2010-02-28 12:14:17 +00:00
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# clj-ml
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2010-10-28 16:31:07 +00:00
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A machine learning library for Clojure built on top of Weka and friends.
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2010-02-28 12:14:17 +00:00
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2010-02-28 12:42:14 +00:00
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## Installation
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In order to install the library you must first install Leiningen.
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### To install from source
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2010-10-27 23:01:18 +00:00
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git clone the project, then run:
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$ lein deps
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$ lein javac
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$ lein uberjar
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2010-02-28 12:42:14 +00:00
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### Installing from Clojars
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2013-08-06 07:40:05 +00:00
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[cc.artifice/clj-ml "0.4.0-SNAPSHOT"]
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2010-02-28 12:42:14 +00:00
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### Installing from Maven
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2010-02-28 12:47:24 +00:00
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(add Clojars repository)
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2010-02-28 12:45:46 +00:00
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2010-10-27 23:01:18 +00:00
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<dependency>
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2013-03-22 12:01:30 +00:00
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<groupId>cc.artifice</groupId>
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2010-10-27 23:01:18 +00:00
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<artifactId>clj-ml</artifactId>
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<version>0.4.0-SNAPSHOT</version>
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2010-10-27 23:01:18 +00:00
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</dependency>
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2010-02-28 12:42:14 +00:00
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2010-02-28 19:29:51 +00:00
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## Supported algorithms
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* Filters
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2013-08-06 07:40:05 +00:00
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* Discretization (supervised, unsupervised, PKI)
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* Nominal to binary (supervised, unsupervised)
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* Numeric to nominal
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* String to word vector
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* Attribute manipulation (reorder, add, remove range, remove percentage, etc.)
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* Resample (supervised, unsupervised)
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2010-02-28 19:29:51 +00:00
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* Classifiers
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2013-08-06 07:40:05 +00:00
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* k-Nearest neighbor
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* Decision trees: C4.5/J4.8, Boosted stump, Random forest, Rotation forest, M5P
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* Naive Bayes
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* Multilayer perceptrons
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* Support vector machines (grid-based training), SMO, Spegasos
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* Regression
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* Linear
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* Logistic
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* Pace
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* Additive gradient boosting
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* Clusterers
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* k-Means
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* Cobweb
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* Expectation-maximization
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2010-02-28 19:29:51 +00:00
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2010-02-28 12:14:17 +00:00
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## Usage
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2013-08-06 07:40:05 +00:00
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API documenation can be found [here](http://clj-ml.artifice.cc/doc/index.html).
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2010-10-28 16:31:07 +00:00
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2010-10-27 23:01:18 +00:00
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### I/O of data
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2010-02-28 12:14:17 +00:00
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2013-08-06 07:40:05 +00:00
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```clojure
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user> (use 'clj-ml.io)
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nil
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user> (def ds (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff"))
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#'user/ds
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user> ds
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#<Instances @relation iris
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@attribute sepallength numeric
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@attribute sepalwidth numeric
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@attribute petallength numeric
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@attribute petalwidth numeric
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@attribute class {Iris-setosa,Iris-versicolor,Iris-virginica}
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@data
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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...
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user> (def ds (load-instances :arff "http://repository.seasr.org/Datasets/UCI/arff/iris.arff"))
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#'user/ds
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user> (save-instances :csv "iris.csv" ds)
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nil
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user> (println (slurp "iris.csv"))
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sepallength,sepalwidth,petallength,petalwidth,class
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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5,3.4,1.5,0.2,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.4,3.7,1.5,0.2,Iris-setosa
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...
