From c0d8674da40116e0c6caa77f5635307106be3391 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 25 Nov 2024 17:29:58 +0100 Subject: [PATCH 01/14] Added datamapplot cluster plotting utility --- turftopic/models/cluster.py | 54 ++++++++++++++++++++++++++++++++++--- 1 file changed, 50 insertions(+), 4 deletions(-) diff --git a/turftopic/models/cluster.py b/turftopic/models/cluster.py index 3fae485..3061dc6 100644 --- a/turftopic/models/cluster.py +++ b/turftopic/models/cluster.py @@ -1,5 +1,9 @@ +import tempfile +import time import warnings +import webbrowser from datetime import datetime +from pathlib import Path from typing import Literal, Optional, Union import numpy as np @@ -11,7 +15,7 @@ from sklearn.feature_extraction.text import CountVectorizer from sklearn.manifold import TSNE from sklearn.metrics.pairwise import cosine_distances -from sklearn.preprocessing import label_binarize +from sklearn.preprocessing import label_binarize, scale from turftopic.base import ContextualModel, Encoder from turftopic.dynamic import DynamicTopicModel @@ -288,6 +292,8 @@ def reduce_topics( """ if not hasattr(self, "original_labels_"): self.original_labels_ = self.labels_ + self.original_names_ = self.topic_names + self.original_classes_ = self.classes_ if reduction_method == "smallest": self.labels_ = self._merge_smallest(n_reduce_to) elif reduction_method == "agglomerative": @@ -313,6 +319,8 @@ def join_topics(self, topic_ids: list[int]): new_topic = topic_ids[0] new_labels = [] self.original_labels_ = self.labels_ + self.original_names_ = self.topic_names + self.original_classes_ = self.classes_ for label in self.labels_: if label in topic_ids: new_labels.append(new_topic) @@ -327,6 +335,7 @@ def reset_topics(self): warnings.warn("Topics have never been reduced, nothing to reset.") else: self.labels_ = self.original_labels_ + self.topic_names_ = self.original_names_ self.estimate_components(self.feature_importance) def estimate_components( @@ -430,12 +439,12 @@ def fit_predict( self.doc_term_matrix = self.vectorizer.fit_transform(raw_documents) console.log("Term extraction done.") status.update("Reducing Dimensionality") - reduced_embeddings = self.dimensionality_reduction.fit_transform( - embeddings + self.reduced_embeddings = ( + self.dimensionality_reduction.fit_transform(embeddings) ) console.log("Dimensionality reduction done.") status.update("Clustering documents") - self.labels_ = self.clustering.fit_predict(reduced_embeddings) + self.labels_ = self.clustering.fit_predict(self.reduced_embeddings) console.log("Clustering done.") status.update("Estimating parameters.") self.estimate_components(self.feature_importance) @@ -569,3 +578,40 @@ def fit_transform_dynamic( time_labels, self.time_bin_edges, self.feature_importance ) return doc_topic_matrix + + @staticmethod + def _labels_to_indices(labels, classes): + n_classes = len(classes) + class_to_index = dict(zip(classes, np.arange(n_classes))) + return np.array([class_to_index[label] for label in labels]) + + def plot_clusters_datamapplot( + self, dimensions: tuple[int, int] = (0, 1), *args, **kwargs + ): + try: + import datamapplot + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "You need to install datamapplot to be able to use plot_clusters_datamapplot()." + ) from e + coordinates = self.reduced_embeddings[:, dimensions] + coordinates = scale(coordinates) * 4 + indices = self._labels_to_indices(self.labels_, self.classes_) + labels = np.array(self.topic_names)[indices] + if -1 in self.classes_: + i_outlier = np.where(self.classes_ == -1)[0][0] + kwargs["noise_label"] = self.topic_names[i_outlier] + plot = datamapplot.create_interactive_plot( + coordinates, labels, *args, **kwargs + ) + + def