def test_emwmv_unbiased():
# bias: True
v = [np.nan, 2, np.nan, 6, 8, 5]
s = Stream(v, "s")
specs = [{
"alpha": 0.68,
"adjust": True,
"ignore_na": True,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": True,
"ignore_na": False,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": False,
"ignore_na": True,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": False,
"ignore_na": False,
"min_periods": 3
}]
for spec in specs:
d = spec.copy()
d["warmup"] = d["min_periods"]
del d["min_periods"]
var = s.ewm(**d).var(bias=False).rename("var")
feed = DataFeed([var])
feed.compile()
expected = list(pd.Series(v).ewm(**spec).var(bias=False))
actual = []
while feed.has_next():
actual += [feed.next()["var"]]
assert all(np.isclose(actual, expected))
def test_ewa_beginning_na():
# adjust: True, ignore_na: False
v = [np.nan, 2, np.nan, 6, 8, 5]
s = Stream(v).rename("s")
specs = [{
"alpha": 0.68,
"adjust": True,
"ignore_na": True,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": True,
"ignore_na": False,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": False,
"ignore_na": True,
"min_periods": 3
}, {
"alpha": 0.68,
"adjust": False,
"ignore_na": False,
"min_periods": 3
}]
for spec in specs:
d = spec.copy()
d["warmup"] = d["min_periods"]
del d["min_periods"]
mean = s.ewm(**d).mean().rename("mean")
feed = DataFeed([mean])
feed.compile()
expected = list(pd.Series(v).ewm(**spec).mean())
actual = []
while feed.has_next():
actual += [feed.next()["mean"]]
assert all(np.isclose(actual, expected))
def test_ewma():
# adjust: True, ignore_na: True
v = [5, 2, 4, 6]
s = Stream(v, "s")
mean = s.ewm(alpha=0.68, adjust=True, ignore_na=True).mean().rename("mean")
feed = DataFeed([mean])
feed.compile()
expected = list(
pd.Series(v).ewm(alpha=0.68, adjust=True, ignore_na=True).mean())
actual = []
while feed.has_next():
actual += [feed.next()["mean"]]
assert all(np.isclose(actual, expected))
# adjust: True, ignore_na: False
v = [5, 2, np.nan, 6]
s = Stream(v, "s")
mean = s.ewm(alpha=0.68, adjust=True,
ignore_na=False).mean().rename("mean")
feed = DataFeed([mean])
feed.compile()
expected = list(
pd.Series(v).ewm(alpha=0.68, adjust=True, ignore_na=False).mean())
actual = []
while feed.has_next():
actual += [feed.next()["mean"]]
assert all(np.isclose(actual, expected))
# adjust: True, ignore_na: False
v = [5, 2, np.nan, 6]
s = Stream(v, "s")
mean = s.ewm(alpha=0.68, adjust=False,
ignore_na=True).mean().rename("mean")
feed = DataFeed([mean])
feed.compile()
expected = list(
pd.Series(v).ewm(alpha=0.68, adjust=False, ignore_na=True).mean())
actual = []
while feed.has_next():
actual += [feed.next()["mean"]]
assert all(np.isclose(actual, expected))
# adjust: True, ignore_na: False
v = [5, 2, np.nan, 6]
s = Stream(v, "s")
mean = s.ewm(alpha=0.68, adjust=False,
ignore_na=False).mean().rename("mean")
feed = DataFeed([mean])
feed.compile()
expected = list(
pd.Series(v).ewm(alpha=0.68, adjust=False, ignore_na=False).mean())
actual = []
while feed.has_next():
actual += [feed.next()["mean"]]
assert all(np.isclose(actual, expected))