x-tabdeveloping · x-tabdeveloping · Feb 18, 2025 · Feb 2, 2025 · Feb 2, 2025 · Feb 2, 2025
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -29,8 +29,7 @@ jobs:
         run: python3 -c "import sys; print(sys.version)"
 
       - name: Install dependencies
-        run: python3 -m pip install --upgrade turftopic[pyro-ppl] pandas pytest
-
+        run: python3 -m pip install --upgrade turftopic[pyro-ppl] pandas pytest plotly igraph
       - name: Run tests
         run: python3 -m pytest tests/
 
diff --git a/README.md b/README.md
@@ -5,41 +5,13 @@
 
 
 ## Features
- - Implementations of transformer-based topic models:
-   - Semantic Signal Separation - S³ 🧭
-   - KeyNMF 🔑 
-   - GMM :gem:
-   - Clustering Topic Models: BERTopic and Top2Vec
-   - Autoencoding Topic Models: CombinedTM and ZeroShotTM
-   - FASTopic
- - Dynamic, Online and Hierarchical Topic Modeling 
- - Streamlined scikit-learn compatible API 🛠️
- - Easy topic interpretation 🔍
- - Automated topic naming with LLMs
- - Topic modeling with keyphrases :key:
- - Lemmatization and Stemming
- - Visualization with [topicwizard](https://github.com/x-tabdeveloping/topicwizard) 🖌️
-
-## New in version 0.12.0: Seeded topic modeling
-
-You can now specify an aspect in KeyNMF from which you want to investigate your corpus by specifying a seed phrase.
-
-```python
-from turftopic import KeyNMF
-
-model = KeyNMF(5, seed_phrase="Is the death penalty moral?")
-model.fit(corpus)
-
-model.print_topics()
-```
-
-| Topic ID | Highest Ranking |
+| | |
 | - | - |
-| 0 | morality, moral, immoral, morals, objective, morally, animals, society, species, behavior |
-| 1 | armenian, armenians, genocide, armenia, turkish, turks, soviet, massacre, azerbaijan, kurdish |
-| 2 | murder, punishment, death, innocent, penalty, kill, crime, moral, criminals, executed |
-| 3 | gun, guns, firearms, crime, handgun, firearm, weapons, handguns, law, criminals |
-| 4 | jews, israeli, israel, god, jewish, christians, sin, christian, palestinians, christianity |
+| SOTA Transformer-based Topic Models | :compass: [S³](https://x-tabdeveloping.github.io/turftopic/s3/), :key: [KeyNMF](https://x-tabdeveloping.github.io/turftopic/KeyNMF/),  :gem: [GMM](https://x-tabdeveloping.github.io/turftopic/GMM/), [Clustering Models](https://x-tabdeveloping.github.io/turftopic/GMM/), [CTMs](https://x-tabdeveloping.github.io/turftopic/ctm/), [FASTopic](https://x-tabdeveloping.github.io/turftopic/FASTopic/) |
+| Models for all Scenarios | :chart_with_upwards_trend: [Dynamic](https://x-tabdeveloping.github.io/turftopic/dynamic/), :ocean: [Online](https://x-tabdeveloping.github.io/turftopic/online/), :herb: [Seeded](https://x-tabdeveloping.github.io/turftopic/seeded/), and :evergreen_tree: [Hierarchical](https://x-tabdeveloping.github.io/turftopic/hierarchical/) topic modeling |
+| [Easy Interpretation](https://x-tabdeveloping.github.io/turftopic/model_interpretation/) | :bookmark_tabs: Pretty Printing, :bar_chart: Interactive Figures, :art: [topicwizard](https://github.com/x-tabdeveloping/topicwizard) compatible |
+| [Topic Naming](https://x-tabdeveloping.github.io/turftopic/namers/) | :robot: LLM-based, N-gram Retrieval, :wave: Manual |
+| [Informative Topic Descriptions](https://x-tabdeveloping.github.io/turftopic/vectorizers/) | :key: Keyphrases, Noun-phrases, Lemmatization, Stemming |
 
 
 ## Basics [(Documentation)](https://x-tabdeveloping.github.io/turftopic/)

diff --git a/docs/clustering.md b/docs/clustering.md
diff --git a/docs/dynamic.md b/docs/dynamic.md
@@ -2,11 +2,13 @@
 
 If you want to examine the evolution of topics over time, you will need a dynamic topic model.
 
-> Note that regular static models can also be used to study the evolution of topics and information dynamics, but they can't capture changes in the topics themselves.
+> You will need to install Plotly for plotting to work.
 
