metricTableList = pool.map(getMetricTable, [groundTruthFiles[i_gt] for i_gt in gtIndices]) pool.close() pool.join() print "Number of metric tables", len(metricTableList), \ "with shape", metricTableList[0].shape for metricTable in metricTableList: print metricTable print "Aggregating rankings from metric tables" rankagg = UnsupervisedLearningRankAggregator() rankagg.aggregate(metricTableList, metricOrder) #averageWeights += rankagg.get_weights() w = rankagg.get_weights() #if t % 20 == 0: # print "Weights", w print "Weights\n", w averageWeights += w print "Average weights after", t+1, "perturbations\n", averageWeights/(t+1) averageWeights /= numPerturbations print "--------------------"