def test_partial_credit(self):
"""Testing Partial Credit Model."""
rng = np.random.default_rng(84445166253145643984335315216)
n_categories = 3
difficulty = np.random.randn(5, n_categories-1)
discrimination = 0.96 * np.sqrt(-2 * np.log(np.random.rand(5)))
theta = np.random.randn(150)
syn_data = create_synthetic_irt_polytomous(difficulty, discrimination,
theta, model='pcm')
girth_model = GirthMCMC(model='PCM', model_args=(n_categories,),
options={'n_tune': 1000, 'n_samples': 1000})
result = girth_model(syn_data, progressbar=False)
def test_graded_response(self):
"""Testing the grm."""
np.random.seed(46899)
n_categories = 3
difficulty = np.random.randn(5, n_categories-1)
difficulty = np.sort(difficulty, 1)
discrimination = 0.96 * np.sqrt(-2 * np.log(np.random.rand(5)))
theta = np.random.randn(150)
syn_data = create_synthetic_irt_polytomous(difficulty, discrimination,
theta, model='grm')
girth_model = GirthMCMC(model='GRM', model_args=(n_categories,),
options={'n_tune': 1000, 'n_samples': 1000})
result = girth_model(syn_data, progressbar=False)
def test_multidimensional_pcm(self):
"""Testing Multidimensional Variational PCM."""
rng = np.random.default_rng(8484959050677840349349)
n_categories = 3
n_factors = 2
discrimnation = rng.uniform(-2, 2, (20, n_factors))
difficulty = np.sort(rng.standard_normal((20, n_categories-1))*.5, axis=1)*-1
thetas = rng.standard_normal((n_factors, 250))
syn_data = create_synthetic_irt_polytomous(difficulty, discrimnation,
thetas, model='grm_md', seed=rng)
girth_model = GirthMCMC(model='PCM_MD', model_args=(n_categories, n_factors),
options={'variational_inference': True,
'variational_samples': 1000,
'n_samples': 1000})
result = girth_model(syn_data, progressbar=False)
def test_multidimensional_pcm(self):
"""Testing Multidimensional PCM."""
rng = np.random.default_rng(29452344633211231635433213)
n_categories = 3
n_factors = 2
discrimnation = rng.uniform(-2, 2, (20, n_factors))
difficulty = np.sort(rng.standard_normal((20, n_categories - 1))*.5, axis=1)*-1
thetas = rng.standard_normal((n_factors, 250))
syn_data = create_synthetic_irt_polytomous(difficulty, discrimnation,
thetas, model='grm_md', seed=rng)
girth_model = GirthMCMC(model='PCM_MD', model_args=(n_categories, n_factors),
options={'n_tune': 1000, 'n_samples': 1000})
result = girth_model(syn_data, progressbar=False)
with self.assertRaises(AssertionError):
girth_model = GirthMCMC(model='PCM_MD', model_args=(n_categories, 1),
options={'n_tune': 1000, 'n_samples': 1000})
result = girth_model(syn_data, progressbar=False)