Class: Disco::Recommender

Inherits:
Object
  • Object
show all
Defined in:
lib/disco/recommender.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(factors: 8, epochs: 20, verbose: nil, top_items: false) ⇒ Recommender

Returns a new instance of Recommender.



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# File 'lib/disco/recommender.rb', line 5

def initialize(factors: 8, epochs: 20, verbose: nil, top_items: false)
  @factors = factors
  @epochs = epochs
  @verbose = verbose
  @user_map = {}
  @item_map = {}
  @top_items = top_items
end

Instance Attribute Details

#global_mean ⇒ Object (readonly)

Returns the value of attribute global_mean.



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# File 'lib/disco/recommender.rb', line 3

def global_mean
  @global_mean
end

Instance Method Details

#fit(train_set, validation_set: nil) ⇒ Object

Raises:

  • (ArgumentError) 


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# File 'lib/disco/recommender.rb', line 14

def fit(train_set, validation_set: nil)
  train_set = to_dataset(train_set)
  validation_set = to_dataset(validation_set) if validation_set

  check_training_set(train_set)

  # TODO option to set in initializer to avoid pass
  # could also just check first few values
  # but may be confusing if they are all missing and later ones aren't
  @implicit = !train_set.any? { |v| v[:rating] }

  if @implicit && train_set.any? { |v| v[:value] }
    warn "[disco] WARNING: Passing `:value` with implicit feedback has no effect on recommendations and can be removed. Earlier versions of the library incorrectly stated this was used."
  end

  # TODO improve performance
  # (catch exception instead of checking ahead of time)
  unless @implicit
    check_ratings(train_set)

    if validation_set
      check_ratings(validation_set)
    end
  end

  @rated = Hash.new { |hash, key| hash[key] = {} }
  input = []
  train_set.each do |v|
    # update maps and build matrix in single pass
    u = (@user_map[v[:user_id]] ||= @user_map.size)
    i = (@item_map[v[:item_id]] ||= @item_map.size)
    @rated[u][i] = true

    # explicit will always have a value due to check_ratings
    input << [u, i, @implicit ? 1 : v[:rating]]
  end
  @rated.default = nil

  # much more efficient than checking every value in another pass
  raise ArgumentError, "Missing user_id" if @user_map.key?(nil)
  raise ArgumentError, "Missing item_id" if @item_map.key?(nil)

  # TODO improve performance
  unless @implicit
    @min_rating, @max_rating = train_set.minmax_by { |o| o[:rating] }.map { |o| o[:rating] }
  end

  if @top_items
    @item_count = [0] * @item_map.size
    @item_sum = [0.0] * @item_map.size
    train_set.each do |v|
      i = @item_map[v[:item_id]]
      @item_count[i] += 1
      @item_sum[i] += (@implicit ? 1 : v[:rating])
    end
  end

  eval_set = nil
  if validation_set
    eval_set = []
    validation_set.each do |v|
      u = @user_map[v[:user_id]]
      i = @item_map[v[:item_id]]

      # set to non-existent item
      u ||= -1
      i ||= -1

      eval_set << [u, i, @implicit ? 1 : v[:rating]]
    end
  end

  loss = @implicit ? 12 : 0
  verbose = @verbose
  verbose = true if verbose.nil? && eval_set
  model = Libmf::Model.new(loss: loss, factors: @factors, iterations: @epochs, quiet: !verbose)
  model.fit(input, eval_set: eval_set)

  @global_mean = model.bias

  @user_factors = model.p_factors(format: :numo)
  @item_factors = model.q_factors(format: :numo)

  @normalized_user_factors = nil
  @normalized_item_factors = nil

  @user_recs_index = nil
  @similar_users_index = nil
  @similar_items_index = nil
end

#inspect ⇒ Object 



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# File 'lib/disco/recommender.rb', line 255

def inspect
  to_s # for now
end

#item_factors(item_id = nil) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 230

def item_factors(item_id = nil)
  if item_id
    i = @item_map[item_id]
    @item_factors[i, true] if i
  else
    @item_factors
  end
end

#item_ids ⇒ Object 



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# File 'lib/disco/recommender.rb', line 217

def item_ids
  @item_map.keys
end

#optimize_similar_items(library: nil) ⇒ Object Also known as: optimize_item_recs 



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# File 'lib/disco/recommender.rb', line 244

def optimize_similar_items(library: nil)
  check_fit
  @similar_items_index = create_index(normalized_item_factors, library: library)
end

#optimize_similar_users(library: nil) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 250

def optimize_similar_users(library: nil)
  check_fit
  @similar_users_index = create_index(normalized_user_factors, library: library)
end

