def get_solve_func(params, options):
"""Get the solve function.
This function takes a model specification and returns the state space of the model
along with components of the solution such as covariates, non-pecuniary rewards,
wages, continuation values and expected value functions as attributes of the class.
Parameters
----------
params : pandas.DataFrame
DataFrame containing parameter series.
options : dict
Dictionary containing model attributes which are not optimized.
Returns
-------
solve : :func:`~respy.solve.solve`
Function with partialed arguments.
Examples
--------
>>> import respy as rp
>>> params, options = rp.get_example_model("robinson_crusoe_basic", with_data=False)
>>> solve = rp.get_solve_func(params, options)
>>> state_space = solve(params)
"""
optim_paras, options = process_params_and_options(params, options)
state_space = create_state_space_class(optim_paras, options)
solve_function = functools.partial(solve,
options=options,
state_space=state_space)
return solve_function
def test_child_indices():
"""Testing existence of properties for calculation of child indices!"""
point_constr = {"n_periods": 2, "n_lagged_choices": 1}
params, options = generate_random_model(point_constr=point_constr)
# Add some inadmissible states
optim_paras, options = process_params_and_options(params, options)
state_space = create_state_space_class(optim_paras, options)
# Create all relevant columns
core_columns = ["period"] + create_core_state_space_columns(optim_paras)
# compose child indices of first choice
initial_state = state_space.core.iloc[0][core_columns].to_numpy()
# Get all the future states
states = []
for i in range(len(optim_paras["choices"])):
child = initial_state.copy()
child[0] += 1
child[i + 1] += 1
child[-1] = i
ix = state_space.indexer[tuple(child)]
states.append(np.array(ix).reshape(1, 2))
manual = np.concatenate(states, axis=0)
np.testing.assert_array_equal(state_space.child_indices[0][0], manual)
def test_choice_restrictions():
"""Basic first test."""
# Load model.
params, options = process_model_or_seed("robinson_crusoe_extended")
# Extend with observable characteristic.
params.loc[("observable_health_well", "probability"), "value"] = 0.9
params.loc[("observable_health_sick", "probability"), "value"] = 0.1
# Sick people can never work.
options["negative_choice_set"] = {
"fishing":
["health == 'sick' & period < 2", "health == 'sick' & period >= 2"],
"friday": ["period < 2", "exp_fishing == 0"],
}
# Create internal specification objects.
optim_paras, options = process_params_and_options(params, options)
state_space = create_state_space_class(optim_paras, options)
for x in state_space.dense_key_to_complex.values():
if (x[0] < 2) & (x[2] == (0, )):
assert x[1] == (False, False, True)
elif x[2] == (0, ):
assert x[1] in [(False, False, True), (False, True, True)]
elif (x[0] < 2) & (x[2] == (1, )):
assert x[1] == (True, False, True)
elif x[2] == (1, ):
assert x[1] in [(True, False, True), (True, True, True)]
def test_dense_period_choice():
params, options = rp.get_example_model("kw_94_one", with_data=False)
options["negative_choice_set"] = {}
options["negative_choice_set"]["b"] = ["period < 5"]
optim_paras, options = process_params_and_options(params, options)
state_space = create_state_space_class(optim_paras, options)
check = _create_dense_period_choice(state_space.core, state_space.dense,
state_space.core_key_to_core_indices,
state_space.core_key_to_complex,
optim_paras, options)
for key in check:
if key[0] < 5:
assert ~key[1][1]