def chunk(self, size="150", axis=None):
"""
Chunks records of a distributed array.
Chunking breaks arrays into subarrays, using an specified
size of chunks along each value dimension. Can alternatively
specify an average chunk byte size (in megabytes) and the size of
chunks (as ints) will be computed automatically.
Parameters
----------
size : tuple, int, or str, optional, default = "150"
A string giving the size in megabytes, or a tuple with the size
of chunks along each dimension.
axis : int or tuple, optional, default = None
One or more axis to chunk array along, if None
will use all axes,
Returns
-------
ChunkedArray
"""
if type(size) is not str:
size = tupleize((size))
axis = tupleize((axis))
from bolt.spark.chunk import ChunkedArray
chnk = ChunkedArray(rdd=self._rdd,
shape=self._shape,
split=self._split,
dtype=self._dtype)
return chnk.chunk(size, axis)
def chunk(self, size="150", axis=None):
"""
Chunks records of a distributed array.
Chunking breaks arrays into subarrays, using an specified
number of chunks along each value dimension. Can alternatively
specify an average chunk size (in megabytes) and the number of
chunks will be computed automatically.
Parameters
----------
size : tuple, int, or str, optional, default = "150"
A string giving the size in megabytes, or a tuple with the number
of chunks along each dimension.
axis : int or tuple, optional, default = None
One or more axis to chunk array along, if None
will use all axes,
Returns
-------
ChunkedArray
"""
if type(size) is not str:
size = tupleize((size))
axis = tupleize((axis))
from bolt.spark.chunk import ChunkedArray
chnk = ChunkedArray(rdd=self._rdd, shape=self._shape, split=self._split, dtype=self._dtype)
return chnk.chunk(size, axis)
def swap(self, key_axes, value_axes, size=150):
key_axes, value_axes = tupleize(key_axes), tupleize(value_axes)
if len(key_axes) == self.keys.ndim and len(value_axes) == 0:
raise ValueError('Cannot perform a swap that would '
'end up with all data on a single key')
if len(key_axes) == 0 and len(value_axes) == 0:
return self
if self.values.ndim == 0:
rdd = self._rdd.mapValues(lambda v: array(v, ndmin=1))
value_shape = (1, )
else:
rdd = self._rdd
value_shape = self.values.shape
from bolt.spark.swap import Swapper, Dims
k = Dims(shape=self.keys.shape, axes=key_axes)
v = Dims(shape=value_shape, axes=value_axes)
s = Swapper(k, v, self.dtype, size)
chunks = s.chunk(rdd)
rdd = s.extract(chunks)
shape = s.getshape()
split = self.split - len(key_axes) + len(value_axes)
if self.values.ndim == 0:
rdd = rdd.mapValues(lambda v: v.squeeze())
shape = shape[:-1]
return self._constructor(rdd, shape=tuple(shape), split=split)
def swap(self, key_axes, value_axes, size=150):
key_axes, value_axes = tupleize(key_axes), tupleize(value_axes)
if len(key_axes) == self.keys.ndim and len(value_axes) == 0:
raise ValueError('Cannot perform a swap that would '
'end up with all data on a single key')
if len(key_axes) == 0 and len(value_axes) == 0:
return self
if self.values.ndim == 0:
rdd = self._rdd.mapValues(lambda v: array(v, ndmin=1))
value_shape = (1,)
else:
rdd = self._rdd
value_shape = self.values.shape
from bolt.spark.swap import Swapper, Dims
k = Dims(shape=self.keys.shape, axes=key_axes)
v = Dims(shape=value_shape, axes=value_axes)
s = Swapper(k, v, self.dtype, size)
chunks = s.chunk(rdd)
rdd = s.extract(chunks)
shape = s.getshape()
split = self.split - len(key_axes) + len(value_axes)
if self.values.ndim == 0:
rdd = rdd.mapValues(lambda v: v.squeeze())
shape = shape[:-1]
return self._constructor(rdd, shape=tuple(shape), split=split)
def swap(self, kaxes, vaxes, size="150"):
"""
Swap axes from keys to values.
This is the core operation underlying shape manipulation
on the Spark bolt array. It exchanges an arbitrary set of axes
between the keys and the valeus. If either is None, will only
move axes in one direction (from keys to values, or values to keys).
Keys moved to values will be placed immediately after the split;
values moved to keys will be placed immediately before the split.
Parameters
----------
kaxes : tuple
Axes from keys to move to values
vaxes : tuple
Axes from values to move to keys
size : tuple or int, optional, default = "150"
Can either provide a string giving the size in megabytes,
or a tuple with the number of chunks along each
value dimension being moved
Returns
-------
BoltArraySpark
"""
kaxes = asarray(tupleize(kaxes), 'int')
vaxes = asarray(tupleize(vaxes), 'int')
if type(size) is not str:
size = tupleize(size)
if len(kaxes) == self.keys.ndim and len(vaxes) == 0:
raise ValueError('Cannot perform a swap that would '
'end up with all data on a single key')
if len(kaxes) == 0 and len(vaxes) == 0:
return self
if self.values.ndim == 0:
rdd = self._rdd.mapValues(lambda v: array(v, ndmin=1))
shape = self._shape + (1,)
else:
rdd = self._rdd
shape = self._shape
from bolt.spark.chunk import ChunkedArray
c = ChunkedArray(rdd, shape=shape, split=self._split, dtype=self._dtype)
chunks = c.chunk(size, axis=vaxes)
barray = chunks.move(kaxes, vaxes)
if self.values.ndim == 0:
barray._rdd = barray._rdd.mapValues(lambda v: v.squeeze())
barray._shape = barray._shape[:-1]
return barray
def swap(self, kaxes, vaxes, size="150"):
"""
Swap axes from keys to values.
This is the core operation underlying shape manipulation
on the Spark bolt array. It exchanges an arbitrary set of axes
between the keys and the valeus. If either is None, will only
move axes in one direction (from keys to values, or values to keys).
