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https://github.com/openai/openai-cookbook
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lint
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Ted Sanders
parent
2a6855133a
commit
46c32a35fe
@ -29,12 +29,11 @@
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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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"\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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"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"
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"matrix.shape\n"
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]
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},
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{
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@ -77,12 +76,12 @@
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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 = 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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"df[\"Cluster\"] = labels\n",
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"\n",
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"df.groupby('Cluster').Score.mean().sort_values()"
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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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@ -125,22 +124,24 @@
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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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"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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"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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"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\")"
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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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@ -199,26 +200,32 @@
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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(df[df.Cluster == i].combined.str.replace(\"Title: \", \"\").str.replace(\"\\n\\nContent: \", \": \").sample(rev_per_cluster, random_state=42).values)\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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" response = openai.Completion.create(\n",
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" engine=\"davinci-instruct-beta-v3\",\n",
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" prompt=f\"What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\\\"\\\"\\\"\\n{reviews}\\n\\\"\\\"\\\"\\n\\nTheme:\",\n",
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" prompt=f'What do the following customer reviews have in common?\\n\\nCustomer reviews:\\n\"\"\"\\n{reviews}\\n\"\"\"\\n\\nTheme:',\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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" presence_penalty=0,\n",
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" )\n",
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" print(response[\"choices\"][0][\"text\"].replace('\\n',''))\n",
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" print(response[\"choices\"][0][\"text\"].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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" 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)"
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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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@ -237,11 +244,9 @@
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8"
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},
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"kernelspec": {
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"display_name": "Python 3.7.3 64-bit ('base': conda)",
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"display_name": "Python 3.9.9 ('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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@ -256,7 +261,12 @@
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"pygments_lexer": "ipython3",
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"version": "3.9.9"
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},
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"orig_nbformat": 4
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"orig_nbformat": 4,
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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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},
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"nbformat": 4,
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"nbformat_minor": 2
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