From bfbfd09f097fb9fa04677fdd75fed702f8e1a43a Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Thu, 18 Nov 2021 11:30:05 +0100 Subject: [PATCH 01/17] Expose diversity parameter --- bertopic/_bertopic.py | 30 ++++++++++++++++++------------ 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 92a20792..9f69844f 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -78,6 +78,7 @@ def __init__(self, nr_topics: Union[int, str] = None, low_memory: bool = False, calculate_probabilities: bool = False, + diversity: float = None, seed_topic_list: List[List[str]] = None, embedding_model=None, umap_model: UMAP = None, @@ -116,6 +117,9 @@ def __init__(self, you do not mind more computation time. NOTE: If false you cannot use the corresponding visualization method `visualize_probabilities`. + diversity: Whether to use MMR to diversify the resulting topic representations. + If set to None, MMR will not be used. Accepted values lie between + 0 and 1 with 0 being not at all diverse and 1 being very diverse. seed_topic_list: A list of seed words per topic to converge around verbose: Changes the verbosity of the model, Set to True if you want to track the stages of the model. @@ -141,6 +145,7 @@ def __init__(self, self.nr_topics = nr_topics self.low_memory = low_memory self.calculate_probabilities = calculate_probabilities + self.diversity = diversity self.verbose = verbose self.seed_topic_list = seed_topic_list @@ -1641,19 +1646,20 @@ def _extract_words_per_topic(self, # Extract word embeddings for the top 30 words per topic and compare it # with the topic embedding to keep only the words most similar to the topic embedding - if self.embedding_model is not None: + if self.diversity is not None: + if self.embedding_model is not None: - for topic, topic_words in topics.items(): - words = [word[0] for word in topic_words] - word_embeddings = self._extract_embeddings(words, - method="word", - verbose=False) - topic_embedding = self._extract_embeddings(" ".join(words), - method="word", - verbose=False).reshape(1, -1) - topic_words = mmr(topic_embedding, word_embeddings, words, - top_n=self.top_n_words, diversity=0) - topics[topic] = [(word, value) for word, value in topics[topic] if word in topic_words] + for topic, topic_words in topics.items(): + words = [word[0] for word in topic_words] + word_embeddings = self._extract_embeddings(words, + method="word", + verbose=False) + topic_embedding = self._extract_embeddings(" ".join(words), + method="word", + verbose=False).reshape(1, -1) + topic_words = mmr(topic_embedding, word_embeddings, words, + top_n=self.top_n_words, diversity=self.diversity) + topics[topic] = [(word, value) for word, value in topics[topic] if word in topic_words] topics = {label: values[:self.top_n_words] for label, values in topics.items()} return topics From f98b429337bf539666cd0f2197cbae6a2e01d458 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Fri, 19 Nov 2021 08:20:00 +0100 Subject: [PATCH 02/17] Improve stability topic reduction --- bertopic/_bertopic.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 9f69844f..d2f44f08 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -1700,10 +1700,7 @@ def _reduce_to_n_topics(self, documents: pd.DataFrame) -> pd.DataFrame: self.merged_topics = [] # Create topic similarity matrix - if self.topic_embeddings is not None: - similarities = cosine_similarity(np.array(self.topic_embeddings)) - else: - similarities = cosine_similarity(self.c_tf_idf) + similarities = cosine_similarity(self.c_tf_idf) np.fill_diagonal(similarities, 0) # Find most similar topic to least common topic From d183359fee379c794ee745d139561d21c61c48f7 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Fri, 26 Nov 2021 11:56:09 +0100 Subject: [PATCH 03/17] Drop python 3.7 support --- README.md | 2 +- setup.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d13d808a..a9bf6d1a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[![PyPI - Python](https://img.shields.io/badge/python-v3.6+-blue.svg)](https://pypi.org/project/bertopic/) +[![PyPI - Python](https://img.shields.io/badge/python-v3.7+-blue.svg)](https://pypi.org/project/bertopic/) [![Build](https://img.shields.io/github/workflow/status/MaartenGr/BERTopic/Code%20Checks/master)](https://pypi.org/project/bertopic/) [![docs](https://img.shields.io/badge/docs-Passing-green.svg)](https://maartengr.github.io/BERTopic/) [![PyPI - PyPi](https://img.shields.io/pypi/v/BERTopic)](https://pypi.org/project/bertopic/) diff --git a/setup.py b/setup.py index b3e1fce2..92b48930 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,7 @@ "Operating System :: Unix", "Operating System :: MacOS", "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.8", ], install_requires=base_packages, @@ -91,5 +91,5 @@ "use": use_packages, "gensim": gensim_packages }, - python_requires='>=3.6', + python_requires='>=3.7', ) From ed8cf8ea42a77f568992cd478871734c542caf6a Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 30 Nov 2021 16:19:12 +0100 Subject: [PATCH 04/17] Start of major documentation overhaul --- README.md | 6 +- bertopic/_bertopic.py | 3 +- docs/{tutorial => }/algorithm/algorithm.md | 47 +++---- .../embeddings/embeddings.md | 0 .../guided/guided.md | 0 .../models/models.md | 0 .../parameter tuning/parametertuning.md | 120 ++++++++++++++++++ .../quickstart/quickstart.md | 0 .../quickstart/viz.html | 0 .../search/search.md | 0 .../supervised/supervised.md | 0 .../topicreduction/topicreduction.md | 0 .../topicrepresentation.md | 0 .../topicsovertime/topicsovertime.md | 0 .../topicsovertime/trump.html | 0 .../topicsperclass/topics_per_class.html | 0 .../topicsperclass/topicsperclass.md | 0 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a9bf6d1a..2eda4c11 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ with one of the examples below: ## Quick Start -We start by extracting topics from the well-known 20 newsgroups dataset which is comprised of english documents: +We start by extracting topics from the well-known 20 newsgroups dataset containing English documents: ```python from bertopic import BERTopic @@ -66,7 +66,7 @@ topic_model = BERTopic() topics, probs = topic_model.fit_transform(docs) ``` -After generating topics, we can access the frequent topics that were generated: +After generating topics and their probabilities, we can access the frequent topics that were generated: ```python >>> topic_model.get_topic_info() @@ -238,7 +238,7 @@ To cite BERTopic in your work, please use the following bibtex reference: title = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.}, year = 2020, publisher = {Zenodo}, - version = {v0.9.2}, + version = {v0.9.3}, doi = {10.5281/zenodo.4381785}, url = {https://doi.org/10.5281/zenodo.4381785} } diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index d2f44f08..577b9e7f 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -106,8 +106,7 @@ def __init__(self, number of topics to the value specified. This reduction can take a while as each reduction in topics (-1) activates a c-TF-IDF calculation. If this is set to None, no reduction is applied. Use - "auto" to automatically reduce topics that have a similarity of at - least 0.9, do not maps all others. + "auto" to automatically reduce topics using HDBSCAN. low_memory: Sets UMAP low memory to True to make sure less memory is used. calculate_probabilities: Whether to calculate the probabilities of all topics per document instead of the probability of the assigned diff --git a/docs/tutorial/algorithm/algorithm.md b/docs/algorithm/algorithm.md similarity index 58% rename from docs/tutorial/algorithm/algorithm.md rename to docs/algorithm/algorithm.md index f3c17f5d..3c81b0cc 100644 --- a/docs/tutorial/algorithm/algorithm.md +++ b/docs/algorithm/algorithm.md @@ -1,4 +1,4 @@ - + The algorithm contains, roughly, 3 stages: @@ -9,38 +9,35 @@ The algorithm contains, roughly, 3 stages: * HDBSCAN to cluster reduced embeddings and create clusters of semantically similar documents 3. **Create topic representation** * Extract and reduce topics with c-TF-IDF - * Improve coherence of words with Maximal Marginal Relevance + * Improve coherence and diversity of words with Maximal Marginal Relevance ## **Embed documents** We start by creating document embeddings from a set of documents using [sentence-transformers](https://github.com/UKPLab/sentence-transformers). These models are pre-trained for many -language and are great for creating either document- or sentence-embeddings. +languages and are great for creating either document- or sentence-embeddings. In BERTopic, you can choose any sentence transformers model but there are two models that are set as defaults: * `"all-MiniLM-L6-v2"` * `"paraphrase-multilingual-MiniLM-L12-v2"` -The first is an English BERT-based model trained specifically for semantic similarity tasks which work quite +The first is an English language model trained specifically for semantic similarity tasks which work quite well for most use-cases. The second model is very similar to the first with one major difference is that the -`xlm` models work for 50+ languages. This model is quite a bit larger than the first and is only selected if +`multilingual` models work for 50+ languages. This model is quite a bit larger than the first and is only selected if you select any language other than English. ## **Cluster Documents** -Next, in order to cluster the documents using a clustering algorithm such as HDBSCAN we first need to -reduce its dimensionality as HDBCAN is prone to the curse of dimensionality. -

- -

+Typically, clustering algorithms have difficulty clustering data in high dimensional space. Before we are +going to cluster our documents, we first need to reduce the dimensionality of the embeddings that we generated. -Thus, we first lower dimensionality with UMAP as it preserves local structure well after which we can -use HDBSCAN to cluster similar documents. +To do so, we use UMAP as it preserves both the local and global structure of embeddings quite well. Then, +we use HDBSCAN to cluster the reduced embeddings as it allows us to identify outliers. ## **Create topic representation** What we want to know from the clusters that we generated, is what makes one cluster, based on its content, -different from another? To solve this, we can modify TF-IDF such that it allows for interesting words per topic -instead of per document. +different from another? To solve this, we can modify TF-IDF such that it allows for interesting words per cluster +of documents instead of per individual document. When you apply TF-IDF as usual on a set of documents, what you are doing is comparing the importance of words between documents. Now, what if, we instead treat all documents in a single category (e.g., a cluster) @@ -50,17 +47,19 @@ if we extract the most important words per cluster, we get descriptions of **top class-based TF-IDF

- +

