def testSyntheticItemResponseTheoryHMC(self):
"""Checks approximate samples from the model against the ground truth."""
model = item_response_theory.SyntheticItemResponseTheory()
self.validate_ground_truth_using_hmc(
model,
num_chains=4,
num_steps=4000,
num_leapfrog_steps=10,
step_size=0.025,
)
def testSyntheticItemResponseTheory(self):
"""Checks that you get finite values given unconstrained samples.
We check `unnormalized_log_prob` as well as the values of the sample
transformations.
"""
model = item_response_theory.SyntheticItemResponseTheory()
self.validate_log_prob_and_transforms(
model,
sample_transformation_shapes=dict(identity={
'mean_student_ability': [],
'student_ability': [400],
'question_difficulty': [100],
}, ),
check_ground_truth_mean_standard_error=True,
check_ground_truth_mean=True,
check_ground_truth_standard_deviation=True,
)
