def test_linear_mixed_model_function(self):
n, f, m = 4, 2, 3
y = np.array([0.0, 1.0, 8.0, 9.0])
x = np.array([[1.0, 0.0],
[1.0, 2.0],
[1.0, 1.0],
[1.0, 4.0]])
z = np.array([[0.0, 0.0, 1.0],
[0.0, 1.0, 2.0],
[1.0, 2.0, 0.0],
[2.0, 0.0, 1.0]])
p_path = utils.new_temp_file()
def make_call(gt):
if gt == 0.0:
return hl.Call([0, 0])
if gt == 1.0:
return hl.Call([0, 1])
if gt == 2.0:
return hl.Call([1, 1])
data = [{'v': j, 's': i, 'y': y[i], 'x1': x[i, 1], 'zt': make_call(z[i, j])}
for i in range(n) for j in range(m)]
ht = hl.Table.parallelize(data, hl.dtype('struct{v: int32, s: int32, y: float64, x1: float64, zt: tcall}'))
mt = ht.to_matrix_table(row_key=['v'], col_key=['s'], col_fields=['x1', 'y'])
colsort = np.argsort(mt.key_cols_by().s.collect()).tolist()
mt = mt.choose_cols(colsort)
rrm = hl.realized_relationship_matrix(mt.zt).to_numpy()
# kinship path agrees with from_kinship
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1], k=rrm, p_path=p_path, overwrite=True)
model0, p0 = LinearMixedModel.from_kinship(y, x, rrm, p_path, overwrite=True)
assert model0._same(model)
assert np.allclose(p0, p)
# random effects path with standardize=True agrees with low-rank rrm
s0, u0 = np.linalg.eigh(rrm)
s0 = np.flip(s0, axis=0)[:m]
p0 = np.fliplr(u0).T[:m, :]
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1], z_t=mt.zt.n_alt_alleles(), p_path=p_path, overwrite=True)
model0 = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x, p_path=p_path)
assert model0._same(model)
# random effects path with standardize=False agrees with from_random_effects
model0, p0 = LinearMixedModel.from_random_effects(y, x, z, p_path, overwrite=True)
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1], z_t=mt.zt.n_alt_alleles(), p_path=p_path, overwrite=True, standardize=False)
assert model0._same(model)
assert np.allclose(p0, p.to_numpy())
def test_linear_mixed_model_function(self):
n, f, m = 4, 2, 3
y = np.array([0.0, 1.0, 8.0, 9.0])
x = np.array([[1.0, 0.0], [1.0, 2.0], [1.0, 1.0], [1.0, 4.0]])
z = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 2.0], [1.0, 2.0, 0.0],
[2.0, 0.0, 1.0]])
p_path = utils.new_temp_file()
def make_call(gt):
if gt == 0.0:
return hl.Call([0, 0])
if gt == 1.0:
return hl.Call([0, 1])
if gt == 2.0:
return hl.Call([1, 1])
data = [{
'v': j,
's': i,
'y': y[i],
'x1': x[i, 1],
'zt': make_call(z[i, j])
} for i in range(n) for j in range(m)]
ht = hl.Table.parallelize(
data,
hl.dtype(
'struct{v: int32, s: int32, y: float64, x1: float64, zt: tcall}'
))
mt = ht.to_matrix_table(row_key=['v'],
col_key=['s'],
col_fields=['x1', 'y'])
colsort = np.argsort(mt.key_cols_by().s.collect()).tolist()
mt = mt.choose_cols(colsort)
rrm = hl.realized_relationship_matrix(mt.zt).to_numpy()
# kinship path agrees with from_kinship
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1],
k=rrm,
p_path=p_path,
overwrite=True)
model0, p0 = LinearMixedModel.from_kinship(y,
x,
rrm,
p_path,
overwrite=True)
assert model0._same(model)
assert np.allclose(p0, p)
# random effects path with standardize=True agrees with low-rank rrm
s0, u0 = np.linalg.eigh(rrm)
s0 = np.flip(s0, axis=0)[:m]
