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RePrec - compute recall precision curves
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RePrec - compute recall precision curves
require RePrec::<Subclass>;
RePrec is an abstract class for computing recall precision curves.
Subclasses implement different recall-precision curve interpretation
measures. Theoretical background is given in detail by the
Information Retrieval Lecture Notes by Norbert Fuhr (chapter 3,
Evaluation). Web address:
http://ls6-www.cs.uni-dortmund.de/ir/teaching/.
- $rp = RePrec::<Subclass>->
new($distribution)
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constructor. Takes as argument a distribution. $distribution is a
reference to an array containing a two element array reference for
each rank (top most rank first). The first element within the
references contains the number of relevant documents while the second
one contains the number of non-relevant documents.
- $visual = $rp->visual
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returns a textual representation of the searchresult.
- ($graph, $average) = $rp->
calculate([$points])
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calculates precision values for $points. $points may be an integer
(specifying for how many recall points precision is to be computed),
an reference to a list of recall points, the string smart (implying
the recall points 0.25, 0.50, and 0.75), the string trec (implying
recall points 0, 0.1, 0.2, ..., 1), or the string rank (implying
one recall point computed after each rank). If argument $points is
omitted precision will be computed for ten recall points (i. e., 0.1,
0.2, ..., 1).
As a result you get a list of (recall, precision) pairs (array of
array references with two elements each) and the averaged precision
(over all recall points computed).
- @precision = $rp->
precision(@recall)
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calculate precision for recall points in @recall. Returned is an array
of (recall, precision) pairs (array of array references with two
elements each). This method is abstract within this class, you need to
choose the proper implementation from the subclasses (or overwrite it
in your own RePrec subclass).
- @precision = $rp->precision_rank
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calculate precision after each rank. Returned is an array
of (recall, precision) pairs (array of array references with two
elements each).
- $rp->
gnuplot([$gnuplot])
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plot curve with gnuplot(1). $gnuplot is a hash reference where
parameters for gnuplot can be set.
- $rp->write_rpdata($file, [$average]);
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Write the recall-precision data to file(s). Writes data for average
precision if $average is true.
Yes. Please let me know!
- Different recall-precision measures:
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RePrec::Ceiling(3),
RePrec::EP(3),
RePrec::EP_ND(3),
RePrec::PRR(3),
RePrec::Raw(3),
RePrec::Salton(3)
- Parsing of searchresults and relevance judgements
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RePrec::Collection(3),
RePrec::Collection::FERMI(3),
RePrec::Collection::Paris(3),
RePrec::Searchresult(3),
RePrec::Searchresult::HySpirit(3)
- Miscellaneous tools
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RePrec::Average(3),
RePrec::Tools(3),
reprec(1)
- Other
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gnuplot(1),
perl(1)
Norbert Gövert <goevert@ls6.cs.uni-dortmund.de>
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RePrec - compute recall precision curves
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