-Each cluster is converted to a single document instead of a set of documents. -Then, the frequency of word `t` are extracted for each class `i` and divided by the total number of words `w`. -This action can now be seen as a form of regularization of frequent words in the class. -Next, the average number of words per class `m` is divided by the total frequency of word `t` across all classes `n`. +Each cluster is converted to a single document instead of a set of documents. Then, we extract the frequency +of word `x` in class `c`, where `c` refers to the cluster we created before. This results in our class-based + `tf` representation. + +Then, we take take the logarithm of the average number of words per class `A` divided by the frequency +of word `x` across all classes. This results in our class-based `idf` representation. -### **Topic Coherence** -This step is executed if you do not use custom embeddings but generate the document embeddings within BERTopic -itself. The embedding model provided by BERTopic will be used to improve the coherence of words within a topic. +Like with the classic TF-IDF, we then multiply `tf` with `idf` to get the importance score per word in each class. + +### **Maximal Marginal Relevance Coherence** After having generated the c-TF-IDF representations, we have a set of words that describe a collection of documents. Technically, this does not mean that this collection of words describes a coherent topic. In practice, we will see that many of the words do describe a similar topic but some words will, in a way, overfit the documents. For @@ -70,3 +69,7 @@ description. To improve the coherence of words, Maximal Marginal Relevance was used to find the most coherent words without having too much overlap between the words themselves. This results in the removal of words that do not contribute to a topic. + +You can also use this technique to diversify the words generated in the topic representation. At times, many variations +of the same word can end up in the topic representation. To reduce the number of synonyms, we can increase the diversity +among words whilst still being similar to the topic representation. diff --git a/docs/tutorial/embeddings/embeddings.md b/docs/getting started/embeddings/embeddings.md similarity index 100% rename from docs/tutorial/embeddings/embeddings.md rename to docs/getting started/embeddings/embeddings.md diff --git a/docs/tutorial/guided/guided.md b/docs/getting started/guided/guided.md similarity index 100% rename from docs/tutorial/guided/guided.md rename to docs/getting started/guided/guided.md diff --git a/docs/tutorial/models/models.md b/docs/getting started/models/models.md similarity index 100% rename from docs/tutorial/models/models.md rename to docs/getting started/models/models.md diff --git a/docs/getting started/parameter tuning/parametertuning.md b/docs/getting started/parameter tuning/parametertuning.md new file mode 100644 index 00000000..243a5af3 --- /dev/null +++ b/docs/getting started/parameter tuning/parametertuning.md @@ -0,0 +1,120 @@ +# Hyperparameter Tuning + +Although BERTopic works quite well out of the box, there are a number of hyperparameters to tune according to your use-case. +This section will focus on important parameters directly accessable in BERTopic but also hyperparameter optimization in sub-models +such as HDBSCAN and UMAP. + +## **BERTopic** + +In this section, we will go through most important hyperparameters in BERTopic that you can change directly when instantiating BERTopic: + +* language +* top_n_words +* n_gram_range +* min_topic_size +* nr_topics +* low_memory +* calculate_probabilities + +### **language** +The `language` parameter is used to simplify the selection of models for those who are not familiar with sentence-transformers models. + +In essence, there are two options to choose from: + +* `language = "english"` or +* `language = "multilingual"` + +The English model is "all-MiniLM-L6-v2" and can be found [here](https://www.sbert.net/docs/pretrained_models.html). It is the default model that is used in BERTopic and works great for English documents. + +The multilingual model is "paraphrase-multilingual-MiniLM-L12-v2" and supports over 50+ languages which can be found [here](https://www.sbert.net/docs/pretrained_models.html). The model is very similar to the base model but is trained on many languages and has a slightly different architecture. + +### **top_n_words** + +`top_n_words` refers to the number of words per topic that you want extracted. In practice, I would advise you to keep this value below 30 and preferably between 10 and 20. The reasoning for this is that the more words you put in a topic the less coherent it can become. The top words are the most representative for the topic and should be focused on. + +### **n_gram_range** +The `n_gram_range` parameter refers to the CountVectorizer used when creating the topic representation. It relates to the number of words you want in your topic representation. For example, "New" and "York" are two seperate words but are often used as "New York" which represents an n-gram of 2. Thus, the `n_gram_range` should be set to (1, 2) if you want "New York" in your topic representation. + +### **min_topic_size** +`min_topic_size` is an important parameter! It is used to specify what the minimum size of a topic can be. The lower this value the more topics are created. If you set this value too high, then it is possible that simply no topics will be created! Set this value too low and you will get many micro clusters. + +It is advised to play around with this value depending on the size of the your dataset. If it nears a million documents, then it advised to set it much higher than the default of 10, for example 100 or even 500. + +### **nr_topics** +`nr_topics` can be a tricky parameter. It specifies, after training the topic model, the number of topics that will be reduced to. For example, if your topic model results in 100 topics but you have set `nr_topics` to 20 then the topic model will try to reduce the number of topics from 100 to 20. + +This reduction can take awhile as each reduction in topics activates a c-TF-IDF calculation. If this is set to None, no reduction is applied. Use "auto" to automatically reduce topics that using HDBSCAN. + +### **low_memory** +`low_memory` sets UMAP's `low_memory` to True to make sure that less memory is used in computation. This slows down computation but allows UMAP to be ran on low memory machines. + +### **calculate_probabilities** +`calculate_probabilities` lets you calculate the probabilities of each topic to each document. This is computationally quite expensive and is turned off by default. + +## **UMAP** + +UMAP is an amazing technique for dimensionality reduction. In BERTopic, it is used to reduce the dimensionality of document embedding into something that is easier to use with HDBSCAN in order to create good clusters. + +However, it does has a significant number of parameters you could take into account. As exposing all parameters in BERTopic would be difficult to manage, we can instantiate our UMAP model and pass it to BERTopic: + +```python +from umap import UMAP + +umap_model = UMAP(n_neighbors=15, n_components=10, metric='cosine', low_memory=False) +topic_model = BERTopic(umap_model=umap_model).fit(docs) +``` + +Here, there are a few parameters that are interesting to us: + +* n_neighbors +* n_components +* metric +* low_memory + +### **n_neighbors** +`n_neighbors` is the numer of neighboring sample points used when making the manifold approximation. Increasing this value typically results in a +more global view of the embedding structure whilst smaller values result in a more local view. Increasing this value often results in larger clusters +being created. + +### **n_components** +`n_components` refers to the dimensionality of the embeddings after reducing them. This is set as a default to `5` in order to reduce dimensionality +as much as possible whilst trying to maximize the information kept in the resulting embeddings. Although lowering or increasing this value has an influence on the quality of embeddings, its effect is largest on the performance of HDBSCAN. Increasing this value too much and HDBSCAN will have a +hard time clustering the high-dimensional embeddings. Lower this value too much and too little information in the resulting embeddings is available +to create proper clusters. If you want to increase this value, I would advise setting using a metric for HDBSCAN that works well in high dimensional data. + +### **metric** +`metric` refers to the method used to compute the distances in high dimensional space. The default is `cosine` as we are dealing with high dimensional data. However, BERTopic is also able to use any input, even regular tabular data, to cluster the documents. Thus, you might want to change the metric +to something that fits with your use case. + +### **low_memory** +`low_memory` is used when datasets may consume a lot of memory. Using millions of documents can lead to memory issues and setting this value to `True` +might alleviate some of the issues. + +## **HDBSCAN** +After reducing the embeddings with UMAP, we use HDBSCAN to cluster our documents into clusters of similar documents. Similar to UMAP, HDBSCAN has many parameters that could be tweaked in order to improve the cluster's quality. + +```python +from hdbscan import HDBSCAN + +hdbscan_model = HDBSCAN(min_cluster_size=10, metric='euclidean', prediction_data=True) +topic_model = BERTopic(hdbscan_model=hdbscan_model).fit(docs) +``` + +### **min_cluster_size** +`min_cluster_size` is arguably the most important parameter in HDBSCAN. It controls the minimum size of a cluster and thereby the amount of clusters +that will be generated. It is set to `10` as a default. Increasing this value results in less clusters but of larger size whereas decreasing this value +results in more micro clusters being generated. Typically, I would advise on increasing this value rather than decreasing it. + +### **min_samples** +`min_samples` is automatically set to `min_cluster_size` and controls the amount of outliers are generated. Setting this value significantly lower than +`min_cluster_size` might help you reduce the amount of noise you will get. Do note that outliers are typically to be expected and forcing the output +to have no outliers may not properly represent the data. + +### **metric** +`metric`, like with HDBSCAN is used to calculate the distances. Here, we went with `euclidean` as, after reducing the dimensionality, we have +low dimensional data and not much optimization is necessary. However, if you increase `n_components` in UMAP, then it would be advised to look into +metrics that work with high dimensional data. + +### **prediction_data** +Make sure you always set this value to `True` as it is needed to predict new points later on. 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zTGJ>Vz=Rd)-W6IqKJ9jNCi1OyTFSPz0i_sA=bJ9{MkVT`Sm4pSQL zZWZD45oM=$3s9~aEwCN*sy6cCc^hXaF3*-dqDFB!YA7|2n=SS@6K?pgr;#f2obZUN zHaFPzIt-O&PgIBFaW}bo<8Sw#1XLsbJ#?gx**g{(+e7}Ml`6B(zOj6b9&^#l$k%7F zR?oF&MEWoYgwPa6bjU=bJu_vHe*H@?-`?q*?su654;seQ{bK8riE7SO!?Zg7s{%Nv z6KkGE25l}nsa-yfCwvM`b%;8>#Fra?JD2W8=|=*SVhzr5i~OI_?Ay*p98yF+rW`(! zOqfvazXxLU>N(8B@0$Fdd9*#LKe^D+EOvN!E{s-)&()P~x->2VDJ@TP6Duzx>G?A_hkN(8eAOcqjske6W>Bk*rxf8+nP z03ckMfGIQ9ZXvcP+h0}C!$Pw>i!