def test_add_to_one_happy(self):
positive_label = "poslabel"
negative_label = "neglabel"
adapter = PythonModelAdapter(model_dir=None, target_type=TargetType.BINARY)
df = pd.DataFrame({positive_label: [0.1, 0.2, 0.3], negative_label: [0.9, 0.8, 0.7]})
adapter._validate_predictions(
to_validate=df, class_labels=[positive_label, negative_label],
)
def test_add_to_one_sad(self):
positive_label = "poslabel"
negative_label = "neglabel"
adapter = PythonModelAdapter(model_dir=None, target_type=TargetType.BINARY)
df = pd.DataFrame({positive_label: [1, 1, 1], negative_label: [-1, 0, 0]})
with pytest.raises(ValueError):
adapter._validate_predictions(
to_validate=df, class_labels=[positive_label, negative_label],
)
def test_regression_predictions_header(self):
adapter = PythonModelAdapter(model_dir=None, target_type=TargetType.REGRESSION)
df = pd.DataFrame({"Predictions": [0.1, 0.2, 0.3]})
adapter._validate_predictions(
to_validate=df, class_labels=None,
)
with pytest.raises(ValueError):
df = pd.DataFrame({"other_name": [0.1, 0.2, 0.3]})
adapter._validate_predictions(
to_validate=df, class_labels=None,
)
def test_add_to_one_happy(self):
positive_label = "poslabel"
negative_label = "neglabel"
adapter = PythonModelAdapter(model_dir=None)
df = pd.DataFrame({
positive_label: [0.1, 0.2, 0.3],
negative_label: [0.9, 0.8, 0.7]
})
adapter._validate_predictions(
to_validate=df,
positive_class_label=positive_label,
negative_class_label=negative_label,
)
def test_class_labels(self):
positive_label = "poslabel"
negative_label = "neglabel"
adapter = PythonModelAdapter(model_dir=None, target_type=TargetType.BINARY)
df = pd.DataFrame({positive_label: [0.1, 0.2, 0.3], negative_label: [0.9, 0.8, 0.7]})
adapter._validate_predictions(
to_validate=df, class_labels=[positive_label, negative_label],
)
with pytest.raises(ValueError):
df = pd.DataFrame({positive_label: [0.1, 0.2, 0.3], negative_label: [0.9, 0.8, 0.7]})
adapter._validate_predictions(
to_validate=df, class_labels=["yes", "no"],
)
def test_add_to_one_sad(self):
positive_label = "poslabel"
negative_label = "neglabel"
adapter = PythonModelAdapter(model_dir=None)
df = pd.DataFrame({
positive_label: [1, 1, 1],
negative_label: [-1, 0, 0]
})
with pytest.raises(ValueError):
adapter._validate_predictions(
to_validate=df,
positive_class_label=positive_label,
negative_class_label=negative_label,
)