This namespace contains several functions for building classifiers using different classification algorithms: Bayes networks, multilayer perceptron, decision tree or support vector machines are available. Some of these classifiers have incremental versions so they can be built without having all the dataset instances in memory.
Functions for evaluating the classifiers built using cross validation or a training set are also provided.
A sample use of the API for classifiers is shown below:
(use 'clj-ml.classifiers)
; Building a classifier using a C4.5 decision tree (def classifier (make-classifier :decision-tree :c45))
; We set the class attribute for the loaded dataset. ; dataset is supposed to contain a set of instances. (dataset-set-class dataset 4)
; Training the classifier (classifier-train classifier dataset)
; We evaluate the classifier using a test dataset (def evaluation (classifier-evaluate classifier :dataset dataset trainingset))
; We retrieve some data from the evaluation result (:kappa evaluation) (:root-mean-squared-error evaluation) (:precision evaluation)
; A trained classifier can be used to classify new instances (def to-classify (make-instance dataset {:class :Iris-versicolor :petalwidth 0.2 :petallength 1.4 :sepalwidth 3.5 :sepallength 5.1}))
; We retrieve the index of the class value assigned by the classifier (classifier-classify classifier to-classify)
; We retrieve a symbol with the value assigned by the classifier ; and assigns it to a certain instance (classifier-label classifier to-classify)
A classifier can also be trained using cross-validation:
(classifier-evaluate classifier :cross-validation dataset 10)
Finally a classifier can be stored in a file for later use:
(use 'clj-ml.utils)
(serialize-to-file classifier "/Users/antonio.garrote/Desktop/classifier.bin")
This namespace contains several functions for building classifiers using different classification algorithms: Bayes networks, multilayer perceptron, decision tree or support vector machines are available. Some of these classifiers have incremental versions so they can be built without having all the dataset instances in memory. Functions for evaluating the classifiers built using cross validation or a training set are also provided. A sample use of the API for classifiers is shown below: (use 'clj-ml.classifiers) ; Building a classifier using a C4.5 decision tree (def *classifier* (make-classifier :decision-tree :c45)) ; We set the class attribute for the loaded dataset. ; *dataset* is supposed to contain a set of instances. (dataset-set-class *dataset* 4) ; Training the classifier (classifier-train *classifier* *dataset*) ; We evaluate the classifier using a test dataset (def *evaluation* (classifier-evaluate *classifier* :dataset *dataset* *trainingset*)) ; We retrieve some data from the evaluation result (:kappa *evaluation*) (:root-mean-squared-error *evaluation*) (:precision *evaluation*) ; A trained classifier can be used to classify new instances (def *to-classify* (make-instance *dataset* {:class :Iris-versicolor :petalwidth 0.2 :petallength 1.4 :sepalwidth 3.5 :sepallength 5.1})) ; We retrieve the index of the class value assigned by the classifier (classifier-classify *classifier* *to-classify*) ; We retrieve a symbol with the value assigned by the classifier ; and assigns it to a certain instance (classifier-label *classifier* *to-classify*) A classifier can also be trained using cross-validation: (classifier-evaluate *classifier* :cross-validation *dataset* 10) Finally a classifier can be stored in a file for later use: (use 'clj-ml.utils) (serialize-to-file *classifier* "/Users/antonio.garrote/Desktop/classifier.bin")
(classifier-classify classifier instance)
Classifies an instance using the provided classifier. Returns the class as a keyword.
Classifies an instance using the provided classifier. Returns the class as a keyword.
(classifier-copy classifier)
Performs a deep copy of the classifier
Performs a deep copy of the classifier
(classifier-copy-and-train classifier dataset)
Performs a deep copy of the classifier, trains the copy, and returns it.
Performs a deep copy of the classifier, trains the copy, and returns it.
Evaluates a trained classifier using the provided dataset or cross-validation. The first argument must be the classifier to evaluate, the second argument is the kind of evaluation to do. Two possible evaluations ara availabe: dataset and cross-validations. The values for the second argument can be:
If dataset evaluation is desired, the function call must receive as the second parameter the keyword :dataset and as third and fourth parameters the original dataset used to build the classifier and the training data:
(classifier-evaluate classifier :dataset training evaluation)
If cross-validation is desired, the function call must receive as the second parameter the keyword :cross-validation and as third and fourth parameters the dataset where for training and the number of folds.
