{"id":550,"date":"2017-02-24T10:48:33","date_gmt":"2017-02-24T10:48:33","guid":{"rendered":"http:\/\/sag.art.uniroma2.it\/kelp_wordpress\/?page_id=550"},"modified":"2017-03-15T10:30:54","modified_gmt":"2017-03-15T10:30:54","slug":"data-manipulation","status":"publish","type":"page","link":"http:\/\/www.kelp-ml.org\/?page_id=550","title":{"rendered":"Data Manipulation"},"content":{"rendered":"<p>KeLP is a general-purpose machine learning platform and does not cover any feature extraction aspect. However it provides some simple data preprocessing features to manipulate the input data. Specific operations on data can be defined by implementing the <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/Manipulator.html\">Manipulator <\/a>interface. Instances of such class can be then passed to the method <em>manipulate<\/em> of the class <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/dataset\/Dataset.html\">Dataset <\/a>in order to perform the manipulation operations on\u00a0the whole dataset.<\/p>\n<p>Some\u00a0implementations of the class <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/Manipulator.html\">Manipulator <\/a>are:<\/p>\n<ul>\n<li><a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/NormalizationManipolator.html\">NormalizationManipolator<\/a>:\u00a0it scales vector representations\u00a0in order to be a unit vector in its explicit feature space. This can be useful when the orientation of the feature vectors is meaningful, while their magnitude is not relevant;<\/li>\n<li><a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/StandardizationManipulator.html\">StandardizationManipulator<\/a>: it standardizes the feature values of a vectorial representation. Let <img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/www.kelp-ml.org\/wp-content\/ql-cache\/quicklatex.com-c8700e0258243116de0d4f288e2e3b44_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#120;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"11\" width=\"15\" style=\"vertical-align: -3px;\"\/> be the value of the <em>i<\/em>-th feature whose mean and standard deviation\u00a0are <img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/www.kelp-ml.org\/wp-content\/ql-cache\/quicklatex.com-65392510548ffdee6a2327533e390149_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#109;&#117;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"16\" style=\"vertical-align: -4px;\"\/> and <img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/www.kelp-ml.org\/wp-content\/ql-cache\/quicklatex.com-c202e7eb4d32c2030102aa2961fdd946_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#115;&#105;&#103;&#109;&#97;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"11\" width=\"15\" style=\"vertical-align: -3px;\"\/> respectively. Then, the standardized value is <img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/www.kelp-ml.org\/wp-content\/ql-cache\/quicklatex.com-ce39db2bcbfa1fa4004a28d99a12c951_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#104;&#97;&#116;&#123;&#120;&#95;&#105;&#125;&#32;&#61;&#32;&#40;&#120;&#95;&#105;&#45;&#92;&#109;&#117;&#95;&#105;&#41;&#47;&#92;&#115;&#105;&#103;&#109;&#97;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"19\" width=\"131\" style=\"vertical-align: -5px;\"\/>. This operation is useful in order to map all the features to a similar range.<\/li>\n<li><a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/VectorConcatenationManipulator.html\">VectorConcatenationManipulator<\/a>:\u00a0it allows to concatenate vectors into a new <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/representation\/vector\/SparseVector.html\">SparseVector<\/a> representation. It is useful when a linear approach must be applied to multiple vectorial representations;<\/li>\n<li><a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/PairSimilarityExtractor.html\">PairSimilarityExtractor<\/a>:\u00a0it analyzes an <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/example\/ExamplePair.html\">ExamplePair <\/a>extracting some similarity scores between the left and the right examples of the pair. The extracted similarity scores are stored in a <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/representation\/vector\/DenseVector.html\">DenseVector<\/a> that is added to the representations set of the <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/example\/ExamplePair.html\">ExamplePair<\/a>.<\/li>\n<li><a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/manipulator\/TreePairRelTagger.html\">TreePairRelTagger<\/a>:\u00a0given an <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/example\/ExamplePair.html\">ExamplePair <\/a>whose left and right examples contain <a href=\"http:\/\/www.kelp-ml.org\/kelp-javadoc\/current-version\/it\/uniroma2\/sag\/kelp\/data\/representation\/tree\/TreeRepresentation.html\">TreeRepresentation<\/a>s, it performs the <em>REL<\/em> tagging described in (Filice et al., 2015).<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>References<\/h3>\n<p>Simone Filice, Giovanni Da San Martino and Alessandro Moschitti<em>. Relational Information for Learning from Structured Text Pairs. <\/em>In Proceedings of the 53<sup>rd<\/sup> Annual Meeting of the Association for Computational Linguistics, ACL 2015.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>KeLP is a general-purpose machine learning platform and does not cover any feature extraction aspect. However it provides some simple data preprocessing features to manipulate the input data. Specific operations on data can be defined by implementing the Manipulator interface. Instances of such class can be then passed to the method manipulate of the class <a href=\"http:\/\/www.kelp-ml.org\/?page_id=550\" rel=\"nofollow\"><span class=\"sr-only\">Read more about Data Manipulation<\/span>[&hellip;]<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/550"}],"collection":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=550"}],"version-history":[{"count":6,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/550\/revisions"}],"predecessor-version":[{"id":896,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/550\/revisions\/896"}],"wp:attachment":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=550"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}