552 lines
18 KiB
Python
552 lines
18 KiB
Python
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"""Compressed Sparse Row matrix format"""
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__docformat__ = "restructuredtext en"
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__all__ = ['csr_array', 'csr_matrix', 'isspmatrix_csr']
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import numpy as np
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from ._matrix import spmatrix
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from ._base import _spbase, sparray
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from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks,
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get_csr_submatrix)
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from ._sputils import upcast
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from ._compressed import _cs_matrix
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class _csr_base(_cs_matrix):
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_format = 'csr'
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# override IndexMixin.__getitem__ for 1d case until fully implemented
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def __getitem__(self, key):
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if self.ndim == 2:
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return super().__getitem__(key)
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if isinstance(key, tuple) and len(key) == 1:
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key = key[0]
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INT_TYPES = (int, np.integer)
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if isinstance(key, INT_TYPES):
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if key < 0:
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key += self.shape[-1]
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if key < 0 or key >= self.shape[-1]:
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raise IndexError('index value out of bounds')
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return self._get_int(key)
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else:
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raise IndexError('array/slice index for 1d csr_array not yet supported')
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# override IndexMixin.__setitem__ for 1d case until fully implemented
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def __setitem__(self, key, value):
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if self.ndim == 2:
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return super().__setitem__(key, value)
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if isinstance(key, tuple) and len(key) == 1:
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key = key[0]
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INT_TYPES = (int, np.integer)
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if isinstance(key, INT_TYPES):
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if key < 0:
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key += self.shape[-1]
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if key < 0 or key >= self.shape[-1]:
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raise IndexError('index value out of bounds')
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return self._set_int(key, value)
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else:
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raise IndexError('array index for 1d csr_array not yet provided')
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def transpose(self, axes=None, copy=False):
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if axes is not None and axes != (1, 0):
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raise ValueError("Sparse arrays/matrices do not support "
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"an 'axes' parameter because swapping "
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"dimensions is the only logical permutation.")
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if self.ndim == 1:
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return self.copy() if copy else self
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M, N = self.shape
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return self._csc_container((self.data, self.indices,
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self.indptr), shape=(N, M), copy=copy)
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transpose.__doc__ = _spbase.transpose.__doc__
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def tolil(self, copy=False):
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if self.ndim != 2:
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raise ValueError("Cannot convert a 1d sparse array to lil format")
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lil = self._lil_container(self.shape, dtype=self.dtype)
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self.sum_duplicates()
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ptr,ind,dat = self.indptr,self.indices,self.data
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rows, data = lil.rows, lil.data
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for n in range(self.shape[0]):
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start = ptr[n]
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end = ptr[n+1]
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rows[n] = ind[start:end].tolist()
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data[n] = dat[start:end].tolist()
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return lil
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tolil.__doc__ = _spbase.tolil.__doc__
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def tocsr(self, copy=False):
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if copy:
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return self.copy()
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else:
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return self
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tocsr.__doc__ = _spbase.tocsr.__doc__
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def tocsc(self, copy=False):
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if self.ndim != 2:
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raise ValueError("Cannot convert a 1d sparse array to csc format")
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M, N = self.shape
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idx_dtype = self._get_index_dtype((self.indptr, self.indices),
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maxval=max(self.nnz, M))
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indptr = np.empty(N + 1, dtype=idx_dtype)
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indices = np.empty(self.nnz, dtype=idx_dtype)
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data = np.empty(self.nnz, dtype=upcast(self.dtype))
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csr_tocsc(M, N,
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self.indptr.astype(idx_dtype),
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self.indices.astype(idx_dtype),
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self.data,
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indptr,
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indices,
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data)
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A = self._csc_container((data, indices, indptr), shape=self.shape)
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A.has_sorted_indices = True
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return A
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tocsc.__doc__ = _spbase.tocsc.__doc__
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def tobsr(self, blocksize=None, copy=True):
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if self.ndim != 2:
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raise ValueError("Cannot convert a 1d sparse array to bsr format")
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if blocksize is None:
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from ._spfuncs import estimate_blocksize
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return self.tobsr(blocksize=estimate_blocksize(self))
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elif blocksize == (1,1):
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arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr)
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return self._bsr_container(arg1, shape=self.shape, copy=copy)
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else:
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R,C = blocksize
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M,N = self.shape
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if R < 1 or C < 1 or M % R != 0 or N % C != 0:
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raise ValueError('invalid blocksize %s' % blocksize)
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blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices)
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idx_dtype = self._get_index_dtype((self.indptr, self.indices),
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maxval=max(N//C, blks))
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indptr = np.empty(M//R+1, dtype=idx_dtype)
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indices = np.empty(blks, dtype=idx_dtype)
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data = np.zeros((blks,R,C), dtype=self.dtype)
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csr_tobsr(M, N, R, C,
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self.indptr.astype(idx_dtype),
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self.indices.astype(idx_dtype),
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self.data,
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indptr, indices, data.ravel())
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return self._bsr_container(
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(data, indices, indptr), shape=self.shape
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)
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tobsr.__doc__ = _spbase.tobsr.__doc__
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# these functions are used by the parent class (_cs_matrix)
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# to remove redundancy between csc_matrix and csr_array
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@staticmethod
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def _swap(x):
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"""swap the members of x if this is a column-oriented matrix
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"""
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return x
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def __iter__(self):
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if self.ndim == 1:
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zero = self.dtype.type(0)
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u = 0
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for v, d in zip(self.indices, self.data):
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for _ in range(v - u):
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yield zero
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yield d
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u = v + 1
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for _ in range(self.shape[0] - u):
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yield zero
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return
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indptr = np.zeros(2, dtype=self.indptr.dtype)
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# return 1d (sparray) or 2drow (spmatrix)
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shape = self.shape[1:] if isinstance(self, sparray) else (1, self.shape[1])
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i0 = 0
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for i1 in self.indptr[1:]:
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indptr[1] = i1 - i0
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indices = self.indices[i0:i1]
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data = self.data[i0:i1]
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yield self.__class__((data, indices, indptr), shape=shape, copy=True)
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i0 = i1
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def _getrow(self, i):
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"""Returns a copy of row i of the matrix, as a (1 x n)
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CSR matrix (row vector).