-## Models
+```bash
+pip install plotly
+```
 
-In Turftopic you can currently use three different topic models for modeling topics over time:
+You can currently use three different topic models for modeling topics over time:
 
 1. [ClusteringTopicModel](clustering.md), where an overall model is fitted on the whole corpus, and then term importances are estimated over time slices.
 2. [GMM](GMM.md), similarly to clustering models, term importances are reestimated per time slice
@@ -33,50 +35,46 @@ model = KeyNMF(5, top_n=5, random_state=42)
 document_topic_matrix = model.fit_transform_dynamic(
     corpus, timestamps=timestamps, bins=10
 )
+# or alternatively:
+topic_data = model.prepare_dynamic_topic_data(corpus, timestamps=timestamps, bins=10)
 ```
+!!! quote "Interpret Topics over Time"
+    === "Interactive Plot"
+
+        ```python
+        model.plot_topics_over_time()
+        # or
+        topic_data.plot_topics_over_time()
+        ```
+
+        <iframe src="../images/dynamic_keynmf.html", title="Topics over time", style="height:800px;width:100%;padding:0px;border:none;"></iframe>
+        <figcaption> Topics over time in a Dynamic KeyNMF model. </figcaption>
+
+    === "Over-time Table"
+
+        ```python
+        model.print_topics_over_time()
+        # or
+        topic_data.print_topics_over_time()
+        ```
+
+        <center>
+
+        | Time Slice | 0_olympics_tokyo_athletes_beijing | 1_covid_vaccine_pandemic_coronavirus | 2_olympic_athletes_ioc_athlete | 3_djokovic_novak_tennis_federer | 4_ronaldo_cristiano_messi_manchester |
+        | - | - | - | - | - | - |
+        | 2012 12 06 - 2013 11 10 | genocide, yugoslavia, karadzic, facts, cnn | cnn, russia, chechnya, prince, merkel | france, cnn, francois, hollande, bike | tennis, tournament, wimbledon, grass, courts | beckham, soccer, retired, david, learn |
+        | 2013 11 10 - 2014 10 14 | keith, stones, richards, musician, author | georgia, russia, conflict, 2008, cnn | civil, rights, hear, why, should | cnn, kidneys, traffickers, organ, nepal | ronaldo, cristiano, goalscorer, soccer, player |
+        | 2014 10 14 - 2015 09 18 | ethiopia, brew, coffee, birthplace, anderson | climate, sutter, countries, snapchat, injustice | women, guatemala, murder, country, worst | cnn, climate, oklahoma, women, topics | sweden, parental, dads, advantage, leave |
+        | 2015 09 18 - 2016 08 22 | snow, ice, winter, storm, pets | climate, crisis, drought, outbreaks, syrian | women, vulnerabilities, frontlines, countries, marcelas | cnn, warming, climate, sutter, theresa | sutter, band, paris, fans, crowd |
+        | 2016 08 22 - 2017 07 26 | derby, epsom, sporting, race, spectacle | overdoses, heroin, deaths, macron, emmanuel | fear, died, indigenous, people, arthur | siblings, amnesia, palombo, racial, mh370 | bobbi, measles, raped, camp, rape |
+        | 2017 07 26 - 2018 06 30 | her, percussionist, drums, she, deported | novichok, hurricane, hospital, deaths, breathing | women, day, celebrate, taliban, international | abuse, harassment, cnn, women, pilgrimage | maradona, argentina, history, jadon, rape |
+        | 2018 06 30 - 2019 06 03 | athletes, teammates, celtics, white, racism | pope, archbishop, francis, vigano, resignation | racism, athletes, teammates, celtics, white | golf, iceland, volcanoes, atlantic, ocean | rape, sudanese, racist, women, soldiers |
+        | 2019 06 03 - 2020 05 07 | esports, climate, ice, racers, culver | esports, coronavirus, pandemic, football, teams | racers, women, compete, zone, bery | serena, stadium, sasha, final, naomi | kobe, bryant, greatest, basketball, influence |
+        | 2020 05 07 - 2021 04 10 | olympics, beijing, xinjiang, ioc, boycott | covid, vaccine, coronavirus, pandemic, vaccination | olympic, japan, medalist, canceled, tokyo | djokovic, novak, tennis, federer, masterclass | ronaldo, cristiano, messi, juventus, barcelona |
+        | 2021 04 10 - 2022 03 16 | olympics, tokyo, athletes, beijing, medal | covid, pandemic, vaccine, vaccinated, coronavirus | olympic, athletes, ioc, medal, athlete | djokovic, novak, tennis, wimbledon, federer | ronaldo, cristiano, messi, manchester, scored |
+
+        </center>
 
-You can use the `print_topics_over_time()` method for producing a table of the topics over the generated time slices.
-
-> This example uses CNN news data.
-
-```python
-model.print_topics_over_time()
-```
-
-<center>
-
-| Time Slice | 0_olympics_tokyo_athletes_beijing | 1_covid_vaccine_pandemic_coronavirus | 2_olympic_athletes_ioc_athlete | 3_djokovic_novak_tennis_federer | 4_ronaldo_cristiano_messi_manchester |
-| - | - | - | - | - | - |
-| 2012 12 06 - 2013 11 10 | genocide, yugoslavia, karadzic, facts, cnn | cnn, russia, chechnya, prince, merkel | france, cnn, francois, hollande, bike | tennis, tournament, wimbledon, grass, courts | beckham, soccer, retired, david, learn |
