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< section id = "datasets" >
< h1 > Datasets< a class = "headerlink" href = "#datasets" title = "Permalink to this heading" > ¶< / a > < / h1 >
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< p > QuaPy makes available several datasets that have been used in
quantification literature, as well as an interface to allow
anyone import their custom datasets.< / p >
< p > A < em > Dataset< / em > object in QuaPy is roughly a pair of < em > LabelledCollection< / em > objects,
one playing the role of the training set, another the test set.
< em > LabelledCollection< / em > is a data class consisting of the (iterable)
instances and labels. This class handles most of the sampling functionality in QuaPy.
Take a look at the following code:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "kn" > import< / span > < span class = "nn" > quapy.functional< / span > < span class = "k" > as< / span > < span class = "nn" > F< / span >
< span class = "n" > instances< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "s1" > ' 1st positive document' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' 2nd positive document' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' the only negative document' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' 1st neutral document' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' 2nd neutral document' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' 3rd neutral document' < / span >
< span class = "p" > ]< / span >
< span class = "n" > labels< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span >
< span class = "n" > data< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > LabelledCollection< / span > < span class = "p" > (< / span > < span class = "n" > instances< / span > < span class = "p" > ,< / span > < span class = "n" > labels< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "n" > strprev< / span > < span class = "p" > (< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > prevalence< / span > < span class = "p" > (),< / span > < span class = "n" > prec< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p > Output the class prevalences (showing 2 digit precision):< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "p" > [< / span > < span class = "mf" > 0.17< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.50< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.33< / span > < span class = "p" > ]< / span >
< / pre > < / div >
< / div >
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< p > One can easily produce new samples at desired class prevalence values:< / p >
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< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > sample_size< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span >
< span class = "n" > prev< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mf" > 0.4< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.1< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.5< / span > < span class = "p" > ]< / span >
< span class = "n" > sample< / span > < span class = "o" > =< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > sampling< / span > < span class = "p" > (< / span > < span class = "n" > sample_size< / span > < span class = "p" > ,< / span > < span class = "o" > *< / span > < span class = "n" > prev< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' instances:' < / span > < span class = "p" > ,< / span > < span class = "n" > sample< / span > < span class = "o" > .< / span > < span class = "n" > instances< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' labels:' < / span > < span class = "p" > ,< / span > < span class = "n" > sample< / span > < span class = "o" > .< / span > < span class = "n" > labels< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s1" > ' prevalence:' < / span > < span class = "p" > ,< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "n" > strprev< / span > < span class = "p" > (< / span > < span class = "n" > sample< / span > < span class = "o" > .< / span > < span class = "n" > prevalence< / span > < span class = "p" > (),< / span > < span class = "n" > prec< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p > Which outputs:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > instances< / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "s1" > ' the only negative document' < / span > < span class = "s1" > ' 2nd positive document' < / span >
< span class = "s1" > ' 2nd positive document' < / span > < span class = "s1" > ' 2nd neutral document' < / span > < span class = "s1" > ' 1st positive document' < / span >
< span class = "s1" > ' the only negative document' < / span > < span class = "s1" > ' the only negative document' < / span >
< span class = "s1" > ' the only negative document' < / span > < span class = "s1" > ' 2nd positive document' < / span >
< span class = "s1" > ' 1st positive document' < / span > < span class = "p" > ]< / span >
< span class = "n" > labels< / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "mi" > 2< / span > < span class = "mi" > 2< / span > < span class = "mi" > 1< / span > < span class = "mi" > 2< / span > < span class = "mi" > 0< / span > < span class = "mi" > 0< / span > < span class = "mi" > 0< / span > < span class = "mi" > 2< / span > < span class = "mi" > 2< / span > < span class = "p" > ]< / span >
< span class = "n" > prevalence< / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "mf" > 0.40< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.10< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.50< / span > < span class = "p" > ]< / span >
< / pre > < / div >
< / div >
< p > Samples can be made consistent across different runs (e.g., to test
different methods on the same exact samples) by sampling and retaining
the indexes, that can then be used to generate the sample:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > index< / span > < span class = "o" > =< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > sampling_index< / span > < span class = "p" > (< / span > < span class = "n" > sample_size< / span > < span class = "p" > ,< / span > < span class = "o" > *< / span > < span class = "n" > prev< / span > < span class = "p" > )< / span >
< span class = "k" > for< / span > < span class = "n" > method< / span > < span class = "ow" > in< / span > < span class = "n" > methods< / span > < span class = "p" > :< / span >
< span class = "n" > sample< / span > < span class = "o" > =< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > sampling_from_index< / span > < span class = "p" > (< / span > < span class = "n" > index< / span > < span class = "p" > )< / span >
< span class = "o" > ...< / span >
< / pre > < / div >
< / div >
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< p > However, generating samples for evaluation purposes is tackled in QuaPy
by means of the evaluation protocols (see the dedicated entries in the Wiki
for < a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation" > evaluation< / a > and
< a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/wiki/Protocols" > protocols< / a > ).< / p >
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< section id = "reviews-datasets" >
< h2 > Reviews Datasets< a class = "headerlink" href = "#reviews-datasets" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > Three datasets of reviews about Kindle devices, Harry Potter’
the well-known IMDb movie reviews can be fetched using a unified interface.
