load_stats(),
ids=lambda stat: stat.__class__.__name__)
def test_pickling(stat):
assert isinstance(pickle.loads(pickle.dumps(stat)), stat.__class__)
assert isinstance(copy.deepcopy(stat), stat.__class__)
# Check the statistic has a working __str__ and name method
assert isinstance(str(stat), str)
if isinstance(stat, stats.Univariate):
assert isinstance(stat.name, str)
@pytest.mark.parametrize(
'stat, func',
[(stats.Kurtosis(bias=True), sp_stats.kurtosis),
(stats.Kurtosis(bias=False),
functools.partial(sp_stats.kurtosis, bias=False)),
(stats.Mean(), statistics.mean), (stats.Skew(bias=True), sp_stats.skew),
(stats.Skew(bias=False), functools.partial(sp_stats.skew, bias=False)),
(stats.Var(ddof=0), np.var),
(stats.Var(), functools.partial(np.var, ddof=1))])
def test_univariate(stat, func):
# Shut up
np.warnings.filterwarnings('ignore')
X = [random.random() for _ in range(30)]
for i, x in enumerate(X):
stat.update(x)
@pytest.mark.parametrize('stat', load_stats(), ids=lambda stat: stat.__class__.__name__)
def test_pickling(stat):
assert isinstance(pickle.loads(pickle.dumps(stat)), stat.__class__)
assert isinstance(copy.deepcopy(stat), stat.__class__)
# Check the statistic has a working __str__ and name method
assert isinstance(str(stat), str)
if isinstance(stat, stats.Univariate):
assert isinstance(stat.name, str)
@pytest.mark.parametrize(
'stat, func',
[
(stats.Kurtosis(bias=True), sp_stats.kurtosis),
(stats.Kurtosis(bias=False), functools.partial(sp_stats.kurtosis, bias=False)),
(stats.Mean(), statistics.mean),
(stats.Skew(bias=True), sp_stats.skew),
(stats.Skew(bias=False), functools.partial(sp_stats.skew, bias=False)),
(stats.Var(ddof=0), np.var),
(stats.Var(), functools.partial(np.var, ddof=1))
]
)
def test_univariate(stat, func):
# Shut up
np.warnings.filterwarnings('ignore')
X = [random.random() for _ in range(30)]