def _custom_model_saving(model_filepath, save_fn, load_fn):
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(8, activation='relu')(inputs)
custom_model = tf.keras.Model(inputs, x)
model = Ivis(k=15, batch_size=16, epochs=2, model=custom_model)
model.fit(X, Y)
save_fn(model, model_filepath)
model_2 = load_fn(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))
_validate_network_equality(model, model_2)
# Train new model
y_pred_2 = model_2.fit_transform(X, Y)
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_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_score_samples_unsupervised():
iris = datasets.load_iris()
x = iris.data
y = iris.target
ivis_iris = Ivis(k=15, batch_size=16, epochs=2)
embeddings = ivis_iris.fit_transform(x)
# Unsupervised model cannot classify
with pytest.raises(Exception):
y_pred = ivis_iris.score_samples(x)
def test_regression():
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
supervision_metric = 'mae'
ivis_boston = Ivis(k=15,
batch_size=16,
epochs=5,
supervision_metric=supervision_metric)
ivis_boston.fit(x_train, y_train)
embeddings = ivis_boston.transform(x_train)
y_pred = ivis_boston.score_samples(x_train)
assert ivis_boston.model_.loss['supervised'] == 'mae'
assert ivis_boston.model_.layers[-1].activation.__name__ == 'linear'
assert ivis_boston.model_.layers[-1].output_shape[-1] == 1
def _supervised_model_save_test(model_filepath, save_fn, load_fn):
model = Ivis(k=15,
batch_size=16,
epochs=2,
supervision_metric='sparse_categorical_crossentropy')
iris = datasets.load_iris()
X = iris.data
Y = iris.target
model.fit(X, Y)
save_fn(model, model_filepath)
model_2 = load_fn(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))
_validate_network_equality(model, model_2)
# Train new model
y_pred_2 = model_2.fit_transform(X, Y)
def test_score_samples():
iris = datasets.load_iris()
x = iris.data
y = iris.target
supervision_metric = 'sparse_categorical_crossentropy'
ivis_iris = Ivis(k=15,
batch_size=16,
epochs=5,
supervision_metric=supervision_metric)
embeddings = ivis_iris.fit_transform(x, y)
y_pred = ivis_iris.score_samples(x)
# Softmax probabilities add to one, correct shape
assert np.sum(y_pred, axis=-1) == pytest.approx(1, 0.01)
assert y_pred.shape[0] == x.shape[0]
assert y_pred.shape[1] == len(np.unique(y))
# Check that loss function and activation are correct
assert ivis_iris.model_.loss['supervised'] == supervision_metric
assert ivis_iris.model_.layers[-1].activation.__name__ == 'softmax'
def test_svm_score_samples():
iris = datasets.load_iris()
x = iris.data
y = iris.target
supervision_metric = 'categorical_hinge'
ivis_iris = Ivis(k=15,
batch_size=16,
epochs=2,
supervision_metric=supervision_metric)
# Correctly formatted one-hot labels train successfully
y = to_categorical(y)
embeddings = ivis_iris.fit_transform(x, y)
y_pred = ivis_iris.score_samples(x)
loss_name = ivis_iris.model_.loss['supervised'].__name__
assert losses.get(loss_name).__name__ == losses.get(
supervision_metric).__name__
assert ivis_iris.model_.layers[-1].activation.__name__ == 'linear'
assert ivis_iris.model_.layers[-1].kernel_regularizer is not None
assert ivis_iris.model_.layers[-1].output_shape[-1] == y.shape[-1]
def test_svm_score_samples():
iris = datasets.load_iris()
x = iris.data
y = iris.target
supervision_metric = 'categorical_hinge'
ivis_iris = Ivis(k=15,
batch_size=16,
epochs=5,
supervision_metric=supervision_metric)
# Incorrectly formatted labels from SVM
with pytest.raises(ValueError):
embeddings = ivis_iris.fit_transform(x, y)
# Correctly formatted labels train successfully
y = to_categorical(y) * 2 - 1
embeddings = ivis_iris.fit_transform(x, y)
y_pred = ivis_iris.score_samples(x)
assert ivis_iris.model_.loss['supervised'] == supervision_metric
assert ivis_iris.model_.layers[-1].activation.__name__ == 'linear'
assert ivis_iris.model_.layers[-1].kernel_regularizer is not None
assert ivis_iris.model_.layers[-1].output_shape[-1] == y.shape[-1]