For example:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "n" > data< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_reviews< / span > < span class = "p" > (< / span > < span class = "s1" > ' kindle' < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > These datasets have been used in:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > Esuli< / span > < span class = "p" > ,< / span > < span class = "n" > A< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "n" > Moreo< / span > < span class = "p" > ,< / span > < span class = "n" > A< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "o" > & < / span > < span class = "n" > Sebastiani< / span > < span class = "p" > ,< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "p" > (< / span > < span class = "mi" > 2018< / span > < span class = "p" > ,< / span > < span class = "n" > October< / span > < span class = "p" > )< / span > < span class = "o" > .< / span >
< span class = "n" > A< / span > < span class = "n" > recurrent< / span > < span class = "n" > neural< / span > < span class = "n" > network< / span > < span class = "k" > for< / span > < span class = "n" > sentiment< / span > < span class = "n" > quantification< / span > < span class = "o" > .< / span >
< span class = "n" > In< / span > < span class = "n" > Proceedings< / span > < span class = "n" > of< / span > < span class = "n" > the< / span > < span class = "mi" > 27< / span > < span class = "n" > th< / span > < span class = "n" > ACM< / span > < span class = "n" > International< / span > < span class = "n" > Conference< / span > < span class = "n" > on< / span >
< span class = "n" > Information< / span > < span class = "ow" > and< / span > < span class = "n" > Knowledge< / span > < span class = "n" > Management< / span > < span class = "p" > (< / span > < span class = "n" > pp< / span > < span class = "o" > .< / span > < span class = "mi" > 1775< / span > < span class = "o" > -< / span > < span class = "mi" > 1778< / span > < span class = "p" > )< / span > < span class = "o" > .< / span >
< / pre > < / div >
< / div >
< p > The list of reviews ids is available in:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > REVIEWS_SENTIMENT_DATASETS< / span >
< / pre > < / div >
< / div >
< p > Some statistics of the fhe available datasets are summarized below:< / p >
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< table class = "docutils align-default" >
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< thead >
< tr class = "row-odd" > < th class = "head" > < p > Dataset< / p > < / th >
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< th class = "head text-center" > < p > classes< / p > < / th >
< th class = "head text-center" > < p > train size< / p > < / th >
< th class = "head text-center" > < p > test size< / p > < / th >
< th class = "head text-center" > < p > train prev< / p > < / th >
< th class = "head text-center" > < p > test prev< / p > < / th >
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< th class = "head" > < p > type< / p > < / th >
< / tr >
< / thead >
< tbody >
< tr class = "row-even" > < td > < p > hp< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 9533< / p > < / td >
< td class = "text-center" > < p > 18399< / p > < / td >
< td class = "text-center" > < p > [0.018, 0.982]< / p > < / td >
< td class = "text-center" > < p > [0.065, 0.935]< / p > < / td >
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< td > < p > text< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > kindle< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 3821< / p > < / td >
< td class = "text-center" > < p > 21591< / p > < / td >
< td class = "text-center" > < p > [0.081, 0.919]< / p > < / td >
< td class = "text-center" > < p > [0.063, 0.937]< / p > < / td >
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< td > < p > text< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > imdb< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 25000< / p > < / td >
< td class = "text-center" > < p > 25000< / p > < / td >
< td class = "text-center" > < p > [0.500, 0.500]< / p > < / td >
< td class = "text-center" > < p > [0.500, 0.500]< / p > < / td >
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< td > < p > text< / p > < / td >
< / tr >
< / tbody >
< / table >
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< / section >
< section id = "twitter-sentiment-datasets" >
< h2 > Twitter Sentiment Datasets< a class = "headerlink" href = "#twitter-sentiment-datasets" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > 11 Twitter datasets for sentiment analysis.
