# frozen_string_literal: true

require 'rumale/values'
require 'rumale/base/base_estimator'
require 'rumale/base/classifier'
require 'rumale/tree/decision_tree_classifier'

module Rumale
  # This module consists of the classes that implement ensemble-based methods.
  module Ensemble
    # RandomForestClassifier is a class that implements random forest for classification.
    #
    # @example
    #   estimator =
    #     Rumale::Ensemble::RandomForestClassifier.new(
    #       n_estimators: 10, criterion: 'gini', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
    #   estimator.fit(training_samples, traininig_labels)
    #   results = estimator.predict(testing_samples)
    #
    class RandomForestClassifier
      include Base::BaseEstimator
      include Base::Classifier

      # Return the set of estimators.
      # @return [Array<DecisionTreeClassifier>]
      attr_reader :estimators

      # Return the class labels.
      # @return [Numo::Int32] (size: n_classes)
      attr_reader :classes

      # Return the importance for each feature.
      # @return [Numo::DFloat] (size: n_features)
      attr_reader :feature_importances

      # Return the random generator for random selection of feature index.
      # @return [Random]
      attr_reader :rng

      # Create a new classifier with random forest.
      #
      # @param n_estimators [Integer] The numeber of decision trees for contructing random forest.
      # @param criterion [String] The function to evalue spliting point. Supported criteria are 'gini' and 'entropy'.
      # @param max_depth [Integer] The maximum depth of the tree.
      #   If nil is given, decision tree grows without concern for depth.
      # @param max_leaf_nodes [Integer] The maximum number of leaves on decision tree.
      #   If nil is given, number of leaves is not limited.
      # @param min_samples_leaf [Integer] The minimum number of samples at a leaf node.
      # @param max_features [Integer] The number of features to consider when searching optimal split point.
      #   If nil is given, split process considers 'Math.sqrt(n_features)' features.
      # @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 random_seed [Integer] The seed value using to initialize the random generator.
      #   It is used to randomly determine the order of features when deciding spliting point.
      def initialize(n_estimators: 10,
                     criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
                     max_features: nil, n_jobs: nil, random_seed: nil)
        check_params_numeric_or_nil(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, n_jobs: n_jobs, random_seed: random_seed)
        check_params_numeric(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
        check_params_string(criterion: criterion)
        check_params_positive(n_estimators: n_estimators, max_depth: max_depth,
                              max_leaf_nodes: max_leaf_nodes, min_samples_leaf: min_samples_leaf,
                              max_features: max_features)
        @params = {}
        @params[:n_estimators] = n_estimators
        @params[:criterion] = criterion
        @params[:max_depth] = max_depth
        @params[:max_leaf_nodes] = max_leaf_nodes
        @params[:min_samples_leaf] = min_samples_leaf
        @params[:max_features] = max_features
        @params[:n_jobs] = n_jobs
        @params[:random_seed] = random_seed
        @params[:random_seed] ||= srand
        @estimators = nil
        @classes = nil
        @feature_importances = 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.
      # @param y [Numo::Int32] (shape: [n_samples]) The labels to be used for fitting the model.
      # @return [RandomForestClassifier] The learned classifier itself.
      def fit(x, y) # rubocop:disable Metrics/AbcSize
        x = check_convert_sample_array(x)
        y = check_convert_label_array(y)
        check_sample_label_size(x, y)
        # Initialize some variables.
        n_samples, n_features = x.shape
        @params[:max_features] = Math.sqrt(n_features).to_i if @params[:max_features].nil?
        @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
        @classes = Numo::Int32.asarray(y.to_a.uniq.sort)
        sub_rng = @rng.dup
        rngs = Array.new(@params[:n_estimators]) { Random.new(sub_rng.rand(Rumale::Values.int_max)) }
        # Construct forest.
        @estimators =
          if enable_parallel?
            # :nocov:
            parallel_map(@params[:n_estimators]) do |n|
              bootstrap_ids = Array.new(n_samples) { rngs[n].rand(0...n_samples) }
              plant_tree(rngs[n].rand(Rumale::Values.int_max)).fit(x[bootstrap_ids, true], y[bootstrap_ids])
            end
            # :nocov:
          else
            Array.new(@params[:n_estimators]) do |n|
              bootstrap_ids = Array.new(n_samples) { rngs[n].rand(0...n_samples) }
              plant_tree(rngs[n].rand(Rumale::Values.int_max)).fit(x[bootstrap_ids, true], y[bootstrap_ids])
            end
          end
        @feature_importances =
          if enable_parallel?
            parallel_map(@params[:n_estimators]) { |n| @estimators[n].feature_importances }.reduce(&:+)
          else
            @estimators.map(&:feature_importances).reduce(&:+)
          end
        @feature_importances /= @feature_importances.sum
        self
      end

      # Predict class labels for samples.
      #
      # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels.
      # @return [Numo::Int32] (shape: [n_samples]) Predicted class label per sample.
      def predict(x)
        x = check_convert_sample_array(x)
        n_samples = x.shape[0]
        n_estimators = @estimators.size
        predicted = if enable_parallel?
                      predict_set = parallel_map(n_estimators) { |n| @estimators[n].predict(x).to_a }.transpose
                      parallel_map(n_samples) { |n| predict_set[n].group_by { |v| v }.max_by { |_k, v| v.size }.first }
                    else
                      predict_set = @estimators.map { |tree| tree.predict(x).to_a }.transpose
                      Array.new(n_samples) { |n| predict_set[n].group_by { |v| v }.max_by { |_k, v| v.size }.first }
                    end
        Numo::Int32.asarray(predicted)
      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)
        x = check_convert_sample_array(x)
        n_estimators = @estimators.size
        if enable_parallel?
          parallel_map(n_estimators) { |n| predict_proba_tree(@estimators[n], x) }.reduce(&:+) / n_estimators
        else
          @estimators.map { |tree| predict_proba_tree(tree, x) }.reduce(&:+) / n_estimators
        end
      end

      # Return the index of the leaf that each sample reached.
      #
      # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels.
      # @return [Numo::Int32] (shape: [n_samples, n_estimators]) Leaf index for sample.
      def apply(x)
        x = check_convert_sample_array(x)
        Numo::Int32[*Array.new(@params[:n_estimators]) { |n| @estimators[n].apply(x) }].transpose.dup
      end

      private

      def plant_tree(rnd_seed)
        Tree::DecisionTreeClassifier.new(
          criterion: @params[:criterion], max_depth: @params[:max_depth],
          max_leaf_nodes: @params[:max_leaf_nodes], min_samples_leaf: @params[:min_samples_leaf],
          max_features: @params[:max_features], random_seed: rnd_seed
        )
      end

      def predict_proba_tree(tree, x)
        # initialize some variables.
        n_samples = x.shape[0]
        base_classes = @classes.to_a
        n_classes = base_classes.size
        class_ids = tree.classes.map { |c| base_classes.index(c) }
        # predict probabilities.
        probs = Numo::DFloat.zeros(n_samples, n_classes)
        tree_probs = tree.predict_proba(x)
        class_ids.each_with_index { |i, j| probs[true, i] = tree_probs[true, j] }
        probs
      end
    end
  end
end