p0 = np.fliplr(u0).T[:m, :]
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1],
z_t=mt.zt.n_alt_alleles(),
p_path=p_path,
overwrite=True)
model0 = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x, p_path=p_path)
assert model0._same(model)
# random effects path with standardize=False agrees with from_random_effects
model0, p0 = LinearMixedModel.from_random_effects(y,
x,
z,
p_path,
overwrite=True)
model, p = hl.linear_mixed_model(mt.y, [1, mt.x1],
z_t=mt.zt.n_alt_alleles(),
p_path=p_path,
overwrite=True,
standardize=False)
assert model0._same(model)
assert np.allclose(p0, p.to_numpy())
def test_linear_mixed_model_math(self):
gamma = 2.0 # testing at fixed value of gamma
n, f, m = 4, 2, 3
y = np.array([0.0, 1.0, 8.0, 9.0])
x = np.array([[1.0, 0.0],
[1.0, 2.0],
[1.0, 1.0],
[1.0, 4.0]])
z = np.array([[0.0, 0.0, 1.0],
[0.0, 1.0, 2.0],
[1.0, 2.0, 4.0],
[2.0, 4.0, 8.0]])
k = z @ z.T
v = k + np.eye(4) / gamma
v_inv = np.linalg.inv(v)
beta = np.linalg.solve(x.T @ v_inv @ x, x.T @ v_inv @ y)
residual = y - x @ beta
sigma_sq = 1 / (n - f) * (residual @ v_inv @ residual)
sv = sigma_sq * v
neg_log_lkhd = 0.5 * (np.linalg.slogdet(sv)[1] + np.linalg.slogdet(x.T @ np.linalg.inv(sv) @ x)[1]) # plus C
x_star = np.array([1.0, 0.0, 1.0, 0.0])
a = x_star.reshape(n, 1)
x1 = np.hstack([a, x])
beta1 = np.linalg.solve(x1.T @ v_inv @ x1, x1.T @ v_inv @ y)
residual1 = y - x1 @ beta1
chi_sq = n * np.log((residual @ v_inv @ residual) / (residual1 @ v_inv @ residual1))
# test from_kinship, full-rank fit
model, p = LinearMixedModel.from_kinship(y, x, k)
s0, u0 = np.linalg.eigh(k)
s0 = np.flip(s0, axis=0)
p0 = np.fliplr(u0).T
self.assertTrue(model._same(LinearMixedModel(p0 @ y, p0 @ x, s0)))
model.fit(np.log(gamma))
self.assertTrue(np.allclose(model.beta, beta))
self.assertAlmostEqual(model.sigma_sq, sigma_sq)
self.assertAlmostEqual(model.compute_neg_log_reml(np.log(gamma)), neg_log_lkhd)
# test full-rank alternative
pa = p @ a
stats = model.fit_alternatives_numpy(pa).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
pa_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(pa.T).write(pa_t_path, force_row_major=True)
stats = model.fit_alternatives(pa_t_path).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
# test from_random_effects, low-rank fit
s0, p0 = s0[:m], p0[:m, :]
# test BlockMatrix path
temp_path = utils.new_temp_file()
model, _ = LinearMixedModel.from_random_effects(y, x,
BlockMatrix.from_numpy(z),
p_path=temp_path,
complexity_bound=0)
lmm = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x, p_path=temp_path)
self.assertTrue(model._same(lmm))
# test ndarray path
model, p = LinearMixedModel.from_random_effects(y, x, z)
lmm = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x)
self.assertTrue(model._same(lmm))
model.fit(np.log(gamma))
self.assertTrue(np.allclose(model.beta, beta))
self.assertAlmostEqual(model.sigma_sq, sigma_sq)
self.assertAlmostEqual(model.compute_neg_log_reml(np.log(gamma)), neg_log_lkhd)
# test low_rank alternative
pa = p @ a
stats = model.fit_alternatives_numpy(pa, a).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
