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rank_bm25.py
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rank_bm25.py
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#!/usr/bin/env python
import math
import numpy as np
from multiprocessing import Pool, cpu_count
"""
All of these algorithms have been taken from the paper:
Trotmam et al, Improvements to BM25 and Language Models Examined
Here we implement all the BM25 variations mentioned.
"""
class BM25:
def __init__(self, corpus, tokenizer=None):
self.corpus_size = len(corpus)
self.avgdl = 0
self.doc_freqs = []
self.idf = {}
self.doc_len = []
self.tokenizer = tokenizer
if tokenizer:
corpus = self._tokenize_corpus(corpus)
nd = self._initialize(corpus)
self._calc_idf(nd)
def _initialize(self, corpus):
nd = {} # word -> number of documents with word
num_doc = 0
for document in corpus:
self.doc_len.append(len(document))
num_doc += len(document)
frequencies = {}
for word in document:
if word not in frequencies:
frequencies[word] = 0
frequencies[word] += 1
self.doc_freqs.append(frequencies)
for word, freq in frequencies.items():
try:
nd[word]+=1
except KeyError:
nd[word] = 1
self.avgdl = num_doc / self.corpus_size
return nd
def _tokenize_corpus(self, corpus):
pool = Pool(cpu_count())
tokenized_corpus = pool.map(self.tokenizer, corpus)
return tokenized_corpus
def _calc_idf(self, nd):
raise NotImplementedError()
def get_scores(self, query):
raise NotImplementedError()
def get_batch_scores(self, query, doc_ids):
raise NotImplementedError()
def get_top_n(self, query, documents, n=5):
assert self.corpus_size == len(documents), "The documents given don't match the index corpus!"
scores = self.get_scores(query)
top_n = np.argsort(scores)[::-1][:n]
return [documents[i] for i in top_n]
class BM25Okapi(BM25):
def __init__(self, corpus, tokenizer=None, k1=1.5, b=0.75, epsilon=0.25):
self.k1 = k1
self.b = b
self.epsilon = epsilon
super().__init__(corpus, tokenizer)
def _calc_idf(self, nd):
"""
Calculates frequencies of terms in documents and in corpus.
This algorithm sets a floor on the idf values to eps * average_idf
"""
# collect idf sum to calculate an average idf for epsilon value
idf_sum = 0
# collect words with negative idf to set them a special epsilon value.
# idf can be negative if word is contained in more than half of documents
negative_idfs = []
for word, freq in nd.items():
idf = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)
self.idf[word] = idf
idf_sum += idf
if idf < 0:
negative_idfs.append(word)
self.average_idf = idf_sum / len(self.idf)
eps = self.epsilon * self.average_idf
for word in negative_idfs:
self.idf[word] = eps
def get_scores(self, query):
"""
The ATIRE BM25 variant uses an idf function which uses a log(idf) score. To prevent negative idf scores,
this algorithm also adds a floor to the idf value of epsilon.
See [Trotman, A., X. Jia, M. Crane, Towards an Efficient and Effective Search Engine] for more info
:param query:
:return:
"""
score = np.zeros(self.corpus_size)
doc_len = np.array(self.doc_len)
for q in query:
q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
score += (self.idf.get(q) or 0) * (q_freq * (self.k1 + 1) /
(q_freq + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)))
return score
def get_batch_scores(self, query, doc_ids):
"""
Calculate bm25 scores between query and subset of all docs
"""
assert all(di < len(self.doc_freqs) for di in doc_ids)
score = np.zeros(len(doc_ids))
doc_len = np.array(self.doc_len)[doc_ids]
for q in query:
q_freq = np.array([(self.doc_freqs[di].get(q) or 0) for di in doc_ids])
score += (self.idf.get(q) or 0) * (q_freq * (self.k1 + 1) /
(q_freq + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)))
return score.tolist()
class BM25L(BM25):
