Class: Network
- Inherits:
-
Object
- Object
- Network
- Defined in:
- lib/NetAnalyzer/network.rb
Instance Attribute Summary collapse
-
#association_values ⇒ Object
Returns the value of attribute association_values.
-
#control_connections ⇒ Object
Returns the value of attribute control_connections.
-
#group_nodes ⇒ Object
Returns the value of attribute group_nodes.
-
#kernels ⇒ Object
Returns the value of attribute kernels.
-
#reference_nodes ⇒ Object
Returns the value of attribute reference_nodes.
-
#threads ⇒ Object
Returns the value of attribute threads.
Instance Method Summary collapse
- #add_edge(nodeID1, nodeID2) ⇒ Object
- #add_nested_record(hash, node1, node2, val) ⇒ Object
- #add_node(nodeID, nodeType = 0) ⇒ Object
- #add_record(hash, node1, node2) ⇒ Object
-
#bfs_shortest_path(start, goal, paths = false) ⇒ Object
pythoninwonderland.wordpress.com/2017/03/18/how-to-implement-breadth-first-search-in-python/ finds shortest path between 2 nodes of a graph using BFS.
- #build_path(previous, startNode, stopNode) ⇒ Object
- #clean_autorelations_on_association_values ⇒ Object
- #collect_nodes(args) ⇒ Object
- #communities_avg_sht_path(coms) ⇒ Object
- #communities_comparative_degree(coms) ⇒ Object
- #compute_adjusted_pvalue(relations, log_val = true) ⇒ Object
- #compute_adjusted_pvalue_benjaminiHochberg(relations) ⇒ Object
- #compute_adjusted_pvalue_bonferroni(relations) ⇒ Object
- #compute_avg_sht_path(com, paths = false) ⇒ Object
-
#compute_comparative_degree(com) ⇒ Object
see Girvan-Newman Benchmark control parameter in networksciencebook.com/chapter/9#testing (communities chapter).
- #compute_group_metrics(output_filename) ⇒ Object
-
#compute_log_transformation(relations) ⇒ Object
Only perform log transform whitout adjust pvalue.
- #compute_node_com_assoc(com, ref_node) ⇒ Object
- #compute_node_com_assoc_in_precomputed_communities(coms, ref_node) ⇒ Object
- #delete_nodes(node_list, mode = 'd') ⇒ Object
- #expand_clusters(expand_method) ⇒ Object
- #generate_adjacency_matrix(layerA, layerB) ⇒ Object
- #get_all_intersections ⇒ Object
- #get_all_pairs(args = {}) ⇒ Object
-
#get_association_by_transference_resources(firstPairLayers, secondPairLayers, lambda_value1 = 0.5, lambda_value2 = 0.5) ⇒ Object
association methods adjacency matrix based ——————————————————— Alaimo 2014, doi: 10.3389/fbioe.2014.00071.
-
#get_association_values(layers, base_layer, meth) ⇒ Object
ASSOCIATION METHODS.
-
#get_associations(layers, base_layer) ⇒ Object
association methods node pairs based ——————————————————— Bass 2013, doi:10.1038/nmeth.2728.
- #get_bipartite_subgraph(from_layer_node_ids, from_layer, to_layer) ⇒ Object
- #get_connected_nodes(node_id, from_layer) ⇒ Object
- #get_cosine_associations(layers, base_layer) ⇒ Object
- #get_csi_associations(layers, base_layer) ⇒ Object
- #get_degree(zscore = false) ⇒ Object
- #get_edge_number ⇒ Object
- #get_geometric_associations(layers, base_layer) ⇒ Object
- #get_hypergeometric_associations(layers, base_layer, pvalue_adj_method = nil) ⇒ Object
- #get_hypergeometric_associations_with_topology(layers, base_layer, mode, thresold = 0.01) ⇒ Object
- #get_jaccard_association(layers, base_layer) ⇒ Object
-
#get_kernel(layer2kernel, kernel, normalization = false) ⇒ Object
KERNEL METHODS.
- #get_node_attributes(attr_names) ⇒ Object
- #get_nodes_from_layer(from_layer) ⇒ Object
- #get_nodes_layer(layers) ⇒ Object
- #get_pcc_associations(layers, base_layer) ⇒ Object
-
#get_pred_rec(meth, cut_number = 100, top_number = 10000) ⇒ Object
Pandey 2007, Association Analysis-based Transformations for Protein Interaction Networks: A Function Prediction Case Study.
- #get_simpson_association(layers, base_layer) ⇒ Object
-
#initialize(layers) ⇒ Network
constructor
BASIC METHODS.
- #intersection(node1, node2) ⇒ Object
- #link_ontology(ontology_file_path, layer_name) ⇒ Object
-
#load_control(ref_array) ⇒ Object
PERFORMANCE METHODS.
- #load_network_by_bin_matrix(input_file, node_file, layers) ⇒ Object
- #load_network_by_pairs(file, layers, split_character = "\t") ⇒ Object
- #load_network_by_plain_matrix(input_file, node_file, layers, splitChar) ⇒ Object
- #load_prediction(pairs_array) ⇒ Object
-
#plot_dot(user_options = {}) ⇒ Object
input keys: layout.