-w6&rCqZ_(#A1N9})&5|j&;Bfu^SUg}sVl)LGF z`br3|b$73sll&UTZy(4*I{dN?eM3)P&8IcrjzQ7N*wSTm3&N6SDT;yC_5(JkG6dDH*%h(jT1yQ+t4KqC + -BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters -allowing for easily interpretable topics whilst keeping important words in the topic descriptions. It even supports -visualizations similar to LDAvis! +BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters +allowing for easily interpretable topics whilst keeping important words in the topic descriptions. + +BERTopic supports +[**guided**](https://maartengr.github.io/BERTopic/tutorial/guided/guided.html), +(semi-) [**supervised**](https://maartengr.github.io/BERTopic/tutorial/supervised/supervised.html), +and [**dynamic**](https://maartengr.github.io/BERTopic/tutorial/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4). @@ -20,7 +24,6 @@ pip install bertopic You may want to install more depending on the transformers and language backends that you will be using. The possible installations are: -To use Flair embeddings, install BERTopic as follows: ```bash pip install bertopic[flair] pip install bertopic[gensim] @@ -28,14 +31,8 @@ pip install bertopic[spacy] pip install bertopic[use] ``` -To install all backends: - -```bash -pip install bertopic[all] -``` - ## **Quick Start** -We start by extracting topics from the well-known 20 newsgroups dataset which is comprised of english documents: +We start by extracting topics from the well-known 20 newsgroups dataset containing English documents: ```python from bertopic import BERTopic @@ -86,35 +83,37 @@ For quick access to common functions, here is an overview of BERTopic's main met | Method | Code | |-----------------------|---| -| Fit the model | `BERTopic().fit(docs)` | -| Fit the model and predict documents | `BERTopic().fit_transform(docs)` | -| Predict new documents | `BERTopic().transform([new_doc])` | -| Access single topic | `BERTopic().get_topic(topic=12)` | -| Access all topics | `BERTopic().get_topics()` | -| Get topic freq | `BERTopic().get_topic_freq()` | -| Get all topic information| `BERTopic().get_topic_info()` | -| Get topics per class | `BERTopic().topics_per_class(docs, topics, classes)` | -| Dynamic Topic Modeling | `BERTopic().topics_over_time(docs, topics, timestamps)` | -| Update topic representation | `BERTopic().update_topics(docs, topics, n_gram_range=(1, 3))` | -| Reduce nr of topics | `BERTopic().reduce_topics(docs, topics, nr_topics=30)` | -| Find topics | `BERTopic().find_topics("vehicle")` | -| Save model | `BERTopic().save("my_model")` | +| Fit the model | `.fit(docs)` | +| Fit the model and predict documents | `.fit_transform(docs)` | +| Predict new documents | `.transform([new_doc])` | +| Access single topic | `.get_topic(topic=12)` | +| Access all topics | `.get_topics()` | +| Get topic freq | `.get_topic_freq()` | +| Get all topic information| `.get_topic_info()` | +| Get representative docs per topic | `.get_representative_docs()` | +| Get topics per class | `.topics_per_class(docs, topics, classes)` | +| Dynamic Topic Modeling | `.topics_over_time(docs, topics, timestamps)` | +| Update topic representation | `.update_topics(docs, topics, n_gram_range=(1, 3))` | +| Reduce nr of topics | `.reduce_topics(docs, topics, nr_topics=30)` | +| Find topics | `.find_topics("vehicle")` | +| Save model | `.save("my_model")` | | Load model | `BERTopic.load("my_model")` | -| Get parameters | `BERTopic().get_params()` | +| Get parameters | `.get_params()` | For an overview of BERTopic's visualization methods: | Method | Code | |-----------------------|---| -| Visualize Topics | `BERTopic().visualize_topics()` | -| Visualize Topic Hierarchy | `BERTopic().visualize_hierarchy()` | -| Visualize Topic Terms | `BERTopic().visualize_barchart()` | -| Visualize Topic Similarity | `BERTopic().visualize_heatmap()` | -| Visualize Term Score Decline | `BERTopic().visualize_term_rank()` | -| Visualize Topic Probability Distribution | `BERTopic().visualize_distribution(probs[0])` | -| Visualize Topics over Time | `BERTopic().visualize_topics_over_time(topics_over_time)` | -| Visualize Topics per Class | `BERTopic().visualize_topics_per_class(topics_per_class)` | - +| Visualize Topics | `.visualize_topics()` | +| Visualize Topic Hierarchy | `.visualize_hierarchy()` | +| Visualize Topic Terms | `.visualize_barchart()` | +| Visualize Topic Similarity | `.visualize_heatmap()` | +| Visualize Term Score Decline | `.visualize_term_rank()` | +| Visualize Topic Probability Distribution | `.visualize_distribution(probs[0])` | +| Visualize Topics over Time | `.visualize_topics_over_time(topics_over_time)` | +| Visualize Topics per Class | `.visualize_topics_per_class(topics_per_class)` | + + ## **Citation** To cite BERTopic in your work, please use the following bibtex reference: @@ -124,7 +123,7 @@ To cite BERTopic in your work, please use the following bibtex reference: title = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.}, year = 2020, publisher = {Zenodo}, - version = {v0.7.0}, + version = {v0.9.3}, doi = {10.5281/zenodo.4381785}, url = {https://doi.org/10.5281/zenodo.4381785} } diff --git a/docs/style.css b/docs/style.css deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 00000000..b82978a2 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,7 @@ +:root { + --md-primary-fg-color: #234E70; + } + +:root>* { +--md-typeset-a-color: #0277BD; +} \ No newline at end of file diff --git a/docs/tutorial/algorithm/algorithm.png b/docs/tutorial/algorithm/algorithm.png deleted file mode 100644 index aa53c1939a56da3c91d47ece45387ed9940f4570..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 176820 zcmd4&c|4TgA3ux_Eh?diil}7Imh7^W%DyG*ShHmrvJGZzMPx5R*|TS77@;AQZDPt` zFotA{!3bk8hWk>zzrWA#{`2?y+{4I>T58a;yeWcfta*3 z?>+#5PTT^47}QQ22i_4>az8ss=7C0uM)B 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zDT!^483qMQnUTL5gTP&rX{u^34oV(+z;Fj>jtk{s(T_s`{h^2kJ|OyF+CmG)Up+MZ zm5yPcIZ=$(=o{Nd2*){qc&YWs83)#V)*LoD=Bob+lto9(cpnA!nzu3`AF{$RHAKM= z0O}u+B0(SZ;WvM4D9m7)765ZszfcMALP=9M{>$S4P11Dq%dk*VUII9U1sEQnR-yE2 zi-I{z<}`c)Bych41u~CBPQQS|u_$I-&?e=W!+L5E^pwr|Q&wiN!MqwoeM?6ibZlSZ zKndzHxPq}PkjU%??TK`M z2g?vsj8-5+s{N@0m2=v5Uw~W$sJ^Bo#Y%;ek|mpy>Vg+fz;DO2uv^v1Om{-fjO0J# z2H^RSqLF={J4d{wK3rTQamYQ)~houn+ zcBV`EvV?cl5m-NaR%ZRn*givyLrzz;veu&b8>lLxxZpLCWcEHW89Z0M$_jU{2S^oe ze3Vq9^6%KI(PR1E|8)4^c(tNBgszYn^&Bb|9`>M(Uo_#}5(DnyhZPZrfG0su2c}yx zhoY%*6f)XGK8QwaDiy(Z3GKiPJ5;>8=vdNf8Y!mjx3`o&ZrmzjZYlVnGpD0YZECXZ z!R3O0zs$yHUHlWFM`_J5s)0$8e Date: Wed, 1 Dec 2021 09:47:33 +0100 Subject: [PATCH 05/17] Implement #351 and update ctfidf params --- bertopic/_bertopic.py | 10 +++++----- bertopic/_ctfidf.py | 18 ++++++++++++++---- tests/test_models.py | 4 ++-- 3 files changed, 21 insertions(+), 11 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 577b9e7f..a96b713c 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -480,7 +480,7 @@ def topics_over_time(self, selection = documents.loc[documents.Timestamps == timestamp, :] documents_per_topic = selection.groupby(['Topic'], as_index=False).agg({'Document': ' '.join, "Timestamps": "count"}) - c_tf_idf, words = self._c_tf_idf(documents_per_topic, m=len(selection), fit=False) + c_tf_idf, words = self._c_tf_idf(documents_per_topic, fit=False) if global_tuning or evolution_tuning: c_tf_idf = normalize(c_tf_idf, axis=1, norm='l1', copy=False) @@ -573,7 +573,7 @@ def topics_per_class(self, selection = documents.loc[documents.Class == class_, :] documents_per_topic = selection.groupby(['Topic'], as_index=False).agg({'Document': ' '.join, "Class": "count"}) - c_tf_idf, words = self._c_tf_idf(documents_per_topic, m=len(selection), fit=False) + c_tf_idf, words = self._c_tf_idf(documents_per_topic, fit=False) # Fine-tune the timestamp c-TF-IDF representation based on the global c-TF-IDF representation # by simply taking the average of the two @@ -1451,7 +1451,7 @@ def _extract_topics(self, documents: pd.DataFrame): c_tf_idf: The resulting matrix giving a value (importance score) for each word per topic """ documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join}) - self.c_tf_idf, words = self._c_tf_idf(documents_per_topic, m=len(documents)) + self.c_tf_idf, words = self._c_tf_idf(documents_per_topic) self.topics = self._extract_words_per_topic(words) self._create_topic_vectors() self.topic_names = {key: f"{key}_" + "_".join([word[0] for word in values[:4]]) @@ -1557,7 +1557,7 @@ def _create_topic_vectors(self): self.topic_embeddings = topic_embeddings - def _c_tf_idf(self, documents_per_topic: pd.DataFrame, m: int, fit: bool = True) -> Tuple[csr_matrix, List[str]]: + def _c_tf_idf(self, documents_per_topic: pd.DataFrame, fit: bool = True) -> Tuple[csr_matrix, List[str]]: """ Calculate a class-based TF-IDF where m is the number of total documents. Arguments: @@ -1585,7 +1585,7 @@ def _c_tf_idf(self, documents_per_topic: pd.DataFrame, m: int, fit: bool = True) multiplier = None if fit: - self.transformer = ClassTFIDF().fit(X, n_samples=m, multiplier=multiplier) + self.transformer = ClassTFIDF().fit(X, multiplier=multiplier) c_tf_idf = self.transformer.transform(X) diff --git a/bertopic/_ctfidf.py b/bertopic/_ctfidf.py index 9953ed6b..d3d8fa2a 100644 --- a/bertopic/_ctfidf.py +++ b/bertopic/_ctfidf.py @@ -21,12 +21,12 @@ class ClassTFIDF(TfidfTransformer): def __init__(self, *args, **kwargs): super(ClassTFIDF, self).__init__(*args, **kwargs) - def fit(self, X: sp.csr_matrix, n_samples: int, multiplier: np.ndarray = None): + def fit(self, X: sp.csr_matrix, multiplier: np.ndarray = None): """Learn the idf vector (global term weights). Arguments: X: A matrix of term/token counts. - n_samples: Number of total documents + multiplier: A multiplier for increasing/decreasing certain IDF scores """ X = check_array(X, accept_sparse=('csr', 'csc')) if not sp.issparse(X): @@ -35,11 +35,21 @@ def fit(self, X: sp.csr_matrix, n_samples: int, multiplier: np.ndarray = None): if self.use_idf: _, n_features = X.shape + + # Calculate the frequency of words across all classes df = np.squeeze(np.asarray(X.sum(axis=0))) + + # Calculate the average number of samples as regularization avg_nr_samples = int(X.sum(axis=1).mean()) - idf = np.log(avg_nr_samples / df) + + # Divide the average number of samples by the word frequency + # +1 is added to force values to be positive + idf = np.log((avg_nr_samples / df)+1) + + # Multiplier to increase/decrease certain idf scores if multiplier is not None: idf = idf * multiplier + self._idf_diag = sp.diags(idf, offsets=0, shape=(n_features, n_features), format='csr', @@ -47,7 +57,7 @@ def fit(self, X: sp.csr_matrix, n_samples: int, multiplier: np.ndarray = None): return self - def transform(self, X: sp.csr_matrix, copy=True): + def transform(self, X: sp.csr_matrix): """Transform a count-based matrix to c-TF-IDF Arguments: diff --git a/tests/test_models.py b/tests/test_models.py index 931eff14..bfb83a03 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -178,7 +178,7 @@ def test_ctfidf(base_bertopic): count = base_bertopic.vectorizer_model.fit(documents) words = count.get_feature_names() X = count.transform(documents) - transformer = ClassTFIDF().fit(X, n_samples=len(newsgroup_docs)) + transformer = ClassTFIDF().fit(X) c_tf_idf = transformer.transform(X) assert len(words) > 1000 @@ -218,7 +218,7 @@ def test_ctfidf_custom_cv(): count = model.vectorizer_model.fit(documents) words = count.get_feature_names() X = count.transform(documents) - transformer = ClassTFIDF().fit(X, n_samples=len(newsgroup_docs)) + transformer = ClassTFIDF().fit(X) c_tf_idf = transformer.transform(X) assert len(words) > 1000 From a05d2e70f94468ddca76cf49b1182bafe04add44 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Thu, 2 Dec 2021 15:12:34 +0100 Subject: [PATCH 06/17] Update cTFIDF documentation --- docs/algorithm/algorithm.md | 5 +++-- docs/img/ctfidf.png | Bin 80368 -> 80948 bytes 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/algorithm/algorithm.md b/docs/algorithm/algorithm.md index 3c81b0cc..a66419a0 100644 --- a/docs/algorithm/algorithm.md +++ b/docs/algorithm/algorithm.md @@ -54,8 +54,9 @@ Each cluster is converted to a single document instead of a set of documents. Th of word `x` in class `c`, where `c` refers to the cluster we created before. This results in our class-based `tf` representation. -Then, we take take the logarithm of the average number of words per class `A` divided by the frequency -of word `x` across all classes. This results in our class-based `idf` representation. +Then, we take take the logarithm of one plus the average number of words per class `A` divided by the frequency +of word `x` across all classes. We add plus one within the logarithm to force values to be positive. +This results in our class-based `idf` representation. 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z8&qu@n_Oj;4$%_d>HgoSotoV0GVw!Ds%)Dl=ta_?ei{e;lwCQd!09)Q+ z(UL%5=jc#(4}Mo2iwwk+0f>*2%L-oBdwwYob$1iCS@qH=_Nwzb%17-wD52zRw{aW@ zDso3}`pDT`cNag!ZUY1}>@vq)a>o~qlmtNnvE|e^LOb8+dCszZSyU~pqRTo zIA4RK#C-927mgYriE90o(N=*S-WesfXLYud#N#E&N4FixjTb$RUjO9P_e3svoALFZ zH$t)HD_5OkQu%K#x^aVqz;*bQ3NK={V?jN!bzc%3jnxaIJ>yVFG3@5v?~4OFR4)O6 zsQ;T=Z&@TbyiN-oLksECmsiX9;6>1#U?pD=up$;Z{y6FT~hij5QK zeyr`+iK4wcX?3SdLOY^n6x8OU!