据我所知,sliding
函数在Python中不可用,SlidinGrdD
是私有类,不能在外部访问MLlib
。
如果要sliding
在现有的RDD上使用,则可以这样创建可怜人sliding
:
def sliding(rdd, n):
assert n > 0
def gen_window(xi, n):
x, i = xi
return [(i - offset, (i, x)) for offset in xrange(n)]
return (
rdd.
zipWithIndex(). # Add index
flatMap(lambda xi: gen_window(xi, n)). # Generate pairs with offset
groupByKey(). # Group to create windows
# Sort values to ensure order inside window and drop indices
mapValues(lambda vals: [x for (i, x) in sorted(vals)]).
sortByKey(). # Sort to makes sure we keep original order
values(). # Get values
filter(lambda x: len(x) == n)) # Drop beginning and end
或者,您可以尝试这样的操作(在的帮助下toolz
)
from toolz.itertoolz import sliding_window, concat
def sliding2(rdd, n):
assert n > 1
def get_last_el(i, iter):
"""Return last n - 1 elements from the partition"""
return [(i, [x for x in iter][(-n + 1):])]
def slide(i, iter):
"""Prepend prevIoUs items and return sliding window"""
return sliding_window(n, concat([last_items.value[i - 1], iter]))
def clean_last_items(last_items):
"""Adjust for empty or to small partitions"""
clean = {-1: [None] * (n - 1)}
for i in range(rdd.getNumPartitions()):
clean[i] = (clean[i - 1] + list(last_items[i]))[(-n + 1):]
return {k: tuple(v) for k, v in clean.items()}
last_items = sc.broadcast(clean_last_items(
rdd.mapPartitionsWithIndex(get_last_el).collectAsMap()))
return rdd.mapPartitionsWithIndex(slide)