- #plot_network(options = {}) ⇒ Object
- #pred_rec(preds, cut, top) ⇒ Object
- #query_edge(nodeA, nodeB) ⇒ Object
- #replace_nil_vals(val) ⇒ Object
- #set_compute_pairs(use_pairs, get_autorelations) ⇒ Object
- #shortest_path(node_start, node_stop, paths = false) ⇒ Object
- #write_kernel(layer2kernel, output_file) ⇒ Object
Constructor Details
#initialize(layers) ⇒ Network
BASIC METHODS
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# File 'lib/NetAnalyzer/network.rb', line 31 def initialize(layers) @threads = 0 @nodes = {} @edges = {} @reference_nodes = [] @group_nodes = {} @adjacency_matrices = {} @kernels = {} @layers = layers @association_values = {} @control_connections = {} @compute_pairs = :conn @compute_autorelations = true @loaded_obos = [] @ontologies = [] @layer_ontologies = {} end |
Instance Attribute Details
#association_values ⇒ Object
Returns the value of attribute association_values.
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# File 'lib/NetAnalyzer/network.rb', line 27 def association_values @association_values end |
#control_connections ⇒ Object
Returns the value of attribute control_connections.
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# File 'lib/NetAnalyzer/network.rb', line 27 def control_connections @control_connections end |
#group_nodes ⇒ Object
Returns the value of attribute group_nodes.
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# File 'lib/NetAnalyzer/network.rb', line 27 def group_nodes @group_nodes end |
#kernels ⇒ Object
Returns the value of attribute kernels.
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# File 'lib/NetAnalyzer/network.rb', line 27 def kernels @kernels end |
#reference_nodes ⇒ Object
Returns the value of attribute reference_nodes.
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# File 'lib/NetAnalyzer/network.rb', line 27 def reference_nodes @reference_nodes end |
#threads ⇒ Object
Returns the value of attribute threads.
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# File 'lib/NetAnalyzer/network.rb', line 27 def threads @threads end |
Instance Method Details
#add_edge(nodeID1, nodeID2) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 58 def add_edge(nodeID1, nodeID2) query_edge(nodeID1, nodeID2) query_edge(nodeID2, nodeID1) end |
#add_nested_record(hash, node1, node2, val) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 753 def add_nested_record(hash, node1, node2, val) query_node1 = hash[node1] if query_node1.nil? hash[node1] = {node2 => val} else query_node1[node2] = val end end |
#add_node(nodeID, nodeType = 0) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 54 def add_node(nodeID, nodeType = 0) @nodes[nodeID] = Node.new(nodeID, nodeType) end |
#add_record(hash, node1, node2) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 744 def add_record(hash, node1, node2) query = hash[node1] if query.nil? hash[node1] = [node2] else query << node2 end end |
#bfs_shortest_path(start, goal, paths = false) ⇒ Object
pythoninwonderland.wordpress.com/2017/03/18/how-to-implement-breadth-first-search-in-python/ finds shortest path between 2 nodes of a graph using BFS
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# File 'lib/NetAnalyzer/network.rb', line 318 def bfs_shortest_path(start, goal, paths=false) dist = nil explored = {} # keep track of explored nodes previous = {} queue = [[start, 0]] # keep track of all the paths to be checked is_goal = false while !queue.empty? && !is_goal # keeps looping until all possible paths have been checked node, dist = queue.pop # pop the first path from the queue if !explored.include?(node) # get the last node from the path neighbours = @edges[node] explored[node] = true # mark node as explored next if neighbours.nil? dist += 1 neighbours.each do |neighbour| # go through all neighbour nodes, construct a new path next if explored.include?(neighbour) queue.unshift([neighbour, dist]) # push it into the queue previous[neighbour] = node if paths if neighbour == goal # return path if neighbour is goal is_goal = true break end end end end if is_goal path = build_path(previous, start, goal) if paths else dist = nil path = [] end return dist, path end |
#build_path(previous, startNode, stopNode) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 351 def build_path(previous, startNode, stopNode) path = [] currentNode = stopNode path << currentNode while currentNode != startNode currentNode = previous[currentNode] path << currentNode end return path end |
#clean_autorelations_on_association_values ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 528 def clean_autorelations_on_association_values @association_values.each do |meth, values| values.select!{|relation| @nodes[relation[0]].type != @nodes[relation[1]].type} end end |
#collect_nodes(args) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 468 def collect_nodes(args) nodeIDsA = nil nodeIDsB = nil if @compute_autorelations if args[:layers] == :all nodeIDsA = @nodes.keys else nodeIDsA = [] args[:layers].each do |layer| nodeIDsA.concat(@nodes.select{|id, node| node.type == layer}.keys) end end else if args[:layers] != :all nodeIDsA = @nodes.select{|id, node| node.type == args[:layers][0]}.keys nodeIDsB = @nodes.select{|id, node| node.type == args[:layers][1]}.keys end end return nodeIDsA, nodeIDsB end |
