def analise(calcDb, topRank,TestGraph, util, method):
order = sorted( list({ 'node1': r['node1'], 'node2': r['node2'], 'value' : r[method]} for r in calcDb) , key=lambda value: value['value'], reverse=True)
BD = None
if not os.path.exists(FormatingDataSets.get_abs_file_path(util.calculated_file + '.' + method +'.base.pdl')):
BD = generate_finalResult(order, topRank, TestGraph, FormatingDataSets.get_abs_file_path(util.calculated_file + '.' + method +'.base.pdl'))
else:
BD = reading_Database(FormatingDataSets.get_abs_file_path(util.calculated_file + '.' + method +'.base.pdl'))
return get_results(BD, method)
class SFrame:
#util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_astroph_1994_1999/AllExecutionScores/configToAG.txt')
util = ParameterUtil(parameter_file = 'data/configuration/arxiv/exemplo_1994_1999/CombinationLinear/configToAG.txt')
#util = ParameterUtil(parameter_file = 'data/configuration/arxiv/condmat_1994_1999/CombinationLinear/configToAG.txt')
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
metrics = sframe.SFrame.read_csv(FormatingDataSets.get_abs_file_path(util.calculated_file+'_normalizated.csv'))
results = sframe.SFrame.read_csv(FormatingDataSets.get_abs_file_path(util.result_random_file))
top = 20
def __init__(self):
pass
@classmethod
def evaluate(cls, individual):
new_metric = float(0)
##print 'individuos: ', individual
for index_score in range(len(cls.myparams.ScoresChoiced)):
#print cls.myparams.ScoresChoiced[index_score][0].getName()
valorMetrica = cls.metrics[ cls.myparams.ScoresChoiced[index_score][0].getName() ]
valorIndividual = individual[index_score]
#print "valores ", valorMetrica, valorIndividual
new_metric = new_metric + (valorMetrica * valorIndividual )
##print 'nova metrica', new_metric
copy_metrics = cls.metrics.copy()
copy_metrics.add_column(new_metric, name='new_metric')
copy_metrics = copy_metrics.topk('new_metric', k=cls.top)
#print 'metrics after topk \n\n', copy_metrics
copy_results = cls.results.copy()
#print 'copy_results before join', copy_results
copy_metrics = copy_metrics.join(copy_results)
#print 'metrics after join \n\n', copy_metrics
copy_metrics = copy_metrics.sort('new_metric', ascending=False)
##print 'copy metrics ', copy_metrics
aux = [0]
copy_metrics = copy_metrics.filter_by(aux,'result')
zero = copy_metrics.num_rows()
#print 'zero', zero
del copy_metrics
del copy_results
result = float(zero) / cls.top,
#print 'resultado ', result
return result
def getTopRank(relativeFilePathRandomAnalised):
absFile = FormatingDataSets.get_abs_file_path(
relativeFilePathRandomAnalised)
f = open(absFile, 'r')
for last in f:
pass
return int(last.split('\t')[1])
def step05(paramFile):
#util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_example_1994_1999.txt')
util = ParameterUtil(parameter_file=paramFile)
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0,
util.t0_, util.t1, util.t1_,
util.FeaturesChoiced, util.graph_file,
util.trainnig_graph_file, util.test_graph_file,
util.decay)
calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file,
util.ordered_file, util.maxmincalculated_file)
myparams.generating_Test_Graph()
analise = Analyse(
myparams, FormatingDataSets.get_abs_file_path(util.calculated_file),
FormatingDataSets.get_abs_file_path(util.analysed_file) +
'.random.analised.txt', calc.qtyDataCalculated)
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'T.EXPERIMENTO_ATUAL_CORE03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodesSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#GET THE AUTHORS THAT PUBLISH AT TRAINNING AND TEST
#A NUMBER OF PAPERS DEFINED AT MIN_EDGES IN CONFIG FILE
nodes = nodesSelection.get_NowellAuthorsCore()
#GET A PAIR OF AUTHORS THAT PUBLISH AT LEAST ONE ARTICLE AT TRAINNING AND TEST.
