From 0c90c9e4775352f093b747783eb5dc8491c94541 Mon Sep 17 00:00:00 2001
From: xuming624 <xuming624@qq.com>
Date: Fri, 27 Dec 2019 18:47:00 +0800
Subject: [PATCH] update sim result.
---
README.md | 10 ++++++----
examples/similarity_demo.py | 5 +++--
text2vec/similarity.py | 18 +++++++++++-------
3 files changed, 20 insertions(+), 13 deletions(-)
diff --git a/README.md b/README.md
index 3caf716..f65cf45 100644
--- a/README.md
+++ b/README.md
@@ -230,16 +230,18 @@ b = '花呗更改绑定银行卡'
c = '我什么时候开通了花呗'
corpus = [a, b, c]
+print(corpus)
search_sim = SearchSimilarity(corpus=corpus)
-print(search_sim.get_scores(query=a))
-print(search_sim.get_similarities(query=a))
+print(a, 'scores:', search_sim.get_scores(query=a))
+print(a, 'rank similarities:', search_sim.get_similarities(query=a))
```
output:
```
-[ 0.9527457 -0.07449248 -0.03204909]
-[['如何', '更换', '花', '呗', '绑定', '银行卡'], ['我', '什么', '时候', '开通', '了', '花', '呗'], ['花', '呗', '更改', '绑定', '银行卡']]
+['如何更换花呗绑定银行卡', '花呗更改绑定银行卡', '我什么时候开通了花呗']
+如何更换花呗绑定银行卡 scores: [ 0.9527457 -0.07449248 -0.03204909]
+如何更换花呗绑定银行卡 rank similarities: ['如何更换花呗绑定银行卡', '我什么时候开通了花呗', '花呗更改绑定银行卡']
```
diff --git a/examples/similarity_demo.py b/examples/similarity_demo.py
index 3c3f77e..fdaffbb 100644
--- a/examples/similarity_demo.py
+++ b/examples/similarity_demo.py
@@ -23,7 +23,8 @@
from text2vec import SearchSimilarity
corpus = [a, b, c]
+print(corpus)
search_sim = SearchSimilarity(corpus=corpus)
-print(search_sim.get_scores(query=a))
-print(search_sim.get_similarities(query=a))
+print(a, 'scores:', search_sim.get_scores(query=a))
+print(a, 'rank similarities:', search_sim.get_similarities(query=a))
diff --git a/text2vec/similarity.py b/text2vec/similarity.py
index 053032b..de6f136 100644
--- a/text2vec/similarity.py
+++ b/text2vec/similarity.py
@@ -4,6 +4,8 @@
@description:
"""
+import numpy as np
+
from text2vec.algorithm.distance import cosine_distance
from text2vec.algorithm.rank_bm25 import BM25Okapi
from text2vec.utils.logger import get_logger
@@ -87,24 +89,26 @@ def init(self):
if isinstance(self.corpus, str):
self.corpus = [self.corpus]
- self.corpus_seg = [self.tokenizer.tokenize(i) for i in self.corpus]
- self.bm25_instance = BM25Okapi(corpus=self.corpus_seg)
+ self.corpus_seg = {k: self.tokenizer.tokenize(k) for k in self.corpus}
+ self.bm25_instance = BM25Okapi(corpus=list(self.corpus_seg.values()))
def get_similarities(self, query, n=5):
"""
Get similarity between `query` and this docs.
:param query: str
:param n: int, num_best
- :return: float scores
+ :return: result, dict, float scores, docs rank
"""
- self.init()
- tokens = self.tokenizer.tokenize(query)
- return self.bm25_instance.get_top_n(tokens, self.corpus_seg, n=n)
+ scores = self.get_scores(query)
+ rank_n = np.argsort(scores)[::-1]
+ if n > 0:
+ rank_n = rank_n[:n]
+ return [self.corpus[i] for i in rank_n]
def get_scores(self, query):
"""
Get scores between query and docs
- :param query:
+ :param query: input str
:return: numpy array, scores for query between docs
"""
self.init()