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Clean + switch to bloom filters and bitarrays

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* Refactor of the repo structure, for better usability.
* README.md refactored.
* Switch to BloomFilters in python script, to decrease the index file.

TODO:
* Handle binary files in JS to pass the BloomFilters from python to JS.

Note: Current implementations of BloomFilters differ in JS and Python
lib.
This commit is contained in:
Phyks 2014-01-02 21:24:22 +01:00
parent 26d95b4cc3
commit d759e7c8ab
8 changed files with 680 additions and 14 deletions
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30
README.md
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@ -10,13 +10,31 @@ An index is generated by a Python script, upon generation of the pages, and is d
## Files
* `generate_index.py` : Python script to generate the index (runs only at page generation) in a nice format for Javascript
* `samples/` : samples for testing purpose (taken from my blog articles)
### Index generation (`index_generation/` folder)
* `generate_index.py`: Python script to generate the index (runs only at page generation) in a nice format for Javascript
* `pybloom.py`: Library to handle bloom filters in Python
* `stemmer.py`: Implementation of Porter Stemming algorithm in Python, from Vivake Gupta.
### Example html search form
* `index.html`
* `js/bloom.js`: main JS code
* `js/bloomfilters.js`: JS library to use BloomFilters
* `js/jquery-2.0.3.min.js`: jQuery to have convenient functions, will mostly be dropped in the future.
### Examples
* `samples/`: samples for testing purpose (taken from my blog articles)
## Notes
I got the idea while reading [this page](http://www.stavros.io/posts/bloom-filter-search-engine/?print) found on [Sebsauvage's shaarli](http://sebsauvage.net/links/). I searched a bit for code doing what I wanted and found these ones :
* I got the idea while reading [this page](http://www.stavros.io/posts/bloom-filter-search-engine/?print) found on [Sebsauvage's shaarli](http://sebsauvage.net/links/). I searched a bit for code doing what I wanted and found these ones:
* https://github.com/olivernn/lunr.js
* https://github.com/reyesr/fullproof
* https://github.com/olivernn/lunr.js
* https://github.com/reyesr/fullproof
But I wasn't fully satisfied by the first one, and I found the second one too heavy and complicated for my purpose, so I ended up coding this.
But I wasn't fully satisfied by the first one, and I found the second one too heavy and complicated for my purpose, so I ended up coding this.
* This code is mainly a proof of concept. As such, it is not fully optimized (actually, I just tweaked until the resulted files and calculations could be considered "acceptable"). For those looking for more effective solutions, here are a few things I found while looking for information on the web:
* The stemming algorithm used may not be the most efficient one. People wanting to work with non-English languages or to optimize the overall computation of the index can easily move to a more effective algorithm. See [Wikipedia](http://en.wikipedia.org/wiki/Stemming) and [the stemming library in Python](https://pypi.python.org/pypi/stemming/1.0) which has C wrappers for best performances.

1
index.json
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File diff suppressed because one or more lines are too long

16
generate_index.py → index_generation/generate_index.py
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@ -3,8 +3,8 @@
import os
from lxml import html
import re
import json
from collections import defaultdict
import stemmer
from pybloom import BloomFilter
# List all files in path directory
@ -22,7 +22,8 @@ def remove_common_words(words):
# =============================================================================
samples = list_directory("samples/")
index = defaultdict(list)
filters = {}
p = stemmer.PorterStemmer()
for sample in samples:
with open(sample, 'r') as sample_fh:
@ -37,10 +38,11 @@ for sample in samples:
# Remove common words
words = remove_common_words(words)
# Stemming to reduce the number of words
# TODO : Could use http://tartarus.org/martin/PorterStemmer/
words = [p.stem(word, 0, len(word)-1) for word in words]
filters[sample] = BloomFilter(capacity=len(words), error_rate=0.1)
for word in words:
index[sample].append(word)
filters[sample].add(word)
with open("index.json", 'w') as index_fh:
index_fh.write(json.dumps(index))
print(sum(len(filter.bitarray.tobytes()) for filter in filters.values()) /
len(filters))

