Python networkx.from_numpy_matrix函数代码示例

    def test_from_numpy_matrix_parallel_edges(self):
        """Tests that the :func:`networkx.from_numpy_matrix` function
        interprets integer weights as the number of parallel edges when
        creating a multigraph.

        """
        A = np.matrix([[1, 1], [1, 2]])
        # First, with a simple graph, each integer entry in the adjacency
        # matrix is interpreted as the weight of a single edge in the graph.
        expected = nx.DiGraph()
        edges = [(0, 0), (0, 1), (1, 0)]
        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
        expected.add_edge(1, 1, weight=2)
        actual = nx.from_numpy_matrix(A, parallel_edges=True,
                                      create_using=nx.DiGraph())
        assert_graphs_equal(actual, expected)
        actual = nx.from_numpy_matrix(A, parallel_edges=False,
                                      create_using=nx.DiGraph())
        assert_graphs_equal(actual, expected)
        # Now each integer entry in the adjacency matrix is interpreted as the
        # number of parallel edges in the graph if the appropriate keyword
        # argument is specified.
        edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
        expected = nx.MultiDiGraph()
        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
        actual = nx.from_numpy_matrix(A, parallel_edges=True,
                                      create_using=nx.MultiDiGraph())
        assert_graphs_equal(actual, expected)
        expected = nx.MultiDiGraph()
        expected.add_edges_from(set(edges), weight=1)
        # The sole self-loop (edge 0) on vertex 1 should have weight 2.
        expected[1][1][0]['weight'] = 2
        actual = nx.from_numpy_matrix(A, parallel_edges=False,
                                      create_using=nx.MultiDiGraph())
        assert_graphs_equal(actual, expected)

def plot_reconstruction_result(res):
    """ Plot original and reconstructed graph plus time series
    """
    fig = plt.figure(figsize=(32, 8))
    gs = mpl.gridspec.GridSpec(1, 4)

    # original graph
    orig_ax = plt.subplot(gs[0])
    plot_graph(nx.from_numpy_matrix(res.A.orig), orig_ax)
    orig_ax.set_title('Original graph')

    # time series
    ax = plt.subplot(gs[1:3])
    sns.tsplot(
        time='time', value='theta',
        unit='source', condition='oscillator',
        estimator=np.mean, legend=False,
        data=compute_solutions(res),
        ax=ax)
    ax.set_title(r'$A_{{err}} = {:.2}, B_{{err}} = {:.2}$'.format(*compute_error(res)))

    # reconstructed graph
    rec_ax = plt.subplot(gs[3])
    tmp = res.A.rec
    tmp[abs(tmp) < 1e-1] = 0
    plot_graph(nx.from_numpy_matrix(tmp), rec_ax)
    rec_ax.set_title('Reconstructed graph')

    plt.tight_layout()
    save(fig, 'reconstruction_overview')

def make_connections(n,density=0.25):
	"""
	This function will return a random adjacency matrix of size
	n x n. You read the matrix like this:
	
	if matrix[2,7] = 1, then cities '2' and '7' are connected.
	if matrix[2,7] = 0, then the cities are _not_ connected.
	
	:param n: number of cities
	:param density: controls the ratio of 1s to 0s in the matrix
	
	:returns: an n x n adjacency matrix
	"""
	
	import networkx
	
	# Generate a random adjacency matrix and use it to build a networkx graph
	a=numpy.int32(numpy.triu((numpy.random.random_sample(size=(n,n))<density)))
	G=networkx.from_numpy_matrix(a)
	
	# If the network is 'not connected' (i.e., there are isolated nodes)
	# generate a new one. Keep doing this until we get a connected one.
	# Yes, there are more elegant ways to do this, but I'm demonstrating
	# while loops!
	while not networkx.is_connected(G):
		a=numpy.int32(numpy.triu((numpy.random.random_sample(size=(n,n))<density)))
		G=networkx.from_numpy_matrix(a)
	
	# Cities should be connected to themselves.
	numpy.fill_diagonal(a,1)
	
	return a + numpy.triu(a,1).T

 def calc_covariance(self, method="graphlassocv", values="cov"):
     """
     Cacl coveriance matrix to make graph
     parameters
     ----------
     method: string
         Type of algorithm for covariance, graphlassocv
     values: string
         Type of values for matrix for graph
         cov: covariance_
         pre: precision_
     """
     if method == "graphlassocv":
         self._model = covariance.GraphLassoCV()
     else:
         assert NotImplementedError
     self._model_name = method
     self._model.fit(self._data)
     if values == "cov":
         self._graph = nx.from_numpy_matrix(self._model.covariance_)
     elif values == "pre":
         self._graph = nx.from_numpy_matrix(self._model.precision_)
     else:
         assert NotImplementedError
     self._modeled = True

    def _run_interface(self, runtime):

        if not have_cv:
            raise ImportError("cviewer library is not available")

