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()