Skip to content

[MRG] Accelerate examples/manifold/plot_t_sne_perplexity.py #21636

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Nov 16, 2021
Merged

[MRG] Accelerate examples/manifold/plot_t_sne_perplexity.py #21636

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 22 additions & 5 deletions examples/manifold/plot_t_sne_perplexity.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,12 +34,14 @@
from sklearn import manifold, datasets
from time import time

n_samples = 300
n_samples = 150
n_components = 2
(fig, subplots) = plt.subplots(3, 5, figsize=(15, 8))
perplexities = [5, 30, 50, 100]

X, y = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
X, y = datasets.make_circles(
n_samples=n_samples, factor=0.5, noise=0.05, random_state=0
)

red = y == 0
green = y == 1
Expand All @@ -56,7 +58,12 @@

t0 = time()
tsne = manifold.TSNE(
n_components=n_components, init="random", random_state=0, perplexity=perplexity
n_components=n_components,
init="random",
random_state=0,
perplexity=perplexity,
learning_rate="auto",
n_iter=300,
)
Y = tsne.fit_transform(X)
t1 = time()
Expand All @@ -81,7 +88,12 @@

t0 = time()
tsne = manifold.TSNE(
n_components=n_components, init="random", random_state=0, perplexity=perplexity
n_components=n_components,
init="random",
random_state=0,
perplexity=perplexity,
learning_rate="auto",
n_iter=300,
)
Y = tsne.fit_transform(X)
t1 = time()
Expand Down Expand Up @@ -114,7 +126,12 @@

t0 = time()
tsne = manifold.TSNE(
n_components=n_components, init="random", random_state=0, perplexity=perplexity
n_components=n_components,
init="random",
random_state=0,
perplexity=perplexity,
learning_rate="auto",
n_iter=400,
)
Y = tsne.fit_transform(X)
t1 = time()
Expand Down