usha2626
(usha2626)
February 12, 2014, 5:32am
1
Hi,
I want to know what are fieldNorm and queryNorm in the output displayed by
the explain api..how are they calculated?
Thanks
Usha
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Ivan
(Ivan Brusic)
February 12, 2014, 6:30am
2
The norms are calculated by Lucene's TFIDF algorithm:
http://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html
The field norm is encoded into a single byte, so there is a loss of
precision.
--
Ivan
On Tue, Feb 11, 2014 at 9:32 PM, usha2626@gmail.com wrote:
Hi,
I want to know what are fieldNorm and queryNorm in the output displayed by
the explain api..how are they calculated?
Thanks
Usha
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"elasticsearch" group.
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Ivan
formula is queryNorm(q) = queryNorm(sumOfSquaredWeights)http://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/similarities/DefaultSimilarity.html#queryNorm(float) =
1 –––––––––––––– sumOfSquaredWeights½
The sum of squared weights (of the query terms) is computed by the query
Weighthttp://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/Weight.html object. For example, a
BooleanQueryhttp://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/BooleanQuery.html computes this value as:
sumOfSquaredWeightshttp://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/Weight.html#getValueForNormalization() =
q.getBoost()http://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/Query.html#getBoost()
2 · ∑ ( idf(t)http://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html#formula_idf ·
t.getBoost()http://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html#formula_termBoost )
2
t in q
for my following search result how it is calculating:
{
took: 16
timed_out: false
_shards: {
total: 5
successful: 5
failed: 0
}
hits: {
total: 2
max_score: 0.2712221
hits: [
{
_shard: 3
_node: k8BXmkARRsaaYlUJTRpIqQ
_index: phone
_type: iphone
_id: 2
_score: 0.2712221
_source: {
title: iphone update
description: this section shows how to update yopur iphone
to work smoothly in your iphone
}
_explanation: {
value: 0.27122214
description: max of:
details: [
{
value: 0.27122214
description: sum of:
details: [
{
value: 0.13561107
description: weight(title:iphone in 0)
[PerFieldSimilarity], result of:
details: [
{
value: 0.13561107
description: score(doc=0,freq=1.0 =
termFreq=1.0 ), product of:
details: [
{
value: 0.70710677
description: queryWeight, product
of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 2.3043842
description: queryNorm
}
]
}
{
value: 0.19178301
description: fieldWeight in 0,
product of:
details: [
{
value: 1
description: tf(freq=1.0),
with freq of:
details: [
{
value: 1
description:
termFreq=1.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.625
description: fieldNorm(doc=0)
}
]
}
]
}
]
}
{
value: 0.13561107
description: weight(title:update in 0)
[PerFieldSimilarity], result of:
details: [
{
value: 0.13561107
description: score(doc=0,freq=1.0 =
termFreq=1.0 ), product of:
details: [
{
value: 0.70710677
description: queryWeight, product
of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 2.3043842
description: queryNorm
}
]
}
{
value: 0.19178301
description: fieldWeight in 0,
product of:
details: [
{
value: 1
description: tf(freq=1.0),
with freq of:
details: [
{
value: 1
description:
termFreq=1.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.625
description: fieldNorm(doc=0)
}
]
}
]
}
]
}
]
}
{
value: 0.16369703
description: sum of:
details: [
{
value: 0.0958915
description: weight(description:iphone in 0)
[PerFieldSimilarity], result of:
details: [
{
value: 0.0958915
description: score(doc=0,freq=2.0 =
termFreq=2.0 ), product of:
details: [
{
value: 0.70710677
description: queryWeight, product
of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 2.3043842
description: queryNorm
}
]
}
{
value: 0.13561106
description: fieldWeight in 0,
product of:
details: [
{
value: 1.4142135
description: tf(freq=2.0),
with freq of:
details: [
{
value: 2
description:
termFreq=2.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.3125
description: fieldNorm(doc=0)
}
]
}
]
}
]
}
{
value: 0.067805536
description: weight(description:update in 0)
[PerFieldSimilarity], result of:
details: [
{
value: 0.067805536
description: score(doc=0,freq=1.0 =
termFreq=1.0 ), product of:
details: [
{
value: 0.70710677
description: queryWeight, product
of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 2.3043842
description: queryNorm
}
]
}
{
value: 0.095891505
description: fieldWeight in 0,
product of:
details: [
{
value: 1
description: tf(freq=1.0),
with freq of:
details: [
{
value: 1
description:
termFreq=1.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.3125
description: fieldNorm(doc=0)
}
]
}
]
}
]
}
]
}
]
}
}
{
_shard: 2
_node: k8BXmkARRsaaYlUJTRpIqQ
_index: phone
_type: iphone
_id: 1
_score: 0.04500804
_source: {
title: iphone
description: welcome to iphone world
}
_explanation: {
value: 0.04500804
description: max of:
details: [
{
value: 0.04500804
description: product of:
details: [
{
value: 0.09001608
description: sum of:
details: [
{
value: 0.09001608
description: weight(title:iphone in 0)
[PerFieldSimilarity], result of:
details: [
{
value: 0.09001608
description: score(doc=0,freq=1.0
= termFreq=1.0 ), product of:
details: [
{
value: 0.29335263
description: queryWeight,
product of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.9560043
description: queryNorm
}
]
}
{
value: 0.30685282
description: fieldWeight in
0, product of:
details: [
{
value: 1
description: tf(freq=1.0),
with freq of:
details: [
{
value: 1
description:
termFreq=1.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 1
description:
fieldNorm(doc=0)
}
]
}
]
}
]
}
]
}
{
value: 0.5
description: coord(1/2)
}
]
}
{
value: 0.02250402
description: product of:
details: [
{
value: 0.04500804
description: sum of:
details: [
{
value: 0.04500804
description: weight(description:iphone
in 0) [PerFieldSimilarity], result of:
details: [
{
value: 0.04500804
description: score(doc=0,freq=1.0
= termFreq=1.0 ), product of:
details: [
{
value: 0.29335263
description: queryWeight,
product of:
details: [
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.9560043
description: queryNorm
}
]
}
{
value: 0.15342641
description: fieldWeight in
0, product of:
details: [
{
value: 1
description: tf(freq=1.0),
with freq of:
details: [
{
value: 1
description:
termFreq=1.0
}
]
}
{
value: 0.30685282
description: idf(docFreq=1,
maxDocs=1)
}
{
value: 0.5
description:
fieldNorm(doc=0)
}
]
}
]
}
]
}
]
}
{
value: 0.5
description: coord(1/2)
}
]
}
]
}
}
]
}
}
On Wednesday, 12 February 2014 12:00:03 UTC+5:30, Ivan Brusic wrote:
The norms are calculated by Lucene's TFIDF algorithm:
TFIDFSimilarity (Lucene 4.6.0 API)
The field norm is encoded into a single byte, so there is a loss of
precision.
--
Ivan
On Tue, Feb 11, 2014 at 9:32 PM, <usha...@gmail.com <javascript:>> wrote:
Hi,
I want to know what are fieldNorm and queryNorm in the output displayed
by the explain api..how are they calculated?
Thanks
Usha
--
You received this message because you are subscribed to the Google Groups
"elasticsearch" group.
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.
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