def test_lead_scoring_objective(X_y_binary):
X, y = X_y_binary
objective = LeadScoring(true_positives=1, false_positives=-1)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective=objective,
max_iterations=1,
random_seed=0)
automl.search()
pipeline = automl.best_pipeline
pipeline.fit(X, y)
pipeline.predict(X)
pipeline.predict_proba(X)
pipeline.score(X, y, [objective])
predicted = pd.Series([1, 10, .5, 5])
out = objective.decision_function(predicted, 1)
y_true = pd.Series([False, True, False, True])
assert out.tolist() == [False, True, False, True]
predicted = np.array([1, 10, .5, 5])
out = objective.decision_function(predicted, 1)
assert out.tolist() == y_true.to_list()
score = objective.score(out, y_true)
assert (score == 0.5)
def test_different_input_lengths():
objective = LeadScoring(true_positives=1, false_positives=-1)
y_predicted = np.array([0, 0])
y_true = np.array([1])
with pytest.raises(ValueError, match="Inputs have mismatched dimensions"):
objective.score(y_true, y_predicted)
y_true = np.array([0, 0])
y_predicted = np.array([1, 2, 0])
with pytest.raises(ValueError, match="Inputs have mismatched dimensions"):
objective.score(y_true, y_predicted)
def test_input_contains_inf(capsys):
objective = LeadScoring(true_positives=1, false_positives=-1)
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"):
objective.score(y_true, y_predicted)
y_true = np.array([np.inf, 0, 0])
y_predicted = np.array([1, 0, 0])
with pytest.raises(ValueError, match="y_true contains NaN or infinity"):
objective.score(y_true, y_predicted)
def test_binary_more_than_two_unique_values():
objective = LeadScoring(true_positives=1, false_positives=-1)
y_predicted = np.array([0, 1, 2])
y_true = np.array([1, 0, 1])
with pytest.raises(
ValueError,
match="y_predicted contains more than two unique values"):
objective.score(y_true, y_predicted)
y_true = np.array([0, 1, 2])
y_predicted = np.array([1, 0, 1])
with pytest.raises(ValueError,
match="y_true contains more than two unique values"):
objective.score(y_true, y_predicted)
def test_zero_input_lengths():
objective = LeadScoring(true_positives=1, false_positives=-1)
y_predicted = np.array([])
y_true = np.array([])
with pytest.raises(ValueError, match="Length of inputs is 0"):
objective.score(y_true, y_predicted)