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Bucketed top-k in Python using heap-ordered arrays
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#!/usr/bin/env python | |
""" | |
Usage: topk [key-column-name] [score-column-name] | |
Reads tsv rows, including a header row, from stdin. | |
Writes to stdout the top 10 rows per distinct value of the given key column. | |
Author Dietrich Featherston | |
""" | |
import sys | |
import re | |
import heapq | |
import string | |
K = 10 | |
# push the row into the heap ordered list for the given bucket | |
# using a heap of bounded size keeps memory use per bucket bound | |
# and limits time complexity of checking N rows to O(NlogK) | |
def add(buckets, key, score, row): | |
h = [] | |
if key in buckets: | |
h = buckets[key] | |
else: | |
buckets[key] = h | |
heapq.heappush(h, (score, row)) | |
if(len(h) > K): | |
heapq.heappop(h) | |
def tsv(row): | |
return string.join(row, '\t') | |
if __name__ == "__main__": | |
buckets = {} | |
columns = [] | |
first = True | |
key_column = sys.argv[1] | |
score_column = sys.argv[2] | |
key_column_index = 0 | |
score_column_index = 0 | |
# read each line | |
for line in sys.stdin: | |
row = re.split("\s+", line) | |
# first row is column names | |
if first: | |
columns = row | |
key_column_index = columns.index(key_column) | |
score_column_index = columns.index(score_column) | |
first = False | |
else: | |
# compute the row key and score | |
key = int(row[key_column_index]) | |
score = int(row[score_column_index]) | |
# add to heap | |
add(buckets, key, score, row) | |
print tsv(columns) | |
# sort the keys | |
keys = sorted(buckets, key=lambda key: buckets[key]) | |
for key in keys: | |
# get the heap for this key | |
h = buckets[key] | |
ordered_list = reversed([heapq.heappop(h) for i in range(len(h))]) | |
# print each item in the heap in order | |
for (score, row) in ordered_list: | |
print tsv(row) |
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