Keys moved to values will be placed immediately after the split;
values moved to keys will be placed immediately before the split.
Parameters
----------
kaxes : tuple
Axes from keys to move to values
vaxes : tuple
Axes from values to move to keys
size : tuple or int, optional, default = "150"
Can either provide a string giving the size in kilobytes,
or a tuple with the number of chunks along each
value dimension being moved
Returns
-------
BoltArraySpark
"""
kaxes = asarray(tupleize(kaxes), 'int')
vaxes = asarray(tupleize(vaxes), 'int')
if type(size) is not str:
size = tupleize(size)
if len(kaxes) == self.keys.ndim and len(vaxes) == 0:
raise ValueError('Cannot perform a swap that would '
'end up with all data on a single key')
if len(kaxes) == 0 and len(vaxes) == 0:
return self
from bolt.spark.chunk import ChunkedArray
c = ChunkedArray(self._rdd,
shape=self._shape,
split=self._split,
dtype=self._dtype)
chunks = c._chunk(size, axis=vaxes)
swapped = chunks.keys_to_values(kaxes).values_to_keys(
[v + len(kaxes) for v in vaxes])
barray = swapped.unchunk()
return barray
def filter(self, func, axis=(0, ), sort=False):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the keys,
which may incur a swap.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
sort: bool, optional, default=False
Whether or not to sort by key before reindexing
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
swapped = self._align(axis)
def f(record):
return func(record[1])
rdd = swapped._rdd.filter(f)
if sort:
rdd = rdd.sortByKey().values()
else:
rdd = rdd.values()
# count the resulting array in order to reindex (linearize) the keys
count, zipped = zip_with_index(rdd)
if not count:
count = zipped.count()
reindexed = zipped.map(lambda kv: (tupleize(kv[1]), kv[0]))
# since we can only filter over one axis, the remaining shape is always the following
remaining = list(swapped.shape[len(axis):])
if count != 0:
shape = tuple([count] + remaining)
else:
shape = (0, )
return self._constructor(reindexed, shape=shape,
split=swapped.split).__finalize__(swapped)
def map(self, func, axis=(0,), value_shape=None):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
Function of a single array to apply
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
value_shape : tuple, optional, default=None
Known shape of values resulting from operation
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
swapped = self._align(axis)
if value_shape is None:
# try to compute the size of each mapped element by applying func to a random array
value_shape = None
try:
value_shape = func(random.randn(*swapped.values.shape).astype(self.dtype)).shape
except Exception:
first = swapped._rdd.first()
if first:
# eval func on the first element
mapped = func(first[1])
value_shape = mapped.shape
shape = tuple([swapped._shape[ax] for ax in range(len(axis))]) + tupleize(value_shape)
rdd = swapped._rdd.mapValues(func)
# reshaping will fail if the elements aren't uniformly shaped
def check(v):
if len(v.shape) > 0 and v.shape != tupleize(value_shape):
raise Exception("Map operation did not produce values of uniform shape.")
return v
rdd = rdd.mapValues(lambda v: check(v))
return self._constructor(rdd, shape=shape, split=swapped.split).__finalize__(swapped)
def filter(self, func, axis=(0,), sort=False):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the keys,
which may incur a swap.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
sort: bool, optional, default=False
Whether or not to sort by key before reindexing
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
swapped = self._align(axis)
def f(record):
return func(record[1])
rdd = swapped._rdd.filter(f)
if sort:
rdd = rdd.sortByKey().values()
else:
rdd = rdd.values()
# count the resulting array in order to reindex (linearize) the keys
count, zipped = zip_with_index(rdd)
if not count:
count = zipped.count()
reindexed = zipped.map(lambda kv: (tupleize(kv[1]), kv[0]))
# since we can only filter over one axis, the remaining shape is always the following
remaining = list(swapped.shape[len(axis):])
if count != 0:
shape = tuple([count] + remaining)
else:
shape = (0,)
return self._constructor(reindexed, shape=shape, split=swapped.split).__finalize__(swapped)
def swap(self, kaxes, vaxes, size="150"):
"""
Swap axes from keys to values.
This is the core operation underlying shape manipulation
on the Spark bolt array. It exchanges an arbitrary set of axes
between the keys and the valeus. If either is None, will only
move axes in one direction (from keys to values, or values to keys).
Keys moved to values will be placed immediately after the split;
values moved to keys will be placed immediately before the split.
Parameters
----------
kaxes : tuple
Axes from keys to move to values
vaxes : tuple
Axes from values to move to keys
size : tuple or int, optional, default = "150"
Can either provide a string giving the size in kilobytes,
or a tuple with the number of chunks along each
value dimension being moved
Returns
-------
BoltArraySpark
"""
kaxes = asarray(tupleize(kaxes), 'int')
vaxes = asarray(tupleize(vaxes), 'int')
if type(size) is not str:
size = tupleize(size)
if len(kaxes) == self.keys.ndim and len(vaxes) == 0:
raise ValueError('Cannot perform a swap that would '
'end up with all data on a single key')
if len(kaxes) == 0 and len(vaxes) == 0:
return self
from bolt.spark.chunk import ChunkedArray
chunks = self.chunk(size)
swapped = chunks.keys_to_values(kaxes).values_to_keys([v+len(kaxes) for v in vaxes])
barray = swapped.unchunk()
return barray
def __getitem__(self, index):
index = tupleize(index)
if len(index) > self.ndim:
raise ValueError("Too many indices for array")
if not all([isinstance(i, (slice, int, list, set, ndarray)) for i in index]):
raise ValueError("Each index must either be a slice, int, list, set, or ndarray")
# fill unspecified axes with full slices
if len(index) < self.ndim:
index += tuple([slice(0, None, None) for _ in range(self.ndim - len(index))])
# convert ints to lists if not all ints and slices
if not all([isinstance(i, (int, slice)) for i in index]):
index = tuple([[i] if isinstance(i, int) else i for i in index])
# select basic or advanced indexing
if all([isinstance(i, (slice, int)) for i in index]):
rdd, shape, split = self._getbasic(index)
elif all([isinstance(i, (set, list, ndarray)) for i in index]):
rdd, shape, split = self._getadvanced(index)
else:
raise NotImplementedError("Cannot mix basic indexing (slices and ints) with "
"advanced indexing (lists and ndarrays) across axes")
result = self._constructor(rdd, shape=shape, split=split).__finalize__(self)
# squeeze out int dimensions (and squeeze to singletons if all ints)
if all([isinstance(i, int) for i in index]):
return result.squeeze().toarray()[()]
else:
tosqueeze = tuple([i for i in index if isinstance(i, int)])
return result.squeeze(tosqueeze)
def reduce(self, func, axis=(0,)):
"""
Reduce an array along an axis.