277
index_generation/pybloom.py Normal file
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@ -0,0 +1,277 @@
import math
import hashlib
from struct import unpack, pack, calcsize
try:
import bitarray
except ImportError:
raise ImportError('pybloom requires bitarray >= 0.3.4')
__version__ = '2.0'
__author__ = "Jay Baird <jay.baird@me.com>, Bob Ippolito <bob@redivi.com>,\
Marius Eriksen <marius@monkey.org>,\
Alex Brasetvik <alex@brasetvik.com>"
def make_hashfuncs(num_slices, num_bits):
if num_bits >= (1 << 31):
fmt_code, chunk_size = 'Q', 8
elif num_bits >= (1 << 15):
fmt_code, chunk_size = 'I', 4
else:
fmt_code, chunk_size = 'H', 2
total_hash_bits = 8 * num_slices * chunk_size
if total_hash_bits > 384:
hashfn = hashlib.sha512
elif total_hash_bits > 256:
hashfn = hashlib.sha384
elif total_hash_bits > 160:
hashfn = hashlib.sha256
elif total_hash_bits > 128:
hashfn = hashlib.sha1
else:
hashfn = hashlib.md5
fmt = fmt_code * (hashfn().digest_size // chunk_size)
num_salts, extra = divmod(num_slices, len(fmt))
if extra:
num_salts += 1
salts = [hashfn(hashfn(pack('I', i)).digest()) for i in range(num_salts)]
def _make_hashfuncs(key):
key = str(key)
rval = []
for salt in salts:
h = salt.copy()
h.update(key.encode('utf-8'))
rval.extend(uint % num_bits for uint in unpack(fmt, h.digest()))
del rval[num_slices:]
return rval
return _make_hashfuncs
class BloomFilter(object):
FILE_FMT = '<dQQQQ'
def __init__(self, capacity, error_rate=0.001):
if not (0 < error_rate < 1):
raise ValueError("Error_Rate must be between 0 and 1.")
if not capacity > 0:
raise ValueError("Capacity must be > 0")
num_slices = int(math.ceil(math.log(1.0 / error_rate, 2)))
bits_per_slice = int(math.ceil(
(capacity * abs(math.log(error_rate))) /
(num_slices * (math.log(2) ** 2))))
self._setup(error_rate, num_slices, bits_per_slice, capacity, 0)
self.bitarray = bitarray.bitarray(self.num_bits, endian='little')
self.bitarray.setall(False)
def _setup(self, error_rate, num_slices, bits_per_slice, capacity, count):
self.error_rate = error_rate
self.num_slices = num_slices
self.bits_per_slice = bits_per_slice
self.capacity = capacity
self.num_bits = num_slices * bits_per_slice
self.count = count
self.make_hashes = make_hashfuncs(self.num_slices, self.bits_per_slice)
def __contains__(self, key):
bits_per_slice = self.bits_per_slice
bitarray = self.bitarray
if not isinstance(key, list):
hashes = self.make_hashes(key)
else:
hashes = key
offset = 0
for k in hashes:
if not bitarray[offset + k]:
return False
offset += bits_per_slice
return True
def __len__(self):
"""Return the number of keys stored by this bloom filter."""
return self.count
def add(self, key, skip_check=False):
bitarray = self.bitarray
bits_per_slice = self.bits_per_slice
hashes = self.make_hashes(key)
if not skip_check and hashes in self:
return True
if self.count > self.capacity:
raise IndexError("BloomFilter is at capacity")
offset = 0
for k in hashes:
self.bitarray[offset + k] = True
offset += bits_per_slice
self.count += 1
return False
def copy(self):
"""Return a copy of this bloom filter.
"""
new_filter = BloomFilter(self.capacity, self.error_rate)
new_filter.bitarray = self.bitarray.copy()
return new_filter
def union(self, other):
""" Calculates the union of the two underlying bitarrays and returns
a new bloom filter object."""
if self.capacity != other.capacity or \
self.error_rate != other.error_rate:
raise ValueError("Unioning filters requires both filters to have \
both the same capacity and error rate")
new_bloom = self.copy()
new_bloom.bitarray = new_bloom.bitarray | other.bitarray
return new_bloom
def __or__(self, other):
return self.union(other)
def intersection(self, other):
""" Calculates the intersection of the two underlying bitarrays and returns
a new bloom filter object."""
if self.capacity != other.capacity or \
self.error_rate != other.error_rate:
raise ValueError("Intersecting filters requires both filters to \