#communities_avg_sht_path(coms) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 260 def communities_avg_sht_path(coms) avg_sht_path = [] coms.each do |com_id, com| dist, paths = compute_avg_sht_path(com) avg_sht_path << dist end return avg_sht_path end |
#communities_comparative_degree(coms) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 252 def communities_comparative_degree(coms) comparative_degrees = [] coms.each do |com_id, com| comparative_degrees << compute_comparative_degree(com) end return comparative_degrees end |
#compute_adjusted_pvalue(relations, log_val = true) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 714 def compute_adjusted_pvalue(relations, log_val=true) relations.each_with_index do |data, i| #p1, p2, pval pval_adj = yield(data.last, i) pval_adj = -Math.log10(pval_adj) if log_val && pval_adj > 0 data[2] = pval_adj end end |
#compute_adjusted_pvalue_benjaminiHochberg(relations) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 737 def compute_adjusted_pvalue_benjaminiHochberg(relations) adj_pvalues = get_benjaminiHochberg_pvalues(relations.map{|rel| rel.last}) compute_adjusted_pvalue(relations) do |pval, index| adj_pvalues[index] end end |
#compute_adjusted_pvalue_bonferroni(relations) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 728 def compute_adjusted_pvalue_bonferroni(relations) n_comparations = relations.length compute_adjusted_pvalue(relations) do |pval, index| adj = pval * n_comparations adj = 1 if adj > 1 adj end end |
#compute_avg_sht_path(com, paths = false) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 290 def compute_avg_sht_path(com, paths=false) path_lengths = [] all_paths = [] group = com.dup while !group.empty? node_start = group.shift sht_paths = Parallel.map(group, in_processes: @threads) do |node_stop| #group.each do |node_stop| dist, path = shortest_path(node_start, node_stop, paths) [dist, path] #path_lengths << dist if !dist.nil? #all_paths << path if !path.empty? end sht_paths.each do |dist, path| path_lengths << dist all_paths << path end end if path_lengths.include?(nil) avg_sht_path = nil else avg_sht_path = path_lengths.inject(0){|sum,l| sum + l}.fdiv(path_lengths.length) end return avg_sht_path, all_paths end |
#compute_comparative_degree(com) ⇒ Object
see Girvan-Newman Benchmark control parameter in networksciencebook.com/chapter/9#testing (communities chapter)
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# File 'lib/NetAnalyzer/network.rb', line 277 def compute_comparative_degree(com) # see Girvan-Newman Benchmark control parameter in http://networksciencebook.com/chapter/9#testing (communities chapter) internal_degree = 0 external_degree = 0 com.each do |nodeID| nodeIDneigh = @edges[nodeID] next if nodeIDneigh.nil? internal_degree += (nodeIDneigh & com).length external_degree += (nodeIDneigh - com).length end comparative_degree = external_degree.fdiv(external_degree + internal_degree) return comparative_degree end |
#compute_group_metrics(output_filename) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 223 def compute_group_metrics(output_filename) metrics = [] header = ['group'] @group_nodes.keys.each do |k| metrics << [k] end header << 'comparative_degree' comparative_degree = communities_comparative_degree(@group_nodes) comparative_degree.each_with_index{|val,i| metrics[i] << replace_nil_vals(val)} header << 'avg_sht_path' avg_sht_path = communities_avg_sht_path(@group_nodes) avg_sht_path.each_with_index{|val,i| metrics[i] << replace_nil_vals(val)} if !@reference_nodes.empty? header.concat(%w[node_com_assoc_by_edge node_com_assoc_by_node]) node_com_assoc = compute_node_com_assoc_in_precomputed_communities(@group_nodes, @reference_nodes.first) node_com_assoc.each_with_index{|val,i| metrics[i].concat(val)} end File.open(output_filename, 'w') do |f| f.puts header.join("\t") metrics.each do |gr| f. puts gr.join("\t") end end end |
#compute_log_transformation(relations) ⇒ Object
Only perform log transform whitout adjust pvalue. Called when adjusted method is not defined
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# File 'lib/NetAnalyzer/network.rb', line 722 def compute_log_transformation(relations) #Only perform log transform whitout adjust pvalue. Called when adjusted method is not defined compute_adjusted_pvalue(relations) do |pval, index| pval end end |
#compute_node_com_assoc(com, ref_node) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 382 def compute_node_com_assoc(com, ref_node) ref_cons = 0 ref_secondary_cons = 0 secondary_nodes = {} other_cons = 0 other_nodes = {} refNneigh = @edges[ref_node] com.each do |nodeID| nodeIDneigh = @edges[nodeID] next if nodeIDneigh.nil? ref_cons += 1 if nodeIDneigh.include?(ref_node) if !refNneigh.nil? common_nodes = nodeIDneigh & refNneigh common_nodes.each {|id| secondary_nodes[id] = true} ref_secondary_cons += common_nodes.length end specific_nodes = nodeIDneigh - refNneigh - [ref_node] specific_nodes.each {|id| other_nodes[id] = true} other_cons += specific_nodes.length end by_edge = (ref_cons + ref_secondary_cons).fdiv(other_cons) by_node = (ref_cons + secondary_nodes.length).fdiv(other_nodes.length) return by_edge, by_node end |
#compute_node_com_assoc_in_precomputed_communities(coms, ref_node) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 269 def compute_node_com_assoc_in_precomputed_communities(coms, ref_node) node_com_assoc = [] coms.each do |com_id, com| node_com_assoc << [compute_node_com_assoc(com, ref_node)] end return node_com_assoc end |
