Updated tutorial in readme.
This commit is contained in:
Joshua Eckroth
parent
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1 changed files with 377 additions and 155 deletions
532
README.md
532
README.md
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@ -16,7 +16,7 @@ git clone the project, then run:
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### Installing from Clojars
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[cc.artifice/clj-ml "0.3.5"]
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[cc.artifice/clj-ml "0.4.0-SNAPSHOT"]
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### Installing from Maven
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@ -25,236 +25,458 @@ git clone the project, then run:
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<dependency>
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<groupId>cc.artifice</groupId>
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<artifactId>clj-ml</artifactId>
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<version>0.3.4</version>
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<version>0.4.0-SNAPSHOT</version>
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</dependency>
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## Supported algorithms
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* Filters
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* supervised discretize
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* unsupervised discretize
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* supervised nominal to binary
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* unsupervised nominal to binary
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* string to word vector
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* reorder attributes
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* resample (supervised, unsupervised)
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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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* Classifiers
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* C4.5 (J4.8)
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* naive Bayes
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* multilayer perceptron
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* support vector machines
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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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* Clusterers
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* k-means
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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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## Usage
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API documenation can be found [here](http://antoniogarrote.github.com/clj-ml/index.html).
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API documenation can be found [here](http://clj-ml.artifice.cc/doc/index.html).
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### I/O of data
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REPL>(use 'clj-ml.io)
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```clojure
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user> (use 'clj-ml.io)
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nil
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REPL>; Loading data from an ARFF file, XRFF and CSV are also supported
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REPL>(def ds (load-instances :arff "file:///Applications/weka-3-6-2/data/iris.arff"))
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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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REPL>; Saving data in a different format
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REPL>(save-instances :csv "file:///Users/antonio.garrote/Desktop/iris.csv" ds)
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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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### Working with datasets
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REPL>(use 'clj-ml.data)
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```clojure
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user> (use 'clj-ml.data)
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nil
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REPL>; Defining a dataset
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REPL>(def ds (make-dataset "name" [:length :width {:kind [:good :bad]}] [ [12 34 :good] [24 53 :bad] ]))
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REPL>ds
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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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#<ClojureInstances @relation name
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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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@attribute length numeric
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@attribute width numeric
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@attribute kind {good,bad}
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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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@data
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12,34,good
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24,53,bad>
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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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REPL>; Using datasets like sequences
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REPL>(dataset-seq ds)
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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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(#<Instance 12,34,good> #<Instance 24,53,bad>)
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REPL>; Transforming instances into maps or vectors
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REPL>(instance-to-map (first (dataset-seq ds)))
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{:kind :good, :width 34.0, :length 12.0}
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REPL>(instance-to-vector (dataset-at ds 0))
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[12.0 34.0 :good]
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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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### Filtering datasets
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REPL>(use '(clj-ml filters io))
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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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REPL>(def ds (load-instances :arff "file:///Applications/weka-3-6-2/data/iris.arff"))
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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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REPL>; Discretizing a numeric attribute using an unsupervised filter
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REPL>(def discretize (make-filter :unsupervised-discretize {:dataset-format ds :attributes [:sepallength :petallength]}))
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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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REPL>(def filtered-ds (filter-apply discretize 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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REPL>; You can also use the filter's fn directly which will create and apply the filter:
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REPL>(def filtered-ds (unsupervised-discretize ds {:attributes [:sepallength :petallength]}))
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REPL>; The above way lends itself to the -> macro and is useful when using multiple filters.
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Equivalently,
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REPL>; The eqivalent operation can be done with the ->> macro and make-apply-filter fn:
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REPL>(def filtered-ds (->> "file:///home/kiran/Downloads/weka/weka-3-6-9/data/iris.arff"
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(load-instances :arff)
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(make-apply-filter :unsupervised-discretize {:attributes [0 2]})))
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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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### Using classifiers
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REPL>(use 'clj-ml.classifiers)
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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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REPL>; Building a classifier using a C4.5 decission tree
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REPL>(def classifier (make-classifier :decision-tree :c45))
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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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REPL>; We set the class attribute for the loaded dataset
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REPL>(dataset-set-class ds 4)
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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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REPL>; Training the classifier
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REPL>(classifier-train classifier ds)