Applies a function of two arguments
cumlutatively to all arrays along an axis.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltSparkArray
"""
from bolt.local.array import BoltArrayLocal
from numpy import ndarray
axis = tupleize(axis)
swapped = self._align(axis)
arr = swapped._rdd.values().reduce(func)
if not isinstance(arr, ndarray):
# the result of a reduce can also be a scalar
return arr
elif arr.shape == (1,):
# ndarrays with single values in them should be converted into scalars
return arr[0]
return BoltArrayLocal(arr)
def reduce(self, func, axis=(0, )):
"""
Reduce an array along an axis.
Applies a function of two arguments
cumlutatively to all arrays along an axis.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltSparkArray
"""
from bolt.local.array import BoltArrayLocal
from numpy import ndarray
axis = tupleize(axis)
swapped = self._align(axis)
arr = swapped._rdd.values().reduce(func)
if not isinstance(arr, ndarray):
# the result of a reduce can also be a scalar
return arr
elif arr.shape == (1, ):
# ndarrays with single values in them should be converted into scalars
return arr[0]
return BoltArrayLocal(arr)
def sample(self, n=100, seed=None):
"""
Extract random sample of records.
Parameters
----------
n : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
if n < 1:
raise ValueError("Number of samples must be larger than 0, got '%g'" % n)
if seed is None:
seed = random.randint(0, 2 ** 32)
if self.mode == 'spark':
result = asarray(self.values.tordd().values().takeSample(False, n, seed))
else:
basedims = [self.shape[d] for d in self.baseaxes]
inds = [unravel_index(int(k), basedims) for k in random.rand(n) * prod(basedims)]
result = asarray([self.values[tupleize(i) + (slice(None, None),)] for i in inds])
return self._constructor(result, index=self.index)
def __getitem__(self, item):
# handle values -- convert ints to slices so no dimensions are dropped
if isinstance(item, int):
item = tuple([slicify(item, self.shape[0])])
if isinstance(item, tuple):
item = tuple([slicify(i, n) if isinstance(i, int) else i for i, n in zip(item, self.shape[:len(item)])])
if isinstance(item, (list, ndarray)):
item = (item,)
new = self._values.__getitem__(item)
result = self._constructor(new).__finalize__(self, noprop=('index', 'labels'))
# handle labels
if self.labels is not None:
if isinstance(item, int):
label_item = ([item],)
elif isinstance(item, (list, ndarray, slice)):
label_item = (item, )
elif isinstance(item, tuple):
label_item = item[:len(self.baseaxes)]
newlabels = self.labels
for (i, s) in enumerate(label_item):
if isinstance(s, slice):
newlabels = newlabels[[s if j==i else slice(None) for j in range(len(label_item))]]
else:
newlabels = newlabels.take(tupleize(s), i)
result.labels = newlabels
return result
def map(self, func, axis=(0, )):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function of a single array to apply
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
mapped = asarray(list(map(func, reshaped)))
elem_shape = mapped[0].shape
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + list(elem_shape)
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered)
def filter(self, func, axis=(0, )):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the
keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
reshaped = self._align(axes)
filtered = asarray(list(filter(func, reshaped)))
return self._constructor(filtered)
def __getitem__(self, item):
# handle values -- convert ints to slices so no dimensions are dropped
if isinstance(item, int):
item = tuple([slicify(item, self.shape[0])])
if isinstance(item, tuple):
item = tuple([slicify(i, n) if isinstance(i, int) else i for i, n in zip(item, self.shape[:len(item)])])
if isinstance(item, (list, ndarray)):
item = (item,)
new = self._values.__getitem__(item)
result = self._constructor(new).__finalize__(self, noprop=('index', 'labels'))
# handle labels
if self.labels is not None:
if isinstance(item, int):
label_item = ([item],)
elif isinstance(item, (list, ndarray, slice)):
label_item = (item, )
elif isinstance(item, tuple):
label_item = item[:len(self.baseaxes)]
newlabels = self.labels
for (i, s) in enumerate(label_item):
if isinstance(s, slice):
newlabels = newlabels[[s if j==i else slice(None) for j in range(len(label_item))]]
else:
newlabels = newlabels.take(tupleize(s), i)
result.labels = newlabels
return result
def sample(self, n=100, seed=None):
"""
Extract random sample of records.
