def formatNetwork2(filePath):
network = linkpred.read_network(filePath)
G = nx.convert_node_labels_to_integers(network, 1, "default", "label")
jsonNodeArray = []
jsonEdgeArray = []
for node in json.loads(nx.jit_data(G)):
authorName = node['data']['label']
authorId = node['data']['id']
jsonNodeArray.append({
"id": int(authorId),
"label": authorName
})
if 'adjacencies' in node:
edges = node['adjacencies']
for edge in edges:
jsonEdgeArray.append({
"from": int(authorId),
"to": int(edge['nodeTo']),
"value": int(edge['data']['weight']),
"edgeID": str(authorId)+'to'+str(edge['nodeTo'])
})
jsonNetwork = {
"nodes": jsonNodeArray,
"edges": jsonEdgeArray,
}
return jsonNetwork
def test_read_network():
with temp_file(suffix=".net") as fname:
with open(fname, "w") as fh:
fh.write("""*vertices 2
1 "A"
2 "B"
*arcs 2
1 2
2 1""")
expected = nx.DiGraph()
expected.add_edges_from([("A", "B"), ("B", "A")])
G = linkpred.read_network(fname)
assert_equal(set(G.edges()), set(expected.edges()))
with open(fname) as fh:
G = linkpred.read_network(fname)
assert_equal(set(G.edges()), set(expected.edges()))
def test_read_network():
with temp_file(suffix=".net") as fname:
with open(fname, "w") as fh:
fh.write("""*vertices 2
1 "A"
2 "B"
*arcs 2
1 2
2 1""")
expected = nx.DiGraph()
expected.add_edges_from([("A", "B"), ("B", "A")])
G = linkpred.read_network(fname)
assert_equal(set(G.edges()), set(expected.edges()))
with open(fname) as fh:
G = linkpred.read_network(fname)
assert_equal(set(G.edges()), set(expected.edges()))
def hit(rate, batch, option):
timestramp1 = datetime.now()
in_file = open('./data/dbpedia50/smallScaleTrain' + str(rate) + '.txt',
'r')
in_file2 = open('./data/dbpedia50/notSimpleData' + str(rate) + '.txt', 'r')
triples = []
for line in in_file:
inputs = line.strip().split('\t')
e1 = inputs[0]
r = inputs[1]
e2 = inputs[2]
triples.append([e1, r, e2])
notInTriples = []
for line in in_file2:
inputs = line.strip().split('\t')
e1 = inputs[0]
r = inputs[1]
e2 = inputs[2]
notInTriples.append([e1, r, e2])
G = linkpred.read_network('./data/dbpedia50_' + str(rate) + '.net')
timestramp2 = datetime.now()
load_data_time = timestramp2 - timestramp1
timestramp1 = datetime.now()
predictor = getPredictor(option, G)
results = predictor.predict()
timestramp2 = datetime.now()
rooted_pagerank_time = timestramp2 - timestramp1
timestramp1 = datetime.now()
batchList_without_check = get_batch_without_check(results, batch)
hitRate_without_check, hitRate_without_check2 = getHitRate(
batchList_without_check, notInTriples)
timestramp2 = datetime.now()
top_check_time = timestramp2 - timestramp1
out_file = open('./log.txt', 'a')
out_file.write('\n')
out_file.write('rate: ' + rate + ' batch: ' + str(batch) + ' option ' +
option + '\n')
out_file.write('load_time: ' + str(load_data_time) + '\n')
out_file.write('rooted_pagerank_time: ' + str(rooted_pagerank_time) + '\n')
out_file.write('top_check_time: ' + str(top_check_time) + '\n')
out_file.write('The hit rate of ' + option + ' is ' +
str(hitRate_without_check) + ' ' +
str(hitRate_without_check2) + '\n')
out_file.close()
def remove():
global H
global filename_path
log = logging.getLogger(__name__) # recherche
G = linkpred.read_network(filename_path)
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
sentence = ("Network contains {} self-loops. "
"Removing..." + format(num_loops))
H.remove_edges_from(nx.selfloop_edges(G))
txt.delete(0.0, END)
txt.insert(0.0, sentence)
def downloadAsCSV():
data = request.json["data"]
filename = data['filename']
G = linkpred.read_network(os.path.join(
app.config['UPLOAD_FOLDER'], filename))
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
H.remove_edges_from(nx.selfloop_edges(G))
G = nx.convert_node_labels_to_integers(H, 1, "default", "label")
for newConnection in data['newConnections']:
source = G.nodes[int(newConnection['from'])]['label']
target = G.nodes[int(newConnection['to'])]['label']
H.add_edge(source, target, weight=1.0)
path = app.config['UPLOAD_FOLDER'] + filename+'FutureLinks'+'.csv'
nx.write_pajek(H, path)
return send_file(path)
'''
CPU(intel xeon cpu e5-2673 v3 @ 2.40ghz), 8GB RAM, Windows OS
Python==3.6.8, Stellargraph==1.2.1, Tensorflow==2.1.0, linkpred==0.5.1.