show_fig(): + with tempfile.TemporaryDirectory() as temp_dir: + file_name = Path(temp_dir).joinpath("fig.html") + plot.save(file_name) + webbrowser.open("file://" + str(file_name.absolute()), new=2) + time.sleep(2) + + plot.show = show_fig + plot.write_html = plot.save + return plot From 1e6c24ec39da1abf2b83f366080a0e98b99024f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 25 Nov 2024 17:31:44 +0100 Subject: [PATCH 02/14] Switched to HDBSCAN as default clustering model --- pyproject.toml | 2 +- turftopic/models/cluster.py | 12 +++++++++--- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 38ad992..f5b98c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ readme = "README.md" [tool.poetry.dependencies] python = "^3.9" numpy = ">=1.23.0" -scikit-learn = "^1.2.0" +scikit-learn = "^1.3.0" sentence-transformers = ">=2.2.0" torch = "^2.1.0" scipy = "^1.10.0" diff --git a/turftopic/models/cluster.py b/turftopic/models/cluster.py index 3061dc6..16d1e55 100644 --- a/turftopic/models/cluster.py +++ b/turftopic/models/cluster.py @@ -10,7 +10,7 @@ from rich.console import Console from sentence_transformers import SentenceTransformer from sklearn.base import ClusterMixin, TransformerMixin -from sklearn.cluster import OPTICS, AgglomerativeClustering +from sklearn.cluster import HDBSCAN, AgglomerativeClustering from sklearn.exceptions import NotFittedError from sklearn.feature_extraction.text import CountVectorizer from sklearn.manifold import TSNE @@ -198,12 +198,18 @@ def __init__( else: self.vectorizer = vectorizer if clustering is None: - self.clustering = OPTICS(min_samples=25) + self.clustering = HDBSCAN( + min_samples=15, + min_cluster_size=25, + ) else: self.clustering = clustering if dimensionality_reduction is None: self.dimensionality_reduction = TSNE( - n_components=2, metric="cosine", random_state=random_state + n_components=2, + metric="cosine", + perplexity=15, + random_state=random_state, ) else: self.dimensionality_reduction = dimensionality_reduction From db71aa6d15d49e42ed0e36f523b0ab3d9de8b5b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 25 Nov 2024 17:45:00 +0100 Subject: [PATCH 03/14] Made HDBSCAN more permissive, allowing for more clusters --- turftopic/models/cluster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/turftopic/models/cluster.py b/turftopic/models/cluster.py index 16d1e55..4df20ad 100644 --- a/turftopic/models/cluster.py +++ b/turftopic/models/cluster.py @@ -199,7 +199,7 @@ def __init__( self.vectorizer = vectorizer if clustering is None: self.clustering = HDBSCAN( - min_samples=15, + min_samples=10, min_cluster_size=25, ) else: From d669831efd4d16f008de275245956a02bcfa6afd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 25 Nov 2024 17:45:30 +0100 Subject: [PATCH 04/14] Added datamapplot interactive plot over 20 newsgroups --- docs/images/cluster_datamapplot.html | 389 +++++++++++++++++++++++++++ 1 file changed, 389 insertions(+) create mode 100644 docs/images/cluster_datamapplot.html diff --git a/docs/images/cluster_datamapplot.html b/docs/images/cluster_datamapplot.html new file mode 100644 index 0000000..33933c6 --- /dev/null +++ b/docs/images/cluster_datamapplot.html @@ -0,0 +1,389 @@ + + + + + Interactive Data Map + + + + + + + + + + + + + + + + + + +
