def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Read solution.
print('Reading solution...')
public_solution, private_solution, ignored_ids = dataset_file_io.ReadSolution(
cmd_args.solution_path, dataset_file_io.RETRIEVAL_TASK_ID)
print('done!')
# Read predictions.
print('Reading predictions...')
public_predictions, private_predictions = dataset_file_io.ReadPredictions(
cmd_args.predictions_path, set(public_solution.keys()),
set(private_solution.keys()), set(ignored_ids),
dataset_file_io.RETRIEVAL_TASK_ID)
print('done!')
# Mean average precision.
print('**********************************************')
print('(Public) Mean Average Precision: %f' %
metrics.MeanAveragePrecision(public_predictions, public_solution))
print('(Private) Mean Average Precision: %f' %
metrics.MeanAveragePrecision(private_predictions, private_solution))
# Mean precision@k.
print('**********************************************')
public_precisions = 100.0 * metrics.MeanPrecisions(public_predictions,
public_solution)
private_precisions = 100.0 * metrics.MeanPrecisions(private_predictions,
private_solution)
print('(Public) Mean precisions: P@1: %.2f, P@5: %.2f, P@10: %.2f, '
'P@50: %.2f, P@100: %.2f' %
(public_precisions[0], public_precisions[4], public_precisions[9],
public_precisions[49], public_precisions[99]))
print('(Private) Mean precisions: P@1: %.2f, P@5: %.2f, P@10: %.2f, '
'P@50: %.2f, P@100: %.2f' %
(private_precisions[0], private_precisions[4], private_precisions[9],
private_precisions[49], private_precisions[99]))
# Mean/median position of first correct.
print('**********************************************')
public_mean_position, public_median_position = metrics.MeanMedianPosition(
public_predictions, public_solution)
private_mean_position, private_median_position = metrics.MeanMedianPosition(
private_predictions, private_solution)
print('(Public) Mean position: %.2f, median position: %.2f' %
(public_mean_position, public_median_position))
print('(Private) Mean position: %.2f, median position: %.2f' %
(private_mean_position, private_median_position))
def testMeanAveragePrecisionWorks(self):
# Define input.
predictions = _CreateRetrievalPredictions()
solution = _CreateRetrievalSolution()
# Run tested function.
mean_ap = metrics.MeanAveragePrecision(predictions, solution)
# Define expected results.
expected_mean_ap = 0.458333
# Compare actual and expected results.
self.assertAllClose(mean_ap, expected_mean_ap)