#DID NOT SEE ANY NEED
collaborations = nodesSelection.get_NowellColaboration()
#GET THE FIRST EDGES MADE BY THE COMBINATION OF NODES IN TRAINNING GRAPH
eOld = nodesSelection.get_NowellE(nodes,myparams.trainnigGraph)
#GET THE FIRST EDGES MADE BY THE COMBINATION OF NODES IN TEST GRAPH THAT DO NOT HAVE EDGES IN TRAINNING
eNew = nodesSelection.get_NowellE2(nodes, eOld, myparams.testGraph)
#GET THE NODES NOT LINKED OVER THE COMBINATION NODES.
nodesNotLinked = nodesSelection.get_PairsofNodesNotinEold(nodes)
#CREATING CALCULATION OBJECT
calc = CalculateInMemory(myparams,nodesNotLinked)
#CALCULATING THE SCORES.
resultsofCalculation = calc.executingCalculate()
#ORDERNING THE RESULTS RETURNING THE TOP N
orderingResults = calc.ordering(len(eNew), resultsofCalculation)
#SAVING THE ORDERED RESULTS.
calc.saving_orderedResult(util.ordered_file, orderingResults)
#ANALISE THE ORDERED RESULTS AND CHECK THE FUTURE.
ScoresResults = Analyse.AnalyseNodesWithScoresInFuture(orderingResults, myparams.testGraph)
#SAVING THE RESULTS.
for index in range(len(ScoresResults)):
Analyse.saving_analyseResult(ScoresResults[index], util.analysed_file + str(myparams.ScoresChoiced[index][0] ) + '.txt')
resultFile.write("TOTAL OF SUCESSS USING METRIC " + str(myparams.ScoresChoiced[index][0]) + " = " + str(Analyse.get_TotalSucess(ScoresResults[index]) ))
resultFile.write("\n")
resultFile.write("\n")
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(collaborations)*2)+ "\t\t" + str(len(nodes)) + "\t" + str(len(eOld))+"\t" + str(len(eNew)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.fuzzyinputy.txt')):
data = calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked, myparams)
saving_files_calculting_input(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.inputFuzzy.txt'), data)
for item in data:
calc = FuzzyCalculation(item['intensityno1'], item['intensityno2'], item['similarity'], item['ageno1'], item['ageno2'])
print item['no1'], item['no2'], calc.potencial_ligacao, calc.grau_potencial_ligacao
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def step05(paramFile):
#util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_example_1994_1999.txt')
util = ParameterUtil(parameter_file = paramFile)
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0, util.t0_, util.t1, util.t1_, util.FeaturesChoiced, util.graph_file, util.trainnig_graph_file, util.test_graph_file, util.decay)
calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
myparams.generating_Test_Graph()
analise = Analyse(myparams, FormatingDataSets.get_abs_file_path(util.calculated_file), FormatingDataSets.get_abs_file_path(util.analysed_file) + '.random.analised.txt', calc.qtyDataCalculated)
def adding_normalize_values_tograph(self, graph, weighted_graph_file):
arquivo = open(self.filepathResult, 'r')
for line in arquivo:
result = Calculate.reading_calculateLine(line)
graph.add_edge(int(result[1]),
int(result[2]),
weight=str(result[0]))
networkx.write_graphml(
graph, FormatingDataSets.get_abs_file_path(weighted_graph_file))
def execution(configFile, metricas):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#CREATING CALCULATION OBJECT
weights = {'cn' : 1, 'aas': 1, 'pa':1, 'jc': 1, 'ts08':1,'ts05': 1, 'ts02':1}
calc = CalculatingCombinationOnlyNowell(myparams, nodeSelection.nodesNotLinked,weights,False )
saving_files_calculting(FormatingDataSets.get_abs_file_path(util.calculated_file), calc.results, metricas)
Analise = nodeSelection.AnalyseAllNodesNotLinkedInFuture(nodeSelection.nodesNotLinked, myparams.testGraph)
salvar_analise(FormatingDataSets.get_abs_file_path(util.analysed_file) + '.allNodes.csv', Analise)
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.fuzzyinputy.txt')):
data = calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked, myparams)
dataSorted = sorted(data, key=lambda value: value['result'], reverse=True)
topRank = len(nodeSelection.eNeW)
totalCalculated = len(dataSorted)
dataToAnalysed = []
if (topRank >= totalCalculated):
for item in range(totalCalculated):
dataToAnalysed.append({'no1': dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result': dataSorted[item]['result'] })
else:
for item in range(topRank):
dataToAnalysed.append({'no1': dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result': dataSorted[item]['result'] })