Text is not accessible, and the documents were made available
in tf-idf format. Each dataset presents two splits: a train/val
split for model selection purposes, and a train+val/test split
for model evaluation. The following code exemplifies how to load
a twitter dataset for model selection.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "n" > data< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_twitter< / span > < span class = "p" > (< / span > < span class = "s1" > ' gasp' < / span > < span class = "p" > ,< / span > < span class = "n" > for_model_selection< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > The datasets were used in:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > Gao< / span > < span class = "p" > ,< / span > < span class = "n" > W< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "o" > & < / span > < span class = "n" > Sebastiani< / span > < span class = "p" > ,< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "p" > (< / span > < span class = "mi" > 2015< / span > < span class = "p" > ,< / span > < span class = "n" > August< / span > < span class = "p" > )< / span > < span class = "o" > .< / span >
< span class = "n" > Tweet< / span > < span class = "n" > sentiment< / span > < span class = "p" > :< / span > < span class = "n" > From< / span > < span class = "n" > classification< / span > < span class = "n" > to< / span > < span class = "n" > quantification< / span > < span class = "o" > .< / span >
< span class = "n" > In< / span > < span class = "mi" > 2015< / span > < span class = "n" > IEEE< / span > < span class = "o" > /< / span > < span class = "n" > ACM< / span > < span class = "n" > International< / span > < span class = "n" > Conference< / span > < span class = "n" > on< / span > < span class = "n" > Advances< / span > < span class = "ow" > in< / span >
< span class = "n" > Social< / span > < span class = "n" > Networks< / span > < span class = "n" > Analysis< / span > < span class = "ow" > and< / span > < span class = "n" > Mining< / span > < span class = "p" > (< / span > < span class = "n" > ASONAM< / span > < span class = "p" > )< / span > < span class = "p" > (< / span > < span class = "n" > pp< / span > < span class = "o" > .< / span > < span class = "mi" > 97< / span > < span class = "o" > -< / span > < span class = "mi" > 104< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > IEEE< / span > < span class = "o" > .< / span >
< / pre > < / div >
< / div >
< p > Three of the datasets (semeval13, semeval14, and semeval15) share the
same training set (semeval), meaning that the training split one would get
when requesting any of them is the same. The dataset “semeval” can only
be requested with “for_model_selection=True”.
The lists of the Twitter dataset’ < / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "c1" > # a list of 11 dataset ids that can be used for model selection or model evaluation< / span >
< span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > TWITTER_SENTIMENT_DATASETS_TEST< / span >
< span class = "c1" > # 9 dataset ids in which " semeval13" , " semeval14" , and " semeval15" are replaced with " semeval" < / span >
< span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > TWITTER_SENTIMENT_DATASETS_TRAIN< / span >
< / pre > < / div >
< / div >
< p > Some details can be found below:< / p >
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< table class = "docutils align-default" >
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< thead >
< tr class = "row-odd" > < th class = "head" > < p > Dataset< / p > < / th >
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< th class = "head text-center" > < p > classes< / p > < / th >
< th class = "head text-center" > < p > train size< / p > < / th >
< th class = "head text-center" > < p > test size< / p > < / th >
< th class = "head text-center" > < p > features< / p > < / th >
< th class = "head text-center" > < p > train prev< / p > < / th >
< th class = "head text-center" > < p > test prev< / p > < / th >
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< th class = "head" > < p > type< / p > < / th >
< / tr >
< / thead >
< tbody >
< tr class = "row-even" > < td > < p > gasp< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 8788< / p > < / td >
< td class = "text-center" > < p > 3765< / p > < / td >
< td class = "text-center" > < p > 694582< / p > < / td >
< td class = "text-center" > < p > [0.421, 0.496, 0.082]< / p > < / td >
< td class = "text-center" > < p > [0.407, 0.507, 0.086]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > hcr< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 1594< / p > < / td >
< td class = "text-center" > < p > 798< / p > < / td >
< td class = "text-center" > < p > 222046< / p > < / td >
< td class = "text-center" > < p > [0.546, 0.211, 0.243]< / p > < / td >
< td class = "text-center" > < p > [0.640, 0.167, 0.193]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > omd< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 1839< / p > < / td >
< td class = "text-center" > < p > 787< / p > < / td >
< td class = "text-center" > < p > 199151< / p > < / td >
< td class = "text-center" > < p > [0.463, 0.271, 0.266]< / p > < / td >
< td class = "text-center" > < p > [0.437, 0.283, 0.280]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > sanders< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 2155< / p > < / td >
< td class = "text-center" > < p > 923< / p > < / td >
< td class = "text-center" > < p > 229399< / p > < / td >
< td class = "text-center" > < p > [0.161, 0.691, 0.148]< / p > < / td >
< td class = "text-center" > < p > [0.164, 0.688, 0.148]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > semeval13< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 11338< / p > < / td >