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"""
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if self.ndim == 1:
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if i not in (0, -1):
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raise IndexError(f'index ({i}) out of range')
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return self.reshape((1, self.shape[0]), copy=True)
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M, N = self.shape
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i = int(i)
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if i < 0:
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i += M
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if i < 0 or i >= M:
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raise IndexError('index (%d) out of range' % i)
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indptr, indices, data = get_csr_submatrix(
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M, N, self.indptr, self.indices, self.data, i, i + 1, 0, N)
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return self.__class__((data, indices, indptr), shape=(1, N),
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dtype=self.dtype, copy=False)
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def _getcol(self, i):
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"""Returns a copy of column i. A (m x 1) sparse array (column vector).
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"""
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if self.ndim == 1:
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raise ValueError("getcol not provided for 1d arrays. Use indexing A[j]")
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M, N = self.shape
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i = int(i)
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if i < 0:
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i += N
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if i < 0 or i >= N:
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raise IndexError('index (%d) out of range' % i)
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indptr, indices, data = get_csr_submatrix(
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M, N, self.indptr, self.indices, self.data, 0, M, i, i + 1)
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return self.__class__((data, indices, indptr), shape=(M, 1),
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dtype=self.dtype, copy=False)
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def _get_intXarray(self, row, col):
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return self._getrow(row)._minor_index_fancy(col)
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def _get_intXslice(self, row, col):
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if col.step in (1, None):
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return self._get_submatrix(row, col, copy=True)
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# TODO: uncomment this once it's faster:
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# return self._getrow(row)._minor_slice(col)
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M, N = self.shape
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start, stop, stride = col.indices(N)
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ii, jj = self.indptr[row:row+2]
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row_indices = self.indices[ii:jj]
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row_data = self.data[ii:jj]
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if stride > 0:
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ind = (row_indices >= start) & (row_indices < stop)
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else:
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ind = (row_indices <= start) & (row_indices > stop)
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if abs(stride) > 1:
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ind &= (row_indices - start) % stride == 0
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row_indices = (row_indices[ind] - start) // stride
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row_data = row_data[ind]
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row_indptr = np.array([0, len(row_indices)])
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if stride < 0:
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row_data = row_data[::-1]
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row_indices = abs(row_indices[::-1])
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shape = (1, max(0, int(np.ceil(float(stop - start) / stride))))
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return self.__class__((row_data, row_indices, row_indptr), shape=shape,
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dtype=self.dtype, copy=False)
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def _get_sliceXint(self, row, col):
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if row.step in (1, None):
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return self._get_submatrix(row, col, copy=True)
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return self._major_slice(row)._get_submatrix(minor=col)
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def _get_sliceXarray(self, row, col):
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return self._major_slice(row)._minor_index_fancy(col)
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def _get_arrayXint(self, row, col):
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return self._major_index_fancy(row)._get_submatrix(minor=col)
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def _get_arrayXslice(self, row, col):
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if col.step not in (1, None):
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col = np.arange(*col.indices(self.shape[1]))
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return self._get_arrayXarray(row, col)
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return self._major_index_fancy(row)._get_submatrix(minor=col)
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def isspmatrix_csr(x):
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"""Is `x` of csr_matrix type?
