def read_gml(path, encoding='latin-1', relabel=False, auto_table=False):
"""NetworkX read_gml method and wrap graph to Orange network.
"""
G = _wrap(rwgml.read_gml(path, encoding, relabel))
if auto_table:
G.set_items(graph_to_table(G))
return G
def de_main(path, f):
#接受准则Adapted Kernighan-Lin
if f == 1:
G = load_graph(path)
else:
G = read_gml(path, label='id')
dea = DEAlgorithm(G, popSize, generations, sub_dim, 0, G.number_of_nodes())
r = dea.move_bat()
return r
def gacd_main(path, f):
#接受准则Adapted Kernighan-Lin
if f == 1:
G = load_graph(path)
else:
G = read_gml(path, label='id')
bestScores = []
avgScores = []
p = Population(popSize, G)
bestScores.append(p.getMax().score)
for i in range(generations):
p.updatePop()
bestScores.append(p.getMax().score)
return bestScores
ind_2 = diccionario[pares_de_metodos[i][1]]
matriz_presicion[ind_1,ind_2] = presicion
matriz_presicion[ind_2,ind_1] = presicion
return matriz_presicion
def plot_matrix(matriz, etiquetas=None, annot = False):
df = pd.DataFrame(matriz, columns=etiquetas, index = etiquetas)
ax = sns.heatmap(df, annot=annot, fmt=".2f", vmin = 0, vmax=1
, square=True)
for label in ax.get_yticklabels():
label.set_size(7)
for label in ax.get_xticklabels():
label.set_size(7)
#%%
if __name__ == '__main__':
dolph = read_gml('Tp3/dolphins.gml')
lista_de_metodos = ["infomap","label_prop", "fastgreedy", "eigenvector",
"louvain", "edge_betweenness", "walktrap"]
part_1 = calcular_particion(dolph, method="fastgreedy", only_labels = True)
part_2 = calcular_particion(dolph, method="walktrap", only_labels = True)
matriz_confusion, presicion = matriz_de_confusion(part_1,part_2,norm=True)
lista_de_metodos_chetos = ["Infomap","Label Propagation", "Fastgreedy",
"Eigenvector", "Louvain", "Edge Betweenness",
"Walktrap"]
mp = matriz_de_presiciones(dolph, lista_de_metodos)
plt.figure()
plot_matrix(matriz_confusion, annot=True)
plt.figure()
plot_matrix(mp, lista_de_metodos_chetos)
#%% Pruebita
a=[1,1,1,2,2,2]
from random import sample
from lectura import ldata
import sys
sys.path.append('./Tp1/')
from dolphins_funciones import (genero_a_color, particionar_por_genero,
crear_leyenda, contar_enlaces_internos,
contar_enlaces_entre_grupos, contar_clases,
p_value, cociente, desarme)
from modularidad import modularidad
from histograma import histograma
from graficar_multipartito import *
from collections import Counter
dolph = read_gml('Tp1/tc01_data/new_dolphins.gml')
genders = dict(ldata('Tp1/tc01_data/dolphinsGender.txt'))
#%%
# Agrego los sexos a los dicts de cada delfín
for nodo, dict_nodo in dict(dolph.nodes).items():
dict_nodo['gender'] = genders[nodo] # agrego el sexo del delfín a su dict
# print('Key = {}, Value = {}'.format(nodo, dict_nodo)) # para chequear que anda
particiones = particionar_por_genero(dolph)
colores = [
genero_a_color(g)
for g in nx.get_node_attributes(dolph, "gender").values()
]
# Hay 34 delfines macho, 24 delfines hembra y 4 delfines sin información
from networkx.readwrite.gml import read_gml
from lectura import ldata
from histograma import histograma
from scipy.odr import Model, RealData, ODR
from scipy import stats
from asortatividad_funciones import (linear, ClosestToOne, annd, gamma,
chi2_iterative, ks_iterative)
#%%Importamos las redes
#Mati: Tomi te comente esto porque solo funciona en tu compu, me parece que los
#path que puse aca abajo funcionan en las compus de todes.
#net_science = read_gml('/home/tomas/Desktop/Redes complejas/Urdimbres-Sofisticadas/Tp1/tc01_data/netscience.gml')
#july = read_gml('/home/tomas/Desktop/Redes complejas/Urdimbres-Sofisticadas/Tp1/tc01_data/as-22july06.gml')
net_science = read_gml('tc01_data/netscience.gml')
july = read_gml('tc01_data/as-22july06.gml')
degree_2, annd_2 = annd(net_science)
degree_1, annd_1 = annd(july)
#%%
#En este plot se puede ver que no aparece una recta, por lo que no hay que ajustar aca.
f, ([ax1, ax2], [ax3, ax4]) = plt.subplots(2, 2)
f.tight_layout()
plt.sca(ax1)
ax1.set_title('(a) Lineal')
ax1.plot(degree_1, annd_1, '.')
ax1.set_ylabel(r'$k_{nn}$')
ax1.set_xlabel('k')
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 9 21:45:45 2018
@author: tomas
"""
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from networkx.readwrite.gml import read_gml
from histograma import histograma
from __future__ import division
internet = read_gml('Tp1/tc01_data/as-22july06.gml')
nodes = []
degrees = []
for a, b in internet.degree():
nodes.append(a)
degrees.append(b)
#%% PUNTO A: Comparación de visualizaciones
# Para comparar los bineados logarítmicos y no logaasdrítmicos, lo justo es
# excluir a los nodos de grado 0 en ambos
fig, axes = plt.subplots(4, 2, figsize=(10, 10))
axes = axes.flatten()
logbinss = [0, 0, 0, 0, 1, 1, 1, 1]
logxs = [0, 0, 1, 1, 0, 0, 1, 1]
logys = [0, 1, 0, 1, 0, 1, 0, 1]
def load_graph(name):
if not name.endswith(".gml"):
name += ".gml"
return gml.read_gml(name)
