Python PriorityQueue.add_task方法代码示例

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def degreeDiscountIC(G, k, p=.01):
    ''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
    Input: G -- networkx graph object
    k -- number of nodes needed
    p -- propagation probability
    Output:
    S -- chosen k nodes
    '''
    S = []
    dd = PQ() # degree discount
    t = dict() # number of adjacent vertices that are in S
    d = dict() # degree of each vertex

    # initialize degree discount
    for u in G.nodes():
        d[u] = sum([G[u][v]['weight'] for v in G[u]]) # each edge adds degree 1
        # d[u] = len(G[u]) # each neighbor adds degree 1
        dd.add_task(u, -d[u]) # add degree of each node
        t[u] = 0

    # add vertices to S greedily
    for i in range(k):
        u, priority = dd.pop_item() # extract node with maximal degree discount
        S.append(u)
        for v in G[u]:
            if v not in S:
                t[v] += G[u][v]['weight'] # increase number of selected neighbors
                priority = d[v] - 2*t[v] - (d[v] - t[v])*t[v]*p # discount of degree
                dd.add_task(v, -priority)
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def generalGreedy(G, k, p=0.01):
    """ Finds initial seed set S using general greedy heuristic
    Input: G -- networkx Graph object
    k -- number of initial nodes needed
    p -- propagation probability
    Output: S -- initial set of k nodes to propagate
    """
    import time

    start = time.time()
    R = 20  # number of times to run Random Cascade
    S = []  # set of selected nodes
    # add node to S if achieves maximum propagation for current chosen + this node
    for i in range(k):
        s = PQ()  # priority queue
        for v in G.nodes():
            if v not in S:
                s.add_task(v, 0)  # initialize spread value
                for j in range(R):  # run R times Random Cascade
                    [priority, count, task] = s.entry_finder[v]
                    s.add_task(v, priority - float(len(runIC(G, S + [v], p))) / R)  # add normalized spread value
        task, priority = s.pop_item()
        S.append(task)
        print i, k, time.time() - start
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def GDD(G, k, Ep):
    ''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
    Input: G -- networkx graph object
    k -- number of nodes needed
    Ep -- propagation probabilities
    Output:
    S -- chosen k nodes
    '''
    S = []
    dd = PQ() # degree discount
    active = dict()
    inactive = dict()

    # initialize degree discount
    for u in G:
        active[u] = 1
        # inactive[u] = sum([Ep[(u,v)]*G[u][v]['weight'] for v in G[u]])
        inactive[u] = sum([1 - (1 - Ep[(u,v)])**G[u][v]["weight"] for v in G[u]])
        priority = active[u]*(1 + inactive[u])
        dd.add_task(u, -priority) # add degree of each node

    # add vertices to S greedily
    for i in range(k):
        u, priority = dd.pop_item() # extract node with maximal degree discount
        S.append(u)
        for v in G[u]:
            if v not in S:
                active[v] *= (1-Ep[(u,v)])**G[u][v]['weight']
                inactive[v] -= 1 - (1 - Ep[(u,v)])**G[u][v]['weight']
                priority = active[v]*(1 + inactive[v])
                dd.add_task(v, -priority)
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def bipartite(w, discrepancy):
    Ebp_edges = []
    Q = PriorityQueue()
    incident_edges = dict()
    for e in w:
        Q.add_task(e, -w[e])
        incident_edges.setdefault(e[0], []).append((e[1], w[e]))
        incident_edges.setdefault(e[1], []).append((e[0], w[e]))
    processed_edges = []
    while len(processed_edges) < len(w):
        (e, weight) = Q.pop_item()
        processed_edges.append(e)
        try:
            incident_edges[e[0]].remove((e[1], -weight))
            incident_edges[e[1]].remove((e[0], -weight))
        except ValueError:
            pass
        Ebp_edges.append(e)

        # discard all edges in Q incident to b (i.e. e[1])
        for (a, weight) in incident_edges[e[1]]:
            try:
                Q.remove_task((a,e[1]))
                processed_edges.append((a,e[1]))
            except KeyError:
                pass
            try:
                incident_edges[a].remove((e[1], weight))
                incident_edges[e[1]].remove((a, weight))
            except ValueError:
                pass
        discrepancy[e[0]] += 1
        discrepancy[e[1]] += 1

        if -1 < discrepancy[e[0]] < .5:
            for (x, _) in incident_edges[e[0]]:
                try:
                    Q.remove_task((e[0], x))
                except KeyError:
                    pass
                new_weight = abs(discrepancy[e[0]]) + 2*abs(discrepancy[x]) - abs(discrepancy[e[0]]) - 1
                if new_weight > 0:
                    Q.add_task((e[0], x), -new_weight)
                else:
                    processed_edges.append((e[0], x))
        elif discrepancy[e[0]] > .5:
            for (x, _) in incident_edges[e[0]]:
                try:
                    Q.remove_task((e[0], x))
                    processed_edges.append((e[0], x))
                except KeyError:
                    pass
    return Ebp_edges

