Change in behavior for rolling_var when win > len(arr) for 0.14: now raises error

[kdiether]

In 0.13 I could pass a window length greater than the length of the Series passed to rolling_var (or, of course, rolling_std). In 0.14 that raises an error. Behavior is unchanged from 0.13 for other rolling functions:

data = """
x
0.1
0.5
0.3
0.2
0.7
"""

df = pd.read_csv(StringIO(data),header=True)

>>> pd.rolling_mean(df['x'],window=6,min_periods=2)

0      NaN
1    0.300
2    0.300
3    0.275
4    0.360
dtype: float64

>>> pd.rolling_skew(df['x'],window=6,min_periods=2)

0             NaN
1             NaN
2    3.903128e-15
3    7.528372e-01
4    6.013638e-01
dtype: float64

>>> pd.rolling_skew(df['x'],window=6,min_periods=6)

0   NaN
1   NaN
2   NaN
3   NaN
4   NaN
dtype: float64

Those work, but not rolling_var:

>>> pd.rolling_var(df['x'],window=6,min_periods=2)

Traceback (most recent call last):
  File "./foo.py", line 187, in <module>
    print pd.rolling_var(df['x'],window=6,min_periods=2)
  File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 594, in f
    center=center, how=how, **kwargs)
  File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 346, in _rolling_moment
    result = calc(values)
  File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 340, in <lambda>
    **kwds)
  File "/usr/lib64/python2.7/site-packages/pandas/stats/moments.py", line 592, in call_cython
    return func(arg, window, minp, **kwds)
  File "algos.pyx", line 1177, in pandas.algos.roll_var (pandas/algos.c:28449)
IndexError: Out of bounds on buffer access (axis 0)

If this is the new desired default behavior for the rolling functions, I can work around it. I do like the behavior of rolling_skew and rolling_mean better. It was nice default behavior for me when I was doing rolling standard deviations for reasonably large financial data panels.

It looks to me like the issue is caused by the fact that the 0.14 algo for rolling variance is implemented such that the initial loop (roll_var (algos.pyx)) is the following:

for i from 0 <= i < win:

So it loops to win even when win > N.

It looks like to me that the other rolling functions try to implement their algos in such a way that the first loop counts over the following:

for i from 0 <= i < minp - 1:

>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.10-200.fc20.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.6.0.dev-b52bc09
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.3
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
Non

Karl D.

[jreback]

this was changed in #6817 to provide numerical stability in rolling_var

cc @jamiefrio

prob just don't have all of the test cases
I don't think this shock have changed nor is it consistent

want to put together some tests for ranges of window lengths (0,less than len of array, equal, greater than Len of array) - so that it systematically tests these (for all of the rolling functions)

?

fix should be easy

[kdiether]

@jreback, sure I should be able to put together some tests.

[jreback]

@kdiether PR for this?

[kdiether]

@jreback Sorry, I've been particularly busy working on a paper. I should be able to get to it soon.

[kdiether]

@jreback,

So looking at the tests in test_moments.py, it seems to me I could capture these 'out of bounds' window lengths with something like the following:

    def _check_out_of_bounds(self, func):
        arr = np.repeat(np.nan,5)

        result = func(arr,6,min_periods=4)
        self.assertTrue(isnull(result).all())

        result = func(arr,6,min_periods=6)
        self.assertTrue(isnull(result).all())


    def test_rolling_sum_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_sum)

    def test_rolling_mean_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_mean)

    def test_rolling_var_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_var)

Would you be ok with a structure like that?

[jreback]

sure also check min_ periods of 8 and 0 just for kicks

[kdiether]

Got it. So something like the following:

    def _check_out_of_bounds(self, func):
        arr = np.repeat(np.nan,5)

        result = func(arr,6,min_periods=0)
        self.assertTrue(isnull(result).all())

        result = func(arr,6,min_periods=4)
        self.assertTrue(isnull(result).all())

        result = func(arr,6,min_periods=6)
        self.assertTrue(isnull(result).all())

        self.assertRaises(ValueError,func,arr,6,min_periods=8)


    def test_rolling_sum_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_sum)

    def test_rolling_mean_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_mean)

    def test_rolling_var_out_of_bounds(self):
        self._check_out_of_bounds(mom.rolling_var)

In my pull request, do you want me to include tests for all the rolling functions or should I exclude rolling variance/stdev from the test for now?

[jreback]

this essentially a smoke test so can test everything

[kdiether]

It looks like to me like the default behavior for rolling_count is designed to be different than the other rolling functions because the NaNs from the rolling_sum call from within rolling_count are converted to zero counts (which makes sense for count ... at least to me).

result[np.isnan(result)] = 0

Should I exclude rolling_count from these smoke tests or carve out a special test for it?

[jreback]

@kdiether ideally, you would not use nan as your value (maybe use 1), of course you need different results then for the different cases. so prob a several cases here