232 lines
6.8 KiB
Plaintext
232 lines
6.8 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## K-means Clustering in Python using OpenAI\n",
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"\n",
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"We use a simple k-means algorithm to demonstrate how clustering can be done. Clustering can help discover valuable, hidden groupings within the data. The dataset is created in the [Get_embeddings_from_dataset Notebook](Get_embeddings_from_dataset.ipynb)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1000, 1536)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# imports\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from ast import literal_eval\n",
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"\n",
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"# load data\n",
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"datafile_path = \"./data/fine_food_reviews_with_embeddings_1k.csv\"\n",
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"\n",
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"df = pd.read_csv(datafile_path)\n",
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"df[\"embedding\"] = df.embedding.apply(literal_eval).apply(np.array) # convert string to numpy array\n",
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"matrix = np.vstack(df.embedding.values)\n",
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"matrix.shape\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1. Find the clusters using K-means"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We show the simplest use of K-means. You can pick the number of clusters that fits your use case best."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/homebrew/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Cluster\n",
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"0 4.105691\n",
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"1 4.191176\n",
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"2 4.215613\n",
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"3 4.306590\n",
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"Name: Score, dtype: float64"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.cluster import KMeans\n",
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"\n",
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"n_clusters = 4\n",
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"\n",
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"kmeans = KMeans(n_clusters=n_clusters, init=\"k-means++\", random_state=42)\n",
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"kmeans.fit(matrix)\n",
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"labels = kmeans.labels_\n",
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"df[\"Cluster\"] = labels\n",
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"\n",
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"df.groupby(\"Cluster\").Score.mean().sort_values()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.manifold import TSNE\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"tsne = TSNE(n_components=2, perplexity=15, random_state=42, init=\"random\", learning_rate=200)\n",
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"vis_dims2 = tsne.fit_transform(matrix)\n",
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"\n",
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"x = [x for x, y in vis_dims2]\n",
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"y = [y for x, y in vis_dims2]\n",
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"\n",
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"for category, color in enumerate([\"purple\", \"green\", \"red\", \"blue\"]):\n",
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" xs = np.array(x)[df.Cluster == category]\n",
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" ys = np.array(y)[df.Cluster == category]\n",
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" plt.scatter(xs, ys, color=color, alpha=0.3)\n",
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"\n",
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" avg_x = xs.mean()\n",
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" avg_y = ys.mean()\n",
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"\n",
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" plt.scatter(avg_x, avg_y, marker=\"x\", color=color, s=100)\n",
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"plt.title(\"Clusters identified visualized in language 2d using t-SNE\")\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualization of clusters in a 2d projection. In this run, the green cluster (#1) seems quite different from the others. Let's see a few samples from each cluster."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Text samples in the clusters & naming the clusters\n",
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"\n",
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"Let's show random samples from each cluster. We'll use gpt-4 to name the clusters, based on a random sample of 5 reviews from that cluster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from openai import OpenAI\n",
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"import os\n",
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"\n",
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"client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"<your OpenAI API key if not set as env var>\"))\n",
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"\n",
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"# Reading a review which belong to each group.\n",
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"rev_per_cluster = 5\n",
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"\n",
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"for i in range(n_clusters):\n",
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" print(f\"Cluster {i} Theme:\", end=\" \")\n",
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"\n",
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" reviews = \"\\n\".join(\n",
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" df[df.Cluster == i]\n",
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" .combined.str.replace(\"Title: \", \"\")\n",
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" .str.replace(\"\\n\\nContent: \", \": \")\n",
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" .sample(rev_per_cluster, random_state=42)\n",
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" .values\n",
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" )\n",
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"\n",
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" messages = [\n",
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" {\"role\": \"user\", \"content\": f'What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\"\"\"\\n{reviews}\\n\"\"\"\\n\\nTheme:'}\n",
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" ]\n",
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"\n",
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" response = client.chat.completions.create(\n",
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" model=\"gpt-4\",\n",
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" messages=messages,\n",
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" temperature=0,\n",
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" max_tokens=64,\n",
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" top_p=1,\n",
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" frequency_penalty=0,\n",
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" presence_penalty=0)\n",
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" print(response.choices[0].message.content.replace(\"\\n\", \"\"))\n",
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"\n",
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" sample_cluster_rows = df[df.Cluster == i].sample(rev_per_cluster, random_state=42)\n",
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" for j in range(rev_per_cluster):\n",
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" print(sample_cluster_rows.Score.values[j], end=\", \")\n",
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" print(sample_cluster_rows.Summary.values[j], end=\": \")\n",
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" print(sample_cluster_rows.Text.str[:70].values[j])\n",
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"\n",
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" print(\"-\" * 100)\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It's important to note that clusters will not necessarily match what you intend to use them for. A larger amount of clusters will focus on more specific patterns, whereas a small number of clusters will usually focus on largest discrepencies in the data."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "openai",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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},
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"vscode": {
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"interpreter": {
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"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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