def test_custom_encoder_decoder(moon_dataset):
"""test using a custom encoder / decoder"""
dims = (2, )
n_components = 2
encoder = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=dims),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=n_components, name="z"),
])
decoder = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=n_components),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=np.product(dims),
name="recon",
activation=None),
tf.keras.layers.Reshape(dims),
])
embedder = ParametricUMAP(
encoder=encoder,
decoder=decoder,
dims=dims,
parametric_reconstruction=True,
verbose=True,
)
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
def test_create_model(moon_dataset):
"""test a simple parametric UMAP network"""
embedder = ParametricUMAP()
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
def test_nonparametric(moon_dataset):
"""test nonparametric embedding"""
embedder = ParametricUMAP(parametric_embedding=False)
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
def test_global_loss(moon_dataset):
"""test a simple parametric UMAP network"""
embedder = ParametricUMAP(global_correlation_loss_weight=1.0)
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
def test_create_model():
""" test a simple parametric UMAP network
"""
X, y = make_moons(100)
embedder = ParametricUMAP()
embedding = embedder.fit_transform(X)
return embedding, y
def test_validation():
"""tests adding a validation dataset"""
X, y = make_moons(100)
X_valid, y = make_moons(100)
embedder = ParametricUMAP(
parametric_reconstruction=True, reconstruction_validation=X_valid, verbose=True,
)
embedding = embedder.fit_transform(X)
def test_save_load():
""" tests saving and loading
"""
X, y = make_moons(100)
embedder = ParametricUMAP()
embedding = embedder.fit_transform(X)
embedder.save("/tmp/model")
embedder = load_ParametricUMAP("/tmp/model")
def test_validation(moon_dataset):
"""tests adding a validation dataset"""
X_train, X_valid = train_test_split(moon_dataset, train_size=0.5)
embedder = ParametricUMAP(parametric_reconstruction=True,
reconstruction_validation=X_valid,
verbose=True)
embedding = embedder.fit_transform(X_train)
# completes successfully
assert embedding is not None
assert embedding.shape == (X_train.shape[0], 2)
def test_save_load():
"""tests saving and loading"""
X, y = make_moons(100)
embedder = ParametricUMAP()
embedding = embedder.fit_transform(X)
# if platform.system() != "Windows":
# Portable tempfile
model_path = tempfile.mkdtemp(suffix="_umap_model")
embedder.save(model_path)
embedder = load_ParametricUMAP(model_path)
def test_inverse_transform():
"""tests inverse_transform"""
def norm(x):
return (x - np.min(x)) / (np.max(x) - np.min(x))
X, y = make_moons(100)
X = norm(X)
embedder = ParametricUMAP(parametric_reconstruction=True)
embedding = embedder.fit_transform(X)
Z = embedder.transform(X)
X_r = embedder.inverse_transform(Z)
def test_inverse_transform(moon_dataset):
"""tests inverse_transform"""
def norm(x):
return (x - np.min(x)) / (np.max(x) - np.min(x))
X = norm(moon_dataset)
embedder = ParametricUMAP(parametric_reconstruction=True)
Z = embedder.fit_transform(X)
X_r = embedder.inverse_transform(Z)
# completes successfully
assert X_r is not None
assert X_r.shape == X.shape
def test_save_load(moon_dataset):
"""tests saving and loading"""
embedder = ParametricUMAP()
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
# if platform.system() != "Windows":
# Portable tempfile
model_path = tempfile.mkdtemp(suffix="_umap_model")
embedder.save(model_path)
loaded_model = load_ParametricUMAP(model_path)
assert loaded_model is not None
def umap_model(optim, batch_size, epochs, verbose=False, save_path=None, config=None):
if save_path is None:
return ParametricUMAP(optimizer=optim,
batch_size=batch_size,
dims=None,
encoder=None, # you could enter another network here
loss_report_frequency=1,
n_training_epochs=epochs,
verbose=verbose)
else:
return load_ParametricUMAP(save_path)
def test_custom_encoder_decoder():
""" test using a custom encoder / decoder
"""
X, y = make_moons(100)
dims = (2,)
n_components = 2
encoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=dims),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=n_components, name="z"),
]
)
decoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=n_components),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(units=100, activation="relu"),
tf.keras.layers.Dense(
units=np.product(dims), name="recon", activation=None
),
tf.keras.layers.Reshape(dims),
]
)
embedder = ParametricUMAP(
encoder=encoder,
decoder=decoder,
dims=dims,
parametric_reconstruction=True,
verbose=True,
)
embedding = embedder.fit_transform(X)
def test_save_load(moon_dataset):
"""tests saving and loading"""
embedder = ParametricUMAP()
embedding = embedder.fit_transform(moon_dataset)
# completes successfully
assert embedding is not None
assert embedding.shape == (moon_dataset.shape[0], 2)
# Portable tempfile
model_path = tempfile.mkdtemp(suffix="_umap_model")
embedder.save(model_path)
loaded_model = load_ParametricUMAP(model_path)
assert loaded_model is not None
loaded_embedding = loaded_model.transform(moon_dataset)
assert_array_almost_equal(
embedding,
loaded_embedding,
decimal=5,
err_msg="Loaded model transform fails to match original embedding",
)
def test_nonparametric():
""" test nonparametric embedding
"""
X, y = make_moons(100)
embedder = ParametricUMAP(parametric_embedding=False)
embedding = embedder.fit_transform(X)
