Visualizing embeddings in 3D

Mar 10, 2022
Open in Github

The example uses PCA to reduce the dimensionality fo the embeddings from 1536 to 3. Then we can visualize the data points in a 3D plot. The small dataset dbpedia_samples.jsonl is curated by randomly sampling 200 samples from DBpedia validation dataset.

import pandas as pd
samples = pd.read_json("data/dbpedia_samples.jsonl", lines=True)
categories = sorted(samples["category"].unique())
print("Categories of DBpedia samples:", samples["category"].value_counts())
Categories of DBpedia samples: Artist                    21
Film                      19
Plant                     19
OfficeHolder              18
Company                   17
NaturalPlace              16
Athlete                   16
Village                   12
WrittenWork               11
Building                  11
Album                     11
Animal                    11
EducationalInstitution    10
MeanOfTransportation       8
Name: category, dtype: int64
text category
0 Morada Limited is a textile company based in ... Company
1 The Armenian Mirror-Spectator is a newspaper ... WrittenWork
2 Mt. Kinka (金華山 Kinka-zan) also known as Kinka... NaturalPlace
3 Planning the Play of a Bridge Hand is a book ... WrittenWork
4 Wang Yuanping (born 8 December 1976) is a ret... Athlete
from utils.embeddings_utils import get_embeddings
# NOTE: The following code will send a query of batch size 200 to /embeddings
matrix = get_embeddings(samples["text"].to_list(), model="text-embedding-ada-002")
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()
%matplotlib widget
import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(projection='3d')
cmap = plt.get_cmap("tab20")

# Plot each sample category individually such that we can set label name.
for i, cat in enumerate(categories):
    sub_matrix = np.array(samples[samples["category"] == cat]["embed_vis"].to_list())
    x=sub_matrix[:, 0]
    y=sub_matrix[:, 1]
    z=sub_matrix[:, 2]
    colors = [cmap(i/len(categories))] * len(sub_matrix)
    ax.scatter(x, y, zs=z, zdir='z', c=colors, label=cat)

ax.legend(bbox_to_anchor=(1.1, 1))
<matplotlib.legend.Legend at 0x1622180a0>
image generated by notebook