Commit d6e7ca12 authored by Pavel KORSHUNOV's avatar Pavel KORSHUNOV

allow missing files scoring

parent bf936a42
Pipeline #12819 passed with stages
in 7 minutes and 7 seconds
......@@ -25,9 +25,8 @@ from import utils
def _compute_scores(algorithm, toscore_objects, allow_missing_files):
"""Compute scores for the given list of objects using provided algorithm.
# the scores to be computed; initialized with NaN
scores = numpy.ones((1, len(toscore_objects)), numpy.float64) * numpy.nan
scores = numpy.reshape(scores, [len(toscore_objects)])
# the scores to be computed
scores = []
# Loops over the toscore sets
for i, toscore_element in enumerate(toscore_objects):
......@@ -39,9 +38,9 @@ def _compute_scores(algorithm, toscore_objects, allow_missing_files):
toscore = algorithm.read_toscore_object(toscore_element)
# compute score
if isinstance(toscore, list) or isinstance(toscore[0], numpy.ndarray):
scores[i] = algorithm.score_for_multiple_projections(toscore)
scores.insert(i, algorithm.score_for_multiple_projections(toscore))
scores[i] = algorithm.score(toscore)
scores.insert(i, algorithm.score(toscore))
# Returns the scores
return scores
......@@ -107,6 +106,10 @@ def _save_scores(score_file, scores, toscore_objects, write_compressed=False):
id_str = (str(toscore_object.client_id)).zfill(3)
sample_name = str(toscore_object.make_path())
# we can have empty score list if allow_missing_files was true in _compute_scores()
if not scores[i]:
scores[i] = [numpy.nan] # create a NaN score for such
# scores[i] is a list, so
# each sample is allowed to have multiple scores
for score in scores[i]:
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