Python networkx.degree_assortativity_coefficient函数代码示例

def classify(request, pk):
	#gets object based on id given
	graph_file = get_object_or_404(Document, pk=pk)
	#reads file into networkx graph based on extension
	if graph_file.extension() == ".gml":
		G = nx.read_gml(graph_file.uploadfile)
	else:
		G = nx.read_gexf(graph_file.uploadfile)
	#closes file so we can delete it
	graph_file.uploadfile.close()
	#loads the algorithm and tests the algorithm against the graph
	g_json = json_graph.node_link_data(G)
	#save graph into json file
	with open(os.path.join(settings.MEDIA_ROOT, 'graph.json'), 'w') as graph:
			json.dump(g_json, graph)
	with open(os.path.join(settings.MEDIA_ROOT, 'rf_classifier.pkl'), 'rb') as malgo:
		algo_loaded = pickle.load(malgo, encoding="latin1")
		dataset = np.array([G.number_of_nodes(), G.number_of_edges(), nx.density(G), nx.degree_assortativity_coefficient(G), nx.average_clustering(G), nx.graph_clique_number(G)])
		print (dataset)
		#creates X to test against
		X = dataset
		prediction = algo_loaded.predict(X)
		
		
		
		graph_type = check_prediction(prediction)
		graph = GraphPasser(G.number_of_nodes(), G.number_of_edges(), nx.density(G), nx.degree_assortativity_coefficient(G), nx.average_clustering(G), nx.graph_clique_number(G))
	#gives certain variables to the view

	return render(
		request,
		'classification/classify.html',
		{'graph': graph, 'prediction': graph_type}
		)

def knn_pack(graph, *kwargs):
	t = dict()
	for k in kwargs:
		t.__setitem__(k, kwargs[k])
	t.__setitem__('asr', nx.degree_assortativity_coefficient(graph))
	t.__setitem__('weighted_asr', nx.degree_assortativity_coefficient(graph, weight = 'weight'))
	if graph.is_directed():
		t.__setitem__('knn', nx.average_degree_connectivity(graph, source = 'out', target = 'in'))
		if len(nx.get_edge_attributes(graph, 'weight')):
			t.__setitem__('weighted_knn', nx.average_degree_connectivity(graph, source = 'out', target = 'in', weight = 'weight'))
	else:
		t.__setitem__('knn', nx.average_degree_connectivity(graph))
		if len(nx.get_edge_attributes(graph, 'weight')):
			t.__setitem__('weighted_knn', nx.average_degree_connectivity(graph, weight = 'weight'))
	return(t)

def gpn_stats(genes, gpn, version):
    LOGGER.info("Computing GPN statistics")
    nodes = sorted(gpn.nodes_iter())
    components = sorted(nx.connected_components(gpn), key=len, reverse=True)
    ass = nx.degree_assortativity_coefficient(gpn)
    deg = [gpn.degree(node) for node in nodes]
    stats = pd.DataFrame(data={
            "version": version,
            "release": pd.to_datetime(RELEASE[version]),
            "num_genes": len(genes),
            "num_nodes": len(nodes),
            "num_links": gpn.size(),
            "density": nx.density(gpn),
            "num_components": len(components),
            "largest_component": len(components[0]),
            "assortativity": ass,
            "avg_deg": mean(deg),
            "hub_deg": max(deg)
        }, index=[1])
    stats["release"] = pd.to_datetime(stats["release"])
    dists = pd.DataFrame(data={
            "version": version,
            "release": [pd.to_datetime(RELEASE[version])] * len(nodes),
            "node": [node.unique_id for node in nodes],
            "degree": deg,
        })
    return (stats, dists)

def Attributes_of_Graph(G):
    print "*Statistic attributes of graphs:"
    print "N", nx.number_of_nodes(G)
    print "M", nx.number_of_edges(G)

    print "C", nx.average_clustering(G)
    #print "<d>", nx.average_shortest_path_length(G)
    print "r", nx.degree_assortativity_coefficient(G)

    degree_list = list(G.degree_iter())
    max_degree = 0
    min_degree = 0
    avg_degree_1 = 0.0
    avg_degree_2 = 0.0
    for node in degree_list:
        avg_degree_1 = avg_degree_1 + node[1]
        avg_degree_2 = avg_degree_2 + node[1]*node[1]
        if node[1] > max_degree:
            max_degree = node[1]
        if node[1] < min_degree:
            min_degree = node[1]
    #end for
    avg_degree = avg_degree_1/len(degree_list)
    avg_degree_square = (avg_degree_2/len(degree_list)) / (avg_degree*avg_degree)
    print "<k>", avg_degree
    print "k_max", max_degree
    print "H", avg_degree_square
    print "DH", float(max_degree-min_degree)/G.number_of_nodes()

def draw_graph(nodes, edges, graphs_dir, default_lang='all'):
    lang_graph = nx.MultiDiGraph()
    lang_graph.add_nodes_from(nodes)
    for edge in edges:
        if edges[edge] == 0:
            lang_graph.add_edge(edge[0], edge[1])
        else:
            lang_graph.add_edge(edge[0], edge[1], weight=float(edges[edge]), label=str(edges[edge]))

