368 lines
12 KiB
Python
368 lines
12 KiB
Python
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"""Indexing mixin for sparse matrix classes.
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"""
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import numpy as np
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from .sputils import isintlike
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try:
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INT_TYPES = (int, long, np.integer)
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except NameError:
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# long is not defined in Python3
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INT_TYPES = (int, np.integer)
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def _broadcast_arrays(a, b):
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"""
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Same as np.broadcast_arrays(a, b) but old writeability rules.
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NumPy >= 1.17.0 transitions broadcast_arrays to return
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read-only arrays. Set writeability explicitly to avoid warnings.
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Retain the old writeability rules, as our Cython code assumes
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the old behavior.
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"""
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x, y = np.broadcast_arrays(a, b)
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x.flags.writeable = a.flags.writeable
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y.flags.writeable = b.flags.writeable
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return x, y
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class IndexMixin(object):
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"""
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This class provides common dispatching and validation logic for indexing.
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"""
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def __getitem__(self, key):
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row, col = self._validate_indices(key)
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# Dispatch to specialized methods.
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if isinstance(row, INT_TYPES):
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if isinstance(col, INT_TYPES):
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return self._get_intXint(row, col)
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elif isinstance(col, slice):
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return self._get_intXslice(row, col)
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elif col.ndim == 1:
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return self._get_intXarray(row, col)
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raise IndexError('index results in >2 dimensions')
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elif isinstance(row, slice):
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if isinstance(col, INT_TYPES):
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return self._get_sliceXint(row, col)
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elif isinstance(col, slice):
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if row == slice(None) and row == col:
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return self.copy()
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return self._get_sliceXslice(row, col)
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elif col.ndim == 1:
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return self._get_sliceXarray(row, col)
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raise IndexError('index results in >2 dimensions')
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elif row.ndim == 1:
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if isinstance(col, INT_TYPES):
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return self._get_arrayXint(row, col)
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elif isinstance(col, slice):
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return self._get_arrayXslice(row, col)
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else: # row.ndim == 2
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if isinstance(col, INT_TYPES):
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return self._get_arrayXint(row, col)
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elif isinstance(col, slice):
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raise IndexError('index results in >2 dimensions')
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elif row.shape[1] == 1 and (col.ndim == 1 or col.shape[0] == 1):
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# special case for outer indexing
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return self._get_columnXarray(row[:,0], col.ravel())
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# The only remaining case is inner (fancy) indexing
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row, col = _broadcast_arrays(row, col)
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if row.shape != col.shape:
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raise IndexError('number of row and column indices differ')
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if row.size == 0:
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return self.__class__(np.atleast_2d(row).shape, dtype=self.dtype)
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return self._get_arrayXarray(row, col)
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def __setitem__(self, key, x):
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row, col = self._validate_indices(key)
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if isinstance(row, INT_TYPES) and isinstance(col, INT_TYPES):
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x = np.asarray(x, dtype=self.dtype)
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if x.size != 1:
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raise ValueError('Trying to assign a sequence to an item')
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self._set_intXint(row, col, x.flat[0])
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return
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if isinstance(row, slice):
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row = np.arange(*row.indices(self.shape[0]))[:, None]
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else:
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row = np.atleast_1d(row)
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if isinstance(col, slice):
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col = np.arange(*col.indices(self.shape[1]))[None, :]
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if row.ndim == 1:
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row = row[:, None]
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else:
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col = np.atleast_1d(col)
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i, j = _broadcast_arrays(row, col)
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if i.shape != j.shape:
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raise IndexError('number of row and column indices differ')
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from .base import isspmatrix
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if isspmatrix(x):
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if i.ndim == 1:
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# Inner indexing, so treat them like row vectors.
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i = i[None]
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j = j[None]
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broadcast_row = x.shape[0] == 1 and i.shape[0] != 1
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broadcast_col = x.shape[1] == 1 and i.shape[1] != 1
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if not ((broadcast_row or x.shape[0] == i.shape[0]) and
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(broadcast_col or x.shape[1] == i.shape[1])):
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raise ValueError('shape mismatch in assignment')
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if x.size == 0:
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return
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x = x.tocoo(copy=True)
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x.sum_duplicates()
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self._set_arrayXarray_sparse(i, j, x)
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else:
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# Make x and i into the same shape
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x = np.asarray(x, dtype=self.dtype)
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x, _ = _broadcast_arrays(x, i)
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if x.size == 0:
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return
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x = x.reshape(i.shape)
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self._set_arrayXarray(i, j, x)
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def _validate_indices(self, key):
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M, N = self.shape
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row, col = _unpack_index(key)
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if isintlike(row):
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row = int(row)
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if row < -M or row >= M:
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raise IndexError('row index (%d) out of range' % row)
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if row < 0:
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row += M
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elif not isinstance(row, slice):
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row = self._asindices(row, M)
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if isintlike(col):
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col = int(col)
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if col < -N or col >= N:
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raise IndexError('column index (%d) out of range' % col)
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if col < 0:
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col += N
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elif not isinstance(col, slice):
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col = self._asindices(col, N)
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return row, col
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def _asindices(self, idx, length):
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"""Convert `idx` to a valid index for an axis with a given length.
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Subclasses that need special validation can override this method.
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"""
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try:
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x = np.asarray(idx)
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except (ValueError, TypeError, MemoryError):
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raise IndexError('invalid index')
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if x.ndim not in (1, 2):
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raise IndexError('Index dimension must be <= 2')
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if x.size == 0:
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return x
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# Check bounds
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max_indx = x.max()
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if max_indx >= length:
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raise IndexError('index (%d) out of range' % max_indx)
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min_indx = x.min()
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if min_indx < 0:
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if min_indx < -length:
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raise IndexError('index (%d) out of range' % min_indx)
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if x is idx or not x.flags.owndata:
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x = x.copy()
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x[x < 0] += length
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return x
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def getrow(self, i):
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"""Return a copy of row i of the matrix, as a (1 x n) row vector.
