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"""Embedding visualization helpers."""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.manifold import TSNE
def plot_tsne(embeddings: np.ndarray, labels: list[str]) -> None:
if embeddings.size == 0 or embeddings.ndim != 2:
raise ValueError("embeddings must be a non-empty 2D array")
if not labels:
raise ValueError("labels must be a non-empty list")
if embeddings.shape[0] != len(labels):
raise ValueError("number of samples in embeddings must equal length of labels")
if embeddings.shape[1] < 2:
raise ValueError("embeddings must have at least 2 features for t-SNE visualization")
reducer = TSNE(n_components=2, init="pca", learning_rate="auto")
projection = reducer.fit_transform(embeddings)
df = pd.DataFrame(
{
"x": projection[:, 0],
"y": projection[:, 1],
"label": labels,
}
)
plt.figure()
sns.scatterplot(data=df, x="x", y="y", hue="label", palette="tab10", s=50)
plt.legend(title="Labels", loc="best")
plt.tight_layout()
plt.show()
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