def _local_read_sparse_mm(array, ex, fn, data_begin):
'''
1. Noted that Matrix Market format doesn't require (row, col) to be sorted.
If the file is sorted (by either row or col), each worker will return
only a part of the array. If the file is unsorted, each worker may
return a very big and sparser sub-array of the original array. In the
worst case, the sub-array can be as large as the original array but
sparser.
2. We can't know how many lines without reading the whole file. So we simply
decide the region this worker should read based on the file size.
'''
data_size = os.path.getsize(fn) - data_begin
array_size = np.product(array.shape)
begin = extent.ravelled_pos(ex.ul, array.shape)
begin = math.ceil(((begin * 1.0) / array_size) * data_size) + data_begin
end = extent.ravelled_pos([(i - 1) for i in ex.lr], array.shape)
end = math.floor(((end * 1.0) / array_size) * data_size) + data_begin
ul = [array.shape[0], array.shape[1]]
lr = [0, 0]
rows = []
cols = []
data = []
with open(fn) as fp:
fp.seek(begin)
if begin != data_begin:
fp.seek(begin - 1)
a = fp.read(1)
if a != '\n':
line = fp.readline()
pos = fp.tell()
for line in fp:
if pos > end + 1: # +1 in case end locates on \n
break
pos += len(line)
(_row, _col), val = _extract_mm_coordinate(line)
_row -= 1
_col -= 1
rows.append(_row)
cols.append(_col)
data.append(float(val))
ul[0] = _row if _row < ul[0] else ul[0]
ul[1] = _col if _col < ul[1] else ul[1]
lr[0] = _row if _row > lr[0] else lr[0]
lr[1] = _col if _col > lr[1] else lr[1]
# Adjust rows and cols based on the ul of this submatrix.
for i in xrange(len(rows)):
rows[i] -= ul[0]
cols[i] -= ul[1]
new_ex = extent.create(ul, [lr[0] + 1, lr[1] + 1], array.shape)
new_array = sp.coo_matrix((data, (rows, cols)), new_ex.shape)
return new_ex, sparse.convert_sparse_array(new_array)
def _local_read_sparse_mm(array, ex, fn, data_begin):
'''
1. Noted that Matrix Market format doesn't require (row, col) to be sorted.
If the file is sorted (by either row or col), each worker will return
only a part of the array. If the file is unsorted, each worker may
return a very big and sparser sub-array of the original array. In the
worst case, the sub-array can be as large as the original array but
sparser.
2. We can't know how many lines without reading the whole file. So we simply
decide the region this worker should read based on the file size.
'''
data_size = os.path.getsize(fn) - data_begin
array_size = np.product(array.shape)
begin = extent.ravelled_pos(ex.ul, array.shape)
begin = math.ceil(((begin * 1.0) / array_size) * data_size) + data_begin
end = extent.ravelled_pos([(i - 1) for i in ex.lr], array.shape)
end = math.floor(((end * 1.0) / array_size) * data_size) + data_begin
ul = [array.shape[0], array.shape[1]]
lr = [0, 0]
rows = []
cols = []
data = []
with open(fn) as fp:
fp.seek(begin)
if begin != data_begin:
fp.seek(begin - 1)
a = fp.read(1)
if a != '\n':
line = fp.readline()
pos = fp.tell()
for line in fp:
if pos > end + 1: # +1 in case end locates on \n
break
pos += len(line)
(_row, _col), val = _extract_mm_coordinate(line)
_row -= 1
_col -= 1
rows.append(_row)
cols.append(_col)
data.append(float(val))
ul[0] = _row if _row < ul[0] else ul[0]
ul[1] = _col if _col < ul[1] else ul[1]
lr[0] = _row if _row > lr[0] else lr[0]
lr[1] = _col if _col > lr[1] else lr[1]
# Adjust rows and cols based on the ul of this submatrix.
for i in xrange(len(rows)):
rows[i] -= ul[0]
cols[i] -= ul[1]
new_ex = extent.create(ul, [lr[0] + 1, lr[1] + 1], array.shape)
new_array = sp.coo_matrix((data, (rows, cols)), new_ex.shape)
return new_ex, sparse.convert_sparse_array(new_array)
def _local_read_sparse_npy(array, ex, fn):
'''
1. Noted that coo_matrix format doesn't require row[] or col[] to be sorted.
If one of row[] or col[] is sorted (by either row or col), each worker will
return only a part of the array. If the file is unsorted, each worker may
return a very big and sparser sub-array of the original array. In the worst
case, the sub-array can be as large as the original array but sparser.
2. For numpy format, we can evenly distribute the files we need to read to
workers.