< td class = "text-center" > < p > 3813< / p > < / td >
< td class = "text-center" > < p > 1215742< / p > < / td >
< td class = "text-center" > < p > [0.159, 0.470, 0.372]< / p > < / td >
< td class = "text-center" > < p > [0.158, 0.430, 0.412]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > semeval14< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 11338< / p > < / td >
< td class = "text-center" > < p > 1853< / p > < / td >
< td class = "text-center" > < p > 1215742< / p > < / td >
< td class = "text-center" > < p > [0.159, 0.470, 0.372]< / p > < / td >
< td class = "text-center" > < p > [0.109, 0.361, 0.530]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > semeval15< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 11338< / p > < / td >
< td class = "text-center" > < p > 2390< / p > < / td >
< td class = "text-center" > < p > 1215742< / p > < / td >
< td class = "text-center" > < p > [0.159, 0.470, 0.372]< / p > < / td >
< td class = "text-center" > < p > [0.153, 0.413, 0.434]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > semeval16< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 8000< / p > < / td >
< td class = "text-center" > < p > 2000< / p > < / td >
< td class = "text-center" > < p > 889504< / p > < / td >
< td class = "text-center" > < p > [0.157, 0.351, 0.492]< / p > < / td >
< td class = "text-center" > < p > [0.163, 0.341, 0.497]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > sst< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 2971< / p > < / td >
< td class = "text-center" > < p > 1271< / p > < / td >
< td class = "text-center" > < p > 376132< / p > < / td >
< td class = "text-center" > < p > [0.261, 0.452, 0.288]< / p > < / td >
< td class = "text-center" > < p > [0.207, 0.481, 0.312]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > wa< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 2184< / p > < / td >
< td class = "text-center" > < p > 936< / p > < / td >
< td class = "text-center" > < p > 248563< / p > < / td >
< td class = "text-center" > < p > [0.305, 0.414, 0.281]< / p > < / td >
< td class = "text-center" > < p > [0.282, 0.446, 0.272]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > wb< / p > < / td >
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< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > 4259< / p > < / td >
< td class = "text-center" > < p > 1823< / p > < / td >
< td class = "text-center" > < p > 404333< / p > < / td >
< td class = "text-center" > < p > [0.270, 0.392, 0.337]< / p > < / td >
< td class = "text-center" > < p > [0.274, 0.392, 0.335]< / p > < / td >
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< td > < p > sparse< / p > < / td >
< / tr >
< / tbody >
< / table >
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< / section >
< section id = "uci-machine-learning" >
< h2 > UCI Machine Learning< a class = "headerlink" href = "#uci-machine-learning" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > A set of 32 datasets from the < a class = "reference external" href = "https://archive.ics.uci.edu/ml/datasets.php" > UCI Machine Learning repository< / a >
used in:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > Pérez< / span > < span class = "o" > -< / span > < span class = "n" > Gállego< / span > < span class = "p" > ,< / span > < span class = "n" > P< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "n" > Quevedo< / span > < span class = "p" > ,< / span > < span class = "n" > J< / span > < span class = "o" > .< / span > < span class = "n" > R< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "o" > & < / span > < span class = "k" > del< / span > < span class = "n" > Coz< / span > < span class = "p" > ,< / span > < span class = "n" > J< / span > < span class = "o" > .< / span > < span class = "n" > J< / span > < span class = "o" > .< / span > < span class = "p" > (< / span > < span class = "mi" > 2017< / span > < span class = "p" > )< / span > < span class = "o" > .< / span >
< span class = "n" > Using< / span > < span class = "n" > ensembles< / span > < span class = "k" > for< / span > < span class = "n" > problems< / span > < span class = "k" > with< / span > < span class = "n" > characterizable< / span > < span class = "n" > changes< / span >
< span class = "ow" > in< / span > < span class = "n" > data< / span > < span class = "n" > distribution< / span > < span class = "p" > :< / span > < span class = "n" > A< / span > < span class = "n" > case< / span > < span class = "n" > study< / span > < span class = "n" > on< / span > < span class = "n" > quantification< / span > < span class = "o" > .< / span >
< span class = "n" > Information< / span > < span class = "n" > Fusion< / span > < span class = "p" > ,< / span > < span class = "mi" > 34< / span > < span class = "p" > ,< / span > < span class = "mi" > 87< / span > < span class = "o" > -< / span > < span class = "mf" > 100.< / span >
< / pre > < / div >
< / div >
< p > The list does not exactly coincide with that used in Pérez-Gállego et al. 2017
since we were unable to find the datasets with ids “diabetes” and “phoneme”.< / p >
< p > These dataset can be loaded by calling, e.g.:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "n" > data< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_UCIDataset< / span > < span class = "p" > (< / span > < span class = "s1" > ' yeast' < / span > < span class = "p" > ,< / span > < span class = "n" > verbose< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > This call will return a < em > Dataset< / em > object in which the training and
test splits are randomly drawn, in a stratified manner, from the whole
