def add_rows(self, matrix_, id2row):
"""
Adds rows to a peripheral space.
Args:
matrix_: Matrix type, the matrix of the elements to be added.
id2row: list, string identifiers of the rows to be added.
Modifies the current space by appending the new rows.
All operations of the core space are projected to the new rows.
Raises:
ValueError: if attempting to add row strings which are already
in the space.
matrix of the new data is not consistent in shape
with the current data matrix.
"""
try:
self._row2id = add_items_to_dict(self.row2id, id2row)
except ValueError:
raise ValueError("Found duplicate keys when appending rows to\
peripheral space.")
if matrix_.mat.shape[0] != len(id2row):
raise ValueError("Matrix shape inconsistent with no. of rows:%s %s"
% (matrix_.mat.shape, len(id2row)))
self._id2row = self.id2row + id2row
matrix_ = self._project_core_operations(matrix_)
self._cooccurrence_matrix = self._cooccurrence_matrix.vstack(matrix_)
assert_shape_consistent(self.cooccurrence_matrix, self.id2row,
self.id2column, self.row2id, self.column2id)
def add_rows(self, matrix_, id2row):
"""
Adds rows to a peripheral space.
Args:
matrix_: Matrix type, the matrix of the elements to be added.
id2row: list, string identifiers of the rows to be added.
Modifies the current space by appending the new rows.
All operations of the core space are projected to the new rows.
Raises:
ValueError: if attempting to add row strings which are already
in the space.
matrix of the new data is not consistent in shape
with the current data matrix.
"""
try:
self._row2id = add_items_to_dict(self.row2id, id2row)
except ValueError:
raise ValueError("Found duplicate keys when appending rows to\
peripheral space.")
if matrix_.mat.shape[0] != len(id2row):
raise ValueError("Matrix shape inconsistent with no. of rows:%s %s"
% (matrix_.mat.shape, len(id2row)))
self._id2row = self.id2row + id2row
matrix_ = self._project_core_operations(matrix_)
self._cooccurrence_matrix = self._cooccurrence_matrix.vstack(matrix_)
assert_shape_consistent(self.cooccurrence_matrix, self.id2row,
self.id2column, self.row2id, self.column2id)
def vstack(cls, space1, space2):
"""
Classmethod. Stacks two semantic spaces.
The rows in the two spaces are concatenated.
Args:
space1, space2: spaces to be stacked, of type Space
Returns:
Stacked space, type Space.
Raises:
ValueError: if the spaces have different number of columns
or their columns are not identical
"""
if space1.cooccurrence_matrix.shape[
1] != space2.cooccurrence_matrix.shape[1]:
raise ValueError("Inconsistent shapes: %s, %s" %
(space1.cooccurrence_matrix.shape[1],
space2.cooccurrence_matrix.shape[1]))
if space1.id2column != space2.id2column:
raise ValueError("Identical columns required")
new_row2id = add_items_to_dict(space1.row2id.copy(), space2.id2row)
new_id2row = space1.id2row + space2.id2row
matrix_type = get_type_of_largest(
[space1.cooccurrence_matrix, space2.cooccurrence_matrix])
[new_mat1, new_mat2] = resolve_type_conflict(
[space1.cooccurrence_matrix, space2.cooccurrence_matrix],
matrix_type)
new_mat = new_mat1.vstack(new_mat2)
log.print_info(logger, 1, "\nVertical stack of two spaces")
log.print_matrix_info(logger, space1.cooccurrence_matrix, 2,
"Semantic space 1:")
log.print_matrix_info(logger, space2.cooccurrence_matrix, 2,
"Semantic space 2:")
log.print_matrix_info(logger, new_mat, 2, "Resulted semantic space:")
return Space(new_mat,
new_id2row,
list(space1.id2column),
new_row2id,
space1.column2id.copy(),
operations=[])
def vstack(cls, space1, space2):
"""
Classmethod. Stacks two semantic spaces.
The rows in the two spaces are concatenated.
Args:
space1, space2: spaces to be stacked, of type Space
Returns:
Stacked space, type Space.
Raises:
ValueError: if the spaces have different number of columns
or their columns are not identical
"""
if space1.cooccurrence_matrix.shape[1] != space2.cooccurrence_matrix.shape[1]:
raise ValueError("Inconsistent shapes: %s, %s"
% (space1.cooccurrence_matrix.shape[1],
space2.cooccurrence_matrix.shape[1]))
if space1.id2column != space2.id2column:
raise ValueError("Identical columns required")
new_row2id = add_items_to_dict(space1.row2id.copy(), space2.id2row)
new_id2row = space1.id2row + space2.id2row
matrix_type = get_type_of_largest([space1.cooccurrence_matrix,
space2.cooccurrence_matrix])
[new_mat1, new_mat2] = resolve_type_conflict([space1.cooccurrence_matrix,
space2.cooccurrence_matrix],
matrix_type)
new_mat = new_mat1.vstack(new_mat2)
log.print_info(logger, 1, "\nVertical stack of two spaces")
log.print_matrix_info(logger, space1.cooccurrence_matrix, 2,
"Semantic space 1:")
log.print_matrix_info(logger, space2.cooccurrence_matrix, 2,
"Semantic space 2:")
log.print_matrix_info(logger, new_mat, 2, "Resulted semantic space:")
return Space(new_mat, new_id2row, list(space1.id2column), new_row2id,
space1.column2id.copy(), operations=[])