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A machine learning library for Clojure built on top of Weka and friends

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README.md

clj-ml

A machine learning library for Clojure built on top of Weka and friends.

Installation

In order to install the library you must first install Leiningen.

To install from source

git clone the project, then run:

$ lein deps
$ lein javac
$ lein uberjar

Installing from Clojars

[com.leadtune/clj-ml "0.2.4"]

Installing from Maven

(add Clojars repository)

<dependency>
  <groupId>clj-ml</groupId>
  <artifactId>clj-ml</artifactId>
  <version>0.2.4</version>
</dependency>

Supported algorithms

  • Filters

    • supervised discretize
    • unsupervised discretize
    • supervised nominal to binary
    • unsupervised nominal to binary
  • Classifiers

    • C4.5 (J4.8)
    • naive Bayes
    • multilayer perceptron
  • Clusterers

    • k-means

Usage

API documenation can be found here.

I/O of data

REPL>(use 'clj-ml.io)

REPL>; Loading data from an ARFF file, XRFF and CSV are also supported
REPL>(def ds (load-instances :arff "file:///Applications/weka-3-6-2/data/iris.arff"))

REPL>; Saving data in a different format
REPL>(save-instances :csv "file:///Users/antonio.garrote/Desktop/iris.csv"  ds)

Working with datasets

REPL>(use 'clj-ml.data)

REPL>; Defining a dataset
REPL>(def ds (make-dataset "name" [:length :width {:kind [:good :bad]}] [ [12 34 :good] [24 53 :bad] ]))
REPL>ds

#<ClojureInstances @relation name

@attribute length numeric
@attribute width numeric
@attribute kind {good,bad}

@data
12,34,good
24,53,bad>

REPL>; Using datasets like sequences
REPL>(dataset-seq ds)

(#<Instance 12,34,good> #<Instance 24,53,bad>)

REPL>; Transforming instances  into maps or vectors
REPL>(instance-to-map (first (dataset-seq ds)))

{:kind :good, :width 34.0, :length 12.0}

REPL>(instance-to-vector (dataset-at ds 0))
[12.0 34.0 :good]

Filtering datasets

REPL>(use '(clj-ml filters io))

REPL>(def ds (load-instances :arff "file:///Applications/weka-3-6-2/data/iris.arff"))

REPL>; Discretizing a numeric attribute using an unsupervised filter
REPL>(def  discretize (make-filter :unsupervised-discretize {:dataset-format ds :attributes [:sepallength :petallength]}))


REPL>(def filtered-ds (filter-apply discretize ds))

REPL>; You can also use the filter's fn directly which will create and apply the filter:
REPL>(def filtered-ds (unsupervised-discretize ds {:attributes [:sepallength :petallength]}))
REPL>; The above way lends itself to the -> macro and is useful when using multiple filters.


REPL>; The eqivalent operation can be done with the ->> macro and make-apply-filter fn:
REPL>(def filtered-ds (->> "file:///Applications/weka-3-6-2/data/iris.arff")
                           (load-instances :arff)
                           (make-apply-filter :unsupervised-discretize {:attributes [0 2]}))

Using classifiers

REPL>(use 'clj-ml.classifiers)

REPL>; Building a classifier using a  C4.5 decission tree
REPL>(def classifier (make-classifier :decission-tree :c45))

REPL>; We set the class attribute for the loaded dataset
REPL>(dataset-set-class ds 4)

REPL>; Training the classifier
REPL>(classifier-train classifier ds)

 #<J48 J48 pruned tree
 ------------------

 petalwidth <= 0.6: Iris-setosa (50.0)
 petalwidth > 0.6
 |  petalwidth <= 1.7
 |  |   petallength <= 4.9: Iris-versicolor (48.0/1.0)
 |  |   petallength > 4.9
 |  |   |   petalwidth <= 1.5: Iris-virginica (3.0)
 |  |   |   petalwidth > 1.5: Iris-versicolor (3.0/1.0)
 |  petalwidth > 1.7: Iris-virginica (46.0/1.0)

 Number of Leaves  :        5

 Size of the tree : 9


REPL>; We evaluate the classifier using a test dataset
REPL>; last parameter should be a different test dataset, here we are using the same
REPL>(def evaluation   (classifier-evaluate classifier  :dataset ds ds))

 === Confusion Matrix ===

   a     b  c   <-- classified as
  50     0  0 |  a = Iris-setosa
   0 49  1 |     b = Iris-versicolor
   0     2 48 |  c = Iris-virginica

 === Summary ===

 Correctly Classified Instances    147           98      %
 Incorrectly Classified Instances        3            2      %
 Kappa statistic                 0.97
 Mean absolute error                 0.0233
 Root mean squared error             0.108
 Relative absolute error             5.2482 %
 Root relative squared error            22.9089 %
 Total Number of Instances         150

REPL>(:kappa evaluation)

 0.97

REPL>(:root-mean-squared-error e)

 0.10799370769526968

REPL>(:precision e)

 {:Iris-setosa 1.0, :Iris-versicolor 0.9607843137254902, :Iris-virginica
  0.9795918367346939}

REPL>; The classifier can also be evaluated using cross-validation
REPL>(classifier-evaluate classifier :cross-validation ds 10)

 === Confusion Matrix ===

   a     b  c   <-- classified as
  49     1  0 |  a = Iris-setosa
   0 47  3 |     b = Iris-versicolor
   0     4 46 |  c = Iris-virginica

 === Summary ===

 Correctly Classified Instances    142           94.6667 %
 Incorrectly Classified Instances        8            5.3333 %
 Kappa statistic                 0.92
 Mean absolute error                 0.0452
 Root mean squared error             0.1892
 Relative absolute error            10.1707 %
 Root relative squared error            40.1278 %
 Total Number of Instances         150

REPL>; A trained classifier can be used to classify new instances
REPL>(def to-classify (make-instance ds
                                                  {:class :Iris-versicolor,
                                                  :petalwidth 0.2,
                                                  :petallength 1.4,
                                                  :sepalwidth 3.5,
                                                  :sepallength 5.1}))
REPL>(classifier-classify classifier to-classify)

 0.0

REPL>(classifier-label classifier to-classify)

 #<Instance 5.1,3.5,1.4,0.2,Iris-setosa>


REPL>; The classifiers can be saved and restored later
REPL>(use 'clj-ml.utils)

REPL>(serialize-to-file classifier "/Users/antonio.garrote/Desktop/classifier.bin")

Using clusterers

REPL>(use 'clj-ml.clusterers)

REPL> ; we build a clusterer using k-means and three clusters
REPL> (def kmeans (make-clusterer :k-means {:number-clusters 3}))

REPL> ; we need to remove the class from the dataset to
REPL> ; use this clustering algorithm
REPL> (dataset-remove-class ds)

REPL> ; we build the clusters
REPL> (clusterer-build kmeans ds)
REPL> kmeans

  #<SimpleKMeans
  kMeans
  ======

  Number of iterations: 3
  Within cluster sum of squared errors: 7.817456892309574
  Missing values globally replaced with mean/mode

  Cluster centroids:
                                            Cluster#
  Attribute                Full Data               0               1               2
                               (150)            (50)            (50)            (50)
  ==================================================================================
  sepallength                 5.8433           5.936           5.006           6.588
  sepalwidth                   3.054            2.77           3.418           2.974
  petallength                 3.7587            4.26           1.464           5.552
  petalwidth                  1.1987           1.326           0.244           2.026
  class                  Iris-setosa Iris-versicolor     Iris-setosa  Iris-virginica

Thanks YourKit!

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License

MIT License

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