def transform(self):
affinities = Affinities()
affinities.P = self.data.tocsr()
if self.learning_rate == "auto":
n = self.init.shape[0]
self.learning_rate = n / self.early_exaggeration
emb = openTSNE.TSNEEmbedding(
embedding=self.init,
affinities=affinities,
negative_gradient_method=self.negative_gradient_method,
learning_rate=self.learning_rate,
n_jobs=self.n_jobs,
max_step_norm=self.max_step_norm,
random_state=self.random_state,
callbacks_every_iters=self.save_iter_freq,
callbacks=self.saver,
optimizer=GDAnneal(),
)
self.data_ = emb.optimize(
n_iter=self.n_iter,
exaggeration=self.exaggeration,
momentum=self.momentum,
eps=self.eps,
a=self.a,
r=self.r,
elastic_const=self.elastic_const,
inplace=True,
n_jobs=self.n_jobs,
propagate_exception=True,
)
return self.data_
def run(self, n_samples=1000):
x, y = self.load_data(n_samples=n_samples)
print("-" * 80)
start = time.time()
start_aff = time.time()
affinity = openTSNE.affinity.PerplexityBasedNN(
x,
perplexity=self.perplexity,
method="approx",
n_jobs=self.n_jobs,
)
print("openTSNE: NN search", time.time() - start_aff)
init = openTSNE.initialization.random(x.shape[0], n_components=2)
start_optim = time.time()
embedding = openTSNE.TSNEEmbedding(
init,
affinity,
learning_rate=self.learning_rate,
n_jobs=self.n_jobs,
negative_gradient_method="fft",
theta=0.5,
min_num_intervals=10,
ints_in_interval=1,
)
embedding.optimize(250, exaggeration=12, momentum=0.8, inplace=True)
embedding.optimize(750, momentum=0.5, inplace=True)
print("openTSNE: Optimization", time.time() - start_optim)
print("openTSNE: Full", time.time() - start)
def test_transform_with_standard_affinity(self):
init = openTSNE.initialization.random(self.x)
aff = openTSNE.affinity.PerplexityBasedNN(self.x, 5, method="exact")
embedding = openTSNE.TSNEEmbedding(init, aff, negative_gradient_method="bh")
embedding.optimize(100, inplace=True)
# This should not raise an error
embedding.transform(self.x_test)
def test_2(self):
init = initialization.pca(self.x)
aff = affinity.MultiscaleMixture(self.x, perplexities=[5, 30])
embedding = openTSNE.TSNEEmbedding(init, aff)
embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True)
self.eval_embedding(embedding, self.y)
new_embedding = embedding.transform(self.x)
self.eval_embedding(new_embedding, self.y, "transform")
def test_1(self):
init = initialization.pca(self.x)
aff = affinity.PerplexityBasedNN(self.x, perplexity=30)
embedding = openTSNE.TSNEEmbedding(init, aff)
embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True)
self.eval_embedding(embedding, self.y)
new_embedding = embedding.transform(self.x)
self.eval_embedding(new_embedding, self.y, "transform")
def test_transform_with_multiscale_affinity(self):
init = openTSNE.initialization.random(self.x)
aff = openTSNE.affinity.Multiscale(self.x, [2, 5], method="exact")
embedding = openTSNE.TSNEEmbedding(init,
aff,
negative_gradient_method="bh")
embedding.optimize(100, inplace=True)
# This should not raise an error
embedding.transform(self.x_test)
def test_transform_with_nonstandard_affinity(self):
"""Should raise an informative error when a non-standard affinity is used
with `transform`."""
init = openTSNE.initialization.random(self.x)
aff = openTSNE.affinity.Multiscale(self.x, [2, 5], method="exact")
embedding = openTSNE.TSNEEmbedding(init, aff, negative_gradient_method="bh")
embedding.optimize(100, inplace=True)
with self.assertRaises(TypeError):
embedding.transform(self.x_test)
def prepare_embedding(self, affinities, initialization):
"""Prepare an embedding object with appropriate parameters, given some
affinities and initialization."""