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user> (def instance (-> (first (dataset-seq ds))
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(instance-set-class-missing)))
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#<J48 J48 pruned tree
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------------------
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user> (classifier-classify classifier instance)
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:Iris-setosa
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```
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petalwidth <= 0.6: Iris-setosa (50.0)
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petalwidth > 0.6
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| petalwidth <= 1.7
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| | petallength <= 4.9: Iris-versicolor (48.0/1.0)
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| | petallength > 4.9
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| | | petalwidth <= 1.5: Iris-virginica (3.0)
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| | | petalwidth > 1.5: Iris-versicolor (3.0/1.0)
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| petalwidth > 1.7: Iris-virginica (46.0/1.0)
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Evaluation:
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Number of Leaves : 5
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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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Size of the tree : 9
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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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REPL>; We evaluate the classifier using a test dataset
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REPL>; last parameter should be a different test dataset, here we are using the same
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REPL>(def evaluation (classifier-evaluate classifier :dataset ds ds))
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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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=== Confusion Matrix ===
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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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50 0 0 | a = Iris-setosa
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0 49 1 | b = Iris-versicolor
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0 2 48 | c = Iris-virginica
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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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=== Summary ===
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nil
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```
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Saving and restoring (trained) classifiers:
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Correctly Classified Instances 147 98 %
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Incorrectly Classified Instances 3 2 %
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Kappa statistic 0.97
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Mean absolute error 0.0233
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Root mean squared error 0.108
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Relative absolute error 5.2482 %
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Root relative squared error 22.9089 %
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Total Number of Instances 150
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```clojure
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REPL>(:kappa evaluation)
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user> (serialize-to-file classifier "my-classifier.bin")
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"my-classifier.bin"
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0.97
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user> (def classifier2 (deserialize-from-file "my-classifier.bin"))
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#'user/classifier2
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REPL>(:root-mean-squared-error e)
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user> (classifier-classify classifier2 instance)
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:Iris-setosa
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```
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0.10799370769526968
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Text document handling:
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REPL>(:precision e)
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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"]}}
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{:title "Another document title"
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:fulltext "Some more \"fulltext\"; rabbit artificial machine bananas"
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:terms {"Topic" ["Politics" "Food"]}}])
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#'user/docs
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{:Iris-setosa 1.0, :Iris-versicolor 0.9607843137254902, :Iris-virginica
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0.9795918367346939}
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user> (docs-to-dataset docs "Topic" "Sports" 1 "/tmp" :stemmer true :lowercase false)
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#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
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REPL>; The classifier can also be evaluated using cross-validation
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REPL>(classifier-evaluate classifier :cross-validation ds 10)
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@attribute class {no,yes}
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@attribute title-1 numeric
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@attribute title-Another numeric
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@attribute title-Document numeric
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@attribute title-document numeric
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@attribute title-titl numeric
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@attribute fulltext-Some numeric
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@attribute fulltext-This numeric
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@attribute fulltext-artifici numeric
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@attribute fulltext-banana numeric
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@attribute fulltext-fulltext numeric
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@attribute fulltext-is numeric
|
||||
@attribute fulltext-machin numeric
|
||||
@attribute fulltext-more numeric
|
||||
@attribute fulltext-rabbit numeric
|
||||
@attribute fulltext-the numeric
|
||||
|
||||
=== Confusion Matrix ===
|
||||
@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>
|
||||
```
|
||||
|
||||
a b c <-- classified as
|
||||
49 1 0 | a = Iris-setosa
|
||||
0 47 3 | b = Iris-versicolor
|
||||
0 4 46 | c = Iris-virginica
|
||||
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.
|
||||
|
||||
=== Summary ===
|
||||
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.
|
||||
|
||||
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
|
||||
```clojure
|
||||
user> (docs-to-dataset docs "Topic" "Sports" 1 "/tmp"
|
||||
:stemmer true :lowercase false :training true)
|
||||
#<Instances @relation 'docs-weka.filters.unsupervised.attribute.StringToWordVector...'
|
||||
|
||||
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)
|
||||
@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
|
||||
|
||||
0.0
|
||||
@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}>
|
||||
|
||||
REPL>(classifier-label classifier to-classify)
|
||||
user> (def docs2 [{:title "Document title 1 foo bar"
|
||||
:fulltext "baz rabbit quux"
|
||||
:terms {"Topic" ["Sports"]}}])
|
||||
#'user/docs2
|
||||
|
||||
#<Instance 5.1,3.5,1.4,0.2,Iris-setosa>
|
||||
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
|
||||
|
||||
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")
|
||||
@data
|
||||
{0 yes,1 0.480453,3 0.480453,14 0.480453}>
|
||||
user>
|
||||
```
|
||||
|
||||
### Using clusterers
|
||||
|
||||
REPL>(use 'clj-ml.clusterers)
|
||||
```clojure
|
||||
user> (use 'clj-ml.clusterers)
|
||||
nil
|
||||
|
||||
REPL> ; we build a clusterer using k-means and three clusters
|
||||
REPL> (def kmeans (make-clusterer :k-means {:number-clusters 3}))
|
||||
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
|
||||
|
||||
REPL> ; we need to remove the class from the dataset to
|
||||
REPL> ; use this clustering algorithm
|
||||
REPL> (dataset-remove-class ds)
|
||||
@attribute sepallength numeric
|
||||
@attribute sepalwidth numeric
|
||||
@attribute petallength numeric
|
||||
@attribute petalwidth numeric
|
||||
|
||||
REPL> ; we build the clusters
|
||||
REPL> (clusterer-build kmeans ds)
|
||||
REPL> kmeans
|
||||
@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
|
||||
...
|
||||
|
||||
#<SimpleKMeans
|
||||
kMeans
|
||||
======
|
||||
user> (def clusterer (make-clusterer :k-means {:number-clusters 3}))
|
||||
#'user/clusterer
|
||||
|
||||
Number of iterations: 3
|
||||
Within cluster sum of squared errors: 7.817456892309574
|
||||
Missing values globally replaced with mean/mode
|
||||
user> (clusterer-build clusterer ds)
|
||||
nil
|
||||
|
||||
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
|
||||
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>
|
||||
```
|
||||
|
||||
## Thanks YourKit!
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue