def _drop_missing(self) -> BoolArray:
data = (self.portfolios, self.factors)
missing = cast(BoolArray, np.any(np.c_[[dh.isnull for dh in data]], 0))
if any(missing):
if all(missing):
raise ValueError("All observations contain missing data. "
"Model cannot be estimated.")
self.portfolios.drop(missing)
self.factors.drop(missing)
missing_warning(missing)
return missing
def _drop_missing(self) -> BoolArray:
missing = self.dependent.isnull.to_numpy()
missing |= self.exog.isnull.to_numpy()
missing |= self._absorb_inter.cat.isnull().any(1).to_numpy()
missing |= self._absorb_inter.cont.isnull().any(1).to_numpy()
for interact in self._interaction_list:
missing |= interact.isnull.to_numpy()
if npany(missing):
self.dependent.drop(missing)
self.exog.drop(missing)
self._absorb_inter.drop(missing)
for interact in self._interaction_list:
interact.drop(missing)
missing_warning(missing)
return missing
def test_missing_warning():
missing = np.zeros(500, dtype=bool)
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 0
missing[0] = True
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 1
original = linearmodels.WARN_ON_MISSING
linearmodels.WARN_ON_MISSING = False
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 0
linearmodels.WARN_ON_MISSING = original