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"""
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M, N = self.shape
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i = int(i)
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if i < -M or i >= M:
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raise IndexError('index (%d) out of range' % i)
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if i < 0:
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i += M
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return self._get_intXslice(i, slice(None))
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def getcol(self, i):
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"""Return a copy of column i of the matrix, as a (m x 1) column vector.
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"""
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M, N = self.shape
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i = int(i)
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if i < -N or i >= N:
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raise IndexError('index (%d) out of range' % i)
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if i < 0:
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i += N
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return self._get_sliceXint(slice(None), i)
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def _get_intXint(self, row, col):
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raise NotImplementedError()
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def _get_intXarray(self, row, col):
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raise NotImplementedError()
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def _get_intXslice(self, row, col):
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raise NotImplementedError()
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def _get_sliceXint(self, row, col):
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raise NotImplementedError()
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def _get_sliceXslice(self, row, col):
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raise NotImplementedError()
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def _get_sliceXarray(self, row, col):
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raise NotImplementedError()
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def _get_arrayXint(self, row, col):
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raise NotImplementedError()
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def _get_arrayXslice(self, row, col):
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raise NotImplementedError()
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def _get_columnXarray(self, row, col):
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raise NotImplementedError()
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def _get_arrayXarray(self, row, col):
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raise NotImplementedError()
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def _set_intXint(self, row, col, x):
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raise NotImplementedError()
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def _set_arrayXarray(self, row, col, x):
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raise NotImplementedError()
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def _set_arrayXarray_sparse(self, row, col, x):
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# Fall back to densifying x
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x = np.asarray(x.toarray(), dtype=self.dtype)
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x, _ = _broadcast_arrays(x, row)
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self._set_arrayXarray(row, col, x)
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def _unpack_index(index):
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""" Parse index. Always return a tuple of the form (row, col).
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Valid type for row/col is integer, slice, or array of integers.
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"""
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# First, check if indexing with single boolean matrix.
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from .base import spmatrix, isspmatrix
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if (isinstance(index, (spmatrix, np.ndarray)) and
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index.ndim == 2 and index.dtype.kind == 'b'):
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return index.nonzero()
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# Parse any ellipses.
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index = _check_ellipsis(index)
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# Next, parse the tuple or object
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if isinstance(index, tuple):
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if len(index) == 2:
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row, col = index
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elif len(index) == 1:
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row, col = index[0], slice(None)
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else:
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raise IndexError('invalid number of indices')
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else:
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idx = _compatible_boolean_index(index)
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if idx is None:
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row, col = index, slice(None)
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elif idx.ndim < 2:
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return _boolean_index_to_array(idx), slice(None)
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elif idx.ndim == 2:
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return idx.nonzero()
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# Next, check for validity and transform the index as needed.
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if isspmatrix(row) or isspmatrix(col):
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# Supporting sparse boolean indexing with both row and col does
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# not work because spmatrix.ndim is always 2.
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raise IndexError(
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'Indexing with sparse matrices is not supported '
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'except boolean indexing where matrix and index '
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'are equal shapes.')
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bool_row = _compatible_boolean_index(row)
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bool_col = _compatible_boolean_index(col)
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if bool_row is not None:
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row = _boolean_index_to_array(bool_row)
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if bool_col is not None:
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col = _boolean_index_to_array(bool_col)
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return row, col
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def _check_ellipsis(index):
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"""Process indices with Ellipsis. Returns modified index."""
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if index is Ellipsis:
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return (slice(None), slice(None))
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if not isinstance(index, tuple):
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return index
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# TODO: Deprecate this multiple-ellipsis handling,
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# as numpy no longer supports it.
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# Find first ellipsis.
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for j, v in enumerate(index):
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if v is Ellipsis:
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first_ellipsis = j
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break
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else:
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return index
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# Try to expand it using shortcuts for common cases
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if len(index) == 1:
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return (slice(None), slice(None))
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if len(index) == 2:
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if first_ellipsis == 0:
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if index[1] is Ellipsis:
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return (slice(None), slice(None))
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return (slice(None), index[1])
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return (index[0], slice(None))
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# Expand it using a general-purpose algorithm
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tail = []
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for v in index[first_ellipsis+1:]:
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if v is not Ellipsis:
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tail.append(v)
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nd = first_ellipsis + len(tail)
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nslice = max(0, 2 - nd)
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return index[:first_ellipsis] + (slice(None),)*nslice + tuple(tail)
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def _maybe_bool_ndarray(idx):
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"""Returns a compatible array if elements are boolean.
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"""
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idx = np.asanyarray(idx)
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if idx.dtype.kind == 'b':
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return idx
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return None
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def _first_element_bool(idx, max_dim=2):
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"""Returns True if first element of the incompatible
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array type is boolean.
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"""
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if max_dim < 1:
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return None
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try:
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first = next(iter(idx), None)
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except TypeError:
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return None
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if isinstance(first, bool):
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return True
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return _first_element_bool(first, max_dim-1)
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def _compatible_boolean_index(idx):
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"""Returns a boolean index array that can be converted to
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integer array. Returns None if no such array exists.
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"""
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# Presence of attribute `ndim` indicates a compatible array type.
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if hasattr(idx, 'ndim') or _first_element_bool(idx):
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return _maybe_bool_ndarray(idx)
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return None
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def _boolean_index_to_array(idx):
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if idx.ndim > 1:
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raise IndexError('invalid index shape')
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return np.where(idx)[0]
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