1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152
|
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",
)
|