#delete_nodes(node_list, mode = 'd') ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 72 def delete_nodes(node_list, mode='d') if mode == 'd' @nodes.reject!{|n| node_list.include?(n)} @edges.reject!{|n, connections| node_list.include?(n)} @edges.each do |n, connections| connections.reject!{|c| node_list.include?(c)} end elsif mode == 'r' @nodes.select!{|n| node_list.include?(n)} @edges.select!{|n, connections| node_list.include?(n)} @edges.each do |n, connections| connections.select!{|c| node_list.include?(c)} end end @edges.reject!{|n, connections| connections.empty?} end |
#expand_clusters(expand_method) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 370 def () clusters = {} @group_nodes.each do |id, nodes| if == 'sht_path' dist, paths = compute_avg_sht_path(nodes, paths=true) # this uses bfs, maybe Dijkstra is the best one new_nodes = paths.flatten.uniq clusters[id] = nodes | new_nodes # If some node pair are not connected, recover them end end return clusters end |
#generate_adjacency_matrix(layerA, layerB) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 510 def generate_adjacency_matrix(layerA, layerB) layerAidNodes = @nodes.select{|id, node| node.type == layerA}.keys layerBidNodes = @nodes.select{|id, node| node.type == layerB}.keys matrix = Numo::DFloat.zeros(layerAidNodes.length, layerBidNodes.length) layerAidNodes.each_with_index do |nodeA, i| layerBidNodes.each_with_index do |nodeB, j| if @edges[nodeB].include?(nodeA) matrix[i, j] = 1 else matrix[i, j] = 0 end end end all_info_matrix = [matrix, layerAidNodes, layerBidNodes] @adjacency_matrices[[layerA, layerB]] = all_info_matrix return all_info_matrix end |
#get_all_intersections ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 408 def get_all_intersections intersection_lengths = get_all_pairs do |node1, node2| intersection(node1, node2).length end return intersection_lengths end |
#get_all_pairs(args = {}) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 415 def get_all_pairs(args = {}) all_pairs = [] default = {:layers => :all} args = default.merge(args) nodeIDsA, nodeIDsB = collect_nodes(args) if @compute_autorelations if @compute_pairs == :all while !nodeIDsA.empty? node1 = nodeIDsA.shift pairs = Parallel.map(nodeIDsA, in_processes: @threads) do |node2| yield(node1, node2) end all_pairs.concat(pairs) end elsif @compute_pairs == :conn # TODO: Review this case to avoid return nil values while !nodeIDsA.empty? node1 = nodeIDsA.shift ids_connected_to_n1 = @edges[node1] pairs = Parallel.map(nodeIDsA, in_processes: @threads) do |node2| result = nil ids_connected_to_n2 = @edges[node2] if exist_connections?(ids_connected_to_n1, ids_connected_to_n2) result = yield(node1, node2) end result end pairs.compact! all_pairs.concat(pairs) end end else #MAIN METHOD if @compute_pairs == :conn all_pairs = Parallel.map(nodeIDsA, in_processes: @threads) do |node1| ids_connected_to_n1 = @edges[node1] node1_pairs = [] nodeIDsB.each do |node2| ids_connected_to_n2 = @edges[node2] if exist_connections?(ids_connected_to_n1, ids_connected_to_n2) node1_pairs << yield(node1, node2) end end node1_pairs end all_pairs.flatten!(1) elsif @compute_pairs == :all raise 'Not implemented' end end return all_pairs end |
#get_association_by_transference_resources(firstPairLayers, secondPairLayers, lambda_value1 = 0.5, lambda_value2 = 0.5) ⇒ Object
association methods adjacency matrix based
Alaimo 2014, doi: 10.3389/fbioe.2014.00071
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# File 'lib/NetAnalyzer/network.rb', line 569 def get_association_by_transference_resources(firstPairLayers, secondPairLayers, lambda_value1 = 0.5, lambda_value2 = 0.5) relations = [] matrix1 = @adjacency_matrices[firstPairLayers].first rowIds = @adjacency_matrices[firstPairLayers][1] matrix2 = @adjacency_matrices[secondPairLayers].first colIds = @adjacency_matrices[secondPairLayers][2] m1rowNumber, m1colNumber = matrix1.shape m2rowNumber, m2colNumber = matrix2.shape #puts m1rowNumber, m1colNumber, m2rowNumber, m2colNumber matrix1Weight = graphWeights(m1colNumber, m1rowNumber, matrix1.transpose, lambda_value1) matrix2Weight = graphWeights(m2colNumber, m2rowNumber, matrix2.transpose, lambda_value2) matrixWeightProduct = Numo::Linalg.dot(matrix1Weight, Numo::Linalg.dot(matrix2, matrix2Weight)) finalMatrix = Numo::Linalg.dot(matrix1, matrixWeightProduct) relations = matrix2relations(finalMatrix, rowIds, colIds) @association_values[:transference] = relations return relations end |
#get_association_values(layers, base_layer, meth) ⇒ Object
ASSOCIATION METHODS
