def test_too_many_neighbors_warns():
u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random")
u.fit(
nn_data[:100,]
)
assert_equal(u._a, 1.2)
assert_equal(u._b, 1.75)
def test_bad_output_metric():
u = UMAP(output_metric="foobar")
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(output_metric="precomputed")
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(output_metric="hamming")
assert_raises(ValueError, u.fit, nn_data)
def test_umap_transform_on_iris():
data = iris.data[iris_selection]
fitter = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(data)
new_data = iris.data[~iris_selection]
embedding = fitter.transform(new_data)
trust = trustworthiness(new_data, embedding, 10)
assert (
trust >= 0.89
), "Insufficiently trustworthy transform for iris dataset: {}".format(trust)
def test_umap_transform_on_iris_modified_dtype():
data = iris.data[iris_selection]
fitter = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(data)
fitter.embedding_ = fitter.embedding_.astype(np.float64)
new_data = iris.data[~iris_selection]
embedding = fitter.transform(new_data)
trust = trustworthiness(new_data, embedding, 10)
assert_greater_equal(
trust,
0.89,
"Insufficiently trustworthy transform for" "iris dataset: {}".format(trust),
)
def metrics(model, data_iterator):
"""
Summary:
Args:
Returns:
"""
umap_proj = UMAP(metric='euclidean', n_neighbors=200, low_memory=True)
hdb_clusterer = hdbscan.HDBSCAN(
min_samples=100,
min_cluster_size=100,
)
ads_pred = []
ads_actual = []
total_duration = []
pred_ads_duration = []
for i, (data, labels) in tqdm(enumerate(data_iterator)):
aud_len = MP3_META(data).info.length
total_duration.append(aud_len)
aud_data = load_audio(data)
embeds, (aud_splits, _) = encoder.embed(aud_data, group=False)
print(data, "Embed done")
try:
projs = umap_proj.fit_transform(embeds)
print(data, "Created Projections")
except Exception as e:
print(e)
continue
clusters = hdb_clusterer.fit_predict(projs)
print(data, "Created Clusters")
ad_dir, ads = segment_ads(aud_data, aud_splits, data, clusters)
pred_ads_duration.append(len(ads) * 10)
ads_pred.append(len(ads))
ads_actual.append(labels)
print(data, "Done segmenting ads")
plt.scatter(projs[:, 0], projs[:, 1], cmap='Spectral')
plt.title(str(Counter(clusters)))
plt.savefig('{}/{}_umap.jpg'.format(ad_dir, data.split('/')[-1]))
plt.close()
plt.plot(clusters)
plt.savefig('{}/{}_hdb_labels.jpg'.format(ad_dir, data.split('/')[-1]))
plt.close()
continue
def test_multi_component_layout():
data, labels = datasets.make_blobs(100,
2,
centers=5,
cluster_std=0.5,
center_box=[-20, 20],
random_state=42)
true_centroids = np.empty((labels.max() + 1, data.shape[1]),
dtype=np.float64)
for label in range(labels.max() + 1):
true_centroids[label] = data[labels == label].mean(axis=0)
true_centroids = normalize(true_centroids, norm="l2")
embedding = UMAP(n_neighbors=4).fit_transform(data)
embed_centroids = np.empty((labels.max() + 1, data.shape[1]),
dtype=np.float64)
embed_labels = KMeans(n_clusters=5).fit_predict(embedding)
for label in range(embed_labels.max() + 1):
embed_centroids[label] = data[embed_labels == label].mean(axis=0)
embed_centroids = normalize(embed_centroids, norm="l2")
error = np.sum((true_centroids - embed_centroids)**2)
assert_less(error, 15.0, msg="Multi component embedding to far astray")
def test_umap_trustworthiness_on_iris():
data = iris.data
embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(data)
trust = trustworthiness(iris.data, embedding, 10)
assert (
trust >= 0.97
), "Insufficiently trustworthy embedding for iris dataset: {}".format(trust)
def test_umap_trustworthiness_on_sphere_iris():
data = iris.data
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
n_epochs=200,
random_state=42,
output_metric="haversine",
).fit_transform(data)
# Since trustworthiness doesn't support haversine, project onto
# a 3D embedding of the sphere and use cosine distance
r = 3
projected_embedding = np.vstack(
[
r * np.sin(embedding[:, 0]) * np.cos(embedding[:, 1]),
r * np.sin(embedding[:, 0]) * np.sin(embedding[:, 1]),
r * np.cos(embedding[:, 0]),
]
).T
trust = trustworthiness(iris.data, projected_embedding, 10, metric="cosine")
assert_greater_equal(
trust,
0.80,
"Insufficiently trustworthy spherical embedding for iris dataset: {}".format(
trust
),
)
def test_bad_too_large_min_dist():
u = UMAP(min_dist=2.0)
# a RuntimeWarning about division by zero in a,b curve fitting is expected
# caught and ignored for this test
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
assert_raises(ValueError, u.fit, nn_data)
def test_umap_transform_embedding_stability():
"""Test that transforming data does not alter the learned embeddings
Issue #217 describes how using transform to embed new data using a
trained UMAP transformer causes the fitting embedding matrix to change
in cases when the new data has the same number of rows as the original
training data.
