{ "cells": [ { "cell_type": "markdown", "id": "967c35da-f951-4a61-9854-3a55248b2a62", "metadata": {}, "source": [ "# How Toponymy Works\n", "\n", "Toponymy can produce compelling desconstructions of large corpora into well named topics at a wide variety of resolutions -- from large scale themes, to fine grained sub-topics. This tutorial will walk you through how it achieves this through a combination of clustering, feature extraction, LLMs, and careful cleanup of initially generated topic names. To get started let's import some initial libraries, and then get start collecting a (small) dataset we can explore with." ] }, { "cell_type": "code", "execution_count": 1, "id": "993e6f9f-1466-4cf5-af0c-9013971565f9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/work/home/lmmcinn/.conda/envs/toponymy_docs/lib/python3.12/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", " from tqdm.autonotebook import tqdm, trange\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import matplotlib.pyplot as plt\n", "\n", "from sentence_transformers import SentenceTransformer\n", "from collections import Counter" ] }, { "cell_type": "markdown", "id": "e1f55b66-dbb4-41e0-97a3-038622e39632", "metadata": {}, "source": [ "For our dataset we are going to use the category theory section of ArXiv -- all the papers tagged with ``math.CT`` as one of the categories they were deemed to fall under. To keep things simple we will work with just the titles of the papers, and we'll use a dataset that has embeddings and a data map pre-built. This should provide a good example, since we have a small enough dataset to work through quickly (only around 8500 items, and the text is just the relatively short title of the paper), but the dataset is still fairly complex, involving a lot of complex domain specific language that can be challenging to work through." ] }, { "cell_type": "code", "execution_count": 2, "id": "166cd82f-0e94-4f0a-952d-b15ad5e7024d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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date_createdtitlecategoriesarxiv_idyearembeddingdata_map
02007-04-04 06:58:08Generic representations of orthogonal groups: ...math.AT math.CT0704.05022007[-0.5840985178947449, 0.9819669723510742, -4.2...[11.470277786254883, 10.948436737060547]
12007-04-11 09:08:03Triangulated categories without modelsmath.AT math.CT math.KT0704.13782007[0.13958382606506348, 1.518001675605774, -4.65...[13.929526329040527, 10.156951904296875]
22007-04-12 17:21:24Complete Segal spaces arising from simplicial ...math.AT math.CT0704.16242007[0.8432340025901794, 1.7251262664794922, -3.68...[13.688016891479492, 7.38051700592041]
32007-04-17 06:55:01Associated Graded Algebras and Coalgebrasmath.CT math.QA0704.21062007[0.7835264801979065, 1.1688741445541382, -4.08...[12.427974700927734, 8.874861717224121]
42007-04-17 17:52:10Adjoint Functors and Heteromorphismsmath.CT math.LO0704.22072007[-0.21176309883594513, 1.6140257120132446, -3....[11.670499801635742, 11.705853462219238]
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84522005-06-16 13:51:21Quantum information-flow, concretely, and axio...quant-ph math.CT math.LOquant-ph/05061322005[1.2231957912445068, 2.196269989013672, -4.278...[9.509384155273438, 4.773165225982666]
84532005-06-16 14:15:14De-linearizing Linearity: Projective Quantum A...quant-ph math.CT math.LOquant-ph/05061342005[0.9992305040359497, 0.9833099842071533, -3.19...[9.6536283493042, 5.01566219329834]
84542005-10-04 15:53:48Kindergarten Quantum Mechanicsquant-ph math.CTquant-ph/05100322005[1.0677509307861328, 1.4395443201065063, -4.65...[9.42551326751709, 4.819393634796143]
84552006-08-03 16:53:06Quantum measurements without sumsquant-ph math.CT math.LO math.QAquant-ph/06080352006[1.2694358825683594, 1.7442468404769897, -4.39...[9.383161544799805, 4.770181179046631]
84562006-08-08 18:10:10POVMs and Naimark's theorem without sumsquant-ph math-ph math.CT math.MP math.QAquant-ph/06080722006[1.1692596673965454, 2.0193357467651367, -4.15...[11.293802261352539, 7.266186237335205]
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" ], "text/plain": [ " date_created title \\\n", "0 2007-04-04 06:58:08 Generic representations of orthogonal groups: ... \n", "1 2007-04-11 09:08:03 Triangulated categories without models \n", "2 2007-04-12 17:21:24 Complete Segal spaces arising from simplicial ... \n", "3 2007-04-17 06:55:01 Associated Graded Algebras and Coalgebras \n", "4 2007-04-17 17:52:10 Adjoint Functors and Heteromorphisms \n", "... ... ... \n", "8452 2005-06-16 13:51:21 Quantum information-flow, concretely, and axio... \n", "8453 2005-06-16 14:15:14 De-linearizing Linearity: Projective Quantum A... \n", "8454 2005-10-04 15:53:48 Kindergarten Quantum Mechanics \n", "8455 2006-08-03 16:53:06 Quantum