lib/svmkit/linear_model/logistic_regression.rb in svmkit-0.6.0 vs lib/svmkit/linear_model/logistic_regression.rb in svmkit-0.6.1

@@ -1,11 +1,10 @@
 # frozen_string_literal: true
 
 require 'svmkit/validation'
-require 'svmkit/base/base_estimator'
+require 'svmkit/linear_model/sgd_linear_estimator'
 require 'svmkit/base/classifier'
-require 'svmkit/optimizer/nadam'
 
 module SVMKit
   module LinearModel
     # LogisticRegression is a class that implements Logistic Regression
     # with mini-batch stochastic gradient descent optimization.
@@ -17,12 +16,11 @@
     #   estimator.fit(training_samples, traininig_labels)
     #   results = estimator.predict(testing_samples)
     #
     # *Reference*
     # - S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM," Mathematical Programming, vol. 127 (1), pp. 3--30, 2011.
-    class LogisticRegression
-      include Base::BaseEstimator
+    class LogisticRegression < SGDLinearEstimator
       include Base::Classifier
       include Validation
 
       # Return the weight vector for Logistic Regression.
       # @return [Numo::DFloat] (shape: [n_classes, n_features])
@@ -56,24 +54,12 @@
         check_params_float(reg_param: reg_param, bias_scale: bias_scale)
         check_params_integer(max_iter: max_iter, batch_size: batch_size)
         check_params_boolean(fit_bias: fit_bias)
         check_params_type_or_nil(Integer, random_seed: random_seed)
         check_params_positive(reg_param: reg_param, bias_scale: bias_scale, max_iter: max_iter, batch_size: batch_size)
-        @params = {}
-        @params[:reg_param] = reg_param
-        @params[:fit_bias] = fit_bias
-        @params[:bias_scale] = bias_scale
-        @params[:max_iter] = max_iter
-        @params[:batch_size] = batch_size
-        @params[:optimizer] = optimizer
-        @params[:optimizer] ||= Optimizer::Nadam.new
-        @params[:random_seed] = random_seed
-        @params[:random_seed] ||= srand
-        @weight_vec = nil
-        @bias_term = nil
+        super
         @classes = nil
-        @rng = Random.new(@params[:random_seed])
       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.
@@ -84,25 +70,23 @@
         check_label_array(y)
         check_sample_label_size(x, y)
 
         @classes = Numo::Int32[*y.to_a.uniq.sort]
         n_classes = @classes.size
-        _n_samples, n_features = x.shape
+        n_features = x.shape[1]
 
         if n_classes > 2
           @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
           @bias_term = Numo::DFloat.zeros(n_classes)
           n_classes.times do |n|
             bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
-            weight, bias = binary_fit(x, bin_y)
-            @weight_vec[n, true] = weight
-            @bias_term[n] = bias
+            @weight_vec[n, true], @bias_term[n] = partial_fit(x, bin_y)
           end
         else
           negative_label = y.to_a.uniq.min
           bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
-          @weight_vec, @bias_term = binary_fit(x, bin_y)
+          @weight_vec, @bias_term = partial_fit(x, bin_y)
         end
 
         self
       end
 
@@ -167,50 +151,11 @@
         nil
       end
 
       private
 
-      def binary_fit(x, y)
-        # Expand feature vectors for bias term.
-        samples = @params[:fit_bias] ? expand_feature(x) : x
-        # Initialize some variables.
-        n_samples, n_features = samples.shape
-        rand_ids = [*0...n_samples].shuffle(random: @rng)
-        weight_vec = Numo::DFloat.zeros(n_features)
-        optimizer = @params[:optimizer].dup
-        # Start optimization.
-        @params[:max_iter].times do |_t|
-          # random sampling
-          subset_ids = rand_ids.shift(@params[:batch_size])
-          rand_ids.concat(subset_ids)
-          data = samples[subset_ids, true]
-          labels = y[subset_ids]
-          # calculate gradient for loss function.
-          loss_grad = loss_gradient(data, labels, weight_vec)
-          # update weight.
-          weight_vec = optimizer.call(weight_vec, weight_gradient(loss_grad, data, weight_vec))
-        end
-        split_weight_vec_bias(weight_vec)
-      end
-
-      def loss_gradient(x, y, weight)
-        z = x.dot(weight)
-        grad = y / (Numo::NMath.exp(-y * z) + 1.0) - y
-        grad
-      end
-
-      def weight_gradient(loss_grad, x, weight)
-        x.transpose.dot(loss_grad) / @params[:batch_size] + @params[:reg_param] * weight
-      end
-
-      def expand_feature(x)
-        Numo::NArray.hstack([x, Numo::DFloat.ones([x.shape[0], 1]) * @params[:bias_scale]])
-      end
-
-      def split_weight_vec_bias(weight_vec)
-        weights = @params[:fit_bias] ? weight_vec[0...-1] : weight_vec
-        bias = @params[:fit_bias] ? weight_vec[-1] : 0.0
-        [weights, bias]
+      def calc_loss_gradient(x, y, weight)
+        y / (Numo::NMath.exp(-y * x.dot(weight)) + 1.0) - y
       end
     end
   end
 end