Parameters
----------
n : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
if n < 1:
raise ValueError(
"Number of samples must be larger than 0, got '%g'" % n)
if seed is None:
seed = random.randint(0, 2**32)
if self.mode == 'spark':
result = asarray(self.values.tordd().values().takeSample(
False, n, seed))
else:
basedims = [self.shape[d] for d in self.baseaxes]
inds = [
unravel_index(int(k), basedims)
for k in random.rand(n) * prod(basedims)
]
result = asarray([
self.values[tupleize(i) + (slice(None, None), )] for i in inds
])
return self._constructor(result, index=self.index)
def __getitem__(self, item):
# handle values
if isinstance(item, int):
item = slice(item, item+1, None)
if isinstance(item, tuple):
item = tuple([slice(i, i+1, None) if isinstance(i, int) else i for i in item])
if isinstance(item, (list, ndarray)):
item = (item,)
new = self._values.__getitem__(item)
result = self._constructor(new).__finalize__(self, noprop=('index', 'labels'))
# handle labels
if self.labels is not None:
if isinstance(item, int):
label_item = ([item],)
elif isinstance(item, (list, ndarray, slice)):
label_item = (item, )
elif isinstance(item, tuple):
label_item = item[:len(self.baseaxes)]
newlabels = self.labels
for (i, s) in enumerate(label_item):
if isinstance(s, slice):
newlabels = newlabels[[s if j==i else slice(None) for j in range(len(label_item))]]
else:
newlabels = newlabels.take(tupleize(s), i)
result.labels = newlabels
return result
def reduce(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
# if the function is a ufunc, it can automatically handle reducing over multiple axes
if isinstance(func, ufunc):
inshape(self.shape, axes)
reduced = func.reduce(self, axis=tuple(axes))
else:
reshaped = self._align(axes)
reduced = reduce(func, reshaped)
new_array = self._constructor(reduced)
# ensure that the shape of the reduced array is valid
expected_shape = [
self.shape[i] for i in range(len(self.shape)) if i not in axes
]
if new_array.shape != tuple(expected_shape):
raise ValueError(
"reduce did not yield a BoltArray with valid dimensions")
return new_array
def map(self, func, axis=(0,)):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function of a single array to apply
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
mapped = asarray(list(map(func, reshaped)))
elem_shape = mapped[0].shape
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + list(elem_shape)
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered)
def filter(self, func, axis=(0,)):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the
keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
reshaped = self._align(axes)
filtered = asarray(list(filter(func, reshaped)))
return self._constructor(filtered)
def map(self, func, value_shape=None, dtype=None, with_keys=False):
"""
Apply an array -> array function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function of a single array to apply. If with_keys=True,
function should be of a (tuple, array) pair.
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
value_shape : tuple, optional, default=None
Known shape of values resulting from operation. Only
valid in spark mode.
dtype: numpy.dtype, optional, default=None
Known shape of dtype resulting from operation. Only
valid in spark mode.
with_keys : bool, optional, default=False
Include keys as an argument to the function
"""
axis = self.baseaxes
if self.mode == 'local':
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
if with_keys:
keys = zip(*unravel_index(range(prod(key_shape)), key_shape))
mapped = asarray(list(map(func, zip(keys, reshaped))))
else:
mapped = asarray(list(map(func, reshaped)))
try:
elem_shape = mapped[0].shape
except:
elem_shape = (1,)
expand = list(elem_shape)
expand = [1] if len(expand) == 0 else expand
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + expand
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered, mode=self.mode).__finalize__(self, noprop=('index'))
if self.mode == 'spark':
expand = lambda x: array(func(x), ndmin=1)
mapped = self.values.map(expand, axis, value_shape, dtype, with_keys)
return self._constructor(mapped, mode=self.mode).__finalize__(self, noprop=('index',))
def filter(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
reshaped = self._align(axes)
filtered = asarray(list(filter(func, reshaped)))
return self._constructor(filtered)
def filter(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
reshaped = self._align(axes)
filtered = asarray(list(filter(func, reshaped)))
return self._constructor(filtered)
def __getitem__(self, index):
"""
Get an item from the array through indexing.
Supports basic indexing with slices and ints, or advanced
indexing with lists or ndarrays of integers.
Mixing basic and advanced indexing across axes is not
currently supported.
Parameters
----------
index : tuple of slices, ints, list, sets, or ndarrays
One or more index specifications
Returns
-------
BoltSparkArray
"""
index = tupleize(index)
if len(index) > self.ndim:
raise ValueError("Too many indices for array")
if not all(
[isinstance(i, (slice, int, list, set, ndarray)) for i in index]):
raise ValueError(
"Each index must either be a slice, int, list, set, or ndarray"
)
# fill unspecified axes with full slices
if len(index) < self.ndim:
index += tuple(
[slice(0, None, None) for _ in range(self.ndim - len(index))])
# convert ints to lists if not all ints and slices
if not all([isinstance(i, (int, slice)) for i in index]):
index = tuple([[i] if isinstance(i, int) else i for i in index])
# select basic or advanced indexing
if all([isinstance(i, (slice, int)) for i in index]):
rdd, shape, split = self._getbasic(index)
elif all([isinstance(i, (set, list, ndarray)) for i in index]):
rdd, shape, split = self._getadvanced(index)
else:
raise NotImplementedError(
"Cannot mix basic indexing (slices and ints) with "
"advanced indexing (lists and ndarrays) across axes")
result = self._constructor(rdd, shape=shape,
split=split).__finalize__(self)
# squeeze out int dimensions (and squeeze to singletons if all ints)
if all([isinstance(i, int) for i in index]):
return result.squeeze().toarray()[()]
else:
tosqueeze = tuple([i for i in index if isinstance(i, int)])
return result.squeeze(tosqueeze)
def _map(self, func, axis=(0,), value_shape=None, dtype=None, with_keys=False):
"""
Apply an array -> array function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function of a single array to apply. If with_keys=True,
function should be of a (tuple, array) pair.