'''
import linkpred
G_train = linkpred.read_network("train1.net") # train dataset
G_entire = linkpred.read_network("whole.net") # entire dataset
test = G_entire
training = G_train
test.remove_edges_from(training.edges()) # create testset
###### 1. Jaccard
jaccard = linkpred.predictors.Jaccard(training, excluded=training.edges()) # train model based on jaccard index
jaccard_results = jaccard.predict() # predict testset based on jaccard index
test_set = set(linkpred.evaluation.Pair(u,v) for u,v in test.edges())
evaluation1 = linkpred.evaluation.EvaluationSheet(jaccard_results, test_set) # compare predicted value with real value
p1 = evaluation1.precision()
f1 = evaluation1.f_score()
r1 = evaluation1.recall()
print("Jaccard p : ",p1[np.argmax(f1)])
print("Jaccard r : ",r1[np.argmax(f1)]) # extract precision and recall when f1-score is optimal
print("Jaccard f1 : ",max(f1)) # extract optimal f1-score
###### 2. CommonNeighbours
CN = linkpred.predictors.CommonNeighbours(training, excluded=training.edges()) # train model based on Common Neighbours
CN_results = CN.predict() # predict testset based on Common Neighbours
test_set = set(linkpred.evaluation.Pair(u,v) for u,v in test.edges()) # compare predicted value with real value
def test_read_unknown_network_type():
fd, fname = tempfile.mkstemp(suffix=".foo")
with assert_raises(linkpred.exceptions.LinkPredError):
linkpred.read_network(fname)
os.close(fd)
os.unlink(fname)
def test_read_unknown_network_type():
with temp_file(suffix=".foo") as fname:
linkpred.read_network(fname)
def view_file():
filename = request.args.get('filename')
if Stuff.query.filter_by(title=filename).first():
initialGraphJson = formatNetwork2(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
G = linkpred.read_network(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
H.remove_edges_from(nx.selfloop_edges(G))
CommonNeighbours = mypred.predictors.CommonNeighboursGF(
H, excluded=H.edges())
CommonNeighbours_results = CommonNeighbours.predict()
top = CommonNeighbours_results.top()
sentence = []
sentenceunsorted = []
newLinks = []
jsonDict = []
# resultsList = []
G = nx.convert_node_labels_to_integers(H, 1, "default", "label")
CommonNeighboursG = mypred.predictors.CommonNeighboursGF(
G, excluded=G.edges())
CommonNeighbours_resultsG = CommonNeighboursG.predict()
topG = CommonNeighbours_resultsG.top()
for authors, score in topG.items():
authorsArray = [authors[0], authors[1]]
common = intersection(list(G.neighbors(authors[0])),
list(G.neighbors(authors[1]))) + authorsArray
subG = G.subgraph(common)
cngfScore = 0
for nodeID, nodeInfo in subG.nodes(data=True):
if nodeID not in authorsArray:
cngfScore = cngfScore + \
(subG.degree[nodeID] /
math.log10(G.degree[nodeID]))
authorOne = G.nodes[authorsArray[1]]
authorTwo = G.nodes[authorsArray[0]]
sentenceunsorted.append({
"text":
authorOne['label'] + " - " + authorTwo['label'] +
" le score est :" + str(cngfScore),
"score":
cngfScore
})
newLinks.append({
"from": authorOne['id'],
"to": authorTwo['id'],
"value": float(1.0),
"authOne": authorOne,
"authTwo": authorTwo,
"score": float("{:.2f}".format(cngfScore))
})
for s in sentenceunsorted:
sentence.append(s['text'])
for authors, score in top.items():
jsonDict.append({
"authorSource": str(authors).split(' - ')[0],
"authorDest": str(authors).split(' - ')[1],
"score": cngfScore
})
return render_template('viewGraph.html',
newLinks=newLinks,
predictions=sentence,
data=initialGraphJson,
filename=filename)
else:
abort(404)
import linkpred
from datetime import datetime
G = linkpred.read_network("./data/Freebase13.net")
a = datetime.now()
neighbour_rank1 = linkpred.predictors.CommonNeighbours(G, excluded=G.edges())
neighbour_rank_results1 = neighbour_rank1.predict()
b = datetime.now()
print("The time of CommonNeighbores: " + str(b - a))
a = datetime.now()
neighbour_rank2 = linkpred.predictors.AdamicAdar(G, excluded=G.edges())
neighbour_rank_results2 = neighbour_rank2.predict()
b = datetime.now()
print("The time of AdamicAdar: " + str(b - a))
a = datetime.now()
neighbour_rank3 = linkpred.predictors.Jaccard(G, excluded=G.edges())