        THRESH = self.inputs.threshold
        K = self.inputs.number_of_permutations
        TAIL = self.inputs.t_tail
        edge_key = self.inputs.edge_key
        details = edge_key + '-thresh-' + str(THRESH) + '-k-' + str(K) + '-tail-' + TAIL + '.pck'

        # Fill in the data from the networks
        X = ntwks_to_matrices(self.inputs.in_group1, edge_key)
        Y = ntwks_to_matrices(self.inputs.in_group2, edge_key)

        PVAL, ADJ, _ = nbs.compute_nbs(X, Y, THRESH, K, TAIL)

        iflogger.info('p-values:')
        iflogger.info(PVAL)

        pADJ = ADJ.copy()
        for idx, _ in enumerate(PVAL):
            x, y = np.where(ADJ == idx + 1)
            pADJ[x, y] = PVAL[idx]

        # Create networkx graphs from the adjacency matrix
        nbsgraph = nx.from_numpy_matrix(ADJ)
        nbs_pval_graph = nx.from_numpy_matrix(pADJ)

        # Relabel nodes because they should not start at zero for our convention
        nbsgraph = nx.relabel_nodes(nbsgraph, lambda x: x + 1)
        nbs_pval_graph = nx.relabel_nodes(nbs_pval_graph, lambda x: x + 1)

        if isdefined(self.inputs.node_position_network):
            node_ntwk_name = self.inputs.node_position_network
        else:
            node_ntwk_name = self.inputs.in_group1[0]

        node_network = nx.read_gpickle(node_ntwk_name)
        iflogger.info('Populating node dictionaries with attributes from %s',
                      node_ntwk_name)

        for nid, ndata in node_network.nodes(data=True):
            nbsgraph.nodes[nid] = ndata
            nbs_pval_graph.nodes[nid] = ndata

        path = op.abspath('NBS_Result_' + details)
        iflogger.info(path)
        nx.write_gpickle(nbsgraph, path)
        iflogger.info('Saving output NBS edge network as %s', path)

        pval_path = op.abspath('NBS_P_vals_' + details)
        iflogger.info(pval_path)
        nx.write_gpickle(nbs_pval_graph, pval_path)
        iflogger.info('Saving output p-value network as %s', pval_path)
        return runtime

def get_connection_densities(network, community_affiliation):
    #================================
    # Get density of within and between module connections
    #================================
    """
    inputs:
    network: adjacency_matrix (NumPy array)
    community_affiliation: array that indicates which community/module an node belongs to

    outputs:
    density of connections within modules
    density of connections between modules
    """

    import networkx as nx
    import numpy as np
    
    network[network > 0] = 1. # binarizing the network
    
    G = nx.from_numpy_matrix(network) # original network
    for node in G.nodes():
         G.node[node]['community'] = community_affiliation[node]

    within_weights = list()
    between_weights = list()

    for edge in G.edges():
        if G.node[edge[0]]['community'] == G.node[edge[1]]['community']:
            within_weights.append(G.edge[edge[0]][edge[1]]['weight'])
        else:
            between_weights.append(G.edge[edge[0]][edge[1]]['weight'])

    connected_G = nx.from_numpy_matrix(np.ones(shape=network.shape)) # fully-connected network
    full_within_weights = list()
    full_between_weights = list()

    for node in connected_G.nodes():
         connected_G.node[node]['community'] = community_affiliation[node]

    for edge in connected_G.edges():
        if connected_G.node[edge[0]]['community'] == connected_G.node[edge[1]]['community']:
            full_within_weights.append(connected_G.edge[edge[0]][edge[1]]['weight'])
        else:
            full_between_weights.append(connected_G.edge[edge[0]][edge[1]]['weight'])

    within_density = sum(within_weights)/sum(full_within_weights)
    between_density = sum(between_weights)/sum(full_between_weights)
    
    return(within_density, between_density)

def export_networkx_graph(adjacency_matrix, weights):
    """Export networkx graph object for an inferred network.