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
value_shape : tuple, optional, default=None
Known shape of values resulting from operation. Only
valid in spark mode.
dtype: numpy.dtype, optional, default=None
Known shape of dtype resulting from operation. Only
valid in spark mode.
with_keys : bool, optional, default=False
Include keys as an argument to the function
"""
if self.mode == 'local':
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
if with_keys:
keys = zip(*unravel_index(range(prod(key_shape)), key_shape))
mapped = asarray(list(map(func, zip(keys, reshaped))))
else:
mapped = asarray(list(map(func, reshaped)))
try:
elem_shape = mapped[0].shape
except:
elem_shape = (1,)
expand = list(elem_shape)
expand = [1] if len(expand) == 0 else expand
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + expand
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered, mode=self.mode).__finalize__(self, noprop=('index'))
if self.mode == 'spark':
expand = lambda x: array(func(x), ndmin=1)
mapped = self.values.map(expand, axis, value_shape, dtype, with_keys)
return self._constructor(mapped, mode=self.mode).__finalize__(self, noprop=('index',))
def filter(self, func, axis=(0,)):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the keys,
which may incur a swap.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
if len(axis) != 1:
raise NotImplementedError("Filtering over multiple axes will not be "
"supported until SparseBoltArray is implemented.")
swapped = self._align(axis)
rdd = swapped._rdd.values().filter(func)
# count the resulting array in order to reindex (linearize) the keys
count, zipped = zip_with_index(rdd)
if not count:
count = zipped.count()
reindexed = zipped.map(lambda kv: (tupleize(kv[1]), kv[0]))
# since we can only filter over one axis, the remaining shape is always the following
remaining = list(swapped.shape[1:])
if count != 0:
shape = tuple([count] + remaining)
else:
shape = (0,)
return self._constructor(reindexed, shape=shape, split=swapped.split).__finalize__(swapped)
def chunk(self, size="150", axis=None, padding=None):
"""
Chunks records of a distributed array.
Chunking breaks arrays into subarrays, using an specified
size of chunks along each value dimension. Can alternatively
specify an average chunk byte size (in kilobytes) and the size of
chunks (as ints) will be computed automatically.
Parameters
----------
size : tuple, int, or str, optional, default = "150"
A string giving the size in kilobytes, or a tuple with the size
of chunks along each dimension.
axis : int or tuple, optional, default = None
One or more axis to chunk array along, if None
will use all axes,
padding: tuple or int, default = None
Number of elements per dimension that will overlap with the adjacent chunk.
If a tuple, specifies padding along each chunked dimension; if a int, same
padding will be applied to all chunked dimensions.
Returns
-------
ChunkedArray
"""
if type(size) is not str:
size = tupleize((size))
axis = tupleize((axis))
padding = tupleize((padding))
from bolt.spark.chunk import ChunkedArray
chnk = ChunkedArray(rdd=self._rdd,
shape=self._shape,
split=self._split,
dtype=self._dtype)
return chnk._chunk(size, axis, padding)
def _stat(self, axis=None, func=None, name=None, keepdims=False):
"""
Compute a statistic over an axis.
Can provide either a function (for use in a reduce)
or a name (for use by a stat counter).
Parameters
----------
axis : tuple or int, optional, default=None
Axis to compute statistic over, if None
will compute over all axes
func : function, optional, default=None
Function for reduce, see BoltArraySpark.reduce
name : str
A named statistic, see StatCounter
keepdims : boolean, optional, default=False
Keep axis remaining after operation with size 1.
"""
if axis is None:
axis = list(range(len(self.shape)))
axis = tupleize(axis)
if func and not name:
return self.reduce(func, axis, keepdims)
if name and not func:
from bolt.local.array import BoltArrayLocal
swapped = self._align(axis)
def reducer(left, right):
return left.combine(right)
counter = swapped._rdd.values()\
.mapPartitions(lambda i: [StatCounter(values=i, stats=name)])\
.treeReduce(reducer, depth=3)
arr = getattr(counter, name)
if keepdims:
for i in axis:
arr = expand_dims(arr, axis=i)
return BoltArrayLocal(arr).toscalar()
else:
raise ValueError(
'Must specify either a function or a statistic name.')
def __getitem__(self, index):
"""
Get an item from the array through indexing.
Supports basic indexing with slices and ints, or advanced
indexing with lists or ndarrays of integers.
Mixing basic and advanced indexing across axes is not
currently supported.
Parameters
----------
index : tuple of slices, ints, list, sets, or ndarrays
One or more index specifications
Returns
-------
BoltSparkArray
"""
index = tupleize(index)
if len(index) > self.ndim:
raise ValueError("Too many indices for array")
if not all([isinstance(i, (slice, int, list, set, ndarray)) for i in index]):
raise ValueError("Each index must either be a slice, int, list, set, or ndarray")
# fill unspecified axes with full slices
if len(index) < self.ndim:
index += tuple([slice(0, None, None) for _ in range(self.ndim - len(index))])
# convert ints to lists if not all ints and slices
if not all([isinstance(i, (int, slice)) for i in index]):
index = tuple([[i] if isinstance(i, int) else i for i in index])
# select basic or advanced indexing
if all([isinstance(i, (slice, int)) for i in index]):
rdd, shape, split = self._getbasic(index)
elif all([isinstance(i, (set, list, ndarray)) for i in index]):
rdd, shape, split = self._getadvanced(index)
else:
raise NotImplementedError("Cannot mix basic indexing (slices and ints) with "
"advanced indexing (lists and ndarrays) across axes")
result = self._constructor(rdd, shape=shape, split=split).__finalize__(self)
# squeeze out int dimensions (and squeeze to singletons if all ints)
if all([isinstance(i, int) for i in index]):
return result.squeeze().toarray()[()]
else:
tosqueeze = tuple([i for i in index if isinstance(i, int)])
return result.squeeze(tosqueeze)
def _stat(self, axis=None, func=None, name=None, keepdims=False):
"""
Compute a statistic over an axis.
Can provide either a function (for use in a reduce)
or a name (for use by a stat counter).
Parameters
----------
axis : tuple or int, optional, default=None
Axis to compute statistic over, if None
will compute over all axes
func : function, optional, default=None
Function for reduce, see BoltArraySpark.reduce
name : str
A named statistic, see StatCounter
keepdims : boolean, optional, default=False
Keep axis remaining after operation with size 1.
