2022年3月10日

在3D中可视化嵌入

该示例使用PCA将嵌入维度从1536降至3。然后我们可以在3D图中可视化数据点。小型数据集dbpedia_samples.jsonl是通过从DBpedia验证数据集中随机抽取200个样本整理而成。

1. 加载数据集和查询嵌入

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())
samples.head()

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

	文本	类别
0	Morada Limited是一家位于...的纺织公司	公司
1	《亚美尼亚镜报》是一份报纸...	书面作品
2	Mt. Kinka (金華山 Kinka-zan) also known as Kinka...	NaturalPlace
3	《桥牌手牌打法规划》是一本书...	WrittenWork
4	王元平（1976年12月8日出生）是一位退役...	运动员

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-3-small")

2. 降低嵌入维度

from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()

3. 绘制低维嵌入图

%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.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.legend(bbox_to_anchor=(1.1, 1))

<matplotlib.legend.Legend at 0x1622180a0>