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Python Parameterization.generating_Training_Graph方法代碼示例

本文整理匯總了Python中parametering.Parameterization.Parameterization.generating_Training_Graph方法的典型用法代碼示例。如果您正苦於以下問題：Python Parameterization.generating_Training_Graph方法的具體用法？Python Parameterization.generating_Training_Graph怎麽用？Python Parameterization.generating_Training_Graph使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類parametering.Parameterization.Parameterization的用法示例。


在下文中一共展示了Parameterization.generating_Training_Graph方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: step08

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
def step08(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)
    myparams.generating_Training_Graph()
    myparams.generating_Test_Graph()
    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)
    
    calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
    calc.reading_Max_min_file()
    print "# pair of Authors with at least 3 articles Calculated: ", calc.qtyDataCalculated  #FormatingDataSets.getTotalLineNumbers(FormatingDataSets.get_abs_file_path(util.calculated_file))
    topRank = Analyse.getTopRank(util.analysed_file+ '.random.analised.txt')
    print "# pair of Authors with at least 3 articles that is connected in Test Graph in a random way: ", topRank
    print "Max values found in calculations: ", str(calc.maxValueCalculated)
    print "Min Values found in calculations: ", str(calc.minValueCalculated)
    for pathFile in calc.getfilePathOrdered_separeted():
        print "File Analised: ", pathFile +  '.analised.txt'
        number_connected =  Analyse.getTopRankABSPathFiles(pathFile + '.analised.txt')
        print "# pair of Authors that is connected in Test Graph: ", number_connected
        print "%: ", Analyse.getLastInfosofResultsABSPathFiles(pathFile + '.analised.txt', topRank)
        print "---------------------------------"
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:30，代碼來源:Step08.py

示例2: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:61，代碼來源:ExecutionNowellFull.py

示例3: step03

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
def step03( paramFile, num_people):
    #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)
    myparams.generating_Training_Graph()
    selection = VariableSelection(myparams.trainnigGraph, util.nodes_notlinked_file,util.min_edges, False, num_people)
    return
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:10，代碼來源:Step03.py

示例4: step04

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
def step04(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)
    myparams.generating_Training_Graph()
 
    calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
    calc.Separating_calculateFile()
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:11，代碼來源:Step04.py

示例5: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:cptullio，項目名稱:Predicao-de-Links，代碼行數:56，代碼來源:FullExecutionGraphNowell_NoCN.py

示例6: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:cptullio，項目名稱:Predicao-de-Links，代碼行數:55，代碼來源:FullExecutionGraphRich_versaoTempo.py

示例7: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
def execution(configFile):
    
    #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')):
    calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked,  myparams)
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:19，代碼來源:PreAnalise.py

示例8: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:andreluizmelo，項目名稱:Predicao-de-Links，代碼行數:48，代碼來源:FullExecutionGraphNowell.py

示例9: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:cptullio，項目名稱:Predicao-de-Links，代碼行數:46，代碼來源:FullExecutionGraphArxiv.py

示例10: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:44，代碼來源:prepareToAG.py

示例11: execution

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
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()
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:40，代碼來源:AfterAG.py

示例12: ParameterUtil

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
Analysing the results
'''
from parametering.ParameterUtil import ParameterUtil
from parametering.Parameterization import Parameterization
from analysing.Analyse import Analyse
from calculating.VariableSelection import VariableSelection
from formating.FormatingDataSets import FormatingDataSets
import networkx
from calculating.CalculateInMemory import CalculateInMemory

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 )
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:32，代碼來源:SingleExecutionInMemory.py

示例13: step02

# 需要導入模塊: from parametering.Parameterization import Parameterization [as 別名]
# 或者: from parametering.Parameterization.Parameterization import generating_Training_Graph [as 別名]
def step02(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)
    myparams.generating_Training_Graph()
    myparams.generating_Test_Graph()
開發者ID:AndersonChaves，項目名稱:Predicao-de-Links，代碼行數:8，代碼來源:Step02.py


注：本文中的parametering.Parameterization.Parameterization.generating_Training_Graph方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。