neighbour_rank_results3 = neighbour_rank3.predict()
b = datetime.now()
print("The time of Jaccard: " + str(b - a))
a = datetime.now()
neighbour_rank4 = linkpred.predictors.ResourceAllocation(G, excluded=G.edges())
neighbour_rank_results4 = neighbour_rank4.predict()
b = datetime.now()
print("The time of ResourceAllocation: " + str(b - a))
a = datetime.now()
neighbour_rank5 = linkpred.predictors.DegreeProduct(G, excluded=G.edges())
def graph():
if request.method == 'POST':
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash('Veillez choisir un fichier')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
initialGraphJson = formatNetwork2(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
G = linkpred.read_network(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
H.remove_edges_from(nx.selfloop_edges(G))
CommonNeighbours = mypred.predictors.CommonNeighboursGF(
H, excluded=H.edges())
CommonNeighbours_results = CommonNeighbours.predict()
top = CommonNeighbours_results.top()
sentence = []
sentenceunsorted = []
newLinks = []
jsonDict = []
# resultsList = []
G = nx.convert_node_labels_to_integers(H, 1, "default", "label")
CommonNeighboursG = mypred.predictors.CommonNeighboursGF(
G, excluded=G.edges())
CommonNeighbours_resultsG = CommonNeighboursG.predict()
topG = CommonNeighbours_resultsG.top()
for authors, score in topG.items():
authorsArray = [authors[0], authors[1]]
common = intersection(list(G.neighbors(
authors[0])), list(G.neighbors(authors[1]))) + authorsArray
subG = G.subgraph(common)
cngfScore = 0
for nodeID, nodeInfo in subG.nodes(data=True):
if nodeID not in authorsArray:
cngfScore = cngfScore + \
(subG.degree[nodeID] /
math.log10(G.degree[nodeID]))
authorOne = G.nodes[authorsArray[1]]
authorTwo = G.nodes[authorsArray[0]]
sentenceunsorted.append({
"text":
authorOne['label'] + " - " + authorTwo['label'] +
" le score est :" + str(cngfScore),
"score":
cngfScore
})
newLinks.append({
"from": authorOne['id'],
"to": authorTwo['id'],
"value": float(1.0),
"authOne": authorOne,
"authTwo": authorTwo,
"score": cngfScore
})
for s in sentenceunsorted:
sentence.append(s['text'])
for authors, score in top.items():
jsonDict.append({
"authorSource": str(authors).split(' - ')[0],
"authorDest": str(authors).split(' - ')[1],
"score": cngfScore
})
# responseDict = {"results": jsonDict}
# return json.dumps(newLinks)
return render_template('generatedGraph.html',
newLinks=newLinks,
predictions=sentence,
data=initialGraphJson,
filename=filename,
DL_AS_NET_URL=DL_AS_NET_URL)
else:
flash(
"format inccorecte, veillez sélectionner un fichier .net valide "
)
return redirect(request.url)
return render_template('downloads.html')
def test_read_unknown_network_type():
with temp_file(suffix=".foo") as fname:
linkpred.read_network(fname)
def hit(rate, batch, option):
timestramp1 = datetime.now()
in_file = open('./data/Freebase13/smallScaleTrain' + str(rate) + '.txt', 'r')
in_file2 = open('./data/Freebase13/notSimpleData' + str(rate) + '.txt', 'r')
triples = []
for line in in_file:
inputs = line.strip().split('\t')
e1 = inputs[0]
r = inputs[1]
e2 = inputs[2]
triples.append([e1, r, e2])
notInTriples = []
for line in in_file2:
inputs = line.strip().split('\t')
e1 = inputs[0]
r = inputs[1]
e2 = inputs[2]
notInTriples.append([e1, r, e2])
G = linkpred.read_network('./data/Freebase13_' + str(rate) + '.net')
timestramp2 = datetime.now()
load_data_time = timestramp2 - timestramp1
timestramp1 = datetime.now()
predictor = getPredictor(option, G)
results = predictor.predict()
timestramp2 = datetime.now()
rooted_pagerank_time = timestramp2 - timestramp1
timestramp1 = datetime.now()
getEntitiesSet(triples)
batchList_with_check = get_batch_with_check(results, batch)
hitRate_with_check, hitRate_with_check2 = getHitRate(batchList_with_check, notInTriples)
timestramp2 = datetime.now()
entities_cluster_check_time = timestramp2 - timestramp1
timestramp1 = datetime.now()
batchList_without_check = get_batch_without_check(results, batch)
hitRate_without_check, hitRate_without_check2 = getHitRate(batchList_without_check, notInTriples)
