Question

我正在尝试使用function_score中定义的不同函数修改普通查询的分数。

为了找出我的函数计算出的分数，我指定＆＃34; boost_mode＆＃34;到＆＃34;替换＆＃34;。但是，这会使所有分数保持不变：全部等于1.

请考虑以下查询：

{
  "query": {
    "function_score": {
      "query": {
        "terms": {
          "name": ["men", "women"]
        }
      },
      "score_mode": "avg",
      "functions": [
        {
          "filter": {
            "terms": {
              "name": ["men","man"]
            }
          },
          "weight": 2
        }
      ],
      "boost_mode": "replace"
    }
  },
  "explain": true,
  "from": 0
}

我希望在这里获得不同的分数，这取决于＆＃34; name＆＃34;字段包含＆＃34; men＆＃34;或者＆＃34;男人＆＃34;。这些文件肯定存在于索引中。

此外，如果我指定＆＃34;解释＆＃34;：是的，我在解释中显示的分数与命中的_score字段中显示的分数不同：

{  
    "_shard":0,
    "_node":"ro26nlDuTfiTaIlIgHqg4g",
    "_index":"products10",
    "_type":"product_basic",
    "_id":"0c25fc90433481aac0cce62dd1a21e06",
    "_score":1,
    "_source":{  
        "category":[  
            "Chicago Blues",
            "Blues",
            "Styles",
            "Digital Music"
        ],
        "site_name":"www.amazon.com",
        "name":"Who's That Women?",
        "url":"http://www.amazon.com/dp/B001125F8I/",
        "price":0.99,
        "reviews":[  

        ],
        "breadcrumb":"Digital Music",
        "in_stock":true,
        "features":[  

        ],
        "pic_urls":[  
            "http://ecx.images-amazon.com/images/I/51CvgPMwtsL.jpg",
            "http://ecx.images-amazon.com/images/I/51CvgPMwtsL.jpg"
        ],
        "name_semantic_core":[  
            "Women ?",
            "?"
        ],
        "category_path":"/Chicago Blues/Blues/Styles/",
        "visit_datetime":"2014-11-04T11:50:34.169779",
        "detected_category":"Digital Music"
    },
    "_explanation":{  
        "value":1.2249949,
        "description":"function score, no filter match, product of:",
        "details":[  
            {  
                "value":1.2249949,
                "description":"product of:",
                "details":[  
                    {  
                        "value":2.4499898,
                        "description":"sum of:",
                        "details":[  
                            {  
                                "value":2.4499898,
                                "description":"weight(name:women in 6181332) [PerFieldSimilarity], result of:",
                                "details":[  
                                    {  
                                        "value":2.4499898,
                                        "description":"score(doc=6181332,freq=1.0), product of:",
                                        "details":[  
                                            {  
                                                "value":0.67790973,
                                                "description":"queryWeight, product of:",
                                                "details":[  
                                                    {  
                                                        "value":7.228071,
                                                        "description":"idf(docFreq=238699, maxDocs=120967660)"
                                                    },
                                                    {  
                                                        "value":0.09378847,
                                                        "description":"queryNorm"
                                                    }
                                                ]
                                            },
                                            {  
                                                "value":3.6140356,
                                                "description":"fieldWeight in 6181332, product of:",
                                                "details":[  
                                                    {  
                                                        "value":1,
                                                        "description":"tf(freq=1.0), with freq of:",
                                                        "details":[  
                                                            {  
                                                                "value":1,
                                                                "description":"termFreq=1.0"
                                                            }
                                                        ]
                                                    },
                                                    {  
                                                        "value":7.228071,
                                                        "description":"idf(docFreq=238699, maxDocs=120967660)"
                                                    },
                                                    {  
                                                        "value":0.5,
                                                        "description":"fieldNorm(doc=6181332)"
                                                    }
                                                ]
                                            }
                                        ]
                                    }
                                ]
                            }
                        ]
                    },
                    {  
                        "value":0.5,
                        "description":"coord(1/2)"
                    }
                ]
            },
            {  
                "value":1,
                "description":"queryBoost"
            }
        ]
    }
}

这里的解释显示＆＃34;值＆＃34;：1.2249949，而＆＃34; _score＆＃34;是1。

我做错了什么？如何使用functinon_score函数[在与原始查询分数结合之前]计算实际分数？

更新：如果找到匹配的产品，我会得到的结果。出于某种原因，得分是1而应该是2：

Answer 1

在您的示例中，该功能不匹配任何文档：function score, no filter match,。此外，在使用替换时documentation，会发生以下情况：only function score is used, the query score is ignored。因此，情况是这样的：过滤器不匹配 - 因此不计算得分 - 而replace将使得查询得分被忽略并使用过滤器的得分（不会过滤） ; t存在，因为它没有匹配）。

当函数不匹配时，函数的默认值为1。您可以使用"boost_mode": "sum"进行检查。我的观点是，这就是你看到1得分的原因。

关于avg行为，这对我来说并不合适，而且很可能是一个错误。我在这里报告了它：https://github.com/elastic/elasticsearch/issues/13732

Elasticsearch：＆＃34; function_score＆＃34;使用＆＃34; boost_mode＆＃34;：＆＃34;替换＆＃34;忽略功能得分

1 个答案: