def test_midas_hyperbolic_update(self):
nobs, resids = self.nobs, self.resids
sigma2, backcast = self.sigma2, self.backcast
parameters = np.array([0.1, 0.6, 0.2])
j = np.arange(1, 22 + 1)
weights = gamma(j + 0.6) / (gamma(j + 1) * gamma(0.6))
weights = weights / weights.sum()
recpy.midas_recursion(parameters, weights, resids, sigma2, nobs,
backcast, self.var_bounds)
sigma2_ref = sigma2.copy()
sigma2[:] = np.nan
parameters = np.array([0.1, 0.6, 0.2, 0.6])
mu = recpy.MIDASUpdater(22, True)
mu.initialize_update(parameters, backcast, nobs)
for t in range(nobs):
mu._update_tester(t, parameters, resids, sigma2, self.var_bounds)
if t == nobs // 2:
mu = pickle.loads(pickle.dumps(mu))
mu.initialize_update(parameters, backcast, nobs)
assert_allclose(sigma2, sigma2_ref)
sigma2[:] = np.nan
mu = recpy.MIDASUpdater(22, True)
mu.initialize_update(parameters, backcast, nobs)
for t in range(nobs):
mu._update_tester(t, parameters, resids, sigma2, self.var_bounds)
if t == nobs // 2:
mu = pickle.loads(pickle.dumps(mu))
mu.initialize_update(parameters, backcast, nobs)
assert_allclose(sigma2, sigma2_ref)
def test_midas_hyperbolic(self):
nobs, resids = self.nobs, self.resids
sigma2, backcast = self.sigma2, self.backcast
parameters = np.array([0.1, 0.8, 0])
j = np.arange(1, 22 + 1)
weights = gamma(j + 0.6) / (gamma(j + 1) * gamma(0.6))
weights = weights / weights.sum()
recpy.midas_recursion(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
sigma2_numba = sigma2.copy()
recpy.midas_recursion_python(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
sigma2_python = sigma2.copy()
rec.midas_recursion(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
assert_almost_equal(sigma2_numba, sigma2)
assert_almost_equal(sigma2_python, sigma2)
mod_resids = resids.copy()
mod_resids[:10] = np.inf
recpy.midas_recursion_python(
parameters, weights, mod_resids, sigma2, nobs, backcast, self.var_bounds
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.1, 10e10, 0])
j = np.arange(1, 22 + 1)
weights = gamma(j + 0.6) / (gamma(j + 1) * gamma(0.6))
weights = weights / weights.sum()
recpy.midas_recursion_python(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
rec.midas_recursion(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.1, -0.4, 0])
recpy.midas_recursion_python(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
rec.midas_recursion(
parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
def test_midas_performance(self):
midas_setup = """
from scipy.special import gamma
parameters = np.array([.1, 0.8, 0])
j = np.arange(1,22+1)
weights = gamma(j+0.6) / (gamma(j+1) * gamma(0.6))
weights = weights / weights.sum()
"""
midas_first = """
recpy.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
midas_second = """
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
timer = Timer(
midas_first,
"Numba",
midas_second,
"Cython",
"MIDAS",
self.timer_setup + midas_setup,
)
timer.display()
assert timer.ratio < 10.0
if not (missing_numba or CYTHON_COVERAGE):
assert 0.1 < timer.ratio
def test_midas_hyperbolic(self):
nobs, resids, = self.nobs, self.resids
sigma2, backcast = self.sigma2, self.backcast
parameters = np.array([.1, 0.8, 0])
j = np.arange(1, 22 + 1)
weights = gamma(j + 0.6) / (gamma(j + 1) * gamma(0.6))
weights = weights / weights.sum()
recpy.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds)
sigma2_numba = sigma2.copy()
recpy.midas_recursion_python(parameters, weights, resids, sigma2, nobs,
backcast, self.var_bounds)
sigma2_python = sigma2.copy()
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds)
assert_almost_equal(sigma2_numba, sigma2)
assert_almost_equal(sigma2_python, sigma2)
mod_resids = resids.copy()
mod_resids[:10] = np.inf
recpy.midas_recursion_python(parameters, weights, mod_resids, sigma2, nobs, backcast,
self.var_bounds)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([.1, 10e10, 0])
j = np.arange(1, 22 + 1)
weights = gamma(j + 0.6) / (gamma(j + 1) * gamma(0.6))
weights = weights / weights.sum()
recpy.midas_recursion_python(parameters, weights, resids, sigma2, nobs, backcast,
self.var_bounds)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([.1, -0.4, 0])
recpy.midas_recursion_python(parameters, weights, resids, sigma2, nobs, backcast,
self.var_bounds)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, self.var_bounds)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
def test_midas_performance(self):
midas_setup = """
from scipy.special import gamma
parameters = np.array([.1, 0.8, 0])
j = np.arange(1,22+1)
weights = gamma(j+0.6) / (gamma(j+1) * gamma(0.6))
weights = weights / weights.sum()
"""
midas_first = """
recpy.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
midas_second = """
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
timer = Timer(midas_first, 'Numba', midas_second, 'Cython', 'GARCH',
self.timer_setup + midas_setup)
timer.display()
assert timer.ratio < 10.0
def test_midas_performance(self):
midas_setup = """
from scipy.special import gamma
parameters = np.array([.1, 0.8, 0])
j = np.arange(1,22+1)
weights = gamma(j+0.6) / (gamma(j+1) * gamma(0.6))
weights = weights / weights.sum()
"""
midas_first = """
recpy.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
midas_second = """
rec.midas_recursion(parameters, weights, resids, sigma2, nobs, backcast, var_bounds)
"""
timer = Timer(midas_first, 'Numba', midas_second, 'Cython', 'MIDAS',
self.timer_setup + midas_setup)
timer.display()
assert timer.ratio < 10.0
if not (missing_numba or CYTHON_COVERAGE):
assert 0.1 < timer.ratio