a_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(a.T).write(a_t_path, force_row_major=True)
pa_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(pa.T).write(pa_t_path, force_row_major=True)
stats = model.fit_alternatives(pa_t_path, a_t_path).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
def test_linear_mixed_model_math(self):
gamma = 2.0 # testing at fixed value of gamma
n, f, m = 4, 2, 3
y = np.array([0.0, 1.0, 8.0, 9.0])
x = np.array([[1.0, 0.0], [1.0, 2.0], [1.0, 1.0], [1.0, 4.0]])
z = np.array([[0.0, 0.0, 1.0], [0.0, 1.0, 2.0], [1.0, 2.0, 4.0],
[2.0, 4.0, 8.0]])
k = z @ z.T
v = k + np.eye(4) / gamma
v_inv = np.linalg.inv(v)
beta = np.linalg.solve(x.T @ v_inv @ x, x.T @ v_inv @ y)
residual = y - x @ beta
sigma_sq = 1 / (n - f) * (residual @ v_inv @ residual)
sv = sigma_sq * v
neg_log_lkhd = 0.5 * (np.linalg.slogdet(sv)[1] + np.linalg.slogdet(
x.T @ np.linalg.inv(sv) @ x)[1]) # plus C
x_star = np.array([1.0, 0.0, 1.0, 0.0])
a = x_star.reshape(n, 1)
x1 = np.hstack([a, x])
beta1 = np.linalg.solve(x1.T @ v_inv @ x1, x1.T @ v_inv @ y)
residual1 = y - x1 @ beta1
chi_sq = n * np.log(
(residual @ v_inv @ residual) / (residual1 @ v_inv @ residual1))
# test from_kinship, full-rank fit
model, p = LinearMixedModel.from_kinship(y, x, k)
s0, u0 = np.linalg.eigh(k)
s0 = np.flip(s0, axis=0)
p0 = np.fliplr(u0).T
self.assertTrue(model._same(LinearMixedModel(p0 @ y, p0 @ x, s0)))
model.fit(np.log(gamma))
self.assertTrue(np.allclose(model.beta, beta))
self.assertAlmostEqual(model.sigma_sq, sigma_sq)
self.assertAlmostEqual(model.compute_neg_log_reml(np.log(gamma)),
neg_log_lkhd)
# test full-rank alternative
pa = p @ a
stats = model.fit_alternatives_numpy(pa).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
pa_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(pa.T).write(pa_t_path, force_row_major=True)
stats = model.fit_alternatives(pa_t_path).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
# test from_random_effects, low-rank fit
s0, p0 = s0[:m], p0[:m, :]
# test BlockMatrix path
temp_path = utils.new_temp_file()
model, _ = LinearMixedModel.from_random_effects(
y,
x,
BlockMatrix.from_numpy(z),
p_path=temp_path,
complexity_bound=0)
lmm = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x, p_path=temp_path)
self.assertTrue(model._same(lmm))
# test ndarray path
model, p = LinearMixedModel.from_random_effects(y, x, z)
lmm = LinearMixedModel(p0 @ y, p0 @ x, s0, y, x)
self.assertTrue(model._same(lmm))
model.fit(np.log(gamma))
self.assertTrue(np.allclose(model.beta, beta))
self.assertAlmostEqual(model.sigma_sq, sigma_sq)
self.assertAlmostEqual(model.compute_neg_log_reml(np.log(gamma)),
neg_log_lkhd)
# test low_rank alternative
pa = p @ a
stats = model.fit_alternatives_numpy(pa, a).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)
a_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(a.T).write(a_t_path, force_row_major=True)
pa_t_path = utils.new_temp_file()
BlockMatrix.from_numpy(pa.T).write(pa_t_path, force_row_major=True)
stats = model.fit_alternatives(pa_t_path, a_t_path).collect()[0]
self.assertAlmostEqual(stats.beta, beta1[0])
self.assertAlmostEqual(stats.chi_sq, chi_sq)