def testRadon(self): dataset = data.radon(train_fraction=0.75) for k, v in dataset.items(): if k.startswith('train_'): self.assertEqual((689,), v.shape) if k.startswith('test_'): self.assertEqual((230,), v.shape)
def __init__(self): dataset = data.radon(state='MN') for key in list(dataset.keys()): if key.startswith('test_'): del dataset[key] super(RadonContextualEffectsMinnesota, self).__init__(name='radon_contextual_effects_minnesota', pretty_name='Radon Contextual Effects Minnesota', **dataset)
def radon_contextual_effects_minnesota(): """Hierarchical radon model with contextual effects, with data from Minnesota. Returns: target: StanModel. """ dataset = data.radon(state='MN') for key in list(dataset.keys()): if key.startswith('test_'): del dataset[key] return radon_contextual_effects.radon_contextual_effects(**dataset)
def __init__(self, dtype=tf.float64): dataset = data.radon(state='MN') 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(RadonContextualEffectsMinnesota, self).__init__(name='radon_contextual_effects_minnesota', pretty_name='Radon Contextual Effects Minnesota', **dataset)