def test_custom_loss_ivis_callable(model_filepath):
iris = datasets.load_iris()
X = iris.data
class EuclideanDistance:
def __init__(self, margin=1):
self.margin = margin
self.__name__ = self.__class__.__name__
def _euclidean_distance(self, x, y):
return K.sqrt(
K.maximum(K.sum(K.square(x - y), axis=-1, keepdims=True),
K.epsilon()))
def __call__(self, y_true, y_pred):
anchor, positive, negative = tf.unstack(y_pred)
return K.mean(
K.maximum(
self._euclidean_distance(anchor, positive) -
self._euclidean_distance(anchor, negative) + self.margin,
0))
model = Ivis(distance=EuclideanDistance(margin=2),
k=15,
batch_size=16,
epochs=5)
y_pred = model.fit_transform(X)
# Test model saving and loading
model.save_model(model_filepath, overwrite=True)
model_2 = Ivis(distance=EuclideanDistance(margin=2))
model_2.load_model(model_filepath)
model_2.fit(X)
def test_ivis_model_saving(model_filepath):
model = Ivis(k=15, batch_size=16, n_epochs_without_progress=5)
iris = datasets.load_iris()
X = iris.data
model.fit(X)
model.save_model(model_filepath)
model_2 = Ivis()
model_2.load_model(model_filepath)
# Check that model predictions are same
assert np.all(model.transform(X) == model_2.transform(X))
assert model.__getstate__() == model_2.__getstate__(
) # Serializable dict eles same
# Check all weights are the same
for model_layer, model_2_layer in zip(model.encoder.layers,
model_2.encoder.layers):
model_layer_weights = model_layer.get_weights()
model_2_layer_weights = model_2_layer.get_weights()
for i in range(len(model_layer_weights)):
assert np.all(model_layer_weights[i] == model_2_layer_weights[i])
# Check optimizer weights are the same
for model_optimizer_weights, model_2_optimizer_weights in zip(
model.model_.optimizer.get_weights(),
model_2.model_.optimizer.get_weights()):
assert np.all(model_optimizer_weights == model_2_optimizer_weights)
# Check that trying to save over an existing folder raises an Exception
with pytest.raises(FileExistsError) as exception_info:
model.save_model(model_filepath)
assert isinstance(exception_info.value, FileExistsError)
def test_custom_model_saving(model_filepath):
iris = datasets.load_iris()
X = iris.data
Y = iris.target
# Create a custom model
inputs = tf.keras.layers.Input(shape=(X.shape[-1], ))
x = tf.keras.layers.Dense(128, activation='relu')(inputs)
custom_model = tf.keras.Model(inputs, x)
model = Ivis(k=15,
batch_size=16,
epochs=5,
supervision_metric='sparse_categorical_crossentropy',
model=custom_model)
model.fit(X, Y)
model.save_model(model_filepath, overwrite=True)
model_2 = Ivis()
model_2.load_model(model_filepath)
# Check that model embeddings are same
assert np.all(model.transform(X) == model_2.transform(X))
# Check that model supervised predictions are same
assert np.all(model.score_samples(X) == model_2.score_samples(X))
# Serializable dict eles same
assert model.__getstate__() == model_2.__getstate__()
# Check all weights are the same
for model_layer, model_2_layer in zip(model.encoder.layers,
model_2.encoder.layers):
model_layer_weights = model_layer.get_weights()
model_2_layer_weights = model_2_layer.get_weights()
for i in range(len(model_layer_weights)):
assert np.all(model_layer_weights[i] == model_2_layer_weights[i])
# Check optimizer weights are the same
for w1, w2 in zip(model.model_.optimizer.get_weights(),
model_2.model_.optimizer.get_weights()):
assert np.all(w1 == w2)
# Train new model
y_pred_2 = model_2.fit_transform(X, Y)
def test_custom_loss_ivis(model_filepath):
iris = datasets.load_iris()
X = iris.data
def euclidean_loss(y_true, y_pred):
margin = 1
anchor, positive, negative = tf.unstack(y_pred)
return K.mean(
K.maximum(
euclidean_distance(anchor, positive) -
euclidean_distance(anchor, negative) + margin, 0))
model = Ivis(distance=euclidean_loss, k=15, batch_size=16, epochs=3)
y_pred = model.fit_transform(X)
# Test model saving and loading
model.save_model(model_filepath, overwrite=True)
model_2 = Ivis(distance=euclidean_loss)
model_2.load_model(model_filepath)
model_3 = Ivis()
with pytest.raises(ValueError):
model_3.load_model(model_filepath)
def test_save_overwriting(model_filepath):
model = Ivis(k=15, batch_size=16, epochs=2)
iris = datasets.load_iris()
X = iris.data
model.fit(X)
model.save_model(model_filepath)
# Check that trying to save over an existing folder raises an Exception
with pytest.raises(FileExistsError) as exception_info:
model.save_model(model_filepath)
assert isinstance(exception_info.value, FileExistsError)
# Check that can overwrite existing model if requested
model.save_model(model_filepath, overwrite=True)
def test_supervised_model_saving(model_filepath):
model = Ivis(k=15,
batch_size=16,
epochs=5,
supervision_metric='sparse_categorical_crossentropy')
iris = datasets.load_iris()
X = iris.data
Y = iris.target
model.fit(X, Y)
model.save_model(model_filepath, overwrite=True)
model_2 = Ivis()
model_2.load_model(model_filepath)
# Check that model embeddings are same
assert np.all(model.transform(X) == model_2.transform(X))
# Check that model supervised predictions are same
assert np.all(model.score_samples(X) == model_2.score_samples(X))
# Serializable dict eles same
assert model.__getstate__() == model_2.__getstate__()
# Check all weights are the same
for model_layer, model_2_layer in zip(model.encoder.layers,
model_2.encoder.layers):
model_layer_weights = model_layer.get_weights()
model_2_layer_weights = model_2_layer.get_weights()
for i in range(len(model_layer_weights)):
assert np.all(model_layer_weights[i] == model_2_layer_weights[i])
# Check optimizer weights are the same
for w1, w2 in zip(model.model_.optimizer.get_weights(),
model_2.model_.optimizer.get_weights()):
assert np.all(w1 == w2)
# Check that trying to save over an existing folder raises an Exception
with pytest.raises(FileExistsError) as exception_info:
model.save_model(model_filepath)
assert isinstance(exception_info.value, FileExistsError)
# Check that can overwrite existing model if requested
model.save_model(model_filepath, overwrite=True)
# Train new model
y_pred_2 = model_2.fit_transform(X, Y)
def test_untrained_model_persistence(model_filepath):
model = Ivis(k=15, batch_size=16, epochs=2)
model.save_model(model_filepath)