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 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_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_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 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",
)