def run(self, n_samples=1000):
x, y = self.load_data(n_samples=n_samples)
print("-" * 80)
start = time.time()
SKLTSNE(
early_exaggeration=12, learning_rate=self.learning_rate, angle=0.5,
perplexity=self.perplexity, init="random", verbose=True,
).fit_transform(x)
print("scikit-learn t-SNE:", time.time() - start)
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)
init = openTSNE.initialization.random(
x, n_components=2, random_state=random_state
)
start = time.time()
SKLTSNE(
early_exaggeration=12,
learning_rate=self.learning_rate,
angle=0.5,
perplexity=self.perplexity,
init=init,
verbose=True,
random_state=random_state
).fit_transform(x)
print("scikit-learn t-SNE:", time.time() - start, flush=True)
def run(perplexity=30, learning_rate=100, n_jobs=4):
x, y = get_mouse_60k()
# x, y = get_fashion_mnist()
angle = 0.5
ee = 12
metric = 'euclidean'
print(x.shape)
start = time.time()
tsne = TSNE(
perplexity=perplexity,
learning_rate=learning_rate,
early_exaggeration=ee,
n_jobs=n_jobs,
theta=angle,
initialization='random',
metric=metric,
n_components=2,
n_iter=750,
early_exaggeration_iter=250,
neighbors='approx',
negative_gradient_method='fft',
min_num_intervals=10,
ints_in_interval=1,
late_exaggeration_iter=0,
late_exaggeration=2.,
callbacks=ErrorLogger(),
)
# x = PCA(n_components=50).fit_transform(x)
embedding = tsne.fit(x)
print('-' * 80)
print('tsne', time.time() - start)
plt.title('tsne')
plot(embedding, y)
return
x = np.ascontiguousarray(x.astype(np.float64))
from fitsne import FItSNE
start = time.time()
embedding = FItSNE(
x,
2,
perplexity=perplexity,
stop_lying_iter=250,
ann_not_vptree=True,
early_exag_coeff=ee,
nthreads=n_jobs,
theta=angle,
)
print('-' * 80)
print('fft interp %.4f' % (time.time() - start))
plt.title('fft interp')
plot(embedding, y)
plt.show()
return
init = PCA(n_components=2).fit_transform(x)
start = time.time()
embedding = MulticoreTSNE(early_exaggeration=ee,
learning_rate=learning_rate,
perplexity=perplexity,
n_jobs=n_jobs,
cheat_metric=False,
angle=angle,
init=init,
metric=metric,
verbose=True).fit_transform(x)
print('-' * 80)
print('mctsne', time.time() - start)
plt.title('mctsne')
plot(embedding, y)
plt.show()
start = time.time()
embedding = SKLTSNE(
early_exaggeration=ee,
learning_rate=learning_rate,
angle=angle,
perplexity=perplexity,
init='pca',
metric=metric,
).fit_transform(x)
print('-' * 80)
print('sklearn', time.time() - start)
plt.title('sklearn')
plot(embedding, y)
plt.show()