diff --git a/src/bob/bio/base/score/load.py b/src/bob/bio/base/score/load.py
index e2c87b816a31ddcc78e16dc9c3ad10be43810c56..47043f85dfebb77a2eb940fdd33e4ebb7abe650c 100644
--- a/src/bob/bio/base/score/load.py
+++ b/src/bob/bio/base/score/load.py
@@ -169,7 +169,7 @@ def get_split_dataframe(filename):
return impostors, genuines
-def split_csv_scores(filename):
+def split_csv_scores(filename, score_column: str = "score"):
"""Loads a score set that was written with :any:`bob.bio.base.pipelines.CSVScoreWriter`
Parameters
@@ -178,6 +178,8 @@ def split_csv_scores(filename):
filename (:py:class:`str`, ``file-like``): The file object that will be
opened with :py:func:`open_file` containing the scores.
+ score_column: The CSV column that contains the score values.
+
Returns
-------
@@ -196,8 +198,8 @@ def split_csv_scores(filename):
impostors = df[df.probe_subject_id != df.bio_ref_subject_id]
return (
- impostors["score"].to_dask_array().compute(),
- genuines["score"].to_dask_array().compute(),
+ impostors[score_column].to_dask_array().compute(),
+ genuines[score_column].to_dask_array().compute(),
)
@@ -358,7 +360,7 @@ def scores(filename, ncolumns=None):
return _iterate_score_file(filename)
-def split(filename, ncolumns=None, sort=False):
+def split(filename, ncolumns=None, sort=False, csv_score_column: str = "score"):
"""Loads the scores from the given score file and splits them into positives
and negatives.
Depending on the score file format, it calls see
@@ -376,6 +378,8 @@ def split(filename, ncolumns=None, sort=False):
estimated automatically
sort : :obj:`bool`, optional
If ``True``, will return sorted negatives and positives
+ csv_score_column :
+ When loading a CSV file, specifies the column that holds scores.
Returns
-------
@@ -388,7 +392,7 @@ def split(filename, ncolumns=None, sort=False):
the ``real_id`` are identical (see :py:func:`four_column`)
"""
if iscsv(filename):
- neg, pos = split_csv_scores(filename)
+ neg, pos = split_csv_scores(filename, score_column=csv_score_column)
else:
ncolumns = _estimate_score_file_format(filename, ncolumns)
if ncolumns == 4:
@@ -404,7 +408,7 @@ def split(filename, ncolumns=None, sort=False):
return neg, pos
-def cmc(filename, ncolumns=None):
+def cmc(filename, ncolumns=None, csv_score_column: str = "score"):
"""cmc(filename, ncolumns=None) -> list
Loads scores to compute CMC curves.
@@ -422,6 +426,9 @@ def cmc(filename, ncolumns=None):
the score file will be assumed to be in the given format. If not
specified, the score file format will be estimated automatically
+ csv_score_column: When loading a CSV file, specifies the column that holds
+ scores.
+
Returns:
:any:`list`: [(neg,pos)] A list of tuples, where each tuple contains the
@@ -462,7 +469,7 @@ def load_score(filename, ncolumns=None, minimal=False, **kwargs):
Returns:
- array: An array which contains not only the actual scores but also the
+ array: An array which contains not only the actual ``score`` but also the
``claimed_id``, ``real_id``, ``test_label`` and ``['model_label']``
"""
@@ -536,15 +543,15 @@ def load_files(filenames, func_load):
return res
-def get_negatives_positives(score_lines):
+def get_negatives_positives(score_lines, score_column: str = "score"):
"""Take the output of load_score and return negatives and positives. This
function aims to replace split_four_column and split_five_column but takes a
different input. It's up to you to use which one.
