def __call__(self, data):
if isinstance(data, Instance):
return self.negate != bn.anynan(data._y)
if isinstance(data, Storage):
try:
return data._filter_has_class(self.negate)
except NotImplementedError:
pass
r = np.fromiter((bn.anynan(inst._y) for inst in data), bool, len(data))
if self.negate:
r = np.logical_not(r)
return data[r]
def __call__(self, data):
if isinstance(data, Instance):
return self.negate == bn.anynan(data._y)
if isinstance(data, Storage):
try:
return data._filter_has_class(self.negate)
except NotImplementedError:
pass
r = np.fromiter((not bn.anynan(inst._y) for inst in data), bool, len(data))
if self.negate:
r = np.logical_not(r)
return data[r]
def __call__(self, data):
if isinstance(data, Instance):
return self.negate == bn.anynan(data._x)
if isinstance(data, Storage):
try:
return data._filter_is_defined(self.columns, self.negate)
except NotImplementedError:
pass
r = np.fromiter((not bn.anynan(inst._x) for inst in data), dtype=bool, count=len(data))
if self.negate:
r = np.logical_not(r)
return data[r]
def __call__(self, data):
if isinstance(data, Instance):
return self.negate != bn.anynan(data._values)
if isinstance(data, Storage):
try:
return data._filter_is_defined(self.columns, self.negate)
except NotImplementedError:
pass
r = np.fromiter((bn.anynan(inst._values) for inst in data),
dtype=bool, count=len(data))
if self.negate:
r = np.logical_not(r)
return data[r]
def _filter_has_class(self, negate=False):
if sp.issparse(self.Y):
if negate:
retain = self.Y.indptr[1:] != self.Y.indptr[-1:] + self.Y.shape[1]
else:
retain = self.Y.indptr[1:] == self.Y.indptr[-1:] + self.Y.shape[1]
else:
retain = bn.anynan(self.Y, axis=1)
if not negate:
retain = np.logical_not(retain)
return Table.from_table_rows(self, retain)
def _filter_is_defined(self, columns=None, negate=False):
if columns is None:
if sp.issparse(self.X):
remove = self.X.indptr[1:] != self.X.indptr[-1:] + self.X.shape[1]
else:
remove = bn.anynan(self.X, axis=1)
if sp.issparse(self.Y):
remove = np.logical_or(remove, self.Y.indptr[1:] != self.Y.indptr[-1:] + self.Y.shape[1])
else:
remove = np.logical_or(remove, bn.anynan(self.Y, axis=1))
else:
remove = np.zeros(len(self), dtype=bool)
for column in columns:
col, sparse = self.get_column_view(column)
if sparse:
remove = np.logical_or(remove, col == 0)
else:
remove = np.logical_or(remove, bn.anynan([col], axis=0))
retain = remove if negate else np.logical_not(remove)
return Table.from_table_rows(self, retain)
def _filter_has_class(self, negate=False):
if sp.issparse(self.Y):
if negate:
retain = (self.Y.indptr[1:] !=
self.Y.indptr[-1:] + self.Y.shape[1])
else:
retain = (self.Y.indptr[1:] == self.Y.indptr[-1:] +
self.Y.shape[1])
else:
retain = bn.anynan(self.Y, axis=1)
if not negate:
retain = np.logical_not(retain)
return Table.from_table_rows(self, retain)
def _filter_is_defined(self, columns=None, negate=False):
if columns is None:
if sp.issparse(self.X):
remove = (self.X.indptr[1:] !=
self.X.indptr[-1:] + self.X.shape[1])
else:
remove = bn.anynan(self.X, axis=1)
if sp.issparse(self.Y):
remove = np.logical_or(remove, self.Y.indptr[1:] !=
self.Y.indptr[-1:] + self.Y.shape[1])
else:
remove = np.logical_or(remove, bn.anynan(self.Y, axis=1))
else:
remove = np.zeros(len(self), dtype=bool)
for column in columns:
col, sparse = self.get_column_view(column)
if sparse:
remove = np.logical_or(remove, col == 0)
else:
remove = np.logical_or(remove, bn.anynan([col], axis=0))
retain = remove if negate else np.logical_not(remove)
return Table.from_table_rows(self, retain)
def has_missing_class(self):
"""Return `True` if there are any missing class values."""
return bn.anynan(self.Y)
def has_missing(self):
"""Return `True` if there are any missing attribute or class values."""
return bn.anynan(self.X) or bn.anynan(self.Y)
def has_missing_class(self):
"""Return `True` if there are any missing class values."""
return bn.anynan(self.Y)
def has_missing(self):
"""Return `True` if there are any missing attribute or class values."""
return bn.anynan(self.X) or bn.anynan(self.Y)
