def testRadonIndiana(self):
dataset = data.radon_indiana(train_fraction=0.75)
for k, v in dataset.items():
if k.startswith('train_'):
self.assertEqual((1388, ), tf.convert_to_tensor(v).shape)
if k.startswith('test_'):
self.assertEqual((463, ), tf.convert_to_tensor(v).shape)
def __init__(self, dtype=tf.float32):
dataset = data.radon_indiana()
for key in list(dataset.keys()):
if key.startswith('test_'):
del dataset[key]
elif dtype_util.is_floating(dataset[key].dtype):
dataset[key] = tf.cast(dataset[key], dtype)
super(RadonContextualEffectsIndiana,
self).__init__(name='radon_contextual_effects_indiana',
pretty_name='Radon Contextual Effects Indiana',
**dataset)
def radon_contextual_effects_indiana():
"""Hierarchical radon model with contextual effects, with data from Indiana.
Returns:
target: StanModel.
"""
dataset = data.radon_indiana()
for key in list(dataset.keys()):
if key.startswith('test_'):
del dataset[key]
return radon_contextual_effects.radon_contextual_effects(**dataset)