svc.rb in svmkit-0.2.9

- old
+ new

@@ -43,30 +43,34 @@
       # @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 iterations.
       # @param batch_size [Integer] The size of the mini batches.
+      # @param probability [Boolean] The flag indicating whether to perform probability estimation.
       # @param normalize [Boolean] The flag indicating whether to normalize the weight vector.
       # @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, normalize: true, random_seed: nil)
+                     max_iter: 100, batch_size: 50, probability: false, normalize: true, random_seed: nil)
         SVMKit::Validation.check_params_float(reg_param: reg_param, bias_scale: bias_scale)
         SVMKit::Validation.check_params_integer(max_iter: max_iter, batch_size: batch_size)
-        SVMKit::Validation.check_params_boolean(fit_bias: fit_bias, normalize: normalize)
+        SVMKit::Validation.check_params_boolean(fit_bias: fit_bias, probability: probability, normalize: normalize)
         SVMKit::Validation.check_params_type_or_nil(Integer, random_seed: random_seed)
-
+        SVMKit::Validation.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[:probability] = probability
         @params[:normalize] = normalize
         @params[:random_seed] = random_seed
         @params[:random_seed] ||= srand
         @weight_vec = nil
         @bias_term = nil
+        @prob_param = nil
         @classes = nil
         @rng = Random.new(@params[:random_seed])
       end
 
       # Fit the model with given training data.
@@ -75,28 +79,40 @@
       # @param y [Numo::Int32] (shape: [n_samples]) The labels to be used for fitting the model.
       # @return [SVC] The learned classifier itself.
       def fit(x, y)
         SVMKit::Validation.check_sample_array(x)
         SVMKit::Validation.check_label_array(y)
+        SVMKit::Validation.check_sample_label_size(x, y)
 
         @classes = Numo::Int32[*y.to_a.uniq.sort]
         n_classes = @classes.size
         _n_samples, n_features = x.shape
 
         if n_classes > 2
           @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
           @bias_term = Numo::DFloat.zeros(n_classes)
+          @prob_param = Numo::DFloat.zeros(n_classes, 2)
           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
+            @prob_param[n, true] = if @params[:probability]
+                                     SVMKit::ProbabilisticOutput.fit_sigmoid(x.dot(weight.transpose) + bias, bin_y)
+                                   else
+                                     Numo::DFloat[1, 0]
+                                   end
           end
         else
           negative_label = y.to_a.uniq.sort.first
           bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
           @weight_vec, @bias_term = binary_fit(x, bin_y)
+          @prob_param = if @params[:probability]
+                          SVMKit::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec.transpose) + @bias_term, bin_y)
+                        else
+                          Numo::DFloat[1, 0]
+                        end
         end
 
         self
       end
 
@@ -122,26 +138,47 @@
         n_samples, = x.shape
         decision_values = decision_function(x)
         Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] })
       end
 
+      # Predict probability for samples.
+      #
+      # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the probailities.
+      # @return [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted probability of each class per sample.
+      def predict_proba(x)
+        SVMKit::Validation.check_sample_array(x)
+
+        if @classes.size > 2
+          probs = 1.0 / (Numo::NMath.exp(@prob_param[true, 0] * decision_function(x) + @prob_param[true, 1]) + 1.0)
+          return (probs.transpose / probs.sum(axis: 1)).transpose
+        end
+
+        n_samples, = x.shape
+        probs = Numo::DFloat.zeros(n_samples, 2)
+        probs[true, 1] = 1.0 / (Numo::NMath.exp(@prob_param[0] * decision_function(x) + @prob_param[1]) + 1.0)
+        probs[true, 0] = 1.0 - probs[true, 1]
+        probs
+      end
+
       # Dump marshal data.
       # @return [Hash] The marshal data about SVC.
       def marshal_dump
         { params: @params,
           weight_vec: @weight_vec,
           bias_term: @bias_term,
+          prob_param: @prob_param,
           classes: @classes,
           rng: @rng }
       end
 
       # Load marshal data.
       # @return [nil]
       def marshal_load(obj)
         @params = obj[:params]
         @weight_vec = obj[:weight_vec]
         @bias_term = obj[:bias_term]
+        @prob_param = obj[:prob_param]
         @classes = obj[:classes]
         @rng = obj[:rng]
         nil
       end
 
@@ -157,14 +194,16 @@
         # Start optimization.
         @params[:max_iter].times do |t|
           # random sampling
           subset_ids = rand_ids.shift(@params[:batch_size])
           rand_ids.concat(subset_ids)
-          target_ids = subset_ids.map { |n| n if weight_vec.dot(samples[n, true]) * bin_y[n] < 1 }.compact
-          n_subsamples = target_ids.size
-          next if n_subsamples.zero?
+          sub_samples = samples[subset_ids, true]
+          sub_bin_y = bin_y[subset_ids]
+          target_ids = (sub_samples.dot(weight_vec.transpose) * sub_bin_y).lt(1.0).where
+          n_targets = target_ids.size
+          next if n_targets.zero?
           # update the weight vector.
-          mean_vec = samples[target_ids, true].transpose.dot(bin_y[target_ids]) / n_subsamples
+          mean_vec = sub_samples[target_ids, true].transpose.dot(sub_bin_y[target_ids]) / n_targets
           weight_vec -= learning_rate(t) * (@params[:reg_param] * weight_vec - mean_vec)
           # scale the weight vector.
           normalize_weight_vec(weight_vec) if @params[:normalize]
         end
         split_weight_vec_bias(weight_vec)