def test_equality_for_myopic_agents_and_tiny_delta():
"""Test equality of simulated data and likelihood with myopia and tiny delta."""
# Get simulated data and likelihood for myopic model.
params, options = generate_random_model(myopic=True)
simulate = rp.get_simulate_func(params, options)
df = simulate(params)
log_like = get_log_like_func(params, options, df)
likelihood = log_like(params)
# Get simulated data and likelihood for model with tiny delta.
params.loc["delta", "value"] = 1e-12
df_ = simulate(params)
log_like = rp.get_log_like_func(params, options, df_)
likelihood_ = log_like(params)
# The continuation values are different because for delta = 0 the backward induction
# is completely skipped and all continuation values are set to zero whereas for a
# tiny delta, the delta ensures that continuation have no impact.
columns = df.filter(like="Continu").columns.tolist()
pd.testing.assert_frame_equal(df.drop(columns=columns),
df_.drop(columns=columns))
np.testing.assert_almost_equal(likelihood, likelihood_, decimal=12)
def test_return_scalar_for_likelihood(model):
params, options = process_model_or_seed(model)
options["n_periods"] = 3
simulate = get_simulate_func(params, options)
df = simulate(params)
log_like = get_log_like_func(params, options, df, return_scalar=True)
value = log_like(params)
assert isinstance(value, float)
log_like_contribs = get_log_like_func(params,
options,
df,
return_scalar=False)
outputs = log_like_contribs(params)
assert isinstance(outputs, dict)
def test_generate_random_model():
"""Test if random model specifications can be simulated and processed."""
params, options = generate_random_model()
df = simulate_truncated_data(params, options)
log_like = get_log_like_func(params, options, df)
crit_val = log_like(params)
assert isinstance(crit_val, float)
def test_return_output_dict_for_likelihood(model):
params, options = process_model_or_seed(model)
options["n_periods"] = 3
simulate = get_simulate_func(params, options)
df = simulate(params)
log_like = get_log_like_func(params, options, df, return_scalar=False)
log_like = log_like(params)
assert isinstance(log_like["value"], float)
assert isinstance(log_like["contributions"], np.ndarray)
assert isinstance(log_like["comparison_plot_data"], pd.DataFrame)
def test_simulation_and_estimation_with_different_models():
"""Test the evaluation of the criterion function not at the true parameters."""
# Simulate a dataset
params, options = generate_random_model()
df = simulate_truncated_data(params, options)
# Evaluate at different points, ensuring that the simulated dataset still fits.
log_like = get_log_like_func(params, options, df)
params_ = add_noise_to_params(params, options)
params.equals(params_)
log_like(params)
def test_invariant_results_for_two_estimations():
params, options = generate_random_model()
df = simulate_truncated_data(params, options)
log_like = get_log_like_func(params, options, df)
# First estimation.
crit_val = log_like(params)
# Second estimation.
crit_val_ = log_like(params)
assert crit_val == crit_val_
def test_return_comparison_plot_data_for_likelihood(model):
params, options = process_model_or_seed(model)
options["n_periods"] = 3
simulate = get_simulate_func(params, options)
df = simulate(params)
log_like = get_log_like_func(params,
options,
df,
return_comparison_plot_data=False)
log_like = log_like(params)
assert isinstance(log_like, float)
log_like = get_log_like_func(params,
options,
df,
return_comparison_plot_data=True)
log_like, df = log_like(params)
assert isinstance(log_like, float)
assert isinstance(df, pd.DataFrame)
def test_invariance_of_model_solution_in_solve_and_criterion_functions(model):
params, options = process_model_or_seed(model)
solve = get_solve_func(params, options)
state_space = solve(params)
simulate = get_simulate_func(params, options)
df = simulate(params)
state_space_sim = simulate.keywords["solve"].keywords["state_space"]
log_like = get_log_like_func(params, options, df)
_ = log_like(params)
state_space_crit = log_like.keywords["solve"].keywords["state_space"]
for state_space_ in [state_space_sim, state_space_crit]:
assert state_space.core.equals(
state_space_.core.reindex_like(state_space.core))
apply_to_attributes_of_two_state_spaces(
state_space.wages,
state_space_.wages,
np.testing.assert_array_equal,
)
apply_to_attributes_of_two_state_spaces(
state_space.nonpecs,
state_space_.nonpecs,
np.testing.assert_array_equal,
)
apply_to_attributes_of_two_state_spaces(
state_space.expected_value_functions,
state_space_.expected_value_functions,
np.testing.assert_array_equal,
)
apply_to_attributes_of_two_state_spaces(
state_space.base_draws_sol,
state_space_.base_draws_sol,
np.testing.assert_array_equal,
)
def compute_log_likelihood(params, options):
df = simulate_truncated_data(params, options)
log_like = get_log_like_func(params, options, df)
crit_val = log_like(params)
return crit_val