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user> (def ds (load-instances :csv "file:///home/josh/git/clj-ml/iris.csv"))
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#'user/ds
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user> ds
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#<Instances @relation stream
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@attribute sepallength numeric
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@attribute sepalwidth numeric
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@attribute petallength numeric
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@attribute petalwidth numeric
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@attribute class {Iris-setosa,Iris-versicolor,Iris-virginica}
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@data
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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5,3.4,1.5,0.2,Iris-setosa
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```
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2010-02-28 12:14:17 +00:00
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2010-10-27 23:01:18 +00:00
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### Working with datasets
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2010-02-28 12:14:17 +00:00
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2013-08-06 07:40:05 +00:00
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```clojure
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user> (use 'clj-ml.data)
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nil
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2010-02-28 12:18:33 +00:00
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2013-08-06 07:40:05 +00:00
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user> (def ds (make-dataset"my-name" [:length :width {:style nil} {:kind [:good :bad]}]
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[[12 24 "longish" :good]
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[8 5 "shortish" :bad]]))
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#'user/ds
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user> ds
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#<ClojureInstances @relation my-name
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2010-02-28 12:14:17 +00:00
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@attribute length numeric
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@attribute width numeric
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@attribute style string
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@attribute kind {good,bad}
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2010-02-28 12:18:33 +00:00
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2013-08-06 07:40:05 +00:00
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@data
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12,24,longish,good
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8,5,shortish,bad>
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2013-08-06 07:40:05 +00:00
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user> (dataset-seq ds)
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(#<Instance 12,24,longish,good> #<Instance 8,5,shortish,bad>)
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2010-02-28 12:18:33 +00:00
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2013-08-06 07:40:05 +00:00
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user> (map instance-to-map (dataset-seq ds))
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({:kind :good, :style "longish", :width 24.0, :length 12.0}
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{:kind :bad, :style "shortish", :width 5.0, :length 8.0})
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2010-02-28 12:18:33 +00:00
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2013-08-06 07:40:05 +00:00
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user> (map instance-to-vector (dataset-seq ds))
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([12.0 24.0 "longish" :good] [8.0 5.0 "shortish" :bad])
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```
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2010-02-28 12:14:17 +00:00
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2010-10-27 23:01:18 +00:00
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### Filtering datasets
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2010-02-28 12:14:17 +00:00
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2013-08-06 07:40:05 +00:00
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```clojure
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user> (use 'clj-ml.filters 'clj-ml.io)
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nil
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user> (def ds (load-instances :csv "file:///home/josh/git/clj-ml/iris.csv"))
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#'user/ds
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user> (def discretize (make-filter :unsupervised-discretize
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{:dataset-format ds
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:attributes [:sepallength :petallength]}))
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#'user/discretize
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user> (def filtered-ds (filter-apply discretize ds))
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#'user/filtered-ds
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user> (map instance-to-map (dataset-seq filtered-ds))
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({:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
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:sepalwidth 3.5, :sepallength :'(5.02-5.38]'}
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{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
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:sepalwidth 3.0, :sepallength :'(4.66-5.02]'}
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{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
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:sepalwidth 3.2, :sepallength :'(4.66-5.02]'}
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{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
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:sepalwidth 3.1, :sepallength :'(-inf-4.66]'}
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{:class :Iris-setosa, :petalwidth 0.2, :petallength :'(-inf-1.59]',
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:sepalwidth 3.6, :sepallength :'(4.66-5.02]'}
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...) ;; the petallength and sepallength attributes are now nominal
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```
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Equivalently,
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```clojure
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user> (def filtered-ds (->> "file:///home/josh/git/clj-ml/iris.csv"
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(load-instances :csv)
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(make-apply-filter :unsupervised-discretize
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{:attributes [:sepallength :petallength]})))
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```
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2010-02-28 12:14:17 +00:00
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2010-10-27 23:01:18 +00:00
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### Using classifiers
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2010-02-28 12:14:17 +00:00
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2013-08-06 07:40:05 +00:00
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```clojure