measurements without sums \n", "8456 2006-08-08 18:10:10 POVMs and Naimark's theorem without sums \n", "\n", " categories arxiv_id year \\\n", "0 math.AT math.CT 0704.0502 2007 \n", "1 math.AT math.CT math.KT 0704.1378 2007 \n", "2 math.AT math.CT 0704.1624 2007 \n", "3 math.CT math.QA 0704.2106 2007 \n", "4 math.CT math.LO 0704.2207 2007 \n", "... ... ... ... \n", "8452 quant-ph math.CT math.LO quant-ph/0506132 2005 \n", "8453 quant-ph math.CT math.LO quant-ph/0506134 2005 \n", "8454 quant-ph math.CT quant-ph/0510032 2005 \n", "8455 quant-ph math.CT math.LO math.QA quant-ph/0608035 2006 \n", "8456 quant-ph math-ph math.CT math.MP math.QA quant-ph/0608072 2006 \n", "\n", " embedding \\\n", "0 [-0.5840985178947449, 0.9819669723510742, -4.2... \n", "1 [0.13958382606506348, 1.518001675605774, -4.65... \n", "2 [0.8432340025901794, 1.7251262664794922, -3.68... \n", "3 [0.7835264801979065, 1.1688741445541382, -4.08... \n", "4 [-0.21176309883594513, 1.6140257120132446, -3.... \n", "... ... \n", "8452 [1.2231957912445068, 2.196269989013672, -4.278... \n", "8453 [0.9992305040359497, 0.9833099842071533, -3.19... \n", "8454 [1.0677509307861328, 1.4395443201065063, -4.65... \n", "8455 [1.2694358825683594, 1.7442468404769897, -4.39... \n", "8456 [1.1692596673965454, 2.0193357467651367, -4.15... \n", "\n", " data_map \n", "0 [11.470277786254883, 10.948436737060547] \n", "1 [13.929526329040527, 10.156951904296875] \n", "2 [13.688016891479492, 7.38051700592041] \n", "3 [12.427974700927734, 8.874861717224121] \n", "4 [11.670499801635742, 11.705853462219238] \n", "... ... \n", "8452 [9.509384155273438, 4.773165225982666] \n", "8453 [9.6536283493042, 5.01566219329834] \n", "8454 [9.42551326751709, 4.819393634796143] \n", "8455 [9.383161544799805, 4.770181179046631] \n", "8456 [11.293802261352539, 7.266186237335205] \n", "\n", "[8457 rows x 7 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arxiv_ct_df = pd.read_parquet(\"hf://datasets/lmcinnes/arxiv_category_theory/data/train-00000-of-00001.parquet\")\n", "arxiv_ct_df" ] }, { "cell_type": "markdown", "id": "949d8366-d766-4389-94c6-78265a80fb0d", "metadata": {}, "source": [ "We are going to need a text embedding model for Toponymy to use internally -- for embedding and comparing keyphrases and topic names. This need not be the same text embedding model used to embed the text itself. It just needs to be good enough to work with comparisons among keyphrases and topic names. Here we'll use the ``all-MiniLM-L6-v2`` model as it is very performant, even on CPU, but still does a decent job, so should be able to handle the tasks here." ] }, { "cell_type": "code", "execution_count": 3, "id": "fb48b1d8-3078-480f-b5cd-7dac386a392a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/work/home/lmmcinn/.conda/envs/toponymy_docs/lib/python3.12/site-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] } ], "source": [ "embedding_model = SentenceTransformer(\"all-MiniLM-L6-v2\")" ] }, { "cell_type": "markdown", "id": "91c6ef33-e6d5-4890-96cd-9928198ccd72", "metadata": {}, "source": [ "Next we will need to import all the pieces we'll need for Toponymy and get API keys loaded for any LLM services we are going to use." ] }, { "cell_type": "code", "execution_count": null, "id": "15a49969-8d51-4a39-afad-9cceb3123da0", "metadata": {}, "outputs": [], "source": [ "from toponymy.toponymy import Toponymy, ToponymyClusterer, KeyphraseBuilder\n", "from toponymy.cluster_layer import ClusterLayerText\n", "from toponymy.llm_wrappers import AzureAINamer\n", "from toponymy.embedding_wrappers import AzureAIEmbedder\n", "\n", "azure_api_key = open(\"../azure_cohere_api_key.txt\").read().strip()" ] }, { "cell_type": "markdown", "id": "3bcd5b1f-75b7-4a80-b5c9-77d65e847cb3", "metadata": {}, "source": [ "Now we can create our Toponymy model that we will be using to demonstrate how things actually work in practice. We do the same way we did in the earlier tutorials, but instead of just calling ``fit`` on the class we are going to run each of the pieces ourselves step by step, setting the relevant attributes in the parent model as we go." ] }, { "cell_type": "code", "execution_count": null, "id": "9274c3fb-1960-464e-8825-29a32fdba2a4", "metadata": {}, "outputs": [], "source": [ "topic_model = Toponymy(\n", " llm_wrapper=AzureAINamer(\n", " azure_api_key, \n", " endpoint=\"https://azureaitimcuse5821437469.services.ai.azure.com/models\",\n", " model=\"Cohere-command-r-08-2024\",\n", " ),\n", " text_embedding_model=embedding_model,\n", " layer_class=ClusterLayerText,\n", " clusterer=ToponymyClusterer(min_clusters=4, verbose=True),\n", " keyphrase_builder=KeyphraseBuilder(ngram_range=(1,6), max_features=15_000, verbose=True),\n", " object_description=\"paper titles\",\n", " corpus_description=\"category theory related