def test_bootstrap_existing_outcomes(setup):
result = bootstrap(
data=setup["df"],
outcome=_outcome_func,
n_draws=2,
)
assert len(result.outcomes) == 2
result = bootstrap(
outcome=_outcome_func,
data=setup["df"],
existing_result=result,
n_draws=1,
)
assert len(result.outcomes) == 1
def test_outcome_not_callable(setup):
expected_msg = "outcome must be a callable."
with pytest.raises(ValueError) as error:
assert bootstrap(data=setup["df"], outcome=setup["estimates_df"])
assert str(error.value) == expected_msg
def test_bootstrap_with_outcome_kwargs(shift, setup):
result = bootstrap(
outcome=_outcome_func,
data=setup["df"],
seed=123,
outcome_kwargs={"shift": shift},
)
expected = pd.Series([2.5, 7.0], index=["x1", "x2"])
aaae(result.base_outcome, expected + shift)
def test_existing_result_wrong_input_type(input_type, setup):
expected_msg = "existing_result must be None or a BootstrapResult."
with pytest.raises(ValueError) as error:
assert bootstrap(
outcome=_outcome_func,
data=setup["df"],
existing_result=setup["estimates_" + input_type],
)
assert str(error.value) == expected_msg
def test_bootstrap_from_outcomes_single_outcome(setup, expected):
result = bootstrap(outcome=_outcome_func,
data=setup["df"]["x1"],
seed=1234)
_ = result.outcomes
ci_lower, ci_upper = result.ci()
aaae(ci_lower, expected["ci_lower_x1"])
aaae(ci_upper, expected["ci_upper_x1"])
def test_cov_wrong_return_type(setup):
result = bootstrap(
outcome=_outcome_func,
data=setup["df"],
)
expected_msg = "return_type must be one of pytree, array, or dataframe, not dict."
with pytest.raises(ValueError) as error:
assert result.cov(return_type="dict")
assert str(error.value) == expected_msg
def test_existing_result(seaborn_example):
first_result = bootstrap(data=seaborn_example["df"],
outcome=_outcome_ols,
seed=1234)
expected_msg = "existing_result must be None or a BootstrapResult."
with pytest.raises(ValueError) as error:
assert bootstrap(
data=seaborn_example["df"],
outcome=_outcome_ols,
existing_result=first_result.outcomes,
)
assert str(error.value) == expected_msg
my_result = bootstrap(
data=seaborn_example["df"],
outcome=_outcome_ols,
existing_result=first_result,
seed=2,
)
lower_ci, upper_ci = my_result.ci(ci_method="t")
aaae(lower_ci, seaborn_example["expected"]["lower_ci"])
aaae(upper_ci, seaborn_example["expected"]["upper_ci"])
def test_bootstrap_from_outcomes_private_methods(setup, expected):
result = bootstrap(outcome=_outcome_func, data=setup["df"], seed=1234)
_ = result.outcomes
ci_lower, ci_upper = result._ci
covariance = result._cov
standard_errors = result._se
with pytest.raises(NotImplementedError):
assert result._p_values
aaae(ci_lower, expected["ci_lower"])
aaae(ci_upper, expected["ci_upper"])
aaae(covariance, expected["cov"])
aaae(standard_errors, expected["se"])
def test_bootstrap_from_outcomes(setup, expected):
result = bootstrap(outcome=_outcome_func, data=setup["df"], seed=1234)
_ = result.outcomes
summary = result.summary()
ci_lower, ci_upper = result.ci()
covariance = result.cov()
standard_errors = result.se()
with pytest.raises(NotImplementedError):
assert result._p_values
aaae(ci_lower, expected["ci_lower"])
aaae(ci_upper, expected["ci_upper"])
aaae(covariance, expected["cov"])
aaae(standard_errors, expected["se"])
aaae(summary["value"], expected["summary"]["value"])
aaae(summary["standard_error"], expected["summary"]["standard_error"])
aaae(summary["ci_lower"], expected["summary"]["ci_lower"])
aaae(summary["ci_upper"], expected["summary"]["ci_upper"])
def test_cov_correct_return_type(return_type, setup):
result = bootstrap(
outcome=_outcome_func,
data=setup["df"],
)
_ = result.cov(return_type=return_type)