    # print graph info in stdout
    # degree centrality
    print('-----------------\n\n')
    print(default_lang)
    print(nx.info(lang_graph))
    try:
        # When ties are associated to some positive aspects such as friendship or collaboration,
        #  indegree is often interpreted as a form of popularity, and outdegree as gregariousness.
        DC = nx.degree_centrality(lang_graph)
        max_dc = max(DC.values())
        max_dc_list = [item for item in DC.items() if item[1] == max_dc]
    except ZeroDivisionError:
        max_dc_list = []
    # https://ru.wikipedia.org/wiki/%D0%9A%D0%BE%D0%BC%D0%BF%D0%BB%D0%B5%D0%BA%D1%81%D0%BD%D1%8B%D0%B5_%D1%81%D0%B5%D1%82%D0%B8
    print('maxdc', str(max_dc_list), sep=': ')
    # assortativity coef
    AC = nx.degree_assortativity_coefficient(lang_graph)
    print('AC', str(AC), sep=': ')
    # connectivity
    print("Слабо-связный граф: ", nx.is_weakly_connected(lang_graph))
    print("количество слабосвязанных компонент: ", nx.number_weakly_connected_components(lang_graph))
    print("Сильно-связный граф: ", nx.is_strongly_connected(lang_graph))
    print("количество сильносвязанных компонент: ", nx.number_strongly_connected_components(lang_graph))
    print("рекурсивные? компоненты: ", nx.number_attracting_components(lang_graph))
    print("число вершинной связности: ", nx.node_connectivity(lang_graph))
    print("число рёберной связности: ", nx.edge_connectivity(lang_graph))
    # other info
    print("average degree connectivity: ", nx.average_degree_connectivity(lang_graph))
    print("average neighbor degree: ", sorted(nx.average_neighbor_degree(lang_graph).items(),
                                              key=itemgetter(1), reverse=True))
    # best for small graphs, and our graphs are pretty small
    print("pagerank: ", sorted(nx.pagerank_numpy(lang_graph).items(), key=itemgetter(1), reverse=True))

    plt.figure(figsize=(16.0, 9.0), dpi=80)
    plt.axis('off')
    pos = graphviz_layout(lang_graph)
    nx.draw_networkx_edges(lang_graph, pos, alpha=0.5, arrows=True)
    nx.draw_networkx(lang_graph, pos, node_size=1000, font_size=12, with_labels=True, node_color='green')
    nx.draw_networkx_edge_labels(lang_graph, pos, edges)

    # saving file to draw it with dot-graphviz
    # changing overall graph view, default is top-bottom
    lang_graph.graph['graph'] = {'rankdir': 'LR'}
    # marking with blue nodes with maximum degree centrality
    for max_dc_node in max_dc_list:
        lang_graph.node[max_dc_node[0]]['fontcolor'] = 'blue'
    write_dot(lang_graph, os.path.join(graphs_dir, default_lang + '_links.dot'))

    # plt.show()
    plt.savefig(os.path.join(graphs_dir, 'python_' + default_lang + '_graph.png'), dpi=100)
    plt.close()

def compute_singlevalued_measures(ntwk, weighted=True, calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info("Computing single valued measures:")
    measures = {}
    iflogger.info("...Computing degree assortativity (pearson number) ...")
    try:
        measures["degree_pearsonr"] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures["degree_pearsonr"] = nx.degree_pearson_correlation_coefficient(ntwk)
    iflogger.info("...Computing degree assortativity...")
    try:
        measures["degree_assortativity"] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures["degree_assortativity"] = nx.degree_assortativity_coefficient(ntwk)
    iflogger.info("...Computing transitivity...")
    measures["transitivity"] = nx.transitivity(ntwk)
    iflogger.info("...Computing number of connected_components...")
    measures["number_connected_components"] = nx.number_connected_components(ntwk)
    iflogger.info("...Computing average clustering...")
    measures["average_clustering"] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info("...Calculating average shortest path length...")
        measures["average_shortest_path_length"] = nx.average_shortest_path_length(ntwk, weighted)
    if calculate_cliques:
        iflogger.info("...Computing graph clique number...")
        measures["graph_clique_number"] = nx.graph_clique_number(ntwk)  # out of memory error
    return measures