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z0VLhr=G7AGz1ZQ}MT0q4`d*t&8C)g(tIu^aU%NIwxFb^z^w1ivOL_J8xG7|Ndm9K= zpN$vv7}&yq@fYBQ>aS~vENfet8@d)Y-p0Z4;hf6+lBAB>*#WWC&N-Grt9!jRU<}maMV!A$~~rh zHXmYp&!YQ*IM?}*IF&QAvPw-`Lhms~*bSE`EQ5Ve_i<-EPDLgD0Dvwx4bR#qu`9)H z)*{#}X#jgr*F+g3!lR-r7e~sU*MXGU2rW5)C0=KBFr5J>;^b-veeErJIO#ikqzGe| z@A4vCPC?k)lu689wd7hoKo{P12HdIEKq@b%`~E0*)|a_k2U-=4JE= z@TQy#2T}?iZkWv9pAPf}c<%o^Ks^UfDCfaD!1MIp!8(EG^_Tz19{B09+QAxt=d;zp z89#UwF8z5``ju@NDk>Ud Date: Mon, 25 Nov 2024 17:59:37 +0100 Subject: [PATCH 06/14] Updated docs with datamapplot visualization --- README.md | 25 +++++++++++++++++++++++++ docs/basics.md | 30 ++++++++++++++++++++++++++++++ docs/clustering.md | 27 +++++++++++++++++++++++++++ 3 files changed, 82 insertions(+) diff --git a/README.md b/README.md index 2f88904..de724af 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,31 @@ > This package is still work in progress and scientific papers on some of the novel methods are currently undergoing peer-review. If you use this package and you encounter any problem, let us know by opening relevant issues. +### New in version 0.10.0 + +You can interactively explore clusters using `datamapplot` directly in Turftopic! +You will first have to install `datamapplot` for this to work. + +```python +from turftopic import ClusteringTopicModel +from turftopic.namers import OpenAITopicNamer + +model = ClusteringTopicModel(feature_importance="centroid").fit(corpus) + +namer = OpenAITopicNamer("gpt-4o-mini") +model.rename_topics(namer) + +fig = model.plot_clusters_datamapplot() +fig.save("clusters_visualization.html") +fig +``` +> If you are not running Turftopic from a Jupyter notebook, make sure to call `fig.show()`. This will open up a new browser tab with the interactive figure. + +

+ +
Interactive figure to explore cluster structure in a clustering topic model.
+
+ ### New in version 0.9.0 #### Dynamic S³ 🧭 diff --git a/docs/basics.md b/docs/basics.md index 8fd142a..b1cb119 100644 --- a/docs/basics.md +++ b/docs/basics.md @@ -282,6 +282,36 @@ model.print_topics() ### Visualization +#### Datamapplot *(clustering models only)* + +You can interactively explore clusters using `datamapplot` directly in Turftopic! +You will first have to install `datamapplot` for this to work. + +```python +from turftopic import ClusteringTopicModel +from turftopic.namers import OpenAITopicNamer + +model = ClusteringTopicModel(feature_importance="centroid").fit(corpus) + +namer = OpenAITopicNamer("gpt-4o-mini") +model.rename_topics(namer) + +fig = model.plot_clusters_datamapplot() +fig.save("clusters_visualization.html") +fig +``` +!!! info + If you are not running Turftopic from a Jupyter notebook, make sure to call `fig.show()`. This will open up a new browser tab with the interactive figure. + +
+ +
Interactive figure to explore cluster structure in a clustering topic model.
+
+ + + +#### topicwizard + Turftopic does not come with built-in visualization utilities, [topicwizard](https://github.com/x-tabdeveloping/topicwizard), a package for interactive topic model interpretation is fully compatible with Turftopic models. ```bash diff --git a/docs/clustering.md b/docs/clustering.md index bab6de9..768aba5 100644 --- a/docs/clustering.md +++ b/docs/clustering.md @@ -174,6 +174,33 @@ To reset topics to the original clustering, use the `reset_topics()` method: model.reset_topics() ``` +### Visualization + +You can interactively explore clusters using `datamapplot` directly in Turftopic! +You will first have to install `datamapplot` for this to work. + +```python +from turftopic import ClusteringTopicModel +from turftopic.namers import OpenAITopicNamer + +model = ClusteringTopicModel(feature_importance="centroid").fit(corpus) + +namer = OpenAITopicNamer("gpt-4o-mini") +model.rename_topics(namer) + +fig = model.plot_clusters_datamapplot() +fig.save("clusters_visualization.html") +fig +``` +!!! info + If you are not running Turftopic from a Jupyter notebook, make sure to call `fig.show()`. This will open up a new browser tab with the interactive figure. + +
+ +
Interactive figure to explore cluster structure in a clustering topic model.
