def test_cbm_zero_input_lengths():
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
y_predicted = pd.Series([])
y_true = pd.Series([])
with pytest.raises(ValueError, match="Length of inputs is 0"):
cbm.score(y_true, y_predicted)
def test_cbm_different_input_lengths():
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
y_predicted = pd.Series([0, 0])
y_true = pd.Series([1])
with pytest.raises(ValueError, match="Inputs have mismatched dimensions"):
cbm.score(y_true, y_predicted)
y_true = pd.Series([0, 0])
y_predicted = pd.Series([1, 2, 0])
with pytest.raises(ValueError, match="Inputs have mismatched dimensions"):
cbm.score(y_true, y_predicted)
def test_cbm_input_contains_nan(X_y_binary):
y_predicted = pd.Series([np.nan, 0, 0])
y_true = pd.Series([1, 2, 1])
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
with pytest.raises(ValueError,
match="y_predicted contains NaN or infinity"):
cbm.score(y_true, y_predicted)
y_true = pd.Series([np.nan, 0, 0])
y_predicted = pd.Series([1, 2, 0])
with pytest.raises(ValueError, match="y_true contains NaN or infinity"):
cbm.score(y_true, y_predicted)
def test_cbm_input_contains_inf(capsys):
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
y_predicted = np.array([np.inf, 0, 0])
y_true = np.array([1, 0, 0])
with pytest.raises(ValueError,
match="y_predicted contains NaN or infinity"):
cbm.score(y_true, y_predicted)
y_true = pd.Series([np.inf, 0, 0])
y_predicted = pd.Series([1, 0, 0])
with pytest.raises(ValueError, match="y_true contains NaN or infinity"):
cbm.score(y_true, y_predicted)
def test_cbm_binary_more_than_two_unique_values():
cbm = CostBenefitMatrix(true_positive=10,
true_negative=-1,
false_positive=-7,
false_negative=-2)
y_predicted = pd.Series([0, 1, 2])
y_true = pd.Series([1, 0, 1])
with pytest.raises(
ValueError,
match="y_predicted contains more than two unique values"):
cbm.score(y_true, y_predicted)
y_true = pd.Series([0, 1, 2])
y_predicted = pd.Series([1, 0, 1])
with pytest.raises(ValueError,
match="y_true contains more than two unique values"):
cbm.score(y_true, y_predicted)