Class: Ai4r::Classifiers::NaiveBayes

Inherits:
Classifier show all
Defined in:
lib/ai4r/classifiers/naive_bayes.rb

Overview

Introduction

This is an implementation of a Naive Bayesian Classifier without any specialisation (ie. for text classification) Probabilities P(a_i | v_j) are estimated using m-estimates, hence the m parameter as second parameter when isntantiating the class. The estimation looks like this: (n_c + mp) / (n + m)

the variables are: n = the number of training examples for which v = v_j n_c = number of examples for which v = v_j and a = a_i p = a priori estimate for P(a_i | v_j) m = the equivalent sample size

stores the conditional probabilities in an array named @pcp and in this form: @pcp[values]

This kind of estimator is useful when the training data set is relatively small. If the data set is big enough, set it to 0, which is also the default value

For further details regarding Bayes and Naive Bayes Classifier have a look at those websites: en.wikipedia.org/wiki/Naive_Bayesian_classification en.wikipedia.org/wiki/Bayes%27_theorem

Parameters

  • :m => Optional. Default value is set to 0. It may be set to a value greater than 0 when

the size of the dataset is relatively small

How to use it

data = DataSet.new.load_csv_with_labels "bayes_data.csv"
b = NaiveBayes.new.
  set_parameters({:m=>3}).
  build data
b.eval(["Red", "SUV", "Domestic"])

Defined Under Namespace

Classes: DataEntry

Instance Method Summary collapse

Methods inherited from Classifier

#get_rules

Methods included from Data::Parameterizable

#get_parameters, included, #set_parameters

Constructor Details

#initializeNaiveBayes

Returns a new instance of NaiveBayes


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# File 'lib/ai4r/classifiers/naive_bayes.rb', line 63

def initialize
  @m = 0
  @class_counts = []
  @class_prob = [] # stores the probability of the classes
  @pcc = [] # stores the number of instances divided into attribute/value/class
  @pcp = [] # stores the conditional probabilities of the values of an attribute
  @klass_index = {} # hashmap for quick lookup of all the used klasses and their indice
  @values = {} # hashmap for quick lookup of all the values
end

Instance Method Details

#build(data) ⇒ Object

counts values of the attribute instances and calculates the probability of the classes and the conditional probabilities Parameter data has to be an instance of CsvDataSet


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# File 'lib/ai4r/classifiers/naive_bayes.rb', line 105

def build(data)
  raise 'Error instance must be passed' unless data.is_a?(Ai4r::Data::DataSet)
  raise 'Data should not be empty' if data.data_items.length == 0

  initialize_domain_data(data)
  initialize_klass_index
  initialize_pc
  calculate_probabilities

  self
end

#eval(data) ⇒ Object

You can evaluate new data, predicting its category. e.g.

b.eval(["Red", "SUV", "Domestic"])
  => 'No'

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# File 'lib/ai4r/classifiers/naive_bayes.rb', line 77

def eval(data)
  prob = @class_prob.dup
  prob = calculate_class_probabilities_for_entry(data, prob)
  index_to_klass(prob.index(prob.max))
end

#get_probability_map(data) ⇒ Object

Calculates the probabilities for the data entry Data. data has to be an array of the same dimension as the training data minus the class column. Returns a map containint all classes as keys: {Class_1 => probability, Class_2 => probability2 … } Probability is <= 1 and of type Float. e.g.

b.get_probability_map(["Red", "SUV", "Domestic"])
  => {"Yes"=>0.4166666666666667, "No"=>0.5833333333333334}

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# File 'lib/ai4r/classifiers/naive_bayes.rb', line 92

def get_probability_map(data)
  prob = @class_prob.dup
  prob = calculate_class_probabilities_for_entry(data, prob)
  prob = normalize_class_probability prob
  probability_map = {}
  prob.each_with_index { |p, i| probability_map[index_to_klass(i)] = p }

  probability_map
end