Python Graph.to_undirected方法代码示例

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import to_undirected [as 别名]
class Physic (BaseInputGraph):
  def __init__(self):
    '''
    @return: Arxiv ASTRO-PH (Astro Physics) collaboration network as iGraph graph instance 
    '''
    edges = []
    weights = []
    f = open("./physic/compact-physic.txt", "r")
    for line in f:
      if line and line[0]!='#':
        seg = line.split()
        edges.append( (int(seg[0]), int(seg[1])) )
        weights.append( 1 )
    maxid = max( edges, key=itemgetter(1) )[1]
    maxid = max( maxid, max(edges,key=itemgetter(0))[0] )
    self.g = Graph()
    self.g.add_vertices(maxid + 1)
    self.g.add_edges(edges)
    self.g.to_undirected()
    self.g.simplify()
    self.g.vs["myID"] = [ str(int(i)) for i in range(maxid+1)]
    print "#nodes=", maxid + 1
    print "#edges=", len(self.g.es)

  def run(self):
    C = BaseInputGraph.unsupervised_logexpand(self)
    BaseInputGraph.run(self, C, p0=np.array([0.04, 0.04]))
    with open("./physic/Physic_weights.pairs", "w+") as txt:
      for e in self.g.es:
        txt.write("%d %d %f\n" %(e.tuple[0], e.tuple[1], e["weight"]) )

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import to_undirected [as 别名]
class Football ( BaseInputGraph ):
  def __init__(self):
    '''
    @return: American Colleage Football Network as iGraph graph instance 
    '''
    edges = []
    weights = []
    f = open("./football/footballTSEinputEL.dat", "r")
    for line in f:
      seg = line.split()
      edges.append( (int(seg[0]), int(seg[1])) )
      weights.append( 1 )
    maxid = max( edges, key=itemgetter(1))[1]
    maxid = max( maxid, max(edges,key=itemgetter(0))[0] )
    self.g = Graph()
    self.g.add_vertices(maxid + 1)
    self.g.add_edges(edges)
    self.g.to_undirected()
    conf = []
    with open("./football/footballTSEinputConference.clu", "r") as fconf:
      conf = (fconf.read()).split()
    self.g.vs["comm"] = [x for x in conf]
    self.g.vs["myID"] = [ str(int(i)) for i in range(maxid+1)]
    print "#nodes=", maxid + 1
    print "#edges=", len(self.g.es)

  def run(self):
    AMI_increase = [[], [], [], [], [], []]
    for igroup in range(10):
      #semi-supervised
      #group = [i for i, conf in enumerate(self.g.vs["comm"]) if conf == str(igroup)]
      #if len(group) < 3: continue
      #C = BaseInputGraph.get_C(self, group)

      #unsupervised
      C = BaseInputGraph.unsupervised_logexpand(self)

      BaseInputGraph.run(self, C)
      AMI_increase[0] += BaseInputGraph.results(self, Graph.community_fastgreedy, hasgnc = True)
      AMI_increase[1] += BaseInputGraph.results(self, Graph.community_label_propagation, hasgnc = True)
      AMI_increase[2] += BaseInputGraph.results(self, Graph.community_leading_eigenvector, hasgnc = True)
      AMI_increase[3] += BaseInputGraph.results(self, Graph.community_walktrap, hasgnc = True)
      AMI_increase[4] += BaseInputGraph.results(self, Graph.community_edge_betweenness, hasgnc = True)
      AMI_increase[5] += BaseInputGraph.results(self, Graph.community_multilevel, hasgnc = True)
    for i in range(6):
      print "& %.5f" % ( 1.0 * sum(AMI_increase[i]) / len(AMI_increase[i]) )

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import to_undirected [as 别名]
class LFR ( BaseInputGraph ):
  def __init__(self, trialval=1):
    ws = []
    edges = []
    self.trial = trialval
    with open("./binary_networks/mu0.5/network%d.dat" % self.trial, "r") as txt:
      for line in txt:
        seg = line.split()
        edges.append((int(seg[0]), int(seg[1])))
        ws.append(1)
    maxid = max( edges, key=itemgetter(1))[1]
    maxid = max( maxid, max(edges,key=itemgetter(0))[0] )
    self.g = Graph()
    print maxid
    self.g.add_vertices(maxid + 1)
    with open("./binary_networks/mu0.5/community%d.dat" % self.trial, "r") as txt:
      for line in txt:
        seg = line.split()
        #print seg[0]
        self.g.vs[int(seg[0])]["comm"] = seg[1] #note: string is returned
    self.g.add_edges(edges)
    self.g.to_undirected()
    self.g.simplify()
    self.g.delete_vertices(0)
    self.g.es["weight"] = ws
    BaseInputGraph.write_ground_truth(self, "./ground_truth_community%d.groups" % self.trial)
    print "#nodes=", maxid + 1
    print "#edges=", len(self.g.es) 

  def run(self):
    #supervised
    C = []
    for i in range(6):
      commval = str(random.randint(0,100))
      group = [i for i, comm in enumerate(self.g.vs["comm"]) if comm == commval]
      C += BaseInputGraph.get_C(self, group)
    #unsupervised
    C = BaseInputGraph.unsupervised_logexpand(self)
    BaseInputGraph.run(self, C, p0=np.array([1 , 1]))
    BaseInputGraph.results(self, Graph.community_fastgreedy, hasgnc = False,\
     filename="%d" %self.trial)