def __init__(self, corpus, tokenizer=None, k1=1.5, b=0.75, delta=0.5):
# Algorithm specific parameters
self.k1 = k1
self.b = b
self.delta = delta
super().__init__(corpus, tokenizer)
def _calc_idf(self, nd):
for word, freq in nd.items():
idf = math.log(self.corpus_size + 1) - math.log(freq + 0.5)
self.idf[word] = idf
def get_scores(self, query):
score = np.zeros(self.corpus_size)
doc_len = np.array(self.doc_len)
for q in query:
q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
ctd = q_freq / (1 - self.b + self.b * doc_len / self.avgdl)
score += (self.idf.get(q) or 0) * q_freq * (self.k1 + 1) * (ctd + self.delta) / \
(self.k1 + ctd + self.delta)
return score
def get_batch_scores(self, query, doc_ids):
"""
Calculate bm25 scores between query and subset of all docs
"""
assert all(di < len(self.doc_freqs) for di in doc_ids)
score = np.zeros(len(doc_ids))
doc_len = np.array(self.doc_len)[doc_ids]
for q in query:
q_freq = np.array([(self.doc_freqs[di].get(q) or 0) for di in doc_ids])
ctd = q_freq / (1 - self.b + self.b * doc_len / self.avgdl)
score += (self.idf.get(q) or 0) * q_freq * (self.k1 + 1) * (ctd + self.delta) / \
(self.k1 + ctd + self.delta)
return score.tolist()
class BM25Plus(BM25):
def __init__(self, corpus, tokenizer=None, k1=1.5, b=0.75, delta=1):
# Algorithm specific parameters
self.k1 = k1
self.b = b
self.delta = delta
super().__init__(corpus, tokenizer)
def _calc_idf(self, nd):
for word, freq in nd.items():
idf = math.log((self.corpus_size + 1) / freq)
self.idf[word] = idf
def get_scores(self, query):
score = np.zeros(self.corpus_size)
doc_len = np.array(self.doc_len)
for q in query:
q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
score += (self.idf.get(q) or 0) * (self.delta + (q_freq * (self.k1 + 1)) /
(self.k1 * (1 - self.b + self.b * doc_len / self.avgdl) + q_freq))
return score
def get_batch_scores(self, query, doc_ids):
"""
Calculate bm25 scores between query and subset of all docs
"""
assert all(di < len(self.doc_freqs) for di in doc_ids)
score = np.zeros(len(doc_ids))
doc_len = np.array(self.doc_len)[doc_ids]
for q in query:
q_freq = np.array([(self.doc_freqs[di].get(q) or 0) for di in doc_ids])
score += (self.idf.get(q) or 0) * (self.delta + (q_freq * (self.k1 + 1)) /
(self.k1 * (1 - self.b + self.b * doc_len / self.avgdl) + q_freq))
return score.tolist()
# BM25Adpt and BM25T are a bit more complicated than the previous algorithms here. Here a term-specific k1
# parameter is calculated before scoring is done
# class BM25Adpt(BM25):
# def __init__(self, corpus, k1=1.5, b=0.75, delta=1):
# # Algorithm specific parameters
# self.k1 = k1
# self.b = b
# self.delta = delta
# super().__init__(corpus)
#
# def _calc_idf(self, nd):
# for word, freq in nd.items():
# idf = math.log((self.corpus_size + 1) / freq)
# self.idf[word] = idf
#
# def get_scores(self, query):
# score = np.zeros(self.corpus_size)
# doc_len = np.array(self.doc_len)
# for q in query:
# q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
# score += (self.idf.get(q) or 0) * (self.delta + (q_freq * (self.k1 + 1)) /
# (self.k1 * (1 - self.b + self.b * doc_len / self.avgdl) + q_freq))
# return score
#
#
# class BM25T(BM25):
# def __init__(self, corpus, k1=1.5, b=0.75, delta=1):
# # Algorithm specific parameters
# self.k1 = k1
# self.b = b
# self.delta = delta
# super().__init__(corpus)
#
# def _calc_idf(self, nd):
# for word, freq in nd.items():
# idf = math.log((self.corpus_size + 1) / freq)
# self.idf[word] = idf
#
# def get_scores(self, query):
# score = np.zeros(self.corpus_size)
# doc_len = np.array(self.doc_len)
# for q in query:
# q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
# score += (self.idf.get(q) or 0) * (self.delta + (q_freq * (self.k1 + 1)) /
# (self.k1 * (1 - self.b + self.b * doc_len / self.avgdl) + q_freq))
# return score