openai-cookbook/examples/Visualizing_embeddings_in_3D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "983ef639-fbf4-4912-b593-9cf08aeb11cd",
   "metadata": {},
   "source": [
    "# Visualizing embeddings in 3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9ea9a8-675d-4e3a-a8f7-6f4563df84ad",
   "metadata": {},
   "source": [
    "The example uses [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) to reduce the dimensionality fo the embeddings from 2048 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](https://www.kaggle.com/danofer/dbpedia-classes?select=DBPEDIA_val.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df5f2c3-ddbb-4cc4-9205-4c0af1670562",
   "metadata": {},
   "source": [
    "### 1. Load the dataset and query embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "133dfc2a-9dbd-4a5a-96fa-477272f7af5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories of DBpedia samples: Artist                    21\n",
      "Film                      19\n",
      "Plant                     19\n",
      "OfficeHolder              18\n",
      "Company                   17\n",
      "NaturalPlace              16\n",
      "Athlete                   16\n",
      "Village                   12\n",
      "WrittenWork               11\n",
      "Building                  11\n",
      "Album                     11\n",
      "Animal                    11\n",
      "EducationalInstitution    10\n",
      "MeanOfTransportation       8\n",
      "Name: category, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Morada Limited is a textile company based in ...</td>\n",
       "      <td>Company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Armenian Mirror-Spectator is a newspaper ...</td>\n",
       "      <td>WrittenWork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mt. Kinka (金華山 Kinka-zan) also known as Kinka...</td>\n",
       "      <td>NaturalPlace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Planning the Play of a Bridge Hand is a book ...</td>\n",
       "      <td>WrittenWork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wang Yuanping (born 8 December 1976) is a ret...</td>\n",
       "      <td>Athlete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text      category\n",
       "0   Morada Limited is a textile company based in ...       Company\n",
       "1   The Armenian Mirror-Spectator is a newspaper ...   WrittenWork\n",
       "2   Mt. Kinka (金華山 Kinka-zan) also known as Kinka...  NaturalPlace\n",
       "3   Planning the Play of a Bridge Hand is a book ...   WrittenWork\n",
       "4   Wang Yuanping (born 8 December 1976) is a ret...       Athlete"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "samples = pd.read_json(\"data/dbpedia_samples.jsonl\", lines=True)\n",
    "categories = sorted(samples[\"category\"].unique())\n",
    "print(\"Categories of DBpedia samples:\", samples[\"category\"].value_counts())\n",
    "samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19874e3e-a216-48cc-a27b-acb73854d832",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai.embeddings_utils import get_embeddings\n",
    "# NOTE: The following code will send a query of batch size 200 to /embeddings\n",
    "matrix = get_embeddings(samples[\"text\"].to_list(), engine=\"text-embedding-ada-002\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d410c268-d8a7-4979-887c-45b1d382dda9",
   "metadata": {},
   "source": [
    "### 2. Reduce the embedding dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5410068-f3da-490c-8576-48e84a8728de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "vis_dims = pca.fit_transform(matrix)\n",
    "samples[\"embed_vis\"] = vis_dims.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6565f57-59c6-4d36-a094-3cbbd9ddeb4c",
   "metadata": {},
   "source": [
    "### 3. Plot the embeddings of lower dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b17caad3-f0de-4115-83eb-55434a132acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15e6af130>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "cmap = plt.get_cmap(\"tab20\")\n",
    "\n",
    "# Plot each sample category individually such that we can set label name.\n",
    "for i, cat in enumerate(categories):\n",
    "    sub_matrix = np.array(samples[samples[\"category\"] == cat][\"embed_vis\"].to_list())\n",
    "    x=sub_matrix[:, 0]\n",
    "    y=sub_matrix[:, 1]\n",
    "    z=sub_matrix[:, 2]\n",
    "    colors = [cmap(i/len(categories))] * len(sub_matrix)\n",
    "    ax.scatter(x, y, zs=z, zdir='z', c=colors, label=cat)\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_zlabel('z')\n",
    "ax.legend(bbox_to_anchor=(1.1, 1))"
   ]
  }
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