158 lines
5.1 KiB
Python
158 lines
5.1 KiB
Python
import math
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import numpy as np
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from typing import Annotated
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from multiprocessing import Pool, cpu_count
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from arcade.sdk.tool import tool
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class BM25:
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def __init__(self, corpus, tokenizer=None):
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self.corpus_size = 0
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self.avgdl = 0
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self.doc_freqs = []
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self.idf = {}
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self.doc_len = []
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self.tokenizer = tokenizer
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if tokenizer:
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corpus = self._tokenize_corpus(corpus)
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else:
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corpus = self._tokenize(corpus)
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nd = self._initialize(corpus)
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self._calc_idf(nd)
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@staticmethod
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def _tokenize(texts: list[str]) -> list[list[str]]:
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return [text.split() for text in texts]
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def _initialize(self, corpus):
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nd = {} # word -> number of documents with word
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num_doc = 0
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for document in corpus:
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self.doc_len.append(len(document))
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num_doc += len(document)
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frequencies = {}
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for word in document:
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if word not in frequencies:
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frequencies[word] = 0
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frequencies[word] += 1
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self.doc_freqs.append(frequencies)
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for word, freq in frequencies.items():
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try:
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nd[word] += 1
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except KeyError:
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nd[word] = 1
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self.corpus_size += 1
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self.avgdl = num_doc / self.corpus_size
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return nd
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def _tokenize_corpus(self, corpus):
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pool = Pool(cpu_count())
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tokenized_corpus = pool.map(self.tokenizer, corpus)
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return tokenized_corpus
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def _calc_idf(self, nd):
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raise NotImplementedError()
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def get_scores(self, query):
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raise NotImplementedError()
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def get_batch_scores(self, query, doc_ids):
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raise NotImplementedError()
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def get_top_n(self, query, documents, n=5):
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assert self.corpus_size == len(
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documents
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), "The documents given don't match the index corpus!"
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query = self._tokenize([query])[0] # tokenize the query
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scores = self.get_scores(query)
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top_n = np.argsort(scores)[::-1][:n]
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return [documents[i] for i in top_n]
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class Okapi(BM25):
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def __init__(self, corpus, tokenizer=None, k1=1.5, b=0.75, epsilon=0.25):
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self.k1 = k1
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self.b = b
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self.epsilon = epsilon
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super().__init__(corpus, tokenizer)
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def _calc_idf(self, nd):
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"""
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Calculates frequencies of terms in documents and in corpus.
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This algorithm sets a floor on the idf values to eps * average_idf
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"""
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# collect idf sum to calculate an average idf for epsilon value
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idf_sum = 0
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# collect words with negative idf to set them a special epsilon value.
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# idf can be negative if word is contained in more than half of documents
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negative_idfs = []
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for word, freq in nd.items():
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idf = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)
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self.idf[word] = idf
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idf_sum += idf
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if idf < 0:
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negative_idfs.append(word)
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self.average_idf = idf_sum / len(self.idf)
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eps = self.epsilon * self.average_idf
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for word in negative_idfs:
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self.idf[word] = eps
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def get_scores(self, query):
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"""
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The ATIRE BM25 variant uses an idf function which uses a log(idf) score. To prevent negative idf scores,
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this algorithm also adds a floor to the idf value of epsilon.
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See [Trotman, A., X. Jia, M. Crane, Towards an Efficient and Effective Search Engine] for more info
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:param query:
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:return:
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"""
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score = np.zeros(self.corpus_size)
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doc_len = np.array(self.doc_len)
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for q in query:
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q_freq = np.array([(doc.get(q) or 0) for doc in self.doc_freqs])
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score += (self.idf.get(q) or 0) * (
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q_freq
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* (self.k1 + 1)
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/ (q_freq + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl))
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)
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return score
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def get_batch_scores(self, query, doc_ids):
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"""
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Calculate bm25 scores between query and subset of all docs
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"""
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assert all(di < len(self.doc_freqs) for di in doc_ids)
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score = np.zeros(len(doc_ids))
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doc_len = np.array(self.doc_len)[doc_ids]
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for q in query:
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q_freq = np.array([(self.doc_freqs[di].get(q) or 0) for di in doc_ids])
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score += (self.idf.get(q) or 0) * (
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q_freq
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* (self.k1 + 1)
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/ (q_freq + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl))
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)
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return score.tolist()
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@tool
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async def text_search(
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query: Annotated[str, "The search query"],
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texts: Annotated[list[str], "The texts through which to search"],
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num_results: Annotated[int, "Number of texts to return"] = 5,
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) -> Annotated[list[str], "Most similar texts"]:
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"""Use the BM25 algorithm to search through texts
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This should only be used for smaller datasets where the number
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of texts is less than 100.
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"""
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bm25 = Okapi(texts)
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return bm25.get_top_n(query, texts, num_results)
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