papers\",\n", ")" ] }, { "cell_type": "markdown", "id": "18add4ae-b523-4198-b258-2664ff03cefa", "metadata": {}, "source": [ "To get started fitting the model we will need both the clusterable vectors and the embedding vectors. We can pull those out of the dataframe and convert them to numpy format for use with Toponymy." ] }, { "cell_type": "code", "execution_count": 6, "id": "1585824e-9d62-40d8-9c07-e4f0dcafd5e1", "metadata": {}, "outputs": [], "source": [ "embedding_vectors=np.stack(arxiv_ct_df[\"embedding\"].values)\n", "clusterable_vectors=np.stack(arxiv_ct_df[\"data_map\"].values)" ] }, { "cell_type": "code", "execution_count": 7, "id": "682beeca-b27c-46bf-aabe-8725a97b10a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model.llm_wrapper.supports_system_prompts" ] }, { "cell_type": "markdown", "id": "3222fa24-40db-48f9-b839-7339b09eda00", "metadata": {}, "source": [ "## Clustering\n", "\n", "The first step in Toponymy's process is to first break the entire corpus down into clusters at multiple different resolution scales, starting with fine-grained clusters, and working up to find large-scale structures in the data. This is handled by the ``Clusterer`` within Toponymy, and you can read more about different clusterers, and options that can be passed in the tutorials on clusterers. For the purposes of this tutorial we will use standard options but we'll call the clusterer directly ourselves, and ensure the results get copied to where the ``Toponymy`` class will expect to find them. Note that there are some keyword arguments here (``show_progress_bar=topic_model.show_progress_bars``, ``exemplar_delimiters=topic_model.exemplar_delimiters``, and ``prompt_format=\"system_user\"``) that are derived from the Toponymy base class and are going to be passed through the set those properties for the instantiated layer classes produced by the clusterer. The ``prompt_format`` is set in the ``Toponymy.fit`` according to ``topic_model.llm_wrapper.supports_system_prompts``, and for the ``AzureAI`` that's true, so we will be using the separate system and user prompts (as opposed to a combined prompt that puts all the instructions in a single prompt)." ] }, { "cell_type": "code", "execution_count": 8, "id": "f8cff7bb-a0bd-45f7-b3f3-272af0523e64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Layer 0 found 226 clusters\n", "Layer 1 found 71 clusters\n", "Layer 2 found 20 clusters\n", "Layer 3 found 6 clusters\n" ] } ], "source": [ "topic_model.clusterer.fit(\n", " clusterable_vectors=clusterable_vectors, \n", " embedding_vectors=embedding_vectors, \n", " show_progress_bar=topic_model.show_progress_bars, \n", " exemplar_delimiters=topic_model.exemplar_delimiters, \n", " prompt_format=\"system_user\"\n", ")\n", "topic_model.cluster_layers_ = topic_model.clusterer.cluster_layers_\n", "topic_model.cluster_tree_ = topic_model.clusterer.cluster_tree_" ] }, { "cell_type": "markdown", "id": "30659ed0-582e-4910-828a-ea71171c2018", "metadata": {}, "source": [ "We found four layers of clusters. Since our ``clusterable_vectors`` are in 2D we can visualize the clustering we got, so let's do that with a simple helper function:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4da3ad4c-f2dc-4b7a-9ab6-b9297feb88c4", "metadata": {}, "outputs": [], "source": [ "def plot_cluster_layers(cluster_layers):\n", " fig, axs = plt.subplots(2, 2, figsize=(8,8))\n", " for i, layer in enumerate(cluster_layers):\n", " max_clusters_ind = layer.cluster_labels.max()\n", " cluster_shuffle = np.arange(max_clusters_ind + 1)\n", " np.random.shuffle(cluster_shuffle)\n", " color_mapping = [plt.cm.Spectral(cluster_shuffle[x] / max_clusters_ind) if x >= 0 else (0.66, 0.66, 0.66, 0.5) for x in layer.cluster_labels]\n", " axs[i // 2, i % 2].scatter(*clusterable_vectors.T, s=0.1, c=color_mapping, alpha=0.25)\n", " axs[i // 2, i % 2].set_title(f\"Layer {i} clusters\")\n", " axs[i // 2, i % 2].axis('off');" ] }, { "cell_type": "markdown", "id": "2a64bce3-a034-4a4f-8bc3-0a257146124d", "metadata": {}, "source": [ "Plotting the clusters we see that we start with quite fine grained pockets of papers, and as we move up we get larger groups and also leave some of the small outlying clusters as background noise -- at a fine scale they are deeply topical, but from a big picture view of the corpus they don't contribute to the major themes." ] }, { "cell_type": "code", "execution_count": 10, "id": "6b92bd99-8b19-4c57-ae2b-87852a9df237", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_cluster_layers(topic_model.cluster_layers_)" ] }, { "cell_type": "markdown", "id": "747be994-c7a3-4c4f-b7c8-8880778617a3", "metadata": {}, "source": [ "## Keyphrases\n", "\n", "Having extracted clusters from the vectors we also need to extract keyphrases from actual documents. If your objects are not text based this is where the ``object_to_text_function`` would be used to generate text from which keyphrases can be extracted (and indeed there are tricks to ensure that not all the objects are converted to text in these cases, just a suitable representative sampling). Since we have text in the form of paper titles for our objects we can run the keyphrase builder directly on the objects.The task of the keyphrase builder is not to create suitable keyphrases for each cluster, but rather to pick apart the corpus as a whole (or some representative sample of it) and extract out *candidate* keyphrases that might be useful, and note which documents they occur in.