have equal capacity and error rate")
new_bloom = self.copy()
new_bloom.bitarray = new_bloom.bitarray & other.bitarray
return new_bloom
def __and__(self, other):
return self.intersection(other)
def tofile(self, f):
"""Write the bloom filter to file object `f'. Underlying bits
are written as machine values. This is much more space
efficient than pickling the object."""
f.write(pack(self.FILE_FMT, self.error_rate, self.num_slices,
self.bits_per_slice, self.capacity, self.count))
self.bitarray.tofile(f)
@classmethod
def fromfile(cls, f, n=-1):
"""Read a bloom filter from file-object `f' serialized with
``BloomFilter.tofile''. If `n' > 0 read only so many bytes."""
headerlen = calcsize(cls.FILE_FMT)
if 0 < n < headerlen:
raise ValueError('n too small!')
filter = cls(1) # Bogus instantiation, we will `_setup'.
filter._setup(*unpack(cls.FILE_FMT, f.read(headerlen)))
filter.bitarray = bitarray.bitarray(endian='little')
if n > 0:
filter.bitarray.fromfile(f, n - headerlen)
else:
filter.bitarray.fromfile(f)
if filter.num_bits != filter.bitarray.length() and \
(filter.num_bits + (8 - filter.num_bits % 8)
!= filter.bitarray.length()):
raise ValueError('Bit length mismatch!')
return filter
def __getstate__(self):
d = self.__dict__.copy()
del d['make_hashes']
return d
def __setstate__(self, d):
self.__dict__.update(d)
self.make_hashes = make_hashfuncs(self.num_slices, self.bits_per_slice)
class ScalableBloomFilter(object):
SMALL_SET_GROWTH = 2 # slower, but takes up less memory
LARGE_SET_GROWTH = 4 # faster, but takes up more memory faster
FILE_FMT = '<idQd'
def __init__(self, initial_capacity=100, error_rate=0.001,
mode=SMALL_SET_GROWTH):
if not error_rate or error_rate < 0:
raise ValueError("Error_Rate must be a decimal less than 0.")
self._setup(mode, 0.9, initial_capacity, error_rate)
self.filters = []
def _setup(self, mode, ratio, initial_capacity, error_rate):
self.scale = mode
self.ratio = ratio
self.initial_capacity = initial_capacity
self.error_rate = error_rate
def __contains__(self, key):
for f in reversed(self.filters):
if key in f:
return True
return False
def add(self, key):
if key in self:
return True
if not self.filters:
filter = BloomFilter(
capacity=self.initial_capacity,
error_rate=self.error_rate * (1.0 - self.ratio))
self.filters.append(filter)
else:
filter = self.filters[-1]
if filter.count >= filter.capacity:
filter = BloomFilter(
capacity=filter.capacity * self.scale,
error_rate=filter.error_rate * self.ratio)
self.filters.append(filter)
filter.add(key, skip_check=True)
return False
@property
def capacity(self):
"""Returns the total capacity for all filters in this SBF"""
return sum([f.capacity for f in self.filters])
@property
def count(self):
return len(self)
def tofile(self, f):
"""Serialize this ScalableBloomFilter into the file-object
`f'."""
f.write(pack(self.FILE_FMT, self.scale, self.ratio,
self.initial_capacity, self.error_rate))
# Write #-of-filters
f.write(pack('<l', len(self.filters)))
if len(self.filters) > 0:
# Then each filter directly, with a header describing
# their lengths.
headerpos = f.tell()
headerfmt = '<' + 'Q'*(len(self.filters))
f.write('.' * calcsize(headerfmt))
filter_sizes = []
for filter in self.filters:
begin = f.tell()
filter.tofile(f)
filter_sizes.append(f.tell() - begin)
f.seek(headerpos)
f.write(pack(headerfmt, *filter_sizes))
@classmethod
def fromfile(cls, f):
"""Deserialize the ScalableBloomFilter in file object `f'."""
filter = cls()
filter._setup(*unpack(cls.FILE_FMT, f.read(calcsize(cls.FILE_FMT))))
nfilters, = unpack('<l', f.read(calcsize('<l')))
if nfilters > 0:
header_fmt = '<' + 'Q'*nfilters
bytes = f.read(calcsize(header_fmt))
filter_lengths = unpack(header_fmt, bytes)
for fl in filter_lengths:
filter.filters.append(BloomFilter.fromfile(f, fl))
else:
filter.filters = []
return filter
def __len__(self):
"""Returns the total number of elements stored in this SBF"""
return sum([f.count for f in self.filters])

