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fix: update the llm+kmeans notebook with recent change #236

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Merged
merged 2 commits into from
Nov 29, 2023
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fix: update the llm+kmeans notebook with recent change #236

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ef81fd0
fix: update the llm+kmeans notebook with recent change
ashleyxuu Nov 28, 2023
63e4826
Merge branch 'main' into ashleyxu-update-notebook
gcf-merge-on-green[bot] Nov 29, 2023
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47 changes: 12 additions & 35 deletions notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -366,18 +366,6 @@
"predicted_embeddings.head() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4H_etYfsEOFP"
},
"outputs": [],
"source": [
"# Join the complaints with their embeddings in the same DataFrame\n",
"combined_df = downsampled_issues_df.join(predicted_embeddings)"
]
},
{
"attachments": {},
"cell_type": "markdown",
Expand Down Expand Up @@ -426,30 +414,19 @@
"outputs": [],
"source": [
"# Use KMeans clustering to calculate our groups. Will take ~3 minutes.\n",
"cluster_model.fit(combined_df[[\"text_embedding\"]])\n",
"clustered_result = cluster_model.predict(combined_df[[\"text_embedding\"]])\n",
"cluster_model.fit(predicted_embeddings[[\"text_embedding\"]])\n",
"clustered_result = cluster_model.predict(predicted_embeddings)\n",
"# Notice the CENTROID_ID column, which is the ID number of the group that\n",
"# each complaint belongs to.\n",
"clustered_result.head(n=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Join the group number to the complaints and their text embeddings\n",
"combined_clustered_result = combined_df.join(clustered_result)\n",
"combined_clustered_result.head(n=5) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Our dataframe combined_clustered_result now has three columns: the complaints, their text embeddings, and an ID from 1-10 (inclusive) indicating which semantically similar group they belong to."
"Our dataframe combined_clustered_result now has three complaint columns: the content, their text embeddings, and an ID from 1-10 (inclusive) indicating which semantically similar group they belong to."
]
},
{
Expand Down Expand Up @@ -480,14 +457,14 @@
"source": [
"# Using bigframes, with syntax identical to pandas,\n",
"# filter out the first and second groups\n",
"cluster_1_result = combined_clustered_result[\n",
" combined_clustered_result[\"CENTROID_ID\"] == 1\n",
"][[\"consumer_complaint_narrative\"]]\n",
"cluster_1_result = clustered_result[\n",
" clustered_result[\"CENTROID_ID\"] == 1\n",
"][[\"content\"]]\n",
"cluster_1_result_pandas = cluster_1_result.head(5).to_pandas()\n",
"\n",
"cluster_2_result = combined_clustered_result[\n",
" combined_clustered_result[\"CENTROID_ID\"] == 2\n",
"][[\"consumer_complaint_narrative\"]]\n",
"cluster_2_result = clustered_result[\n",
" clustered_result[\"CENTROID_ID\"] == 2\n",
"][[\"content\"]]\n",
"cluster_2_result_pandas = cluster_2_result.head(5).to_pandas()"
]
},
Expand All @@ -503,15 +480,15 @@
"prompt1 = 'comment list 1:\\n'\n",
"for i in range(5):\n",
" prompt1 += str(i + 1) + '. ' + \\\n",
" cluster_1_result_pandas[\"consumer_complaint_narrative\"].iloc[i] + '\\n'\n",
" cluster_1_result_pandas[\"content\"].iloc[i] + '\\n'\n",
"\n",
"prompt2 = 'comment list 2:\\n'\n",
"for i in range(5):\n",
" prompt2 += str(i + 1) + '. ' + \\\n",
" cluster_2_result_pandas[\"consumer_complaint_narrative\"].iloc[i] + '\\n'\n",
" cluster_2_result_pandas[\"content\"].iloc[i] + '\\n'\n",
"\n",
"print(prompt1)\n",
"print(prompt2)\n"
"print(prompt2)"
]
},
{
Expand Down