(classifier-evaluate classifier :cross-validation training 10)
An optional seed can be provided for generation of the cross validation folds.
(classifier-evaluate classifier :cross-validation training 10 {:random-seed 29})
The metrics available in the evaluation are listed below:
Evaluates a trained classifier using the provided dataset or cross-validation. The first argument must be the classifier to evaluate, the second argument is the kind of evaluation to do. Two possible evaluations ara availabe: dataset and cross-validations. The values for the second argument can be: - :dataset - :cross-validation * :dataset If dataset evaluation is desired, the function call must receive as the second parameter the keyword :dataset and as third and fourth parameters the original dataset used to build the classifier and the training data: (classifier-evaluate *classifier* :dataset *training* *evaluation*) * :cross-validation If cross-validation is desired, the function call must receive as the second parameter the keyword :cross-validation and as third and fourth parameters the dataset where for training and the number of folds. (classifier-evaluate *classifier* :cross-validation *training* 10) An optional seed can be provided for generation of the cross validation folds. (classifier-evaluate *classifier* :cross-validation *training* 10 {:random-seed 29}) The metrics available in the evaluation are listed below: - :correct Number of instances correctly classified - :incorrect Number of instances incorrectly evaluated - :unclassified Number of instances incorrectly classified - :percentage-correct Percentage of correctly classified instances - :percentage-incorrect Percentage of incorrectly classified instances - :percentage-unclassified Percentage of not classified instances - :error-rate - :mean-absolute-error - :relative-absolute-error - :root-mean-squared-error - :root-relative-squared-error - :correlation-coefficient - :average-cost - :kappa The kappa statistic - :kb-information - :kb-mean-information - :kb-relative-information - :sf-entropy-gain - :sf-mean-entropy-gain - :roc-area - :false-positive-rate - :false-negative-rate - :f-measure - :precision - :recall - :evaluation-object The underlying Weka's Java object containing the evaluation
(classifier-label classifier instance)
Classifies and assign a label to a dataset instance. The function returns the newly classified instance. This call is destructive, the instance passed as an argument is modified.
Classifies and assign a label to a dataset instance. The function returns the newly classified instance. This call is destructive, the instance passed as an argument is modified.
(classifier-predict-numeric classifier instance)
Predicts the class attribute of an instance using the provided classifier. Returns the value as a floating-point value (e.g., for regression).
Predicts the class attribute of an instance using the provided classifier. Returns the value as a floating-point value (e.g., for regression).
(classifier-predict-probability classifier instance)
Classifies an instance using the provided classifier. Returns the probability distribution across classes for the instance
Classifies an instance using the provided classifier. Returns the probability distribution across classes for the instance
(classifier-train classifier dataset)
Trains a classifier with the given dataset as the training data.
Trains a classifier with the given dataset as the training data.
(classifier-update classifier instance-s)
If the classifier is updateable it updates the classifier with the given instance or set of instances.
If the classifier is updateable it updates the classifier with the given instance or set of instances.
Creates a new classifier for the given kind algorithm and options.
The first argument identifies the kind of classifier and the second argument the algorithm to use, e.g. :decision-tree :c45.
The classifiers currently supported are:
Optionally, a map of options can also be passed as an argument with a set of classifier specific options.
This is the description of the supported classifiers and the accepted option parameters for each of them:
:lazy :ibk
K-nearest neighbor classification.
Parameters:
:decision-tree :c45
A classifier building a pruned or unpruned C 4.5 decision tree using Weka J 4.8 implementation.
Parameters:
:bayes :naive
Classifier based on the Bayes' theorem with strong independence assumptions, among the probabilistic variables.
Parameters:
:neural-network :multilayer-perceptron
Classifier built using a feedforward artificial neural network with three or more layers of neurons and nonlinear activation functions. It is able to distinguish data that is not linearly separable.
Parameters:
:support-vector-machine :smo
Support vector machine (SVM) classifier built using the sequential minimal optimization (SMO) training algorithm.
Parameters:
:support-vector-machine :libsvm
TODO
:regression :linear
Parameters:
- :attribute-selection
Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
- :keep-colinear
Do not try to eliminate colinear attributes.
- :ridge
Set ridge parameter (default 1.0e-8).
Parameters:
- :max-iterations
Set the maximum number of iterations (default -1, until convergence).
- :ridge
Set the ridge in the log-likelihood.