367
index_generation/stemmer.py Normal file
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@ -0,0 +1,367 @@
#!/usr/bin/env python3
"""Porter Stemming Algorithm
This is the Porter stemming algorithm, ported to Python from the
version coded up in ANSI C by the author. It may be be regarded
as canonical, in that it follows the algorithm presented in
Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
no. 3, pp 130-137,
only differing from it at the points maked --DEPARTURE-- below.
See also http://www.tartarus.org/~martin/PorterStemmer
The algorithm as described in the paper could be exactly replicated
by adjusting the points of DEPARTURE, but this is barely necessary,
because (a) the points of DEPARTURE are definitely improvements, and
(b) no encoding of the Porter stemmer I have seen is anything like
as exact as this version, even with the points of DEPARTURE!
Vivake Gupta (v@nano.com)
Release 1: January 2001
Further adjustments by Santiago Bruno (bananabruno@gmail.com)
to allow word input not restricted to one word per line, leading
to:
release 2: July 2008
"""
import sys
class PorterStemmer:
def __init__(self):
"""The main part of the stemming algorithm starts here.
b is a buffer holding a word to be stemmed. The letters are in b[k0],
b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is
readjusted downwards as the stemming progresses. Zero termination is
not in fact used in the algorithm.
Note that only lower case sequences are stemmed. Forcing to lower case
should be done before stem(...) is called.
"""
self.b = "" # buffer for word to be stemmed
self.k = 0
self.k0 = 0
self.j = 0 # j is a general offset into the string
def cons(self, i):
"""cons(i) is TRUE <=> b[i] is a consonant."""
if self.b[i] == 'a' or self.b[i] == 'e' or self.b[i] == 'i' or self.b[i] == 'o' or self.b[i] == 'u':
return 0
if self.b[i] == 'y':
if i == self.k0:
return 1
else:
return (not self.cons(i - 1))
return 1
def m(self):
"""m() measures the number of consonant sequences between k0 and j.
if c is a consonant sequence and v a vowel sequence, and <..>
indicates arbitrary presence,
<c><v> gives 0
<c>vc<v> gives 1
<c>vcvc<v> gives 2
<c>vcvcvc<v> gives 3
....
"""
n = 0
i = self.k0
while 1:
if i > self.j:
return n
if not self.cons(i):
break
i = i + 1
i = i + 1
while 1:
while 1:
if i > self.j:
return n
if self.cons(i):
break
i = i + 1
i = i + 1
n = n + 1
while 1:
if i > self.j:
return n
if not self.cons(i):
break
i = i + 1
i = i + 1
def vowelinstem(self):
"""vowelinstem() is TRUE <=> k0,...j contains a vowel"""
for i in range(self.k0, self.j + 1):
if not self.cons(i):
return 1
return 0
def doublec(self, j):
"""doublec(j) is TRUE <=> j,(j-1) contain a double consonant."""
if j < (self.k0 + 1):
return 0
if (self.b[j] != self.b[j-1]):
return 0
return self.cons(j)
def cvc(self, i):
"""cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant
and also if the second c is not w,x or y. this is used when trying to
restore an e at the end of a short e.g.
cav(e), lov(e), hop(e), crim(e), but
snow, box, tray.
"""
if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2):
return 0
ch = self.b[i]
if ch == 'w' or ch == 'x' or ch == 'y':
return 0
return 1
def ends(self, s):
"""ends(s) is TRUE <=> k0,...k ends with the string s."""
length = len(s)