\n", "\n", "That means that the output of the keyphrase builder is simply a list of all the possible keyphrases we might use, and a large sparse matrix noting which keyphrases occur (and how often they occur) in each of the objects. Here we will stick with standard options, but there is an entire tutorial on keyphrase builders and how they work if you want to dig in further." ] }, { "cell_type": "code", "execution_count": 11, "id": "9db88681-11e4-4690-8d69-4011b569aefb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building keyphrase matrix ... \n", "Chunking into 1 chunks of size 20000 for keyphrase identification.\n", "Combining count dictionaries ...\n", "Found 7493 keyphrases.\n", "Chunking into 1 chunks of size 20000 for keyphrase count construction.\n", "Combining count matrix chunks ...\n" ] }, { "data": { "text/plain": [ "(['categories',\n", " 'theory',\n", " 'category',\n", " 'algebras',\n", " 'homotopy',\n", " 'categorical',\n", " 'functors',\n", " 'monoidal',\n", " 'structures',\n", " 'model'],\n", " )" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model.keyphrase_builder.fit(arxiv_ct_df[\"title\"])\n", "topic_model.keyphrase_list_ = topic_model.keyphrase_builder.keyphrase_list_\n", "topic_model.object_x_keyphrase_matrix_ = topic_model.keyphrase_builder.object_x_keyphrase_matrix_\n", "topic_model.keyphrase_list_[:10], topic_model.object_x_keyphrase_matrix_" ] }, { "cell_type": "markdown", "id": "92ded7f4-46f5-4360-8d15-fa8ca537dba7", "metadata": {}, "source": [ "As we said, what we get out is a list of keyphrases (here we are just looking at the top 10) and a large sparse matrix. As with the clusterer as need to ensure they get copied to where the usual fit method will store results for later use.\n", "\n", "Now that we have all our candidates for keyphrases we can start applying keyphrases to clusters and finding good keyphrases that can help summarise a cluster. Because we are following the process that Toponymy uses we are just going to do this for the bottom layer of the most fine-grained clusters for now, rather than all the clusters at all the layers. To actually pick out good representative keyphrases we will need a few things: the actual keyphrases, the sparse matrix of where and how often the keyphrases appear in our documents, and vector representations of the keyphrases. The first two are obviously what the keyphrase builder built for us, and are clearly relevant, but why do we need vector representations? We want to ensure diversity among keyphrases, and that they represent ideas central to the cluster -- to do that we need to be able to make semantic comparisons between keyphrases. Since the keyphrases are only being compared amongst each other the keyphrase embeddings don't need to match the document embeddings, so we can potentially use an entirely different embedding model (as we are about to).\n", "\n", "Internally Toponymy is going to look at an information weighted occurrence rates of keyphrases per cluster. What is the \"information weighting\"? It is a weighting determined by the information gained by knowing the distribution of a given keyphrase over documents compared to a baseline expected distribution. You can think of it as a more theoretically grounded version of class-based TF-IDF; it will pick out keyphrases that are most distinctive or distinguishing of a given cluster based on their relative distributions over clusters and over documents. Having selected the most distinguishing keyphrases Toponymy then uses the embedding vectors to ensure a diversity of keyphrases are ultimately selected.\n", "\n", "Each cluster layer object keeps track of relevant information about the clusters in the layer, and provides a simple ``make_keyphrases`` method that handles all of this." ] }, { "cell_type": "code", "execution_count": 12, "id": "f7533213-3882-4f56-94ec-3cec79c4cef4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "98cb8d4c260c430391a3d6dc7e1fc277", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/235 [00:00, \"topic_specificity\":}\n", "where NAME is the topic name you generate and SCORE is a float value between 0.0 and 1.0,\n", "representing how specific and well-defined the topic name is given the input information.