Class: Eps::NaiveBayes
- Inherits:
-
BaseEstimator
- Object
- BaseEstimator
- Eps::NaiveBayes
- Defined in:
- lib/eps/naive_bayes.rb
Instance Attribute Summary collapse
-
#probabilities ⇒ Object
readonly
Returns the value of attribute probabilities.
Class Method Summary collapse
Instance Method Summary collapse
Methods inherited from BaseEstimator
#evaluate, extract_text_features, #initialize, #predict, #summary, #to_pmml
Constructor Details
This class inherits a constructor from Eps::BaseEstimator
Instance Attribute Details
#probabilities ⇒ Object (readonly)
Returns the value of attribute probabilities.
3 4 5 |
# File 'lib/eps/naive_bayes.rb', line 3 def probabilities @probabilities end |
Class Method Details
.load_pmml(data) ⇒ Object
pmml
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
# File 'lib/eps/naive_bayes.rb', line 11 def self.load_pmml(data) super do |data| # TODO more validation node = data.css("NaiveBayesModel") prior = {} node.css("BayesOutput TargetValueCount").each do |n| prior[n.attribute("value").value] = n.attribute("count").value.to_f end legacy = false conditional = {} features = {} node.css("BayesInput").each do |n| prob = {} # numeric n.css("TargetValueStat").each do |n2| n3 = n2.css("GaussianDistribution") prob[n2.attribute("value").value] = { mean: n3.attribute("mean").value.to_f, stdev: Math.sqrt(n3.attribute("variance").value.to_f) } end # detect bad form in Eps < 0.3 bad_format = n.css("PairCounts").map { |n2| n2.attribute("value").value } == prior.keys # categorical n.css("PairCounts").each do |n2| if bad_format n2.css("TargetValueCount").each do |n3| prob[n3.attribute("value").value] ||= {} prob[n3.attribute("value").value][n2.attribute("value").value] = BigDecimal(n3.attribute("count").value) end else boom = {} n2.css("TargetValueCount").each do |n3| boom[n3.attribute("value").value] = BigDecimal(n3.attribute("count").value) end prob[n2.attribute("value").value] = boom end end if bad_format legacy = true prob.each do |k, v| prior.keys.each do |k| v[k] ||= 0.0 end end end name = n.attribute("fieldName").value conditional[name] = prob features[name] = n.css("TargetValueStat").any? ? "numeric" : "categorical" end target = node.css("BayesOutput").attribute("fieldName").value probabilities = { prior: prior, conditional: conditional } # get derived fields derived = {} data.css("DerivedField").each do |n| name = n.attribute("name").value field = n.css("NormDiscrete").attribute("field").value value = n.css("NormDiscrete").attribute("value").value features.delete(name) features[field] = "derived" derived[field] ||= {} derived[field][name] = value end Evaluators::NaiveBayes.new(probabilities: probabilities, features: features, derived: derived, legacy: legacy) end end |