if s[length - 1] != self.b[self.k]: # tiny speed-up
return 0
if length > (self.k - self.k0 + 1):
return 0
if self.b[self.k-length+1:self.k+1] != s:
return 0
self.j = self.k - length
return 1
def setto(self, s):
"""setto(s) sets (j+1),...k to the characters in the string s, readjusting k."""
length = len(s)
self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:]
self.k = self.j + length
def r(self, s):
"""r(s) is used further down."""
if self.m() > 0:
self.setto(s)
def step1ab(self):
"""step1ab() gets rid of plurals and -ed or -ing. e.g.
caresses -> caress
ponies -> poni
ties -> ti
caress -> caress
cats -> cat
feed -> feed
agreed -> agree
disabled -> disable
matting -> mat
mating -> mate
meeting -> meet
milling -> mill
messing -> mess
meetings -> meet
"""
if self.b[self.k] == 's':
if self.ends("sses"):
self.k = self.k - 2
elif self.ends("ies"):
self.setto("i")
elif self.b[self.k - 1] != 's':
self.k = self.k - 1
if self.ends("eed"):
if self.m() > 0:
self.k = self.k - 1
elif (self.ends("ed") or self.ends("ing")) and self.vowelinstem():
self.k = self.j
if self.ends("at"): self.setto("ate")
elif self.ends("bl"): self.setto("ble")
elif self.ends("iz"): self.setto("ize")
elif self.doublec(self.k):
self.k = self.k - 1
ch = self.b[self.k]
if ch == 'l' or ch == 's' or ch == 'z':
self.k = self.k + 1
elif (self.m() == 1 and self.cvc(self.k)):
self.setto("e")
def step1c(self):
"""step1c() turns terminal y to i when there is another vowel in the stem."""
if (self.ends("y") and self.vowelinstem()):
self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]
def step2(self):
"""step2() maps double suffices to single ones.
so -ization ( = -ize plus -ation) maps to -ize etc. note that the
string before the suffix must give m() > 0.
"""
if self.b[self.k - 1] == 'a':
if self.ends("ational"): self.r("ate")
elif self.ends("tional"): self.r("tion")
elif self.b[self.k - 1] == 'c':
if self.ends("enci"): self.r("ence")
elif self.ends("anci"): self.r("ance")
elif self.b[self.k - 1] == 'e':
if self.ends("izer"): self.r("ize")
elif self.b[self.k - 1] == 'l':
if self.ends("bli"): self.r("ble") # --DEPARTURE--
# To match the published algorithm, replace this phrase with
# if self.ends("abli"): self.r("able")
elif self.ends("alli"): self.r("al")
elif self.ends("entli"): self.r("ent")
elif self.ends("eli"): self.r("e")
elif self.ends("ousli"): self.r("ous")
elif self.b[self.k - 1] == 'o':
if self.ends("ization"): self.r("ize")
elif self.ends("ation"): self.r("ate")
elif self.ends("ator"): self.r("ate")
elif self.b[self.k - 1] == 's':
if self.ends("alism"): self.r("al")
elif self.ends("iveness"): self.r("ive")
elif self.ends("fulness"): self.r("ful")
elif self.ends("ousness"): self.r("ous")
elif self.b[self.k - 1] == 't':
if self.ends("aliti"): self.r("al")
elif self.ends("iviti"): self.r("ive")
elif self.ends("biliti"): self.r("ble")
elif self.b[self.k - 1] == 'g': # --DEPARTURE--
if self.ends("logi"): self.r("log")
# To match the published algorithm, delete this phrase
def step3(self):
"""step3() dels with -ic-, -full, -ness etc. similar strategy to step2."""
if self.b[self.k] == 'e':
if self.ends("icate"): self.r("ic")
elif self.ends("ative"): self.r("")
elif self.ends("alize"): self.r("al")
elif self.b[self.k] == 'i':
if self.ends("iciti"): self.r("ic")
elif self.b[self.k] == 'l':