+
+ + ### Manual Topic Merging You can also manually merge topics using the `join_topics()` method. From db9f5db9acf4b26db3f7fed4695da2767714f5ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 25 Nov 2024 18:00:06 +0100 Subject: [PATCH 07/14] Version bump --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f5b98c5..29b8fa0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ line-length=79 [tool.poetry] name = "turftopic" -version = "0.9.0" +version = "0.10.0" description = "Topic modeling with contextual representations from sentence transformers." authors = ["Márton Kardos "] license = "MIT" From 0cfe8907e9c499716f9e19d3529e0973240c00ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Wed, 27 Nov 2024 16:00:40 +0100 Subject: [PATCH 08/14] Clustering models switch to openTSNE automatically if it's installed --- turftopic/models/cluster.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/turftopic/models/cluster.py b/turftopic/models/cluster.py index 4df20ad..a6eea27 100644 --- a/turftopic/models/cluster.py +++ b/turftopic/models/cluster.py @@ -13,7 +13,6 @@ from sklearn.cluster import HDBSCAN, AgglomerativeClustering from sklearn.exceptions import NotFittedError from sklearn.feature_extraction.text import CountVectorizer -from sklearn.manifold import TSNE from sklearn.metrics.pairwise import cosine_distances from sklearn.preprocessing import label_binarize, scale @@ -99,6 +98,24 @@ def calculate_topic_vectors( return centroids +def build_tsne(*args, **kwargs): + try: + from openTSNE import TSNE + + model = TSNE(*args, **kwargs) + model.fit_transform = model.fit + return model + except ModuleNotFoundError: + from sklearn.manifold import TSNE + + warnings.warn( + """OpenTSNE is not installed, default scikit-learn implementation will be used. + Your model could potentially run orders of magnitudes faster by installing openTSNE. + """ + ) + return TSNE(*args, **kwargs) + + class ClusteringTopicModel(ContextualModel, ClusterMixin, DynamicTopicModel): """Topic models, which assume topics to be clusters of documents in semantic space. @@ -205,7 +222,7 @@ def __init__( else: self.clustering = clustering if dimensionality_reduction is None: - self.dimensionality_reduction = TSNE( + self.dimensionality_reduction = build_tsne( n_components=2, metric="cosine", perplexity=15, From c4a0b4ec4a1a236cfddac1f54a5160f354b0f705 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Wed, 27 Nov 2024 16:09:03 +0100 Subject: [PATCH 09/14] Updated docs to reflect openTSNE changes --- docs/clustering.md | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/clustering.md b/docs/clustering.md index 768aba5..313443d 100644 --- a/docs/clustering.md +++ b/docs/clustering.md @@ -18,14 +18,21 @@ that the other libraries boast. from sklearn.manifold import TSNE from turftopic import ClusteringTopicModel -model = ClusteringTopicModel(clustering=TSNE()) +model = ClusteringTopicModel(dimensionality_reduction=TSNE()) ``` It is common practice to reduce the dimensionality of the embeddings before clustering them. This is to avoid the curse of dimensionality, an issue, which many clustering models are affected by. -Dimensionality reduction by default is done with scikit-learn's **TSNE** implementation in Turftopic, +Dimensionality reduction by default is done with **TSNE** in Turftopic, but users are free to specify the model that will be used for dimensionality reduction. +!!! tip "Use openTSNE for better performance!" + By default, a scikit-learn implementation is used, but if you have the openTSNE package installed on your system, Turftopic will automatically use it. + You can potentially speed up your clustering topic models by multiple orders of magnitude. + ```bash + pip install opentsne + ``` + ??? note "What reduction model should I choose?" Our knowledge about the impacts of choice of dimensionality reduction is limited, and has not yet been explored in the literature. Top2Vec and BERTopic both use UMAP, which has a number of desirable properties over alternatives (arranging data points into cluster-like structures, better preservation of global