def __init__(self): dataset = data.german_credit_numeric() del dataset['test_features'] del dataset['test_labels'] super(GermanCreditNumericLogisticRegression, self).__init__( name='german_credit_numeric_logistic_regression', pretty_name='German Credit Numeric Logistic Regression', **dataset)
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 german_credit_numeric_logistic_regression(): """German credit (numeric) logistic regression. Returns: target: StanModel. """ dataset = data.german_credit_numeric() del dataset['test_features'] del dataset['test_labels'] return logistic_regression.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 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]), dataset['train_features'].mean(0), atol=1e-5) self.assertAllClose( np.ones([24]), dataset['train_features'].std(0), atol=1e-5) self.assertAllClose( np.zeros([24]), dataset['test_features'].mean(0), atol=0.3) self.assertAllClose( np.ones([24]), dataset['test_features'].std(0), atol=0.3)