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user> (use 'clj-ml.classifiers 'clj-ml.data 'clj-ml.utils)
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nil
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user> (def ds (-> (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff")
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(dataset-set-class :class)))
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#'user/ds
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user> (def classifier (-> (make-classifier :decision-tree :c45)
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(classifier-train ds)))
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#'user/classifier
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user> (def instance (-> (first (dataset-seq ds))
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(instance-set-class-missing)))
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user> (classifier-classify classifier instance)
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:Iris-setosa
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```
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Evaluation:
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```clojure
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user> (def evaluation (classifier-evaluate classifier :cross-validation ds 10))
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#'user/evaluation
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user> (clojure.pprint/pprint (dissoc evaluation :summary :confusion-matrix))
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{:incorrect 7.0,
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:root-relative-squared-error 36.693518966642074,
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:sf-entropy-gain -4076.3670930399717,
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:recall
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{:Iris-setosa 0.9795918367346939,
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:Iris-versicolor 0.94,
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:Iris-virginica 0.94},
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:kb-information 217.7935138195151,
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:kb-relative-information 13741.240800360849,
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:false-positive-rate
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{:Iris-setosa 0.0,
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:Iris-versicolor 0.04040404040404041,
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:Iris-virginica 0.030303030303030304},
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:percentage-correct 95.30201342281879,
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:roc-area
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{:Iris-setosa 0.984845423317842,
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:Iris-versicolor 0.9456,
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:Iris-virginica 0.9496},
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:kb-mean-information 1.4617014350303028,
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:percentage-unclassified 0.0,
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:percentage-incorrect 4.697986577181208,
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:root-mean-squared-error 0.17297908222448935,
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:unclassified 0.0,
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:correlation-coefficient
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{:nan "Can't compute correlation coefficient: class is nominal!"},
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:correct 142.0,
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:sf-mean-entropy-gain -27.358168409664238,
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:mean-absolute-error 0.04083212821368881,
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:relative-absolute-error 9.187228848079984,
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:error-rate 0.04697986577181208,
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:kappa 0.9295222650179066,
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:f-measure
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{:Iris-setosa 0.9896907216494846,
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:Iris-versicolor 0.9306930693069307,
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:Iris-virginica 0.94},
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:false-negative-rate
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{:Iris-setosa 0.02040816326530612,
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:Iris-versicolor 0.06,
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:Iris-virginica 0.06},
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:evaluation-object #<Evaluation weka.classifiers.Evaluation@6a7272ca>,
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:average-cost 0.0,
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:precision
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{:Iris-setosa 1.0,
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:Iris-versicolor 0.9215686274509803,
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:Iris-virginica 0.94}}
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user> (println (:summary evaluation))
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Correctly Classified Instances 142 95.302 %
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Incorrectly Classified Instances 7 4.698 %
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Kappa statistic 0.9295
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Mean absolute error 0.0408
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Root mean squared error 0.173
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Relative absolute error 9.1872 %
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Root relative squared error 36.6935 %
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Total Number of Instances 149
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Ignored Class Unknown Instances 1
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nil
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user> (println (:confusion-matrix evaluation))
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=== Confusion Matrix ===
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a b c <-- classified as
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48 1 0 | a = Iris-setosa
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0 47 3 | b = Iris-versicolor
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0 3 47 | c = Iris-virginica
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nil
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```
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Saving and restoring (trained) classifiers:
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```clojure
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user> (serialize-to-file classifier "my-classifier.bin")
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"my-classifier.bin"
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user> (def classifier2 (deserialize-from-file "my-classifier.bin"))
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#'user/classifier2
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user> (classifier-classify classifier2 instance)
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:Iris-setosa
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```
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Text document handling:
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```clojure
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user> (def docs [{:title "Document title 1"
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:fulltext "This is the fulltext..."