"""
pos_mask = score_lines["claimed_id"] == score_lines["real_id"]
- positives = score_lines["score"][pos_mask]
- negatives = score_lines["score"][numpy.logical_not(pos_mask)]
+ positives = score_lines[score_column][pos_mask]
+ negatives = score_lines[score_column][numpy.logical_not(pos_mask)]
return (negatives, positives)
@@ -552,17 +559,19 @@ def get_negatives_positives_from_file(filename, **kwargs):
"""Loads the scores first efficiently and then calls
get_negatives_positives"""
score_lines = load_score(filename, minimal=True, **kwargs)
- return get_negatives_positives(score_lines)
+ return get_negatives_positives(score_lines, score_column="score")
-def get_negatives_positives_all(score_lines_list):
+def get_negatives_positives_all(score_lines_list, score_column: str = "score"):
"""Take a list of outputs of load_score and return stacked negatives and
positives.
"""
negatives, positives = [], []
for score_lines in score_lines_list:
- neg_pos = get_negatives_positives(score_lines)
+ neg_pos = get_negatives_positives(
+ score_lines, score_column=score_column
+ )
negatives.append(neg_pos[0])
positives.append(neg_pos[1])
negatives = numpy.vstack(negatives).T
@@ -570,11 +579,11 @@ def get_negatives_positives_all(score_lines_list):
return (negatives, positives)
-def get_all_scores(score_lines_list):
+def get_all_scores(score_lines_list, score_column: str = "score"):
"""Take a list of outputs of load_score and return stacked scores"""
return numpy.vstack(
- [score_lines["score"] for score_lines in score_lines_list]
+ [score_lines[score_column] for score_lines in score_lines_list]
).T
@@ -614,18 +623,20 @@ def _estimate_score_file_format(filename, ncolumns=None):
return ncolumns
-def _iterate_score_file(filename):
+def _iterate_score_file(filename, csv_score_column: str = "score"):
"""Opens the score file for reading and yields the score file line by line in a tuple/list.
The last element of the line (which is the score) will be transformed to float, the other elements will be str
"""
if iscsv(filename):
- for row in _iterate_csv_score_file(filename):
+ for row in _iterate_csv_score_file(
+ filename, score_column=csv_score_column
+ ):
yield [
row["bio_ref_subject_id"],
row["probe_subject_id"],
row["probe_key"],
- row["score"],
+ row[csv_score_column],
]
else:
opened = open_file(filename, "rb")
@@ -640,16 +651,16 @@ def _iterate_score_file(filename):
yield splits
-def _iterate_csv_score_file(filename):
+def _iterate_csv_score_file(filename, score_column: str = "score"):
"""Opens a CSV score file for reading and yields each line in a dict.
- The `score` field of the line will be cast to float, the other elements will
- be str.
+ The ``score_column`` field of the line will be cast to float, the other
+ elements will be str.
"""
opened = open_file(filename)
reader = csv.DictReader(opened)
for row in reader:
- row["score"] = float(row["score"])
+ row[score_column] = float(row[score_column])
yield row
@@ -712,13 +723,13 @@ def _split_cmc_scores(
]
-def split_csv_vuln(filename):
+def split_csv_vuln(filename, score_column: str = "score"):
"""Loads vulnerability scores from a CSV score file.
Returns the scores split between positive and negative as well as licit
and presentation attack (spoof).
- The CSV must contain a `probe_attack_type` column with each field either
+ The CSV must contain a ``probe_attack_type`` column with each field either
containing a str defining the attack type (spoof), or empty (licit).
Parameters
@@ -735,14 +746,14 @@ def split_csv_vuln(filename):
"""
logger.debug(f"Loading CSV score file: '{filename}'")
split_scores = {"licit_neg": [], "licit_pos": [], "spoof": []}
- for row in _iterate_csv_score_file(filename):
+ for row in _iterate_csv_score_file(filename, score_column=score_column):
if not row["probe_attack_type"]: # licit
if row["probe_subject_id"] == row["bio_ref_subject_id"]:
- split_scores["licit_pos"].append(row["score"])
+ split_scores["licit_pos"].append(row[score_column])
else:
- split_scores["licit_neg"].append(row["score"])
+ split_scores["licit_neg"].append(row[score_column])
else:
- split_scores["spoof"].append(row["score"])
+ split_scores["spoof"].append(row[score_column])
logger.debug(
f"Found {len(split_scores['licit_neg'])} negative (ZEI), "
f"{len(split_scores['licit_pos'])} positive (licit), and "