"""
data = iris.data[iris_selection]
fitter = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(data)
original_embedding = fitter.embedding_.copy()
# The important point is that the new data has the same number of rows
# as the original fit data
new_data = np.random.random(data.shape)
embedding = fitter.transform(new_data)
assert_array_equal(
original_embedding,
fitter.embedding_,
"Transforming new data changed the original embeddings",
)
# Example from issue #217
a = np.random.random((1000, 10))
b = np.random.random((1000, 5))
umap = UMAP()
u1 = umap.fit_transform(a[:, :5])
u1_orig = u1.copy()
assert_array_equal(u1_orig, umap.embedding_)
u2 = umap.transform(b)
assert_array_equal(u1_orig, umap.embedding_)
def test_umap_sparse_trustworthiness():
embedding = UMAP(n_neighbors=10).fit_transform(sparse_test_data[:100])
trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, 10)
assert_greater_equal(
trust,
0.91,
"Insufficiently trustworthy embedding for"
"sparse test dataset: {}".format(trust),
)
def test_supervised_umap_trustworthiness():
data, labels = datasets.make_blobs(50, cluster_std=0.5, random_state=42)
embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(
data, labels
)
trust = trustworthiness(data, embedding, 10)
assert_greater_equal(
trust,
0.97,
"Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
)
def test_semisupervised_umap_trustworthiness_on_iris():
data = iris.data
target = iris.target.copy()
target[25:75] = -1
embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(
data, target
)
trust = trustworthiness(iris.data, embedding, 10)
assert (
trust >= 0.97
), "Insufficiently trustworthy embedding for iris dataset: {}".format(trust)
def test_supervised_umap_trustworthiness_on_iris():
data = iris.data
embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(
data, iris.target
)
trust = trustworthiness(iris.data, embedding, 10)
assert_greater_equal(
trust,
0.97,
"Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust),
)
def test_sklearn_digits():
digits = datasets.load_digits()
data = digits.data
embedding = UMAP(n_neighbors=5, min_dist=0.01,
random_state=42).fit_transform(data)
#np.save('digits_embedding_42.npy', embedding)
to_match = np.load(os.path.join(os.path.dirname(__file__),
'digits_embedding_42.npy'))
assert_array_almost_equal(embedding, to_match, err_msg='Digits embedding '
'is not consistent '
'with previous runs')
def test_initialized_umap_trustworthiness_on_iris():
data = iris.data
embedding = UMAP(
n_neighbors=10, min_dist=0.01, init=data[:, 2:], n_epochs=200, random_state=42
).fit_transform(data)
trust = trustworthiness(iris.data, embedding, 10)
assert_greater_equal(
trust,
0.97,
"Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust),
)
def test_umap_trustworthiness_random_init():
data = nn_data[:50]
embedding = UMAP(
n_neighbors=10, min_dist=0.01, random_state=42, init="random"
).fit_transform(data)
trust = trustworthiness(data, embedding, 10)
assert_greater_equal(
trust,
0.75,
"Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust),
)
def test_umap_sparse_transform_on_iris():
data = sparse.csr_matrix(iris.data[iris_selection])
assert sparse.issparse(data)
fitter = UMAP(
n_neighbors=10,
min_dist=0.01,
random_state=42,
n_epochs=100,
force_approximation_algorithm=True,
).fit(data)
new_data = sparse.csr_matrix(iris.data[~iris_selection])
assert sparse.issparse(new_data)
embedding = fitter.transform(new_data)
trust = trustworthiness(new_data, embedding, 10)
assert_greater_equal(
trust,
0.85,
"Insufficiently trustworthy transform for" "iris dataset: {}".format(trust),
)
def plot(features, labels, classes, path):
print(features.shape, labels.shape)
features, labels = shuffle(features, labels)
print('Plotting UMAP...', end='')
features = features.reshape(features.shape[0], -1)
embedding = UMAP(n_neighbors=20, min_dist=1, metric='correlation', random_state=1, transform_seed=1).fit_transform(features)
colours = ListedColormap(colour_list[:len(classes)])
scatter = plt.scatter(embedding[:, 0], embedding[:, 1], c=labels, s=3, alpha=1, cmap='plasma')
plt.legend(handles=scatter.legend_elements()[0], labels=classes, loc='best',ncol=1, fontsize=6)
plt.savefig(str(path / 'umap.png'), dpi=300)
plt.close()
print('done.')
