def simulate_params(n_groups):
var1 = Variable('constant_one', lambda x: x, 0.0, 0.0, fe_bounds=[-np.inf, 0.0])
var2 = Variable('constant_one', np.exp, 0.0, 0.0, fe_bounds=[-np.inf, 0.0])
if n_groups == 1:
var1.re_bounds = [0.0, 0.0]
var2.re_bounds = [0.0, 0.0]
else:
var1.re_bounds = [-1.0, 1.0]
var2.re_bounds = [-1.0, 1.0]
param1 = Parameter('p1', np.exp, [var1])
param2 = Parameter('p2', lambda x: x, [var1, var2])
param3 = Parameter('p3', np.exp, [var1])
n_var = 4
fe_true = -np.random.rand(n_var) * 3
if n_groups == 1:
re_true = np.zeros((n_groups, n_var))
else:
re_true = np.random.randn(n_groups, n_var)
x_true = re_true + fe_true
param_true = np.zeros((n_groups, 3))
param_true[:, 0] = np.exp(x_true[:, 0])
param_true[:, 1] = x_true[:, 1] + np.exp(x_true[:, 2])
param_true[:, 2] = np.exp(x_true[:, 3])
return ParameterSet([param1, param2, param3]), param_true, x_true
def parameter_set():
var1 = Variable(covariate='intercept',
var_link_fun=lambda x: x,
fe_init=0.,
re_init=0.)
var2 = Variable(covariate='intercept',
var_link_fun=lambda x: x,
fe_init=0.,
re_init=0.)
alpha = Parameter(param_name='alpha',
link_fun=lambda x: x,
variables=[var1])
beta = Parameter(param_name='beta', link_fun=lambda x: x, variables=[var2])
ln_alpha_beta = ParameterFunction(
param_function_name='ln-alpha-beta',
param_function=lambda params: np.log(params[0] * params[1]))
param_set = ParameterSet(parameters=[alpha, beta],
parameter_functions=[ln_alpha_beta])
return param_set
def param_set():
variable1 = Variable('intercept',
lambda x: x,
0.0,
0.3,
re_bounds=[0.0, 1.0])
variable2 = Variable('intercept',
lambda x: x,
0.1,
0.4,
re_bounds=[0.0, 2.0])
variable3 = Variable('intercept',
lambda x: x,
0.2,
0.5,
re_bounds=[0.0, 3.0])
parameter1 = Parameter('p1', np.exp, [variable1])
parameter2 = Parameter('p2', np.exp, [variable2])
parameter3 = Parameter('p3', np.exp, [variable3] * 2)
parameter_set = ParameterSet([parameter1, parameter2, parameter3])
assert parameter_set.num_fe == 4
return parameter_set
phi_intercept = Variable(covariate='cov_one',
var_link_fun=identity_fun,
fe_init=p_true / 3,
re_init=0.0,
fe_bounds=[-numpy.inf, numpy.inf],
re_bounds=[0.0, 0.0])
alpha = Parameter(param_name='alpha',
link_fun=exp_fun,
variables=[a_intercept])
beta = Parameter(param_name='beta',
link_fun=identity_fun,
variables=[b_intercept, b_social_distance])
p = Parameter(param_name='p', link_fun=exp_fun, variables=[phi_intercept])
parameters = ParameterSet([alpha, beta, p])
optimizer_options = {
'ftol': 1e-12,
'gtol': 1e-12,
}
model = CoreModel(param_set=parameters,
curve_fun=gaussian_cdf,
loss_fun=normal_loss)
solver = ScipyOpt(model)
solver.fit(data=data._get_df(copy=True, return_specs=True),
options=optimizer_options)
params_estimate = model.get_params(solver.x_opt, expand=True)
# -------------------------------------------------------------------------
def parameter_set(param1, param2, parameter_function):
return ParameterSet(
parameters=[param1, param2],
parameter_functions=[parameter_function],
)
def test_parameter_set_duplicates(param1):
with pytest.raises(RuntimeError):
ParameterSet(
parameters=[param1, param1]
)