    Export a weighted, directed graph object from the network of inferred
    (multivariate) interactions (e.g., multivariate TE), using the networkx
    class for directed graphs (DiGraph). Multiple options for the weight are
    available (see documentation of method get_adjacency_matrix for details).

    Args:
        adjacency_matrix : 2D numpy array
            adjacency matrix to be exported, returned by get_adjacency_matrix()
            method of Results() class
        weights : str
            weights for the adjacency matrix (see documentation of method
            get_adjacency_matrix for details)
        fdr : bool [optional]
            return FDR-corrected results (default=True)

    Returns: DiGraph instance
        directed graph of networkx package's DiGraph() class
    """
    # use 'weights' parameter (string) as networkx edge property name and use
    # adjacency matrix entries as edge property values
    custom_type = [(weights, type(adjacency_matrix[0, 0]))]
    custom_npmatrix = np.matrix(adjacency_matrix, dtype=custom_type)
    return nx.from_numpy_matrix(custom_npmatrix, create_using=nx.DiGraph())

def sort_sentences(sentences, words, sim_func = get_similarity, pagerank_config = {'alpha': 0.85,}):
    """将句子按照关键程度从大到小排序

    Keyword arguments:
    sentences         --  列表，元素是句子
    words             --  二维列表，子列表和sentences中的句子对应，子列表由单词组成
    sim_func          --  计算两个句子的相似性，参数是两个由单词组成的列表
    pagerank_config   --  pagerank的设置
    """
    sorted_sentences = []
    _source = words
    sentences_num = len(_source)        
    graph = np.zeros((sentences_num, sentences_num))
    
    for x in xrange(sentences_num):
        for y in xrange(x, sentences_num):
            similarity = sim_func( _source[x], _source[y] )
            graph[x, y] = similarity
            graph[y, x] = similarity
            
    nx_graph = nx.from_numpy_matrix(graph)
    scores = nx.pagerank(nx_graph, **pagerank_config)              # this is a dict
    sorted_scores = sorted(scores.items(), key = lambda item: item[1], reverse=True)

    for index, score in sorted_scores:
        item = AttrDict(index=index, sentence=sentences[index], weight=score)
        sorted_sentences.append(item)

    return sorted_sentences

def atoms_to_nxgraph(atoms, cutoff):
    ni, nj = neighbour_list('ij', atoms, cutoff)
    adjacency_matrix = np.zeros((len(atoms), len(atoms))).astype(np.int)
    for i, j in zip (ni, nj):
        adjacency_matrix[i,j] = 1
    graph = nx.from_numpy_matrix(np.array(adjacency_matrix))
    return graph

def node_closeness_centrality(X):
    """
    based on networkx function: closeness_centrality
    """
    XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
    for i, value in enumerate(X):
        adj_mat = value.reshape((np.sqrt(len(value)), -1))
        adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
        adj_mat = 1 - adj_mat

#        th = np.mean(adj_mat) - 0.23
#        adj_mat = np.where(adj_mat < th, adj_mat, 0.)

        percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.27) # in this context the percentage
        print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))

        g = nx.from_numpy_matrix(adj_mat)
        print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
        print "\nEdge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))

        deg_cent = nx.closeness_centrality(g, normalized=True)
        node_cent = np.zeros(g.number_of_nodes())

        for k in deg_cent:
            node_cent[k] = deg_cent[k]
        XX[i] = node_cent
        print "graph {0} => mean {1}, min {2}, max {3}".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))

    return XX

def edge_betweeness_centrality(X):
    """
    based on networkx function: edge_betweenness_centrality
    """
    XX = np.zeros(X.shape)
    for i, value in enumerate(X):
        adj_mat = value.reshape((np.sqrt(len(value)),-1))
        adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
        adj_mat = 1 - adj_mat

#        th = np.mean(adj_mat) + 0.1
#        adj_mat = np.where(adj_mat < th, adj_mat, 0.)

        percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.43) # 43 #63 #73
        print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))

        g = nx.from_numpy_matrix(adj_mat)
        print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
        print "\nEdge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))

        bet_cent = nx.edge_betweenness_centrality(g, weight = 'weight', normalized = True)
        edge_cent = np.zeros(adj_mat.shape)

        for k in bet_cent:
            edge_cent[k[0],k[1]] = bet_cent[k]
        XX[i] = edge_cent.reshape(-1)
        print "graph {0} => mean {1}, min {2}, max {3}".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))

    return XX

def draw_network_by_years(df, start_year, end_year, trim):