collection at 70% and 30%, respectively. The < em > verbose=True< / em > option indicates
that the dataset description should be printed in standard output.
The original data is not split,
and some papers submit the entire collection to a kFCV validation.
In order to accommodate with these practices, one could first instantiate
the entire collection, and then creating a generator that will return one
training+test dataset at a time, following a kFCV protocol:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "n" > collection< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_UCILabelledCollection< / span > < span class = "p" > (< / span > < span class = "s2" > " yeast" < / span > < span class = "p" > )< / span >
< span class = "k" > for< / span > < span class = "n" > data< / span > < span class = "ow" > in< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > Dataset< / span > < span class = "o" > .< / span > < span class = "n" > kFCV< / span > < span class = "p" > (< / span > < span class = "n" > collection< / span > < span class = "p" > ,< / span > < span class = "n" > nfolds< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "n" > nrepeats< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ):< / span >
< span class = "o" > ...< / span >
< / pre > < / div >
< / div >
< p > Above code will allow to conduct a 2x5FCV evaluation on the “yeast” dataset.< / p >
< p > All datasets come in numerical form (dense matrices); some statistics
are summarized below.< / p >
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< table class = "docutils align-default" >
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< thead >
< tr class = "row-odd" > < th class = "head" > < p > Dataset< / p > < / th >
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< th class = "head text-center" > < p > classes< / p > < / th >
< th class = "head text-center" > < p > instances< / p > < / th >
< th class = "head text-center" > < p > features< / p > < / th >
< th class = "head text-center" > < p > prev< / p > < / th >
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< th class = "head" > < p > type< / p > < / th >
< / tr >
< / thead >
< tbody >
< tr class = "row-even" > < td > < p > acute.a< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 120< / p > < / td >
< td class = "text-center" > < p > 6< / p > < / td >
< td class = "text-center" > < p > [0.508, 0.492]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > acute.b< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 120< / p > < / td >
< td class = "text-center" > < p > 6< / p > < / td >
< td class = "text-center" > < p > [0.583, 0.417]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > balance.1< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 625< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.539, 0.461]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > balance.2< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 625< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.922, 0.078]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > balance.3< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 625< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.539, 0.461]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > breast-cancer< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 683< / p > < / td >
< td class = "text-center" > < p > 9< / p > < / td >
< td class = "text-center" > < p > [0.350, 0.650]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > cmc.1< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1473< / p > < / td >
< td class = "text-center" > < p > 9< / p > < / td >
< td class = "text-center" > < p > [0.573, 0.427]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > cmc.2< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1473< / p > < / td >
< td class = "text-center" > < p > 9< / p > < / td >
< td class = "text-center" > < p > [0.774, 0.226]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > cmc.3< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1473< / p > < / td >
< td class = "text-center" > < p > 9< / p > < / td >
< td class = "text-center" > < p > [0.653, 0.347]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > ctg.1< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 2126< / p > < / td >
< td class = "text-center" > < p > 22< / p > < / td >
< td class = "text-center" > < p > [0.222, 0.778]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > ctg.2< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 2126< / p > < / td >
< td class = "text-center" > < p > 22< / p > < / td >
< td class = "text-center" > < p > [0.861, 0.139]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > ctg.3< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 2126< / p > < / td >
< td class = "text-center" > < p > 22< / p > < / td >
< td class = "text-center" > < p > [0.917, 0.083]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > german< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > 24< / p > < / td >
< td class = "text-center" > < p > [0.300, 0.700]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > haberman< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 306< / p > < / td >
< td class = "text-center" > < p > 3< / p > < / td >
< td class = "text-center" > < p > [0.735, 0.265]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > ionosphere< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 351< / p > < / td >
< td class = "text-center" > < p > 34< / p > < / td >
