def finish_evaluation(self):
self.log1()
self.report.summary()
if self.args.statistics:
self.evalDetails.output_statistic_details(
self.output_folder + "/statistics/rescalsim_" + self.start_time,
self.ground_truth.getHeaders(), self.args.fbeta)
gexf = create_gexf_graph(self.ground_truth, self.evalDetails)
output_file = open(self.output_folder + "/statistics/rescalsim_" + self.start_time + "/graph.gexf", "w")
gexf.write(output_file)
output_file.close()
def finish_evaluation(self):
self.AUC_test = np.array(self.AUC_test)
self.logger.info('AUC-PR Test Mean / Std: %f / %f' % (self.AUC_test.mean(), self.AUC_test.std()))
self.logger.info('----------------------------------------------------')
self.log1()
self.report.summary()
if self.args.statistics:
self.evalDetails.output_statistic_details(
self.output_folder + "/statistics/rescal_" + self.start_time,
self.ground_truth.getHeaders(), self.args.fbeta, True)
gexf = create_gexf_graph(self.ground_truth, self.evalDetails)
output_file = open(self.output_folder + "/statistics/rescal_" + self.start_time + "/graph.gexf", "w")
gexf.write(output_file)
output_file.close()
def finish_evaluation(self):
self.logEvaluationLine()
self.report.summary()
if self.args.statistics:
folder = "/statistics/cosine_"
if self.weighted:
folder = "/statistics/wcosine_"
self.evalDetails.output_statistic_details(
self.output_folder + folder + self.start_time,
self.ground_truth.getHeaders(), self.args.fbeta)
gexf = create_gexf_graph(self.ground_truth, self.evalDetails)
output_file = open(self.output_folder + folder +
self.start_time + "/graph.gexf", "w")
gexf.write(output_file)
output_file.close()
