result - 1) < 1e-2)
s = Series([1.0, 2.0, 4.0, 8.0])
expected = Series([1.0, 1.6, 2.736842, 4.923077])
for f in [lambda s: mom.ewma(s, com=2.0, adjust=True),
lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=False),
lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=True),
]:
result = f(s)
assert_series_equal(result, expected)
expected = Series([1.0, 1.333333, 2.222222, 4.148148])
for f in [lambda s: mom.ewma(s, com=2.0, adjust=False),
lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=False),
lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=True),
]:
result = f(s)
assert_series_equal(result, expected)
def test_ewma_nan_handling(self):
s = Series([1.] + [np.nan] * 5 + [1.])
result = mom.ewma(s, com=5)
assert_almost_equal(result, [1.] * len(s))
s = Series([np.nan] * 2 + [1.] + [np.nan] * 2 + [1.])
result = mom.ewma(s, com=5)
assert_almost_equal(result, [np.nan] * 2 + [1.] * 4)
# GH 7603
s0 = Series([np.nan, 1., 101.])
s1 = Series([1., np.nan, 101.])
s2 = Series([np.nan, 1., np.nan, np.nan, 101., np.nan])
s3 = Series([1., np.nan, 101., 50.])
com = 2.
alpha = 1. / (1. + com)
def simple_wma(s, w):
return (s.multiply(w).cumsum() / w.cumsum()).fillna(method='ffill')
for (s, adjust, ignore_na, w) in [
(s0, True, False, [np.nan, (1. - alpha), 1.]),
(s0, True, True, [np.nan, (1. - alpha), 1.]),
(s0, False, False, [np.nan, (1. - alpha), alpha]),
(s0, False, True, [np.nan, (1. - alpha), alpha]),
(s1, True, False, [(1. - alpha)**2, np.nan, 1.]),
(s1, True, True, [(1. - alpha), np.nan, 1.]),
(s1, False, False, [(1. - alpha)**2, np.nan, alpha]),
(s1, False, True, [(1. - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, 1., np.nan]),
(s2, True, True, [np.nan, (1. - alpha), np.nan, np.nan, 1., np.nan]),
(s2, False, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, alpha, np.nan]),
(s2, False, True, [np.nan, (1. - alpha), np.nan, np.nan, alpha, np.nan]),
(s3, True, False, [(1. - alpha)**3, np.nan, (1. - alpha), 1.]),
(s3, True, True, [(1. - alpha)**2, np.nan, (1. - alpha), 1.]),
(s3, False, False, [(1. - alpha)**3, np.nan, (1. - alpha) * alpha, alpha * ((1. - alpha)**2 + alpha)]),
(s3, False, True, [(1. - alpha)**2, np.nan, (1. - alpha) * alpha, alpha]),
]:
expected = simple_wma(s, Series(w))
result = mom.ewma(s, com=com, adjust=adjust, ignore_na=ignore_na)
assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = mom.ewma(s, com=com, adjust=adjust)
assert_series_equal(result, expected)
def test_ewmvar(self):
self._check_ew(mom.ewmvar)
def test_ewmvol(self):
self._check_ew(mom.ewmvol)
def test_ewma_span_com_args(self):
A = mom.ewma(self.arr, com=9.5)
B = mom.ewma(self.arr, span=20)
assert_almost_equal(A, B)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20)
self.assertRaises(Exception, mom.ewma, self.arr)
def test_ewma_halflife_arg(self):
A = mom.ewma(self.arr, com=13.932726172912965)
B = mom.ewma(self.arr, halflife=10.0)
assert_almost_equal(A, B)
self.assertRaises(Exception, mom.ewma, self.arr, span=20, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr)
def test_moment_preserve_series_name(self):
# GH 10565
s = Series(np.arange(100), name='foo')
s2 = mom.rolling_mean(s, 30)
s3 = mom.rolling_sum(s, 20)
self.assertEqual(s2.name, 'foo')
self.assertEqual(s3.name, 'foo')
def test_ew_empty_arrays(self):
arr = np.array([], dtype=np.float64)
funcs = [mom.ewma, mom.ewmvol, mom.ewmvar]
for f in funcs:
result = f(arr, 3)
assert_almost_equal(result, arr)
def _check_ew(self, func):
self._check_ew_ndarray(func)
self._check_ew_structures(func)
def _check_ew_ndarray(self, func, preserve_nan=False):
result = func(self.arr, com=10)
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
s = Series(arr)
# check min_periods
# GH 7898
result = func(s, 50, min_periods=2)
self.assertTrue(np.isnan(result.values[:11]).all())
self.assertFalse(np.isnan(result.values[11:]).any())
for min_periods in (0, 1):
result = func(s, 50, min_periods=min_periods)
if func == mom.ewma:
self.assertTrue(np.isnan(result.values[:10]).all())
self.assertFalse(np.isnan(result.values[10:]).any())
else:
# ewmstd, ewmvol, ewmvar (with bias=False) require at least two values
self.assertTrue(np.isnan(result.values[:11]).all())
self.assertFalse(np.isnan(result.values[11:]).any())
# check series of length 0
result = func(Series([]), 50, min_periods=min_periods)
assert_series_equal(result, Series([]))
# check series of length 1
result = func(Series([1.]), 50, min_periods=min_periods)
if func == mom.ewma:
assert_series_equal(result, Series([1.]))
