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 "--------------------"
