openai-cookbook/examples/Clustering.ipynb
2022-06-03 12:56:03 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering\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 [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1000, 2048)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"\n",
"df = pd.read_csv('output/embedded_1k_reviews.csv')\n",
"df['text-similarity-babbage-001'] = df.babbage_similarity.apply(eval).apply(np.array)\n",
"matrix = np.vstack(df.babbage_similarity.values)\n",
"matrix.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Find the clusters using K-means"
]
},
{
"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": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Cluster\n",
"2 2.543478\n",
"3 4.374046\n",
"0 4.709402\n",
"1 4.832099\n",
"Name: Score, dtype: float64"
]
},
"execution_count": 34,
"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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like cluster 2 focused on negative reviews, while cluster 0 and 1 focused on positive reviews."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Clusters identified visualized in language 2d using t-SNE')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"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\")"
]
},
{
"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": 39,
"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' dogs love them.\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(df[df.Cluster == i].combined.str.replace(\"Title: \", \"\").str.replace(\"\\n\\nContent: \", \": \").sample(rev_per_cluster, random_state=42).values)\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)"
]
},
{
"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."
]
}
],
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