Class: Mjai::Manue::DangerEstimator

Inherits:
Object
  • Object
show all
Defined in:
lib/mjai/manue/danger_estimator.rb

Defined Under Namespace

Classes: DecisionNode, DecisionTree, Scene, StoredKyoku, StoredScene

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ DangerEstimator

Returns a new instance of DangerEstimator.



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# File 'lib/mjai/manue/danger_estimator.rb', line 444

def initialize()
  @min_gap = 0.0
end

Instance Attribute Details

#min_gap ⇒ Object

Returns the value of attribute min_gap.



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# File 'lib/mjai/manue/danger_estimator.rb', line 449

def min_gap
  @min_gap
end

#verbose ⇒ Object

Returns the value of attribute verbose.



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# File 'lib/mjai/manue/danger_estimator.rb', line 448

def verbose
  @verbose
end

Class Method Details

.bool_array_to_bit_vector(bool_array) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 732

def self.bool_array_to_bit_vector(bool_array)
  vector = 0
  bool_array.reverse_each() do |value|
    vector <<= 1
    vector |= 1 if value
  end
  return vector
end

.feature_vector_to_str(feature_vector) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 741

def self.feature_vector_to_str(feature_vector)
  return (0...Scene.feature_names.size).select(){ |i| feature_vector[i] != 0 }.
      map(){ |i| Scene.feature_names[i] }.join(" ")
end

.get_feature_value(feature_vector, feature_name) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 746

def self.get_feature_value(feature_vector, feature_name)
  return feature_vector[Scene.feature_names.index(feature_name)] != 0
end

Instance Method Details

#aggregate_probabilities(criteria) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 678

def aggregate_probabilities(criteria)
  result = {}
  for criterion in criteria
    kyoku_probs = @kyoku_probs_map[criterion.object_id]
    next if !kyoku_probs
    result[criterion] = node = DecisionNode.new(
        kyoku_probs.inject(:+) / kyoku_probs.size,
        ConfidenceInterval.calculate(kyoku_probs, :min => 0.0, :max => 1.0),
        kyoku_probs.size)
    print("%p\n  %.2f [%.2f, %.2f] (%d samples)\n\n" %
        [criterion,
         node.average_prob * 100.0,
         node.conf_interval[0] * 100.0,
         node.conf_interval[1] * 100.0,
         node.num_samples])
  end
  return result
end

#calculate_probabilities(features_path, criteria) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 628

def calculate_probabilities(features_path, criteria)
  create_kyoku_probs_map(features_path, criteria)
  return aggregate_probabilities(criteria)
end

#calculate_single_probabilities(features_path) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 550

def calculate_single_probabilities(features_path)
  criteria = Scene.feature_names.map(){ |s| [{s => false}, {s => true}] }.flatten()
  calculate_probabilities(features_path, criteria)
end

#create_kyoku_probs_map(features_path, criteria) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 633

def create_kyoku_probs_map(features_path, criteria)
  
  require "with_progress"
  
  @kyoku_probs_map = {}
  
  criterion_masks = {}
  for criterion in criteria
    positive_ary = [false] * Scene.feature_names.size
    negative_ary = [true] * Scene.feature_names.size
    for name, value in criterion
      index = Scene.feature_names.index(name)
      raise("no such feature: %p" % name) if !index
      if value
        positive_ary[index] = true
      else
        negative_ary[index] = false
      end
    end
    criterion_masks[criterion] = [
      DangerEstimator.bool_array_to_bit_vector(positive_ary),
      DangerEstimator.bool_array_to_bit_vector(negative_ary),
    ]
  end
  
  open(features_path, "rb") do |f|
    meta_data = Marshal.load(f)
    if meta_data[:feature_names] != Scene.feature_names
      raise("feature set has been changed")
    end
    f.with_progress() do
      begin
        while true
          stored_kyokus = Marshal.load(f)
          for stored_kyoku in stored_kyokus
            update_metrics_for_kyoku(stored_kyoku, criterion_masks)
          end
        end
      rescue EOFError
      end
    end
  end
  
end

#extract_features_from_file(input_path, listener) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 471

def extract_features_from_file(input_path, listener)
  begin
    stored_kyoku = nil
    reacher = nil
    waited = nil
    prereach_sutehais = nil
    skip = false
    archive = Archive.load(input_path)
    archive.each_action() do |action|
      archive.dump_action(action) if self.verbose
      case action.type
        
        when :start_kyoku
          stored_kyoku = StoredKyoku.new([])
          reacher = nil
          skip = false
        
        when :end_kyoku
          next if skip
          raise("should not happen") if !stored_kyoku
          @stored_kyokus.push(stored_kyoku)
          stored_kyoku = nil
        
        when :reach_accepted
          if ["ASAPIN", "（≧▽≦）"].include?(action.actor.name) || reacher
            skip = true
          end
          next if skip
          reacher = action.actor
          waited = TenpaiAnalysis.new(action.actor.tehais).waited_pais
          prereach_sutehais = reacher.sutehais.dup()
        
        when :dahai
          next if skip || !reacher || action.actor.reach?
          scene = Scene.new({
              :game => archive,
              :me => action.actor,
              :dapai => action.pai,
              :reacher => reacher,
              :prereach_sutehais => prereach_sutehais,
          })
          stored_scene = StoredScene.new([])
          #p [:candidates, action.actor, reacher, scene.candidates.join(" ")]
          puts("reacher: %d" % reacher.id) if self.verbose
          candidates = []
          for pai in scene.candidates
            hit = waited.include?(pai)
            feature_vector = scene.feature_vector(pai)
            stored_scene.candidates.push([feature_vector, hit])
            candidates.push({
                :pai => pai,
                :hit => hit,
                :feature_vector => feature_vector,
            })
            if self.verbose
              puts("candidate %s: hit=%d, %s" % [
                  pai,
                  hit ? 1 : 0,
                  DangerEstimator.feature_vector_to_str(feature_vector)])
            end
          end
          stored_kyoku.scenes.push(stored_scene)
          if listener
            listener.on_dahai({
                :game => archive,
                :action => action,
                :reacher => reacher,
                :candidates => candidates,
            })
          end
          
      end
    end
  rescue Exception
    $stderr.puts("at #{input_path}")
    raise()
  end
end