def degree_assortativity(G):
#this wrapper helps avoid error due to change in interface name
    if hasattr(nx, 'degree_assortativity_coefficient'):
        return nx.degree_assortativity_coefficient(G)
    elif hasattr(nx, 'degree_assortativity'):
        return nx.degree_assortativity(G)
    else:
        raise ValueError, 'Cannot compute degree assortativity: method not available'

def SR_nx_assortativity():
	#os.chdir("SR_graphs")
	os.chdir(IN_DIR_SR)

	SR=nx.read_edgelist(f_in_graph_SR, create_using=nx.Graph()) #, data=(('weight',int),))
	print(len(SR.nodes(data=True)))

	print "Degree assortativity of UNWEIGHTED is %f " % nx.degree_assortativity_coefficient(SR)
	#print "Sentiment (by value) numeric assortativity is %f " % nx.numeric_assortativity_coefficient(MENT, 'sentiment_val')


	SR=nx.read_edgelist(f_in_graph_SR, create_using=nx.Graph(), data=(('weight',int),))
	print(len(SR.nodes(data=True)))
	print "Degree assortativity of WEIGHTED is %f " % nx.degree_assortativity_coefficient(SR, weight='weight')

	cnt = 0
	d=defaultdict(int)
	d_val = defaultdict(int)
	d1 = defaultdict(int)
	with open(f_in_user_sentiment) as f:
	    for line in f:
	        (uid, label, val) = line.split()
	        uid = unicode(uid)
	        d1[uid]= int(float(val)*10000)
	        if uid in SR.nodes():
	        	d[uid]= int(float(val)*10000)
	        	d_val[uid] = int(label)
	        else:
	        	cnt += 1
	print "Number of nodes for which we have sentminet but are not in the mention graph is ", cnt
	cnt = 0
	for node in SR.nodes():
		if not node in d1:
			cnt += 1
			SR.remove_node(node)
	print "Number of nodes that do not have sentiment value, so we remove them from the mention graph", cnt
	nx.set_node_attributes(SR, 'sentiment' , d)
	nx.set_node_attributes(SR, 'sentiment_val' , d_val)
	print "Final number of nodes in the graph ", (len(SR.nodes(data=True)))

	print "Sentiment (by label) nominal numeric assortativity is %f " % nx.numeric_assortativity_coefficient(SR, 'sentiment')
	print "Sentiment (by value) numeric assortativity is %f " % nx.numeric_assortativity_coefficient(SR, 'sentiment_val')

def trn_stats(genes, trn, t_factors, version):
    LOGGER.info("Computing TRN statistics")
    nodes = sorted(trn.nodes_iter())
    node2id = {n: i for (i, n) in enumerate(nodes)}
    id2node = {i: n for (i, n) in enumerate(nodes)}
    (grn, node2id) = to_simple(trn.to_grn(), return_map=True)
    nodes = sorted(grn.nodes_iter())
    regulating = {node for (node, deg) in grn.out_degree_iter() if deg > 0}
    regulated = set(nodes) - regulating
    components = sorted(nx.weakly_connected_components(grn), key=len,
            reverse=True)
    data = dict()
    for (a, b) in itertools.product(("in", "out"), repeat=2):
        data["{a}_{b}_ass".format(a=a, b=b)] = nx.degree_assortativity_coefficient(grn, x=a, y=b)
    census = triadic_census(grn)
    forward = census["030T"]
    feedback = census["030C"]
    num_cycles = sum(1 for cyc in nx.simple_cycles(grn) if len(cyc) > 2)
    in_deg = [grn.in_degree(node) for node in regulated]
    out_deg = [grn.out_degree(node) for node in regulating]
    data["version"] = version,
    data["release"] = pd.to_datetime(RELEASE[version]),
    data["num_genes"] = len(genes),
    data["num_tf"] = len(t_factors),
    data["num_nodes"] = len(nodes),
    data["num_regulating"] = len(regulating),
    data["num_regulated"] = len(regulated),
    data["num_links"] = grn.size(),
    data["density"] = nx.density(grn),
    data["num_components"] = len(components),
    data["largest_component"] = len(components[0]),
    data["feed_forward"] = forward,
    data["feedback"] = feedback,
    data["fis_out"] = trn.out_degree(TranscriptionFactor[FIS_ID, version]),
    data["hns_out"] = trn.out_degree(TranscriptionFactor[HNS_ID, version]),
    data["cycles"] = num_cycles,
    data["regulated_in_deg"] = mean(in_deg),
    data["regulating_out_deg"] = mean(out_deg),
    data["hub_out_deg"] = max(out_deg)
    stats = pd.DataFrame(data, index=[1])
    in_deg = [grn.in_degree(node) for node in nodes]
    out_deg = [grn.out_degree(node) for node in nodes]
    bc = nx.betweenness_centrality(grn)
    bc = [bc[node] for node in nodes]
    dists = pd.DataFrame({
            "version": version,
            "release": [pd.to_datetime(RELEASE[version])] * len(nodes),
            "node": [id2node[node].unique_id for node in nodes],
            "regulated_in_degree": in_deg,
            "regulating_out_degree": out_deg,
            "betweenness": bc
        })
    return (stats, dists)

