openai-cookbook/examples/Visualizing_embeddings_in_2D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the embeddings in 2D\n",
    "\n",
    "We will use t-SNE to reduce the dimensionality of the embeddings from 2048 to 2. Once the embeddings are reduced to two dimensions, we can plot them in a 2D scatter plot. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Reduce dimensionality\n",
    "\n",
    "We reduce the dimensionality to 2 dimensions using t-SNE decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 2)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "\n",
    "# Load the embeddings\n",
    "# If you have not run the \"Obtain_dataset.ipynb\" notebook, you can download the datafile from here: https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\n",
    "datafile_path = \"./data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "df = pd.read_csv(datafile_path)\n",
    "\n",
    "# Convert to a list of lists of floats\n",
    "matrix = np.array(df.ada_similarity.apply(eval).to_list())\n",
    "\n",
    "# Create a t-SNE model and transform the data\n",
    "tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)\n",
    "vis_dims = tsne.fit_transform(matrix)\n",
    "vis_dims.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Plotting the embeddings\n",
    "\n",
    "We colour each review by its star rating, ranging from red to green."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe a decent data separation even in the reduced 2 dimensions. There seems to be a cluster of mostly negative reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amazon ratings visualized in language using t-SNE')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "colors = [\"red\", \"darkorange\", \"gold\", \"turquoise\", \"darkgreen\"]\n",
    "x = [x for x,y in vis_dims]\n",
    "y = [y for x,y in vis_dims]\n",
    "color_indices = df.Score.values - 1\n",
    "\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)\n",
    "for score in [0,1,2,3,4]:\n",
    "    avg_x = np.array(x)[df.Score-1==score].mean()\n",
    "    avg_y = np.array(y)[df.Score-1==score].mean()\n",
    "    color = colors[score]\n",
    "    plt.scatter(avg_x, avg_y, marker='x', color=color, s=100)\n",
    "\n",
    "plt.title(\"Amazon ratings visualized in language using t-SNE\")"
   ]
  }
 ],
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