"""
if axis is None:
axis = list(range(len(self.shape)))
axis = tupleize(axis)
if func and not name:
return self.reduce(func, axis, keepdims)
if name and not func:
from bolt.local.array import BoltArrayLocal
swapped = self._align(axis)
def reducer(left, right):
return left.combine(right)
counter = swapped._rdd.values()\
.mapPartitions(lambda i: [StatCounter(values=i, stats=name)])\
.reduce(reducer)
arr = getattr(counter, name)
if keepdims:
for i in axis:
arr = expand_dims(arr, axis=i)
return BoltArrayLocal(arr).toscalar()
else:
raise ValueError('Must specify either a function or a statistic name.')
def chunk(self, size="150", axis=None, padding=None):
"""
Chunks records of a distributed array.
Chunking breaks arrays into subarrays, using an specified
size of chunks along each value dimension. Can alternatively
specify an average chunk byte size (in kilobytes) and the size of
chunks (as ints) will be computed automatically.
Parameters
----------
size : tuple, int, or str, optional, default = "150"
A string giving the size in kilobytes, or a tuple with the size
of chunks along each dimension.
axis : int or tuple, optional, default = None
One or more axis to chunk array along, if None
will use all axes,
padding: tuple or int, default = None
Number of elements per dimension that will overlap with the adjacent chunk.
If a tuple, specifies padding along each chunked dimension; if a int, same
padding will be applied to all chunked dimensions.
Returns
-------
ChunkedArray
"""
if type(size) is not str:
size = tupleize((size))
axis = tupleize((axis))
padding = tupleize((padding))
from bolt.spark.chunk import ChunkedArray
chnk = ChunkedArray(rdd=self._rdd, shape=self._shape, split=self._split, dtype=self._dtype)
return chnk._chunk(size, axis, padding)
def map(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
mapped = asarray(list(map(func, reshaped)))
elem_shape = mapped[0].shape
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + list(elem_shape)
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered)
def map(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
key_shape = [self.shape[axis] for axis in axes]
reshaped = self._align(axes, key_shape=key_shape)
mapped = asarray(list(map(func, reshaped)))
elem_shape = mapped[0].shape
# invert the previous reshape operation, using the shape of the map result
linearized_shape_inv = key_shape + list(elem_shape)
reordered = mapped.reshape(*linearized_shape_inv)
return self._constructor(reordered)
def _reshapebasic(self, shape):
"""
Check if the requested reshape can be broken into independant reshapes
on the keys and values. If it can, returns the index in the new shape
separating keys from values, otherwise returns -1
"""
new = tupleize(shape)
old_key_size = prod(self.keys.shape)
old_value_size = prod(self.values.shape)
for i in range(len(new)):
new_key_size = prod(new[:i])
new_value_size = prod(new[i:])
if new_key_size == old_key_size and new_value_size == old_value_size:
return i
return -1
def _reshapebasic(self, shape):
"""
Check if the requested reshape can be broken into independant reshapes
on the keys and values. If it can, returns the index in the new shape
separating keys from values, otherwise returns -1
"""
new = tupleize(shape)
old_key_size = prod(self.keys.shape)
old_value_size = prod(self.values.shape)
for i in range(len(new)):
new_key_size = prod(new[:i])
new_value_size = prod(new[i:])
if new_key_size == old_key_size and new_value_size == old_value_size:
return i
return -1
def filter(self, func, axis=(0, )):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
Returns
-------
BoltSparkArray
"""
axis = tupleize(axis)
if len(axis) != 1:
raise NotImplementedError(
"Filtering over multiple axes will not be "
"supported until SparseBoltArray is implemented.")
swapped = self._align(axis)
rdd = swapped._rdd.values().filter(func)
# count the resulting array in order to reindex (linearize) the keys
count, zipped = zip_with_index(rdd)
if not count:
count = zipped.count()
reindexed = zipped.map(lambda kv: (kv[1], kv[0]))
remaining = [
swapped.shape[dim] for dim in range(len(swapped.shape))
if dim not in axis
]
if count != 0:
shape = tuple([count] + remaining)
else:
shape = (0, )
return self._constructor(reindexed, shape=shape,
split=swapped.split).__finalize__(swapped)
def _stat(self, axis=None, func=None, name=None):
"""
Compute a statistic over an axis.
Can provide either a function (for use in a reduce)
or a name (for use by a stat counter)
Parameters
----------
axis : tuple or int, optional, default=None
Axis to compute statistic over, if None
will compute over all axes
func : function, optional, default=None
Function for reduce, see BoltSparkArray.reduce
name : str
A named statistic, see StatCounter
"""
if axis is None:
axis = list(range(len(self.shape)))
axis = tupleize(axis)
if func and not name:
return self.reduce(func, axis)
if name and not func:
from bolt.local.array import BoltArrayLocal
swapped = self._align(axis)
def reducer(left, right):
return left.combine(right)
counter = swapped._rdd.values()\
.mapPartitions(lambda i: [StatCounter(values=i, stats=name)])\
.reduce(reducer)
res = BoltArrayLocal(getattr(counter, name))
return res.toscalar()
else:
raise ValueError(
'Must specify either a function or a statistic name.')
def reduce(self, func, axis=0):
"""
Reduce an array along an axis.
