Ilan Bigio
231
codex-cli/examples/prompt-analyzer/template/Clustering.ipynb
Normal file
231
codex-cli/examples/prompt-analyzer/template/Clustering.ipynb
Normal file
@@ -0,0 +1,231 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## K-means Clustering in Python using OpenAI\n",
|
||||
"\n",
|
||||
"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)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1000, 1536)"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# imports\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from ast import literal_eval\n",
|
||||
"\n",
|
||||
"# load data\n",
|
||||
"datafile_path = \"./data/fine_food_reviews_with_embeddings_1k.csv\"\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(datafile_path)\n",
|
||||
"df[\"embedding\"] = df.embedding.apply(literal_eval).apply(np.array) # convert string to numpy array\n",
|
||||
"matrix = np.vstack(df.embedding.values)\n",
|
||||
"matrix.shape\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1. Find the clusters using K-means"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We show the simplest use of K-means. You can pick the number of clusters that fits your use case best."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/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",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Cluster\n",
|
||||
"0 4.105691\n",
|
||||
"1 4.191176\n",
|
||||
"2 4.215613\n",
|
||||
"3 4.306590\n",
|
||||
"Name: Score, dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.cluster import KMeans\n",
|
||||
"\n",
|
||||
"n_clusters = 4\n",
|
||||
"\n",
|
||||
"kmeans = KMeans(n_clusters=n_clusters, init=\"k-means++\", random_state=42)\n",
|
||||
"kmeans.fit(matrix)\n",
|
||||
"labels = kmeans.labels_\n",
|
||||
"df[\"Cluster\"] = labels\n",
|
||||
"\n",
|
||||
"df.groupby(\"Cluster\").Score.mean().sort_values()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.manifold import TSNE\n",
|
||||
"import matplotlib\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"tsne = TSNE(n_components=2, perplexity=15, random_state=42, init=\"random\", learning_rate=200)\n",
|
||||
"vis_dims2 = tsne.fit_transform(matrix)\n",
|
||||
"\n",
|
||||
"x = [x for x, y in vis_dims2]\n",
|
||||
"y = [y for x, y in vis_dims2]\n",
|
||||
"\n",
|
||||
"for category, color in enumerate([\"purple\", \"green\", \"red\", \"blue\"]):\n",
|
||||
" xs = np.array(x)[df.Cluster == category]\n",
|
||||
" ys = np.array(y)[df.Cluster == category]\n",
|
||||
" plt.scatter(xs, ys, color=color, alpha=0.3)\n",
|
||||
"\n",
|
||||
" avg_x = xs.mean()\n",
|
||||
" avg_y = ys.mean()\n",
|
||||
"\n",
|
||||
" plt.scatter(avg_x, avg_y, marker=\"x\", color=color, s=100)\n",
|
||||
"plt.title(\"Clusters identified visualized in language 2d using t-SNE\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 2. Text samples in the clusters & naming the clusters\n",
|
||||
"\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from openai import OpenAI\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"<your OpenAI API key if not set as env var>\"))\n",
|
||||
"\n",
|
||||
"# Reading a review which belong to each group.\n",
|
||||
"rev_per_cluster = 5\n",
|
||||
"\n",
|
||||
"for i in range(n_clusters):\n",
|
||||
" print(f\"Cluster {i} Theme:\", end=\" \")\n",
|
||||
"\n",
|
||||
" reviews = \"\\n\".join(\n",
|
||||
" df[df.Cluster == i]\n",
|
||||
" .combined.str.replace(\"Title: \", \"\")\n",
|
||||
" .str.replace(\"\\n\\nContent: \", \": \")\n",
|
||||
" .sample(rev_per_cluster, random_state=42)\n",
|
||||
" .values\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" messages = [\n",
|
||||
" {\"role\": \"user\", \"content\": f'What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\"\"\"\\n{reviews}\\n\"\"\"\\n\\nTheme:'}\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" response = client.chat.completions.create(\n",
|
||||
" model=\"gpt-4\",\n",
|
||||
" messages=messages,\n",
|
||||
" temperature=0,\n",
|
||||
" max_tokens=64,\n",
|
||||
" top_p=1,\n",
|
||||
" frequency_penalty=0,\n",
|
||||
" presence_penalty=0)\n",
|
||||
" print(response.choices[0].message.content.replace(\"\\n\", \"\"))\n",
|
||||
"\n",
|
||||
" sample_cluster_rows = df[df.Cluster == i].sample(rev_per_cluster, random_state=42)\n",
|
||||
" for j in range(rev_per_cluster):\n",
|
||||
" print(sample_cluster_rows.Score.values[j], end=\", \")\n",
|
||||
" print(sample_cluster_rows.Summary.values[j], end=\": \")\n",
|
||||
" print(sample_cluster_rows.Text.str[:70].values[j])\n",
|
||||
"\n",
|
||||
" print(\"-\" * 100)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "openai",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user