analise = AnalyseNodesInFuture(dataToAnalysed, myparams.testGraph)
resultFile.write( repr(get_TotalSucess(analise)) )
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.fuzzyinputy.txt')):
data = calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked, myparams)
dataSorted = sorted(data, key=lambda value: value['result'], reverse=True)
topRank = len(nodeSelection.eNeW)
totalCalculated = len(dataSorted)
dataToAnalysed = []
if (topRank >= totalCalculated):
for item in range(totalCalculated):
dataToAnalysed.append({'no1': dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result': dataSorted[item]['result'] })
else:
for item in range(topRank):
dataToAnalysed.append({'no1': dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result': dataSorted[item]['result'] })
analise = AnalyseNodesInFuture(dataToAnalysed, myparams.testGraph)
resultFile.write( repr(get_TotalSucess(analise)) )
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def analise(calcDb, topRank, TestGraph, util, method):
order = sorted(list({
'node1': r['node1'],
'node2': r['node2'],
'value': r[method]
} for r in calcDb),
key=lambda value: value['value'],
reverse=True)
BD = None
if not os.path.exists(
FormatingDataSets.get_abs_file_path(util.calculated_file + '.' +
method + '.base.pdl')):
BD = generate_finalResult(
order, topRank, TestGraph,
FormatingDataSets.get_abs_file_path(util.calculated_file + '.' +
method + '.base.pdl'))
else:
BD = reading_Database(
FormatingDataSets.get_abs_file_path(util.calculated_file + '.' +
method + '.base.pdl'))
return get_results(BD, method)
class SFrame:
util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_astroph_1994_1999/AllExecutionScores/configToAG.txt')
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
metrics = sframe.SFrame.read_csv(FormatingDataSets.get_abs_file_path(util.calculated_file+'_normalizated.csv'))
results = sframe.SFrame.read_csv(FormatingDataSets.get_abs_file_path(util.result_random_file))
top = 20
def __init__(self):
pass
@classmethod
def evaluate(cls, individual):
new_metric = float(0)
for index_score in range(len(cls.myparams.ScoresChoiced)):
new_metric = new_metric + (cls.metrics[ cls.myparams.ScoresChoiced[index_score][0].getName() ] * individual[index_score] )
print new_metric
copy_metrics = cls.metrics.copy()
copy_metrics.add_column(new_metric, name='new_metric')
copy_metrics = copy_metrics.topk('new_metric', k=cls.top)
copy_results = cls.results.copy()
copy_metrics = copy_metrics.join(copy_results)
copy_metrics = copy_metrics.sort('new_metric', ascending=False)
aux = [0]
copy_metrics = copy_metrics.filter_by(aux,'result')
zero = copy_metrics.num_rows()
del copy_metrics
del copy_results
return float(zero) / cls.top,
def saving_analyseResult(AnalysedNodesnotLinkedInFuture, filepath):
f = codecs.open(FormatingDataSets.get_abs_file_path(filepath), 'w',encoding='utf-8')
f.write('no1,no2,result\n')
for item in AnalysedNodesnotLinkedInFuture:
value = item[0] + ',' +item[1] + ','
for item_index in range(len(item)-2):
value = value + repr( item[item_index+2] )
if (item_index < (len(item) -2) ):
value = value + ','
final = value + '\n'
f.write( final.replace(',\n', '\n') )
f.close()
def reading_analyseResult( filepath):
result = []
firstLine = 0
f = open(FormatingDataSets.get_abs_file_path(filepath), 'r')
for line in f:
if firstLine == 0:
firstLine = 1
continue
cols = line.strip().replace('\n','').split(',')
item_result = []
for col in cols:
try:
item_result.append(eval(col))
except Exception:
item_result.append(str(col))
result.append(item_result)
return result
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03_onlyinteraction.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
db = None
if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl')):
db = generateWeights(myparams.trainnigGraph, FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl') , myparams)
else:
db = reading_Database(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl'))
calcDb = None
if not os.path.exists(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl')):
calcDb = calculatingWeights(myparams.trainnigGraph, nodeSelection.nodesNotLinked, db, FormatingDataSets.get_abs_file_path(util.calculated_file) + '.base.pdl')
else:
