"""Compressed Sparse Row matrix format"""
__docformat__ = "restructuredtext en"
__all__ = ['csr_matrix', 'isspmatrix_csr']
import numpy as np
from ._base import spmatrix
from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks,
get_csr_submatrix)
from ._sputils import upcast, get_index_dtype
from ._compressed import _cs_matrix
[docs]class csr_matrix(_cs_matrix):
"""
Compressed Sparse Row matrix
This can be instantiated in several ways:
csr_matrix(D)
with a dense matrix or rank-2 ndarray D
csr_matrix(S)
with another sparse 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
Number of stored values, including explicit zeros
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
Whether indices are sorted
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)
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]])
"""
format = 'csr'
[docs] def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
return self._csc_container((self.data, self.indices,
self.indptr), shape=(N, M), copy=copy)
transpose.__doc__ = spmatrix.transpose.__doc__
[docs] def tolil(self, copy=False):
lil = self._lil_container(self.shape, dtype=self.dtype)
self.sum_duplicates()
ptr,ind,dat = self.indptr,self.indices,self.data
rows, data = lil.rows, lil.data
for n in range(self.shape[0]):
start = ptr[n]
end = ptr[n+1]
rows[n] = ind[start:end].tolist()
data[n] = dat[start:end].tolist()
return lil
tolil.__doc__ = spmatrix.tolil.__doc__
[docs] def tocsr(self, copy=False):
if copy:
return self.copy()
else:
return self
tocsr.__doc__ = spmatrix.tocsr.__doc__
[docs] def tocsc(self, copy=False):
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(self.nnz, self.shape[0]))
indptr = np.empty(self.shape[1] + 1, dtype=idx_dtype)
indices = np.empty(self.nnz, dtype=idx_dtype)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csr_tocsc(self.shape[0], self.shape[1],
self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype),
self.data,
indptr,
indices,
data)
A = self._csc_container((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
tocsc.__doc__ = spmatrix.tocsc.__doc__
[docs] def tobsr(self, blocksize=None, copy=True):
if blocksize is None:
from ._spfuncs import estimate_blocksize
return self.tobsr(blocksize=estimate_blocksize(self))
elif blocksize == (1,1):
arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr)
return self._bsr_container(arg1, shape=self.shape, copy=copy)
else:
R,C = blocksize
M,N = self.shape
if R < 1 or C < 1 or M % R != 0 or N % C != 0:
raise ValueError('invalid blocksize %s' % blocksize)
blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices)
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(N//C, blks))
indptr = np.empty(M//R+1, dtype=idx_dtype)
indices = np.empty(blks, dtype=idx_dtype)
data = np.zeros((blks,R,C), dtype=self.dtype)
csr_tobsr(M, N, R, C,
self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype),
self.data,
indptr, indices, data.ravel())
return self._bsr_container(
(data, indices, indptr), shape=self.shape
)
tobsr.__doc__ = spmatrix.tobsr.__doc__
# these functions are used by the parent class (_cs_matrix)
# to remove redundancy between csc_matrix and csr_matrix
def _swap(self, x):
"""swap the members of x if this is a column-oriented matrix
"""
return x
[docs] def __iter__(self):
indptr = np.zeros(2, dtype=self.indptr.dtype)
shape = (1, self.shape[1])
i0 = 0
for i1 in self.indptr[1:]:
indptr[1] = i1 - i0
indices = self.indices[i0:i1]
data = self.data[i0:i1]
yield self.__class__(
(data, indices, indptr), shape=shape, copy=True
)
i0 = i1
[docs] def getrow(self, i):
"""Returns a copy of row i of the matrix, as a (1 x n)
CSR matrix (row vector).
"""
M, N = self.shape
i = int(i)
if i < 0:
i += M
if i < 0 or i >= M:
raise IndexError('index (%d) out of range' % i)
indptr, indices, data = get_csr_submatrix(
M, N, self.indptr, self.indices, self.data, i, i + 1, 0, N)
return self.__class__((data, indices, indptr), shape=(1, N),
dtype=self.dtype, copy=False)
[docs] def getcol(self, i):
"""Returns a copy of column i of the matrix, as a (m x 1)
CSR matrix (column vector).
"""
M, N = self.shape
i = int(i)
if i < 0:
i += N
if i < 0 or i >= N:
raise IndexError('index (%d) out of range' % i)
indptr, indices, data = get_csr_submatrix(
M, N, self.indptr, self.indices, self.data, 0, M, i, i + 1)
return self.__class__((data, indices, indptr), shape=(M, 1),
dtype=self.dtype, copy=False)
def _get_intXarray(self, row, col):
return self.getrow(row)._minor_index_fancy(col)
def _get_intXslice(self, row, col):
if col.step in (1, None):
return self._get_submatrix(row, col, copy=True)
# TODO: uncomment this once it's faster:
# return self.getrow(row)._minor_slice(col)
M, N = self.shape
start, stop, stride = col.indices(N)
ii, jj = self.indptr[row:row+2]
row_indices = self.indices[ii:jj]
row_data = self.data[ii:jj]
if stride > 0:
ind = (row_indices >= start) & (row_indices < stop)
else:
ind = (row_indices <= start) & (row_indices > stop)
if abs(stride) > 1:
ind &= (row_indices - start) % stride == 0
row_indices = (row_indices[ind] - start) // stride
row_data = row_data[ind]
row_indptr = np.array([0, len(row_indices)])
if stride < 0:
row_data = row_data[::-1]
row_indices = abs(row_indices[::-1])
shape = (1, max(0, int(np.ceil(float(stop - start) / stride))))
return self.__class__((row_data, row_indices, row_indptr), shape=shape,
dtype=self.dtype, copy=False)
def _get_sliceXint(self, row, col):
if row.step in (1, None):
return self._get_submatrix(row, col, copy=True)
return self._major_slice(row)._get_submatrix(minor=col)
def _get_sliceXarray(self, row, col):
return self._major_slice(row)._minor_index_fancy(col)
def _get_arrayXint(self, row, col):
return self._major_index_fancy(row)._get_submatrix(minor=col)
def _get_arrayXslice(self, row, col):
if col.step not in (1, None):
col = np.arange(*col.indices(self.shape[1]))
return self._get_arrayXarray(row, col)
return self._major_index_fancy(row)._get_submatrix(minor=col)
def isspmatrix_csr(x):
"""Is x of csr_matrix type?
Parameters
----------
x
object to check for being a csr matrix
Returns
-------
bool
True if x is a csr matrix, False otherwise
Examples
--------
>>> from scipy.sparse import csr_matrix, isspmatrix_csr
>>> isspmatrix_csr(csr_matrix([[5]]))
True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc
>>> isspmatrix_csr(csc_matrix([[5]]))
False
"""
from ._arrays import csr_array
return isinstance(x, csr_matrix) or isinstance(x, csr_array)