#extract_features_from_files(input_paths, output_path, listener = nil) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 451

def extract_features_from_files(input_paths, output_path, listener = nil)
  require "with_progress"
  $stderr.puts("%d files." % input_paths.size)
  open(output_path, "wb") do |f|
    meta_data = {
      :feature_names => Scene.feature_names,
    }
    Marshal.dump(meta_data, f)
    @stored_kyokus = []
    input_paths.enum_for(:each_with_progress).each_with_index() do |path, i|
      if i % 100 == 0 && i > 0
        Marshal.dump(@stored_kyokus, f)
        @stored_kyokus.clear()
      end
      extract_features_from_file(path, listener)
    end
    Marshal.dump(@stored_kyokus, f)
  end
end

#generate_decision_tree(features_path, base_criterion = {}, base_node = nil, root = nil) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 555

def generate_decision_tree(features_path, base_criterion = {}, base_node = nil, root = nil)
  p [:generate_decision_tree, base_criterion]
  targets = {}
  criteria = []
  criteria.push(base_criterion) if !base_node
  for name in Scene.feature_names
    next if base_criterion.has_key?(name)
    negative_criterion = base_criterion.merge({name => false})
    positive_criterion = base_criterion.merge({name => true})
    targets[name] = [negative_criterion, positive_criterion]
    criteria.push(negative_criterion, positive_criterion)
  end
  node_map = calculate_probabilities(features_path, criteria)
  base_node = node_map[base_criterion] if !base_node
  root = base_node if !root
  gaps = {}
  for name, (negative_criterion, positive_criterion) in targets
    negative = node_map[negative_criterion]
    positive = node_map[positive_criterion]
    next if !positive || !negative
    if positive.average_prob >= negative.average_prob
      gap = positive.conf_interval[0] - negative.conf_interval[1]
    else
      gap = negative.conf_interval[0] - positive.conf_interval[1]
    end
    p [name, gap]
    gaps[name] = gap if gap > @min_gap
  end
  max_name = gaps.keys.max_by(){ |s| gaps[s] }
  p [:max_name, max_name]
  if max_name
    (negative_criterion, positive_criterion) = targets[max_name]
    base_node.feature_name = max_name
    base_node.negative = node_map[negative_criterion]
    base_node.positive = node_map[positive_criterion]
    render_decision_tree(root, "all")
    generate_decision_tree(features_path, negative_criterion, base_node.negative, root)
    generate_decision_tree(features_path, positive_criterion, base_node.positive, root)
  end
  return base_node
end

#match?(feature_vector, positive_mask, negative_mask) ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/mjai/manue/danger_estimator.rb', line 727

def match?(feature_vector, positive_mask, negative_mask)
  return (feature_vector & positive_mask) == positive_mask &&
      (feature_vector | negative_mask) == negative_mask
end

#node_to_hash(node) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 613

def node_to_hash(node)
  if node
    return {
        "average_prob" => node.average_prob,
        "conf_interval" => node.conf_interval,
        "num_samples" => node.num_samples,
        "feature_name" => node.feature_name,
        "negative" => node_to_hash(node.negative),
        "positive" => node_to_hash(node.positive),
    }
  else
    return nil
  end
end

#render_decision_tree(node, label, indent = 0) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 597

def render_decision_tree(node, label, indent = 0)
  puts("%s%s : %.2f [%.2f, %.2f] (%d samples)" %
      ["  " * indent,
       label,
       node.average_prob * 100.0,
       node.conf_interval[0] * 100.0,
       node.conf_interval[1] * 100.0,
       node.num_samples])
  if node.feature_name
    for value, child in [[false, node.negative], [true, node.positive]].
        sort_by(){ |v, c| c.average_prob }
      render_decision_tree(child, "%s = %p" % [node.feature_name, value], indent + 1)
    end
  end
end

#update_metrics_for_kyoku(stored_kyoku, criterion_masks) ⇒ Object 



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# File 'lib/mjai/manue/danger_estimator.rb', line 697

def update_metrics_for_kyoku(stored_kyoku, criterion_masks)
  scene_prob_sums = Hash.new(0.0)
  scene_counts = Hash.new(0)
  for stored_scene in stored_kyoku.scenes
    pai_freqs = {}
    for feature_vector, hit in stored_scene.candidates
      for criterion, (positive_mask, negative_mask) in criterion_masks
        if match?(feature_vector, positive_mask, negative_mask)
          # Uses object_id as key for efficiency.
          pai_freqs[criterion.object_id] ||= Hash.new(0)
          pai_freqs[criterion.object_id][hit] += 1
        end
      end
      #p [pai, hit, feature_vector]
    end
    for criterion_id, freqs in pai_freqs
      scene_prob = freqs[true].to_f() / (freqs[false] + freqs[true])
      #p [:scene_prob, criterion, scene_prob]
      scene_prob_sums[criterion_id] += scene_prob
      scene_counts[criterion_id] += 1
    end
  end
  for criterion_id, count in scene_counts
    kyoku_prob = scene_prob_sums[criterion_id] / count
    #p [:kyoku_prob, criterion, kyoku_prob]
    @kyoku_probs_map[criterion_id] ||= []
    @kyoku_probs_map[criterion_id].push(kyoku_prob)
  end
end