\n", "A score of 1.0 means a perfectly descriptive and specific name, while 0.0 would be a completely generic or unrelated name.\n", "\n", "\n", "Ensure your entire response is only the JSON object, with no other text before or after it.\n", "\n", "\n", "### User prompt:\n", " \n", "Here is the information about the group of paper titles:\n", "\n", "- Keywords for this group include: bundle gerbes, 2-gerbes, non-abelian gerbes, stacks and gerbes, differential geometry of gerbes, 2-hilbert space, introducing, prequantum, tame, dixmier-douady, symbols, combinatorics, holonomy, tate, characteristic classes, weyl\n", "\n", "- Sample paper titles from this group include:\n", " * \"Equivariant bundle gerbes\"\n", "\n", " * \"More Morphisms between Bundle Gerbes\"\n", "\n", " * \"Notes on 1- and 2-gerbes\"\n", "\n", " * \"Differential Geometry of Gerbes\"\n", "\n", " * \"Central Extensions of Gerbes\"\n", "\n", " * \"Four Equivalent Versions of Non-Abelian Gerbes\"\n", "\n", " * \"2-gerbes and 2-Tate spaces\"\n", "\n", " * \"On two notions of a Gerbe over a stack\"\n", "\n", "\n", "Based on this information, provide a domain expert level (8 to 15 word) name for this group.\n", "Recall the output format: {\"topic_name\":, \"topic_specificity\":}.\n" ] } ], "source": [ "topic_model.cluster_layers_[0].make_prompts(\n", " detail_level=0.0,\n", " all_topic_names=[[]], # We have none yet\n", " object_description=topic_model.object_description,\n", " corpus_description=topic_model.corpus_description,\n", " cluster_tree=topic_model.cluster_tree_,\n", ");\n", "print(\"### System prompt:\\n\", topic_model.cluster_layers_[0].prompts[0][\"system\"])\n", "print(\"\\n\\n### User prompt:\\n\", topic_model.cluster_layers_[0].prompts[0][\"user\"])" ] }, { "cell_type": "markdown", "id": "66c85830-c0c8-4d1b-8bd0-64056cc471e1", "metadata": {}, "source": [ "So we have a system prompt that sets up the context -- what kinds of objects are being modeled, the nature of the collection they are drawn from -- and provides instructions on the task and output format. That means that the user prompt just contains information about a given cluster. The information here amounts to the extracted keywords, and the exemplars of the cluster. You can also see, in the second to last sentence, that the ``detail_level`` has been translated into instructions for the kind of topic name to be provided. Each and every cluster will have its own prompt like this." ] }, { "cell_type": "markdown", "id": "370dd46d-5a70-44c2-9109-9099c77c50ba", "metadata": {}, "source": [ "## Naming base layer topics\n", "\n", "With a prompt set up for every cluster, the next step is going to be using an LLM to name the clusters. It turns out that this is a little more subtle than one imagine at first. Let's have a look at what the actual ``name_topics`` method actually does:" ] }, { "cell_type": "code", "execution_count": 16, "id": "5d36aec1-648a-4c5b-84e6-64789a688d27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m\n", "\u001b[0mtopic_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_layers_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_topics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdetail_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mall_topic_names\u001b[0m\u001b[0;34m:\u001b[0m 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generate a name\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopic_names\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopic_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_topic_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopic_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprompts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopic_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/src/topicnaming/toponymy/cluster_layer.py\n", "\u001b[0;31mType:\u001b[0m method" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "topic_model.cluster_layers_[0].name_topics??" ] }, { "cell_type": "markdown", "id": "320ddbfa-044c-4ac2-91b0-05ed2cf7382e", "metadata": {}, "source": [ "So there are a few things going on here. First of all we have a loop in a list comprehension that generates the topic names, but it has an odd clause:\n", "\n", "```python\n", " if isinstance(prompt, dict) or not prompt.startswith(\"[!SKIP!]: \")\n", " else prompt.removeprefix(\"[!SKIP!]: \")\n", "```\n", "\n", "This is not relevant yet, but for other layers it will start to matter. In some cases a cluster at one layer is essentially identical to a cluster below. When that happens we can generate a special prompt that skips the LLM and just re-uses the name given in the lower layer. We'll reproduce that logic here for consistency.