-| 2013 11 10 - 2014 10 14 | keith, stones, richards, musician, author | georgia, russia, conflict, 2008, cnn | civil, rights, hear, why, should | cnn, kidneys, traffickers, organ, nepal | ronaldo, cristiano, goalscorer, soccer, player |
-| 2014 10 14 - 2015 09 18 | ethiopia, brew, coffee, birthplace, anderson | climate, sutter, countries, snapchat, injustice | women, guatemala, murder, country, worst | cnn, climate, oklahoma, women, topics | sweden, parental, dads, advantage, leave |
-| 2015 09 18 - 2016 08 22 | snow, ice, winter, storm, pets | climate, crisis, drought, outbreaks, syrian | women, vulnerabilities, frontlines, countries, marcelas | cnn, warming, climate, sutter, theresa | sutter, band, paris, fans, crowd |
-| 2016 08 22 - 2017 07 26 | derby, epsom, sporting, race, spectacle | overdoses, heroin, deaths, macron, emmanuel | fear, died, indigenous, people, arthur | siblings, amnesia, palombo, racial, mh370 | bobbi, measles, raped, camp, rape |
-| 2017 07 26 - 2018 06 30 | her, percussionist, drums, she, deported | novichok, hurricane, hospital, deaths, breathing | women, day, celebrate, taliban, international | abuse, harassment, cnn, women, pilgrimage | maradona, argentina, history, jadon, rape |
-| 2018 06 30 - 2019 06 03 | athletes, teammates, celtics, white, racism | pope, archbishop, francis, vigano, resignation | racism, athletes, teammates, celtics, white | golf, iceland, volcanoes, atlantic, ocean | rape, sudanese, racist, women, soldiers |
-| 2019 06 03 - 2020 05 07 | esports, climate, ice, racers, culver | esports, coronavirus, pandemic, football, teams | racers, women, compete, zone, bery | serena, stadium, sasha, final, naomi | kobe, bryant, greatest, basketball, influence |
-| 2020 05 07 - 2021 04 10 | olympics, beijing, xinjiang, ioc, boycott | covid, vaccine, coronavirus, pandemic, vaccination | olympic, japan, medalist, canceled, tokyo | djokovic, novak, tennis, federer, masterclass | ronaldo, cristiano, messi, juventus, barcelona |
-| 2021 04 10 - 2022 03 16 | olympics, tokyo, athletes, beijing, medal | covid, pandemic, vaccine, vaccinated, coronavirus | olympic, athletes, ioc, medal, athlete | djokovic, novak, tennis, wimbledon, federer | ronaldo, cristiano, messi, manchester, scored |
-
-</center>
-
-You can also display the topics over time on an interactive HTML figure.
-The most important words for topics get revealed by hovering over them.
-
-> You will need to install Plotly for this to work.
-
-```bash
-pip install plotly
-```
-
-```python
-model.plot_topics_over_time()
-```
-
-<figure>
-  <iframe src="../images/dynamic_keynmf.html", title="Topics over time", style="height:800px;width:1000px;padding:0px;border:none;"></iframe>
-  <figcaption> Topics over time in a Dynamic KeyNMF model. </figcaption>
-</figure>
 
 ## API reference
 

diff --git a/docs/hierarchical.md b/docs/hierarchical.md
@@ -1,16 +1,27 @@
 # Hierarchical Topic Modeling
 
-> Note: Hierarchical topic modeling in Turftopic is still in its early stages, you can expect more visualization utilities, tools and models in the future :sparkles:
-
 You might expect some topics in your corpus to belong to a hierarchy of topics.
-Some models in Turftopic (currently only [KeyNMF](KeyNMF.md)) allow you to investigate hierarchical relations and build a taxonomy of topics in a corpus.
+Some models in Turftopic allow you to investigate hierarchical relations and build a taxonomy of topics in a corpus.
+
+Models in Turftopic that can model hierarchical relations will have a `hierarchy` property, that you can manipulate and print/visualize:
+
+```python
+from turftopic import ClusteringTopicModel
+
+model = ClusteringTopicModel(n_reduce_to=10).fit(corpus)
+# We cut at level 3 for plotting, since the hierarchy is very deep
+model.hierarchy.cut(3).plot_tree()
+```
+
+_Drag and click to zoom, hover to see word importance_
+
+<iframe src="../images/tree_plot.html", title="Topic hierarchy in a clustering model", style="height:800px;width:100%;padding:0px;border:none;"></iframe>
+
 
-## Divisive Hierarchical Modeling
+## 1. Divisive/Top-down Hierarchical Modeling
 
-Currently Turftopic, in contrast with other topic modeling libraries only allows for hierarchical modeling in a divisive context.
-This means that topics can be divided into subtopics in a **top-down** manner.
-[KeyNMF](KeyNMF.md) does not discover a topic hierarchy automatically,
- but you can manually instruct the model to find subtopics in larger topics.
+In divisive modeling, you start from larger structures, higher up in the hierarchy, and  divide topics into smaller sub-topics on-demand.
+This is how hierarchical modeling works in [KeyNMF](keynmf.md), which, by default does not discover a topic hierarchy, but you can divide topics to as many subtopics as you see fit.
 
 As a demonstration, let's load a corpus, that we know to have hierarchical themes.
 