@WQ%_c~qzU^CWMa+vCKXwK|FbS2@#lB%nSK=nDcrg1OYwuQxF+piofJ#Dp4#a|KtSar)1&SBF*Kj$6%ELGTKOjH&PFKq0ROZY zM=gxF^@)8q0H8BAC#n9D$P*CAI^c7xZGOlg8Mrz3u;LOmIYZILX zt}aZR3*==M8X^aC%>+U8{%sKf&(^e_f5-EDXnOgFDK$UH(%OCX& z5FZ1(bTrF&FclKU&*W@et@7rj_SYe`fqQ5CY<~)HMZYP z0m$zY=kGS;_YC}RbH@VzwZ?Bh`1Sg)HU7V>!PnI^lSbV=5$3!zf`!?&%NP^S`2PXP CNkW4F From d8374bf98cc8233c92440d7b78314ce0e1667ee1 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Thu, 2 Dec 2021 16:49:05 +0100 Subject: [PATCH 07/17] More documentation changes --- docs/algorithm/algorithm.md | 8 +- .../custom_embeddings/embeddings.md | 98 ++++++++++++++ docs/getting started/embeddings/embeddings.md | 124 ++---------------- docs/getting started/guided/guided.md | 1 - .../parameter tuning/parametertuning.md | 17 +-- docs/getting started/quickstart/quickstart.md | 18 +-- docs/getting started/search/search.md | 4 +- .../topicreduction/topicreduction.md | 26 +--- .../topicrepresentation.md | 2 +- .../topicsovertime/topicsovertime.md | 3 +- .../visualization/visualization.md | 18 ++- mkdocs.yml | 5 + 12 files changed, 149 insertions(+), 175 deletions(-) create mode 100644 docs/getting started/custom_embeddings/embeddings.md diff --git a/docs/algorithm/algorithm.md b/docs/algorithm/algorithm.md index a66419a0..59d62023 100644 --- a/docs/algorithm/algorithm.md +++ b/docs/algorithm/algorithm.md @@ -11,7 +11,7 @@ The algorithm contains, roughly, 3 stages: * Extract and reduce topics with c-TF-IDF * Improve coherence and diversity of words with Maximal Marginal Relevance -## **Embed documents** +## **1. Embed documents** We start by creating document embeddings from a set of documents using [sentence-transformers](https://github.com/UKPLab/sentence-transformers). These models are pre-trained for many languages and are great for creating either document- or sentence-embeddings. @@ -26,7 +26,7 @@ well for most use-cases. The second model is very similar to the first with one `multilingual` models work for 50+ languages. This model is quite a bit larger than the first and is only selected if you select any language other than English. -## **Cluster Documents** +## **2. Cluster Documents** Typically, clustering algorithms have difficulty clustering data in high dimensional space. Before we are going to cluster our documents, we first need to reduce the dimensionality of the embeddings that we generated. @@ -34,7 +34,7 @@ going to cluster our documents, we first need to reduce the dimensionality of th To do so, we use UMAP as it preserves both the local and global structure of embeddings quite well. Then, we use HDBSCAN to cluster the reduced embeddings as it allows us to identify outliers. -## **Create topic representation** +## **3. Create topic representation** What we want to know from the clusters that we generated, is what makes one cluster, based on its content, different from another? To solve this, we can modify TF-IDF such that it allows for interesting words per cluster of documents instead of per individual document. @@ -60,7 +60,7 @@ This results in our class-based `idf` representation. Like with the classic TF-IDF, we then multiply `tf` with `idf` to get the importance score per word in each class. -### **Maximal Marginal Relevance Coherence** +## **(Optional) Maximal Marginal Relevance Coherence** After having generated the c-TF-IDF representations, we have a set of words that describe a collection of documents. Technically, this does not mean that this collection of words describes a coherent topic. In practice, we will see that many of the words do describe a similar topic but some words will, in a way, overfit the documents. For diff --git a/docs/getting started/custom_embeddings/embeddings.md b/docs/getting started/custom_embeddings/embeddings.md new file mode 100644 index 00000000..de4c9fb9 --- /dev/null +++ b/docs/getting started/custom_embeddings/embeddings.md @@ -0,0 +1,98 @@ +Over the last years, many new embedding models have been released that could be interesting to use as a +backend in BERTopic. Although not all might be implemented, BERTopic knows several ways to add these embeddings while still +allowing for full functionality of BERTopic. As a result, you can use any language model as a back-end with just a few lines of code. +Here, we will go through several ways of approaching this. + +### **Word + Document Embeddings** +You might want to be using different language models for creating document- and word-embeddings. For example, +while SentenceTransformers might be great in embedding sentences and documents, you might prefer to use +FastText to create the word embeddings. + +```python +from bertopic.backend import WordDocEmbedder +import gensim.downloader as api +from sentence_transformers import SentenceTransformer + +# Word embedding model +ft = api.load('fasttext-wiki-news-subwords-300') + +# Document embedding model +embedding_model = SentenceTransformer("all-MiniLM-L6-v2") + +# Create a model that uses both language models and pass it through BERTopic +word_doc_embedder = WordDocEmbedder(embedding_model=embedding_model, word_embedding_model=ft) +topic_model = BERTopic(embedding_model=word_doc_embedder) +``` + +### **Custom Backend** +If your backend or model cannot be found in the ones currently available, you can use the `bertopic.backend.BaseEmbedder` class to +create your backend. Below, you will find an example of creating a SentenceTransformer backend for BERTopic: + +```python +from bertopic.backend import BaseEmbedder +from sentence_transformers import SentenceTransformer + +class CustomEmbedder(BaseEmbedder): + def __init__(self, embedding_model): + super().__init__() + self.embedding_model = embedding_model + + def embed(self, documents, verbose=False): + embeddings = self.embedding_model.encode(documents, show_progress_bar=verbose) + return embeddings + +# Create custom backend +embedding_model = SentenceTransformer("all-MiniLM-L6-v2") +custom_embedder = CustomEmbedder(embedding_model=embedding_model) + +# Pass custom backend to bertopic +topic_model = BERTopic(embedding_model=custom_embedder) +``` + +### **Custom Embeddings** +The base models in BERTopic are BERT-based models that work well with document similarity tasks. Your documents, +however, might be too specific for a general pre-trained model to be used. Fortunately, you can use embedding +model in BERTopic to create document features. + +You only need to prepare the document embeddings yourself and pass them through `fit_transform` of BERTopic: +```python +from sklearn.datasets import fetch_20newsgroups +from sentence_transformers import SentenceTransformer + +# Prepare embeddings +docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] +sentence_model = SentenceTransformer("all-MiniLM-L6-v2") +embeddings = sentence_model.encode(docs, show_progress_bar=False) + +# Train our topic model using our pre-trained sentence-transformers embeddings +topic_model = BERTopic() +topics, probs = topic_model.fit_transform(docs, embeddings) +``` + +As you can see above, we used a SentenceTransformer model to create the embedding. You could also have used +`🤗 transformers`, `Doc2Vec`, or any other embedding method. + +#### **TF-IDF** +As mentioned above, any embedding technique can be used. However, when running umap, the typical distance metric is +`cosine` which does not work quite well for a TF-IDF matrix. Instead, BERTopic will recognize that a sparse matrix +is passed and use `hellinger` instead which works quite well for the similarity between probability distributions. + +We simply create a TF-IDF matrix and use them as embeddings in our `fit_transform` method: + +```python +from sklearn.datasets import fetch_20newsgroups +from sklearn.feature_extraction.text import TfidfVectorizer + +# Create TF-IDF sparse matrix +docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] +vectorizer = TfidfVectorizer(min_df=5) +embeddings = vectorizer.fit_transform(docs) + +# Train our topic model using TF-IDF vectors +topic_model = BERTopic(stop_words="english") +topics, probs = topic_model.fit_transform(docs, embeddings) +``` + +Here, you will probably notice that creating the embeddings is quite fast whereas `fit_transform` is quite slow. +This is to be expected as reducing the dimensionality of a large sparse matrix takes some time. The inverse of using +transformer embeddings is true: creating the embeddings is slow whereas `fit_transform` is quite fast. \ No newline at end of file diff --git a/docs/getting started/embeddings/embeddings.md b/docs/getting started/embeddings/embeddings.md index e4fbdaa1..453d830b 100644 --- a/docs/getting started/embeddings/embeddings.md +++ b/docs/getting started/embeddings/embeddings.md @@ -21,6 +21,10 @@ sentence_model = SentenceTransformer("all-MiniLM-L6-v2") topic_model = BERTopic(embedding_model=sentence_model) ``` +!!! tip "Tip!" + This embedding back-end was put here first for a reason, sentence-transformers works amazing out-of-the-box! Playing around with different models can give you great results. Also, make sure to frequently visit [this](https://www.sbert.net/docs/pretrained_models.html) page as new models are often released. + + ### **Flair** [Flair](https://github.com/flairNLP/flair) allows you to choose almost any embedding model that is publicly available. Flair can be used as follows: @@ -56,7 +60,8 @@ To use Spacy's non-transformer models in BERTopic: ```python import spacy -nlp = spacy.load("en_core_web_md", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer']) +nlp = spacy.load("en_core_web_md", exclude=['tagger', 'parser', 'ner', + 'attribute_ruler', 'lemmatizer']) topic_model = BERTopic(embedding_model=nlp) ``` @@ -67,7 +72,8 @@ Using spacy-transformer models: import spacy spacy.prefer_gpu() -nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer']) +nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', + 'attribute_ruler', 'lemmatizer']) topic_model = BERTopic(embedding_model=nlp) ``` @@ -78,7 +84,8 @@ If you run into memory issues with spacy-transformer models, try: import spacy from thinc.api import set_gpu_allocator, require_gpu -nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer']) +nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', + 'attribute_ruler', 'lemmatizer']) set_gpu_allocator("pytorch") require_gpu(0) @@ -99,9 +106,8 @@ topic_model = BERTopic(embedding_model=embedding_model) ``` ### **Gensim** -For Gensim, BERTopic supports its `gensim.downloader` module. Here, we can download any model word embedding model -to be used in BERTopic. Note that Gensim is primarily used for Word Embedding models. This works typically -best for short documents since the word embeddings are pooled. +BERTopic supports the `gensim.downloader` module, which allows it to download any word embedding model supported by Gensim. +Typically, these are Glove, Word2Vec, or FastText embeddings: ```python import gensim.downloader as api @@ -109,107 +115,5 @@ ft = api.load('fasttext-wiki-news-subwords-300') topic_model = BERTopic(embedding_model=ft) ``` -## **Customization** -Over the last years, many new embedding models have been released that could be interesting to use as a -backend in BERTopic. It is not always feasible to implement them all as there are simply too many to follow. - -In order to still allow to use those embeddings, BERTopic knows several ways to add these embeddings while still -allowing for full functionality of BERTopic. - -Moreover, there are several customization options that allow for a bit more control over which embedding to use when. - -### **Word + Document Embeddings** -You might want to be using different language models for creating document- and word-embeddings. For example, -while SentenceTransformers might be great in embedding sentences and documents, you might prefer to use -FastText to create the word embeddings. - -```python -from bertopic.backend import WordDocEmbedder -import gensim.downloader as api -from sentence_transformers import SentenceTransformer - -# Word embedding model -ft = api.load('fasttext-wiki-news-subwords-300') - -# Document embedding model -embedding_model = SentenceTransformer("all-MiniLM-L6-v2") - -# Create a model that uses both language models and pass it through BERTopic -word_doc_embedder = WordDocEmbedder(embedding_model=embedding_model, word_embedding_model=ft) -topic_model = BERTopic(embedding_model=word_doc_embedder) -``` - -### **Custom Backend** -If your backend or model cannot be found in the ones currently available, you can use the `bertopic.backend.BaseEmbedder` class to -create your backend. Below, you will find an example of creating a SentenceTransformer backend for BERTopic: - -```python -from bertopic.backend import BaseEmbedder -from sentence_transformers import SentenceTransformer - -class CustomEmbedder(BaseEmbedder): - def __init__(self, embedding_model): - super().