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Parameters
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----------
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x
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object to check for being a csr matrix
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Returns
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-------
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bool
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True if `x` is a csr matrix, False otherwise
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Examples
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--------
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>>> from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr
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>>> isspmatrix_csr(csr_matrix([[5]]))
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True
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>>> isspmatrix_csr(csr_array([[5]]))
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False
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>>> isspmatrix_csr(coo_matrix([[5]]))
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False
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"""
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return isinstance(x, csr_matrix)
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# This namespace class separates array from matrix with isinstance
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class csr_array(_csr_base, sparray):
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"""
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Compressed Sparse Row array.
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This can be instantiated in several ways:
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csr_array(D)
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where D is a 2-D ndarray
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csr_array(S)
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with another sparse array or matrix S (equivalent to S.tocsr())
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csr_array((M, N), [dtype])
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to construct an empty array with shape (M, N)
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dtype is optional, defaulting to dtype='d'.
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csr_array((data, (row_ind, col_ind)), [shape=(M, N)])
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where ``data``, ``row_ind`` and ``col_ind`` satisfy the
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relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
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csr_array((data, indices, indptr), [shape=(M, N)])
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is the standard CSR representation where the column indices for
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row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their
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corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.
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If the shape parameter is not supplied, the array dimensions
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are inferred from the index arrays.
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Attributes
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----------
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dtype : dtype
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Data type of the array
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shape : 2-tuple
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Shape of the array
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ndim : int
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Number of dimensions (this is always 2)
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nnz
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size
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data
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CSR format data array of the array
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indices
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CSR format index array of the array
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indptr
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CSR format index pointer array of the array
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has_sorted_indices
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has_canonical_format
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T
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Notes
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-----
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Sparse arrays can be used in arithmetic operations: they support
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addition, subtraction, multiplication, division, and matrix power.
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Advantages of the CSR format
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- efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
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- efficient row slicing
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- fast matrix vector products
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Disadvantages of the CSR format
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- slow column slicing operations (consider CSC)
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- changes to the sparsity structure are expensive (consider LIL or DOK)
|
||
|
|
|
||
|
|
Canonical Format
|
||
|
|
- Within each row, indices are sorted by column.
|
||
|
|
- There are no duplicate entries.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from scipy.sparse import csr_array
|
||
|
|
>>> csr_array((3, 4), dtype=np.int8).toarray()
|
||
|
|
array([[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=int8)
|
||
|
|
|
||
|
|
>>> row = np.array([0, 0, 1, 2, 2, 2])
|
||
|
|
>>> col = np.array([0, 2, 2, 0, 1, 2])
|
||
|
|
>>> data = np.array([1, 2, 3, 4, 5, 6])
|
||
|
|
>>> csr_array((data, (row, col)), shape=(3, 3)).toarray()
|
||
|
|
array([[1, 0, 2],
|
||
|
|
[0, 0, 3],
|
||
|
|
[4, 5, 6]])
|
||
|
|
|
||
|
|
>>> indptr = np.array([0, 2, 3, 6])
|
||
|
|
>>> indices = np.array([0, 2, 2, 0, 1, 2])
|
||
|
|
>>> data = np.array([1, 2, 3, 4, 5, 6])
|
||
|
|
>>> csr_array((data, indices, indptr), shape=(3, 3)).toarray()
|
||
|
|
array([[1, 0, 2],
|
||
|
|
[0, 0, 3],
|
||
|
|
[4, 5, 6]])
|
||
|
|
|
||
|
|
Duplicate entries are summed together:
|
||
|
|
|
||
|
|
>>> row = np.array([0, 1, 2, 0])
|
||
|
|
>>> col = np.array([0, 1, 1, 0])
|
||
|
|
>>> data = np.array([1, 2, 4, 8])
|
||
|
|
>>> csr_array((data, (row, col)), shape=(3, 3)).toarray()
|
||
|
|
array([[9, 0, 0],
|
||
|
|
[0, 2, 0],
|
||
|
|
[0, 4, 0]])
|
||
|
|
|
||
|
|
As an example of how to construct a CSR array incrementally,
|
||
|
|
the following snippet builds a term-document array from texts:
|
||
|
|
|
||
|
|
>>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
|
||
|
|
>>> indptr = [0]
|
||
|
|
>>> indices = []
|
||
|
|
>>> data = []
|
||
|
|
>>> vocabulary = {}
|
||
|
|
>>> for d in docs:
|
||
|
|
... for term in d:
|
||
|
|
... index = vocabulary.setdefault(term, len(vocabulary))
|
||
|
|
... indices.append(index)
|
||
|
|
... data.append(1)
|
||
|
|
... indptr.append(len(indices))
|
||
|
|
...
|
||
|
|
>>> csr_array((data, indices, indptr), dtype=int).toarray()
|
||
|
|
array([[2, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
class csr_matrix(spmatrix, _csr_base):
|
||
|
|
"""
|
||
|
|
Compressed Sparse Row matrix.