else:
# ewmstd, ewmvol, ewmvar with bias=False require at least two values
assert_series_equal(result, Series([np.NaN]))
# pass in ints
result2 = func(np.arange(50), span=10)
self.assertEqual(result2.dtype, np.float_)
def _check_ew_structures(self, func):
series_result = func(self.series, com=10)
tm.assertIsInstance(series_result, Series)
frame_result = func(self.frame, com=10)
self.assertEqual(type(frame_result), DataFrame)
# create the data only once as we are not setting it
def _create_consistency_data():
def create_series():
return [Series(),
Series([np.nan]),
Series([np.nan, np.nan]),
Series([3.]),
Series([np.nan, 3.]),
Series([3., np.nan]),
Series([1., 3.]),
Series([2., 2.]),
Series([3., 1.]),
Series([5., 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, np.nan, 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7., 12., 13., 14., 15.]),
Series([np.nan, 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3., 12., 13., 14., 15.]),
Series([2., 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7., 12., 13., 14., 15.]),
Series([2., 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3., 12., 13., 14., 15.]),
Series(range(10)),
Series(range(20, 0, -2)),
]
def create_dataframes():
return [DataFrame(),
DataFrame(columns=['a']),
DataFrame(columns=['a', 'a']),
DataFrame(columns=['a', 'b']),
DataFrame(np.arange(10).reshape((5, 2))),
DataFrame(np.arange(25).reshape((5, 5))),
DataFrame(np.arange(25).reshape((5, 5)), columns=['a', 'b', 99, 'd', 'd']),
] + [DataFrame(s) for s in create_series()]
def is_constant(x):
values = x.values.ravel()
return len(set(values[notnull(values)])) == 1
def no_nans(x):
return x.notnull().all().all()
# data is a tuple(object, is_contant, no_nans)
data = create_series() + create_dataframes()
return [ (x, is_constant(x), no_nans(x)) for x in data ]
_consistency_data = _create_consistency_data()
class TestMomentsConsistency(Base):
base_functions = [
(lambda v: Series(v).count(), None, 'count'),
(lambda v: Series(v).max(), None, 'max'),
(lambda v: Series(v).min(), None, 'min'),
(lambda v: Series(v).sum(), None, 'sum'),
(lambda v: Series(v).mean(), None, 'mean'),
(lambda v: Series(v).std(), 1, 'std'),
(lambda v: Series(v).cov(Series(v)), None, 'cov'),
(lambda v: Series(v).corr(Series(v)), None, 'corr'),
(lambda v: Series(v).var(), 1, 'var'),
#(lambda v: Series(v).skew(), 3, 'skew'), # restore once GH 8086 is fixed
#(lambda v: Series(v).kurt(), 4, 'kurt'), # restore once GH 8086 is fixed
#(lambda x, min_periods: mom.expanding_quantile(x, 0.3, min_periods=min_periods, 'quantile'),
# lambda v: Series(v).quantile(0.3), None, 'quantile'), # restore once GH 8084 is fixed
(lambda v: Series(v).median(), None ,'median'),
(np.nanmax, 1, 'max'),
(np.nanmin, 1, 'min'),
(np.nansum, 1, 'sum'),
]
if np.__version__ >= LooseVersion('1.8.0'):
base_functions += [
(np.nanmean, 1, 'mean'),
(lambda v: np.nanstd(v, ddof=1), 1 ,'std'),
(lambda v: np.nanvar(v, ddof=1), 1 ,'var'),
]
if np.__version__ >= LooseVersion('1.9.0'):
base_functions += [
(np.nanmedian, 1, 'median'),
]
no_nan_functions = [
(np.max, None, 'max'),
(np.min, None, 'min'),
(np.sum, None, 'sum'),
(np.mean, None, 'mean'),
(lambda v: np.std(v, ddof=1), 1 ,'std'),
(lambda v: np.var(v, ddof=1), 1 ,'var'),
(np.median, None, 'median'),
]
def _create_data(self):
