def test_bootstrap(poisson_obs, poisson_obs_control):
st = ab.sample(poisson_obs)
sc = ab.sample(poisson_obs_control)
_subtest_bootstrap(st, sc)
st = st.reweigh()
_subtest_bootstrap(st, sc)
def test_compress(poisson_obs, poisson_obs_control):
s = ab.sample(poisson_obs)
sc = s.compress(n_buckets=100)
_subtest_equality(s, sc)
sc = ab.sample(poisson_obs)
sc.compress(n_buckets=100, inplace=True)
_subtest_equality(s, sc)
s = ab.sample(poisson_obs, poisson_obs_control + 1)
sc = s.compress(n_buckets=100, sort_by='den')
assert s.ratio() == pytest.approx(sc.ratio(), 1e-6)
sc = s.compress(n_buckets=100, sort_by='num', weights_dist='multinomial')
assert s.ratio() == pytest.approx(sc.ratio(), 1e-6)
sc = s.copy()
sc.compress(n_buckets=100, sort_by='taylor', reweigh=True)
assert s.ratio() == pytest.approx(sc.ratio(), 1e-6)
with pytest.raises(ValueError):
sc = s.compress(n_buckets=100, sort_by='num', weights_dist='')
with pytest.raises(ValueError):
sc = s.compress(n_buckets=100, sort_by='num', stat='sum')
def test_trim(normal_obs, poisson_obs, poisson_obs_control):
s = ab.sample(normal_obs)
assert s.trim(rtrim=0.01, ltrim=0.01).nobs == 980
s = ab.sample(poisson_obs, poisson_obs_control).reweigh()
assert s.trim(rtrim=0.01, ltrim=0.01, sort_by='num').nobs == 980
assert s.trim(rtrim=0.01, ltrim=0.01, sort_by='den').nobs == 980
assert s.trim(rtrim=0.01, ltrim=0.01, sort_by='taylor').nobs == 980
def test_exceptions():
res = ab.sample([1, 2]).shapiro_test()
assert np.isnan(res.statistic)
assert np.isnan(res.p_value)
res = ab.sample(np.ones(100)).median_test(ab.sample(np.ones(100)))
assert np.isnan(res.statistic)
assert np.isnan(res.p_value)
assert np.isnan(res.grand_median)
with pytest.raises(ValueError):
s = ab.sample([1, 2], [1, 1]).linearize('')
ar = np.array([1, 2, 3], dtype='float')
rw = ab.compress.reweigh(ar)
def test_linearize(poisson_obs, poisson_obs_control):
s = ab.sample(poisson_obs, poisson_obs_control + 1).reweigh()
lin = s.linearize(strategy='taylor')
assert s.ratio() == pytest.approx(lin.mean(), 1e-6)
lin = s.linearize(strategy='naive')
assert lin.mean() != s.ratio()
def test_ratio(poisson_obs, poisson_obs_control):
s = ab.sample(poisson_obs, poisson_obs_control).reweigh()
assert s.nobs == 1000
assert s.ratio() == poisson_obs.sum() / poisson_obs_control.sum()
np.testing.assert_array_equal(s.weights, s.numsamp.weights)
np.testing.assert_array_equal(s.weights, s.densamp.weights)
np.testing.assert_array_equal(s.num, s.numsamp.obs)
np.testing.assert_array_equal(s.den, s.densamp.obs)
def test_sample_weighted(poisson_obs):
np.random.seed(1)
treatment = ab.sample(poisson_obs).reweigh()
shuffle_ind = np.arange(treatment.obs.shape[0])
np.random.shuffle(shuffle_ind)
treatment.obs = treatment.obs[shuffle_ind]
treatment.weights = treatment.weights[shuffle_ind]
_subtest_compare_sample_vs_obs(treatment, poisson_obs)
def test_significance_tests(normal_obs, normal_obs_control):
treatment = ab.sample(normal_obs)
control = ab.sample(normal_obs_control)
res = treatment.t_test(control, equal_var=True)
res_expected = ttest_ind(normal_obs, normal_obs_control, equal_var=True)
assert res.p_value == res_expected.pvalue
assert res.statistic == res_expected.statistic
res = treatment.t_test(control, equal_var=False)
res_expected = ttest_ind(normal_obs, normal_obs_control, equal_var=False)
assert res.p_value == res_expected.pvalue
assert res.statistic == res_expected.statistic
res = treatment.t_test_1samp(101)
res_expected = ttest_1samp(normal_obs, 101)
assert res.p_value == res_expected.pvalue
assert res.statistic == res_expected.statistic
res = treatment.mann_whitney_u_test(control)
res_expected = mannwhitneyu(normal_obs_control, normal_obs, alternative='two-sided')
assert res.p_value == pytest.approx(res_expected.pvalue, 1e-6)
assert res.u_statistic == res_expected.statistic
res = treatment.shapiro_test()
res_expected = shapiro(normal_obs)
assert res.statistic == res_expected[0]
assert res.p_value == res_expected[1]
res = treatment.median_test(control)
res_expected = median_test(normal_obs, normal_obs_control)
assert res.p_value == res_expected[1]
assert res.statistic == res_expected[0]
assert res.grand_median == res_expected[2]
res = treatment.levene_test(control)
res_expected = levene(normal_obs, normal_obs_control)
assert res.p_value == res_expected.pvalue
assert res.statistic == res_expected.statistic
res = treatment.mood_test(control)
res_expected = mood(normal_obs, normal_obs_control)
assert res.p_value == res_expected[1]
assert res.statistic == res_expected[0]
def test_sample_stats(normal_obs):
np.random.seed(1)
treatment = ab.sample(normal_obs)
_subtest_compare_sample_vs_obs(treatment, normal_obs)
def test_sample_factory():
s = ab.sample([1, 2], weights=[2, 5])
assert s.mean() == 12 / 7
def test_reweigh(poisson_obs):
s = ab.sample(poisson_obs)
sc = ab.sample(poisson_obs).reweigh()
sc.reweigh(inplace=True)
_subtest_equality(s, sc)