structure than TSNE, speed). @@ -33,14 +40,14 @@ but users are free to specify the model that will be used for dimensionality red ### Clustering ```python -from sklearn.cluster import OPTICS +from sklearn.cluster import HDBSCAN from turftopic import ClusteringTopicModel -model = ClusteringTopicModel(clustering=OPTICS()) +model = ClusteringTopicModel(clustering=HDBSCAN()) ``` After reducing the dimensionality of the embeddings, they are clustered with a clustering model. -As HDBSCAN has only been part of scikit-learn since version 1.3.0, Turftopic uses **OPTICS** as its default. +Turftopic uses **HDBSCAN** as its default. ??? note "What clustering model should I choose?" Some clustering models are capable of discovering the number of clusters in the data (HDBSCAN, DBSCAN, OPTICS, etc.). From d003591a840c640ead552abe42853fcd9cbe8dee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 28 Nov 2024 08:55:56 +0100 Subject: [PATCH 10/14] Update README.md Co-authored-by: Kenneth Enevoldsen --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index de724af..ffd82f3 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,8 @@ You will first have to install `datamapplot` for this to work. from turftopic import ClusteringTopicModel from turftopic.namers import OpenAITopicNamer -model = ClusteringTopicModel(feature_importance="centroid").fit(corpus) +model = ClusteringTopicModel(feature_importance="centroid") +model.fit(corpus) namer = OpenAITopicNamer("gpt-4o-mini") model.rename_topics(namer) From 6039095229225ab4b529f1be2a73df2e32ac70b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 28 Nov 2024 08:56:04 +0100 Subject: [PATCH 11/14] Update docs/basics.md Co-authored-by: Kenneth Enevoldsen --- docs/basics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/basics.md b/docs/basics.md index b1cb119..e094c98 100644 --- a/docs/basics.md +++ b/docs/basics.md @@ -312,7 +312,7 @@ fig #### topicwizard -Turftopic does not come with built-in visualization utilities, [topicwizard](https://github.com/x-tabdeveloping/topicwizard), a package for interactive topic model interpretation is fully compatible with Turftopic models. +Turftopic integrates with [topicwizard](https://github.com/x-tabdeveloping/topicwizard), a package for interactive topic visualization. ```bash pip install topic-wizard From 951f2c5c41b0f3a367110396caf67bc401fa86e3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 28 Nov 2024 08:58:30 +0100 Subject: [PATCH 12/14] Added openTSNE as optional dependency --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 29b8fa0..db661f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,7 @@ scipy = "^1.10.0" rich = "^13.6.0" huggingface-hub = "^0.23.2" joblib = "^1.2.0" +openTSNE = "^1.0.0" pyro-ppl = { version = "^1.8.0", optional = true } openai = { version = "^1.40.0", optional = true } mkdocs = { version = "^1.5.2", optional = true } @@ -31,6 +32,7 @@ mkdocstrings = { version = "^0.24.0", extras = ["python"], optional = true } [tool.poetry.extras] pyro-ppl = ["pyro-ppl"] openai = ["openai"] +opentsne = ["openTSNE"] docs = ["mkdocs", "mkdocs-material", "mkdocstrings"] [build-system] From a604e55043b5e4a2065a7fe8d87b822a3e529588 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 28 Nov 2024 09:03:00 +0100 Subject: [PATCH 13/14] Added datamapplot as optional dependency --- pyproject.toml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index db661f9..89af5cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,8 @@ scipy = "^1.10.0" rich = "^13.6.0" huggingface-hub = "^0.23.2" joblib = "^1.2.0" -openTSNE = "^1.0.0" +openTSNE = {version="^1.0.0", optional=true} +datamapplot = {version="^0.4.2", optional=true} pyro-ppl = { version = "^1.8.0", optional = true } openai = { version = "^1.40.0", optional = true } mkdocs = { version = "^1.5.2", optional = true } @@ -33,6 +34,7 @@ mkdocstrings = { version = "^0.24.0", extras = ["python"], optional = true } pyro-ppl = ["pyro-ppl"] openai = ["openai"] opentsne = ["openTSNE"] +datamapplot=["datamapplot"] docs = ["mkdocs", "mkdocs-material", "mkdocstrings"] [build-system] From 