[ https://issues.apache.org/jira/browse/LUCENE-10650 ]
Nathan Meisels deleted comment on LUCENE-10650:
-----------------------------------------
was (Author: JIRAUSER292626):
Hi [~jpountz]!
Appreciate your help until now!
Another question.
I did a reindex and I get different scores.
query is:
{code:java}
{
"query": {
"term": {
"sessionIds": "1234-1234"
}
}
}{code}
New index explain:
{code:java}
{
"_index": "entities-new",
"_type": "entity",
"_id": "AWByRrSPIGshPfnDk4hN",
"matched": true,
"explanation": {
"value": 22.941677,
"description": "weight(sessionIds:1234-1234 in 1400) [PerFieldSimilarity],
result of:",
"details": [
{
"value": 22.941677,
"description": "score from
ScriptedSimilarity(weightScript=[Script{type=inline, lang='painless',
idOrCode='return query.boost *
Math.log((field.docCount+1.0)/(term.docFreq+0.5)) / Math.log(2);', options={},
params={}}], script=[Script{type=inline, lang='painless', idOrCode='return
weight;', options={}, params={}}]) computed from:",
"details": [
{
"value": 22.941677,
"description": "weight",
"details": []
},
{
"value": 1.0,
"description": "query.boost",
"details": []
},
{
"value": 12084378,
"description": "field.docCount",
"details": []
},
{
"value": 4.730932E+7,
"description": "field.sumDocFreq",
"details": []
},
{
"value": -1.0,
"description": "field.sumTotalTermFreq",
"details": []
},
{
"value": 1.0,
"description": "term.docFreq",
"details": []
},
{
"value": -1.0,
"description": "term.totalTermFreq",
"details": []
},
{
"value": 1.0,
"description": "doc.freq",
"details": []
},
{
"value": 1.0,
"description": "doc.length",
"details": []
}
]
}
]
}
}{code}
Old index explain:
{code:java}
{
"_index" : "entities-old",
"_type" : "entity",
"_id" : "AWByRrSPIGshPfnDk4hN",
"matched" : true,
"explanation" : {
"value" : 21.23644,
"description" : "weight(sessionIds:1234-1234 in 527154)
[PerFieldSimilarity], result of:",
"details" : [
{
"value" : 21.23644,
"description" : "score(DFRSimilarity, doc=527154, freq=1.0), computed
from:",
"details" : [
{
"value" : 1.0,
"description" : "no normalization",
"details" : [ ]
},
{
"value" : 21.23644,
"description" : "BasicModelIn, computed from: ",
"details" : [
{
"value" : 1.605901E7,
"description" : "numberOfDocuments",
"details" : [ ]
},
{
"value" : 6.0,
"description" : "docFreq",
"details" : [ ]
}
]
},
{
"value" : 1.0,
"description" : "no aftereffect",
"details" : [ ]
}
]
}
]
}
}{code}
Does this make sense? I need the scores to stay the same.
Thanks
> "after_effect": "no" was removed what replaces it?
> --------------------------------------------------
>
> Key: LUCENE-10650
> URL: https://issues.apache.org/jira/browse/LUCENE-10650
> Project: Lucene - Core
> Issue Type: Wish
> Reporter: Nathan Meisels
> Priority: Major
>
> Hi!
> We have been using an old version of elasticsearch with the following
> settings:
>
> {code:java}
> "default": {
> "queryNorm": "1",
> "type": "DFR",
> "basic_model": "in",
> "after_effect": "no",
> "normalization": "no"
> }{code}
>
> I see [here|https://issues.apache.org/jira/browse/LUCENE-8015] that
> "after_effect": "no" was removed.
> In
> [old|https://github.com/apache/lucene-solr/blob/releases/lucene-solr/5.5.0/lucene/core/src/java/org/apache/lucene/search/similarities/BasicModelIn.java#L33]
> version score was:
> {code:java}
> return tfn * (float)(log2((N + 1) / (n + 0.5)));{code}
> In
> [new|https://github.com/apache/lucene-solr/blob/releases/lucene-solr/8.11.2/lucene/core/src/java/org/apache/lucene/search/similarities/BasicModelIn.java#L43]
> version it's:
> {code:java}
> long N = stats.getNumberOfDocuments();
> long n = stats.getDocFreq();
> double A = log2((N + 1) / (n + 0.5));
> // basic model I should return A * tfn
> // which we rewrite to A * (1 + tfn) - A
> // so that it can be combined with the after effect while still guaranteeing
> // that the result is non-decreasing with tfn
> return A * aeTimes1pTfn * (1 - 1 / (1 + tfn));
> {code}
> I tried changing {color:#172b4d}after_effect{color} to "l" but the scoring is
> different than what we are used to. (We depend heavily on the exact scoring).
> Do you have any advice how we can keep the same scoring as before?
> Thanks
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