def test_linear_component(self):
vars_to_create = {
"sigma", "sigma_interval__", "y_obs", "lm_x0", "lm_Intercept"
}
with Model() as model:
lm = LinearComponent(self.data_linear["x"],
self.data_linear["y"],
name="lm") # yields lm_x0, lm_Intercept
sigma = Uniform("sigma", 0, 20) # yields sigma_interval__
Normal("y_obs", mu=lm.y_est, sigma=sigma,
observed=self.y_linear) # yields y_obs
start = find_MAP(vars=[sigma])
step = Slice(model.vars)
trace = sample(500,
tune=0,
step=step,
start=start,
progressbar=False,
random_seed=self.random_seed)
assert round(abs(np.mean(trace["lm_Intercept"]) - self.intercept),
1) == 0
assert round(abs(np.mean(trace["lm_x0"]) - self.slope), 1) == 0
assert round(abs(np.mean(trace["sigma"]) - self.sd), 1) == 0
assert vars_to_create == set(model.named_vars.keys())
def test_linear_component(self):
vars_to_create = {
'sigma', 'sigma_interval__', 'y_obs', 'lm_x0', 'lm_Intercept'
}
with Model() as model:
lm = LinearComponent(self.data_linear['x'],
self.data_linear['y'],
name='lm') # yields lm_x0, lm_Intercept
sigma = Uniform('sigma', 0, 20) # yields sigma_interval__
Normal('y_obs', mu=lm.y_est, sigma=sigma,
observed=self.y_linear) # yields y_obs
start = find_MAP(vars=[sigma])
step = Slice(model.vars)
trace = sample(500,
tune=0,
step=step,
start=start,
progressbar=False,
random_seed=self.random_seed)
assert round(abs(np.mean(trace['lm_Intercept']) - self.intercept),
1) == 0
assert round(abs(np.mean(trace['lm_x0']) - self.slope), 1) == 0
assert round(abs(np.mean(trace['sigma']) - self.sd), 1) == 0
assert vars_to_create == set(model.named_vars.keys())
def test_linear_component_from_formula(self):
with Model() as model:
lm = LinearComponent.from_formula('y ~ x', self.data_linear)
sigma = Uniform('sigma', 0, 20)
Normal('y_obs', mu=lm.y_est, sd=sigma, observed=self.y_linear)
start = find_MAP(vars=[sigma])
step = Slice(model.vars)
trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept, 1)
self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
self.assertAlmostEqual(np.mean(trace['sigma']), self.sd, 1)
def test_linear_component_from_formula(self):
with Model() as model:
lm = LinearComponent.from_formula('y ~ x', self.data_linear)
sigma = Uniform('sigma', 0, 20)
Normal('y_obs', mu=lm.y_est, sd=sigma, observed=self.y_linear)
start = find_MAP(vars=[sigma])
step = Slice(model.vars)
trace = sample(500, step=step, start=start, progressbar=False, random_seed=self.random_seed)
assert round(abs(np.mean(trace['Intercept'])-self.intercept), 1) == 0
assert round(abs(np.mean(trace['x'])-self.slope), 1) == 0
assert round(abs(np.mean(trace['sigma'])-self.sd), 1) == 0
def test_linear_component_from_formula(self):
with Model() as model:
lm = LinearComponent.from_formula("y ~ x", self.data_linear)
sigma = Uniform("sigma", 0, 20)
Normal("y_obs", mu=lm.y_est, sigma=sigma, observed=self.y_linear)
start = find_MAP(vars=[sigma])
step = Slice(model.vars)
trace = sample(
500, tune=0, step=step, start=start, progressbar=False, random_seed=self.random_seed
)
assert round(abs(np.mean(trace["Intercept"]) - self.intercept), 1) == 0
assert round(abs(np.mean(trace["x"]) - self.slope), 1) == 0
assert round(abs(np.mean(trace["sigma"]) - self.sigma), 1) == 0