Applies an associative/commutative function of two arguments
cumulatively to all arrays along an axis. Array will be aligned
so that the desired set of axes are in the keys, which may
require a transpose/reshape.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
# if the function is a ufunc, it can automatically handle reducing over multiple axes
if isinstance(func, ufunc):
inshape(self.shape, axes)
reduced = func.reduce(self, axis=tuple(axes))
else:
reshaped = self._align(axes)
reduced = reduce(func, reshaped)
new_array = self._constructor(reduced)
# ensure that the shape of the reduced array is valid
expected_shape = [
self.shape[i] for i in range(len(self.shape)) if i not in axes
]
if new_array.shape != tuple(expected_shape):
raise ValueError(
"reduce did not yield a BoltArray with valid dimensions")
return new_array
def __getitem__(self, index):
index = tupleize(index)
if len(index) > self.ndim:
raise ValueError("Too many indices for array")
if not all(
[isinstance(i, (slice, int, list, set, ndarray)) for i in index]):
raise ValueError(
"Each index must either be a slice, int, list, set, or ndarray"
)
# fill unspecified axes with full slices
if len(index) < self.ndim:
index += tuple(
[slice(0, None, None) for _ in range(self.ndim - len(index))])
# convert ints to lists if not all ints and slices
if not all([isinstance(i, (int, slice)) for i in index]):
index = tuple([[i] if isinstance(i, int) else i for i in index])
# select basic or advanced indexing
if all([isinstance(i, (slice, int)) for i in index]):
rdd, shape, split = self._getbasic(index)
elif all([isinstance(i, (set, list, ndarray)) for i in index]):
rdd, shape, split = self._getadvanced(index)
else:
raise NotImplementedError(
"Cannot mix basic indexing (slices and ints) with "
"advanced indexing (lists and ndarrays) across axes")
result = self._constructor(rdd, shape=shape,
split=split).__finalize__(self)
# squeeze out int dimensions (and squeeze to singletons if all ints)
if all([isinstance(i, int) for i in index]):
return result.squeeze().toarray()[()]
else:
tosqueeze = tuple([i for i in index if isinstance(i, int)])
return result.squeeze(tosqueeze)
def filter(self, func, axis=(0,)):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes.
Parameters
----------
func : function
Function to apply, should return boolean
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to filter along.
Returns
-------
BoltSparkArray
"""
axis = tupleize(axis)
if len(axis) != 1:
raise NotImplementedError("Filtering over multiple axes will not be "
"supported until SparseBoltArray is implemented.")
swapped = self._align(axis)
rdd = swapped._rdd.values().filter(func)
# count the resulting array in order to reindex (linearize) the keys
count, zipped = zip_with_index(rdd)
if not count:
count = zipped.count()
reindexed = zipped.map(lambda kv: (kv[1], kv[0]))
remaining = [swapped.shape[dim] for dim in range(len(swapped.shape)) if dim not in axis]
if count != 0:
shape = tuple([count] + remaining)
else:
shape = (0,)
return self._constructor(reindexed, shape=shape, split=swapped.split).__finalize__(swapped)
def reduce(self, func, axis=(0,), keepdims=False):
"""
Reduce an array along an axis.
Applies a commutative/associative function of two
arguments cumulatively to all arrays along an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltArraySpark
"""
from bolt.local.array import BoltArrayLocal
from numpy import ndarray
axis = tupleize(axis)
swapped = self._align(axis)
arr = swapped._rdd.values().reduce(func)
if keepdims:
for i in axis:
arr = expand_dims(arr, axis=i)
if not isinstance(arr, ndarray):
# the result of a reduce can also be a scalar
return arr
elif arr.shape == (1,):
# ndarrays with single values in them should be converted into scalars
return arr[0]
return BoltArrayLocal(arr)
def reduce(self, func, axis=(0, ), keepdims=False):
"""
Reduce an array along an axis.
Applies a commutative/associative function of two
arguments cumulatively to all arrays along an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltArraySpark
"""
from bolt.local.array import BoltArrayLocal
from numpy import ndarray
axis = tupleize(axis)
swapped = self._align(axis)
arr = swapped._rdd.values().treeReduce(func, depth=3)
if keepdims:
for i in axis:
arr = expand_dims(arr, axis=i)
if not isinstance(arr, ndarray):
# the result of a reduce can also be a scalar
return arr
elif arr.shape == (1, ):
# ndarrays with single values in them should be converted into scalars
return arr[0]
return BoltArrayLocal(arr)
def reduce(self, func, axis=0):
"""
"""
axes = sorted(tupleize(axis))
# if the function is a ufunc, it can automatically handle reducing over multiple axes
if isinstance(func, ufunc):
inshape(self.shape, axes)
reduced = func.reduce(self, axis=tuple(axes))
else:
reshaped = self._align(axes)
reduced = reduce(func, reshaped)
new_array = self._constructor(reduced)
# ensure that the shape of the reduced array is valid
expected_shape = [self.shape[i] for i in range(len(self.shape)) if i not in axes]
if new_array.shape != tuple(expected_shape):
raise ValueError("reduce did not yield a BoltArray with valid dimensions")
return new_array
def chunk(self, size, axis=None):
"""
Split values of distributed array into chunks.
Transforms an underlying pair RDD of (key, value) into
records of the form: (key, chunk id), (chunked value).
Here, chunk id is a tuple identifying the chunk and
chunked value is a subset of the data from each original value,
that has been divided along the specified dimensions.
Parameters
----------
size : str or tuple or int
If str, the average size (in MB) of the chunks in all value dimensions.
If int or tuple, an explicit specification of the number chunks in
each value dimension.
axis : tuple, optional, default=None
One or more axes to estimate chunks for, if provided any
other axes will use one chunk.
"""
axis = tupleize(axis)
plan = self.getplan(size, axis)
if any([x > y for x, y in zip(plan, self.vshape)]):
raise ValueError("Chunk sizes %s cannot exceed value dimensions %s along any axis"
% (tuple(plan), tuple(self.vshape)))
slices = self.getslices(plan, self.vshape)
labels = list(product(*[list(enumerate(s)) for s in slices]))
scheme = [list(zip(*s)) for s in labels]
def _chunk(record):
k, v = record[0], record[1]
for (chk, slc) in scheme:
yield (k, chk), v[slc]
rdd = self._rdd.flatMap(_chunk)
return self._constructor(rdd, plan=plan).__finalize__(self)
def reduce(self, func, axis=0):
"""
Reduce an array along an axis.
Applies an associative/commutative function of two arguments
cumulatively to all arrays along an axis. Array will be aligned
so that the desired set of axes are in the keys, which may
require a transpose/reshape.