__init__() - self.embedding_model = embedding_model - - def embed(self, documents, verbose=False): - embeddings = self.embedding_model.encode(documents, show_progress_bar=verbose) - return embeddings - -# Create custom backend -embedding_model = SentenceTransformer("all-MiniLM-L6-v2") -custom_embedder = CustomEmbedder(embedding_model=embedding_model) - -# Pass custom backend to bertopic -topic_model = BERTopic(embedding_model=custom_embedder) -``` - -### **Custom Embeddings** -The base models in BERTopic are BERT-based models that work well with document similarity tasks. Your documents, -however, might be too specific for a general pre-trained model to be used. Fortunately, you can use embedding -model in BERTopic to create document features. - -You only need to prepare the document embeddings yourself and pass them through `fit_transform` of BERTopic: -```python -from sklearn.datasets import fetch_20newsgroups -from sentence_transformers import SentenceTransformer - -# Prepare embeddings -docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] -sentence_model = SentenceTransformer("all-MiniLM-L6-v2") -embeddings = sentence_model.encode(docs, show_progress_bar=False) - -# Create topic model and use the custom embeddings -topic_model = BERTopic() -topics, probs = topic_model.fit_transform(docs, embeddings) -``` - -As you can see above, we used a SentenceTransformer model to create the embedding. You could also have used -`🤗 transformers`, `Doc2Vec`, or any other embedding method. - -#### **TF-IDF** -As mentioned above, any embedding technique can be used. However, when running umap, the typical distance metric is -`cosine` which does not work quite well for a TF-IDF matrix. Instead, BERTopic will recognize that a sparse matrix -is passed and use `hellinger` instead which works quite well for the similarity between probability distributions. - -We simply create a TF-IDF matrix and use them as embeddings in our `fit_transform` method: - -```python -from sklearn.datasets import fetch_20newsgroups -from sklearn.feature_extraction.text import TfidfVectorizer - -# Create TF-IDF sparse matrix -docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] -vectorizer = TfidfVectorizer(min_df=5) -embeddings = vectorizer.fit_transform(docs) - -# -topic_model = BERTopic(stop_words="english") -topics, probs = topic_model.fit_transform(docs, embeddings) -``` - -Here, you will probably notice that creating the embeddings is quite fast whereas `fit_transform` is quite slow. -This is to be expected as reducing the dimensionality of a large sparse matrix takes some time. The inverse of using -transformer embeddings is true: creating the embeddings is slow whereas `fit_transform` is quite fast. - -You can play around with different models until you find the best suiting model for you. +!!! tip "Tip!" + Gensim is primarily used for Word Embedding models. This works typically best for short documents since the word embeddings are pooled. \ No newline at end of file diff --git a/docs/getting started/guided/guided.md b/docs/getting started/guided/guided.md index 5a2b81bd..ffbdc6f7 100644 --- a/docs/getting started/guided/guided.md +++ b/docs/getting started/guided/guided.md @@ -1,4 +1,3 @@ -## **Guided Topic Modeling** Guided Topic Modeling or Seeded Topic Modeling is a collection of techniques that guides the topic modeling approach by setting a number of seed topics in which the model will converge to. These techniques allow the user to set a pre-defined number of topic representations that are sure to be in documents. For example, take an IT-business diff --git a/docs/getting started/parameter tuning/parametertuning.md b/docs/getting started/parameter tuning/parametertuning.md index 243a5af3..e906f050 100644 --- a/docs/getting started/parameter tuning/parametertuning.md +++ b/docs/getting started/parameter tuning/parametertuning.md @@ -6,15 +6,7 @@ such as HDBSCAN and UMAP. ## **BERTopic** -In this section, we will go through most important hyperparameters in BERTopic that you can change directly when instantiating BERTopic: - -* language -* top_n_words -* n_gram_range -* min_topic_size -* nr_topics -* low_memory -* calculate_probabilities +When instantiating BERTopic, there are a number of hyperparameters that you can directly adjust that could significantly improve the performance of your topic model. In this section, we will go through the most impactful parameters in BERTopic and directions on how to optimize them. ### **language** The `language` parameter is used to simplify the selection of models for those who are not familiar with sentence-transformers models. @@ -64,13 +56,6 @@ umap_model = UMAP(n_neighbors=15, n_components=10, metric='cosine', low_memory=F topic_model = BERTopic(umap_model=umap_model).fit(docs) ``` -Here, there are a few parameters that are interesting to us: - -* n_neighbors -* n_components -* metric -* low_memory - ### **n_neighbors** `n_neighbors` is the numer of neighboring sample points used when making the manifold approximation. Increasing this value typically results in a more global view of the embedding structure whilst smaller values result in a more local view. Increasing this value often results in larger clusters diff --git a/docs/getting started/quickstart/quickstart.md b/docs/getting started/quickstart/quickstart.md index 81a78d2b..7a9e5575 100644 --- a/docs/getting started/quickstart/quickstart.md +++ b/docs/getting started/quickstart/quickstart.md @@ -16,12 +16,6 @@ pip install bertopic[spacy] pip install bertopic[use] ``` -To install all backends: - -```bash -pip install bertopic[all] -``` - ## **Quick Start** We start by extracting topics from the well-known 20 newsgroups dataset which is comprised of English documents: @@ -66,10 +60,11 @@ frequent topic that was generated, topic 0: ('pc', 0.003047105930670237)] ``` -**NOTE**: Use `BERTopic(language="multilingual")` to select a model that supports 50+ languages. +!!! Tip "Tip!" + Use `BERTopic(language="multilingual")` to select a model that supports 50+ languages. ## **Visualize Topics** -After having trained our `BERTopic` model, we can iteratively go through perhaps a hundred topic to get a good +After having trained our `BERTopic` model, we can iteratively go through perhaps a hundred topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to [LDAvis](https://github.com/cpsievert/LDAvis): @@ -93,6 +88,7 @@ Then, we can load the model in one line: topic_model = BERTopic.load("my_model") ``` -If you do not want to save the embedding model because it is loaded from the cloud, simply run -`model.save("my_model", save_embedding_model=False)` instead. Then, you can load in the model -with `BERTopic.load("my_model", embedding_model="whatever_model_you_used")`. \ No newline at end of file +!!! Tip "Tip!" + If you do not want to save the embedding model because it is loaded from the cloud, simply run + `model.save("my_model", save_embedding_model=False)` instead. Then, you can load in the model + with `BERTopic.load("my_model", embedding_model="whatever_model_you_used")`. \ No newline at end of file diff --git a/docs/getting started/search/search.md b/docs/getting started/search/search.md index a4d1a86f..f03583cf 100644 --- a/docs/getting started/search/search.md +++ b/docs/getting started/search/search.md @@ -35,4 +35,6 @@ search term "motor". Then, we extract the most similar topic and check the resul It definitely seems that a topic was found that closely matches with "motor". The topic seems to be motorcycle related and therefore matches with our "motor" input. You can use the `similarity` variable to see how similar the extracted topics are to the search term. - \ No newline at end of file + +!!! note + You can only use this method if an embedding model was supplied to BERTopic using `embedding_model`. \ No newline at end of file diff --git a/docs/getting started/topicreduction/topicreduction.md b/docs/getting started/topicreduction/topicreduction.md index 79bb35d2..f3bb7c21 100644 --- a/docs/getting started/topicreduction/topicreduction.md +++ b/docs/getting started/topicreduction/topicreduction.md @@ -1,24 +1,4 @@ -In `BERTopic`, there are several arguments that might be helpful if you tend to end up with too many or too few -topics. - -## **Topic Parameters** -The arguments discussed here all relate to the clustering step of BERTopic. - -#### **Minimum topic size** -The `min_topic_size` parameter is actually used in `HDBSCAN`. It tells HDBSCAN what the minimum size of a cluster -should be before it is accepted as a cluster. When you set this parameter very high, you will get very few clusters -as they all need to be high. In contrast, if you set this too low you might end with too many extremely specific -clusters. - -```python -from bertopic import BERTopic -topic_model = BERTopic(min_topic_size=10) -``` - -You can increase this value if you have more data available or if you expect clusters to be quite large. - -## **Hierarchical Topic Reduction** -HDBSCAN can't specify the number of clusters you would want. To a certain extent, +BERTopic uses HDSCAN for clustering the data and it cannot specify the number of clusters you would want. To a certain extent, this is an advantage, as we can trust HDBSCAN to be better in finding the number of clusters than we are. Instead, we can try to reduce the number of topics that have been created. Below, you will find three methods of doing so. @@ -57,11 +37,11 @@ from sklearn.datasets import fetch_20newsgroups # Create topics -> Typically over 50 topics docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] -topic_model = BERTopic(calculate_probabilities=True) +topic_model = BERTopic() topics, probs = topic_model.fit_transform(docs) # Further reduce topics -new_topics, new_probs = topic_model.reduce_topics(docs, topics, probabilities=probs, nr_topics=30) +new_topics, new_probs = topic_model.reduce_topics(docs, topics, nr_topics=30) ``` The reasoning for putting `docs` and `topics` (and optionally `probabilities`) as parameters is that these values are not saved within diff --git a/docs/getting started/topicrepresentation/topicrepresentation.md b/docs/getting started/topicrepresentation/topicrepresentation.md index f0368608..6f5d3c12 100644 --- a/docs/getting started/topicrepresentation/topicrepresentation.md +++ b/docs/getting started/topicrepresentation/topicrepresentation.md @@ -2,7 +2,7 @@ The topics that are extracted from BERTopic are represented by words. These word occupying their topics using a class-based TF-IDF. This allows us to extract words that are interesting to a topic but less so to another. -### Update Topic Representation after Training +### **Update Topic Representation after Training** When you have trained a model and viewed the topics and the words that represent them, you might not be satisfied with the representation. Perhaps you forgot to remove stop_words or you want