super(TestMomentsConsistency, self)._create_data()
self.data = _consistency_data
def setUp(self):
self._create_data()
warnings.simplefilter("ignore", category=FutureWarning)
def _test_moments_consistency(self,
min_periods,
count, mean, mock_mean, corr,
var_unbiased=None, std_unbiased=None, cov_unbiased=None,
var_biased=None, std_biased=None, cov_biased=None,
var_debiasing_factors=None):
def _non_null_values(x):
values = x.values.ravel()
return set(values[notnull(values)].tolist())
for (x, is_constant, no_nans) in self.data:
assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
count_x = count(x)
mean_x = mean(x)
if mock_mean:
# check that mean equals mock_mean
expected = mock_mean(x)
assert_equal(mean_x, expected.astype('float64'))
# check that correlation of a series with itself is either 1 or NaN
corr_x_x = corr(x, x)
# self.assertTrue(_non_null_values(corr_x_x).issubset(set([1.]))) # restore once rolling_cov(x, x) is identically equal to var(x)
if is_constant:
exp = x.max() if isinstance(x, Series) else x.max().max()
# check mean of constant series
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = exp
assert_equal(mean_x, expected)
# check correlation of constant series with itself is NaN
expected[:] = np.nan
assert_equal(corr_x_x, expected)
if var_unbiased and var_biased and var_debiasing_factors:
# check variance debiasing factors
var_unbiased_x = var_unbiased(x)
var_biased_x = var_biased(x)
var_debiasing_factors_x = var_debiasing_factors(x)
assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
for (std, var, cov) in [(std_biased, var_biased, cov_biased),
(std_unbiased, var_unbiased, cov_unbiased)]:
# check that var(x), std(x), and cov(x) are all >= 0
var_x = var(x)
std_x = std(x)
self.assertFalse((var_x < 0).any().any())
self.assertFalse((std_x < 0).any().any())
if cov:
cov_x_x = cov(x, x)
self.assertFalse((cov_x_x < 0).any().any())
# check that var(x) == cov(x, x)
assert_equal(var_x, cov_x_x)
# check that var(x) == std(x)^2
assert_equal(var_x, std_x * std_x)
if var is var_biased:
# check that biased var(x) == mean(x^2) - mean(x)^2
mean_x2 = mean(x * x)
assert_equal(var_x, mean_x2 - (mean_x * mean_x))
if is_constant:
# check that variance of constant series is identically 0
self.assertFalse((var_x > 0).any().any())
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = 0.
if var is var_unbiased:
expected[count_x < 2] = np.nan
assert_equal(var_x, expected)
if isinstance(x, Series):
for (y, is_constant, no_nans) in self.data:
if not x.isnull().equals(y.isnull()):
# can only easily test two Series with similar structure
continue
# check that cor(x, y) is symmetric
corr_x_y = corr(x, y)
corr_y_x = corr(y, x)
assert_equal(corr_x_y, corr_y_x)
if cov:
# check that cov(x, y) is symmetric
cov_x_y = cov(x, y)
cov_y_x = cov(y, x)
assert_equal(cov_x_y, cov_y_x)
# check that cov(x, y) == (var(x+y) - var(x) - var(y)) / 2
var_x_plus_y = var(x + y)
var_y = var(y)
assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y))
# check that corr(x, y) == cov(x, y) / (std(x) * std(y))
std_y = std(y)
assert_equal(corr_x_y, cov_x_y / (std_x * std_y))
if cov is cov_biased:
# check that biased cov(x, y) == mean(x*y) - mean(x)*mean(y)
mean_y = mean(y)
mean_x_times_y = mean(x * y)
assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y))
@slow