calcDb = reading_Database(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl'))
ordering = get_ordering(calcDb, len(nodeSelection.eNeW))
result = get_analyseNodesInFuture(ordering, myparams.testGraph)
resultFile.write(repr(result))
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'wTScore03_010304.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
db = None
if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl')):
db = generateWeights(myparams.trainnigGraph, FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl') , myparams)
else:
db = reading_Database(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl'))
calcDb = None
if not os.path.exists(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl')):
calcDb = calculatingWeights(myparams.trainnigGraph, nodeSelection.nodesNotLinked, db, FormatingDataSets.get_abs_file_path(util.calculated_file) + '.base.pdl')
else:
calcDb = reading_Database(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl'))
ordering = get_ordering(calcDb, len(nodeSelection.eNeW))
result = get_analyseNodesInFuture(ordering, myparams.testGraph)
resultFile.write(repr(result))
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03_execucaoFinal_cstT02.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#CREATING CALCULATION OBJECT
calc = CalculatingTogether(myparams, nodeSelection.nodesNotLinked)
ordering = calc.ordering(len(nodeSelection.eNeW))
#calc.saving_orderedResult(util.ordered_file, ordering)
calc.AnalyseNodesInFuture(ordering, myparams.testGraph)
resultFile.write(repr(calc.get_TotalSucess()))
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def execution(configFile):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
#CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
#GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
myparams.generating_Training_Graph()
#GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
myparams.generating_Test_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#CREATING CALCULATION OBJECT
calc = CalculatingTogether(myparams, nodeSelection.nodesNotLinked)
ordering = calc.ordering(len(nodeSelection.eNeW))
#calc.saving_orderedResult(util.ordered_file, ordering)
calc.AnalyseNodesInFuture(ordering, myparams.testGraph)
resultFile.write(repr(calc.get_TotalSucess()))
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.datetime.now()))
resultFile.close()
def get_analyseNodesInFuture(calcDb, topRank, TestGraph, util):
result = []
WCNFI_ORDERED = sorted(list({
'node1': r['node1'],
'node2': r['node2'],
'value': r['WCNFI']
} for r in calcDb),
key=lambda value: value['value'],
reverse=True)
WCNFIBD = None
if not os.path.exists(
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WCNFI.base.pdl')):
WCNFIBD = generate_finalResult(
WCNFI_ORDERED, topRank, TestGraph,
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WCNFI.base.pdl'))
else:
WCNFIBD = reading_Database(
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WCNFI.base.pdl'))
result.append(get_results(WCNFIBD, 'WCNFI'))
WAAFI_ORDERED = sorted(list({
'node1': r['node1'],
'node2': r['node2'],
'value': r['WAAFI']
} for r in calcDb),
key=lambda value: value['value'],
reverse=True)
WAAFIBD = None
if not os.path.exists(
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WAAFI.base.pdl')):
WAAFIBD = generate_finalResult(
WAAFI_ORDERED, topRank, TestGraph,
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WAAFI.base.pdl'))
else:
WAAFIBD = reading_Database(
FormatingDataSets.get_abs_file_path(util.calculated_file +
'.WAAFI.base.pdl'))
result.append(get_results(WAAFIBD, 'WAAFI'))
return result
def execution(configFile, weights):
#DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
resultFile.write("Inicio da operacao\n")
resultFile.write(str(datetime.now()))
resultFile.write("\n")
#READING THE CONFIG FILE
util = ParameterUtil(parameter_file = configFile)
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
myparams.generating_Test_Graph()
myparams.generating_Training_Graph()
nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
#CREATING CALCULATION OBJECT
calc = CalculatingCombinationOnlyNowell(myparams, nodeSelection.nodesNotLinked, weights, True)
ordering = calc.ordering(len(nodeSelection.eNeW))
calc.AnalyseNodesInFuture(ordering, myparams.testGraph)
resultFile.write(repr(calc.get_TotalSucess()))