to try out a different n_gram_range. We can use the function `update_topics` to update diff --git a/docs/getting started/topicsovertime/topicsovertime.md b/docs/getting started/topicsovertime/topicsovertime.md index b0c2766c..c0f9d665 100644 --- a/docs/getting started/topicsovertime/topicsovertime.md +++ b/docs/getting started/topicsovertime/topicsovertime.md @@ -1,4 +1,3 @@ -## **Dynamic Topic Modeling** Dynamic topic modeling (DTM) is a collection of techniques aimed at analyzing the evolution of topics over time. These methods allow you to understand how a topic is represented across different times. For example, in 1995 people may talk differently about environmental awareness than those in 2015. Although the @@ -24,7 +23,7 @@ the representations to evolve over time. Both fine-tuning methods are set to `True` as a default and allow for interesting representations to be created. -### **Example** +## **Example** To demonstrate DTM in BERTopic, we first need to prepare our data. A good example of where DTM is useful is topic modeling on Twitter data. We can analyze how certain people have talked about certain topics in the years they have been on Twitter. Due to the controversial nature of his tweets, we are going to be using all diff --git a/docs/getting started/visualization/visualization.md b/docs/getting started/visualization/visualization.md index ba2dfac5..9d3636c4 100644 --- a/docs/getting started/visualization/visualization.md +++ b/docs/getting started/visualization/visualization.md @@ -1,3 +1,7 @@ +Visualizing BERTopic and its derivatives is important in understanding the model, how it works, but more importantly, where it works. +Since topic modeling can be quite a subjective field it is difficult for users to validate their models. Looking at the topics and seeing +if they make sense is an important factor in eliviating this issue. + ## **Visualize Topics** After having trained our `BERTopic` model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extract. However, that takes quite some time and lacks a global representation. @@ -37,9 +41,10 @@ of topics that you have created. To visualize this hierarchy, simply call `topic -Do note that this is not the actual procedure of `.reduce_topics()` when `nr_topics` is set to -auto since HDBSCAN is used to automatically extract topics. The visualization above closely resembles -the actual procedure of `.reduce_topics()` when any number of `nr_topics` is selected. +!!! note + Do note that this is not the actual procedure of `.reduce_topics()` when `nr_topics` is set to + auto since HDBSCAN is used to automatically extract topics. The visualization above closely resembles + the actual procedure of `.reduce_topics()` when any number of `nr_topics` is selected. ## **Visualize Terms** We can visualize the selected terms for a few topics by creating bar charts out of the c-TF-IDF scores @@ -181,7 +186,8 @@ topic_model.visualize_distribution(probabilities[0]) -**NOTE**: The distribution of the probabilities does not give an indication to -the distribution of the frequencies of topics across a document. It merely shows -how confident BERTopic is that certain topics can be found in a document. +!!! note + The distribution of the probabilities does not give an indication to + the distribution of the frequencies of topics across a document. It merely shows + how confident BERTopic is that certain topics can be found in a document. diff --git a/mkdocs.yml b/mkdocs.yml index ed78e71a..58dd80c1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -18,6 +18,7 @@ nav: - Topic Representation: getting started/topicrepresentation/topicrepresentation.md - Search Topics: getting started/search/search.md - Customization: + - Custom Embedding Models: getting started/custom_embeddings/embeddings.md - Custom Sub-Models: getting started/models/models.md - Parameter tuning: getting started/parameter tuning/parametertuning.md - Variations: @@ -77,7 +78,11 @@ theme: icon: material/weather-night name: Switch to light mode markdown_extensions: + - admonition + - pymdownx.details - pymdownx.highlight: - pymdownx.superfences: + - pymdownx.tabbed: + alternate_style: true - toc: permalink: true From 8565459b8f8e484de8df8a67290c0ef149280e05 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Sun, 5 Dec 2021 09:36:02 +0100 Subject: [PATCH 08/17] More documentation updates, prepare for 0.9.4 --- README.md | 12 ++-- bertopic/__init__.py | 2 +- bertopic/plotting/_barchart.py | 2 +- bertopic/plotting/_distribution.py | 2 +- bertopic/plotting/_heatmap.py | 2 +- bertopic/plotting/_hierarchy.py | 2 +- bertopic/plotting/_term_rank.py | 4 +- bertopic/plotting/_topics.py | 2 +- bertopic/plotting/_topics_over_time.py | 2 +- bertopic/plotting/_topics_per_class.py | 2 +- docs/changelog.md | 19 +++++- docs/faq.md | 57 +++++++++++++++--- .../custom_embeddings/embeddings.md | 0 .../embeddings/embeddings.md | 0 .../guided/guided.md | 0 .../models/models.md | 0 .../parameter tuning/parametertuning.md | 0 .../quickstart/quickstart.md | 0 .../quickstart/viz.html | 0 .../search/search.md | 0 .../supervised/supervised.md | 0 .../topicreduction/topicreduction.md | 0 .../topicrepresentation.md | 0 .../topicsovertime/topicsovertime.md | 0 .../topicsovertime/trump.html | 0 .../topicsperclass/topics_per_class.html | 0 .../topicsperclass/topicsperclass.md | 0 .../visualization/bar_chart.html | 0 .../visualization/heatmap.html | 0 .../visualization/hierarchy.html | 0 .../visualization/probabilities.html | 0 .../visualization/probabilities.png | Bin .../visualization/term_rank.html | 0 .../visualization/term_rank_log.html | 0 .../visualization/topics_per_class.html | 0 .../visualization/trump.html | 0 .../visualization/visualization.md | 0 .../visualization/viz.html | 0 docs/index.md | 8 +-- mkdocs.yml | 35 ++++++----- setup.py | 2 +- 41 files changed, 102 insertions(+), 51 deletions(-) rename docs/{getting started => getting_started}/custom_embeddings/embeddings.md (100%) rename docs/{getting started => getting_started}/embeddings/embeddings.md (100%) rename docs/{getting started => getting_started}/guided/guided.md (100%) rename docs/{getting started => getting_started}/models/models.md (100%) rename docs/{getting started => getting_started}/parameter tuning/parametertuning.md (100%) rename docs/{getting started => getting_started}/quickstart/quickstart.md (100%) rename docs/{getting started => getting_started}/quickstart/viz.html (100%) rename docs/{getting started => getting_started}/search/search.md (100%) rename docs/{getting started => getting_started}/supervised/supervised.md (100%) rename docs/{getting started => getting_started}/topicreduction/topicreduction.md (100%) rename docs/{getting started => getting_started}/topicrepresentation/topicrepresentation.md (100%) rename docs/{getting started => getting_started}/topicsovertime/topicsovertime.md (100%) rename docs/{getting started => getting_started}/topicsovertime/trump.html (100%) rename docs/{getting started => getting_started}/topicsperclass/topics_per_class.html (100%) rename docs/{getting started => getting_started}/topicsperclass/topicsperclass.md (100%) rename docs/{getting started => getting_started}/visualization/bar_chart.html (100%) rename docs/{getting started => getting_started}/visualization/heatmap.html (100%) rename docs/{getting started => getting_started}/visualization/hierarchy.html (100%) rename docs/{getting started => getting_started}/visualization/probabilities.html (100%) rename docs/{getting started => getting_started}/visualization/probabilities.png (100%) rename docs/{getting started => getting_started}/visualization/term_rank.html (100%) rename docs/{getting started => getting_started}/visualization/term_rank_log.html (100%) rename docs/{getting started => getting_started}/visualization/topics_per_class.html (100%) rename docs/{getting started => getting_started}/visualization/trump.html (100%) rename docs/{getting started => getting_started}/visualization/visualization.md (100%) rename docs/{getting started => getting_started}/visualization/viz.html (100%) diff --git a/README.md b/README.md index 2eda4c11..5a71e791 100644 --- a/README.md +++ b/README.md @@ -13,9 +13,9 @@ BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports -[**guided**](https://maartengr.github.io/BERTopic/tutorial/guided/guided.html), -(semi-) [**supervised**](https://maartengr.github.io/BERTopic/tutorial/supervised/supervised.html), -and [**dynamic**](https://maartengr.github.io/BERTopic/tutorial/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis! +[**guided**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html), +(semi-) [**supervised**](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), +and [**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4). @@ -123,7 +123,7 @@ topic_model.visualize_barchart() Find all possible visualizations with interactive examples in the documentation -[here](https://maartengr.github.io/BERTopic/tutorial/visualization/visualization.html). +[here](https://maartengr.github.io/BERTopic/getting_started/visualization/visualization.html). ## Embedding Models BERTopic supports many embedding models that can be used to embed the documents and words: @@ -151,7 +151,7 @@ roberta = TransformerDocumentEmbeddings('roberta-base') topic_model = BERTopic(embedding_model=roberta) ``` -Click [here](https://maartengr.github.io/BERTopic/tutorial/embeddings/embeddings.html) +Click [here](https://maartengr.github.io/BERTopic/getting_started/embeddings/embeddings.html) for a full overview of all supported embedding models. ## Dynamic Topic Modeling @@ -238,7 +238,7 @@ To cite BERTopic in your work, please use the following bibtex reference: title = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.}, year = 2020, publisher = {Zenodo}, - version = {v0.9.3}, + version = {v0.9.4}, doi = {10.5281/zenodo.4381785}, url = {https://doi.org/10.5281/zenodo.4381785} } diff --git a/bertopic/__init__.py b/bertopic/__init__.py index d158b5f5..ba37e017 100644 --- a/bertopic/__init__.py +++ b/bertopic/__init__.py @@ -1,6 +1,6 @@ from bertopic._bertopic import BERTopic -__version__ = "0.9.3" +__version__ = "0.9.4" __all__ = [ "BERTopic", diff --git a/bertopic/plotting/_barchart.py b/bertopic/plotting/_barchart.py index ab098b08..8f283a1d 100644 --- a/bertopic/plotting/_barchart.py +++ b/bertopic/plotting/_barchart.py @@ -39,7 +39,7 @@ def visualize_barchart(topic_model, fig = topic_model.visualize_barchart() fig.write_html("path/to/file.html") ``` - """ # Select topics based on top_n and topics args diff --git a/bertopic/plotting/_distribution.py b/bertopic/plotting/_distribution.py index 3bbbc21d..ea2ea0a3 100644 --- a/bertopic/plotting/_distribution.py +++ b/bertopic/plotting/_distribution.py @@ -32,7 +32,7 @@ def visualize_distribution(topic_model, fig = topic_model.visualize_distribution(probabilities[0]) fig.write_html("path/to/file.html") ``` - """ if len(probabilities.shape) != 1: diff --git a/bertopic/plotting/_heatmap.py b/bertopic/plotting/_heatmap.py index d4e71d3a..ceec4a6a 100644 --- a/bertopic/plotting/_heatmap.py +++ b/bertopic/plotting/_heatmap.py @@ -45,7 +45,7 @@ def visualize_heatmap(topic_model, fig = topic_model.visualize_heatmap() fig.write_html("path/to/file.html") ``` - """ diff --git a/bertopic/plotting/_hierarchy.py b/bertopic/plotting/_hierarchy.py index c266ac66..cd92292d 100644 --- a/bertopic/plotting/_hierarchy.py +++ b/bertopic/plotting/_hierarchy.py @@ -46,7 +46,7 @@ def visualize_hierarchy(topic_model, fig = topic_model.visualize_hierarchy() fig.write_html("path/to/file.html") ``` - """ diff --git a/bertopic/plotting/_term_rank.py b/bertopic/plotting/_term_rank.py index 35f917a5..ced45ceb 100644 --- a/bertopic/plotting/_term_rank.py +++ b/bertopic/plotting/_term_rank.py @@ -42,10 +42,10 @@ def