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- import sys
- sys.path.insert(0, "../")
- #import pathlib
- import numpy as np
- import networkx as nx
- import time
-
- from gedlibpy import librariesImport, gedlibpy
- #import script
- sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/")
- import gklearn
- from gklearn.utils.graphfiles import loadDataset
-
- def replace_graph_in_env(script, graph, old_id, label='median'):
- """
- Replace a graph in script
-
- If old_id is -1, add a new graph to the environnemt
-
- """
- if(old_id > -1):
- script.PyClearGraph(old_id)
- new_id = script.PyAddGraph(label)
- for i in graph.nodes():
- script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib
- for e in graph.edges:
- script.PyAddEdge(new_id, str(e[0]),str(e[1]), {})
- script.PyInitEnv()
- script.PySetMethod("IPFP", "")
- script.PyInitMethod()
-
- return new_id
-
- #Dessin median courrant
- def draw_Letter_graph(graph, savepath=''):
- import numpy as np
- import networkx as nx
- import matplotlib.pyplot as plt
- plt.figure()
- pos = {}
- for n in graph.nodes:
- pos[n] = np.array([float(graph.node[n]['attributes'][0]),
- float(graph.node[n]['attributes'][1])])
- nx.draw_networkx(graph, pos)
- if savepath != '':
- plt.savefig(savepath + str(time.time()) + '.eps', format='eps', dpi=300)
- plt.show()
- plt.clf()
-
- #compute new mappings
- def update_mappings(script,median_id,listID):
- med_distances = {}
- med_mappings = {}
- sod = 0
- for i in range(0,len(listID)):
- script.PyRunMethod(median_id,listID[i])
- med_distances[i] = script.PyGetUpperBound(median_id,listID[i])
- med_mappings[i] = script.PyGetForwardMap(median_id,listID[i])
- sod += med_distances[i]
- return med_distances, med_mappings, sod
-
- def calcul_Sij(all_mappings, all_graphs,i,j):
- s_ij = 0
- for k in range(0,len(all_mappings)):
- cur_graph = all_graphs[k]
- cur_mapping = all_mappings[k]
- size_graph = cur_graph.order()
- if ((cur_mapping[i] < size_graph) and
- (cur_mapping[j] < size_graph) and
- (cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)):
- s_ij += 1
-
- return s_ij
-
- # def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings):
- # from scipy.stats.mstats import gmean
-
- # for i in median.nodes():
- # for k in listIdSet:
- # vectors = [] #np.zeros((len(listIdSet),2))
- # if(k != median_id):
- # phi_i = mappings[k][i]
- # if(phi_i < dataset[k].order()):
- # vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])])
-
- # new_labels = gmean(vectors)
- # median.node[i]['x'] = str(new_labels[0])
- # median.node[i]['y'] = str(new_labels[1])
- # return median
-
- def update_median_nodes(median,dataset,mappings):
- #update node attributes
- for i in median.nodes():
- nb_sub=0
- mean_label = {'x' : 0, 'y' : 0}
- for k in range(0,len(mappings)):
- phi_i = mappings[k][i]
- if ( phi_i < dataset[k].order() ):
- nb_sub += 1
- mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x'])
- mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y'])
- median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub))
- median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub))
- return median
-
- def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425):
- #for letter high, ceir = 1.7, alpha = 0.75
- size_dataset = len(dataset)
- ratio_cei_cer = cer/(cei + cer)
- threshold = size_dataset*ratio_cei_cer
- order_graph_median = median.order()
- for i in range(0,order_graph_median):
- for j in range(i+1,order_graph_median):
- s_ij = calcul_Sij(mappings,dataset,i,j)
- if(s_ij > threshold):
- median.add_edge(i,j)
- else:
- if(median.has_edge(i,j)):
- median.remove_edge(i,j)
- return median
-
-
-
- def compute_median(script, listID, dataset,verbose=False):
- """Compute a graph median of a dataset according to an environment
-
- Parameters
-
- script : An gedlib initialized environnement
- listID (list): a list of ID in script: encodes the dataset
- dataset (list): corresponding graphs in networkX format. We assume that graph
- listID[i] corresponds to dataset[i]
-
- Returns:
- A networkX graph, which is the median, with corresponding sod
- """
- print(len(listID))
- median_set_index, median_set_sod = compute_median_set(script, listID)
- print(median_set_index)
- print(median_set_sod)
- sods = []
- #Ajout median dans environnement
- set_median = dataset[median_set_index].copy()
- median = dataset[median_set_index].copy()
- cur_med_id = replace_graph_in_env(script,median,-1)
- med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
- sods.append(cur_sod)
- if(verbose):
- print(cur_sod)
- ite_max = 50
- old_sod = cur_sod * 2
- ite = 0
- epsilon = 0.001
-
- best_median
- while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )):
- median = update_median_nodes(median,dataset, med_mappings)
- median = update_median_edges(dataset,med_mappings,median)
-
- cur_med_id = replace_graph_in_env(script,median,cur_med_id)
- med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
-
-
- sods.append(cur_sod)
- if(verbose):
- print(cur_sod)
- ite += 1
- return median, cur_sod, sods, set_median
-
- draw_Letter_graph(median)
-
-
- def compute_median_set(script,listID):
- 'Returns the id in listID corresponding to median set'
- #Calcul median set
- N=len(listID)
- map_id_to_index = {}
- map_index_to_id = {}
- for i in range(0,len(listID)):
- map_id_to_index[listID[i]] = i
- map_index_to_id[i] = listID[i]
-
- distances = np.zeros((N,N))
- for i in listID:
- for j in listID:
- script.PyRunMethod(i,j)
- distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j)
-
- median_set_index = np.argmin(np.sum(distances,0))
- sod = np.min(np.sum(distances,0))
-
- return median_set_index, sod
-
- if __name__ == "__main__":
- #Chargement du dataset
- script.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml')
- script.PySetEditCost("LETTER")
- script.PyInitEnv()
- script.PySetMethod("IPFP", "")
- script.PyInitMethod()
-
- dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl")
-
- listID = script.PyGetAllGraphIds()
- median, sod = compute_median(script,listID,dataset,verbose=True)
-
- print(sod)
- draw_Letter_graph(median)
-
-
- #if __name__ == '__main__':
- # # test draw_Letter_graph
- # ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
- # 'extra_params': {}} # node nsymb
- # Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
- # print(y_all)
- # for g in Gn:
- # draw_Letter_graph(g)
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