ridge.rb in rumale-linear_model-0.25.0

- old
+ new
@@ -2,205 +2,136 @@
 
 require 'lbfgsb'
 
 require 'rumale/base/regressor'
 require 'rumale/validation'
-require 'rumale/linear_model/base_sgd'
 
+require_relative 'base_estimator'
+
 module Rumale
   module LinearModel
     # Ridge is a class that implements Ridge Regression
-    # with stochastic gradient descent (SGD) optimization,
-    # singular value decomposition (SVD), or L-BFGS optimization.
+    # with singular value decomposition (SVD) or L-BFGS optimization.
     #
     # @example
     #   require 'rumale/linear_model/ridge'
     #
-    #   estimator =
-    #     Rumale::LinearModel::Ridge.new(reg_param: 0.1, max_iter: 1000, batch_size: 20, random_seed: 1)
+    #   estimator = Rumale::LinearModel::Ridge.new(reg_param: 0.1)
     #   estimator.fit(training_samples, traininig_values)
     #   results = estimator.predict(testing_samples)
     #
     #   # If Numo::Linalg is installed, you can specify 'svd' for the solver option.
     #   require 'numo/linalg/autoloader'
     #   require 'rumale/linear_model/ridge'
     #
     #   estimator = Rumale::LinearModel::Ridge.new(reg_param: 0.1, solver: 'svd')
     #   estimator.fit(training_samples, traininig_values)
     #   results = estimator.predict(testing_samples)
-    #
-    # *Reference*
-    # - Bottou, L., "Large-Scale Machine Learning with Stochastic Gradient Descent," Proc. COMPSTAT'10, pp. 177--186, 2010.
-    class Ridge < BaseSGD
-      include ::Rumale::Base::Regressor
+    class Ridge < Rumale::LinearModel::BaseEstimator
+      include Rumale::Base::Regressor
 
-      # Return the weight vector.
-      # @return [Numo::DFloat] (shape: [n_outputs, n_features])
-      attr_reader :weight_vec
-
-      # Return the bias term (a.k.a. intercept).
-      # @return [Numo::DFloat] (shape: [n_outputs])
-      attr_reader :bias_term
-
-      # Return the random generator for random sampling.
-      # @return [Random]
-      attr_reader :rng
-
       # Create a new Ridge regressor.
       #
-      # @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).
-      #   If solver is not 'sgd', this parameter is ignored.
-      # @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'.
-      #   If solver is not 'sgd', this parameter is ignored.
-      # @param momentum [Float] The momentum factor.
-      #   If solver is not 'sgd', this parameter is ignored.
       # @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 max_iter [Integer] The maximum number of epochs that indicates
       #   how many times the whole data is given to the training process.
       #   If solver is 'svd', this parameter is ignored.
-      # @param batch_size [Integer] The size of the mini batches.
-      #   If solver is not 'sgd', this parameter is ignored.
       # @param tol [Float] The tolerance of loss for terminating optimization.
       #   If solver is 'svd', this parameter is ignored.
-      # @param solver [String] The algorithm to calculate weights. ('auto', 'sgd', 'svd', or 'lbfgs').
+      # @param solver [String] The algorithm to calculate weights. ('auto', 'svd', or 'lbfgs').
       #   'auto' chooses the 'svd' solver if Numo::Linalg is loaded. Otherwise, it chooses the 'lbfgs' solver.
-      #   'sgd' uses the stochastic gradient descent optimization.
       #   'svd' performs singular value decomposition of samples.
       #   'lbfgs' uses the L-BFGS method for 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 or solver is not 'sgd'.
       # @param verbose [Boolean] The flag indicating whether to output loss during iteration.
       #   If solver is 'svd', this parameter is ignored.
-      # @param random_seed [Integer] The seed value using to initialize the random generator.
-      def initialize(learning_rate: 0.01, decay: nil, momentum: 0.9,
-                     reg_param: 1.0, fit_bias: true, bias_scale: 1.0,
-                     max_iter: 1000, batch_size: 50, tol: 1e-4,
-                     solver: 'auto',
-                     n_jobs: nil, verbose: false, random_seed: nil)
+      def initialize(reg_param: 1.0, fit_bias: true, bias_scale: 1.0, max_iter: 1000, tol: 1e-4, solver: 'auto', verbose: false)
         super()
-        @params.merge!(method(:initialize).parameters.to_h { |_t, arg| [arg, binding.local_variable_get(arg)] })
+        @params = {
+          reg_param: reg_param,
+          fit_bias: fit_bias,
+          bias_scale: bias_scale,
+          max_iter: max_iter,
+          tol: tol,
+          verbose: verbose
+        }
         @params[:solver] = if solver == 'auto'
                              enable_linalg?(warning: false) ? 'svd' : 'lbfgs'
                            else
-                             solver.match?(/^svd$|^sgd$|^lbfgs$/) ? solver : 'lbfgs'
+                             solver.match?(/^svd$|^lbfgs$/) ? solver : 'lbfgs'
                            end
-        @params[:decay] ||= @params[:reg_param] * @params[:learning_rate]
-        @params[:random_seed] ||= srand
-        @rng = Random.new(@params[:random_seed])
-        @penalty_type = L2_PENALTY
-        @loss_func = ::Rumale::LinearModel::Loss::MeanSquaredError.new
       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 [Ridge] 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)
 