\n", "\n", "The more interesting aspect is that after having generated the topic names we then have a further call that \"disambiguates\" the topic names, followed by some further fallback logic that tries to catch anything that went wrong along the way. Since the disambiguation step is important, and worth going into some detail on, but will get swept under the carpet if we just call ``name_topics`` let's step through these stages individually and look at what is happening as we go.\n", "\n", "First of all let's generate topic names with the list comprehension, and save them off within the topic model object:" ] }, { "cell_type": "code", "execution_count": 17, "id": "2515d3b2-a226-4dc8-9121-a049e799ee97", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Gerbe Theory and Applications'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model.cluster_layers_[0].topic_names = [\n", " (\n", " topic_model.llm_wrapper.generate_topic_name(prompt)\n", " if isinstance(prompt, dict) or not prompt.startswith(\"[!SKIP!]: \")\n", " else prompt.removeprefix(\"[!SKIP!]: \")\n", " )\n", " for prompt in topic_model.cluster_layers_[0].prompts\n", "]\n", "topic_model.topic_names_ = [topic_model.cluster_layers_[0].topic_names, []]\n", "topic_model.topic_names_[0][0]" ] }, { "cell_type": "markdown", "id": "09fb471e-e123-4ae6-b858-fd09cc724b91", "metadata": {}, "source": [ "As you can see we actually get reasonably good and specific topic names out as long as you use a reasonably powerful LLM. Now that we have all the topic names for the layer, let's have a look at why we might want to \"disambiguate\" topic names. We can start by looking at the counts of occurences of topic names within the layer." ] }, { "cell_type": "code", "execution_count": 18, "id": "4e332672-c635-4f0a-8e06-9e896a504895", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Advanced Category Theory Topics', 3),\n", " ('Category Theory: Advanced Topics and Applications', 2),\n", " ('Gerbe Theory and Applications', 1),\n", " ('Ann-Category Theory and Applications', 1),\n", " ('Quiver Representations and Categories', 1),\n", " ('Euler Characteristic and Zeta Functions in Category Theory', 1),\n", " ('French Category Theory Research', 1),\n", " ('Mobius Inversion and Categorification', 1),\n", " ('Sheaf Theory and Applications', 1),\n", " ('Quasi-Coherent Sheaves and Derived Categories', 1)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(topic_model.topic_names_[0]).most_common(10)" ] }, { "cell_type": "markdown", "id": "840ac5ed-16cc-4f86-87c2-e5fa345f9dd3", "metadata": {}, "source": [ "We see we have some duplicated topic names -- specifically topic names that were relatively generic given the corpus we are working with. It is almost certain that \"Advanced Category Theory Topics\" is a reasonable description of the papers in those clusters, but it is also not a useful description for our purposes here. What we care about is what differentiates these clusters from each other. Of course the LLM was not provided with information about the other clusters, so it cannot constrast them. So perhaps we should, at this point, make an effort to provide the LLM with that information so it can disambiguate those topics? Just looking at exactly duplicated topic names may not quite be sufficient of course. Depending on the LLM changes of case or captilaisation, spacing, or subtle word changes may prevent exact duplicates, but still result in almost identical topic names. The solution, given the tools we have at hand, is to find semantically similar topic names using text embeddings. This is built into Toponymy with a clustering function specifically designed for this use case. We can deploy it here and see what sets of potentially overly similar topic names we get out. " ] }, { "cell_type": "code", "execution_count": 19, "id": "7def6b68-bcef-4ba0-9571-e5ce713b6052", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cf372be7eb9e443dacbe5bd69481da39", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/8 [00:00, \"topic_specificity\":}\n", "where NAME is the topic name you generate and SCORE is a float value between 0.0 and 1.0,\n", "representing how specific and well-defined the topic name is given the input information.\n", "A score of 1.0 means a perfectly descriptive and specific name, while 0.0 would be a completely generic or unrelated name.\n", "\n", "\n", "You should primarily make use of the major and minor subtopics of this group to generate a name,\n", "and ensure the topic name reflects the core essence of *all* major subtopics.\n", "\n", "Ensure your entire response is only the JSON object, with no other text before or after it.