基于python实现分析爬取的中国电影票房数据并可视化源码.zip

# -*- coding: utf-8 -*- """ This module implements community detection. """ from __future__ import print_function import array import random import networkx as nx from community_status import Status __author__ = """Thomas Aynaud (thomas.aynaud@lip6.fr)""" # Copyright (C) 2009 by # Thomas Aynaud <thomas.aynaud@lip6.fr> # All rights reserved. # BSD license. __PASS_MAX = -1 __MIN = 0.0000001 def partition_at_level(dendrogram, level): """Return the partition of the nodes at the given level A dendrogram is a tree and each level is a partition of the graph nodes. Level 0 is the first partition, which contains the smallest communities, and the best is len(dendrogram) - 1. The higher the level is, the bigger are the communities Parameters ---------- dendrogram : list of dict a list of partitions, ie dictionnaries where keys of the i+1 are the values of the i. level : int the level which belongs to [0..len(dendrogram)-1] Returns ------- partition : dictionnary A dictionary where keys are the nodes and the values are the set it belongs to Raises ------ KeyError If the dendrogram is not well formed or the level is too high See Also -------- best_partition which directly combines partition_at_level and generate_dendrogram to obtain the partition of highest modularity Examples -------- >>> G=nx.erdos_renyi_graph(100, 0.01) >>> dendrogram = generate_dendrogram(G) >>> for level in range(len(dendrogram) - 1) : >>> print("partition at level", level, "is", partition_at_level(dendrogram, level)) # NOQA """ partition = dendrogram[0].copy() for index in range(1, level + 1): for node, community in partition.items(): partition[node] = dendrogram[index][community] return partition def modularity(partition, graph, weight='weight'): """Compute the modularity of a partition of a graph Parameters ---------- partition : dict the partition of the nodes, i.e a dictionary where keys are their nodes and values the communities graph : networkx.Graph the networkx graph which is decomposed weight : str, optional the key in graph to use as weight. Default to 'weight' Returns ------- modularity : float The modularity Raises ------ KeyError If the partition is not a partition of all graph nodes ValueError If the graph has no link TypeError If graph is not a networkx.Graph References ---------- .. 1. Newman, M.E.J. & Girvan, M. Finding and evaluating community structure in networks. Physical Review E 69, 26113(2004). Examples -------- >>> G=nx.erdos_renyi_graph(100, 0.01) >>> part = best_partition(G) >>> modularity(part, G) """ if graph.is_directed(): raise TypeError("Bad graph type, use only non directed graph") inc = dict([]) deg = dict([]) links = graph.size(weight=weight) if links == 0: raise ValueError("A graph without link has an undefined modularity") for node in graph: com = partition[node] deg[com] = deg.get(com, 0.) + graph.degree(node, weight=weight) for neighbor, datas in graph[node].items(): edge_weight = datas.get(weight, 1) if partition[neighbor] == com: if neighbor == node: inc[com] = inc.get(com, 0.) + float(edge_weight) else: inc[com] = inc.get(com, 0.) + float(edge_weight) / 2. res = 0. for com in set(partition.values()): res += (inc.get(com, 0.) / links) - \ (deg.get(com, 0.) / (2. * links)) ** 2 return res def best_partition(graph, partition=None, weight='weight', resolution=1., randomize=False): """Compute the partition of the graph nodes which maximises the modularity (or try..) using the Louvain heuristices This is the partition of highest modularity, i.e. the highest partition of the dendrogram generated by the Louvain algorithm. Parameters ---------- graph : networkx.Graph the networkx graph which is decomposed partition : dict, optional the algorithm will start using this partition of the nodes. It's a dictionary where keys are their nodes and values the communities weight : str, optional the key in graph to use as weight. Default to 'weight' resolution : double, optional Will change the size of the communities, default to 1. represents the time described in "Laplacian Dynamics and Multiscale Modular Structure in Networks", R. Lambiotte, J.-C. Delvenne, M. Barahona randomize : boolean, optional Will randomize the node evaluation order and the community evaluation order to get different partitions at each call Returns ------- partition : dictionnary The partition, with communities numbered from 0 to number of communities Raises ------ NetworkXError If the graph is not Eulerian. See Also -------- generate_dendrogram to obtain all the decompositions levels Notes ----- Uses Louvain algorithm References ---------- .. 1. Blondel, V.D. et al. Fast unfolding of communities in large networks. J. Stat. Mech 10008, 1-12(2008). Examples -------- >>> #Basic usage >>> G=nx.erdos_renyi_graph(100, 0.01) >>> part = best_partition(G) >>> #other example to display a graph with its community : >>> #better with karate_graph() as defined in networkx examples >>> #erdos renyi don't have true community structure >>> G = nx.erdos_renyi_graph(30, 0.05) >>> #first compute the best partition >>> partition = community.best_partition(G) >>> #drawing >>> size = float(len(set(partition.values()))) >>> pos = nx.spring_layout(G) >>> count = 0. >>> for com in set(partition.values()) : >>> count += 1. >>> list_nodes = [nodes for nodes in partition.keys() >>> if partition[nodes] == com] >>> nx.draw_networkx_nodes(G, pos, list_nodes, node_size = 20, node_color = str(count / size)) >>> nx.draw_networkx_edges(G, pos, alpha=0.5) >>> plt.show() """ dendo = generate_dendrogram(graph, partition, weight, resolution, randomize) return partition_at_level(dendo, len(dendo) - 1) def generate_dendrogram(graph, part_init=None, weight='weight', resolution=1., randomize=False): """Find communities in the graph and return the associated dendrogram A dendrogram is a tree and each level is a partition of the graph nodes. Level 0 is the first partition, which contains the smallest communities, and the best is len(dendrogram) - 1. The higher the level is, the bigger are the communities Parameters ---------- graph : networkx.Graph the networkx graph which will be decomposed part_init : dict, optional the algorithm will start using this partition of the nodes. It's a dictionary where keys are their nodes and values the communities weight : str, optional the key in graph to use as weight. Default to 'weight' resolution : double, optional Will change the size of the communities, default to 1. represents the time described in "Laplacian Dynamics and Multiscale Modular Structure in Networks", R. Lambiotte, J.-C. Delvenne, M. Barahona Returns ------- dendrogram : list of dictionaries a list of partitions, ie di