Creates a new classifier for the given kind algorithm and options. The first argument identifies the kind of classifier and the second argument the algorithm to use, e.g. :decision-tree :c45. The classifiers currently supported are: - :lazy :ibk - :decision-tree :c45 - :decision-tree :boosted-stump - :decision-tree :M5P - :decision-tree :random-forest - :decision-tree :rotation-forest - :bayes :naive - :neural-network :multilayer-perceptron - :support-vector-machine :smo - :regression :linear - :regression :logistic - :regression :pace - :regression :pls Optionally, a map of options can also be passed as an argument with a set of classifier specific options. This is the description of the supported classifiers and the accepted option parameters for each of them: * :lazy :ibk K-nearest neighbor classification. Parameters: - :inverse-weighted Neighbors will be weighted by the inverse of their distance when voting. (default equal weighting) Sample value: true - :similarity-weighted Neighbors will be weighted by their similarity when voting. (default equal weighting) Sample value: true - :no-normalization Turns off normalization. Sample value: true - :num-neighbors Set the number of nearest neighbors to use in prediction (default 1) Sample value: 3 * :decision-tree :c45 A classifier building a pruned or unpruned C 4.5 decision tree using Weka J 4.8 implementation. Parameters: - :unpruned Use unpruned tree. Sample value: true - :reduce-error-pruning Sample value: true - :only-binary-splits Sample value: true - :no-raising Sample value: true - :no-cleanup Sample value: true - :laplace-smoothing For predicted probabilities. Sample value: true - :pruning-confidence Threshold for pruning. Default value: 0.25 - :minimum-instances Minimum number of instances per leave. Default value: 2 - :pruning-number-folds Set number of folds for reduced error pruning. Default value: 3 - :random-seed Seed for random data shuffling. Default value: 1 * :bayes :naive Classifier based on the Bayes' theorem with strong independence assumptions, among the probabilistic variables. Parameters: - :kernel-estimator Use kernel desity estimator rather than normal. Sample value: true - :supervised-discretization Use supervised discretization to to process numeric attributes (see :supervised-discretize filter in clj-ml.filters/make-filter function). Sample value: true * :neural-network :multilayer-perceptron Classifier built using a feedforward artificial neural network with three or more layers of neurons and nonlinear activation functions. It is able to distinguish data that is not linearly separable. Parameters: - :no-nominal-to-binary A :nominal-to-binary filter will not be applied by default. (see :supervised-nominal-to-binary filter in clj-ml.filters/make-filter function). Default value: false - :no-numeric-normalization A numeric class will not be normalized. Default value: false - :no-normalization No attribute will be normalized. Default value: false - :no-reset Reseting the network will not be allowed. Default value: false - :learning-rate-decay Learning rate decay will occur. Default value: false - :learning-rate Learning rate for the backpropagation algorithm. Value should be between [0,1]. Default value: 0.3 - :momentum Momentum rate for the backpropagation algorithm. Value shuld be between [0,1]. Default value: 0.2 - :epochs Number of iteration to train through. Default value: 500 - :percentage-validation-set Percentage size of validation set to use to terminate training. If it is not zero it takes precende over the number of epochs to finish training. Values should be between [0,100]. Default value: 0 - :random-seed Value of the seed for the random generator. Values should be longs greater than 0. Default value: 1 - :threshold-number-errors The consequetive number of errors allowed for validation testing before the network terminates. Values should be greater thant 0. Default value: 20 * :support-vector-machine :smo Support vector machine (SVM) classifier built using the sequential minimal optimization (SMO) training algorithm. Parameters: - :fit-logistic-models Fit logistic models to SVM outputs. Default value :false - :complexity-constant The complexity constance. Default value: 1 - :tolerance Tolerance parameter. Default value: 1.0e-3 - :epsilon-roundoff Epsilon round-off error. Default value: 1.0e-12 - :folds-for-cross-validation Number of folds for the internal cross-validation. Sample value: 10 - :random-seed Value of the seed for the random generator. Values should be longs greater than 0. Default value: 1 * :support-vector-machine :libsvm TODO * :regression :linear Parameters: - :attribute-selection Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method) - :keep-colinear Do not try to eliminate colinear attributes. - :ridge Set ridge parameter (default 1.0e-8). * :regression :logistic Parameters: - :max-iterations Set the maximum number of iterations (default -1, until convergence). - :ridge Set the ridge in the log-likelihood.
cljdoc is a website building & hosting documentation for Clojure/Script libraries
× close