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def representativeNodes(G, k, metric=1):
    ''' Finds the most distinguishable (representative) nodes in graph G greedily.
    Takes the most furthest node to the already chosen nodes at each step.

    Input: G -- networkx object graph with weighted edges
    k -- number of nodes needed
    metric -- parameter for differentiating representative qualities
    metric == 1 trying to maximize total distance in the chosen set of k nodes
    metric == 2 trying to maximize minimal distance between a pair of k nodes
    Output:
    S -- chosen k nodes
    objv -- objective value according to the chosen metric and set of nodes
    '''

    S = [] # set of chosen nodes
    S_dist = PQ() # distances from each node in G to set S according to metric

    # initialize S with furthest vertices
    try:
        u,v,d = max(G.edges(data=True), key=lambda (u, v, d): d['weight'])
    except KeyError:
        raise KeyError, 'Most likely you have no weight attribute'
    S.extend([u,v])

    # compute distances from each node in G to S
    for v in G.nodes():
        if v not in S: # calculate only for nodes in G
            if metric == 1:
                S_dist.add_task(v, - _sumDist(G, S, v)) # take minus to pop the maximum value from priority queue
            elif metric == 2:
                S_dist.add_task(v, - _minDist(G, S, v)) # take minus to pop the maximum value from priority queue

    # add new nodes to the set greedily
    while len(S) < k:
        u, priority = S_dist.pop_item() # find maximum value of distance to set S
        S.append(u) # append that node to S

        # only increase distance for nodes that are connected to u
        for v in G[u].keys():
            if v not in S: # add only remained nodes
                [priority, count, task] = S_dist.entry_finder[v] # finds distance for the previous step
                try:
                    if metric == 1:
                        S_dist.add_task(v, priority-G[u][v]['weight']) # adds distance to the new member of S
                    elif metric == 2:
                        S_dist.add_task(v, max(priority, -G[u][v]['weight'])) # update min distance to the set S
                except:
                    raise u,v, "These are vertices that caused the problem"

    # extract objective value of the chosen set
    if metric == 1:
        objv = 0
        for u in S:
            objv += _sumDist(G, S, u)
    elif metric == 2:
        objv = float('Inf')
        for u in S:
            objv = min(objv, _minDist(G, S, u))

    return S, objv

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def stopDegreeDiscount(G, tsize, ic_step=1, p=.01, iterations=200):
    ''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
    Input: G -- networkx graph object
    tsize -- number of nodes necessary to reach
    ic_step -- step of change in k between 2 iterations of IC
    p -- propagation probability
    Output:
    S -- seed set
    Tspread -- spread values for different sizes of seed set
    '''
    S = []
    dd = PQ() # degree discount
    t = dict() # number of adjacent vertices that are in S
    d = dict() # degree of each vertex

    # initialize degree discount
    for u in G.nodes():
        d[u] = sum([G[u][v]['weight'] for v in G[u]]) # each edge adds degree 1
        # d[u] = len(G[u]) # each neighbor adds degree 1
        dd.add_task(u, -d[u]) # add degree of each node
        t[u] = 0

    # add vertices to S greedily
    # until necessary number of nodes can be reached
    Tspread = dict() # spread for different k
    k = 0
    Tspread[k] = 0
    stepk = 1
    while Tspread[k] < tsize:
        u, priority = dd.pop_item() # extract node with maximal degree discount
        S.append(u)
        for v in G[u]:
            if v not in S:
                t[v] += G[u][v]['weight'] # increase number of selected neighbors
                priority = d[v] - 2*t[v] - (d[v] - t[v])*t[v]*p # discount of degree
                dd.add_task(v, -priority)
        # calculate IC spread with ic_step
        if stepk == ic_step:
            k = len(S)
            Tspread[k] = avgSize(G, S, p, iterations)
            print k, Tspread[k]
            stepk = 0
        stepk += 1

    # search precise boundary
    if abs(int(math.ceil(float(ic_step)/2))) == 1:
        return S, Tspread
    else:
        return binarySearchBoundary(G, k, Tspread, tsize, ic_step, p, iterations)