timestramp2 = datetime.now()
top_check_time = timestramp2 - timestramp1
out_file = open('./log.txt', 'a')
out_file.write('\n')
out_file.write('rate: ' + rate + ' batch: ' + str(batch) + ' option ' + option + '\n')
out_file.write('load_time: ' + str(load_data_time) + '\n')
out_file.write('rooted_pagerank_time: ' + str(rooted_pagerank_time) + '\n')
out_file.write('entities_cluster_check_time: ' + str(entities_cluster_check_time) + '\n')
out_file.write('top_check_time: ' + str(top_check_time) + '\n')
out_file.write('The hit rate without check of ' + option + ' is ' + str(hitRate_without_check) + ' ' + str(
hitRate_without_check2) + '\n')
out_file.write('The hit rate with check of ' + option + ' is ' + str(hitRate_with_check) + ' ' + str(
hitRate_with_check2) + '\n')
out_file.close()
results_file = open('./data/results/Freebase13_' + str(rate) + '_' + str(batch) + '_results.txt', 'w')
for pair in batchList_with_check:
x, y = pair
results_file.write(x + '\t' + y + '\n')
results_file.close()
def upload_graph():
if request.method == 'POST':
if 'file' not in request.files:
flash('No file part', 'netErrors')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash('Veillez choisir un fichier', 'netErrors')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file_save_name = datetime.utcnow().strftime(
"%Y_%d_%m_%H_%M_%S_") + filename
file_path = os.path.join(app.config['UPLOAD_FOLDER'],
file_save_name)
file.save(file_path)
originalFile = Stuff(title=file_save_name,
type="net",
user=current_user)
db.session.add(originalFile)
initialGraphJson = formatNetwork2(file_path)
G = linkpred.read_network(file_path)
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
H.remove_edges_from(nx.selfloop_edges(G))
CommonNeighbours = mypred.predictors.CommonNeighboursGF(
H, excluded=H.edges())
CommonNeighbours_results = CommonNeighbours.predict()
top = CommonNeighbours_results.top()
topAll = CommonNeighbours_results.top(0)
sentence = []
sentenceunsorted = []
newLinks = []
jsonDict = []
# resultsList = []
G = nx.convert_node_labels_to_integers(H, 1, "default", "label")
CommonNeighboursG = mypred.predictors.CommonNeighboursGF(
G, excluded=G.edges())
CommonNeighbours_resultsG = CommonNeighboursG.predict()
topG = CommonNeighbours_resultsG.top(13)
for authors, score in topG.items():
authorsArray = [authors[0], authors[1]]
common = intersection(list(G.neighbors(
authors[0])), list(G.neighbors(authors[1]))) + authorsArray
subG = G.subgraph(common)
cngfScore = 0
for nodeID, nodeInfo in subG.nodes(data=True):
if nodeID not in authorsArray:
cngfScore = cngfScore + \
(subG.degree[nodeID] /
math.log10(G.degree[nodeID]))
authorOne = G.nodes[authorsArray[1]]
authorTwo = G.nodes[authorsArray[0]]
sentenceunsorted.append({
"text":
authorOne['label'] + " - " + authorTwo['label'] +
" le score est :" + str(cngfScore),
"score":
cngfScore
})
newLinks.append({
"from": authorOne['id'],
"to": authorTwo['id'],
"value": float(1.0),
"authOne": authorOne,
"authTwo": authorTwo,
"score": float("{:.4f}".format(cngfScore))
})
for s in sentenceunsorted:
sentence.append(s['text'])
for authors, score in topAll.items():
jsonDict.append({
"authorSource":
str(authors).split(' - ')[0].encode("utf-8"),
"authorDest":
str(authors).split(' - ')[1].encode("utf-8"),
"score":
score
})
# responseDict = {"results": jsonDict}
# return json.dumps(newLinks)
csv_columns = ['authorSource', 'authorDest', 'score']
csv_file = file_path + ".csv"
try:
with open(csv_file, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
for data in jsonDict:
writer.writerow(data)
CSV_results = Stuff(title=file_save_name + ".csv",
type="csv",
user=current_user)
db.session.add(CSV_results)
except IOError:
print("I/O error")
db.session.commit()
return render_template('generatedGraph.html',
newLinks=newLinks,
predictions=sentence,
data=initialGraphJson,
filename=file_save_name)
else:
flash(
"format inccorecte, veillez sélectionner un fichier .net valide ",
'netErrors')
return redirect(request.url)
return render_template('upload_graph.html', title='Upload')
import linkpred