\n", "\n", "\n", "### User prompt:\n", " \n", "Here is the information about the group of paper titles:\n", "\n", "- Keywords for this group include: quasi-coherent sheaves, sheaf theory, derived categories of sheaves, probs and perverse sheaves, algebraic set theory, predicative, topos, quantales, cellular sheaves, cotilting sheaves, godement, lawvere-tierney sheaves, torsion, dynamical, factorization systems, hochschild cochains\n", "\n", "- Major subtopics of this group are:\n", " * Sheaf Theory and Applications\n", " * Quasi-Coherent Sheaves and Derived Categories\n", "- Sample paper titles from this group include:\n", " * \"Sheaves as modules\"\n", "\n", " * \"Sheaves on Grothendieck constructions\"\n", "\n", " * \"Sheaves and Duality\"\n", "\n", " * \"Dynamical sheaves\"\n", "\n", " * \"Sheaves and conditional sets\"\n", "\n", " * \"Derived categories of coherent sheaves\"\n", "\n", " * \"Abstract sheaf theory\"\n", "\n", " * \"Various notions of (co)simplicial (pre)sheaves\"\n", "\n", "\n", "Based on this information, provide a specific and detailed (4 to 8 word) name for this group.\n", "Recall the output format: {\"topic_name\":, \"topic_specificity\":}.\n" ] } ], "source": [ "topic_model.cluster_layers_[1].make_prompts(\n", " detail_level=0.33,\n", " all_topic_names=topic_model.topic_names_,\n", " object_description=topic_model.object_description,\n", " corpus_description=topic_model.corpus_description,\n", " cluster_tree=topic_model.cluster_tree_,\n", ");\n", "print(\"### System prompt:\\n\", topic_model.cluster_layers_[1].prompts[0][\"system\"])\n", "print(\"\\n\\n### User prompt:\\n\", topic_model.cluster_layers_[1].prompts[0][\"user\"])" ] }, { "cell_type": "markdown", "id": "567d45c6-a8dd-4b4d-909f-9c5636121371", "metadata": {}, "source": [ "We have prompts similar to before, but are asking for a slightly less specific topic name (based on the detail level) and we now also include \"Major subtopics of this group\". Note that in this case the \"Major subtopics\" are identical to the selected exemplar subtopics (because there were, in fact, only two subtopics of this cluster at any layer level). As we move to higher layers that will cease to be the case.\n", "\n", "We can now run ``name_topics`` for this layer, this time with the ``[!SKIP!]`` instruction potentially coming into play." ] }, { "cell_type": "code", "execution_count": 26, "id": "3b12326b-3465-41d6-83ac-c209515be472", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Sheaf Theory and Derived Categories'" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model.cluster_layers_[1].topic_names = [\n", " (\n", " topic_model.llm_wrapper.generate_topic_name(prompt)\n", " if isinstance(prompt, dict) or not prompt.startswith(\"[!SKIP!]: \")\n", " else prompt.removeprefix(\"[!SKIP!]: \")\n", " )\n", " for prompt in topic_model.cluster_layers_[1].prompts\n", "]\n", "topic_model.topic_names_ = [topic_model.cluster_layers_[0].topic_names, topic_model.cluster_layers_[1].topic_names, []]\n", "topic_model.topic_names_[1][0]" ] }, { "cell_type": "markdown", "id": "36b2b7f6-0613-460d-8e6c-54b614e29e5c", "metadata": {}, "source": [ "Great, we have some decent looking topic names. Let's check for duplicates as we did before:" ] }, { "cell_type": "code", "execution_count": 27, "id": "9bc6e227-6d38-4b9c-8f1e-ae59d6c2eb67", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Sheaf Theory and Derived Categories', 1),\n", " ('Quiver Representations and Categories', 1),\n", " ('Factorization Systems and Functors', 1),\n", " ('Fusion Category Classification', 1),\n", " ('Modular Category Theory and Applications', 1),\n", " ('Tensor Triangulation & Support Theories', 1),\n", " ('Tensor Category Theory and Applications', 1),\n", " ('Tannakian Categories and Duality', 1),\n", " ('Advanced Mackey Functor Theory', 1),\n", " ('Fibrations and Lifting Properties', 1)]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(topic_model.topic_names_[1]).most_common(10)" ] }, { "cell_type": "markdown", "id": "8c1dcce9-7845-43b0-9173-a29e6593b560", "metadata": {}, "source": [ "Since there are fewer clusters in this layer, and many of them are more distinct, we don't have any actual duplicate topic names this time. On the other hand we can still try clustering the topic names to see if there are some potentially overly similar topic names that we can still try to distinguish between:" ] }, { "cell_type": "code", "execution_count": 28, "id": "79ab1977-2430-4a5e-b49d-ed25183831c8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1074967739084c988cfb792eee783135", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/3 [00:00, \"topic_specificity\":}\n", "where NAME is the topic name you generate and SCORE is a float value between 0.0 and 1.0,\n", "representing how specific and well-defined the topic name is given the input information.