Added learning_rate option for LightGBM - #2

CHANGELOG.md

@@ -1,3 +1,7 @@
+## 0.3.5 (unreleased)
+
+- Added `learning_rate` option for LightGBM
+
 ## 0.3.4 (2020-04-05)
 
 - Added `predict_probability` for classification

lib/eps/base_estimator.rb

@@ -83,7 +83,7 @@ def _predict(data, probabilities)
       singular ? predictions.first : predictions
     end
 
-    def train(data, y = nil, target: nil, weight: nil, split: nil, validation_set: nil, verbose: nil, text_features: nil, early_stopping: nil)
+    def train(data, y = nil, target: nil, weight: nil, split: nil, validation_set: nil, text_features: nil, **options)
       data, @target = prep_data(data, y, target, weight)
       @target_type = Utils.column_type(data.label, @target)
 
@@ -175,7 +175,7 @@ def train(data, y = nil, target: nil, weight: nil, split: nil, validation_set: n
       raise "No data in validation set" if validation_set && validation_set.empty?
 
       @validation_set = validation_set
-      @evaluator = _train(verbose: verbose, early_stopping: early_stopping)
+      @evaluator = _train(**options)
 
       # reset pmml
       @pmml = nil

lib/eps/lightgbm.rb

@@ -17,7 +17,7 @@ def _summary(extended: false)
       str
     end
 
-    def _train(verbose: nil, early_stopping: nil)
+    def _train(verbose: nil, early_stopping: nil, learning_rate: 0.1)
       train_set = @train_set
       validation_set = @validation_set.dup
       summary_label = train_set.label
@@ -66,6 +66,7 @@ def _train(verbose: nil, early_stopping: nil)
         params[:min_data_in_bin] = 1
         params[:min_data_in_leaf] = 1
       end
+      params[:learning_rate] = learning_rate
 
       # create datasets
       categorical_idx = @features.values.map.with_index.select { |type, _| type == "categorical" }.map(&:last)

test/lightgbm_test.rb

@@ -218,6 +218,16 @@ def test_text_features_classification
     assert_equal ["ham", "spam"], model.predict(test_data)
   end
 
+  def test_learning_rate
+    data = mpg_data
+    model = Eps::LightGBM.new(data, target: :hwy, split: false, learning_rate: 1)
+    assert model.summary
+
+    expected = [30.80980036, 34.39919293, 17.99841545, 17.298401, 28.4685196, 29.31558087, 27.79557906, 18.44068633, 24.64178236, 29.31558087]
+    predictions = model.predict(data.first(10))
+    assert_elements_in_delta expected, predictions
+  end
+
   private
 
   def model