<s> import time , argparse , sys , json <EOL> from hashlib import sha1 <EOL> from sklearn . datasets import fetch_20newsgroups <EOL> import numpy as np <EOL> from datasketch import MinHashLSH , MinHash <EOL> def get_ngrams ( text , n = <NUM_LIT:3> ) : <EOL> onegrams = text . split ( ) <EOL> if n == <NUM_LIT:1> : <EOL> return onegrams <EOL> if len ( onegrams ) < n : <EOL> return text <EOL> return [ "<STR_LIT:U+0020>" . join ( onegrams [ i : i + n ] ) for i in range ( <NUM_LIT:0> , len ( onegrams ) - n + <NUM_LIT:1> ) ] <EOL> def get_newsgroup_data ( num_perm , subset ) : <EOL> print ( "<STR_LIT>" ) <EOL> newsgroup = fetch_20newsgroups ( subset = subset , <EOL> remove = ( "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" ) ) <EOL> print ( "<STR_LIT>" ) <EOL> minhashes = [ None for _ in range ( len ( newsgroup . data ) ) ] <EOL> shingles = [ None for _ in range ( len ( newsgroup . data ) ) ] <EOL> for i , text in enumerate ( newsgroup . data ) : <EOL> minhashes [ i ] = MinHash ( num_perm ) <EOL> shingles [ i ] = set ( get_ngrams ( text ) ) <EOL> for ngram in shingles [ i ] : <EOL> minhashes [ i ] . update ( ngram . encode ( "<STR_LIT:utf8>" ) ) <EOL> newsgroup . minhashes = minhashes <EOL> newsgroup . shingles = shingles <EOL> return newsgroup <EOL> def benchmark_lsh ( threshold , index_data , query_data ) : <EOL> print ( "<STR_LIT>" ) <EOL> num_perm = len ( index_data . minhashes [ <NUM_LIT:0> ] . hashvalues ) <EOL> lsh = MinHashLSH ( threshold , num_perm ) <EOL> for key , minhash in zip ( index_data . filenames , index_data . minhashes ) : <EOL> lsh . insert ( key , minhash ) <EOL> print ( "<STR_LIT>" ) <EOL> times = [ ] <EOL> results = [ ] <EOL> for minhash in query_data . minhashes : <EOL> start = time . clock ( ) <EOL> result = lsh . query ( minhash ) <EOL> duration = time . clock ( ) - start <EOL> times . append ( duration ) <EOL> results . append ( result ) <EOL> return times , results <EOL> def benchmark_linearscan ( threshold , index_data , query_data ) : <EOL> times = [ ] <EOL> results = [ ] <EOL> for q in query_data . minhashes : <EOL> start = time . clock ( ) <EOL> result = [ ] <EOL> for key , m in zip ( index_data . filenames , index_data . minhashes ) : <EOL> j = q . jaccard ( m ) <EOL> if j >= threshold : <EOL> result . append ( key ) <EOL> duration = time . clock ( ) - start <EOL> times . append ( duration ) <EOL> results . append ( result ) <EOL> return times , results <EOL> def benchmark_groud_truth ( threshold , index_data , query_data ) : <EOL> times = [ ] <EOL> results = [ ] <EOL> for q in query_data . shingles : <EOL> start = time . clock ( ) <EOL> result = [ ] <EOL> for key , a in zip ( index_data . filenames , index_data . shingles ) : <EOL> intersection = <NUM_LIT:0> <EOL> for w in a : <EOL> if w in q : <EOL> intersection += <NUM_LIT:1> <EOL> j = float ( intersection ) / float ( len ( a ) + len ( q ) - intersection ) <EOL> if j >= threshold : <EOL> result . append ( key ) <EOL> duration = time . clock ( ) - start <EOL> results . append ( result ) <EOL> return times , results <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> parser = argparse . ArgumentParser ( ) <EOL> parser . add_argument ( "<STR_LIT>" , type = str , default = "<STR_LIT>" ) <EOL> args = parser . parse_args ( sys . argv [ <NUM_LIT:1> : ] ) <EOL> num_perms = [ <NUM_LIT:32> , <NUM_LIT:64> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> ] <EOL> output = { "<STR_LIT>" : num_perms , <EOL> "<STR_LIT>" : [ ] , "<STR_LIT>" : [ ] , <EOL> "<STR_LIT>" : [ ] , "<STR_LIT>" : [ ] , <EOL> "<STR_LIT>" : None , "<STR_LIT>" : None } <EOL> for num_perm in num_perms : <EOL> print ( "<STR_LIT>" % num_perm ) <EOL> index_data = get_newsgroup_data ( num_perm , "<STR_LIT:train>" ) <EOL> query_data = get_newsgroup_data ( num_perm , "<STR_LIT:test>" ) <EOL> result = { } <EOL> print ( "<STR_LIT>" ) <EOL> linearscan_times , linearscan_results = benchmark_linearscan ( <NUM_LIT:0.5> , index_data , query_data ) <EOL> print ( "<STR_LIT>" ) <EOL> lsh_times , lsh_results = benchmark_lsh ( <NUM_LIT:0.5> , index_data , query_data ) <EOL> output [ "<STR_LIT>" ] . append ( lsh_times ) <EOL> output [ "<STR_LIT>" ] . append ( lsh_results ) <EOL> output [ "<STR_LIT>" ] . append ( linearscan_times ) <EOL> output [ "<STR_LIT>" ] . append ( linearscan_results ) <EOL> print ( "<STR_LIT>" ) <EOL> output [ "<STR_LIT>" ] , output [ "<STR_LIT>" ] = benchmark_groud_truth ( <NUM_LIT:0.5> , index_data , query_data ) <EOL> average_cardinality = np . mean ( [ len ( s ) for s in <EOL> index_data . shingles + query_data . shingles ] ) <EOL> print ( "<STR_LIT>" , average_cardinality ) <EOL> with open ( args . output , '<STR_LIT:w>' ) as f : <EOL> json . dump ( output , f ) </s>
