def load_dataset_in_xla():
dataset = data.german_credit_numeric()
# The actual dataset loading will happen in Eager mode, courtesy of the
# init_scope.
return (
tf.convert_to_tensor(dataset['train_features']),
tf.convert_to_tensor(dataset['train_labels']),
)
def __init__(self):
dataset = data.german_credit_numeric()
del dataset['test_features']
del dataset['test_labels']
super(GermanCreditNumericProbitRegression, self).__init__(
name='german_credit_numeric_probit_regression',
pretty_name='German Credit Numeric Probit Regression',
**dataset)
def __init__(self):
dataset = data.german_credit_numeric()
del dataset['test_features']
del dataset['test_labels']
super(GermanCreditNumericSparseLogisticRegression, self).__init__(
name='german_credit_numeric_sparse_logistic_regression',
pretty_name='German Credit Numeric Sparse Logistic Regression',
**dataset)
def german_credit_numeric_sparse_logistic_regression():
"""German credit (numeric) logistic regression with a sparsity-inducing prior.
Returns:
target: StanModel.
"""
dataset = data.german_credit_numeric()
del dataset['test_features']
del dataset['test_labels']
return sparse_logistic_regression.sparse_logistic_regression(**dataset)
def german_credit_numeric_probit_regression():
"""German credit (numeric) probit regression.
Returns:
target: StanModel.
"""
dataset = data.german_credit_numeric()
del dataset['test_features']
del dataset['test_labels']
return probit_regression.probit_regression(**dataset)
def testGermanCreditNumeric(self):
dataset = data.german_credit_numeric(train_fraction=0.75)
self.assertEqual((750, 24), dataset['train_features'].shape)
self.assertEqual((750,), dataset['train_labels'].shape)
self.assertEqual((250, 24), dataset['test_features'].shape)
self.assertEqual((250,), dataset['test_labels'].shape)
self.assertAllClose(
np.zeros([24]), np.mean(dataset['train_features'], 0), atol=1e-5)
self.assertAllClose(
np.ones([24]), np.std(dataset['train_features'], 0), atol=1e-5)
self.assertAllClose(
np.zeros([24]), np.mean(dataset['test_features'], 0), atol=0.3)
self.assertAllClose(
np.ones([24]), np.std(dataset['test_features'], 0), atol=0.3)
