mirror of
https://github.com/openai/openai-cookbook
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274 lines
79 KiB
Plaintext
274 lines
79 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Clustering\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 [Obtain_dataset Notebook](Obtain_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": 1,
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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, 2048)"
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]
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},
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"execution_count": 1,
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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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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"datafile_path = \"https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\" # for your convenience, we precomputed the embeddings\n",
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"df = pd.read_csv(datafile_path)\n",
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"df[\"babbage_similarity\"] = df.babbage_similarity.apply(eval).apply(np.array)\n",
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"matrix = np.vstack(df.babbage_similarity.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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"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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"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": 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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"Cluster\n",
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"2 2.543478\n",
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"3 4.374046\n",
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"0 4.709402\n",
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"1 4.832099\n",
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"Name: Score, dtype: float64"
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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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"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": "markdown",
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"metadata": {},
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"source": [
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"It looks like cluster 2 focused on negative reviews, while cluster 0 and 1 focused on positive reviews."
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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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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Clusters identified visualized in language 2d using t-SNE')"
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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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"data": {
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"image/png": 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",
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"text/plain": [
|
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"<Figure size 432x288 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
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"needs_background": "light"
|
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},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"from sklearn.manifold import TSNE\n",
|
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"import matplotlib\n",
|
|
"import matplotlib.pyplot as plt\n",
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"\n",
|
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"tsne = TSNE(\n",
|
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" n_components=2, perplexity=15, random_state=42, init=\"random\", learning_rate=200\n",
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")\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",
|
|
"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",
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"\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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Visualization of clusters in a 2d projection. The red cluster clearly represents negative reviews. The blue cluster seems quite different from the others. Let's see a few samples from each cluster."
|
|
]
|
|
},
|
|
{
|
|
"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 davinci-instruct-beta-v3 to name the clusters, based on a random sample of 6 reviews from that cluster."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Cluster 0 Theme: All of the customer reviews mention the great flavor of the product.\n",
|
|
"5, French Vanilla Cappuccino: Great price. Really love the the flavor. No need to add anything to \n",
|
|
"5, great coffee: A bit pricey once you add the S & H but this is one of the best flavor\n",
|
|
"5, Love It: First let me say I'm new to drinking tea. So you're not getting a well\n",
|
|
"----------------------------------------------------------------------------------------------------\n",
|
|
"Cluster 1 Theme: All three reviews mention the quality of the product.\n",
|
|
"5, Beautiful: I don't plan to grind these, have plenty other peppers for that. I go\n",
|
|
"5, Awesome: I can't find this in the stores and thought I would like it. So I bou\n",
|
|
"5, Came as expected: It was tasty and fresh. The other one I bought was old and tasted mold\n",
|
|
"----------------------------------------------------------------------------------------------------\n",
|
|
"Cluster 2 Theme: All reviews are about customer's disappointment.\n",
|
|
"1, Disappointed...: I should read the fine print, I guess. I mostly went by the picture a\n",
|
|
"5, Excellent but Price?: I first heard about this on America's Test Kitchen where it won a blin\n",
|
|
"1, Disappointed: I received the offer from Amazon and had never tried this brand before\n",
|
|
"----------------------------------------------------------------------------------------------------\n",
|
|
"Cluster 3 Theme: The reviews for these products have in common that the customers are happy with the product.\n",
|
|
"5, My Dog's Favorite Snack!: I was first introduced to this snack at my dog's training classes at p\n",
|
|
"4, Fruitables Crunchy Dog Treats: My lab goes wild for these and I am almost tempted to have a go at som\n",
|
|
"5, Happy with the product: My dog was suffering with itchy skin. He had been eating Natural Choi\n",
|
|
"----------------------------------------------------------------------------------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import openai\n",
|
|
"\n",
|
|
"# Reading a review which belong to each group.\n",
|
|
"rev_per_cluster = 3\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",
|
|
" response = openai.Completion.create(\n",
|
|
" engine=\"davinci-instruct-beta-v3\",\n",
|
|
" prompt=f'What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\"\"\"\\n{reviews}\\n\"\"\"\\n\\nTheme:',\n",
|
|
" temperature=0,\n",
|
|
" max_tokens=64,\n",
|
|
" top_p=1,\n",
|
|
" frequency_penalty=0,\n",
|
|
" presence_penalty=0,\n",
|
|
" )\n",
|
|
" print(response[\"choices\"][0][\"text\"].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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can see based on the average ratings per cluster, that Cluster 2 contains mostly negative reviews. Cluster 0 and 1 contain mostly positive reviews, whilst Cluster 3 appears to contain reviews about dog products."
|
|
]
|
|
},
|
|
{
|
|
"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": "Python 3.9.9 ('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": "3.9.9"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|