svr.rb in rumale-linear_model-0.25.0

- old
+ new

@@ -1,99 +1,70 @@
 # frozen_string_literal: true
 
+require 'lbfgsb'
+
 require 'rumale/base/regressor'
 require 'rumale/validation'
-require 'rumale/linear_model/base_sgd'
 
+require_relative 'base_estimator'
+
 module Rumale
   module LinearModel
-    # SVR is a class that implements Support Vector Regressor
-    # with stochastic gradient descent optimization.
+    # SVR is a class that implements Support Vector Regressor with the squared epsilon-insensitive loss.
     #
     # @note
     #   Rumale::SVM provides linear and kernel support vector regressor based on LIBLINEAR and LIBSVM.
     #   If you prefer execution speed, you should use Rumale::SVM::LinearSVR.
     #   https://github.com/yoshoku/rumale-svm
     #
     # @example
     #   require 'rumale/linear_model/svr'
     #
-    #   estimator =
-    #     Rumale::LinearModel::SVR.new(reg_param: 1.0, epsilon: 0.1, max_iter: 1000, batch_size: 50, random_seed: 1)
+    #   estimator = Rumale::LinearModel::SVR.new(reg_param: 1.0, epsilon: 0.1)
     #   estimator.fit(training_samples, traininig_target_values)
     #   results = estimator.predict(testing_samples)
-    #
-    # *Reference*
-    # - Shalev-Shwartz, S., and Singer, Y., "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM," Proc. ICML'07, pp. 807--814, 2007.
-    # - Tsuruoka, Y., Tsujii, J., and Ananiadou, S., "Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty," Proc. ACL'09, pp. 477--485, 2009.
-    # - Bottou, L., "Large-Scale Machine Learning with Stochastic Gradient Descent," Proc. COMPSTAT'10, pp. 177--186, 2010.
-    class SVR < BaseSGD
-      include ::Rumale::Base::Regressor
+    class SVR < Rumale::LinearModel::BaseEstimator
+      include Rumale::Base::Regressor
 
-      # Return the weight vector for SVR.
-      # @return [Numo::DFloat] (shape: [n_outputs, n_features])
-      attr_reader :weight_vec
-
-      # Return the bias term (a.k.a. intercept) for SVR.
-      # @return [Numo::DFloat] (shape: [n_outputs])
-      attr_reader :bias_term
-
-      # Return the random generator for performing random sampling.
-      # @return [Random]
-      attr_reader :rng
-
       # Create a new regressor with Support Vector Machine by the SGD optimization.
       #
-      # @param learning_rate [Float] The initial value of learning rate.
-      #   The learning rate decreases as the iteration proceeds according to the equation: learning_rate / (1 + decay * t).
-      # @param decay [Float] The smoothing parameter for decreasing learning rate as the iteration proceeds.
-      #   If nil is given, the decay sets to 'reg_param * learning_rate'.
-      # @param momentum [Float] The momentum factor.
-      # @param penalty [String] The regularization type to be used ('l1', 'l2', and 'elasticnet').
-      # @param l1_ratio [Float] The elastic-net type regularization mixing parameter.
-      #   If penalty set to 'l2' or 'l1', this parameter is ignored.
-      #   If l1_ratio = 1, the regularization is similar to Lasso.
-      #   If l1_ratio = 0, the regularization is similar to Ridge.
-      #   If 0 < l1_ratio < 1, the regularization is a combination of L1 and L2.
       # @param reg_param [Float] The regularization parameter.
       # @param fit_bias [Boolean] The flag indicating whether to fit the bias term.
       # @param bias_scale [Float] The scale of the bias term.
       # @param epsilon [Float] The margin of tolerance.
       # @param max_iter [Integer] The maximum number of epochs that indicates
       #   how many times the whole data is given to the training process.
-      # @param batch_size [Integer] The size of the mini batches.
       # @param tol [Float] The tolerance of loss for terminating optimization.
       # @param n_jobs [Integer] The number of jobs for running the fit method in parallel.
       #   If nil is given, the method does not execute in parallel.
       #   If zero or less is given, it becomes equal to the number of processors.
       #   This parameter is ignored if the Parallel gem is not loaded.
       # @param verbose [Boolean] The flag indicating whether to output loss during iteration.
-      # @param random_seed [Integer] The seed value using to initialize the random generator.
-      def initialize(learning_rate: 0.01, decay: nil, momentum: 0.9,
-                     penalty: 'l2', reg_param: 1.0, l1_ratio: 0.5,
-                     fit_bias: true, bias_scale: 1.0,
-                     epsilon: 0.1,
-                     max_iter: 1000, batch_size: 50, tol: 1e-4,
-                     n_jobs: nil, verbose: false, random_seed: nil)
+      def initialize(reg_param: 1.0, fit_bias: true, bias_scale: 1.0, epsilon: 0.1, max_iter: 1000, tol: 1e-4,
+                     n_jobs: nil, verbose: false)
         super()
-        @params.merge!(method(:initialize).parameters.to_h { |_t, arg| [arg, binding.local_variable_get(arg)] })
-        @params[:decay] ||= @params[:reg_param] * @params[:learning_rate]
-        @params[:random_seed] ||= srand
-        @rng = Random.new(@params[:random_seed])
-        @penalty_type = @params[:penalty]
-        @loss_func = ::Rumale::LinearModel::Loss::EpsilonInsensitive.new(epsilon: @params[:epsilon])
+        @params = {
+          reg_param: reg_param,
+          fit_bias: fit_bias,
+          bias_scale: bias_scale,
+          epsilon: epsilon,
+          max_iter: max_iter,
+          tol: tol,
+          n_jobs: n_jobs,
+          verbose: verbose
+        }
       end
 