@@ -78,30 +89,12 @@ model.hierarchy.divide_children(n_subtopics=3)
 │   ├── <b style="color: magenta">0.0</b>: dos, file, disk, files, program, windows, disks, shareware, norton, memory <br>
 │   ├── <b style="color: magenta">0.1</b>: os, unix, windows, microsoft, apps, nt, ibm, ms, os2, platform <br>
 │   └── <b style="color: magenta">0.2</b>: card, drivers, monitor, driver, vga, ram, motherboard, cards, graphics, ati <br>
-└── <b style="color: blue">1</b>: atheism, atheist, atheists, religion, christians, religious, belief, christian, god, beliefs <br>
-.    ├── <b style="color: magenta">1.0</b>: atheism, alt, newsgroup, reading, faq, islam, questions, read, newsgroups, readers <br>
-.    ├── <b style="color: magenta">1.1</b>: atheists, atheist, belief, theists, beliefs, religious, religion, agnostic, gods, religions <br>
-.    └── <b style="color: magenta">1.2</b>: morality, bible, christian, christians, moral, christianity, biblical, immoral, god, religion <br>
+...
 </tt>
 </div>
 
 As you can see, the model managed to identify meaningful subtopics of the two larger topics we found earlier.
-Topic 0 got divided into a topic mostly concerned with dos and windows, a topic on operating systems in general, and one about hardware,
-while Topic 1 contains a topic about newsgroups, one about atheism, and one about morality and christianity.
-
-You can also easily access nodes of the hierarchy by indexing it:
-```python
-model.hierarchy[0]
-```
-
-<div style="background-color: #F5F5F5; padding: 10px; padding-left: 20px; padding-right: 20px;">
-<tt style="font-size: 11pt">
-<b style="color: blue">0</b>: windows, dos, os, disk, card, drivers, file, pc, files, microsoft <br>
-├── <b style="color: magenta">0.0</b>: dos, file, disk, files, program, windows, disks, shareware, norton, memory <br>
-├── <b style="color: magenta">0.1</b>: os, unix, windows, microsoft, apps, nt, ibm, ms, os2, platform <br>
-└── <b style="color: magenta">0.2</b>: card, drivers, monitor, driver, vga, ram, motherboard, cards, graphics, ati <br>
-</tt>
-</div>
+Topic 0 got divided into a topic mostly concerned with dos and windows, a topic on operating systems in general, and one about hardware.
 
 You can also divide individual topics to a number of subtopics, by using the `divide()` method.
 Let us divide Topic 0.0 to 5 subtopics.
@@ -118,35 +111,58 @@ model.hierarchy
 │   ├── <b style="color: magenta">0.0</b>: dos, file, disk, files, program, windows, disks, shareware, norton, memory <br>
 │   │   ├── <b style="color: green">0.0.1</b>: file, files, ftp, bmp, program, windows, shareware, directory, bitmap, zip <br>
 │   │   ├── <b style="color: green">0.0.2</b>: os, windows, unix, microsoft, crash, apps, crashes, nt, pc, operating <br>
-│   │   ├── <b style="color: green">0.0.3</b>: disk, disks, floppy, drive, drives, scsi, boot, hd, norton, ide <br>
-│   │   ├── <b style="color: green">0.0.4</b>: dos, modem, command, ms, emm386, serial, commands, 386, drivers, batch <br>
-│   │   └── <b style="color: green">0.0.5</b>: printer, print, printing, fonts, font, postscript, hp, printers, output, driver <br>
-│   ├── <b style="color: magenta">0.1</b>: os, unix, windows, microsoft, apps, nt, ibm, ms, os2, platform <br>
-│   └── <b style="color: magenta">0.2</b>: card, drivers, monitor, driver, vga, ram, motherboard, cards, graphics, ati <br>
-└── <b style="color: blue">1</b>: atheism, atheist, atheists, religion, christians, religious, belief, christian, god, beliefs <br>
-.    ├── <b style="color: magenta">1.0</b>: atheism, alt, newsgroup, reading, faq, islam, questions, read, newsgroups, readers <br>
-.    ├── <b style="color: magenta">1.1</b>: atheists, atheist, belief, theists, beliefs, religious, religion, agnostic, gods, religions <br>
-.    └── <b style="color: magenta">1.2</b>: morality, bible, christian, christians, moral, christianity, biblical, immoral, god, religion <br>
+...
 </tt>
 </div>
 
-## Visualization
-You can visualize hierarchies in Turftopic by using the `plot_tree()` method of a topic hierarchy.
-The plot is interactive and you can zoom in or hover on individual topics to get an overview of the most important words.
+## 2. Agglomerative/Bottom-up Hierarchical Modeling
+
+In other models, hierarchies arise from starting from smaller, more specific topics, and then merging them together based on their similarity until a desired number of top-level topics are obtained.
+
+This is how it is done in [clustering topic models](clustering.md) like BERTopic and Top2Vec.
+Clustering models typically find a lot of topics, and it can help with interpretation to merge topics until you gain 10-20 top-level topics.
+
+You can either do this by default on a clustering model by setting `n_reduce_to` on initialization or you can do it manually with `reduce_topics()`.
+For more details, check our guide on [Clustering models](clustering.md).
 