|
||
|
|
|
||
|
|
This can be instantiated in several ways:
|
||
|
|
csr_matrix(D)
|
||
|
|
where D is a 2-D ndarray
|
||
|
|
|
||
|
|
csr_matrix(S)
|
||
|
|
with another sparse array or matrix S (equivalent to S.tocsr())
|
||
|
|
|
||
|
|
csr_matrix((M, N), [dtype])
|
||
|
|
to construct an empty matrix with shape (M, N)
|
||
|
|
dtype is optional, defaulting to dtype='d'.
|
||
|
|
|
||
|
|
csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
|
||
|
|
where ``data``, ``row_ind`` and ``col_ind`` satisfy the
|
||
|
|
relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
|
||
|
|
|
||
|
|
csr_matrix((data, indices, indptr), [shape=(M, N)])
|
||
|
|
is the standard CSR representation where the column indices for
|
||
|
|
row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their
|
||
|
|
corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.
|
||
|
|
If the shape parameter is not supplied, the matrix dimensions
|
||
|
|
are inferred from the index arrays.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
dtype : dtype
|
||
|
|
Data type of the matrix
|
||
|
|
shape : 2-tuple
|
||
|
|
Shape of the matrix
|
||
|
|
ndim : int
|
||
|
|
Number of dimensions (this is always 2)
|
||
|
|
nnz
|
||
|
|
size
|
||
|
|
data
|
||
|
|
CSR format data array of the matrix
|
||
|
|
indices
|
||
|
|
CSR format index array of the matrix
|
||
|
|
indptr
|
||
|
|
CSR format index pointer array of the matrix
|
||
|
|
has_sorted_indices
|
||
|
|
has_canonical_format
|
||
|
|
T
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
Sparse matrices can be used in arithmetic operations: they support
|
||
|
|
addition, subtraction, multiplication, division, and matrix power.
|
||
|
|
|
||
|
|
Advantages of the CSR format
|
||
|
|
- efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
|
||
|
|
- efficient row slicing
|
||
|
|
- fast matrix vector products
|
||
|
|
|
||
|
|
Disadvantages of the CSR format
|
||
|
|
- slow column slicing operations (consider CSC)
|
||
|
|
- changes to the sparsity structure are expensive (consider LIL or DOK)
|
||
|
|
|
||
|
|
Canonical Format
|
||
|
|
- Within each row, indices are sorted by column.
|
||
|
|
- There are no duplicate entries.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from scipy.sparse import csr_matrix
|
||
|
|
>>> csr_matrix((3, 4), dtype=np.int8).toarray()
|
||
|
|
array([[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=int8)
|
||
|
|
|
||
|
|
>>> row = np.array([0, 0, 1, 2, 2, 2])
|
||
|
|
>>> col = np.array([0, 2, 2, 0, 1, 2])
|
||
|
|
>>> data = np.array([1, 2, 3, 4, 5, 6])
|
||
|
|
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
|
||
|
|
array([[1, 0, 2],
|
||
|
|
[0, 0, 3],
|
||
|
|
[4, 5, 6]])
|
||
|
|
|
||
|
|
>>> indptr = np.array([0, 2, 3, 6])
|
||
|
|
>>> indices = np.array([0, 2, 2, 0, 1, 2])
|
||
|
|
>>> data = np.array([1, 2, 3, 4, 5, 6])
|
||
|
|
>>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
|
||
|
|
array([[1, 0, 2],
|
||
|
|
[0, 0, 3],
|
||
|
|
[4, 5, 6]])
|
||
|
|
|
||
|
|
Duplicate entries are summed together:
|
||
|
|
|
||
|
|
>>> row = np.array([0, 1, 2, 0])
|
||
|
|
>>> col = np.array([0, 1, 1, 0])
|
||
|
|
>>> data = np.array([1, 2, 4, 8])
|
||
|
|
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
|
||
|
|
array([[9, 0, 0],
|
||
|
|
[0, 2, 0],
|
||
|
|
[0, 4, 0]])
|
||
|
|
|
||
|
|
As an example of how to construct a CSR matrix incrementally,
|
||
|
|
the following snippet builds a term-document matrix from texts:
|
||
|
|
|
||
|
|
>>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
|
||
|
|
>>> indptr = [0]
|
||
|
|
>>> indices = []
|
||
|
|
>>> data = []
|
||
|
|
>>> vocabulary = {}
|
||
|
|
>>> for d in docs:
|
||
|
|
... for term in d:
|
||
|
|
... index = vocabulary.setdefault(term, len(vocabulary))
|
||
|
|
... indices.append(index)
|
||
|
|
... data.append(1)
|
||
|
|
... indptr.append(len(indices))
|
||
|
|
...
|
||
|
|
>>> csr_matrix((data, indices, indptr), dtype=int).toarray()
|
||
|
|
array([[2, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
|