Quick Start: Embed Digits into 2D¶

This notebook demonstrates SG-t-SNE-Pi on the sklearn digits dataset (1,797 samples, 64 features). It runs in about 30 seconds and requires no external downloads.

In [1]:

from sklearn.datasets import load_digits

digits = load_digits()
X, y = digits.data, digits.target
print(f"Data shape: {X.shape}  (n_samples, n_features)")
print(f"Labels: {sorted(set(y))}")

from sklearn.datasets import load_digits digits = load_digits() X, y = digits.data, digits.target print(f"Data shape: {X.shape} (n_samples, n_features)") print(f"Labels: {sorted(set(y))}")

Data shape: (1797, 64)  (n_samples, n_features)
Labels: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9)]

In [2]:

from pysgtsnepi import SGtSNEpi

model = SGtSNEpi(d=2, lambda_=10, n_neighbors=15, random_state=0)
Y = model.fit_transform(X)
print(f"Embedding shape: {Y.shape}")

from pysgtsnepi import SGtSNEpi model = SGtSNEpi(d=2, lambda_=10, n_neighbors=15, random_state=0) Y = model.fit_transform(X) print(f"Embedding shape: {Y.shape}")

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

Embedding shape: (1797, 2)

In [3]:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 8))
scatter = ax.scatter(Y[:, 0], Y[:, 1], c=y, cmap="tab10", s=5, alpha=0.7)
ax.set_aspect("equal")
ax.set_title("SG-t-SNE-Pi embedding of sklearn digits")
ax.set_xlabel("Dimension 1")
ax.set_ylabel("Dimension 2")
cbar = fig.colorbar(scatter, ax=ax, ticks=range(10))
cbar.set_label("Digit")
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(8, 8)) scatter = ax.scatter(Y[:, 0], Y[:, 1], c=y, cmap="tab10", s=5, alpha=0.7) ax.set_aspect("equal") ax.set_title("SG-t-SNE-Pi embedding of sklearn digits") ax.set_xlabel("Dimension 1") ax.set_ylabel("Dimension 2") cbar = fig.colorbar(scatter, ax=ax, ticks=range(10)) cbar.set_label("Digit") plt.tight_layout() plt.show()