 ```python
-model.hierarchy.plot_tree()
+from turftopic import ClusteringTopicModel
+
+model = ClusteringTopicModel(
+    n_reduce_to=10,
+    feature_importance="centroid",
+    reduction_method="smallest",
+    reduction_topic_representation="centroid",
+    reduction_distance_metric="cosine",
+)
+model.fit(corpus)
+
+print(model.hierarchy)
 ```
 
-<figure>
-  <img src="../images/hierarchy_tree.png" width="90%" style="margin-left: auto;margin-right: auto;">
-  <figcaption>Tree plot of the hierarchy.</figcaption>
-</figure>
+<div style="background-color: #F5F5F5; padding: 10px; padding-left: 20px; padding-right: 20px;">
+<tt style="font-size: 11pt">
+<b>Root</b>: <br>
+├── <b style="color:blue">-1</b>: documented, obsolete, et4000, concerns, dubious, embedded, hardware, xfree86, alternative, seeking<br>
+├── <b style="color:blue">20</b>: hitter, pitching, batting, hitters, pitchers, fielder, shortstop, inning, baseman, pitcher<br>
+├── <b style="color:blue">284</b>: nhl, goaltenders, canucks, sabres, hockey, bruins, puck, oilers, canadiens, flyers<br>
+│   ├── <b style="color:magenta">242</b>: sportschannel, espn, nbc, nhl, broadcasts, broadcasting, broadcast, mlb, cbs, cbc<br>
+│   │   ├── <b style="color:green">171</b>: stadium, tickets, mlb, ticket, sportschannel, mets, inning, nationals, schedule, cubs<br>
+│   │   │   └── ...<br>
+│   │   └── <b style="color:green">21</b>: sportschannel, nbc, espn, nhl, broadcasting, broadcasts, broadcast, hockey, cbc, cbs<br>
+│   └── <b style="color:magenta">236</b>: nhl, goaltenders, canucks, sabres, puck, oilers, andreychuk, bruins, goaltender, leafs<br>
+...
+</tt>
+</div>
 
 
 ## API reference
 
 ::: turftopic.hierarchical.TopicNode
 
+::: turftopic.hierarchical.DivisibleTopicNode
+
+::: turftopic.models._hierarchical_clusters.ClusterNode
+
 
 
diff --git a/docs/images/docs_per_second.png b/docs/images/docs_per_second.png
diff --git a/docs/images/performance_20ng.png b/docs/images/performance_20ng.png