-        if @params[:solver] == 'svd' && enable_linalg?(warning: false)
-          fit_svd(x, y)
-        elsif @params[:solver] == 'lbfgs'
-          fit_lbfgs(x, y)
-        else
-          fit_sgd(x, y)
-        end
+        @weight_vec, @bias_term = if @params[:solver] == 'svd' && enable_linalg?(warning: false)
+                                    partial_fit_svd(x, y)
+                                  else
+                                    partial_fit_lbfgs(x, y)
+                                  end
 
         self
       end
 
       # Predict values for samples.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the values.
       # @return [Numo::DFloat] (shape: [n_samples, n_outputs]) Predicted values per sample.
       def predict(x)
-        x = ::Rumale::Validation.check_convert_sample_array(x)
+        x = Rumale::Validation.check_convert_sample_array(x)
 
         x.dot(@weight_vec.transpose) + @bias_term
       end
 
       private
 
-      def fit_svd(x, y)
+      def partial_fit_svd(x, y)
         x = expand_feature(x) if fit_bias?
-
         s, u, vt = Numo::Linalg.svd(x, driver: 'sdd', job: 'S')
         d = (s / (s**2 + @params[:reg_param])).diag
         w = vt.transpose.dot(d).dot(u.transpose).dot(y)
-
-        @weight_vec, @bias_term = single_target?(y) ? split_weight(w) : split_weight_mult(w)
+        w = w.transpose.dup unless single_target?(y)
+        split_weight(w)
       end
 
-      def fit_lbfgs(x, y)
-        fnc = proc do |w, x, y, a| # rubocop:disable Lint/ShadowingOuterLocalVariable
+      def partial_fit_lbfgs(base_x, base_y)
+        fnc = proc do |w, x, y, a|
           n_samples, n_features = x.shape
           w = w.reshape(y.shape[1], n_features) unless y.shape[1].nil?
           z = x.dot(w.transpose)
           d = z - y
           loss = (d**2).sum.fdiv(n_samples) + a * (w * w).sum
           gradient = 2.fdiv(n_samples) * d.transpose.dot(x) + 2.0 * a * w
           [loss, gradient.flatten.dup]
         end
 
-        x = expand_feature(x) if fit_bias?
+        base_x = expand_feature(base_x) if fit_bias?
 
-        n_features = x.shape[1]
-        n_outputs = single_target?(y) ? 1 : y.shape[1]
+        n_features = base_x.shape[1]
+        n_outputs = single_target?(base_y) ? 1 : base_y.shape[1]
+        w_init = Numo::DFloat.zeros(n_outputs * n_features)
 
         res = Lbfgsb.minimize(
-          fnc: fnc, jcb: true, x_init: init_weight(n_features, n_outputs), args: [x, y, @params[:reg_param]],
+          fnc: fnc, jcb: true, x_init: w_init, args: [base_x, base_y, @params[:reg_param]],
           maxiter: @params[:max_iter], factr: @params[:tol] / Lbfgsb::DBL_EPSILON,
           verbose: @params[:verbose] ? 1 : -1
         )
 
-        @weight_vec, @bias_term =
-          if single_target?(y)
-            split_weight(res[:x])
-          else
-            split_weight_mult(res[:x].reshape(n_outputs, n_features).transpose)
-          end
+        w = single_target?(base_y) ? res[:x] : res[:x].reshape(n_outputs, n_features)
+        split_weight(w)
       end
 
-      def fit_sgd(x, y)
-        if single_target?(y)
-          @weight_vec, @bias_term = partial_fit(x, y)
-        else
-          n_outputs = y.shape[1]
-          n_features = x.shape[1]
-          @weight_vec = Numo::DFloat.zeros(n_outputs, n_features)
-          @bias_term = Numo::DFloat.zeros(n_outputs)
-          if enable_parallel?
-            models = parallel_map(n_outputs) { |n| partial_fit(x, y[true, n]) }
-            n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = models[n] }
-          else
-            n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = partial_fit(x, y[true, n]) }
-          end
-        end
-      end
-
       def single_target?(y)
         y.ndim == 1
-      end
-
-      def init_weight(n_features, n_outputs)
-        ::Rumale::Utils.rand_normal([n_outputs, n_features], @rng.dup).flatten.dup
-      end
-
-      def split_weight_mult(w)
-        if fit_bias?
-          [w[0...-1, true].dup, w[-1, true].dup]
-        else
-          [w.dup, Numo::DFloat.zeros(w.shape[1])]
-        end
       end
     end
   end
 end