#optimize_user_recs ⇒ Object 



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# File 'lib/disco/recommender.rb', line 239

def optimize_user_recs
  check_fit
  @user_recs_index = create_index(item_factors, library: "faiss")
end

#predict(data) ⇒ Object

generates a prediction even if a user has already rated the item



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# File 'lib/disco/recommender.rb', line 106

def predict(data)
  data = to_dataset(data)

  u = data.map { |v| @user_map[v[:user_id]] }
  i = data.map { |v| @item_map[v[:item_id]] }

  new_index = data.each_index.select { |index| u[index].nil? || i[index].nil? }
  new_index.each do |j|
    u[j] = 0
    i[j] = 0
  end

  predictions = @user_factors[u, true].inner(@item_factors[i, true])
  predictions.inplace.clip(@min_rating, @max_rating) if @min_rating
  predictions[new_index] = @global_mean
  predictions.to_a
end

#similar_items(item_id, count: 5) ⇒ Object Also known as: item_recs 



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# File 'lib/disco/recommender.rb', line 168

def similar_items(item_id, count: 5)
  check_fit
  similar(item_id, @item_map, normalized_item_factors, count, @similar_items_index)
end

#similar_users(user_id, count: 5) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 174

def similar_users(user_id, count: 5)
  check_fit
  similar(user_id, @user_map, normalized_user_factors, count, @similar_users_index)
end

#top_items(count: 5) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 179

def top_items(count: 5)
  check_fit
  raise "top_items not computed" unless @top_items

  if @implicit
    scores = Numo::UInt64.cast(@item_count)
  else
    require "wilson_score"

    range = @min_rating..@max_rating
    scores = Numo::DFloat.cast(@item_sum.zip(@item_count).map { |s, c| WilsonScore.rating_lower_bound(s / c, c, range) })

    # TODO uncomment in 0.3.0
    # wilson score with continuity correction
    # https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval_with_continuity_correction
    # z = 1.96 # 95% confidence
    # range = @max_rating - @min_rating
    # n = Numo::DFloat.cast(@item_count)
    # phat = (Numo::DFloat.cast(@item_sum) - (@min_rating * n)) / range / n
    # phat = (phat - (1 / 2 * n)).clip(0, 100) # continuity correction
    # scores = (phat + z**2 / (2 * n) - z * Numo::DFloat::Math.sqrt((phat * (1 - phat) + z**2 / (4 * n)) / n)) / (1 + z**2 / n)
    # scores = scores * range + @min_rating
  end

  indexes = scores.sort_index.reverse
  indexes = indexes[0...[count, indexes.size].min] if count
  scores = scores[indexes]

  keys = @item_map.keys
  indexes.size.times.map do |i|
    {item_id: keys[indexes[i]], score: scores[i]}
  end
end

#user_factors(user_id = nil) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 221

def user_factors(user_id = nil)
  if user_id
    u = @user_map[user_id]
    @user_factors[u, true] if u
  else
    @user_factors
  end
end

#user_ids ⇒ Object 



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# File 'lib/disco/recommender.rb', line 213

def user_ids
  @user_map.keys
end

#user_recs(user_id, count: 5, item_ids: nil) ⇒ Object 



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# File 'lib/disco/recommender.rb', line 124

def user_recs(user_id, count: 5, item_ids: nil)
  check_fit
  u = @user_map[user_id]

  if u
    rated = item_ids ? {} : @rated[u]

    if item_ids
      ids = Numo::NArray.cast(item_ids.map { |i| @item_map[i] }.compact)
      return [] if ids.size == 0

      predictions = @item_factors[ids, true].inner(@user_factors[u, true])
      indexes = predictions.sort_index.reverse
      indexes = indexes[0...[count + rated.size, indexes.size].min] if count
      predictions = predictions[indexes]
      ids = ids[indexes]
    elsif @user_recs_index && count
      predictions, ids = @user_recs_index.search(@user_factors[u, true].expand_dims(0), count + rated.size).map { |v| v[0, true] }
    else
      predictions = @item_factors.inner(@user_factors[u, true])
      indexes = predictions.sort_index.reverse # reverse just creates view
      indexes = indexes[0...[count + rated.size, indexes.size].min] if count
      predictions = predictions[indexes]
      ids = indexes
    end

    predictions.inplace.clip(@min_rating, @max_rating) if @min_rating

    keys = @item_map.keys
    result = []
    ids.each_with_index do |item_id, i|
      next if rated[item_id]

      result << {item_id: keys[item_id], score: predictions[i]}
      break if result.size == count
    end
    result
  elsif @top_items
    top_items(count: count)
  else
    []
  end
end