def test_check_convergence():
N = 500
nu = 0.1
M_test = 200
N_test = int(nu * N)
mu = np.array([0.0, 0.1, 2 * np.pi, 0.1 * np.pi], dtype=np.float32)
lb_a12 = 0.0
ub_a12 = 10.0
lb_a21 = -10.0
ub_a21 = 0.0
a11 = Parameter("a11", 1, 0.0)
a12 = Parameter("a12", 1, lb_a12, ub_a12)
a21 = Parameter("a21", 1, lb_a21, ub_a21)
a22 = Parameter("a22", 1, ub=0.0)
params = [a11, a12, a21, a22]
M = Model("lds_2D", params)
M.set_eps(linear2D_freq)
q_theta, opt_data, epi_path, failed = M.epi(
mu,
num_iters=1000,
K=10,
N=N,
stop_early=True,
save_movie_data=False,
random_seed=1,
)
assert not failed
assert (opt_data["converged"] == True).sum() > 0
epi_df = M.get_epi_df()
epi_df_row = epi_df[epi_df["iteration"] == epi_df["iteration"].max()].iloc[0]
init = epi_df_row["init"]
init_params = {"mu": init["mu"], "Sigma": init["Sigma"]}
nf = M._df_row_to_nf(epi_df_row)
aug_lag_hps = M._df_row_to_al_hps(epi_df_row)
best_k, converged, best_H = M.get_convergence_epoch(
init_params, nf, mu, aug_lag_hps, alpha=0.05, nu=0.1,
)
assert converged
return None
def test_epi():
mu = np.array([0.0, 0.1, 2 * np.pi, 0.1 * np.pi])
lb_a12 = 0.0
ub_a12 = 10.0
lb_a21 = -10.0
ub_a21 = 0.0
a11 = Parameter("a11", 1, 0.0)
a12 = Parameter("a12", 1, lb_a12, ub_a12)
a21 = Parameter("a21", 1, lb_a21, ub_a21)
a22 = Parameter("a22", 1, ub=0.0)
params = [a11, a12, a21, a22]
M = Model("lds_2D", params)
M.set_eps(linear2D_freq)
q_theta, opt_data, epi_path, failed = M.epi(
mu, num_iters=100, K=1, save_movie_data=True, log_rate=10,
)
z = q_theta(50)
g = q_theta.plot_dist(z)
M.epi_opt_movie(epi_path)
params = [a11, a12, a21, a22]
# should load from prev epi
M = Model("lds_2D", params)
M.set_eps(linear2D_freq)
q_theta, opt_data, epi_path, failed = M.epi(
mu, num_iters=100, K=1, save_movie_data=True
)
print("epi_path", epi_path)
epi_df = M.get_epi_df()
epi_df_row = epi_df[epi_df["iteration"] == 100].iloc[0]
q_theta = M.get_epi_dist(epi_df_row)
opt_data_filename = os.path.join(epi_path, "opt_data.csv")
M.set_eps(linear2D_freq)
q_theta, opt_data, epi_path, failed = M.epi(
mu, num_iters=100, K=1, save_movie_data=True, log_rate=10,
)
opt_data_cols = ["k", "iteration", "H", "cost", "converged"] + [
"R%d" % i for i in range(1, M.m + 1)
]
for x, y in zip(opt_data.columns, opt_data_cols):
assert x == y
assert q_theta is not None
z = q_theta(1000)
log_q_z = q_theta.log_prob(z)
assert np.sum(z[:, 0] < 0.0) == 0
assert np.sum(z[:, 1] < lb_a12) == 0
assert np.sum(z[:, 1] > ub_a12) == 0
assert np.sum(z[:, 2] < lb_a21) == 0
assert np.sum(z[:, 2] > ub_a21) == 0
assert np.sum(z[:, 3] > 0.0) == 0
assert np.sum(1 - np.isfinite(z)) == 0
# Intentionally swap order in list to insure proper handling.
params = [a22, a21, a12, a11]
M = Model("lds", params)
M.set_eps(linear2D_freq)
q_theta, opt_data, epi_path, _ = M.epi(
mu,
K=2,
num_iters=100,
stop_early=True,
verbose=True,
save_movie_data=True,
log_rate=10,
)
M.epi_opt_movie(epi_path)
z = q_theta(1000)
log_q_z = q_theta.log_prob(z)
assert np.sum(z[:, 0] < 0.0) == 0
assert np.sum(z[:, 1] < lb_a12) == 0
assert np.sum(z[:, 1] > ub_a12) == 0
assert np.sum(z[:, 2] < lb_a21) == 0
assert np.sum(z[:, 2] > ub_a21) == 0
assert np.sum(z[:, 3] > 0.0) == 0
assert np.sum(1 - np.isfinite(z)) == 0
print("DOING ABC NOW")
# Need finite support for ABC
a11 = Parameter("a11", 1, -10.0, 10.0)
a12 = Parameter("a12", 1, -10.0, 10.0)
a21 = Parameter("a21", 1, -10.0, 10.0)
a22 = Parameter("a22", 1, -10.0, 10.0)
params = [a11, a12, a21, a22]
M = Model("lds_2D", params)
M.set_eps(linear2D_freq)
init_type = "abc"
init_params = {"num_keep": 50, "mean": mu[:2], "std": np.sqrt(mu[2:])}
q_theta, opt_data, epi_path, failed = M.epi(
mu,
num_iters=100,
K=1,
init_type=init_type,
init_params=init_params,
save_movie_data=True,
log_rate=10,
)
params = [a11, a12, a21, a22]
M = Model("lds2", params)
M.set_eps(linear2D_freq)
# This should cause opt to fail with nan since c0=1e20 is too high.
q_theta, opt_data, epi_path, _ = M.epi(
mu,
K=3,
num_iters=1000,
c0=1e20,
stop_early=True,
verbose=True,
save_movie_data=False,
log_rate=10,
)
with raises(IOError):
M.epi_opt_movie(epi_path)
for x, y in zip(opt_data.columns, opt_data_cols):
assert x == y
with raises(ValueError):
def bad_f(a11, a12, a21, a22):
return tf.expand_dims(a11 + a12 + a21 + a22, 0)
M.set_eps(bad_f)
params = [a11, a12, a21, a22]
M = Model("lds2", params)
init_params = {"mu": 2 * np.zeros((4,)), "Sigma": np.eye(4)}
nf = NormalizingFlow("autoregressive", 4, 1, 2, 10)
al_hps = AugLagHPs()
epi_path, exists = M.get_epi_path(init_params, nf, mu, al_hps, eps_name="foo")
assert not exists
return None