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def degreeHeuristic(G, k, p=.01):
    ''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
    Input: G -- networkx graph object
    k -- number of nodes needed
    p -- propagation probability
    Output:
    S -- chosen k nodes
    '''
    S = []
    d = PQ()
    for u in G:
        degree = sum([G[u][v]['weight'] for v in G[u]])
        # degree = len(G[u])
        d.add_task(u, -degree)
    for i in range(k):
        u, priority = d.pop_item()
        S.append(u)
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def getScores(G, Ep):
    '''Finds scores for GDD.
    Scores are degree for each node.
    '''

    scores = PQ() # degree discount
    active = dict()
    inactive = dict()

    # initialize degree discount
    for u in G:
        active[u] = 1
        # inactive[u] = sum([Ep[(u,v)]*G[u][v]['weight'] for v in G[u]])
        inactive[u] = sum([1 - (1 - Ep[(u,v)])**G[u][v]["weight"] for v in G[u]])
        priority = active[u]*(1 + inactive[u])
        scores.add_task(u, -priority) # add degree of each node

    return scores, active, inactive

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def FIND_LDAG(G, v, t, Ew):
    '''
    Compute local DAG for vertex v.
    Reference: W. Chen "Scalable Influence Maximization in Social Networks under LT model" Algorithm 3
    INPUT:
        G -- networkx DiGraph object
        v -- vertex of G
        t -- parameter theta
        Ew -- influence weights of G
        NOTE: Since graph G can have multiple edges between u and v,
        total influence weight between u and v will be
        number of edges times influence weight of one edge.
    OUTPUT:
        D -- networkx DiGraph object that is also LDAG
    '''
    # intialize Influence of nodes
    Inf = PQ()
    Inf.add_task(v, -1)
    x, priority = Inf.pop_item()
    M = -priority
    X = [x]

    D = nx.DiGraph()
    while M >= t:
        out_edges = G.out_edges([x], data=True)
        for (v1,v2,edata) in out_edges:
            if v2 in X:
                D.add_edge(v1, v2, edata)
        in_edges = G.in_edges([x])
        for (u,_) in in_edges:
            if u not in X:
                try:
                    [pr, _, _] = Inf.entry_finder[u]
                except KeyError:
                    pr = 0
                Inf.add_task(u, pr - G[u][x]['weight']*Ew[(u,x)]*M)
        try:
            x, priority = Inf.pop_item()
        except KeyError:
            return D
        M = -priority
        X.append(x)

    return D

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def degreeDiscountStar(G,k,p=.01):
    
    S = []
    scores = PQ()
    d = dict()
    t = dict()
    for u in G:
        d[u] = sum([G[u][v]['weight'] for v in G[u]])
        t[u] = 0
        score = -((1-p)**t[u])*(1+(d[u]-t[u])*p)
        scores.add_task(u, )
    for iteration in range(k):
        u, priority = scores.pop_item()
        print iteration, -priority
        S.append(u)
        for v in G[u]:
            if v not in S:
                t[v] += G[u][v]['weight']
                score = -((1-p)**t[u])*(1+(d[u]-t[u])*p)
                scores.add_task(v, score)
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def singleDiscount(G, k, p=.1):
    ''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
    Input: G -- networkx graph object
    k -- number of nodes needed
    p -- propagation probability
    Output:
    S -- chosen k nodes
    '''
    S = [] # set of activated nodes
    d = PQ() # degrees
    for u in G:
        degree = sum([G[u][v]['weight'] for v in G[u]])
        d.add_task(u, -degree)
    for i in range(k):
        u, priority = d.pop_item()
        S.append(u)
        for v in G[u]:
            if v not in S:
                [priority, count, task] = d.entry_finder[v]
                d.add_task(v, priority + G[u][v]['weight']) # discount degree by the weight of the edge
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def LDAG_heuristic(G, Ew, k, t):
    ''' LDAG algorithm for seed selection.
    Reference: [1] Algorithm 5
    Input:
    G -- directed graph (nx.DiGraph)
    Ew -- inlfuence weights of edges (eg. uniform, random) (dict)
    k -- size of seed set (int)
    t -- parameter theta for finding LDAG (0 <= t <= 1; typical value: 1/320) (int)
    Output:
    S -- seed set (list)
    '''
    # define variables
    S = []
    IncInf = PQ()
    for node in G:
        IncInf.add_task(node, 0)
    # IncInf = dict(zip(G.nodes(), [0]*len(G))) # in case of usage dict instead of PQ
    LDAGs = dict()
    InfSet = dict()
    ap = dict()
    A = dict()