< td class = "text-center" > < p > [0.641, 0.359]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > iris.1< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 150< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.667, 0.333]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > iris.2< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 150< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.667, 0.333]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > iris.3< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 150< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.667, 0.333]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > mammographic< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 830< / p > < / td >
< td class = "text-center" > < p > 5< / p > < / td >
< td class = "text-center" > < p > [0.514, 0.486]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > pageblocks.5< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 5473< / p > < / td >
< td class = "text-center" > < p > 10< / p > < / td >
< td class = "text-center" > < p > [0.979, 0.021]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > semeion< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1593< / p > < / td >
< td class = "text-center" > < p > 256< / p > < / td >
< td class = "text-center" > < p > [0.901, 0.099]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > sonar< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 208< / p > < / td >
< td class = "text-center" > < p > 60< / p > < / td >
< td class = "text-center" > < p > [0.534, 0.466]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > spambase< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 4601< / p > < / td >
< td class = "text-center" > < p > 57< / p > < / td >
< td class = "text-center" > < p > [0.606, 0.394]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > spectf< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 267< / p > < / td >
< td class = "text-center" > < p > 44< / p > < / td >
< td class = "text-center" > < p > [0.794, 0.206]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > tictactoe< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 958< / p > < / td >
< td class = "text-center" > < p > 9< / p > < / td >
< td class = "text-center" > < p > [0.653, 0.347]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > transfusion< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 748< / p > < / td >
< td class = "text-center" > < p > 4< / p > < / td >
< td class = "text-center" > < p > [0.762, 0.238]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > wdbc< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 569< / p > < / td >
< td class = "text-center" > < p > 30< / p > < / td >
< td class = "text-center" > < p > [0.627, 0.373]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > wine.1< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 178< / p > < / td >
< td class = "text-center" > < p > 13< / p > < / td >
< td class = "text-center" > < p > [0.669, 0.331]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > wine.2< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 178< / p > < / td >
< td class = "text-center" > < p > 13< / p > < / td >
< td class = "text-center" > < p > [0.601, 0.399]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > wine.3< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 178< / p > < / td >
< td class = "text-center" > < p > 13< / p > < / td >
< td class = "text-center" > < p > [0.730, 0.270]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > wine-q-red< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1599< / p > < / td >
< td class = "text-center" > < p > 11< / p > < / td >
< td class = "text-center" > < p > [0.465, 0.535]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > wine-q-white< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 4898< / p > < / td >
< td class = "text-center" > < p > 11< / p > < / td >
< td class = "text-center" > < p > [0.335, 0.665]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > yeast< / p > < / td >
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< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 1484< / p > < / td >
< td class = "text-center" > < p > 8< / p > < / td >
< td class = "text-center" > < p > [0.711, 0.289]< / p > < / td >
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< td > < p > dense< / p > < / td >
< / tr >
< / tbody >
< / table >
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< section id = "issues" >
< h3 > Issues:< a class = "headerlink" href = "#issues" title = "Permalink to this heading" > ¶< / a > < / h3 >
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< p > All datasets will be downloaded automatically the first time they are requested, and
stored in the < em > quapy_data< / em > folder for faster further reuse.
However, some datasets require special actions that at the moment are not fully
automated.< / p >
< ul class = "simple" >
< li > < p > Datasets with ids “ctg.1”, “ctg.2”, and “ctg.3” (< em > Cardiotocography Data Set< / em > ) load
an Excel file, which requires the user to install the < em > xlrd< / em > Python module in order
to open it.< / p > < / li >
< li > < p > The dataset with id “pageblocks.5” (< em > Page Blocks Classification (5)< / em > ) needs to
open a “unix compressed file” (extension .Z), which is not directly doable with
standard Pythons packages like gzip or zip. This file would need to be uncompressed using
OS-dependent software manually. Information on how to do it will be printed the first
time the dataset is invoked.< / p > < / li >
< / ul >
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< / section >
< / section >
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< section id = "lequa-datasets" >
< h2 > LeQua Datasets< a class = "headerlink" href = "#lequa-datasets" title = "Permalink to this heading" > ¶< / a > < / h2 >
< p > QuaPy also provides the datasets used for the LeQua competition.
