lib/eps.rb in eps-0.3.0 vs lib/eps.rb in eps-0.3.1

@@ -1,20 +1,15 @@
 # dependencies
-require "bigdecimal"
 require "json"
 require "lightgbm"
 require "matrix"
 require "nokogiri"
 
 # modules
 require "eps/base"
 require "eps/base_estimator"
 require "eps/data_frame"
-require "eps/evaluators/linear_regression"
-require "eps/evaluators/lightgbm"
-require "eps/evaluators/naive_bayes"
-require "eps/evaluators/node"
 require "eps/label_encoder"
 require "eps/lightgbm"
 require "eps/linear_regression"
 require "eps/metrics"
 require "eps/model"
@@ -22,20 +17,34 @@
 require "eps/statistics"
 require "eps/text_encoder"
 require "eps/utils"
 require "eps/version"
 
+# pmml
+require "eps/pmml"
+require "eps/pmml/generator"
+require "eps/pmml/loader"
+
+# evaluators
+require "eps/evaluators/linear_regression"
+require "eps/evaluators/lightgbm"
+require "eps/evaluators/naive_bayes"
+require "eps/evaluators/node"
+
 module Eps
-  def self.metrics(y_true, y_pred)
+  class Error < StandardError; end
+  class UnstableSolution < Error; end
+
+  def self.metrics(y_true, y_pred, weight: nil)
     if Utils.column_type(y_true, "actual") == "numeric"
       {
-        rmse: Metrics.rmse(y_true, y_pred),
-        mae: Metrics.mae(y_true, y_pred),
-        me: Metrics.me(y_true, y_pred)
+        rmse: Metrics.rmse(y_true, y_pred, weight: weight),
+        mae: Metrics.mae(y_true, y_pred, weight: weight),
+        me: Metrics.me(y_true, y_pred, weight: weight)
       }
     else
       {
-        accuracy: Metrics.accuracy(y_true, y_pred)
+        accuracy: Metrics.accuracy(y_true, y_pred, weight: weight)
       }
     end
   end
 
   # backwards compatibility