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# File 'lib/NetAnalyzer/network.rb', line 536 def get_association_values(layers, base_layer, meth) relations = [] #node A, node B, val if meth == :jaccard #all networks relations = get_jaccard_association(layers, base_layer) elsif meth == :simpson #all networks relations = get_simpson_association(layers, base_layer) elsif meth == :geometric #all networks relations = get_geometric_associations(layers, base_layer) elsif meth == :cosine #all networks relations = get_cosine_associations(layers, base_layer) elsif meth == :pcc #all networks relations = get_pcc_associations(layers, base_layer) elsif meth == :hypergeometric #all networks relations = get_hypergeometric_associations(layers, base_layer) elsif meth == :hypergeometric_bf #all networks relations = get_hypergeometric_associations(layers, base_layer, :bonferroni) elsif meth == :hypergeometric_bh #all networks relations = get_hypergeometric_associations(layers, base_layer, :benjamini_hochberg) elsif meth == :hypergeometric_elim #tripartite networks? relations = get_hypergeometric_associations_with_topology(layers, base_layer, :elim) elsif meth == :hypergeometric_weight #tripartite networks? relations = get_hypergeometric_associations_with_topology(layers, base_layer, :weight) elsif meth == :csi #all networks relations = get_csi_associations(layers, base_layer) elsif meth == :transference #tripartite networks relations = get_association_by_transference_resources(layers, base_layer) end return relations end |
#get_associations(layers, base_layer) ⇒ Object
association methods node pairs based
Bass 2013, doi:10.1038/nmeth.2728
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# File 'lib/NetAnalyzer/network.rb', line 590 def get_associations(layers, base_layer) # BASE METHOD associations = get_all_pairs(layers: layers) do |node1, node2| associatedIDs_node1 = @edges[node1].map{|id| @nodes[id]}.select{|node| node.type == base_layer}.map{|node| node.id} associatedIDs_node2 = @edges[node2].map{|id| @nodes[id]}.select{|node| node.type == base_layer}.map{|node| node.id} intersectedIDs = associatedIDs_node1 & associatedIDs_node2 associationValue = yield(associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2) [node1, node2, associationValue] end return associations end |
#get_bipartite_subgraph(from_layer_node_ids, from_layer, to_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 97 def get_bipartite_subgraph(from_layer_node_ids, from_layer, to_layer) bipartite_subgraph = {} from_layer_node_ids.each do |from_layer_node_id| connected_nodes = @edges[from_layer_node_id] connected_nodes.each do |connected_node| if @nodes[connected_node].type == to_layer query = bipartite_subgraph[connected_node] if query.nil? bipartite_subgraph[connected_node] = get_connected_nodes(connected_node, from_layer) end end end end return bipartite_subgraph end |
#get_connected_nodes(node_id, from_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 89 def get_connected_nodes(node_id, from_layer) return @edges[node_id].map{|id| @nodes[id]}.select{|node| node.type == from_layer}.map{|node| node.id} end |
#get_cosine_associations(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 630 def get_cosine_associations(layers, base_layer) relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| productLength = Math.sqrt(associatedIDs_node1.length * associatedIDs_node2.length) cosineValue = intersectedIDs.length/productLength end @association_values[:cosine] = relations return relations end |
#get_csi_associations(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 763 def get_csi_associations(layers, base_layer) pcc_relations = get_pcc_associations(layers, base_layer) clean_autorelations_on_association_values if layers.length > 1 nx = get_nodes_layer(layers).length pcc_vals = {} node_rels = {} pcc_relations.each do |node1, node2, assoc_index| add_nested_record(pcc_vals, node1, node2, assoc_index.abs) add_nested_record(pcc_vals, node2, node1, assoc_index.abs) add_record(node_rels, node1, node2) add_record(node_rels, node2, node1) end relations = [] pcc_relations.each do |node1, node2 ,assoc_index| pccAB = assoc_index - 0.05 valid_nodes = 0 node_rels[node1].each do |node| valid_nodes += 1 if pcc_vals[node1][node] >= pccAB end node_rels[node2].each do |node| valid_nodes += 1 if pcc_vals[node2][node] >= pccAB end csiValue = 1 - (valid_nodes-1).fdiv(nx) # valid_nodes-1 is done due to the connection node1-node2 is counted twice (one for each loop) relations << [node1, node2, csiValue] end @association_values[:csi] = relations return relations end |
#get_degree(zscore = false) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 140 def get_degree(zscore=false) degree = {} @edges.each do |id, nodes| degree[id] = nodes.length end if !zscore degree_values = degree.values mean_degree = degree_values.mean std_degree = degree_values.standard_deviation degree.transform_values!{|v| (v - mean_degree).fdiv(std_degree)} end return degree end |
#get_edge_number ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 135 def get_edge_number node_connections = get_degree.values.inject(0){|sum, n| sum + n} return node_connections/2 end |
#get_geometric_associations(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 619 def get_geometric_associations(layers, base_layer) #wang 2016 method relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| intersectedIDs = intersectedIDs.length**2 productLength = Math.sqrt(associatedIDs_node1.length * associatedIDs_node2.length) geometricValue = intersectedIDs.to_f/productLength end @association_values[:geometric] = relations return relations end |
