def test_expected_columns_ok():
validator = ExpectedColumnNamesValidator(expected=(
'foo',
'bar',
))
df = DataFrame(columns=['foo', 'bar'])
validator.validate(df=df)
def test_expected_columns_overdefined():
validator = ExpectedColumnNamesValidator(expected=(
'foo',
'bar',
))
df = DataFrame(columns=['foo', 'bar', 'baz'])
with pytest.raises(ValidationError):
validator.validate(df=df)
def __init__(self):
super().__init__(
file_loader=CSVLoader(),
validators=(
ExpectedColumnNamesValidator(
expected=('image_id', 'x', 'y', 'score')
),
),
join_key='image_id',
ground_truth_path=(
Path(__file__).parent /
'resources' /
'detection' /
'reference'
),
predictions_path=(
Path(__file__).parent /
'resources' /
'detection' /
'submission'
),
output_file=Path('/tmp/metrics.json'),
)
self.detection_threshold = 0.5
self.detection_radius = 1.0
def __init__(self):
super().__init__(
file_loader=CSVLoader(),
ground_truth_path=(Path(__file__).parent / 'resources' /
'classification' / 'reference'),
predictions_path=(Path(__file__).parent / 'resources' /
'classification' / 'submission'),
output_file=Path('/tmp/metrics.json'),
join_key='case',
validators=(ExpectedColumnNamesValidator(expected=(
'case',
'class',
)), ),
)
def __init__(self):
super().__init__(
file_loader=CSVLoader(),
validators=(ExpectedColumnNamesValidator(expected=(
"image_id_roi_id",
"pixel_size",
"x",
"y",
"score",
)), ),
join_key="image_id_roi_id",
)
self.detection_threshold = 0.0
self.detection_radius = 4 / 0.24309392273426056
def __init__(self, outdir):
super().__init__(
file_loader=CSVLoader(),
validators=(ExpectedColumnNamesValidator(expected=("image_id", "x",
"y",
"score")), ),
join_key="image_id",
ground_truth_path=(Path(__file__).parent / "resources" /
"detection" / "reference"),
predictions_path=(Path(__file__).parent / "resources" /
"detection" / "submission"),
output_file=Path(outdir) / "metrics.json",
detection_threshold=0.5,
detection_radius=1.0,
)
def __init__(self, outdir):
super().__init__(
file_loader=CSVLoader(),
ground_truth_path=(
Path(__file__).parent
/ "resources"
/ "classification"
/ "reference"
),
predictions_path=(
Path(__file__).parent
/ "resources"
/ "classification"
/ "submission"
),
output_file=Path(outdir) / "metrics.json",
join_key="case",
validators=(
ExpectedColumnNamesValidator(expected=("case", "class")),
),
)
def test_expected_columns_ok():
validator = ExpectedColumnNamesValidator(expected=("foo", "bar"))
df = DataFrame(columns=["foo", "bar"])
validator.validate(df=df)
def test_expected_columns_overdefined():
validator = ExpectedColumnNamesValidator(expected=("foo", "bar"))
df = DataFrame(columns=["foo", "bar", "baz"])
with pytest.raises(ValidationError):
validator.validate(df=df)
def test_expected_columns_creation():
with pytest.raises(ValueError):
# noinspection PyTypeChecker
ExpectedColumnNamesValidator(expected=())