Method: Nimbus::ClassificationTree#build_node

Defined in:
lib/nimbus/classification_tree.rb

#build_node(individuals_ids, y_hat) ⇒ Object

Creates a node by taking a random sample of the SNPs and computing the loss function for every split by SNP of that sample.

  • If SNP_min is the SNP with smaller loss function and it is < the loss function of the node, it splits the individuals sample in two:

(the average of the 0,1,2 values for the SNP_min in the individuals is computed, and they are splitted in [<=avg], [>avg]) then it builds these 2 new nodes.

  • Otherwise every individual in the node gets labeled with the average of the fenotype values of all of them.


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# File 'lib/nimbus/classification_tree.rb', line 40

def build_node(individuals_ids, y_hat)
  # General loss function value for the node
  individuals_count = individuals_ids.size
  return label_node(y_hat, individuals_ids) if individuals_count < @node_min_size
  node_loss_function = Nimbus::LossFunctions.gini_index individuals_ids, @id_to_fenotype, @classes

  # Finding the SNP that minimizes loss function
  snps = snps_random_sample
  min_loss, min_SNP, split, split_type, ginis = node_loss_function, nil, nil, nil, nil

  snps.each do |snp|
    individuals_split_by_snp_value, node_split_type = split_by_snp_avegare_value individuals_ids, snp
    y_hat_0 = Nimbus::LossFunctions.majority_class(individuals_split_by_snp_value[0], @id_to_fenotype, @classes)
    y_hat_1 = Nimbus::LossFunctions.majority_class(individuals_split_by_snp_value[1], @id_to_fenotype, @classes)

    gini_0 = Nimbus::LossFunctions.gini_index individuals_split_by_snp_value[0], @id_to_fenotype, @classes
    gini_1 = Nimbus::LossFunctions.gini_index individuals_split_by_snp_value[1], @id_to_fenotype, @classes
    loss_snp = (individuals_split_by_snp_value[0].size * gini_0 +
                individuals_split_by_snp_value[1].size * gini_1) / individuals_count

    min_loss, min_SNP, split, split_type, ginis = loss_snp, snp, individuals_split_by_snp_value, node_split_type, [y_hat_0, y_hat_1] if loss_snp < min_loss
  end
  return build_branch(min_SNP, split, split_type, ginis, y_hat) if min_loss < node_loss_function
  return label_node(y_hat, individuals_ids)
end