def main():
    domain_name = 'baidu.com'
    domain_pkts = get_data(domain_name)
    node_cname, node_ip, visit_total, edges, node_main = get_ip_cname(domain_pkts[0]['details'])
    for i in domain_pkts[0]['details']:
        for v in i['answers']:
            edges.append((v['domain_name'],v['dm_data']))

    DG = nx.DiGraph()
    DG.add_edges_from(edges)

    # 分析域名直接解析为IP的node
    for node in DG:
        if node in node_main and DG.successors(node) in node_ip:
            print node

    # 分析cname关联的IP数量分布
    for node in DG:
        if node in node_cname and DG.successors(node) not in node_cname:  # 查找与ip直接连接的cname
            print "node",DG.out_degree(node),DG.in_degree(node),DG.degree(node)
    # 与cname关联的域名个数
    # for node in DG:
    #     if node in node_cname and DG.predecessors(node) not in node_cname:
    #         print len(DG.predecessors(node))

    for node in DG:
        if node in  node_main:
            if len(DG.successors(node)) ==3:
                print node
                print DG.successors(node)
    # print sorted(nx.degree(DG).values())

    print nx.degree_assortativity_coefficient(DG)
    average_degree = sum(nx.degree(DG).values())/(len(node_cname)+len(node_ip)+len(node_main))
    print average_degree
    print len(node_cname)+len(node_ip)+len(node_main)
    print len(edges)
    print nx.degree_histogram(DG)

def compute_singlevalued_measures(ntwk, weighted=True,
                                  calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info('Computing single valued measures:')
    measures = {}
    iflogger.info('...Computing degree assortativity (pearson number) ...')
    try:
        measures['degree_pearsonr'] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures[
            'degree_pearsonr'] = nx.degree_pearson_correlation_coefficient(
                ntwk)
    iflogger.info('...Computing degree assortativity...')
    try:
        measures['degree_assortativity'] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures['degree_assortativity'] = nx.degree_assortativity_coefficient(
            ntwk)
    iflogger.info('...Computing transitivity...')
    measures['transitivity'] = nx.transitivity(ntwk)
    iflogger.info('...Computing number of connected_components...')
    measures['number_connected_components'] = nx.number_connected_components(
        ntwk)
    iflogger.info('...Computing graph density...')
    measures['graph_density'] = nx.density(ntwk)
    iflogger.info('...Recording number of edges...')
    measures['number_of_edges'] = nx.number_of_edges(ntwk)
    iflogger.info('...Recording number of nodes...')
    measures['number_of_nodes'] = nx.number_of_nodes(ntwk)
    iflogger.info('...Computing average clustering...')
    measures['average_clustering'] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info('...Calculating average shortest path length...')
        measures[
            'average_shortest_path_length'] = nx.average_shortest_path_length(
                ntwk, weighted)
    else:
        iflogger.info('...Calculating average shortest path length...')
        measures[
            'average_shortest_path_length'] = nx.average_shortest_path_length(
                nx.connected_component_subgraphs(ntwk)[0], weighted)
    if calculate_cliques:
        iflogger.info('...Computing graph clique number...')
        measures['graph_clique_number'] = nx.graph_clique_number(
            ntwk)  # out of memory error
    return measures

def main():
    """ 
    Entry point. 
    """
    if len(sys.argv) == 1:
        sys.exit("Usage: python evolving_network.py <params file>")