resultFile.write("\n")
#
resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
resultFile.write("\n")
resultFile.write("Fim da Operacao\n")
resultFile.write(str(datetime.now()))
resultFile.close()
if __name__ == '__main__':
util = ParameterUtil(parameter_file = 'data/formatado/exemplomenor/config/config.txt')
myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_,
filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None)
myparams.generating_Training_Graph()
myparams.generating_Test_Graph()
selection = VariableSelection(myparams.trainnigGraph, util.min_edges)
nodesNotLinked = selection.get_pair_nodes_not_linked()
calc = CalculateInMemory(myparams, nodesNotLinked)
resultsCalculate = calc.executingCalculate()
calc.Separating_calculateFile()
analise = Analyse(myparams, FormatingDataSets.get_abs_file_path(util.calculated_file), FormatingDataSets.get_abs_file_path(util.analysed_file) + '.random.analised.txt', calc.qtyDataCalculated)
topRank = Analyse.getTopRank(util.analysed_file + '.random.analised.txt')
calc.Ordering_separating_File(topRank)
for OrderingFilePath in calc.getfilePathOrdered_separeted():
analise = Analyse(myparams, OrderingFilePath, OrderingFilePath + '.analised.txt', topRank )
print "Trainning Period:", myparams.t0, " - ", myparams.t0_
print "Test Period:", myparams.t1, " - ", myparams.t1_
print "# Papers in Trainning: ", myparams.get_edges(myparams.trainnigGraph)
print "# Authors in Training: ", myparams.get_nodes(myparams.trainnigGraph)
print "# Papers in Test: ", myparams.get_edges(myparams.testGraph)
print "# Authors in Test", myparams.get_nodes(myparams.testGraph)
print "# pair of Authors with at least 3 articles Calculated: ", calc.qtyDataCalculated #FormatingDataSets.getTotalLineNumbers(FormatingDataSets.get_abs_file_path(util.calculated_file))
for line in calculatedFile:
if texto in line:
result = line
break
elif textov2 in line:
result = line
break
calculatedFile.seek(0)
return result
if __name__ == '__main__':
util = ParameterUtil(
parameter_file='data/formatado/arxiv/nowell_astroph_1994_1999.txt')
calculatedFile = open(
FormatingDataSets.get_abs_file_path(util.calculated_file), 'r')
for linha in calculatedFile:
x.append(Calculate.reading_calculateLine(linha))
calculatedFile.close()
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0,
util.t0_, util.t1, util.t1_,
util.FeaturesChoiced, util.graph_file,
util.trainnig_graph_file, util.test_graph_file,
util.decay)
myparams.generating_Training_Graph()
Nodes_notLinked = VariableSelection(myparams.trainnigGraph,
util.nodes_notlinked_file,
util.min_edges)
nodes_notlinkedFile = open(
FormatingDataSets.get_abs_file_path(util.nodes_notlinked_file), 'r')
qtyLine = 0
'''
Created on Aug 22, 2015
@author: cptullio
Analysing the results
'''
from parametering.ParameterUtil import ParameterUtil
from parametering.Parameterization import Parameterization
from calculating.Calculate import Calculate
from analysing.Analyse import Analyse
from calculating.VariableSelection import VariableSelection
from formating.FormatingDataSets import FormatingDataSets
import networkx
if __name__ == '__main__':
util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_example_1994_1999.txt')
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0, util.t0_, util.t1, util.t1_, util.FeaturesChoiced, util.graph_file, util.trainnig_graph_file, util.test_graph_file, util.decay)
myparams.generating_Training_Graph()
selection = VariableSelection(myparams.trainnigGraph, util.nodes_notlinked_file,util.min_edges, True)
calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
wg = calc.adding_normalize_values_tograph(myparams.trainnigGraph)
networkx.write_graphml(wg, FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.weighted.txt'))
node993 =set(n for n,d in wg.edges(data=True) if n == 993 and d == 994)
print node993
def getTopRank(relativeFilePathRandomAnalised):
absFile = FormatingDataSets.get_abs_file_path(relativeFilePathRandomAnalised)
f = open(absFile, 'r')
for last in f:
pass
return int(last.split('\t')[1])
from analysing.Analyse import Analyse
from calculating.VariableSelection import VariableSelection
from formating.FormatingDataSets import FormatingDataSets
import networkx
import mysql.connector
if __name__ == '__main__':