496fe73cbd66e7bb7b62f9431fc103bbaaaeafbb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 28 Nov 2024 09:05:32 +0100 Subject: [PATCH 14/14] Implemented requested changes in docs --- docs/basics.md | 9 ++++++--- docs/clustering.md | 18 +++++++++++------- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/docs/basics.md b/docs/basics.md index e094c98..7b0003b 100644 --- a/docs/basics.md +++ b/docs/basics.md @@ -284,8 +284,12 @@ model.print_topics() #### Datamapplot *(clustering models only)* -You can interactively explore clusters using `datamapplot` directly in Turftopic! -You will first have to install `datamapplot` for this to work. +You can interactively explore clusters using [datamapplot](https://github.com/TutteInstitute/datamapplot) directly in Turftopic! +You will first have to install `datamapplot` for this to work: + +```bash +pip install turftopic[datamapplot] +``` ```python from turftopic import ClusteringTopicModel @@ -309,7 +313,6 @@ fig - #### topicwizard Turftopic integrates with [topicwizard](https://github.com/x-tabdeveloping/topicwizard), a package for interactive topic visualization. diff --git a/docs/clustering.md b/docs/clustering.md index 313443d..8178f1d 100644 --- a/docs/clustering.md +++ b/docs/clustering.md @@ -23,19 +23,19 @@ model = ClusteringTopicModel(dimensionality_reduction=TSNE()) It is common practice to reduce the dimensionality of the embeddings before clustering them. This is to avoid the curse of dimensionality, an issue, which many clustering models are affected by. -Dimensionality reduction by default is done with **TSNE** in Turftopic, +Dimensionality reduction by default is done with [**TSNE**](https://scikit-learn.org/stable/modules/manifold.html#t-distributed-stochastic-neighbor-embedding-t-sne) in Turftopic, but users are free to specify the model that will be used for dimensionality reduction. !!! tip "Use openTSNE for better performance!" - By default, a scikit-learn implementation is used, but if you have the openTSNE package installed on your system, Turftopic will automatically use it. + By default, a scikit-learn implementation is used, but if you have the [openTSNE](https://github.com/pavlin-policar/openTSNE) package installed on your system, Turftopic will automatically use it. You can potentially speed up your clustering topic models by multiple orders of magnitude. ```bash - pip install opentsne + pip install turftopic[opentsne] ``` ??? note "What reduction model should I choose?" Our knowledge about the impacts of choice of dimensionality reduction is limited, and has not yet been explored in the literature. - Top2Vec and BERTopic both use UMAP, which has a number of desirable properties over alternatives (arranging data points into cluster-like structures, better preservation of global structure than TSNE, speed). + Top2Vec and BERTopic both use [UMAP](https://umap-learn.readthedocs.io/en/latest/basic_usage.html), which has a number of desirable properties over alternatives (arranging data points into cluster-like structures, better preservation of global structure than TSNE, speed). ### Clustering @@ -47,7 +47,7 @@ model = ClusteringTopicModel(clustering=HDBSCAN()) ``` After reducing the dimensionality of the embeddings, they are clustered with a clustering model. -Turftopic uses **HDBSCAN** as its default. +Turftopic uses [**HDBSCAN**](https://scikit-learn.org/stable/modules/clustering.html#hdbscan) as its default. ??? note "What clustering model should I choose?" Some clustering models are capable of discovering the number of clusters in the data (HDBSCAN, DBSCAN, OPTICS, etc.). @@ -183,8 +183,12 @@ model.reset_topics() ### Visualization -You can interactively explore clusters using `datamapplot` directly in Turftopic! -You will first have to install `datamapplot` for this to work. +You can interactively explore clusters using [datamapplot](https://github.com/TutteInstitute/datamapplot) directly in Turftopic! +You will first have to install `datamapplot` for this to work: + +```bash +pip install turftopic[datamapplot] +``` ```python from turftopic import ClusteringTopicModel