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From "Ahmed Adel (Jira)" <j...@apache.org>
Subject [jira] [Updated] (SOLR-13903) Classification Model Confusion Matrix Discrepancy
Date Fri, 08 Nov 2019 04:08:00 GMT

     [ https://issues.apache.org/jira/browse/SOLR-13903?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ahmed Adel updated SOLR-13903:
------------------------------
    Description: 
Using features and train stream sources generate a model with TP, TN, FP, FN fields. For some
reason, the summation of the values of these fields is sometimes less than the training set
size.

 How to regenerate:

1. Create two collections: cellphones and cellphones-model

2. Indexing the attached dataset into cellphones

3. Run the following expression:

{{commit(cellphones-model,update(cellphones-model,batchSize=500,}}}}
 {{   train(cellphones,}}
 {{     features(cellphones, q="*:*", featureSet="featureSet",}}
 {{ field="title_t",}}
 {{ outcome="brand_i", numTerms=25),}}
 {{ q="*:*",}}
 {{ name="cellphones-classification-model",}}
 {{ field="title_t",}}
 {{ outcome="brand_i",}}
 {{ maxIterations=100)))}}

4. Run the following query to retrieve confusion matrix:

{{search q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}

The summation of the metrics TP, TN, FP, FN is always less than the training set size by one
in this instance for all iterations.

  was:
Using features and train stream sources generate a model with TP, TN, FP, FN fields. For some
reason, the summation of the values of these fields is sometimes less than the training set
size.

 How to regenerate:

1. Create two collections: cellphones and cellphones-model

2. Indexing the attached dataset into cellphones

3. Run the following expression:

{{{{commit(cellphones-model,update(cellphones-model,batchSize=500,}}}}
{{   train(cellphones,}}
{{     features(cellphones, q="*:*", featureSet="featureSet",}}
{{ field="title_t",}}
{{ outcome="brand_i", numTerms=25),}}
{{ q="*:*",}}
{{ name="cellphones-classification-model",}}
{{ field="title_t",}}
{{ outcome="brand_i",}}
{{ maxIterations=100)))}}

4. Run the following query to retrieve confusion matrix:

{{search q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}

The summation of the metrics TP, TN, FP, FN is always less than the training set size by one
in this instance for all iterations.


> Classification Model Confusion Matrix Discrepancy
> -------------------------------------------------
>
>                 Key: SOLR-13903
>                 URL: https://issues.apache.org/jira/browse/SOLR-13903
>             Project: Solr
>          Issue Type: Bug
>      Security Level: Public(Default Security Level. Issues are Public) 
>          Components: streaming expressions
>    Affects Versions: 8.2
>            Reporter: Ahmed Adel
>            Priority: Major
>              Labels: classification
>         Attachments: cellphones.csv
>
>
> Using features and train stream sources generate a model with TP, TN, FP, FN fields.
For some reason, the summation of the values of these fields is sometimes less than the training
set size.
>  How to regenerate:
> 1. Create two collections: cellphones and cellphones-model
> 2. Indexing the attached dataset into cellphones
> 3. Run the following expression:
> {{commit(cellphones-model,update(cellphones-model,batchSize=500,}}}}
>  {{   train(cellphones,}}
>  {{     features(cellphones, q="*:*", featureSet="featureSet",}}
>  {{ field="title_t",}}
>  {{ outcome="brand_i", numTerms=25),}}
>  {{ q="*:*",}}
>  {{ name="cellphones-classification-model",}}
>  {{ field="title_t",}}
>  {{ outcome="brand_i",}}
>  {{ maxIterations=100)))}}
> 4. Run the following query to retrieve confusion matrix:
> {{search q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}
> The summation of the metrics TP, TN, FP, FN is always less than the training set size
by one in this instance for all iterations.



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