def test_umap_trustworthiness_fast_approx():
data = nn_data[:50]
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
random_state=42,
n_epochs=100,
force_approximation_algorithm=True,
).fit_transform(data)
trust = trustworthiness(data, embedding, 10)
assert_greater_equal(
trust,
0.75,
"Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust),
)
def test_discrete_metric_supervised_umap_trustworthiness():
data, labels = datasets.make_blobs(50, cluster_std=0.5, random_state=42)
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
target_metric="ordinal",
target_weight=0.8,
n_epochs=100,
random_state=42,
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, 10)
assert_greater_equal(
trust,
0.95,
"Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
)
def nmf_training(self, nmf_training_params: NMFTrainingParams):
# 创建训练状态信息对象
self.nmf_training_progress = NMFTrainingProgress()
self.umap_progress = UMAPProgress()
# 创建SSNMF和UMAP对象
self.ss_nmf = SSNMFTopicModel(self.callback_set_nmf_training_progress,
self.text_preprocessing.tfidfWD,
nmf_training_params.topic_num,
min_iter=nmf_training_params.min_iter,
max_iter=nmf_training_params.max_iter,
tol=nmf_training_params.tolerance,
seed=nmf_training_params.random_seed)
"""
self.t_sne = TSNEWithCallback(
update_progress_callback=self.callback_set_tsne_progress,
update_progress_each_iter=10,
n_components=3,
perplexity=nmf_training_params.perplexity,
learning_rate=nmf_training_params.learning_rate,
n_iter=nmf_training_params.tsne_max_iter,
metric=custom_doc_distance_callback(nmf_training_params.scaling_ratio),
init="pca",
random_state=nmf_training_params.random_seed
)
"""
print("参数值：")
print(nmf_training_params.n_neighbors)
print(nmf_training_params.min_dist)
self.umap = UMAP(n_neighbors=nmf_training_params.n_neighbors,
n_components=nmf_training_params.dimension,
metric=custom_doc_distance_callback(
nmf_training_params.scaling_ratio),
min_dist=nmf_training_params.min_dist)
# 开始训练
self.start_nmf_and_umap()
X = dataset.data y = dataset.target # Generate shape graph using KeplerMapper mapper = KeplerMapper(verbose=1) lens = mapper.fit_transform(X, projection=[0]) graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.2) # Convert to a DyNeuGraph dG = DyNeuGraph(G=graph, y=y) # Define some custom_layouts dG.add_custom_layout(lens, name='lens') dG.add_custom_layout(nx.spring_layout, name='nx.spring') dG.add_custom_layout(nx.kamada_kawai_layout, name='nx.kamada_kawai') dG.add_custom_layout(nx.spectral_layout, name='nx.spectral') dG.add_custom_layout(nx.circular_layout, name='nx.circular') # Configure some projections pca = PCA(2, random_state=1) tsne = TSNE(2, init='pca', random_state=1) umap = UMAP(n_components=2, init=pca.fit_transform(X)) # Add projections as custom_layouts dG.add_custom_layout(pca.fit_transform(X), name='PCA') dG.add_custom_layout(tsne.fit_transform(X), name='TSNE') dG.add_custom_layout(umap.fit_transform(X, y=None), name='UMAP') # Visualize dG.visualize(static=True, show=True)
def test_bad_transform_data():
u = UMAP().fit([[1, 1, 1, 1]])
assert_raises(ValueError, u.transform, [[0, 0, 0, 0]])
def test_haversine_embed_to_highd():
u = UMAP(n_components=3, output_metric="haversine")
assert_raises(ValueError, u.fit, nn_data)
def repeated_points_large_n():
model = UMAP(n_neighbors=5, unique=True).fit(repetition_dense)
assert_equal(model._n_neighbors, 3)
def repeated_points_small_dense_binary():
model = UMAP(n_neighbors=3, unique=True).fit(binary_repeats)
# assert_equal(np.unique(binary_repeats[0:2], axis=0).shape[0],1)
assert_equal(np.unique(model.embedding_[0:2], axis=0).shape[0], 1)
def repeated_points_large_dense_binary():
model = UMAP(n_neighbors=3, unique=True, force_approximation_algorithm=True).fit(
binary_repeats
)
assert_equal(np.unique(model.embedding_[0:2], axis=0).shape[0], 1)
def test_umap_fit_params():
# x and y are required to be the same length
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(257,))
assert_raises(ValueError, u.fit, x, y)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(255,))
assert_raises(ValueError, u.fit, x, y)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
assert_raises(ValueError, u.fit, x, [])
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(256,))
res = u.fit(x, y)
assert isinstance(res, UMAP)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
res = u.fit(x)
assert isinstance(res, UMAP)
def repeated_points_small_sparse_spatial():
model = UMAP(n_neighbors=3, unique=True).fit(sparse_spatial_data_repeats)
assert_equal(np.unique(model.embedding_[0:2], axis=0).shape[0], 1)