'''
#data_begin = {}
#dtype = {}
#dtype_size = {}
#shape = {}
#fp = {}
#read_next = {}
attr = {'data_begin':{}, 'dtype':{}, 'shape':None,
'read_next':{}, 'fn':{}}
types = ['row', 'col', 'data']
dtype_name = {'float64':'d', 'float32':'f', 'int64':'q', 'int32':'i'}
for i in types:
_fn = '%s_%s.npy' % (fn, i)
attr['fn'][i] = _fn
_shape, attr['dtype'][i], attr['data_begin'][i] = _parse_npy_header(_fn)
if attr['shape'] != None:
assert attr['shape'] == _shape
else:
attr['shape'] = _shape
#shape['row'], dtype['row'], data_begin['row'] = _parse_npy_header(fn + '_row.npy')
#shape['col'], dtype['col'], data_begin['col'] = _parse_npy_header(fn + '_col.npy')
#shape['data'], dtype['data'], data_begin['data'] = _parse_npy_header(fn + '_data.npy')
item_count = np.product(array.shape)
begin_item = extent.ravelled_pos(ex.ul, array.shape)
begin_item = int(math.ceil(((begin_item * 1.0) / item_count) * attr['shape'][0]))
end_item = extent.ravelled_pos([(i - 1) for i in ex.lr], array.shape)
end_item = int(math.floor((end_item * 1.0) / item_count * attr['shape'][0])) + 1
end_item = attr['shape'][0] if end_item > attr['shape'][0] else end_item
ul = [array.shape[0], array.shape[1]]
lr = [0, 0]
rows = []
cols = []
data = []
with FileHelper(row=open(attr['fn']['row'], 'rb'),
col=open(attr['fn']['col'], 'rb'),
data=open(attr['fn']['data'], 'rb')) as fp:
for k in types:
_dtype = attr['dtype'][k]
_dtype_size = _dtype.itemsize
_fp = getattr(fp, k)
_fp.seek(attr['data_begin'][k] + begin_item * _dtype_size)
attr['read_next'][k] = _bulk_read(_fp, _dtype_size)
attr['dtype'][k] = dtype_name[_dtype.name]
for i in xrange(begin_item, end_item):
_row = struct.unpack(attr['dtype']['row'], attr['read_next']['row'].next())[0]
rows.append(_row)
_col = struct.unpack(attr['dtype']['col'], attr['read_next']['col'].next())[0]
cols.append(_col)
_data = struct.unpack(attr['dtype']['data'], attr['read_next']['data'].next())[0]
data.append(_data)
ul[0] = _row if _row < ul[0] else ul[0]
ul[1] = _col if _col < ul[1] else ul[1]
lr[0] = _row if _row > lr[0] else lr[0]
lr[1] = _col if _col > lr[1] else lr[1]
for i in xrange(len(rows)):
rows[i] -= ul[0]
cols[i] -= ul[1]
new_ex = extent.create(ul, [lr[0] + 1, lr[1] + 1], array.shape)
new_array = sp.coo_matrix((data, (rows, cols)), new_ex.shape)
return new_ex, sparse.convert_sparse_array(new_array)
def _local_read_sparse_npy(array, ex, fn):
'''
1. Noted that coo_matrix format doesn't require row[] or col[] to be sorted.
If one of row[] or col[] is sorted (by either row or col), each worker will
return only a part of the array. If the file is unsorted, each worker may
return a very big and sparser sub-array of the original array. In the worst
case, the sub-array can be as large as the original array but sparser.
2. For numpy format, we can evenly distribute the files we need to read to
workers.
'''
#data_begin = {}
#dtype = {}
#dtype_size = {}
#shape = {}
#fp = {}
#read_next = {}
attr = {
'data_begin': {},
'dtype': {},
'shape': None,
'read_next': {},
'fn': {}
}
types = ['row', 'col', 'data']
dtype_name = {'float64': 'd', 'float32': 'f', 'int64': 'q', 'int32': 'i'}
for i in types:
_fn = '%s_%s.npy' % (fn, i)
attr['fn'][i] = _fn
_shape, attr['dtype'][i], attr['data_begin'][i] = _parse_npy_header(
_fn)
if attr['shape'] != None:
assert attr['shape'] == _shape
else:
attr['shape'] = _shape
#shape['row'], dtype['row'], data_begin['row'] = _parse_npy_header(fn + '_row.npy')
#shape['col'], dtype['col'], data_begin['col'] = _parse_npy_header(fn + '_col.npy')
#shape['data'], dtype['data'], data_begin['data'] = _parse_npy_header(fn + '_data.npy')
item_count = np.product(array.shape)
begin_item = extent.ravelled_pos(ex.ul, array.shape)
begin_item = int(
math.ceil(((begin_item * 1.0) / item_count) * attr['shape'][0]))
end_item = extent.ravelled_pos([(i - 1) for i in ex.lr], array.shape)
end_item = int(math.floor(
(end_item * 1.0) / item_count * attr['shape'][0])) + 1
end_item = attr['shape'][0] if end_item > attr['shape'][0] else end_item
ul = [array.shape[0], array.shape[1]]
lr = [0, 0]
rows = []
cols = []
data = []
with FileHelper(row=open(attr['fn']['row'], 'rb'),
col=open(attr['fn']['col'], 'rb'),
data=open(attr['fn']['data'], 'rb')) as fp:
for k in types:
_dtype = attr['dtype'][k]
_dtype_size = _dtype.itemsize
_fp = getattr(fp, k)
_fp.seek(attr['data_begin'][k] + begin_item * _dtype_size)
attr['read_next'][k] = _bulk_read(_fp, _dtype_size)
attr['dtype'][k] = dtype_name[_dtype.name]
for i in xrange(begin_item, end_item):
_row = struct.unpack(attr['dtype']['row'],
attr['read_next']['row'].next())[0]
rows.append(_row)
_col = struct.unpack(attr['dtype']['col'],
attr['read_next']['col'].next())[0]
cols.append(_col)
_data = struct.unpack(attr['dtype']['data'],
attr['read_next']['data'].next())[0]
data.append(_data)
ul[0] = _row if _row < ul[0] else ul[0]
ul[1] = _col if _col < ul[1] else ul[1]
lr[0] = _row if _row > lr[0] else lr[0]
lr[1] = _col if _col > lr[1] else lr[1]
for i in xrange(len(rows)):
rows[i] -= ul[0]
cols[i] -= ul[1]
new_ex = extent.create(ul, [lr[0] + 1, lr[1] + 1], array.shape)
new_array = sp.coo_matrix((data, (rows, cols)), new_ex.shape)
return new_ex, sparse.convert_sparse_array(new_array)