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   "source": [
    "## Visualizing the embeddings in W&B\n",
    "\n",
    "We will upload the data to [Weights & Biases](http://wandb.ai) and use an [Embedding Projector](https://docs.wandb.ai/ref/app/features/panels/weave/embedding-projector) to visualize the embeddings using common dimension reduction algorithms like PCA, UMAP, and t-SNE. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Log the data to W&B\n",
    "\n",
    "We create a [W&B Table](https://docs.wandb.ai/guides/data-vis/log-tables) with the original data and the embeddings. Each review is a new row and the 1536 embedding floats are given their own column named `emb_{i}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "\n",
    "# Load the embeddings\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.embedding.apply(eval).to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb\n",
    "\n",
    "original_cols = df.columns[1:-1].tolist()\n",
    "embedding_cols = ['emb_'+str(idx) for idx in range(len(matrix[0]))]\n",
    "table_cols = original_cols + embedding_cols\n",
    "\n",
    "with wandb.init(project='openai_embeddings'):\n",
    "    table = wandb.Table(columns=table_cols)\n",
    "    for i, row in enumerate(df.to_dict(orient=\"records\")):\n",
    "        original_data = [row[col_name] for col_name in original_cols]\n",
    "        embedding_data = matrix[i].tolist()\n",
    "        table.add_data(*(original_data + embedding_data))\n",
    "    wandb.log({'openai_embedding_table': table})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Render as 2D Projection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After navigating to the W&B run link, we click the ⚙️ icon in the top right of the Table and change \"Render As:\" to \"Combined 2D Projection\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: http://wandb.me/openai_embeddings"
   ]
  }
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