return openTSNE.TSNEEmbedding(
initialization,
affinities,
learning_rate=self.learning_rate,
theta=self.theta,
min_num_intervals=self.min_num_intervals,
ints_in_interval=self.ints_in_interval,
n_jobs=self.n_jobs,
negative_gradient_method=self.negative_gradient_method,
callbacks=self.callbacks,
callbacks_every_iters=self.callbacks_every_iters,
)
def test_fft_transform_with_point_subsets_using_perplexity_nn(self):
x_train, x_test = train_test_split(
self.iris.data, test_size=0.33, random_state=42
)
# Set up the initial embedding
init = openTSNE.initialization.pca(x_train)
affinity = openTSNE.affinity.PerplexityBasedNN(x_train, method="exact")
embedding = openTSNE.TSNEEmbedding(
init, affinity, negative_gradient_method="fft", random_state=42
)
embedding.optimize(n_iter=100, inplace=True)
# The test set contains 50 samples, so let's verify on half of those
transform_params = dict(n_iter=0)
new_embedding_1 = embedding.transform(x_test, **transform_params)[:25]
new_embedding_2 = embedding.transform(x_test[:25], **transform_params)
np.testing.assert_equal(new_embedding_1, new_embedding_2)
def run(self, n_samples=1000, random_state=None):
x, y = self.load_data(n_samples=n_samples)
print("-" * 80)
print("Random state", random_state)
print("-" * 80, flush=True)
random_state = check_random_state(random_state)
start = time.time()
start_aff = time.time()
affinity = openTSNE.affinity.PerplexityBasedNN(
x,
perplexity=self.perplexity,
method="annoy",
n_jobs=self.n_jobs,
random_state=random_state,
verbose=True,
)
print("openTSNE: NN search", time.time() - start_aff, flush=True)
init = openTSNE.initialization.random(
x,
n_components=2,
random_state=random_state,
verbose=True,
)
start_optim = time.time()
embedding = openTSNE.TSNEEmbedding(
init,
affinity,
learning_rate=self.learning_rate,
n_jobs=self.n_jobs,
negative_gradient_method="fft",
random_state=random_state,
verbose=True,
)
embedding.optimize(250, exaggeration=12, momentum=0.8, inplace=True)
embedding.optimize(750, momentum=0.5, inplace=True)
print("openTSNE: Optimization", time.time() - start_optim)
print("openTSNE: Full", time.time() - start, flush=True)
# fast_tsne(x, nthreads=1)
affinities = openTSNE.affinity.PerplexityBasedNN(
x,
perplexity=30,
metric="cosine",
method="approx",
n_jobs=-1,
random_state=0,
verbose=True,
)
init = openTSNE.initialization.spectral(affinities.P, verbose=True)
embedding = openTSNE.TSNEEmbedding(
init,
affinities,
negative_gradient_method="fft",
n_jobs=-1,
random_state=0,
verbose=True,
)
embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(n_iter=500, momentum=0.8, inplace=True)
import matplotlib.pyplot as plt
plt.scatter(embedding[:, 0], embedding[:, 1], s=1)
plt.show()
def fit(self,
X: np.ndarray,
Y: np.ndarray = None) -> openTSNE.TSNEEmbedding:
# Sparse data are not supported
if sp.issparse(X):
raise TypeError(
"A sparse matrix was passed, but dense data is required. Use "
"X.toarray() to convert to a dense numpy array.")
# Build up the affinity matrix, using multiscale if needed
if self.multiscale:
# The local perplexity should be on the order ~50 while the higher
# perplexity should be on the order ~N/50
if not isinstance(self.perplexity, Iterable):
raise ValueError(
"Perplexity should be an instance of `Iterable`, `%s` "
"given." % type(self.perplexity).__name__)
affinities = openTSNE.affinity.Multiscale(
X,
perplexities=self.perplexity,
metric=self.metric,
method=self.neighbors,
random_state=self.random_state,
n_jobs=self.n_jobs)
else:
if isinstance(self.perplexity, Iterable):
raise ValueError(
"Perplexity should be an instance of `float`, `%s` "
"given." % type(self.perplexity).__name__)
affinities = openTSNE.affinity.PerplexityBasedNN(
X,
perplexity=self.perplexity,
metric=self.metric,
method=self.neighbors,
random_state=self.random_state,
n_jobs=self.n_jobs)
# Create an initial embedding
if isinstance(self.initialization, np.ndarray):
initialization = self.initialization
elif self.initialization == "pca":
initialization = openTSNE.initialization.pca(
X, self.n_components, random_state=self.random_state)
elif self.initialization == "random":
initialization = openTSNE.initialization.random(
X, self.n_components, random_state=self.random_state)
else:
raise ValueError(
"Invalid initialization `%s`. Please use either `pca` or "
"`random` or provide a numpy array." % self.initialization)
embedding = openTSNE.TSNEEmbedding(
initialization,
affinities,
learning_rate=self.learning_rate,
theta=self.theta,
min_num_intervals=self.min_num_intervals,
ints_in_interval=self.ints_in_interval,
n_jobs=self.n_jobs,
negative_gradient_method=self.negative_gradient_method,
callbacks=self.callbacks,
callbacks_every_iters=self.callbacks_every_iters,
)
# Run standard t-SNE optimization
embedding.optimize(
n_iter=self.early_exaggeration_iter,
exaggeration=self.early_exaggeration,
inplace=True,
momentum=0.5,
propagate_exception=True,
)
embedding.optimize(
n_iter=self.n_iter,
exaggeration=self.exaggeration,
inplace=True,
momentum=0.8,
propagate_exception=True,
)
return embedding
with Timer("Loading data..."):
with gzip.open("../examples/data/macosko_2015.pkl.gz", "rb") as f:
data = pickle.load(f)
x = data["pca_50"]
y, cluster_ids = data["CellType1"], data["CellType2"]
with Timer("Finding nearest neighbors..."):
affinities = openTSNE.affinity.PerplexityBasedNN(
x, perplexity=30, method="approx", n_jobs=8, random_state=3
)
with Timer("Creating initial embedding..."):
init = openTSNE.initialization.random(x, random_state=3)
with Timer("Creating embedding object..."):
embedding = openTSNE.TSNEEmbedding(
init,
affinities,
negative_gradient_method="fft",
n_jobs=8,
callbacks=openTSNE.callbacks.ErrorLogger(),
random_state=3,
)
with Timer("Running optimization..."):
embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(n_iter=750, momentum=0.8, inplace=True)