    print 'Initialization phase'
    for v in G:
        LDAGs[v] = FIND_LDAG(G, v, t, Ew)
        # update influence set for each node in LDAGs[v] with its root
        for u in LDAGs[v]:
            InfSet.setdefault(u, []).append(v)
        alpha = computeAlpha(LDAGs[v], Ew, S, v)
        A.update(alpha) # add new linear coefficients to A
        # update incremental influence of all nodes in LDAGs[v] with alphas
        for u in LDAGs[v]:
            ap[(v, u)] = 0 # additionally set initial activation probability (line 7)
            priority, _, _ = IncInf.entry_finder[u] # find previous value of IncInf
            IncInf.add_task(u, priority - A[(v, u)]) # and add alpha
            # IncInf[u] += A[(v, u)] # in case of using dict instead of PQ

    print 'Main loop'
    for it in range(k):
        s, priority = IncInf.pop_item() # chose node with biggest incremental influence
        print it+1, s, -priority
        for v in InfSet[s]: # for all nodes that s can influence
            if v not in S:
                D = LDAGs[v]
                # update alpha_v_u for all u that can reach s in D (lines 17-22)
                alpha_v_s = A[(v,s)]
                dA = computeAlpha(D, Ew, S, s, val=-alpha_v_s)
                for (s,u) in dA:
                    if u not in S + [s]: # don't update IncInf if it's already in S
                        A[(v,u)] += dA[(s,u)]
                        priority, _, _ = IncInf.entry_finder[u] # find previous value of incremental influence of u
                        IncInf.add_task(u, priority - dA[(s,u)]*(1 - ap[(v,u)])) # and update it accordingly
                # update ap_v_u for all u reachable from s in D (liens 23-28)
                dap = computeActProb(D, Ew, S + [s], s, val=1-ap[(v,s)])
                for (s,u) in dap:
                    if u not in S + [s]:
                        ap[(v,u)] += dap[(s,u)]
                        priority, _, _ = IncInf.entry_finder[u] # find previous value of incremental influence of u
                        IncInf.add_task(u, priority + A[(v,u)]*dap[(s,u)]) # and update it accordingly
        S.append(s)
    return S

# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import add_task [as 别名]
def spreadNewGreedyIC(G, targeted_size, step=1, p=.01, S0=[], iterations = 200):
    ''' Finds initial set of nodes to propagate in Independent Cascade.
    Input: G -- networkx graph object
    k -- number of nodes needed
    p -- propagation probability
    Output: S -- set of k nodes chosen

    TODO: add step functionality
    '''

    import time
    start = time.time()

    assert type(S0) == list, "S0 must be a list. %s provided instead" % type(S0)
    S = S0 # set of selected nodes
    tsize = 0
    R = iterations
    for i in range(R):
        T = runIC(G, S, p)
        tsize += float(len(T))/R

    while tsize <= targeted_size:
        s = PQ() # number of additional nodes each remained mode will bring to the set S in R iterations
        Rv = dict() # number of reachable nodes for node v
        # initialize values of s
        for v in G.nodes():
            if v not in S:
                s.add_task(v, 0)
        # calculate potential additional spread for each vertex not in S
        prg_idx = 1
        idx = 1
        prcnt = .1 # for progress to print
        R = iterations # number of iterations to run RanCas
        for j in range(R):
            # create new pruned graph E
            E = deepcopy(G)
            edge_rem = [] # edges to remove
            for (u,v) in E.edges():
                w = G[u][v]['weight']
                if random() < 1 - (1 - p)**w:
                    edge_rem.append((u,v))
            E.remove_edges_from(edge_rem)
            # find reachable vertices from S
            Rs = bfs(E, S)
            # find additional nodes each vertex would bring to the set S
            for v in G.nodes():
                if v not in S + Rs: # if node has not chosen in S and has chosen by spread from S
                    [priority, c, task] = s.entry_finder[v]
                    s.add_task(v, priority - float(len(bfs(E, [v])))/R)

            if idx == int(prg_idx*prcnt*R):
                print '%s%%...' %(int(prg_idx*prcnt*100))
                prg_idx += 1
            idx += 1
        # add vertex with maximum potential spread
        task, priority = s.pop_item()
        S.append(task)
        print i, len(S), task, -priority, time.time() - start

        tsize = 0
        for j in range(R):
            T = runIC(G, S, p)
            tsize += float(len(T))/R
    return S