In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide
raw documents instead.
Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B
are multiclass quantification problems consisting of estimating the class prevalence
values of 28 different merchandise products.< / p >
< p > Every task consists of a training set, a set of validation samples (for model selection)
and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection
(training) and two generation protocols (for validation and test samples), as follows:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > training< / span > < span class = "p" > ,< / span > < span class = "n" > val_generator< / span > < span class = "p" > ,< / span > < span class = "n" > test_generator< / span > < span class = "o" > =< / span > < span class = "n" > fetch_lequa2022< / span > < span class = "p" > (< / span > < span class = "n" > task< / span > < span class = "o" > =< / span > < span class = "n" > task< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > See the < code class = "docutils literal notranslate" > < span class = "pre" > lequa2022_experiments.py< / span > < / code > in the examples folder for further details on how to
carry out experiments using these datasets.< / p >
< p > The datasets are downloaded only once, and stored for fast reuse.< / p >
< p > Some statistics are summarized below:< / p >
< table class = "docutils align-default" >
< thead >
< tr class = "row-odd" > < th class = "head" > < p > Dataset< / p > < / th >
< th class = "head text-center" > < p > classes< / p > < / th >
< th class = "head text-center" > < p > train size< / p > < / th >
< th class = "head text-center" > < p > validation samples< / p > < / th >
< th class = "head text-center" > < p > test samples< / p > < / th >
< th class = "head text-center" > < p > docs by sample< / p > < / th >
< th class = "head text-center" > < p > type< / p > < / th >
< / tr >
< / thead >
< tbody >
< tr class = "row-even" > < td > < p > T1A< / p > < / td >
< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 250< / p > < / td >
< td class = "text-center" > < p > vector< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > T1B< / p > < / td >
< td class = "text-center" > < p > 28< / p > < / td >
< td class = "text-center" > < p > 20000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > vector< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > T2A< / p > < / td >
< td class = "text-center" > < p > 2< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 250< / p > < / td >
< td class = "text-center" > < p > text< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > T2B< / p > < / td >
< td class = "text-center" > < p > 28< / p > < / td >
< td class = "text-center" > < p > 20000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > 5000< / p > < / td >
< td class = "text-center" > < p > 1000< / p > < / td >
< td class = "text-center" > < p > text< / p > < / td >
< / tr >
< / tbody >
< / table >
< p > For further details on the datasets, we refer to the original
< a class = "reference external" href = "https://ceur-ws.org/Vol-3180/paper-146.pdf" > paper< / a > :< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > Esuli< / span > < span class = "p" > ,< / span > < span class = "n" > A< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "n" > Moreo< / span > < span class = "p" > ,< / span > < span class = "n" > A< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "n" > Sebastiani< / span > < span class = "p" > ,< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "p" > ,< / span > < span class = "o" > & < / span > < span class = "n" > Sperduti< / span > < span class = "p" > ,< / span > < span class = "n" > G< / span > < span class = "o" > .< / span > < span class = "p" > (< / span > < span class = "mi" > 2022< / span > < span class = "p" > )< / span > < span class = "o" > .< / span >
< span class = "n" > A< / span > < span class = "n" > Detailed< / span > < span class = "n" > Overview< / span > < span class = "n" > of< / span > < span class = "n" > LeQua< / span > < span class = "o" > @< / span > < span class = "n" > CLEF< / span > < span class = "mi" > 2022< / span > < span class = "p" > :< / span > < span class = "n" > Learning< / span > < span class = "n" > to< / span > < span class = "n" > Quantify< / span > < span class = "o" > .< / span >
< / pre > < / div >
< / div >
< / section >
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< section id = "adding-custom-datasets" >
< h2 > Adding Custom Datasets< a class = "headerlink" href = "#adding-custom-datasets" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > QuaPy provides data loaders for simple formats dealing with
text, following the format:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > class< / span > < span class = "o" > -< / span > < span class = "nb" > id< / span > \< span class = "n" > t< / span > < span class = "n" > first< / span > < span class = "n" > document< / span > < span class = "s1" > ' s pre-processed text < / span > < span class = "se" > \n< / span >