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:terms {"Topic" ["Sports"]}}
|
|
|
|
{:title "Another document title"
|
|
|
|
:fulltext "Some more \"fulltext\"; rabbit artificial machine bananas"
|
|
|
|
:terms {"Topic" ["Politics" "Food"]}}])
|
|
|
|
#'user/docs
|
|
|
|
|
|
|
|
user> (docs-to-dataset docs "Topic" "Sports" 1 "/tmp" :stemmer true :lowercase false)
|
|
|
|
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
|
|
|
|
|
|
|
|
@attribute class {no,yes}
|
|
|
|
@attribute title-1 numeric
|
|
|
|
@attribute title-Another numeric
|
|
|
|
@attribute title-Document numeric
|
|
|
|
@attribute title-document numeric
|
|
|
|
@attribute title-titl numeric
|
|
|
|
@attribute fulltext-Some numeric
|
|
|
|
@attribute fulltext-This numeric
|
|
|
|
@attribute fulltext-artifici numeric
|
|
|
|
@attribute fulltext-banana numeric
|
|
|
|
@attribute fulltext-fulltext numeric
|
|
|
|
@attribute fulltext-is numeric
|
|
|
|
@attribute fulltext-machin numeric
|
|
|
|
@attribute fulltext-more numeric
|
|
|
|
@attribute fulltext-rabbit numeric
|
|
|
|
@attribute fulltext-the numeric
|
|
|
|
|
|
|
|
@data
|
|
|
|
{0 yes,1 0.480453,3 0.480453,7 0.480453,11 0.480453,15 0.480453}
|
|
|
|
{2 0.480453,4 0.480453,6 0.480453,8 0.480453,9 0.480453,12 0.480453,13 0.480453,14 0.480453}>
|
|
|
|
user>
|
|
|
|
```
|
|
|
|
|
|
|
|
Words appearing in the dataset will only be those appearing in the
|
|
|
|
documents (or a subset; by default, the most common 1000 words). This
|
|
|
|
presents a problem when new documents are loaded and used in a
|
|
|
|
classifier trained on other documents. The classifier will not know
|
|
|
|
how to handle word attributes that are not present in the training
|
|
|
|
set.
|
|
|
|
|
|
|
|
The `docs-to-dataset` function provides the ability to save the
|
|
|
|
training documents dataset and "filter" the testing documents through
|
|
|
|
this dataset to ensure the same word attributes are extracted for both
|
|
|
|
sets. The following example shows that the words "foo, bar, baz, quux"
|
|
|
|
are ignored in the new (testing) documents, and all the original
|
|
|
|
attributes in the training dataset are retained.
|
|
|
|
|
|
|
|
```clojure
|
|
|
|
user> (docs-to-dataset docs "Topic" "Sports" 1 "/tmp"
|
|
|
|
:stemmer true :lowercase false :training true)
|
|
|
|
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
|
|
|
|
|
|
|
|
@attribute class {no,yes}
|
|
|
|
@attribute title-1 numeric
|
|
|
|
@attribute title-Another numeric
|
|
|
|
@attribute title-Document numeric
|
|
|
|
@attribute title-document numeric
|
|
|
|
@attribute title-titl numeric
|
|
|
|
@attribute fulltext-Some numeric
|
|
|
|
@attribute fulltext-This numeric
|
|
|
|
@attribute fulltext-artifici numeric
|
|
|
|
@attribute fulltext-banana numeric
|
|
|
|
@attribute fulltext-fulltext numeric
|
|
|
|
@attribute fulltext-is numeric
|
|
|
|
@attribute fulltext-machin numeric
|
|
|
|
@attribute fulltext-more numeric
|
|
|
|
@attribute fulltext-rabbit numeric
|
|
|
|
@attribute fulltext-the numeric
|
|
|
|
|
|
|
|
@data
|
|
|
|
{2 0.480453,4 0.480453,6 0.480453,8 0.480453,9 0.480453,12 0.480453,13 0.480453,14 0.480453}
|
|
|
|
{0 yes,1 0.480453,3 0.480453,7 0.480453,11 0.480453,15 0.480453}>
|
|
|
|
|
|
|
|
user> (def docs2 [{:title "Document title 1 foo bar"
|
|
|
|
:fulltext "baz rabbit quux"
