def testModelRaisesErrorForBadClass(self):
with self.assertRaisesRegex(TypeError,
'must be a class.*given.*not_a_class'):
_ = utils.get_uci_model(model_class='not_a_class')
with self.assertRaisesRegex(
TypeError, 'must be a subclass of.*keras.Model.*given.*str'):
_ = utils.get_uci_model(model_class=type('bad_class'))
with self.assertRaisesRegex(
TypeError, 'must support the Functional API.*cannot be a subclass '
'of.*Sequential.*given.*Sequential'):
_ = utils.get_uci_model(model_class=tf.keras.Sequential)
def testModelStructure(self):
model = utils.get_uci_model()
self.assertIsInstance(model, tf.keras.Model)
expected_inputs = utils._UCI_COLUMN_NAMES.copy()
expected_inputs.remove('income')
expected_inputs.remove('race')
expected_inputs.remove('fnlwgt')
self.assertSetEqual(set([layer.name for layer in model.inputs]),
set(expected_inputs))
self.assertIsInstance(model.layers[-1], tf.keras.layers.Dense)
def testModelRunsOnUCIData(self):
model = utils.get_uci_model()
model.compile(optimizer='adam', loss='binary_crossentropy')
data = utils.get_uci_data()
print('DATA:', data)
dataset = utils.df_to_dataset(data, batch_size=64)
# Model can train on UCI data
model.fit(dataset, epochs=1)
# Model can evaluate on UCI data
model.evaluate(dataset)
def testModelStructureWithCustomClass(self):
class CustomClass(tf.keras.Model):
pass # No additional implementation needed for this test.
model = utils.get_uci_model(model_class=CustomClass)
self.assertIsInstance(model, CustomClass)
expected_inputs = utils._UCI_COLUMN_NAMES.copy()
expected_inputs.remove('income')
expected_inputs.remove('race')
expected_inputs.remove('fnlwgt')
self.assertSetEqual(set([layer.name for layer in model.inputs]),
set(expected_inputs))
self.assertIsInstance(model.layers[-1], tf.keras.layers.Dense)