    # Load the parameters.
    params = json.load((open(sys.argv[1], "r")))
    seedNetwork = params["seedNetwork"]

    # Setup the seed network.
    if seedNetwork["name"] == "read_graphml":
        G = networkx.convert_node_labels_to_integers(\
            networkx.read_graphml(seedNetwork["args"]["path"]))
    else:
        G = getattr(networkx, seedNetwork["name"])(**seedNetwork["args"])

    # Evolve G.
    R = robustness(G, params["attackStrategy"], params["sequentialMode"])
    countDown = params["stagnantEpochs"]
    while countDown > 0:
        if params["verbose"]:
            v = numpy.var(G.degree().values())               # degree variance
            l = networkx.average_shortest_path_length(G)     # avg. path length
            C = networkx.average_clustering(G)                 # clustering
            r = networkx.degree_assortativity_coefficient(G)   # assortativity
            print("%.4f %.4f %.4f %.4f %.4f" %(R, v, l, C, r))
        mutants = genMutants(G, params)
        prevR = R
        for mutant in mutants:
            mutantR = robustness(mutant, params["attackStrategy"], 
                                 params["sequentialMode"])
            if params["maximizeRobustness"] and mutantR > R or \
               not params["maximizeRobustness"] and mutantR < R:
                R = mutantR
                G = mutant
        if params["maximizeRobustness"] and R > prevR or \
           not params["maximizeRobustness"] and R < prevR:
            countDown = params["stagnantEpochs"]
        else:
            countDown -= 1

    # Save G.
    networkx.write_graphml(G, params["outFile"])

 def analysis(self):
     self.compute_density()
     self.degree_correlation = nx.degree_assortativity_coefficient(self)
     self.compute_complexity()
     self.compute_paths()
     self.compute_overlap()
     self.compute_variance()
     self.pattern_rank = numpy.linalg.matrix_rank(self.ideal_pattern)
     self.binary_rank = numpy.linalg.matrix_rank(self.ideal_pattern > 0)
     try:
         self.compute_modularity()
     except nx.NetworkXError:
         pass
     self.generate_random_ensemble()
     self.compute_zscores()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", RuntimeWarning)
         self.compute_essentialities()

def compute_network_stats(G, inst):
    print 'RECIP:%.5f' % reciprocity(G)
    print 'MEAN_DEGREE:%.5f' % mean_degree(G)
    print 'MEAN_NB_DEGREE:%.5f' % mean_nb_degree(G)

    Gu = G.to_undirected()
    print 'AVG_CLUSTER:%.5f' % nx.average_clustering(Gu)
    print 'DEGREE_ASSORT:%.5f' % nx.degree_assortativity_coefficient(Gu)
    print 'MEAN_GEODESIC:%.5f' % nx.average_shortest_path_length(Gu)
    mg, d = mean_max_geodesic(Gu)
    print 'MEAN_GEODESIC:%.5f' % mg
    print 'DIAMETER:%d' % int(d)

    keep = []
    for n in Gu.nodes_iter():
        if n in inst:
            Gu.node[n]['region'] = inst[n]['Region']
            keep.append(n)
    
    H = Gu.subgraph(keep)
    print 'MOD_REGION:%.5f' % (nx.attribute_assortativity_coefficient(H, 'region'))

def getStats(graph):
    stats = dict()
    stats["Nodes"] = nx.number_of_nodes(graph)
    stats["Edges"] = nx.number_of_edges(graph)
    stats["Neighbors/node"] = 2 * float(stats["Edges"])/ stats["Nodes"]
        
    c = nx.average_clustering(graph)
    stats["Clustering coefficient"] = "%3.2f"%c

    try:
        r = nx.degree_assortativity_coefficient(graph)
        stats["Degree assortativity"] = "%3.2f"%r
        r = get_assortativity_coeff(graph)
        # stats["Degree assortativity - own"] = "%3.2f"%r
    except:
        print("Impossible to compute degree assortativity")
    
    if (nx.is_connected(graph)):
        stats['Diameter'] = nx.diameter(graph)
        p = nx.average_shortest_path_length(graph)
        stats["Characteristic path length"] = "%3.2f"%p
        stats["Connected components"] = 1
    else:
        d = 0.0
        p = 0.0
        i = 0
        for g in nx.connected_component_subgraphs(graph):
            i += 1
            d += nx.diameter(g)
            if len(nx.nodes(g)) > 1:
                p += nx.average_shortest_path_length(g)
        p /= i
        stats["Connected components"] = i
        stats["Diameter - sum on cc"] = "%3.2f"%d
        stats["Characteristic path length - avg on cc"] = "%3.2f"%p 
    
    dd = nx.degree_histogram(graph)
    stats["Max degree"] = len(dd) - 1

    return stats