该示例使用 PCA 将嵌入的维数从 1536 减少到 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 个样本来管理的。
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
| text | category |
|---|---|
| Morada Limited is a textile company based in … | Company |
| The Armenian Mirror-Spectator is a newspaper … | WrittenWork |
| Mt. Kinka (金華山 Kinka-zan) also known as Kinka… | NaturalPlace |
| Planning the Play of a Bridge Hand is a book … | WrittenWork |
| Wang Yuanping (born 8 December 1976) is a ret… | Athlete |
from openai.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(), engine="text-embedding-ada-002")
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.绘制较低维度的嵌入
%matplotlib widget
import matplotlib.pyplot as plt
import numpy as npfig = 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>
