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)