|
|
|
|
:terms {"Topic" ["Sports"]}}])
|
|
|
|
#'user/docs2
|
|
|
|
|
|
|
|
user> (docs-to-dataset docs2 "Topic" "Sports" 1 "/tmp"
|
|
|
|
:stemmer true :lowercase false :testing true)
|
|
|
|
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
|
|
|
|
|
|
|
|
@attribute class {no,yes}
|
|
|
|
@attribute title-1 numeric
|
|
|
|
@attribute title-Another numeric
|
|
|
|
@attribute title-Document numeric
|
|
|
|
@attribute title-document numeric
|
|
|
|
@attribute title-titl numeric
|
|
|
|
@attribute fulltext-Some numeric
|
|
|
|
@attribute fulltext-This numeric
|
|
|
|
@attribute fulltext-artifici numeric
|
|
|
|
@attribute fulltext-banana numeric
|
|
|
|
@attribute fulltext-fulltext numeric
|
|
|
|
@attribute fulltext-is numeric
|
|
|
|
@attribute fulltext-machin numeric
|
|
|
|
@attribute fulltext-more numeric
|
|
|
|
@attribute fulltext-rabbit numeric
|
|
|
|
@attribute fulltext-the numeric
|
|
|
|
|
|
|
|
@data
|
|
|
|
{0 yes,1 0.480453,3 0.480453,14 0.480453}>
|
|
|
|
user>
|
|
|
|
```
|
2010-02-28 12:14:17 +00:00
|
|
|
|
2010-10-27 23:01:18 +00:00
|
|
|
### Using clusterers
|
2010-02-28 19:29:51 +00:00
|
|
|
|
2013-08-06 07:40:05 +00:00
|
|
|
```clojure
|
|
|
|
user> (use 'clj-ml.clusterers)
|
|
|
|
nil
|
|
|
|
|
|
|
|
user> (def ds (-> (load-instances :arff "file:///home/josh/git/clj-ml/iris.arff")
|
|
|
|
(dataset-remove-attribute-at 4)))
|
|
|
|
#'user/ds
|
|
|
|
user> ds
|
|
|
|
#<Instances @relation iris
|
|
|
|
|
|
|
|
@attribute sepallength numeric
|
|
|
|
@attribute sepalwidth numeric
|
|
|
|
@attribute petallength numeric
|
|
|
|
@attribute petalwidth numeric
|
|
|
|
|
|
|
|
@data
|
|
|
|
5.1,3.5,1.4,0.2
|
|
|
|
4.9,3,1.4,0.2
|
|
|
|
4.7,3.2,1.3,0.2
|
|
|
|
4.6,3.1,1.5,0.2
|
|
|
|
5,3.6,1.4,0.2
|
|
|
|
5.4,3.9,1.7,0.4
|
|
|
|
4.6,3.4,1.4,0.3
|
|
|
|
...
|
|
|
|
|
|
|
|
user> (def clusterer (make-clusterer :k-means {:number-clusters 3}))
|
|
|
|
#'user/clusterer
|
|
|
|
|
|
|
|
user> (clusterer-build clusterer ds)
|
|
|
|
nil
|
|
|
|
|
|
|
|
user> clusterer
|
|
|
|
#<SimpleKMeans
|
|
|
|
kMeans
|
|
|
|
======
|
|
|
|
|
|
|
|
Number of iterations: 6
|
|
|
|
Within cluster sum of squared errors: 6.998114004826762
|
|
|
|
Missing values globally replaced with mean/mode
|
|
|
|
|
|
|
|
Cluster centroids:
|
|
|
|
Cluster#
|
|
|
|
Attribute Full Data 0 1 2
|
|
|
|
(150) (61) (50) (39)
|
|
|
|
=========================================================
|
|
|
|
sepallength 5.8433 5.8885 5.006 6.8462
|
|
|
|
sepalwidth 3.054 2.7377 3.418 3.0821
|
|
|
|
petallength 3.7587 4.3967 1.464 5.7026
|
|
|
|
petalwidth 1.1987 1.418 0.244 2.0795
|
|
|
|
|
|
|
|
|
|
|
|
>
|
|
|
|
user>
|
|
|
|
```
|
2010-02-28 19:29:51 +00:00
|
|
|
|
2012-09-07 15:46:37 +00:00
|
|
|
## Thanks YourKit!
|
|
|
|
|
|
|
|
YourKit is kindly supporting open source projects with its full-featured Java Profiler.
|
|
|
|
YourKit, LLC is the creator of innovative and intelligent tools for profiling
|
|
|
|
Java and .NET applications. Take a look at YourKit's leading software products:
|
|
|
|
<a href="http://www.yourkit.com/java/profiler/index.jsp">YourKit Java Profiler</a> and
|
|
|
|
<a href="http://www.yourkit.com/.net/profiler/index.jsp">YourKit .NET Profiler</a>.
|
2010-02-28 19:29:51 +00:00
|
|
|
|
2010-02-28 12:14:17 +00:00
|
|
|
## License
|
|
|
|
|
|
|
|
MIT License
|