visualize_term_rank(topic_model, fig.write_html("path/to/file.html") ``` - - Reference: diff --git a/bertopic/plotting/_topics.py b/bertopic/plotting/_topics.py index 75533768..0d20cb1a 100644 --- a/bertopic/plotting/_topics.py +++ b/bertopic/plotting/_topics.py @@ -39,7 +39,7 @@ def visualize_topics(topic_model, fig = topic_model.visualize_topics() fig.write_html("path/to/file.html") ``` - """ # Select topics based on top_n and topics args diff --git a/bertopic/plotting/_topics_over_time.py b/bertopic/plotting/_topics_over_time.py index e5b94111..cb66594f 100644 --- a/bertopic/plotting/_topics_over_time.py +++ b/bertopic/plotting/_topics_over_time.py @@ -41,7 +41,7 @@ def visualize_topics_over_time(topic_model, fig = topic_model.visualize_topics_over_time(topics_over_time) fig.write_html("path/to/file.html") ``` - """ colors = ["#E69F00", "#56B4E9", "#009E73", "#F0E442", "#D55E00", "#0072B2", "#CC79A7"] diff --git a/bertopic/plotting/_topics_per_class.py b/bertopic/plotting/_topics_per_class.py index d66170a9..3f064017 100644 --- a/bertopic/plotting/_topics_per_class.py +++ b/bertopic/plotting/_topics_per_class.py @@ -41,7 +41,7 @@ def visualize_topics_per_class(topic_model, fig = topic_model.visualize_topics_per_class(topics_per_class) fig.write_html("path/to/file.html") ``` - """ colors = ["#E69F00", "#56B4E9", "#009E73", "#F0E442", "#D55E00", "#0072B2", "#CC79A7"] diff --git a/docs/changelog.md b/docs/changelog.md index 98a25d6e..f7c45b22 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,3 +1,18 @@ +## **Version 0.9.4** +*Release date: XXXXX, 2021* + +A number of fixes, documentation updates, and small features: + +* Expose diversity parameter + * Use `diversity=0.1` to change how diverse the words in a topic representation are (ranges from 0 to 1) +* Improve stability of topic reduction by only computing the cosine similarity within c-TF-IDF and not the topic embeddings + * This also means that the topic embeddings could be improved or do not work properly with certain language models + * Perhaps this could be improved by averaging out the exemplars but that still would not fully represent a topic + * c-TF-IDF seems to hold the most information with respect to the topics +* Drop python 3.7 ([#333](https://github.com/MaartenGr/BERTopic/issues/333)) +* Major documentation overhaul (mkdocs, tutorials, FAQ, images, etc. ) ([#330](https://github.com/MaartenGr/BERTopic/issues/330)) +* Add property to c-TF-IDF that all IDF values should be positive ([#351](https://github.com/MaartenGr/BERTopic/issues/351)) + ## **Version 0.9.3** *Release date: 17 October, 2021* @@ -49,7 +64,7 @@ A release focused on fixing several issues: **Highlights**: -* Implemented a [**Guided BERTopic**](https://maartengr.github.io/BERTopic/tutorial/guided/guided.html) -> Use seeds to steer the Topic Modeling +* Implemented a [**Guided BERTopic**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html) -> Use seeds to steer the Topic Modeling * Get the most representative documents per topic: `topic_model.get_representative_docs(topic=1)` * This allows users to see which documents are good representations of a topic and better understand the topics that were created * Added `normalize_frequency` parameter to `visualize_topics_per_class` and `visualize_topics_over_time` in order to better compare the relative topic frequencies between topics @@ -148,7 +163,7 @@ and several **backends** to use instead of Flair and SentenceTransformers! * Added Spacy, Gensim, USE (TFHub) * Use a different backend for document embeddings and word embeddings * Create your own backends with `bertopic.backend.BaseEmbedder` - * Click [here](https://maartengr.github.io/BERTopic/tutorial/embeddings/embeddings.html) for an overview of all new backends + * Click [here](https://maartengr.github.io/BERTopic/getting_started/embeddings/embeddings.html) for an overview of all new backends * Calculate and visualize topics per class * Calculate: `topics_per_class = topic_model.topics_per_class(docs, topics, classes)` * Visualize: `topic_model.visualize_topics_per_class(topics_per_class)` diff --git a/docs/faq.md b/docs/faq.md index 82ee23f9..6c25d191 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -4,7 +4,19 @@ multiple times. Using custom embeddings allows you to try out BERTopic several t topics that suit you best. You only need to generate the embeddings itself once and run BERTopic several times with different parameters. -## **Which embedding model works best for which language?** +If you want to reproduce the results, at the expense of [performance](https://umap-learn.readthedocs.io/en/latest/reproducibility.html), you can set a `random_state` in UMAP to prevent +any stochastic behavior: + +```python +from bertopic import BERTopic +from umap import UMAP + +umap_model = UMAP(n_neighbors=15, n_components=5, + min_dist=0.0, metric='cosine', random_state=42) +topic_model = BERTopic(umap_model=umap_model) +``` + +## **Which embedding model should I choose?** Unfortunately, there is not a definitive list of the best models for each language, this highly depends on your data, the model, and your specific use-case. However, the default model in BERTopic (`"all-MiniLM-L6-v2"`) works great for **English** documents. In contrast, for **multi-lingual** @@ -22,8 +34,33 @@ BERTopic allows you to use any 🤗 transformers model. These models are typica a word/sentence level but can easily be pooled using Flair (see Guides/Embeddings). If you have a specific language for which you want to generate embeddings, you can choose the model [here](https://huggingface.co/models). +## **How do I reduce topic outliers?** +There are two ways in reducing outliers. + +First, the amount of datapoint classified as outliers is handled by the `min_samples` parameters in HDBSCAN. This value is automatically set to the +same value of `min_cluster_size`. However, you can set it indepedently if you want to reduce the number of generated outliers. Lowering this value will +result in less noise being generated. + +```python +from bertopic import BERTopic +from hdbscan import HDBSCAN + +hdbscan_model = HDBSCAN(min_cluster_size=10, metric='euclidean', + cluster_selection_method='eom', prediction_data=True, min_samples=5) +topic_model = BERTopic(hdbscan_model=hdbscan_model) +``` + +!!! note "Note" + Although this will lower outliers found in the data, this might force outliers to be put into topics where they do not belong. So make + sure to strike a balance between keeping noise and reducing outliers. + +Second, after training our BERTopic model, we can assign outliers to topics. By setting `calculate_probabilities=True`, we calculate the probability +of a document belonging to any topic. That way, we can select, for each document, the topic with the the highest probability. Thus, although we do +generate an outlier class in our BERTopic model, we can assign documents to an actual topic. + + ## **How can I speed up BERTopic?** -You can speed up BERTopic by either generating your embeddings beforehand, which is not advised, or by +You can speed up BERTopic by either generating your embeddings beforehand or by setting `calculate_probabilities` to False. Calculating the probabilities is quite expensive and can significantly increase the computation time. Thus, only use it if you do not mind waiting a bit before the model is done running or if you have less than 50_000 documents. @@ -59,21 +96,21 @@ that can currently only be installed through their master branch: `pip install --upgrade git+https://github.com/scikit-learn-contrib/hdbscan` -If the problem persists, then this could be an issue related to your available memory. The processing of +If the problem persists, then this could be an issue related to your available memory. The processing of millions of documents is quite computationally expensive and sufficient RAM is necessary. ## **I have only a few topics, how do I increase them?** -There are several reasons why your topic model results in only a few topics. +There are several reasons why your topic model may result in only a few topics: -First, you might only have a few documents (~1000). This makes it very difficult to properly +* First, you might only have a few documents (~1000). This makes it very difficult to properly extract topics due to the little amount of data available. Increasing the number of documents might solve your issues. -Second, `min_topic_size` might be simply too large for your number of documents. If you decrease +* Second, `min_topic_size` might be simply too large for your number of documents. If you decrease the minimum size of topics, then you are much more likely to increase the number of topics generated. You could also decrease the `n_neighbors` parameter used in `UMAP` if this does not work. -Third, although this does not happen very often, there simply aren't that many topics to be found +* Third, although this does not happen very often, there simply aren't that many topics to be found in your documents. You can often see this when you have many `-1` topics, which is actually not a topic but a category of outliers. @@ -84,16 +121,16 @@ a few hundred topics at most in order to interpret them nicely. There are a few ways of increasing the number of generated topics: -First, we can set the `min_topic_size` in the BERTopic initialization much higher (e.g., 300) +* First, we can set the `min_topic_size` in the BERTopic initialization much higher (e.g., 300) to make sure that those small clusters will not be generated. This is a HDBSCAN parameter that specifies what the minimum number of documents are needed in a cluster. More documents in a cluster means less topics will be generated. -Second, you can create a custom UMAP model and set `n_neighbors` much higher than the default 15 (e.g., 200). +* Second, you can create a custom UMAP model and set `n_neighbors` much higher than the default 15 (e.g., 200). This also prevents those micro clusters to be generated as it will needs quite a number of neighboring documents to create a cluster. -Third, we can set `nr_topics` to a value that seems logical to the user. Do note that topics are forced +* Third, we can set `nr_topics` to a value that seems logical to the user. Do note that topics are forced to merge together which might result in a lower quality of topics. In practice, I would advise using `nr_topic="auto"` as that will merge topics together that are very similar. Dissimilar topics will therefore remain separated. diff --git a/docs/getting started/custom_embeddings/embeddings.md b/docs/getting_started/custom_embeddings/embeddings.md similarity index 100% rename from docs/getting started/custom_embeddings/embeddings.md rename to docs/getting_started/custom_embeddings/embeddings.md diff --git a/docs/getting started/embeddings/embeddings.md b/docs/getting_started/embeddings/embeddings.md similarity index 100% rename from docs/getting started/embeddings/embeddings.md rename to docs/getting_started/embeddings/embeddings.md diff --git a/docs/getting started/guided/guided.md b/docs/getting_started/guided/guided.md similarity index 100% rename from docs/getting started/guided/guided.md rename to docs/getting_started/guided/guided.md diff --git a/docs/getting started/models/models.md b/docs/getting_started/models/models.md similarity index 100% rename from docs/getting started/models/models.md rename to docs/getting_started/models/models.md diff --git 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started/visualization/topics_per_class.html rename to docs/getting_started/visualization/topics_per_class.html diff --git a/docs/getting started/visualization/trump.html b/docs/getting_started/visualization/trump.html similarity index 100% rename from docs/getting started/visualization/trump.html rename to docs/getting_started/visualization/trump.html diff --git a/docs/getting started/visualization/visualization.md b/docs/getting_started/visualization/visualization.md similarity index 100% rename from docs/getting started/visualization/visualization.md rename to docs/getting_started/visualization/visualization.md diff --git a/docs/getting started/visualization/viz.html b/docs/getting_started/visualization/viz.html similarity index 100% rename from docs/getting started/visualization/viz.html rename to docs/getting_started/visualization/viz.html diff --git a/docs/index.md b/docs/index.md index 16128399..2da9e721 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,9 +6,9 @@ BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports -[**guided**](https://maartengr.github.io/BERTopic/tutorial/guided/guided.html), -(semi-) [**supervised**](https://maartengr.github.io/BERTopic/tutorial/supervised/supervised.html), -and [**dynamic**](https://maartengr.github.io/BERTopic/tutorial/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis! +[**guided**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html), +(semi-) [**supervised**](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), +and [**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4). @@ -123,7 +123,7 @@ To cite BERTopic in your work, please use the following bibtex reference: title = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.