       # Fit the model with given training data.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
       # @param y [Numo::DFloat] (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.
       # @return [SVR] The learned regressor itself.
       def fit(x, y)
-        x = ::Rumale::Validation.check_convert_sample_array(x)
-        y = ::Rumale::Validation.check_convert_target_value_array(y)
-        ::Rumale::Validation.check_sample_size(x, y)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_target_value_array(y)
+        Rumale::Validation.check_sample_size(x, y)
 
         n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
         n_features = x.shape[1]
 
         if n_outputs > 1
@@ -118,9 +89,40 @@
       # @return [Numo::DFloat] (shape: [n_samples, n_outputs]) Predicted values per sample.
       def predict(x)
         x = ::Rumale::Validation.check_convert_sample_array(x)
 
         x.dot(@weight_vec.transpose) + @bias_term
+      end
+
+      private
+
+      def partial_fit(base_x, single_y)
+        fnc = proc do |w, x, y, eps, reg_param|
+          n_samples = x.shape[0]
+          z = x.dot(w)
+          d = y - z
+          loss = 0.5 * reg_param * w.dot(w) + (x.class.maximum(0, d.abs - eps)**2).sum.fdiv(n_samples)
+          c = x.class.zeros(n_samples)
+          indices = d.gt(eps)
+          c[indices] = -d[indices] + eps if indices.count.positive?
+          indices = d.lt(eps)
+          c[indices] = -d[indices] - eps if indices.count.positive?
+          grad = reg_param * w + 2.fdiv(n_samples) * x.transpose.dot(c)
+          [loss, grad]
+        end
+
+        base_x = expand_feature(base_x) if fit_bias?
+
+        n_features = base_x.shape[1]
+        w_init = Numo::DFloat.zeros(n_features)
+
+        res = Lbfgsb.minimize(
+          fnc: fnc, jcb: true, x_init: w_init, args: [base_x, single_y, @params[:epsilon], @params[:reg_param]],
+          maxiter: @params[:max_iter], factr: @params[:tol] / Lbfgsb::DBL_EPSILON,
+          verbose: @params[:verbose] ? 1 : -1
+        )
+
+        split_weight(res[:x])
       end
     end
   end
 end