import random
from matplotlib import pyplot as plt
emb = input("Select the dataset" )
random.seed(100)
# Read network
G = linkpred.read_network(emb)
# Create test network
test = G.subgraph(random.sample(G.nodes(), 20))
# Exclude test network from learning phase
training = G.copy()
training.remove_edges_from(test.edges())
simrank = linkpred.predictors.SimRank(training, excluded=training.edges())
simrank_results = simrank.predict(c=0.5)
test_set = set(linkpred.evaluation.Pair(u, v) for u, v in test.edges())
evaluation = linkpred.evaluation.EvaluationSheet(simrank_results, test_set)
recall = evaluation.recall()
precision = evaluation.precision()
plt.clf()
plt.plot(recall, precision, color='navy',
label='Precision-Recall curve')
plt.xlabel('Recall')
def upload_file():
if request.method == 'POST':
# check if the post request has the file part
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
# if user does not select file, browser also
# submit an empty part without filename
# return Response(file).get_data()
if file.filename == '':
flash('No selected file')
return redirect(request.url)
if file and allowed_file(file.filename):
# return redirect(url_for('uploaded_file',filename=filename))
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
initialGraphJson = formatNetwork2(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
G = linkpred.read_network(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
H = G.copy()
num_loops = nx.number_of_selfloops(G)
if num_loops:
H.remove_edges_from(nx.selfloop_edges(G))
CommonNeighbours = linkpred.predictors.CommonNeighbours(
H, excluded=H.edges())
CommonNeighbours_results = CommonNeighbours.predict()
top = CommonNeighbours_results.top()
sentence = []
sentenceunsorted = []
newLinks = []
jsonDict = []
# resultsList = []
G = nx.convert_node_labels_to_integers(H, 1, "default", "label")
CommonNeighboursG = linkpred.predictors.CommonNeighbours(
G, excluded=G.edges())
CommonNeighbours_resultsG = CommonNeighboursG.predict()
topG = CommonNeighbours_resultsG.top(10)
# for authors, score in top.items():
# sentence.append(
# str(authors) + " le score est :" + str(score))
# newLinks.append({
# "from": getNodefromLabelFromJson(str(authors[0]), initialGraphJson['nodes'])['id'],
# "to": getNodefromLabelFromJson(str(authors[1]), initialGraphJson['nodes'])['id'],
# "value": float(1.0)
# })
for authors, score in topG.items():
authorsArray = [authors[0], authors[1]]
common = intersection(list(G.neighbors(
authors[0])), list(G.neighbors(authors[1]))) + authorsArray
subG = G.subgraph(common)
cngfScore = 0
for nodeID, nodeInfo in subG.nodes(data=True):
if nodeID not in authorsArray:
cngfScore = cngfScore + \
(subG.degree[nodeID] /
math.log10(G.degree[nodeID]))
authorOne = G.nodes[authorsArray[1]]
authorTwo = G.nodes[authorsArray[0]]
sentenceunsorted.append({
"text":
authorOne['label'] + " - " + authorTwo['label'] +
" le score est :" + str(cngfScore),
"score":
cngfScore
})
newLinks.append({
"from": authorOne['id'],
"to": authorTwo['id'],
"value": float(1.0),
"authOne": authorOne,
"authTwo": authorTwo,
"score": cngfScore
})
# return json.dumps(newLinks)
# newLinks.append([str(authors[0]), str(authors[1])])
newLinks = sorted(newLinks, key=lambda i: i['score'], reverse=True)
sentenceunsorted = sorted(sentenceunsorted,
key=lambda i: i['score'],
reverse=True)
for s in sentenceunsorted:
sentence.append(s['text'])
for authors, score in top.items():
jsonDict.append({
"authorSource": str(authors).split(' - ')[0],
"authorDest": str(authors).split(' - ')[1],
"score": cngfScore
})
# responseDict = {"results": jsonDict}
# return json.dumps(newLinks)
return render_template('generatedGraph.html',
newLinks=newLinks,
predictions=sentence,
data=initialGraphJson,
filename=filename)
else:
flash(
"format inccorecte, veillez sélectionner un fichier .net valide "
)
return redirect(request.url)
return render_template('downloads.html')
def test_read_unknown_network_type():
with temp_file(suffix=".foo") as fname:
with pytest.raises(linkpred.exceptions.LinkPredError):
linkpred.read_network(fname)