#get_hypergeometric_associations(layers, base_layer, pvalue_adj_method = nil) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 655 def get_hypergeometric_associations(layers, base_layer, pvalue_adj_method= nil) ny = get_nodes_layer([base_layer]).length fet = Rubystats::FishersExactTest.new relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| fisher = 0 intersection_lengths = intersectedIDs.length if intersection_lengths > 0 n1_items = associatedIDs_node1.length n2_items = associatedIDs_node2.length fisher = fet.calculate( intersection_lengths, n1_items - intersection_lengths, n2_items - intersection_lengths, ny - (n1_items + n2_items - intersection_lengths) ) fisher = fisher[:right] end fisher end if pvalue_adj_method == :bonferroni meth = :hypergeometric_bf compute_adjusted_pvalue_bonferroni(relations) elsif pvalue_adj_method == :benjamini_hochberg meth = :hypergeometric_bh compute_adjusted_pvalue_benjaminiHochberg(relations) else meth = :hypergeometric compute_log_transformation(relations) end @association_values[meth] = relations return relations end |
#get_hypergeometric_associations_with_topology(layers, base_layer, mode, thresold = 0.01) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 688 def get_hypergeometric_associations_with_topology(layers, base_layer, mode, thresold = 0.01) relations = [] reference_layer = (layers - @layer_ontologies.keys).first ontology_layer = (layers - [reference_layer]).first ref_nodes = get_nodes_from_layer(reference_layer) # get nodes from NOT ontology layer ontology = @layer_ontologies[ontology_layer] base_layer_length = @nodes.values.count{|n| n.type == base_layer} ref_nodes.each do |ref_node| base_nodes = get_connected_nodes(ref_node, base_layer) ontology_base_subgraph = get_bipartite_subgraph(base_nodes, base_layer, ontology_layer) # get shared nodes between nodes from NOT ontology layer and ONTOLOGY layer. Also get the conections between shared nodes and ontology nodes. next if ontology_base_subgraph.empty? ontology_base_subgraph.transform_keys!{|k| k.to_sym} ontology.load_item_relations_to_terms(ontology_base_subgraph, remove_old_relations = true) term_pvals = ontology.compute_relations_to_items(base_nodes, base_layer_length, mode, thresold) relations.concat(term_pvals.map{|term| [ref_node, term[0], term[1]]}) end compute_log_transformation(relations) if mode == :elim meth = :hypergeometric_elim elsif mode == :weight meth = :hypergeometric_weight end @association_values[meth] = relations return relations end |
#get_jaccard_association(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 601 def get_jaccard_association(layers, base_layer) relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| unionIDS = associatedIDs_node1 | associatedIDs_node2 jaccValue = intersectedIDs.length.to_f/unionIDS.length end @association_values[:jaccard] = relations return relations end |
#get_kernel(layer2kernel, kernel, normalization = false) ⇒ Object
KERNEL METHODS
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# File 'lib/NetAnalyzer/network.rb', line 871 def get_kernel(layer2kernel, kernel, normalization=false) matrix, node_names = @adjacency_matrices[layer2kernel] #I = identity matrix #D = Diagonal matrix #A = adjacency matrix #L = laplacian matrix = D − A matrix_result = nil dimension_elements = matrix.shape.last # In scuba code, the diagonal values of A is set to 0. In weighted matrix the kernel result is the same with or without this operation. Maybe increases the computing performance? # In the md kernel this operation affects the values of the final kernel #dimension_elements.times do |n| # matrix[n,n] = 0.0 #end if kernel == 'el' || kernel == 'ct' || kernel == 'rf' || kernel.include?('vn') || kernel.include?('rl') || kernel == 'me' diagonal_matrix = matrix.sum(1).diag # get the total sum for each row, for this reason the sum method takes the 1 value. If sum colums is desired, use 0 # Make a matrix whose diagonal is row_sum matrix_L = diagonal_matrix - matrix if kernel == 'el' #Exponential Laplacian diffusion kernel(active). F Fouss 2012 | doi: 10.1016/j.neunet.2012.03.001 beta = 0.02 beta_product = matrix_L * -beta #matrix_result = beta_product.expm matrix_result = Numo::Linalg.expm(beta_product, 14) elsif kernel == 'ct' # Commute time kernel (active). J.-K. Heriche 2014 | doi: 10.1091/mbc.E13-04-0221 matrix_result = Numo::Linalg.pinv(matrix_L) # Anibal saids that this kernel was normalized. Why?. Paper do not seem to describe this operation for ct, it describes for Kvn or for all kernels, it is not clear. elsif kernel == 'rf' # Random forest kernel. J.-K. Heriche 2014 | doi: 10.1091/mbc.E13-04-0221 matrix_result = Numo::Linalg.inv(Numo::DFloat.eye(dimension_elements) + matrix_L) #Krf = (I +L ) ^ −1 elsif kernel.include?('vn') # von Neumann diffusion kernel. J.-K. Heriche 2014 | doi: 10.1091/mbc.E13-04-0221 alpha = kernel.gsub('vn', '').to_f * matrix.max_eigenvalue ** -1 # alpha = impact_of_penalization (1, 0.5 or 0.1) * spectral radius of A. spectral radius of A = absolute value of max eigenvalue of A matrix_result = Numo::Linalg.inv(Numo::DFloat.eye(dimension_elements) - matrix * alpha ) # (I -alphaA ) ^ −1 elsif kernel.include?('rl') # Regularized Laplacian kernel matrix (active) alpha = kernel.gsub('rl', '').to_f * matrix.max_eigenvalue ** -1 # alpha = impact_of_penalization (1, 0.5 or 0.1) * spectral radius of A. spectral radius of A = absolute value of max eigenvalue of A matrix_result = Numo::Linalg.inv(Numo::DFloat.eye(dimension_elements) + matrix_L * alpha ) # (I + alphaL ) ^ −1 elsif kernel == 'me' # Markov exponential diffusion kernel (active). G Zampieri 2018 | doi.org/10.1186/s12859-018-2025-5 . Taken from compute_kernel script beta=0.04 #(beta/N)*(N*I - D + A) id_mat = Numo::DFloat.eye(dimension_elements) m_matrix = (id_mat * dimension_elements - diagonal_matrix + matrix ) * (beta/dimension_elements) #matrix_result = m_matrix.expm matrix_result = Numo::Linalg.expm(m_matrix, 16) end elsif kernel == 'ka' # Kernelized adjacency matrix (active). J.