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import to_undirected [as 别名]
class Enron (BaseInputGraph):
  def __init__(self):
    '''
    @return: Enron email communication network as iGraph graph instance 
    '''
    edges = []
    weights = []
    f = open("./enron/email-Enron.txt", "r")
    for line in f:
      if line and line[0]!='#':
        seg = line.split()
        edges.append( (int(seg[0]), int(seg[1])) )
        weights.append( 1 )
    maxid = max( edges, key=itemgetter(1) )[1]
    maxid = max( maxid, max(edges,key=itemgetter(0))[0] )
    self.g = Graph()
    self.g.add_vertices(maxid + 1)
    self.g.add_edges(edges)
    self.g.to_undirected()
    self.g.simplify()
    self.g.vs["myID"] = [ str(int(i)) for i in range(maxid+1)]
    print "#nodes=", maxid + 1
    print "#edges=", len(self.g.es)

  def run(self):
    C = BaseInputGraph.unsupervised_logexpand(self)
    BaseInputGraph.run(self, C, p0=np.array([0.04, 0.04]))
    with open("./enron/email-Enron_weights.pairs", "w+") as txt:
      for e in self.g.es:
        txt.write("%d %d %f\n" %(e.tuple[0], e.tuple[1], e["weight"]) )
    with open("./enron/email-Enron_unweights.pairs", "w+") as txt:
      count = 0
      for e in self.g.es:
        txt.write("%d %d\n" %(e.tuple[0], e.tuple[1]) )
        count += 1
      print count , "edges written."

# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import to_undirected [as 别名]
class Execution:
  def results(self, algo, filename):
    title = self.__class__.__name__
    #=====Community Detection====#
    # weighted graph 
    print "Weighted: %s" % algo 
    vd = algo(self.g, weights = [ (lambda w: max(w,0.001) )(w) for w in self.g.es["weight"]] )
    try:
      vc1 = vd.as_clustering()
    except:
      vc1 = vd #in case a VertexCluster instance is returned
    self.write_vertex_clustering(vc1, "_weighted_%s" % filename)

    # unweighted graph
    print "Un-weighted: %s" % algo
    vd = algo(self.g)
    try:
      vc2 = vd.as_clustering()
    except:
      vc2 = vd #in case a VertexCluster instance is returned
    self.write_vertex_clustering(vc2, "_unweighted_%s" % filename)

    #=====Validation====#
    if "football" in filename and validate_foot_ball_only:
      self.quality(vc1, vc2)

  def write_vertex_clustering(self, vc, tag):
      title = "community" 
      with open("./output/" + title + tag + ".group", "w+") as txt:
        for cc in range(len(vc)):
            if len(vc[cc]) > 1:
          	txt.write(" ".join([str(self.reverse_id(_)) for _ in vc[cc]]) + "\n")
      print "Write to file", "./output/" + title + tag + ".group"
  
  def quality(self, vc1, vc2):
    global display

    with open("./input/football_raw_data/footballTSEinputConference.clu", "r") as fconf:
      conf = (fconf.read()).split()
    self.g.vs["comm"] = [x for x in conf]

    for index, vc in enumerate([vc1, vc2]):
      print "=================="
      for cc in range(len(vc)):
        for cci in vc[cc]:
          self.g.vs[ self.reverse_id(cci) ]["detected"] = str(cc)
      ami = metrics.adjusted_mutual_info_score(self.g.vs["detected"], self.g.vs["comm"])
      print "AMI", ami
      display["AMI"][index].append(ami)

      ari = metrics.adjusted_rand_score(self.g.vs["detected"], self.g.vs["comm"])
      print "ARI", ari
      display["ARI"][index].append(ari)

  def compact_id(self, i):
    if i in self.ID:
      return self.ID[i]
    else:
      self.ID[i] = self.cap
      self.rID[self.cap] = i
      self.cap += 1
      return self.ID[i]

  def reverse_id(self, i):
    assert i in self.rID
    return self.rID[i]

  def __init__(self, filename):
    print "Load file", filename
    self.cap = 0
    self.ID = {} 
    self.rID = {} 
    edges = []
    weights = []
    with open(filename, "r") as txt:
      for line in txt:
        seg = line.split()
        edges.append( (self.compact_id(int(seg[0])), self.compact_id(int(seg[1]))) )
        if len(seg) == 3:
          weights.append( float(seg[2]) )
	else:
          weights.append(1) 
    self.g = Graph()
    self.g.add_vertices(self.cap)
    self.g.add_edges(edges)
    self.g.to_undirected()
    self.g.es["weight"] = weights