Class: Ai4r::Experiment::ClassifierEvaluator

Inherits:
Object
  • Object
show all
Defined in:
lib/ai4r/experiment/classifier_evaluator.rb

Overview

The ClassifierEvaluator is useful to compare different classifiers algorithms. The evaluator builds the Classifiers using the same data examples, and provides methods to evalute their performance in parallel. It is a nice tool to compare and evaluate the performance of different algorithms, the same algorithm with different parameters, or your own new algorithm against the classic classifiers.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initializeClassifierEvaluator

Returns a new instance of ClassifierEvaluator


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 19

def initialize
  @classifiers = []
end

Instance Attribute Details

#build_timesObject (readonly)

Returns the value of attribute build_times


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 17

def build_times
  @build_times
end

#classifiersObject (readonly)

Returns the value of attribute classifiers


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 17

def classifiers
  @classifiers
end

#eval_timesObject (readonly)

Returns the value of attribute eval_times


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 17

def eval_times
  @eval_times
end

Instance Method Details

#add_classifier(classifier) ⇒ Object Also known as: <<

Add a classifier instance to the test batch


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 24

def add_classifier(classifier)
  @classifiers << classifier
  return self
end

#build(data_set) ⇒ Object

Build all classifiers, using data examples found in data_set. The last attribute of each item is considered as the item class. Building times are measured by separate, and can be accessed through build_times attribute reader.


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 36

def build(data_set)
  @build_times = []
  @classifiers.each do |classifier|
    @build_times << Benchmark.measure { classifier.build data_set }
  end
  return self
end

#eval(data) ⇒ Object

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

classifier.eval(['New York',  '<30', 'F'])  
=> ['Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N']

Evaluation times are measured by separate, and can be accessed through eval_times attribute reader.


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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 50

def eval(data)
  @eval_times = []
  results = []
  @classifiers.each do |classifier|
    @eval_times << Benchmark.measure { results << classifier.eval(data) }
  end
  return results
end

#test(data_set) ⇒ Object

Test classifiers using a data set. The last attribute of each item is considered as the expected class. Data items are evaluated using all classifiers: evalution times, sucess rate, and quantity of classification errors are returned in a data set. The return data set has a row for every classifier tested, and the following attributes:

["Classifier", "Testing Time", "Errors", "Success rate"]

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# File 'lib/ai4r/experiment/classifier_evaluator.rb', line 66

def test(data_set)
  result_data_items = []
  @classifiers.each do |classifier|
    result_data_items << test_classifier(classifier, data_set)
  end
  return Ai4r::Data::DataSet.new(:data_items => result_data_items, 
    :data_labels => ["Classifier","Testing Time","Errors","Success rate"])
end