util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_astroph_1994_1999.txt')
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0, util.t0_, util.t1, util.t1_, util.FeaturesChoiced, util.graph_file, util.trainnig_graph_file, util.test_graph_file, util.decay)
myparams.generating_Training_Graph()
AllNodes = VariableSelection(myparams.trainnigGraph, util.nodes_file,util.min_edges, True)
calc = Calculate(myparams, util.nodes_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
print 'armazenando resultados'
cnx = mysql.connector.connect(user='******', password='******',
host='127.0.0.1',
database='calculos')
add_result = ("INSERT INTO resultadopesos "
"(no1, no2, resultados) "
"VALUES (%s, %s, %s)")
cursor = cnx.cursor()
calculatedFile = open(FormatingDataSets.get_abs_file_path(util.calculated_file), 'r')
for linha in calculatedFile:
dado = Calculate.reading_calculateLine(linha)
data_result = (dado[1], dado[2].replace('\n',''),str(dado[0]))
cursor.execute(add_result, data_result)
calculatedFile.close()
cnx.commit()
cursor.close()
cnx.close()
'''
Created on Aug 22, 2015
@author: cptullio
Generating TopRank
'''
from parametering.ParameterUtil import ParameterUtil
from parametering.Parameterization import Parameterization
from calculating.Calculate import Calculate
from analysing.Analyse import Analyse
from formating.FormatingDataSets import FormatingDataSets
if __name__ == '__main__':
util = ParameterUtil(
parameter_file='data/formatado/duarte/nowell_duarte_1994_1999.txt')
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0,
util.t0_, util.t1, util.t1_,
util.FeaturesChoiced, util.graph_file,
util.trainnig_graph_file, util.test_graph_file,
util.decay)
calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file,
util.ordered_file, util.maxmincalculated_file)
myparams.generating_Test_Graph()
analise = Analyse(
myparams, FormatingDataSets.get_abs_file_path(util.calculated_file),
FormatingDataSets.get_abs_file_path(util.analysed_file) +
'.random.analised.txt', calc.qtyDataCalculated)
parameter_file='data/formatado/arxiv/nowell_astroph_1994_1999.txt')
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0,
util.t0_, util.t1, util.t1_,
util.FeaturesChoiced, util.graph_file,
util.trainnig_graph_file, util.test_graph_file,
util.decay)
myparams.generating_Training_Graph()
AllNodes = VariableSelection(myparams.trainnigGraph, util.nodes_file,
util.min_edges, True)
calc = Calculate(myparams, util.nodes_file, util.calculated_file,
util.ordered_file, util.maxmincalculated_file)
print 'armazenando resultados'
cnx = mysql.connector.connect(user='******',
password='******',
host='127.0.0.1',
database='calculos')
add_result = ("INSERT INTO resultadopesos "
"(no1, no2, resultados) "
"VALUES (%s, %s, %s)")
cursor = cnx.cursor()
calculatedFile = open(
FormatingDataSets.get_abs_file_path(util.calculated_file), 'r')
for linha in calculatedFile:
dado = Calculate.reading_calculateLine(linha)
data_result = (dado[1], dado[2].replace('\n', ''), str(dado[0]))
cursor.execute(add_result, data_result)
calculatedFile.close()
cnx.commit()
cursor.close()
cnx.close()
for line in calculatedFile:
if texto in line:
result = line
break
elif textov2 in line:
result = line
break
calculatedFile.seek(0)
return result
if __name__ == '__main__':
util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_astroph_1994_1999.txt')
calculatedFile = open(FormatingDataSets.get_abs_file_path(util.calculated_file), 'r')
for linha in calculatedFile:
x.append(Calculate.reading_calculateLine(linha))
calculatedFile.close()
myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0, util.t0_, util.t1, util.t1_, util.FeaturesChoiced, util.graph_file, util.trainnig_graph_file, util.test_graph_file, util.decay)
myparams.generating_Training_Graph()
Nodes_notLinked = VariableSelection(myparams.trainnigGraph, util.nodes_notlinked_file,util.min_edges)
nodes_notlinkedFile = open(FormatingDataSets.get_abs_file_path(util.nodes_notlinked_file), 'r')
qtyLine = 0
qtyCalculated = 0
f = open(FormatingDataSets.get_abs_file_path(util.calculated_file )+ '.weight.txt', 'w')
minValueCalculated = list(99999 for x in myparams.featuresChoice)
maxValueCalculated = list(0 for x in myparams.featuresChoice)
qtyFeatures = len(myparams.featuresChoice)
for line in nodes_notlinkedFile:
qtyLine = qtyLine + 1