Parameters
----------
func : function
Function of two arrays that returns a single array
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to reduce along.
Returns
-------
BoltArrayLocal
"""
axes = sorted(tupleize(axis))
# if the function is a ufunc, it can automatically handle reducing over multiple axes
if isinstance(func, ufunc):
inshape(self.shape, axes)
reduced = func.reduce(self, axis=tuple(axes))
else:
reshaped = self._align(axes)
reduced = reduce(func, reshaped)
new_array = self._constructor(reduced)
# ensure that the shape of the reduced array is valid
expected_shape = [self.shape[i] for i in range(len(self.shape)) if i not in axes]
if new_array.shape != tuple(expected_shape):
raise ValueError("reduce did not yield a BoltArray with valid dimensions")
return new_array
def __getitem__(self, item):
# handle values
if isinstance(item, int):
item = slice(item, item + 1, None)
if isinstance(item, tuple):
item = tuple([
slice(i, i + 1, None) if isinstance(i, int) else i
for i in item
])
if isinstance(item, (list, ndarray)):
item = (item, )
new = self._values.__getitem__(item)
result = self._constructor(new).__finalize__(self,
noprop=('index',
'labels'))
# handle labels
if self.labels is not None:
if isinstance(item, int):
label_item = ([item], )
elif isinstance(item, (list, ndarray, slice)):
label_item = (item, )
elif isinstance(item, tuple):
label_item = item[:len(self.baseaxes)]
newlabels = self.labels
for (i, s) in enumerate(label_item):
if isinstance(s, slice):
newlabels = newlabels[[
s if j == i else slice(None)
for j in range(len(label_item))
]]
else:
newlabels = newlabels.take(tupleize(s), i)
result.labels = newlabels
return result
def check(v):
if len(v.shape) > 0 and v.shape != tupleize(value_shape):
raise Exception("Map operation did not produce values of uniform shape.")
return v
def map(self, func, axis=(0,), value_shape=None, dtype=None, with_keys=False):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
Function of a single array to apply. If with_keys=True,
function should be of a (tuple, array) pair.
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
value_shape : tuple, optional, default=None
Known shape of values resulting from operation
dtype: numpy.dtype, optional, default=None
Known dtype of values resulting from operation
with_keys : bool, optional, default=False
Include keys as an argument to the function
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
swapped = self._align(axis)
if with_keys:
test_func = lambda x: func(((0,), x))
else:
test_func = func
if value_shape is None or dtype is None:
# try to compute the size of each mapped element by applying func to a random array
try:
mapped = test_func(random.randn(*swapped.values.shape).astype(self.dtype))
except Exception:
first = swapped._rdd.first()
if first:
# eval func on the first element
mapped = test_func(first[1])
if value_shape is None:
value_shape = mapped.shape
if dtype is None:
dtype = mapped.dtype
shape = tuple([swapped._shape[ax] for ax in range(len(axis))]) + tupleize(value_shape)
if with_keys:
rdd = swapped._rdd.map(lambda kv: (kv[0], func(kv)))
else:
rdd = swapped._rdd.mapValues(func)
# reshaping will fail if the elements aren't uniformly shaped
def check(v):
if len(v.shape) > 0 and v.shape != tupleize(value_shape):
raise Exception("Map operation did not produce values of uniform shape.")
return v
rdd = rdd.mapValues(lambda v: check(v))
return self._constructor(rdd, shape=shape, dtype=dtype, split=swapped.split).__finalize__(swapped)
def test_tupleize():
assert tupleize([1, 2, 3]) == (1, 2, 3)
assert tupleize((1, 2, 3)) == (1, 2, 3)
assert tupleize((1,)) == (1,)
assert tupleize(1) == (1,)
def test_tupleize():
assert tupleize([1, 2, 3]) == (1, 2, 3)
assert tupleize((1, 2, 3)) == (1, 2, 3)
assert tupleize((1, )) == (1, )
assert tupleize(1) == (1, )
def check(v):
if len(v.shape) > 0 and v.shape != tupleize(value_shape):
raise Exception(
"Map operation did not produce values of uniform shape.")
return v
def map(self,
func,
axis=(0, ),
value_shape=None,
dtype=None,
with_keys=False):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
Function of a single array to apply. If with_keys=True,
function should be of a (tuple, array) pair.
axis : tuple or int, optional, default=(0,)
Axis or multiple axes to apply function along.
value_shape : tuple, optional, default=None
Known shape of values resulting from operation
dtype: numpy.dtype, optional, default=None
Known dtype of values resulting from operation
with_keys : bool, optional, default=False
Include keys as an argument to the function
Returns
-------
BoltArraySpark
"""
axis = tupleize(axis)
swapped = self._align(axis)
if with_keys:
test_func = lambda x: func(((0, ), x))
else:
test_func = func
if value_shape is None or dtype is None:
# try to compute the size of each mapped element by applying func to a random array
try:
mapped = test_func(
random.randn(*swapped.values.shape).astype(self.dtype))
except Exception:
first = swapped._rdd.first()
if first:
# eval func on the first element
mapped = test_func(first[1])
if value_shape is None:
value_shape = mapped.shape
if dtype is None:
dtype = mapped.dtype
shape = tuple([swapped._shape[ax]
for ax in range(len(axis))]) + tupleize(value_shape)
if with_keys:
rdd = swapped._rdd.map(lambda kv: (kv[0], func(kv)))
else:
rdd = swapped._rdd.mapValues(func)
# reshaping will fail if the elements aren't uniformly shaped
def check(v):
if len(v.shape) > 0 and v.shape != tupleize(value_shape):
raise Exception(
"Map operation did not produce values of uniform shape.")
return v
rdd = rdd.mapValues(lambda v: check(v))
return self._constructor(rdd,
shape=shape,
dtype=dtype,
split=swapped.split).__finalize__(swapped)