< span class = "n" > class< / span > < span class = "o" > -< / span > < span class = "nb" > id< / span > \< span class = "n" > t< / span > < span class = "n" > second< / span > < span class = "n" > document< / span > < span class = "s1" > ' s pre-processed text < / span > < span class = "se" > \n< / span >
< span class = "o" > ...< / span >
< / pre > < / div >
< / div >
< p > and sparse representations of the form:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "p" > {< / span > < span class = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "ow" > or< / span > < span class = "o" > +< / span > < span class = "mi" > 1< / span > < span class = "p" > }< / span > < span class = "n" > col< / span > < span class = "p" > (< / span > < span class = "nb" > int< / span > < span class = "p" > ):< / span > < span class = "n" > val< / span > < span class = "p" > (< / span > < span class = "nb" > float< / span > < span class = "p" > )< / span > < span class = "n" > col< / span > < span class = "p" > (< / span > < span class = "nb" > int< / span > < span class = "p" > ):< / span > < span class = "n" > val< / span > < span class = "p" > (< / span > < span class = "nb" > float< / span > < span class = "p" > )< / span > < span class = "o" > ...< / span > \< span class = "n" > n< / span >
< span class = "o" > ...< / span >
< / pre > < / div >
< / div >
< p > The code in charge in loading a LabelledCollection is:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nd" > @classmethod< / span >
< span class = "k" > def< / span > < span class = "nf" > load< / span > < span class = "p" > (< / span > < span class = "bp" > cls< / span > < span class = "p" > ,< / span > < span class = "n" > path< / span > < span class = "p" > :< / span > < span class = "nb" > str< / span > < span class = "p" > ,< / span > < span class = "n" > loader_func< / span > < span class = "p" > :< / span > < span class = "n" > callable< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > LabelledCollection< / span > < span class = "p" > (< / span > < span class = "o" > *< / span > < span class = "n" > loader_func< / span > < span class = "p" > (< / span > < span class = "n" > path< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p > indicating that any < em > loader_func< / em > (e.g., a user-defined one) which
returns valid arguments for initializing a < em > LabelledCollection< / em > object will allow
to load any collection. In particular, the < em > LabelledCollection< / em > receives as
arguments the instances (as an iterable) and the labels (as an iterable) and,
additionally, the number of classes can be specified (it would otherwise be
inferred from the labels, but that requires at least one positive example for
all classes to be present in the collection).< / p >
< p > The same < em > loader_func< / em > can be passed to a Dataset, along with two
paths, in order to create a training and test pair of < em > LabelledCollection< / em > ,
e.g.:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
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< span class = "n" > train_path< / span > < span class = "o" > =< / span > < span class = "s1" > ' ../my_data/train.dat' < / span >
< span class = "n" > test_path< / span > < span class = "o" > =< / span > < span class = "s1" > ' ../my_data/test.dat' < / span >
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< span class = "k" > def< / span > < span class = "nf" > my_custom_loader< / span > < span class = "p" > (< / span > < span class = "n" > path< / span > < span class = "p" > ):< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "n" > path< / span > < span class = "p" > ,< / span > < span class = "s1" > ' rb' < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > fin< / span > < span class = "p" > :< / span >
< span class = "o" > ...< / span >
< span class = "k" > return< / span > < span class = "n" > instances< / span > < span class = "p" > ,< / span > < span class = "n" > labels< / span >
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< span class = "n" > data< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > Dataset< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > train_path< / span > < span class = "p" > ,< / span > < span class = "n" > test_path< / span > < span class = "p" > ,< / span > < span class = "n" > my_custom_loader< / span > < span class = "p" > )< / span >
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< section id = "data-processing" >
< h3 > Data Processing< a class = "headerlink" href = "#data-processing" title = "Permalink to this heading" > ¶< / a > < / h3 >
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< p > QuaPy implements a number of preprocessing functions in the package < em > qp.data.preprocessing< / em > , including:< / p >
< ul class = "simple" >
< li > < p > < em > text2tfidf< / em > : tfidf vectorization< / p > < / li >
< li > < p > < em > reduce_columns< / em > : reducing the number of columns based on term frequency< / p > < / li >
< li > < p > < em > standardize< / em > : transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so
that the column values have zero mean and unit variance).< / p > < / li >
< li > < p > < em > index< / em > : transforms textual tokens into lists of numeric ids)< / p > < / li >
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