    """ Constructs and draws the co-word networks for the years
    Parameters
    -----------------------------------------
    df: WoS references
    start_year:
    end_year:
    trim: degree of nodes to include in the graph

    Returns
    ----------------------------------
    coword networkx object
    """

    df_sub = df[(df.PY> start_year) & (df.PY <= end_year)]
    keys = keyword_counts(df_sub)

    print('Calculating co-word matrix')
    coword_df = coword_matrix(df_sub,keys.keys())

    coword_array = coword_df.as_matrix()
    np.fill_diagonal(coword_array, 0)
    coword_net  = nx.from_numpy_matrix(coword_array)
    col_names = coword_df.columns.tolist()
    labels = {col_names.index(l):l for l in col_names}
    nx.set_node_attributes(coword_net, 'keyword', labels)
    nx.set_node_attributes(coword_net, 'between_central', nx.betweenness_centrality(coword_net))
    if trim > 0:
        coword_net = trim_nodes(coword_net, trim)
        labels = {n:labels[n] for n in coword_net.nodes()}

    return coword_net

def r_perturbSa(g,p=None):
    '''固定参数的随机扰动方法，p伯努利实验成功的概率'''
    A=nx.to_scipy_sparse_matrix(g)
    B=sparse.triu(A).toarray()
    #print B
    n=len(g)
    e_num=len(g.edges())#图中存在的边数

    q = e_num * (1 - p) / ((n * (n - 1)) / 2 - e_num)
    #print q
    i = 0
    ts=0
    listp=stats.bernoulli.rvs(p,size=e_num)
    listp=listp.tolist()
    listq=stats.bernoulli.rvs(q,size=(n * (n - 1)) / 2 - e_num)
    listq=listq.tolist()

    while i<n:
        j=i+1#略过对角线上的0
        while j<n:
            if(B[i,j]==1):
                B[i,j] = listp.pop()#参数p伯努利实验成功的概率
                #ts=ts + 1
                # print "+",ts, ":", i, ",", j, ",", B[i, j]
            else:
                B[i,j] = listq.pop()#参数q伯努利实验成功的概率
                #ts=ts + 1
                # print "-",ts, ":", i, ",", j, ",", B[i, j]
            j = j + 1
        i=i+1

    return nx.from_numpy_matrix(B,create_using=nx.Graph())#重新构建了Graph类型的返回对象

    def __init__(self, graph, communities=None):
        """ initialize partition of graph, with optional communities

        Parameters
        ----------
        graph : networkx graph
        communities : list of sets, optional
            a list of sets with nodes in each set
            if communities is None, will initialize with
            one  per node

        Returns
        -------
        part : WeightedPartition object
        """
        # assert graph has edge weights, and no negative weights
        mat = nx.adjacency_matrix(graph).todense()
        if mat.min() < 0:
            raise ValueError("Graph has invalid negative weights")

        self.graph = nx.from_numpy_matrix(mat)
        if communities is None:
            self._communities = self._init_communities_from_nodes()
        else:
            self.set_communities(communities)
        self.total_edge_weight = graph.size(weight="weight")
        self.degrees = graph.degree(weight="weight")

def sortSentences(sentences, words, sim_func = getSimilarity, pagerank_config = {'alpha': 0.85,}):
    '''
    :param sentences: 用于计算权重的句子列表
    :param words: 与sentences相对应的每个句子的单词列表，该参数的类型为二维列表
    :param sim_func: 用于计算句子相似度的函数名
    :param pagerank_config:
    :return:
    '''
    sortedSentences = []
    _source = words
    sentencesNum = len(_source)   #获得图的大小
    graph = np.zeros((sentencesNum, sentencesNum))

    for x in xrange(sentencesNum):
        for y in xrange(x, sentencesNum):
            similarity = sim_func( _source[x], _source[y] )
            graph[x, y] = similarity
            graph[y, x] = similarity

    nx_graph = nx.from_numpy_matrix(graph)
    scores = nx.pagerank(nx_graph, **pagerank_config)              # this is a dict
    sorted_scores = sorted(scores.items(), key = lambda item: item[1], reverse=True)

    for index, score in sorted_scores:
        item = AttrDict(sentence=sentences[index], weight=score)
        sortedSentences.append(item)

    return sortedSentences