... | @@ -84,6 +84,8 @@ During optimization the software gives further information about the parameters |
... | @@ -84,6 +84,8 @@ During optimization the software gives further information about the parameters |
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Further, the system gives information of the optimization process itself, and the meaningfulness of single parameter analysis results.
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Further, the system gives information of the optimization process itself, and the meaningfulness of single parameter analysis results.
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We choosed to explain the aspects with an example, where we optimized fulltext search attribute weightings (e.g. document title matches should be higher rated as bodies). The fitness values are calculated result list nDCG values. These values shows the quality of document orders inside result lists against a given ground truth.
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### Convergation status
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### Convergation status
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You can check if the optimization converges by enabling the max and average generation fitness monitor gui in the config file with `showMonitorGui=true`. Here you can see an example:
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You can check if the optimization converges by enabling the max and average generation fitness monitor gui in the config file with `showMonitorGui=true`. Here you can see an example:
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... | @@ -103,7 +105,7 @@ The 'independence analysis' is more or less the naive loop over the single param |
... | @@ -103,7 +105,7 @@ The 'independence analysis' is more or less the naive loop over the single param |
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The attributes are sorted according to their best fitness values, parameters that have achieved better results are at the top. The fitness diversity columns 'EntropyImpact, StdDeviation, DeviationImpact' show entropy and standard deviation based values calculated from the fitness heatmap histograms on the right. The contents of the histograms depend on the configured value ranges of the parameters. If one value is considered independent, all other values are set to their default value (here zero in any case).
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The attributes are sorted according to their best fitness values, parameters that have achieved better results are at the top. The fitness diversity columns 'EntropyImpact, StdDeviation, DeviationImpact' show entropy and standard deviation based values calculated from the fitness heatmap histograms on the right. The contents of the histograms depend on the configured value ranges of the parameters. If one value is considered independent, all other values are set to their default value (here zero in any case).
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In this example, we optimized fulltext search attribute weightings, the fitness values are calculated result list nDCG values. These values shows the quality of document orders inside result lists against a given ground truth.
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As mentioned, our fitness values in this example are calculated result list nDCG values. These values shows the quality of document orders inside result lists against a given ground truth.
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If you change the attribute weighting within the value range of a parameter and leave all others at 0, the absolute scores of the documents in the results lists change, but their sorting order remains identical. The nDCGs derived from the sequence thus remain constant. This is reflected in the identical values for a parameter within the histogram. The entropy derived from it also remains constant - it is independent of the absolute numbers in the histogram, the entropies over the parameters are largely constant. The resulting entropy impact, which shows the reverse, percentage entropy (1-entropy, normalized to [0-1]), is also constant.
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If you change the attribute weighting within the value range of a parameter and leave all others at 0, the absolute scores of the documents in the results lists change, but their sorting order remains identical. The nDCGs derived from the sequence thus remain constant. This is reflected in the identical values for a parameter within the histogram. The entropy derived from it also remains constant - it is independent of the absolute numbers in the histogram, the entropies over the parameters are largely constant. The resulting entropy impact, which shows the reverse, percentage entropy (1-entropy, normalized to [0-1]), is also constant.
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