def test_swap_random(data, seed):
a, b = data
np.random.seed(seed)
a_orig, b_orig = original.swap_random(a, b)
dcst_private._seed_numba(seed)
a_out, b_out = dcst.swap_random(a, b)
assert len(a_out) == len(b_out) == len(a) == len(b)
# Each entry should be present same number of times
ab = np.sort(np.concatenate((a, b)))
ab_out = np.sort(np.concatenate((a_out, b_out)))
assert np.allclose(ab, ab_out, atol=atol, equal_nan=True)
# Check for swaps matching
for i in range(len(a)):
ab = np.array([a[i], b[i]])
ab_out = np.array([a_out[i], b_out[i]])
assert ab[0] in ab_out
assert ab[1] in ab_out
def test_pandas_conversion(seed):
df = pd.DataFrame({
"a": [3, 2, 1, 4],
"b": [8, 6, 7, 5],
"c": [9.1, 10.1, 11.1, np.nan]
})
x, y = dcst.ecdf(df.loc[:, "a"])
assert (x == np.array([1, 2, 3, 4])).all()
assert (y == np.array([0.25, 0.5, 0.75, 1.0])).all()
x, y = dcst.ecdf(df.loc[:, "c"])
assert np.allclose(x, np.array([9.1, 10.1, 11.1]))
assert np.allclose(y, np.array([1 / 3, 2 / 3, 1.0]))
df = pd.DataFrame({
"a":
np.concatenate((np.random.normal(0, 1, size=10), [np.nan] * 990)),
"b":
np.random.normal(0, 1, size=1000),
})
correct, _ = st.ks_2samp(df["a"].dropna(), df["b"])
assert np.isclose(dcst.ks_stat(df["a"], df["b"]), correct)
df = pd.DataFrame({
"a":
np.concatenate((np.random.normal(0, 1, size=80), [np.nan] * 20)),
"b":
np.random.normal(0, 1, size=100),
})
dcst_private._seed_numba(seed)
correct = dcst.draw_bs_reps(df["a"].values, np.mean, size=100)
dcst_private._seed_numba(seed)
assert np.allclose(dcst.draw_bs_reps(df["a"], np.mean, size=100),
correct,
atol=atol)
dcst_private._seed_numba(seed)
correct = dcst.draw_bs_reps(df["b"].values, np.mean, size=100)
dcst_private._seed_numba(seed)
assert np.allclose(dcst.draw_bs_reps(df["b"], np.mean, size=100),
correct,
atol=atol)
dcst_private._seed_numba(seed)
correct = dcst.draw_perm_reps(df["a"].values,
df["b"].values,
dcst.diff_of_means,
size=100)
dcst_private._seed_numba(seed)
assert np.allclose(
dcst.draw_perm_reps(df["a"], df["b"], dcst.diff_of_means, size=100),
correct,
atol=atol,
)