lib/lightgbm/regressor.rb in lightgbm-0.1.3 vs lib/lightgbm/regressor.rb in lightgbm-0.1.4

@@ -1,34 +1,25 @@
 module LightGBM
-  class Regressor
-    def initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: nil)
-      @params = {
-        num_leaves: num_leaves,
-        learning_rate: learning_rate
-      }
-      @params[:objective] = objective if objective
-      @n_estimators = n_estimators
+  class Regressor < Model
+    def initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options)
+      super
     end
 
-    def fit(x, y)
-      train_set = Dataset.new(x, label: y)
-      @booster = LightGBM.train(@params, train_set, num_boost_round: @n_estimators)
+    def fit(x, y, categorical_feature: "auto", eval_set: nil, eval_names: [], early_stopping_rounds: nil, verbose: true)
+      train_set = Dataset.new(x, label: y, categorical_feature: categorical_feature)
+      valid_sets = Array(eval_set).map { |v| Dataset.new(v[0], label: v[1], reference: train_set) }
+
+      @booster = LightGBM.train(@params, train_set,
+        num_boost_round: @n_estimators,
+        early_stopping_rounds: early_stopping_rounds,
+        verbose_eval: verbose,
+        valid_sets: valid_sets,
+        valid_names: eval_names
+      )
       nil
     end
 
-    def predict(data)
-      @booster.predict(data)
-    end
-
-    def save_model(fname)
-      @booster.save_model(fname)
-    end
-
-    def load_model(fname)
-      @booster = Booster.new(params: @params, model_file: fname)
-    end
-
-    def feature_importances
-      @booster.feature_importance
+    def predict(data, num_iteration: nil)
+      @booster.predict(data, num_iteration: num_iteration)
     end
   end
 end