Python Graph.vertex_properties['confidence']方法代码示例

# 需要导入模块: from graph_tool import Graph [as 别名]
# 或者: from graph_tool.Graph import vertex_properties['confidence'] [as 别名]
def build_graph(df_list, sens='ST', top=410, min_sens=0.01,
                edge_cutoff=0.0):
    """
    Initializes and constructs a graph where vertices are the parameters
    selected from the first dataframe in 'df_list', subject to the
    constraints set by 'sens', 'top', and 'min_sens'.  Edges are the second
    order sensitivities of the interactions between those vertices,
    with sensitivities greater than 'edge_cutoff'.

    Parameters
    -----------
    df_list     : list
                  A list of two dataframes.  The first dataframe should be
                  the first/total order sensitivities collected by the
                  function data_processing.get_sa_data().
    sens        : str, optional
                  A string with the name of the sensitivity that you would
                  like to use for the vertices ('ST' or 'S1').
    top         : int, optional
                  An integer specifying the number of vertices to display (
                  the top sensitivity values).
    min_sens    : float, optional
                  A float with the minimum sensitivity to allow in the graph.
    edge_cutoff : float, optional
                  A float specifying the minimum second order sensitivity to
                  show as an edge in the graph.

    Returns
    --------
    g : graph-tool object
        a graph-tool graph object of the network described above.  Each
        vertex has properties 'param', 'sensitivity', and 'confidence'
        corresponding to the name of the parameter, value of the sensitivity
        index, and it's confidence interval.  The only edge property is
        'second_sens', the second order sensitivity index for the
        interaction between the two vertices it connects.
    """

    # get the first/total index dataframe and second order dataframe
    df = df_list[0]
    df2 = df_list[1]

    # Make sure sens is ST or S1
    if sens not in set(['ST', 'S1']):
        raise ValueError('sens must be ST or S1')
    # Make sure that there is a second order index dataframe
    try:
        if not df2:
            raise Exception('Missing second order dataframe!')
    except:
        pass

    # slice the dataframes so the resulting graph will only include the top
    # 'top' values of 'sens' greater than 'min_sens'.
    df = df.sort_values(sens, ascending=False)
    df = df.ix[df[sens] > min_sens, :].head(top)
    df = df.reset_index()

    # initialize a graph
    g = Graph()

    vprop_sens = g.new_vertex_property('double')
    vprop_conf = g.new_vertex_property('double')
    vprop_name = g.new_vertex_property('string')
    eprop_sens = g.new_edge_property('double')

    g.vertex_properties['param'] = vprop_name
    g.vertex_properties['sensitivity'] = vprop_sens
    g.vertex_properties['confidence'] = vprop_conf
    g.edge_properties['second_sens'] = eprop_sens

    # keep a list of all the vertices
    v_list = []

    # Add the vertices to the graph
    for i, param in enumerate(df['Parameter']):
        v = g.add_vertex()
        vprop_sens[v] = df.ix[i, sens]
        vprop_conf[v] = 1 + df.ix[i, '%s_conf' % sens] / df.ix[i, sens]
        vprop_name[v] = param
        v_list.append(v)

    # Make two new columns in second order dataframe that point to the vertices
    # connected on each row.
    df2['vertex1'] = -999
    df2['vertex2'] = -999
    for vertex in v_list:
        param = g.vp.param[vertex]
        df2.ix[df2['Parameter_1'] == param, 'vertex1'] = vertex
        df2.ix[df2['Parameter_2'] == param, 'vertex2'] = vertex

    # Only allow edges for vertices that we've defined
    df_edges = df2[(df2['vertex1'] != -999) & (df2['vertex2'] != -999)]
    # eliminate edges below a certain cutoff value
    pruned = df_edges[df_edges['S2'] > edge_cutoff]
    pruned.reset_index(inplace=True)
    # Add the edges for the graph
    for i, sensitivity in enumerate(pruned['S2']):
        v1 = pruned.ix[i, 'vertex1']
        v2 = pruned.ix[i, 'vertex2']
#.........这里部分代码省略.........