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
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 ) print "Trainning Period:", myparams.t0, " - ", myparams.t0_ print "Test Period:", myparams.t1, " - ", myparams.t1_
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.nodes_notlinked_file, util.min_edges) calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file) 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_
''' 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 from calculating.VariableSelection import VariableSelection if __name__ == '__main__': util = ParameterUtil(parameter_file = 'data/formatado/arxiv/exemplomenor/config/config.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) myparams.generating_Training_Graph() myparams.generating_Test_Graph() selection = VariableSelection(myparams.trainnigGraph) nodesNotLinked = selection.readingResultsFile(util.nodes_notlinked_file) resultsRank = Analyse.AnalyseNodesInFuture(nodesNotLinked, myparams.testGraph) Analyse.saving_analyseResult(resultsRank, util.result_random_file) print resultsRank print Analyse.reading_analyseResult(util.result_random_file)
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)