-K. Heriche 2014 | doi: 10.1091/mbc.E13-04-0221 lambda_value = matrix.min_eigenvalue matrix_result = matrix + Numo::DFloat.eye(dimension_elements) * lambda_value.abs # Ka = A + lambda*I # lambda = the absolute value of the smallest eigenvalue of A elsif kernel.include?('md') # Markov diffusion kernel matrix. G Zampieri 2018 | doi.org/10.1186/s12859-018-2025-5 . Taken from compute_kernel script t = kernel.gsub('md', '').to_i #TODO: check implementation with Numo::array col_sum = matrix.sum(1) p_mat = matrix.div_by_vector(col_sum) p_temp_mat = p_mat.clone zt_mat = p_mat.clone (t-1).times do p_temp_mat = p_temp_mat.dot(p_mat) zt_mat = zt_mat + p_temp_mat end zt_mat = zt_mat * (1.0/t) matrix_result = zt_mat.dot(zt_mat.transpose) else matrix_result = matrix warn('Warning: The kernel method was not specified or not exists. The adjacency matrix will be given as result') # This allows process a previous kernel and perform the normalization in a separated step. end matrix_result = matrix_result.cosine_normalization if normalization #TODO: check implementation with Numo::array @kernels[layer2kernel] = matrix_result end |
#get_node_attributes(attr_names) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 154 def get_node_attributes(attr_names) attrs = [] attr_names.each do |attr_name| if attr_name == 'get_degree' attrs << get_degree elsif attr_name == 'get_degreeZ' attrs << get_degree(zscore=true) end end node_ids = attrs.first.keys node_attrs = [] node_ids.each do |n| node_attrs << [n].concat(attrs.map{|at| at[n]}) end return node_attrs end |
#get_nodes_from_layer(from_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 93 def get_nodes_from_layer(from_layer) return @nodes.values.select{|node| node.type == from_layer}.map{|node| node.id} end |
#get_nodes_layer(layers) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 490 def get_nodes_layer(layers) #for creating ny value in hypergeometric and pcc index nodes = [] layers.each do |layer| nodes.concat(@nodes.select{|nodeId, node| node.type == layer}.values) end return nodes end |
#get_pcc_associations(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 639 def get_pcc_associations(layers, base_layer) #for Ny calcule use get_nodes_layer base_layer_nodes = get_nodes_layer([base_layer]) ny = base_layer_nodes.length relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| intersProd = intersectedIDs.length * ny nodesProd = associatedIDs_node1.length * associatedIDs_node2.length nodesSubs = intersProd - nodesProd nodesAInNetwork = ny - associatedIDs_node1.length nodesBInNetwork = ny - associatedIDs_node2.length pccValue = nodesSubs.to_f / Math.sqrt(nodesProd * nodesAInNetwork * nodesBInNetwork) end @association_values[:pcc] = relations return relations end |
#get_pred_rec(meth, cut_number = 100, top_number = 10000) ⇒ Object
Pandey 2007, Association Analysis-based Transformations for Protein Interaction Networks: A Function Prediction Case Study
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# File 'lib/NetAnalyzer/network.rb', line 836 def get_pred_rec(meth, cut_number = 100, top_number = 10000) performance = [] #cut, pred, rec preds, limits = load_prediction(@association_values[meth]) cuts = get_cuts(limits, cut_number) cuts.each do |cut| prec, rec = pred_rec(preds, cut, top_number) performance << [cut, prec, rec] end return performance end |
#get_simpson_association(layers, base_layer) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 610 def get_simpson_association(layers, base_layer) relations = get_associations(layers, base_layer) do |associatedIDs_node1, associatedIDs_node2, intersectedIDs, node1, node2| minLength = [associatedIDs_node1.length, associatedIDs_node2.length].min simpsonValue = intersectedIDs.length.to_f/minLength end @association_values[:simpson] = relations return relations end |
#intersection(node1, node2) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 499 def intersection(node1, node2) shared_nodes = [] associatedIDs_node1 = @edges[node1] associatedIDs_node2 = @edges[node2] intersectedIDs = associatedIDs_node1 & associatedIDs_node2 intersectedIDs.each do |id| shared_nodes << @nodes[id] end return shared_nodes end |
#link_ontology(ontology_file_path, layer_name) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 941 def link_ontology(ontology_file_path, layer_name) if !@loaded_obos.include?(ontology_file_path) #Load new ontology ontology = Ontology.new(file: ontology_file_path, load_file: true) @loaded_obos << ontology_file_path @ontologies << ontology else #Link loaded ontology to current layer ontology = @ontologies[@loaded_obos.index(ontology_file_path)] end @layer_ontologies[layer_name] = ontology end |
#load_control(ref_array) ⇒ Object
PERFORMANCE METHODS
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# File 'lib/NetAnalyzer/network.rb', line 796 def load_control(ref_array) control = {} ref_array.each do |node1, node2| if node2 != '-' query = control[node1] if query.nil? control[node1] = [node2] else query << node2 end end end @control_connections = control return control end |
