clj-ml.filters

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Clojure only.

This namespace defines a set of functions that can be applied to data sets to modify the dataset in some way: transforming nominal attributes into binary attributes, removing attributes etc.

There are a number of ways to use the filtering API. The most straight forward and idomatic clojure way is to use the provided filter fns:

;; ds is the dataset (def ds (make-dataset :test [:a :b {:c [:g :m]}] [ [1 2 :g] [2 3 :m] [4 5 :g]])) (def filtered-ds (-> ds (add-attribute {:type :nominal, :column 1, :name "pet", :labels ["dog" "cat"]}) (remove-attributes {:attributes [:a :c]})))

The above functions rely on lower level fns that create and apply the filters which you may also use if you need more control over the actual filter objects:

(def filter (make-filter :remove-attributes {:dataset-format ds :attributes [:a :c]}))

;; We apply the filter to the original data set and obtain the new one (def filtered-ds (filter-apply filter ds))

The previous sample of code could be rewritten with the make-apply-filter function:

(def filtered-ds (make-apply-filter :remove-attributes {:attributes [:a :c]} ds))

This namespace defines a set of functions that can be applied to data sets to modify the
dataset in some way: transforming nominal attributes into binary attributes, removing
attributes etc.

There are a number of ways to use the filtering API.  The most straight forward and
idomatic clojure way is to use the provided filter fns:

  ;; ds is the dataset
  (def ds (make-dataset :test [:a :b {:c [:g :m]}]
                                  [ [1 2 :g]
                                    [2 3 :m]
                                    [4 5 :g]]))
  (def filtered-ds
     (-> ds
         (add-attribute {:type :nominal, :column 1, :name "pet", :labels ["dog" "cat"]})
         (remove-attributes {:attributes [:a :c]})))


The above functions rely on lower level fns that create and apply the filters which you may
also use if you need more control over the actual filter objects:

  (def filter (make-filter :remove-attributes {:dataset-format ds :attributes [:a :c]}))


  ;; We apply the filter to the original data set and obtain the new one
  (def filtered-ds (filter-apply filter ds))


The previous sample of code could be rewritten with the make-apply-filter function:

  (def filtered-ds (make-apply-filter :remove-attributes {:attributes [:a :c]} ds))

raw docstring

add-attribute^clj

(add-attribute ds__1214__auto__)

(add-attribute ds__1214__auto__ attributes__1215__auto__)

clj-ml.filters

add-attributeclj

attribute-typesclj

clj-batchclj

clj-streamableclj

deffiltercljmacro

filter-aliasesclj

filter-applyclj

make-apply-filterclj

make-apply-filtersclj

make-filterclj

normalizeclj

numeric-to-nominalclj

pki-unsupervised-discretizeclj

project-attributesclj

random-subsetclj

remove-attributesclj

remove-percentageclj

remove-rangeclj

remove-useless-attributesclj

reorder-attributesclj

replace-missing-valuesclj

resample-supervisedclj

resample-unsupervisedclj

select-append-attributesclj

stratified-remove-folds-supervisedclj

string-to-word-vectorclj

supervised-discretizeclj

supervised-nominal-to-binaryclj

unsupervised-discretizeclj

unsupervised-nominal-to-binaryclj

add-attribute^clj

attribute-types^clj

clj-batch^clj

clj-streamable^clj

deffilter^cljmacro

filter-aliases^clj

filter-apply^clj

make-apply-filter^clj

make-apply-filters^clj

make-filter^clj

normalize^clj

numeric-to-nominal^clj

pki-unsupervised-discretize^clj

project-attributes^clj

random-subset^clj

remove-attributes^clj

remove-percentage^clj

remove-range^clj

remove-useless-attributes^clj

reorder-attributes^clj

replace-missing-values^clj

resample-supervised^clj

resample-unsupervised^clj

select-append-attributes^clj

stratified-remove-folds-supervised^clj

string-to-word-vector^clj

supervised-discretize^clj

supervised-nominal-to-binary^clj

unsupervised-discretize^clj

unsupervised-nominal-to-binary^clj