Class: Rumale::NearestNeighbors::KNeighborsRegressor

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Regressor
Defined in:
lib/rumale/nearest_neighbors/k_neighbors_regressor.rb

Overview

KNeighborsRegressor is a class that implements the regressor with the k-nearest neighbors rule. The current implementation uses the Euclidean distance for finding the neighbors.

Examples:

estimator =
  Rumale::NearestNeighbors::KNeighborsRegressor.new(n_neighbors = 5)
estimator.fit(training_samples, traininig_target_values)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(n_neighbors: 5) ⇒ KNeighborsRegressor

Create a new regressor with the nearest neighbor rule.

Parameters:

  • n_neighbors (Integer) (defaults to: 5) —

    The number of neighbors.



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 32

def initialize(n_neighbors: 5)
  check_params_integer(n_neighbors: n_neighbors)
  check_params_positive(n_neighbors: n_neighbors)
  @params = {}
  @params[:n_neighbors] = n_neighbors
  @prototypes = nil
  @values = nil
end

Instance Attribute Details

#prototypes ⇒ Numo::DFloat (readonly)

Return the prototypes for the nearest neighbor regressor.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_features])



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 23

def prototypes
  @prototypes
end

#values ⇒ Numo::DFloat (readonly)

Return the values of the prototypes

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_outputs])



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 27

def values
  @values
end

Instance Method Details

#fit(x, y) ⇒ KNeighborsRegressor

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model.

  • y (Numo::DFloat) —

    (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.

Returns:



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 46

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  @prototypes = x.dup
  @values = y.dup
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about KNeighborsRegressor.



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 77

def marshal_dump
  { params: @params,
    prototypes: @prototypes,
    values: @values }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 85

def marshal_load(obj)
  @params = obj[:params]
  @prototypes = obj[:prototypes]
  @values = obj[:values]
  nil
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the values.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_outputs]) Predicted values per sample.



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# File 'lib/rumale/nearest_neighbors/k_neighbors_regressor.rb', line 59

def predict(x)
  check_sample_array(x)
  # Initialize some variables.
  n_samples, = x.shape
  n_prototypes, n_outputs = @values.shape
  n_neighbors = [@params[:n_neighbors], n_prototypes].min
  # Calculate distance matrix.
  distance_matrix = PairwiseMetric.euclidean_distance(x, @prototypes)
  # Predict values for the given samples.
  predicted_values = Array.new(n_samples) do |n|
    neighbor_ids = distance_matrix[n, true].to_a.each_with_index.sort.map(&:last)[0...n_neighbors]
    n_outputs.nil? ? @values[neighbor_ids].mean : @values[neighbor_ids, true].mean(0).to_a
  end
  Numo::DFloat[*predicted_values]
end