/usr/share/pyshared/pandas/stats/tests/test_moments.py is in python-pandas 0.7.0-1.
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import nose
from datetime import datetime
from numpy.random import randn
import numpy as np
from pandas.core.api import Series, DataFrame, DateRange
from pandas.util.testing import assert_almost_equal
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
N, K = 100, 10
class TestMoments(unittest.TestCase):
_nan_locs = np.arange(20, 40)
_inf_locs = np.array([])
def setUp(self):
arr = randn(N)
arr[self._nan_locs] = np.NaN
self.arr = arr
self.rng = DateRange(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))
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)
def test_rolling_mean(self):
self._check_moment_func(mom.rolling_mean, np.mean)
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)
def test_rolling_max(self):
self._check_moment_func(mom.rolling_max, np.max)
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, time_rule=None):
return mom.rolling_quantile(x, window, q,
min_periods=min_periods,
time_rule=time_rule)
def alt(x):
return scoreatpercentile(x, q)
self._check_moment_func(f, alt)
def test_rolling_apply(self):
def roll_mean(x, window, min_periods=None, time_rule=None):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
time_rule=time_rule)
self._check_moment_func(roll_mean, np.mean)
def test_rolling_std(self):
self._check_moment_func(mom.rolling_std,
lambda x: np.std(x, ddof=1))
def test_rolling_var(self):
self._check_moment_func(mom.rolling_var,
lambda x: np.var(x, ddof=1))
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 _check_moment_func(self, func, static_comp, window=50,
has_min_periods=True,
has_time_rule=True,
preserve_nan=True):
self._check_ndarray(func, static_comp, window=window,
has_min_periods=has_min_periods,
preserve_nan=preserve_nan)
self._check_structures(func, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule)
def _check_ndarray(self, func, static_comp, window=50,
has_min_periods=True,
preserve_nan=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.assert_(np.isnan(result[23]))
self.assert_(not np.isnan(result[24]))
self.assert_(not np.isnan(result[-6]))
self.assert_(np.isnan(result[-5]))
# 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]))
def _check_structures(self, func, static_comp,
has_min_periods=True, has_time_rule=True):
series_result = func(self.series, 50)
self.assert_(isinstance(series_result, Series))
frame_result = func(self.frame, 50)
self.assertEquals(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,
time_rule='WEEKDAY')
frame_result = func(self.frame[::2], win, min_periods=minp,
time_rule='WEEKDAY')
else:
series_result = func(self.series[::2], win, time_rule='WEEKDAY')
frame_result = func(self.frame[::2], win, time_rule='WEEKDAY')
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))
def test_ewma(self):
self._check_ew(mom.ewma)
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 _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
# ??? check something
# pass in ints
result2 = func(np.arange(50), span=10)
self.assert_(result.dtype == np.float_)
def _check_ew_structures(self, func):
series_result = func(self.series, com=10)
self.assert_(isinstance(series_result, Series))
frame_result = func(self.frame, com=10)
self.assertEquals(type(frame_result), DataFrame)
# binary moments
def test_rolling_cov(self):
A = self.series
B = A + randn(len(A))
result = mom.rolling_cov(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = mom.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = mom.rolling_corr(a, b, len(a), min_periods=1)
assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr_pairwise(self):
panel = mom.rolling_corr_pairwise(self.frame, 10, min_periods=5)
correl = panel.ix[:, 1, 5]
exp = mom.rolling_corr(self.frame[1], self.frame[5],
10, min_periods=5)
tm.assert_series_equal(correl, exp)
def test_flex_binary_frame(self):
def _check(method):
series = self.frame[1]
res = method(series, self.frame, 10)
res2 = method(self.frame, series, 10)
exp = self.frame.apply(lambda x: method(series, x, 10))
tm.assert_frame_equal(res, exp)
tm.assert_frame_equal(res2, exp)
frame2 = self.frame.copy()
frame2.values[:] = np.random.randn(*frame2.shape)
res3 = method(self.frame, frame2, 10)
exp = DataFrame(dict((k, method(self.frame[k], frame2[k], 10))
for k in self.frame))
tm.assert_frame_equal(res3, exp)
methods = [mom.rolling_corr, mom.rolling_cov]
for meth in methods:
_check(meth)
def test_ewmcov(self):
self._check_binary_ew(mom.ewmcov)
def test_ewmcorr(self):
self._check_binary_ew(mom.ewmcorr)
def _check_binary_ew(self, func):
A = Series(randn(50), index=np.arange(50))
B = A[2:] + randn(48)
A[:10] = np.NaN
B[-10:] = np.NaN
result = func(A, B, 20, min_periods=5)
self.assert_(np.isnan(result.values[:15]).all())
self.assert_(not np.isnan(result.values[15:]).any())
self.assertRaises(Exception, func, A, randn(50), 20, min_periods=5)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
exit=False)
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