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Ted Sanders 2022-07-11 15:38:07 -07:00
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examples/Clustering.ipynb
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@ -29,12 +29,11 @@
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n", "import numpy as np\n",
"\n", "\n",
"\n",
"datafile_path = \"https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\" # for your convenience, we precomputed the embeddings\n", "datafile_path = \"https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\" # for your convenience, we precomputed the embeddings\n",
"df = pd.read_csv(datafile_path)\n", "df = pd.read_csv(datafile_path)\n",
"df['babbage_similarity'] = df.babbage_similarity.apply(eval).apply(np.array)\n", "df[\"babbage_similarity\"] = df.babbage_similarity.apply(eval).apply(np.array)\n",
"matrix = np.vstack(df.babbage_similarity.values)\n", "matrix = np.vstack(df.babbage_similarity.values)\n",
"matrix.shape" "matrix.shape\n"
] ]
}, },
{ {
@ -77,12 +76,12 @@
"\n", "\n",
"n_clusters = 4\n", "n_clusters = 4\n",
"\n", "\n",
"kmeans = KMeans(n_clusters = n_clusters,init='k-means++',random_state=42)\n", "kmeans = KMeans(n_clusters=n_clusters, init=\"k-means++\", random_state=42)\n",
"kmeans.fit(matrix)\n", "kmeans.fit(matrix)\n",
"labels = kmeans.labels_\n", "labels = kmeans.labels_\n",
"df['Cluster'] = labels\n", "df[\"Cluster\"] = labels\n",
"\n", "\n",
"df.groupby('Cluster').Score.mean().sort_values()" "df.groupby(\"Cluster\").Score.mean().sort_values()\n"
] ]
}, },
{ {
@ -125,22 +124,24 @@
"import matplotlib\n", "import matplotlib\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)\n", "tsne = TSNE(\n",
" n_components=2, perplexity=15, random_state=42, init=\"random\", learning_rate=200\n",
")\n",
"vis_dims2 = tsne.fit_transform(matrix)\n", "vis_dims2 = tsne.fit_transform(matrix)\n",
"\n", "\n",
"x = [x for x,y in vis_dims2]\n", "x = [x for x, y in vis_dims2]\n",
"y = [y for x,y in vis_dims2]\n", "y = [y for x, y in vis_dims2]\n",
"\n", "\n",
"for category, color in enumerate(['purple', 'green', 'red', 'blue']):\n", "for category, color in enumerate([\"purple\", \"green\", \"red\", \"blue\"]):\n",
" xs = np.array(x)[df.Cluster==category]\n", " xs = np.array(x)[df.Cluster == category]\n",
" ys = np.array(y)[df.Cluster==category]\n", " ys = np.array(y)[df.Cluster == category]\n",
" plt.scatter(xs, ys, color=color, alpha=0.3)\n", " plt.scatter(xs, ys, color=color, alpha=0.3)\n",
"\n", "\n",
" avg_x = xs.mean()\n", " avg_x = xs.mean()\n",
" avg_y = ys.mean()\n", " avg_y = ys.mean()\n",
" \n", "\n",
" plt.scatter(avg_x, avg_y, marker='x', color=color, s=100)\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\")" "plt.title(\"Clusters identified visualized in language 2d using t-SNE\")\n"
] ]
}, },
{ {
@ -199,26 +200,32 @@
"\n", "\n",
"for i in range(n_clusters):\n", "for i in range(n_clusters):\n",
" print(f\"Cluster {i} Theme:\", end=\" \")\n", " print(f\"Cluster {i} Theme:\", end=\" \")\n",
" \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", " 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", " response = openai.Completion.create(\n",
" engine=\"davinci-instruct-beta-v3\",\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", " prompt=f'What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\"\"\"\\n{reviews}\\n\"\"\"\\n\\nTheme:',\n",
" temperature=0,\n", " temperature=0,\n",
" max_tokens=64,\n", " max_tokens=64,\n",
" top_p=1,\n", " top_p=1,\n",
" frequency_penalty=0,\n", " frequency_penalty=0,\n",
" presence_penalty=0\n", " presence_penalty=0,\n",
" )\n", " )\n",
" print(response[\"choices\"][0][\"text\"].replace('\\n',''))\n", " print(response[\"choices\"][0][\"text\"].replace(\"\\n\", \"\"))\n",
"\n", "\n",
" sample_cluster_rows = df[df.Cluster == i].sample(rev_per_cluster, random_state=42) \n", " sample_cluster_rows = df[df.Cluster == i].sample(rev_per_cluster, random_state=42)\n",
" for j in range(rev_per_cluster):\n", " for j in range(rev_per_cluster):\n",
" print(sample_cluster_rows.Score.values[j], end=\", \")\n", " print(sample_cluster_rows.Score.values[j], end=\", \")\n",
" print(sample_cluster_rows.Summary.values[j], end=\": \")\n", " print(sample_cluster_rows.Summary.values[j], end=\": \")\n",
" print(sample_cluster_rows.Text.str[:70].values[j])\n", " print(sample_cluster_rows.Text.str[:70].values[j])\n",
" \n", "\n",
" print(\"-\" * 100)" " print(\"-\" * 100)\n"
] ]
}, },
{ {
@ -237,11 +244,9 @@
} }
], ],
"metadata": { "metadata": {
"interpreter": {
"hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8"
},
"kernelspec": { "kernelspec": {
"display_name": "Python 3.7.3 64-bit ('base': conda)", "display_name": "Python 3.9.9 ('openai')",
"language": "python",
"name": "python3" "name": "python3"
}, },
"language_info": { "language_info": {
@ -256,7 +261,12 @@
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.9" "version": "3.9.9"
}, },
"orig_nbformat": 4 "orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
}
}
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2