Add support for categorical feature auto-encoding

lib/lightgbm/booster.rb

@@ -1,3 +1,5 @@
+require_relative "categorical_feature_encoder"
+
 module LightGBM
   class Booster
     attr_accessor :best_iteration, :train_data_name
@@ -6,9 +8,11 @@ def initialize(params: nil, train_set: nil, model_file: nil, model_str: nil)
       @handle = ::FFI::MemoryPointer.new(:pointer)
       if model_str
         model_from_string(model_str)
+        @categorical_feature_encoder = CategoricalFeatureEncoder.new(model_str.each_line)
       elsif model_file
         out_num_iterations = ::FFI::MemoryPointer.new(:int)
         check_result FFI.LGBM_BoosterCreateFromModelfile(model_file, out_num_iterations, @handle)
+        @categorical_feature_encoder = CategoricalFeatureEncoder.new(File.foreach(model_file))
       else
         params ||= {}
         set_verbosity(params)
@@ -152,7 +156,12 @@ def predict(input, start_iteration: nil, num_iteration: nil, **params)
       num_iteration ||= best_iteration
       num_class ||= num_class()
 
-      flat_input = input.flatten
+      flat_input = if @categorical_feature_encoder
+        input.flat_map { |row| @categorical_feature_encoder.apply(row) }
+      else
+        input.flatten
+      end
+
       handle_missing(flat_input)
       data = ::FFI::MemoryPointer.new(:double, input.count * input.first.count)
       data.write_array_of_double(flat_input)

lib/lightgbm/categorical_feature_encoder.rb

@@ -0,0 +1,82 @@
+require "json"
+
+module LightGBM
+  # Converts LightGBM categorical featulres to Float, using label encoding.
+  # The categorical and mappings are extracted from the LightGBM model file.
+  class CategoricalFeatureEncoder
+    # Initializes a new CategoricalFeatureEncoder instance.
+    #
+    # @param model_enumerable [Enumerable] Enumerable with each line of LightGBM model file.
+    def initialize(model_enumerable)
+      @categorical_feature = []
+      @pandas_categorical = []
+
+      load_categorical_features(model_enumerable)
+    end
+
+    # Returns a new array with categorical features converted to Float, using label encoding.
+    def apply(feature_values)
+      return feature_values if @categorical_feature.empty?
+
+      transformed_features = feature_values.dup
+
+      @categorical_feature.each_with_index do |feature_index, pandas_categorical_index|
+        value = feature_values[feature_index]
+
+        pandas_categorical_entry = @pandas_categorical[pandas_categorical_index]
+        transformed_value = pandas_categorical_entry[value]
+        transformed_features[feature_index] = transformed_value.nil? ? Float::NAN : transformed_value.to_f
+      end
+
+      transformed_features
+    end
+
+    private
+
+    def load_categorical_features(model_enumerable)
+      categorical_found = false
+      pandas_found = false
+
+      model_enumerable.each_entry do |line|
+        # Format: "[categorical_feature: 0,1,2,3,4,5]"
+        if line.start_with?("[categorical_feature:")
+          parts = line.split("categorical_feature:")
+          last_part = parts.last
+          next if last_part.nil?
+
+          values = last_part.strip[0...-1]
+          next if values.nil?
+
+          @categorical_feature = values.split(",").map(&:to_i)
+          categorical_found = true
+        end
+
+        # Format: "pandas_categorical:[[-1.0, 0.0, 1.0], ["", "shop_pay"], [false, true]]"
+        if line.start_with?("pandas_categorical:")
+          parts = line.split("pandas_categorical:")
+          values = parts[1]
+          next if values.nil?
+
+          @pandas_categorical = JSON.parse(values).map do |array|
+            array.each_with_index.to_h
+          end
+          pandas_found = true
+        end
+
+        # Break the loop if both lines are found
+        break if categorical_found && pandas_found
+      end
+
+      if @categorical_feature.size != @pandas_categorical.size
+        raise "categorical_feature and pandas_categorical mismatch"
+      end
+    end
+
+    def transform_categorical_value(value, index)
+      pandas_categorical_entry = @pandas_categorical[index]
+      transformed_value = pandas_categorical_entry.find_index(value)
+
+      transformed_value.nil? ? Float::NAN : transformed_value.to_f
+    end
+  end
+end

test/booster_test.rb

@@ -8,13 +8,27 @@ def test_model_file
     assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2)
   end
 
+  def test_model_file_with_categorical_features
+    x_test = [[false, "green", 7.2, 9.0], [true, "blue", 7.9, 0.0]]
+    booster = LightGBM::Booster.new(model_file: "test/support/model_with_categorical_features.txt")
+    y_pred = booster.predict(x_test)
+    assert_elements_in_delta [0.9948804305465, 0.792909968121466], y_pred.first(2)
+  end
+
   def test_model_str
     x_test = [[3.7, 1.2, 7.2, 9.0], [7.5, 0.5, 7.9, 0.0]]
     booster = LightGBM::Booster.new(model_str: File.read("test/support/model.txt"))
     y_pred = booster.predict(x_test)
     assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2)
   end
 
+  def test_model_str_with_categorical_features
+    x_test = [[false, "green", 7.2, 9.0], [true, "blue", 7.9, 0.0]]
+    booster = LightGBM::Booster.new(model_str: File.read("test/support/model_with_categorical_features.txt"))
+    y_pred = booster.predict(x_test)
+    assert_elements_in_delta [0.9948804305465, 0.792909968121466], y_pred.first(2)
+  end
+
   def test_feature_importance
     assert_equal [280, 285, 335, 148], booster.feature_importance
   end

test/categorical_feature_encoder_test.rb

@@ -0,0 +1,41 @@
+require_relative "test_helper"
+
+class CategoricalFeatureEncoder < Minitest::Test
+  def setup
+    model = <<~MODEL
+      [categorical_feature: 1,2,3]
+      pandas_categorical:[[-1.0, 0.0, 1.0], ["red", "green", "blue"], [false, true]]
+    MODEL
+
+    @encoder = LightGBM::CategoricalFeatureEncoder.new(model.each_line)
+  end
+
+  def test_apply_with_categorical_features
+    input = [42.0, 0.0, "green", true]
+    expected = [42.0, 1.0, 1.0, 1.0]
+
+    assert_equal(expected, @encoder.apply(input))
+  end
+
+  def test_apply_with_non_categorical_features
+    input = [42.0, "non_categorical", 39.0, false]
+    expected = [42.0, Float::NAN, Float::NAN, 0]
+
+    assert_equal(expected, @encoder.apply(input))
+  end
+
+  def test_apply_with_missing_values
+    input = [42.0, nil, "red", nil]
+    expected = [42.0, Float::NAN, 0.0, Float::NAN]
+    result = @encoder.apply(input)
+
+    assert_equal(expected, result)
+  end
+
+  def test_apply_with_boolean_values
+    input = [42.0, -1.0, "green", false]
+    expected = [42.0, 0.0, 1.0, 0.0]
+
+    assert_equal(expected, @encoder.apply(input))
+  end
+end