\n", "A score of 1.0 means a perfectly descriptive and specific name, while 0.0 would be a completely generic or unrelated name.\n", "\n", "\n", "You should primarily make use of the major and minor subtopics of this group to generate a name,\n", "and ensure the topic name reflects the core essence of *all* major subtopics.\n", "\n", "Ensure your entire response is only the JSON object, with no other text before or after it.\n", "\n", "\n", "### User prompt:\n", " \n", "Here is the information about the group of paper titles:\n", "\n", "- Keywords for this group include: finite tensor categories, twisted tensor product, tensor products, noncommutative tensor triangular geometry, tensor triangulated categories, braided tensor categories, symmetric tensor categories, frobenius, quantum, rigid tensor categories, drinfeld, tensor functors, fusion, subfactors, mackey, tensor triangular chow\n", "\n", "- Major subtopics of this group are:\n", " * Tensor Category Theory and Applications\n", " * Tensor Triangulation & Support Theories\n", "- Other miscellaneous detailed subtopics of this group in order of relevance (from most to least) include:\n", " * Tensor Category Theory\n", " * Tensor Triangulation & Support Theories\n", " * Tensor Triangular Geometry and Support Theory\n", " * C*-Tensor Categories and Subfactors\n", " * Gray Tensor Product Theory\n", " * Twisted Tensor Products and Operads\n", "- Sample paper titles from this group include:\n", " * \"Tensor categories\"\n", "\n", " * \"Semisimple algebraic tensor categories\"\n", "\n", " * \"Crossed product tensor categories\"\n", "\n", " * \"Tensor-triangulated categories and dualities\"\n", "\n", " * \"Lectures on Symmetric Tensor Categories\"\n", "\n", " * \"Abstract Tensor Systems as Monoidal Categories\"\n", "\n", " * \"Quasisymmetric and unipotent tensor categories\"\n", "\n", " * \"A construction of a quotient tensor category\"\n", "\n", "\n", "Based on this information, provide a focussed and brief (2 to 5 word) name for this group.\n", "Recall the output format: {\"topic_name\":, \"topic_specificity\":}.\n" ] } ], "source": [ "topic_model.cluster_layers_[2].make_exemplar_texts(arxiv_ct_df[\"title\"], embedding_vectors);\n", "topic_model.cluster_layers_[2].make_keyphrases(\n", " topic_model.keyphrase_list_, \n", " topic_model.object_x_keyphrase_matrix_, \n", " keyphrase_vectors=keyphrase_vectors, \n", " embedding_model=embedding_model\n", ");\n", "topic_model.cluster_layers_[2].make_subtopics(\n", " topic_model.topic_names_[0],\n", " topic_model.cluster_layers_[0].cluster_labels,\n", " topic_model.cluster_layers_[0].topic_name_embeddings,\n", " topic_model.embedding_model,\n", ");\n", "topic_model.cluster_layers_[2].make_prompts(\n", " detail_level=0.66,\n", " all_topic_names=topic_model.topic_names_,\n", " object_description=topic_model.object_description,\n", " corpus_description=topic_model.corpus_description,\n", " cluster_tree=topic_model.cluster_tree_,\n", ");\n", "print(\"### System prompt:\\n\", topic_model.cluster_layers_[2].prompts[0][\"system\"])\n", "print(\"\\n\\n### User prompt:\\n\", topic_model.cluster_layers_[2].prompts[0][\"user\"])" ] }, { "cell_type": "markdown", "id": "d88d61eb-ac2e-431c-b1f1-7ee3af055260", "metadata": {}, "source": [ "Looking at the one prompt, now that we have more layers and more papers in a cluster we get more topic information -- we have major sub-topics and also a list of detailed subtopics. Otherwise the prompt look essentially the same in form.\n", "\n", "Let's run the loop to name all of the clusters." ] }, { "cell_type": "code", "execution_count": 32, "id": "3e1c1c4c-2000-4dc9-a920-a6512543ca40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Tensor Category Theory'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model.cluster_layers_[2].topic_names = [\n", " (\n", " topic_model.llm_wrapper.generate_topic_name(prompt)\n", " if isinstance(prompt, dict) or not prompt.startswith(\"[!SKIP!]: \")\n", " else prompt.removeprefix(\"[!SKIP!]: \")\n", " )\n", " for prompt in topic_model.cluster_layers_[2].prompts\n", "]\n", "topic_model.topic_names_ = [topic_model.cluster_layers_[0].topic_names, topic_model.cluster_layers_[1].topic_names, topic_model.cluster_layers_[2].topic_names, []]\n", "topic_model.topic_names_[2][0]" ] }, { "cell_type": "markdown", "id": "3abe2d40-21db-4a86-9fe1-ae974dc9317b", "metadata": {}, "source": [ "We get a good brief name, reflecting the higher level topic we are at now. Let's check for any overly similar names -- much less likely now that we have fewer clusters." ] }, { "cell_type": "code", "execution_count": 33, "id": "5c3a3d45-b049-4fb4-b5c5-f6e10c91d96d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6a29b93993f447959303c087edf6850f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00