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import numpy as np
import tempfile
import pytest
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from numpy.testing import assert_array_almost_equal
import platform
try:
import tensorflow as tf
IMPORT_TF = True
except ModuleNotFoundError:
IMPORT_TF = False
else:
from umap.parametric_umap import ParametricUMAP, load_ParametricUMAP
tf_only = pytest.mark.skipif(not IMPORT_TF, reason="TensorFlow >= 2.0 is not installed")
not_windows = pytest.mark.skipif(platform.system() == "Windows", reason="Windows file access issues")
@pytest.fixture(scope="session")
def moon_dataset():
X, _ = make_moons(200)
return X
@tf_only
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)
@tf_only
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)
@tf_only
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
@tf_only
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)
@tf_only
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)
@tf_only
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)
@not_windows
@tf_only
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",
)
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