#load_network_by_bin_matrix(input_file, node_file, layers) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 125 def load_network_by_bin_matrix(input_file, node_file, layers) node_names = load_input_list(node_file) @adjacency_matrices[layers.map{|l| l.first}] = [Numo::NArray.load(input_file, type='npy'), node_names, node_names] end |
#load_network_by_pairs(file, layers, split_character = "\t") ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 113 def load_network_by_pairs(file, layers, split_character="\t") File.open(file).each do |line| line.chomp! pair = line.split(split_character) node1 = pair[0] node2 = pair[1] add_node(node1, set_layer(layers, node1)) add_node(node2, set_layer(layers, node2)) add_edge(node1, node2) end end |
#load_network_by_plain_matrix(input_file, node_file, layers, splitChar) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 130 def load_network_by_plain_matrix(input_file, node_file, layers, splitChar) node_names = load_input_list(node_file) @adjacency_matrices[layers.map{|l| l.first}] = [Numo::NArray.load(input_file, type='txt', splitChar=splitChar), node_names, node_names] end |
#load_prediction(pairs_array) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 812 def load_prediction(pairs_array) pred = {} min = nil max = nil pairs_array.each do |key, label, score| query = pred[key] if !min.nil? && !max.nil? min = score if score < min max = score if score > max else min = score; max = score end if query.nil? pred[key] = [[label], [score]] else query.first << label query.last << score end end return pred, [min, max] end |
#plot_dot(user_options = {}) ⇒ Object
input keys: layout
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# File 'lib/NetAnalyzer/network.rb', line 187 def plot_dot( = {}) # input keys: layout = {layout: "sfdp"} = .merge() graphviz_colors = %w[lightsteelblue1 lightyellow1 lightgray orchid2] palette = {} @layers.each do |layer| palette[layer] = graphviz_colors.shift end graph = GV::Graph.open('g', type = :undirected) plotted_edges = {} @edges.each do |nodeID, associatedIDs| associatedIDs.each do |associatedID| pair = [nodeID, associatedID].sort.join('_').to_sym if !plotted_edges[pair] graph.edge 'e', graph.node(nodeID, label: '', style: 'filled', fillcolor: palette[@nodes[nodeID].type]), graph.node(associatedID, label: '', style: 'filled' , fillcolor: palette[@nodes[associatedID].type]) plotted_edges[pair] = true end end end @reference_nodes.each do |nodeID| graph.node(nodeID, style: 'filled', fillcolor: 'firebrick1', label: '') end graphviz_border_colors = %w[blue darkorange red olivedrab4] @group_nodes.each do |groupID, gNodes| border_color = graphviz_border_colors.shift gNodes.each do |nodeID| graph.node(nodeID, color: border_color, penwidth: '10', label: '') end end graph[:overlap] = false STDERR.puts 'Save graph' graph.save([:output_file] + '.png', format='png', layout=[:layout]) end |
#plot_network(options = {}) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 171 def plot_network( = {}) if [:method] == 'graphviz' plot_dot() else if [:method] == 'elgrapho' template = 'el_grapho' elsif [:method] == 'cytoscape' template = 'cytoscape' elsif [:method] == 'sigma' template = 'sigma' end renderered_template = ERB.new(File.open(File.join(TEMPLATES, template + '.erb')).read).result(binding) File.open([:output_file] + '.html', 'w'){|f| f.puts renderered_template} end end |
#pred_rec(preds, cut, top) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 847 def pred_rec(preds, cut, top) predicted_labels = 0 #m true_labels = 0 #n common_labels = 0 # k @control_connections.each do |key, c_labels| true_labels += c_labels.length #n pred_info = preds[key] if !pred_info.nil? labels, scores = pred_info reliable_labels = get_reliable_labels(labels, scores, cut, top) predicted_labels += reliable_labels.length #m common_labels += (c_labels & reliable_labels).length #k end end #puts "cut: #{cut} trueL: #{true_labels} predL: #{predicted_labels} commL: #{common_labels}" prec = common_labels.to_f/predicted_labels rec = common_labels.to_f/true_labels prec = 0.0 if prec.nan? rec = 0.0 if rec.nan? return prec, rec end |
#query_edge(nodeA, nodeB) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 63 def query_edge(nodeA, nodeB) query = @edges[nodeA] if query.nil? @edges[nodeA] = [nodeB] else query << nodeB end end |
#replace_nil_vals(val) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 248 def replace_nil_vals(val) return val.nil? ? 'NULL' : val end |
#set_compute_pairs(use_pairs, get_autorelations) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 49 def set_compute_pairs(use_pairs, get_autorelations) @compute_pairs = use_pairs @compute_autorelations = get_autorelations end |
#shortest_path(node_start, node_stop, paths = false) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 362 def shortest_path(node_start, node_stop, paths=false) #https://betterprogramming.pub/5-ways-to-find-the-shortest-path-in-a-graph-88cfefd0030f #return bidirectionalSearch(node_start, node_stop) #https://efficientcodeblog.wordpress.com/2017/12/13/bidirectional-search-two-end-bfs/ dist, all_paths = bfs_shortest_path(node_start, node_stop, paths) return dist, all_paths end |
#write_kernel(layer2kernel, output_file) ⇒ Object
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# File 'lib/NetAnalyzer/network.rb', line 937 def write_kernel(layer2kernel, output_file) @kernels[layer2kernel].save(output_file) end |