def test_iid_unequal_equiv():
rs = RandomState(0)
x = rs.standard_normal(500)
rs1 = RandomState(0)
bs1 = IIDBootstrap(x, random_state=rs1)
rs2 = RandomState(0)
bs2 = IndependentSamplesBootstrap(x, random_state=rs2)
v1 = bs1.var(np.mean)
v2 = bs2.var(np.mean)
assert_allclose(v1, v2)
def test_unequal_bs():
def mean_diff(*args):
return args[0].mean() - args[1].mean()
rs = RandomState(0)
x = rs.randn(800)
y = rs.randn(200)
bs = IndependentSamplesBootstrap(x, y, random_state=rs)
variance = bs.var(mean_diff)
assert variance > 0
ci = bs.conf_int(mean_diff)
assert ci[0] < ci[1]
applied = bs.apply(mean_diff, 1000)
assert len(applied) == 1000
x = pd.Series(x)
y = pd.Series(y)
bs = IndependentSamplesBootstrap(x, y)
variance = bs.var(mean_diff)
assert variance > 0
def test_unequal_reset():
def mean_diff(*args):
return args[0].mean() - args[1].mean()
rs = RandomState(0)
x = rs.standard_normal(800)
y = rs.standard_normal(200)
orig_state = rs.get_state()
bs = IndependentSamplesBootstrap(x, y, random_state=rs)
variance = bs.var(mean_diff)
assert variance > 0
bs.reset()
state = bs.get_state()
assert_equal(state[1], orig_state[1])
bs = IndependentSamplesBootstrap(x, y)
bs.seed(0)
orig_state = bs.get_state()
bs.var(mean_diff)
bs.reset(use_seed=True)
state = bs.get_state()
assert_equal(state[1], orig_state[1])
def test_unequal_bs_kwargs():
def mean_diff(x, y):
return x.mean() - y.mean()
rs = RandomState(0)
x = rs.standard_normal(800)
y = rs.standard_normal(200)
bs = IndependentSamplesBootstrap(x=x, y=y, random_state=rs)
variance = bs.var(mean_diff)
assert variance > 0
ci = bs.conf_int(mean_diff)
assert ci[0] < ci[1]
applied = bs.apply(mean_diff, 1000)
x = pd.Series(x)
y = pd.Series(y)
bs = IndependentSamplesBootstrap(x=x, y=y, random_state=rs)
variance = bs.var(mean_diff)
assert variance > 0
assert len(applied) == 1000
def test_unequal_bs():
def mean_diff(*args):
return args[0].mean() - args[1].mean()
rs = RandomState(0)
x = rs.standard_normal(800)
y = rs.standard_normal(200)
bs = IndependentSamplesBootstrap(x, y, random_state=rs)
variance = bs.var(mean_diff)
assert variance > 0
ci = bs.conf_int(mean_diff)
assert ci[0] < ci[1]
applied = bs.apply(mean_diff, 1000)
assert len(applied) == 1000
x = pd.Series(x)
y = pd.Series(y)
bs = IndependentSamplesBootstrap(x, y)
variance = bs.var(mean_diff)
assert variance > 0
with pytest.raises(ValueError, match="BCa cannot be applied"):
bs.conf_int(mean_diff, method="bca")
def test_iid_unequal_equiv():
rs = RandomState(0)
x = rs.standard_normal(500)
rs1 = RandomState(0)
bs1 = IIDBootstrap(x, random_state=rs1)
rs2 = RandomState(0)
bs2 = IndependentSamplesBootstrap(x, random_state=rs2)
v1 = bs1.var(np.mean)
v2 = bs2.var(np.mean)
assert_allclose(v1, v2)
assert isinstance(bs2.index, tuple)
assert isinstance(bs2.index[0], list)
assert isinstance(bs2.index[0][0], np.ndarray)
assert bs2.index[0][0].shape == x.shape