Class: Cmfrec::Recommender

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

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(factors: 8, epochs: 10, verbose: true, user_bias: true, item_bias: true, add_implicit_features: false) ⇒ Recommender

Returns a new instance of Recommender.



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

def initialize(factors: 8, epochs: 10, verbose: true, user_bias: true, item_bias: true, add_implicit_features: false)
  set_params(
    k: factors,
    niter: epochs,
    verbose: verbose,
    user_bias: user_bias,
    item_bias: item_bias,
    add_implicit_features: add_implicit_features
  )

  @fit = false
  @user_map = {}
  @item_map = {}
  @user_info_map = {}
  @item_info_map = {}
end

Instance Attribute Details

#global_mean ⇒ Object (readonly)

Returns the value of attribute global_mean.



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

def global_mean
  @global_mean
end

Instance Method Details

#fit(train_set, user_info: nil, item_info: nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 22

def fit(train_set, user_info: nil, item_info: nil)
  reset
  partial_fit(train_set, user_info: , item_info: item_info)
end

#item_bias(item_id = nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 108

def item_bias(item_id = nil)
  read_bias(@bias_b, item_id, @item_map) if @bias_b
end

#item_factors(item_id = nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 100

def item_factors(item_id = nil)
  read_factors(@b, [@n, @n_i].max, @k_item + @k + @k_main, item_id, @item_map)
end

#item_ids ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 92

def item_ids
  @item_map.keys
end

#new_user_recs(data, count: 5, user_info: nil, item_ids: nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 81

def new_user_recs(data, count: 5, user_info: nil, item_ids: nil)
  check_fit

  a_vec, a_bias, rated = factors_warm(data, user_info: )
  top_n(a_vec: a_vec, a_bias: a_bias, count: count, rated: rated, item_ids: item_ids)
end

#predict(data) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 27

def predict(data)
  check_fit

  data = to_dataset(data)

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

  row = int_ptr(u)
  col = int_ptr(i)
  n_predict = data.size
  predicted = Fiddle::Pointer.malloc(n_predict * Fiddle::SIZEOF_DOUBLE)

  if @implicit
    check_status FFI.predict_X_old_collective_implicit(
      row, col, predicted, n_predict,
      @a, @b,
      @k, @k_user, @k_item, @k_main,
      @m, @n,
      @nthreads
    )
  else
    check_status FFI.predict_X_old_collective_explicit(
      row, col, predicted, n_predict,
      @a, @bias_a,
      @b, @bias_b,
      @global_mean,
      @k, @k_user, @k_item, @k_main,
      @m, @n,
      @nthreads
    )
  end

  predictions = real_array(predicted)
  predictions.map! { |v| v.nan? ? @global_mean : v } if @implicit
  predictions
end

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



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# File 'lib/cmfrec/recommender.rb', line 112

def similar_items(item_id, count: 5)
  check_fit
  similar(item_id, @item_map, item_factors, count, item_index)
end

#similar_users(user_id, count: 5) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 118

def similar_users(user_id, count: 5)
  check_fit
  similar(user_id, @user_map, user_factors, count, user_index)
end

#user_bias(user_id = nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 104

def user_bias(user_id = nil)
  read_bias(@bias_a, user_id, @user_map) if @bias_a
end

#user_factors(user_id = nil) ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 96

def user_factors(user_id = nil)
  read_factors(@a, [@m, @m_u].max, @k_user + @k + @k_main, user_id, @user_map)
end

#user_ids ⇒ Object 



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# File 'lib/cmfrec/recommender.rb', line 88

def user_ids
  @user_map.keys
end

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



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# File 'lib/cmfrec/recommender.rb', line 65

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

  if user
    a_vec = @a[user * @k * Fiddle::SIZEOF_DOUBLE, @k * Fiddle::SIZEOF_DOUBLE]
    a_bias = @bias_a ? @bias_a[user * Fiddle::SIZEOF_DOUBLE, Fiddle::SIZEOF_DOUBLE].unpack1("d") : 0
    # @rated[user] will be nil for recommenders saved before 0.1.5
    top_n(a_vec: a_vec, a_bias: a_bias, count: count, rated: (@rated[user] || {}).keys, item_ids: item_ids)
  else
    # no items if user is unknown
    # TODO maybe most popular items
    []
  end
end