if self.ends("ical"): self.r("ic")
elif self.ends("ful"): self.r("")
elif self.b[self.k] == 's':
if self.ends("ness"): self.r("")
def step4(self):
"""step4() takes off -ant, -ence etc., in context <c>vcvc<v>."""
if self.b[self.k - 1] == 'a':
if self.ends("al"): pass
else: return
elif self.b[self.k - 1] == 'c':
if self.ends("ance"): pass
elif self.ends("ence"): pass
else: return
elif self.b[self.k - 1] == 'e':
if self.ends("er"): pass
else: return
elif self.b[self.k - 1] == 'i':
if self.ends("ic"): pass
else: return
elif self.b[self.k - 1] == 'l':
if self.ends("able"): pass
elif self.ends("ible"): pass
else: return
elif self.b[self.k - 1] == 'n':
if self.ends("ant"): pass
elif self.ends("ement"): pass
elif self.ends("ment"): pass
elif self.ends("ent"): pass
else: return
elif self.b[self.k - 1] == 'o':
if self.ends("ion") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass
elif self.ends("ou"): pass
# takes care of -ous
else: return
elif self.b[self.k - 1] == 's':
if self.ends("ism"): pass
else: return
elif self.b[self.k - 1] == 't':
if self.ends("ate"): pass
elif self.ends("iti"): pass
else: return
elif self.b[self.k - 1] == 'u':
if self.ends("ous"): pass
else: return
elif self.b[self.k - 1] == 'v':
if self.ends("ive"): pass
else: return
elif self.b[self.k - 1] == 'z':
if self.ends("ize"): pass
else: return
else:
return
if self.m() > 1:
self.k = self.j
def step5(self):
"""step5() removes a final -e if m() > 1, and changes -ll to -l if
m() > 1.
"""
self.j = self.k
if self.b[self.k] == 'e':
a = self.m()
if a > 1 or (a == 1 and not self.cvc(self.k-1)):
self.k = self.k - 1
if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:
self.k = self.k -1
def stem(self, p, i, j):
"""In stem(p,i,j), p is a char pointer, and the string to be stemmed
is from p[i] to p[j] inclusive. Typically i is zero and j is the
offset to the last character of a string, (p[j+1] == '\0'). The
stemmer adjusts the characters p[i] ... p[j] and returns the new
end-point of the string, k. Stemming never increases word length, so
i <= k <= j. To turn the stemmer into a module, declare 'stem' as
extern, and delete the remainder of this file.
"""
# copy the parameters into statics
self.b = p
self.k = j
self.k0 = i
if self.k <= self.k0 + 1:
return self.b # --DEPARTURE--
# With this line, strings of length 1 or 2 don't go through the
# stemming process, although no mention is made of this in the
# published algorithm. Remove the line to match the published
# algorithm.
self.step1ab()
self.step1c()
self.step2()
self.step3()
self.step4()
self.step5()
return self.b[self.k0:self.k+1]
if __name__ == '__main__':
p = PorterStemmer()
if len(sys.argv) > 1:
for f in sys.argv[1:]:
infile = open(f, 'r')
while 1:
output = ''
word = ''
line = infile.readline()
if line == '':
break
for c in line:
if c.isalpha():
word += c.lower()
else:
if word:
output += p.stem(word, 0,len(word)-1)
word = ''
output += c.lower()
print(output)
infile.close()

3
bloom.js → js/bloom.js
View File

@ -44,6 +44,9 @@ function callback_change() {
$("#results").append("<p>"+key+"</p>");
}
}
if(!$("#results p").length) {
$("#results").append("<p>No results...</p>");
}
}
$("#search").on('input', callback_change);

0
bloomfilter.js → js/bloomfilter.js
View File

0
jquery-2.0.3.min.js → js/jquery-2.0.3.min.js vendored
View File