I want to change the scoring system in elasticsearch to get rid of counting multiple term expressions. For example, I want:
"texas texas texas"
and
"Texas"
to make the same mark. I found this mapping that elasticsearch said to turn off time frequency counting, but my searches do not go beyond one point:
"mappings":{
"business": {
"properties" : {
"name" : {
"type" : "string",
"index_options" : "docs",
"norms" : { "enabled": false}}
}
}
}
}
Any help would be appreciated; I could not find much information about this.
Edit:
I add my search code, and that returns when I use the explanation.
My search code:
Settings settings = ImmutableSettings.settingsBuilder().put("cluster.name", "escluster").build();
Client client = new TransportClient(settings)
.addTransportAddress(new InetSocketTransportAddress("127.0.0.1", 9300));
SearchRequest request = Requests.searchRequest("businesses")
.source(SearchSourceBuilder.searchSource().query(QueryBuilders.boolQuery()
.should(QueryBuilders.matchQuery("name", "Texas")
.minimumShouldMatch("1")))).searchType(SearchType.DFS_QUERY_THEN_FETCH);
ExplainRequest request2 = client.prepareIndex("businesses", "business")
and when I search with an explanation, I get:
"took" : 14,
"timed_out" : false,
"_shards" : {
"total" : 3,
"successful" : 3,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [ {
"_shard" : 1,
"_node" : "BTqBPVDET5Kr83r-CYPqfA",
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9U5KBks4zEorv9YI4n",
"_score" : 1.0,
"_source":{
"name" : "texas"
}
,
"_explanation" : {
"value" : 1.0,
"description" : "weight(_all:texas in 0) [PerFieldSimilarity], result of:",
"details" : [ {
"value" : 1.0,
"description" : "fieldWeight in 0, product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(freq=1.0), with freq of:",
"details" : [ {
"value" : 1.0,
"description" : "termFreq=1.0"
} ]
}, {
"value" : 1.0,
"description" : "idf(docFreq=2, maxDocs=3)"
}, {
"value" : 1.0,
"description" : "fieldNorm(doc=0)"
} ]
} ]
}
}, {
"_shard" : 1,
"_node" : "BTqBPVDET5Kr83r-CYPqfA",
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9U5K6Ks4zEorv9YI4o",
"_score" : 0.8660254,
"_source":{
"name" : "texas texas texas"
}
,
"_explanation" : {
"value" : 0.8660254,
"description" : "weight(_all:texas in 0) [PerFieldSimilarity], result of:",
"details" : [ {
"value" : 0.8660254,
"description" : "fieldWeight in 0, product of:",
"details" : [ {
"value" : 1.7320508,
"description" : "tf(freq=3.0), with freq of:",
"details" : [ {
"value" : 3.0,
"description" : "termFreq=3.0"
} ]
}, {
"value" : 1.0,
"description" : "idf(docFreq=2, maxDocs=3)"
}, {
"value" : 0.5,
"description" : "fieldNorm(doc=0)"
} ]
} ]
}
} ]
}
It looks like it is still considering the frequency and frequency of the doc. Any ideas? Sorry for the poor formatting, I don't know why it looks so grotesque.
Edit Edit:
http://localhost:9200/businesses/business/_search?pretty=true&qname=texas
:
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 3,
"successful" : 3,
"failed" : 0
},
"hits" : {
"total" : 4,
"max_score" : 1.0,
"hits" : [ {
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9YcCKjKvtg8NgyozGK",
"_score" : 1.0,
"_source":{"business" : {
"name" : "texas texas texas texas" }
}
}, {
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9YateBKvtg8Ngyoy-p",
"_score" : 1.0,
"_source":{
"name" : "texas" }
}, {
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9YavVnKvtg8Ngyoy-4",
"_score" : 1.0,
"_source":{
"name" : "texas texas texas" }
}, {
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9Yb7NgKvtg8NgyozFf",
"_score" : 1.0,
"_source":{"business" : {
"name" : "texas texas texas" }
}
} ]
}
}
4 , , .
API- java , :
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 3,
"successful" : 3,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.287682,
"hits" : [ {
"_shard" : 1,
"_node" : "BTqBPVDET5Kr83r-CYPqfA",
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9YateBKvtg8Ngyoy-p",
"_score" : 1.287682,
"_source":{
"name" : "texas" }
,
"_explanation" : {
"value" : 1.287682,
"description" : "weight(name:texas in 0) [PerFieldSimilarity], result of:",
"details" : [ {
"value" : 1.287682,
"description" : "fieldWeight in 0, product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(freq=1.0), with freq of:",
"details" : [ {
"value" : 1.0,
"description" : "termFreq=1.0"
} ]
}, {
"value" : 1.287682,
"description" : "idf(docFreq=2, maxDocs=4)"
}, {
"value" : 1.0,
"description" : "fieldNorm(doc=0)"
} ]
} ]
}
}, {
"_shard" : 1,
"_node" : "BTqBPVDET5Kr83r-CYPqfA",
"_index" : "businesses",
"_type" : "business",
"_id" : "AU9YavVnKvtg8Ngyoy-4",
"_score" : 1.1151654,
"_source":{
"name" : "texas texas texas" }
,
"_explanation" : {
"value" : 1.1151654,
"description" : "weight(name:texas in 0) [PerFieldSimilarity], result of:",
"details" : [ {
"value" : 1.1151654,
"description" : "fieldWeight in 0, product of:",
"details" : [ {
"value" : 1.7320508,
"description" : "tf(freq=3.0), with freq of:",
"details" : [ {
"value" : 3.0,
"description" : "termFreq=3.0"
} ]
}, {
"value" : 1.287682,
"description" : "idf(docFreq=2, maxDocs=4)"
}, {
"value" : 0.5,
"description" : "fieldNorm(doc=0)"
} ]
} ]
}
} ]
}
}