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Add support for lesion_id in classification ground truth #133

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Merged
brianhelba merged 1 commit into from
Jul 19, 2025
Merged

Add support for lesion_id in classification ground truth #133

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33 changes: 23 additions & 10 deletions isic_challenge_scoring/load_csv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,16 @@


def parse_truth_csv(csv_file_stream: TextIO) -> tuple[pd.DataFrame, pd.DataFrame]:
table = pd.read_csv(csv_file_stream, header=0)
table = pd.read_csv(csv_file_stream, header=0, index_col=False)

table.set_index('image', drop=True, inplace=True, verify_integrity=False)
if 'image' in table.columns:
index_name = 'image'
elif 'lesion_id' in table.columns:
index_name = 'lesion_id'
else:
raise KeyError('Missing column in CSV: "image" or "lesion_id".')

table.set_index(index_name, drop=True, inplace=True, verify_integrity=False)

# Support legacy truth files
if 'score_weight' not in table.columns:
Expand Down Expand Up @@ -42,25 +49,31 @@ def parse_csv(csv_file_stream: TextIO, categories: pd.Index) -> pd.DataFrame:
except UnicodeDecodeError:
raise ScoreError('Could not parse CSV: could not decode file as UTF-8.')

if 'image' not in probabilities.columns:
raise ScoreError("Missing column in CSV: 'image'.")
if 'image' in probabilities.columns:
index_name = 'image'
elif 'lesion_id' in probabilities.columns:
index_name = 'lesion_id'
else:
raise ScoreError('Missing column in CSV: "image" or "lesion_id".')

# Pandas represents strings as 'O' (object)
if probabilities['image'].dtype != np.dtype('O'):
if probabilities[index_name].dtype != np.dtype('O'):
# Coercing to 'U' (unicode) ensures that even NaN values are converted;
# however, the resulting type is still 'O'
probabilities['image'] = probabilities['image'].astype(np.dtype('U'))
probabilities[index_name] = probabilities[index_name].astype(np.dtype('U'))

probabilities['image'] = probabilities['image'].str.replace(
probabilities[index_name] = probabilities[index_name].str.replace(
r'\.jpg$', '', case=False, regex=True
)

if not probabilities['image'].is_unique:
duplicate_images = probabilities['image'][probabilities['image'].duplicated()].unique()
if not probabilities[index_name].is_unique:
duplicate_images = probabilities[index_name][
probabilities[index_name].duplicated()
].unique()
raise ScoreError(f'Duplicate image rows detected in CSV: {duplicate_images.tolist()}.')

# The duplicate check is the same as performed by 'verify_integrity'
probabilities.set_index('image', drop=True, inplace=True, verify_integrity=False)
probabilities.set_index(index_name, drop=True, inplace=True, verify_integrity=False)

missing_columns = categories.difference(probabilities.columns)
if not missing_columns.empty:
Expand Down
24 changes: 19 additions & 5 deletions tests/test_load_csv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,16 @@
from isic_challenge_scoring.types import ScoreError


def test_parse_truth_csv(categories):
@pytest.mark.parametrize(
'index_column',
[
'image',
'lesion_id',
],
)
def test_parse_truth_csv(categories, index_column):
truth_file_stream = io.StringIO(
'image,MEL,NV,BCC,AKIEC,BKL,DF,VASC,score_weight,validation_weight\n'
f'{index_column},MEL,NV,BCC,AKIEC,BKL,DF,VASC,score_weight,validation_weight\n'
'ISIC_0000123,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0\n'
'ISIC_0000124,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0\n'
'ISIC_0000125,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0\n'
Expand Down Expand Up @@ -67,9 +74,16 @@ def test_parse_truth_csv_legacy(categories):
)


def test_parse_csv(categories):
@pytest.mark.parametrize(
'index_column',
[
'image',
'lesion_id',
],
)
def test_parse_csv(categories, index_column):
prediction_file_stream = io.StringIO(
'image,MEL,NV,BCC,AKIEC,BKL,DF,VASC\n'
f'{index_column},MEL,NV,BCC,AKIEC,BKL,DF,VASC\n'
'ISIC_0000123,1.0,0.0,0.0,0.0,0.0,0.0,0.0\n'
'ISIC_0000124.jpg,0.0,1.0,0.0,0.0,0.0,0.0,0.0\n'
'ISIC_0000125.JPG,0.0,0.0,1.0,0.0,0.0,0.0,0.0\n'
Expand Down Expand Up @@ -204,7 +218,7 @@ def test_parse_csv_missing_index(categories):
'MEL,NV,BCC,AKIEC,BKL,DF,VASC\n' '1.0,0.0,0.0,0.0,0.0,0.0,0.0\n'
)

with pytest.raises(ScoreError, match=r"^Missing column in CSV: 'image'\.$"):
with pytest.raises(ScoreError, match=r'^Missing column in CSV: "image" or "lesion_id"\.$'):
load_csv.parse_csv(prediction_file_stream, categories)


Expand Down