lib/eps/model.rb in eps-0.2.1 vs lib/eps/model.rb in eps-0.3.0

@@ -1,86 +1,52 @@
 module Eps
   class Model
-    def initialize(data = nil, y = nil, target: nil, estimator: nil, **options)
-      @options = options
-
+    def initialize(data = nil, y = nil, estimator: nil, **options)
       if estimator
         @estimator = estimator
-      elsif data # legacy
-        train(data, y, target: target)
+      elsif data
+        train(data, y, **options)
       end
     end
 
     # pmml
 
     def self.load_pmml(data)
       if data.is_a?(String)
-        require "nokogiri"
         data = Nokogiri::XML(data) { |config| config.strict }
       end
 
       estimator_class =
-        if data.css("RegressionModel").any?
+        if data.css("Segmentation").any?
+          Eps::LightGBM
+        elsif data.css("RegressionModel").any?
           Eps::LinearRegression
         elsif data.css("NaiveBayesModel").any?
           Eps::NaiveBayes
         else
           raise "Unknown model"
         end
 
       new(estimator: estimator_class.load_pmml(data))
     end
 
-    # ruby - legacy
-
-    def self.load(data)
-      new(estimator: Eps::LinearRegression.load(data))
-    end
-
-    # json - legacy
-
-    def self.load_json(data)
-      new(estimator: Eps::LinearRegression.load_json(data))
-    end
-
-    def to_json
-      @estimator ? @estimator.to_json : super
-    end
-
-    # pfa - legacy
-
-    def self.load_pfa(data)
-      new(estimator: Eps::LinearRegression.load_pfa(data))
-    end
-
-    # metrics
-
-    def self.metrics(actual, estimated)
-      estimator_class =
-        if numeric?(actual)
-          Eps::LinearRegression
-        else
-          Eps::NaiveBayes
-        end
-
-      estimator_class.metrics(actual, estimated)
-    end
-
     private
 
-    def train(data, y = nil, target: nil)
-      y ||= daru?(data) ? data[target].to_a : data.map { |r| r[target] }
-
+    def train(data, y = nil, target: nil, algorithm: :lightgbm, **options)
       estimator_class =
-        if self.class.numeric?(y)
+        case algorithm
+        when :lightgbm
+          Eps::LightGBM
+        when :linear_regression
           Eps::LinearRegression
-        else
+        when :naive_bayes
           Eps::NaiveBayes
+        else
+          raise ArgumentError, "Unknown algorithm: #{algorithm}"
         end
 
-      @estimator = estimator_class.new(**@options)
-      @estimator.train(data, y, target: target)
+      @estimator = estimator_class.new(data, y, target: target, **options)
     end
 
     def respond_to_missing?(name, include_private = false)
       if @estimator
         @estimator.respond_to?(name, include_private)
@@ -88,21 +54,13 @@
         super
       end
     end
 
     def method_missing(method, *args, &block)
-      if @estimator
+      if @estimator && @estimator.respond_to?(method)
         @estimator.public_send(method, *args, &block)
       else
         super
       end
-    end
-
-    def self.numeric?(y)
-      y.first.is_a?(Numeric)
-    end
-
-    def daru?(x)
-      defined?(Daru) && x.is_a?(Daru::DataFrame)
     end
   end
 end