logistic_regression.rb in svmkit-0.2.1

- old
+ new

@@ -18,20 +18,10 @@
     # 1. 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
       include Base::Classifier
 
-      # @!visibility private
-      DEFAULT_PARAMS = {
-        reg_param: 1.0,
-        fit_bias: false,
-        bias_scale: 1.0,
-        max_iter: 100,
-        batch_size: 50,
-        random_seed: nil
-      }.freeze
-
       # Return the weight vector for Logistic Regression.
       # @return [Numo::DFloat] (shape: [n_features])
       attr_reader :weight_vec
 
       # Return the bias term (a.k.a. intercept) for Logistic Regression.
@@ -50,21 +40,21 @@
       #   If fit_bias is true, the feature vector v becoms [v; bias_scale].
       # @param max_iter [Integer] The maximum number of iterations.
       # @param batch_size [Integer] The size of the mini batches.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, max_iter: 100, batch_size: 50, random_seed: nil)
-        self.params = {}
-        self.params[:reg_param] = reg_param
-        self.params[:fit_bias] = fit_bias
-        self.params[:bias_scale] = bias_scale
-        self.params[:max_iter] = max_iter
-        self.params[:batch_size] = batch_size
-        self.params[:random_seed] = random_seed
-        self.params[:random_seed] ||= srand
+        @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[:random_seed] = random_seed
+        @params[:random_seed] ||= srand
         @weight_vec = nil
         @bias_term = 0.0
-        @rng = Random.new(self.params[:random_seed])
+        @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.
@@ -75,41 +65,41 @@
         # Generate binary labels.
         negative_label = y.to_a.uniq.sort.shift
         bin_y = y.to_a.map { |l| l != negative_label ? 1 : 0 }
         # Expand feature vectors for bias term.
         samples = x
-        if params[:fit_bias]
+        if @params[:fit_bias]
           samples = Numo::NArray.hstack(
-            [samples, Numo::DFloat.ones([x.shape[0], 1]) * params[:bias_scale]]
+            [samples, Numo::DFloat.ones([x.shape[0], 1]) * @params[:bias_scale]]
           )
         end
         # Initialize some variables.
         n_samples, n_features = samples.shape
         rand_ids = [*0..n_samples - 1].shuffle(random: @rng)
         weight_vec = Numo::DFloat.zeros(n_features)
         # Start optimization.
-        params[:max_iter].times do |t|
+        @params[:max_iter].times do |t|
           # random sampling
-          subset_ids = rand_ids.shift(params[:batch_size])
+          subset_ids = rand_ids.shift(@params[:batch_size])
           rand_ids.concat(subset_ids)
           # update the weight vector.
-          eta = 1.0 / (params[:reg_param] * (t + 1))
+          eta = 1.0 / (@params[:reg_param] * (t + 1))
           mean_vec = Numo::DFloat.zeros(n_features)
           subset_ids.each do |n|
             z = weight_vec.dot(samples[n, true])
             coef = bin_y[n] / (1.0 + Math.exp(bin_y[n] * z))
             mean_vec += samples[n, true] * coef
           end
-          mean_vec *= eta / params[:batch_size]
-          weight_vec = weight_vec * (1.0 - eta * params[:reg_param]) + mean_vec
+          mean_vec *= eta / @params[:batch_size]
+          weight_vec = weight_vec * (1.0 - eta * @params[:reg_param]) + mean_vec
           # scale the weight vector.
           norm = Math.sqrt(weight_vec.dot(weight_vec))
-          scaler = (1.0 / params[:reg_param]**0.5) / (norm + 1.0e-12)
+          scaler = (1.0 / @params[:reg_param]**0.5) / (norm + 1.0e-12)
           weight_vec *= [1.0, scaler].min
         end
         # Store the learned model.
-        if params[:fit_bias]
+        if @params[:fit_bias]
           @weight_vec = weight_vec[0...n_features - 1]
           @bias_term = weight_vec[n_features - 1]
         else
           @weight_vec = weight_vec[0...n_features]
           @bias_term = 0.0
@@ -154,16 +144,16 @@
       end
 
       # Dump marshal data.
       # @return [Hash] The marshal data about LogisticRegression.
       def marshal_dump
-        { params: params, weight_vec: @weight_vec, bias_term: @bias_term, rng: @rng }
+        { params: @params, weight_vec: @weight_vec, bias_term: @bias_term, rng: @rng }
       end
 
       # Load marshal data.
       # @return [nil]
       def marshal_load(obj)
-        self.params = obj[:params]
+        @params = obj[:params]
         @weight_vec = obj[:weight_vec]
         @bias_term = obj[:bias_term]
         @rng = obj[:rng]
         nil
       end