}, year = 2020, publisher = {Zenodo}, - version = {v0.9.3}, + version = {v0.9.4}, doi = {10.5281/zenodo.4381785}, url = {https://doi.org/10.5281/zenodo.4381785} } diff --git a/mkdocs.yml b/mkdocs.yml index 58dd80c1..e2e51e7a 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -11,21 +11,22 @@ nav: - Home: index.md - The Algorithm: algorithm/algorithm.md - Getting Started: - - getting started/quickstart/quickstart.md - - Embedding Models: getting started/embeddings/embeddings.md - - Topic Visualization: getting started/visualization/visualization.md - - Topic Reduction: getting started/topicreduction/topicreduction.md - - Topic Representation: getting started/topicrepresentation/topicrepresentation.md - - Search Topics: getting started/search/search.md + - getting_started/quickstart/quickstart.md + - Embedding Models: getting_started/embeddings/embeddings.md + - Topic Visualization: getting_started/visualization/visualization.md + - Topic Reduction: getting_started/topicreduction/topicreduction.md + - Topic Representation: getting_started/topicrepresentation/topicrepresentation.md + - Search Topics: getting_started/search/search.md - Customization: - - Custom Embedding Models: getting started/custom_embeddings/embeddings.md - - Custom Sub-Models: getting started/models/models.md - - Parameter tuning: getting started/parameter tuning/parametertuning.md + - Custom Embedding Models: getting_started/custom_embeddings/embeddings.md + - Custom Sub-Models: getting_started/models/models.md + - Parameter tuning: getting_started/parameter tuning/parametertuning.md - Variations: - - Topics per Class: getting started/topicsperclass/topicsperclass.md - - (semi)-Supervised Topic Modeling: getting started/supervised/supervised.md - - Dynamic Topic Modeling: getting started/topicsovertime/topicsovertime.md - - Guided Topic Modeling: getting started/guided/guided.md + - Topics per Class: getting_started/topicsperclass/topicsperclass.md + - (semi)-Supervised Topic Modeling: getting_started/supervised/supervised.md + - Dynamic Topic Modeling: getting_started/topicsovertime/topicsovertime.md + - Guided Topic Modeling: getting_started/guided/guided.md + - FAQ: faq.md - API: - BERTopic: api/bertopic.md - cTFIDF: api/ctfidf.md @@ -42,7 +43,6 @@ nav: - Topics: api/plotting/topics.md - DTM: api/plotting/dtm.md - Topics per Class: api/plotting/topics_per_class.md - - FAQ: faq.md - Changelog: changelog.md plugins: - mkdocstrings: @@ -80,9 +80,8 @@ theme: markdown_extensions: - admonition - pymdownx.details - - pymdownx.highlight: - - pymdownx.superfences: - - pymdownx.tabbed: - alternate_style: true + - pymdownx.highlight + - pymdownx.superfences - toc: permalink: true + diff --git a/setup.py b/setup.py index 92b48930..a3efd795 100644 --- a/setup.py +++ b/setup.py @@ -53,7 +53,7 @@ setup( name="bertopic", packages=find_packages(exclude=["notebooks", "docs"]), - version="0.9.3", + version="0.9.4", author="Maarten P. Grootendorst", author_email="maartengrootendorst@gmail.com", description="BERTopic performs topic Modeling with state-of-the-art transformer models.", From 0172b0974af74ef455b05029b613d6112f9d4730 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Mon, 6 Dec 2021 15:14:33 +0100 Subject: [PATCH 09/17] Additional logging for .transform (#356) --- bertopic/_bertopic.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index a96b713c..fb8835dc 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -374,10 +374,14 @@ def transform(self, verbose=self.verbose) umap_embeddings = self.umap_model.transform(embeddings) + logger.info("Reduced dimensionality with UMAP") + predictions, probabilities = hdbscan.approximate_predict(self.hdbscan_model, umap_embeddings) + logger.info("Predicted clusters with HDBSCAN") if self.calculate_probabilities: probabilities = hdbscan.membership_vector(self.hdbscan_model, umap_embeddings) + logger.info("Calculated probabilities with HDBSCAN") else: probabilities = None From 5fc0089e2146596df518db754af3029fa8f69424 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 7 Dec 2021 14:33:52 +0100 Subject: [PATCH 10/17] Improve stability barchart --- bertopic/plotting/_barchart.py | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/bertopic/plotting/_barchart.py b/bertopic/plotting/_barchart.py index 8f283a1d..cd859694 100644 --- a/bertopic/plotting/_barchart.py +++ b/bertopic/plotting/_barchart.py @@ -1,3 +1,4 @@ +import itertools import numpy as np from typing import List @@ -9,8 +10,8 @@ def visualize_barchart(topic_model, topics: List[int] = None, top_n_topics: int = 6, n_words: int = 5, - width: int = 800, - height: int = 600) -> go.Figure: + width: int = 250, + height: int = 250) -> go.Figure: """ Visualize a barchart of selected topics Arguments: @@ -18,8 +19,8 @@ def visualize_barchart(topic_model, topics: A selection of topics to visualize. top_n_topics: Only select the top n most frequent topics. n_words: Number of words to show in a topic - width: The width of the figure. - height: The height of the figure. + width: The width of each figure. + height: The height of each figure. Returns: fig: A plotly figure @@ -42,6 +43,8 @@ def visualize_barchart(topic_model, """ + colors = itertools.cycle(["#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#009E73", "#F0E442"]) + # Select topics based on top_n and topics args if topics is not None: topics = list(topics) @@ -52,13 +55,13 @@ def visualize_barchart(topic_model, # Initialize figure subplot_titles = [f"Topic {topic}" for topic in topics] - columns = 3 + columns = 4 rows = int(np.ceil(len(topics) / columns)) fig = make_subplots(rows=rows, cols=columns, - shared_xaxes=True, - horizontal_spacing=.15, - vertical_spacing=.15, + shared_xaxes=False, + horizontal_spacing=.1, + vertical_spacing=.4 / rows if rows > 1 else 0, subplot_titles=subplot_titles) # Add barchart for each topic @@ -71,7 +74,8 @@ def visualize_barchart(topic_model, fig.add_trace( go.Bar(x=scores, y=words, - orientation='h'), + orientation='h', + marker_color=next(colors)), row=row, col=column) if column == columns: @@ -86,7 +90,6 @@ def visualize_barchart(topic_model, showlegend=False, title={ 'text': "Topic Word Scores", - 'y': .95, 'x': .15, 'xanchor': 'center', 'yanchor': 'top', @@ -94,8 +97,8 @@ def visualize_barchart(topic_model, size=22, color="Black") }, - width=width, - height=height, + width=width*4, + height=height*rows if rows > 1 else height * 1.3, hoverlabel=dict( bgcolor="white", font_size=16, From 2584ac012a3f180fcc4675bb6adcadbaf0d0126a Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 7 Dec 2021 14:46:51 +0100 Subject: [PATCH 11/17] Update barchart params in bertopic.py --- bertopic/_bertopic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index fb8835dc..07454545 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -881,8 +881,8 @@ def reduce_topics(self, def visualize_topics(self, topics: List[int] = None, top_n_topics: int = None, - width: int = 650, - height: int = 650) -> go.Figure: + width: int = 250, + height: int = 250) -> go.Figure: """ Visualize topics, their sizes, and their corresponding words This visualization is highly inspired by LDAvis, a great visualization From 5f9306db275e7cf5abb3c54e58c17e99b829b54f Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 7 Dec 2021 15:07:21 +0100 Subject: [PATCH 12/17] Fix plotting --- bertopic/_bertopic.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 07454545..5544740f 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -881,8 +881,8 @@ def reduce_topics(self, def visualize_topics(self, topics: List[int] = None, top_n_topics: int = None, - width: int = 250, - height: int = 250) -> go.Figure: + width: int = 650, + height: int = 650) -> go.Figure: """ Visualize topics, their sizes, and their corresponding words This visualization is highly inspired by LDAvis, a great visualization @@ -1195,16 +1195,16 @@ def visualize_barchart(self, topics: List[int] = None, top_n_topics: int = 6, n_words: int = 5, - width: int = 800, - height: int = 600) -> go.Figure: + width: int = 250, + height: int = 250) -> go.Figure: """ Visualize a barchart of selected topics Arguments: topics: A selection of topics to visualize. top_n_topics: Only select the top n most frequent topics. n_words: Number of words to show in a topic - width: The width of the figure. - height: The height of the figure. + width: The width of each figure. + height: The height of each figure. Returns: fig: A plotly figure From 634e4960e712bb53e2f9ec0c4c856e8f5540699a Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Wed, 8 Dec 2021 14:17:29 +0100 Subject: [PATCH 13/17] Improve stability .visualize_hierarcy() --- bertopic/_bertopic.py | 4 ++-- bertopic/plotting/_hierarchy.py | 22 ++++++++++++++-------- docs/changelog.md | 7 ++++--- 3 files changed, 20 insertions(+), 13 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 5544740f..5ef2d034 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -1115,8 +1115,8 @@ def visualize_hierarchy(self, Either 'left' or 'bottom' topics: A selection of topics to visualize top_n_topics: Only select the top n most frequent topics - width: The width of the figure. - height: The height of the figure. + width: The width of the figure. Only works if orientation is set to 'left' + height: The height of the figure. Only works if orientation is set to 'bottom' Returns: fig: A plotly figure diff --git a/bertopic/plotting/_hierarchy.py b/bertopic/plotting/_hierarchy.py index cd92292d..ca8b9510 100644 --- a/bertopic/plotting/_hierarchy.py +++ b/bertopic/plotting/_hierarchy.py @@ -25,8 +25,8 @@ def visualize_hierarchy(topic_model, Either 'left' or 'bottom' topics: A selection of topics to visualize top_n_topics: Only select the top n most frequent topics - width: The width of the figure. - height: The height of the figure. + width: The width of the figure. Only works if orientation is set to 'left' + height: The height of the figure. Only works if orientation is set to 'bottom' Returns: fig: A plotly figure @@ -89,7 +89,6 @@ def visualize_hierarchy(topic_model, template="plotly_white", title={ 'text': "Hierarchical Clustering", - 'y': .95, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top', @@ -97,21 +96,28 @@ def visualize_hierarchy(topic_model, size=22, color="Black") }, - width=width, - height=height, hoverlabel=dict( bgcolor="white", font_size=16, font_family="Rockwell" ), - ) # Stylize orientation if orientation == "left": - fig.update_layout(yaxis=dict(tickmode="array", + fig.update_layout(height=200+(15*len(topics)), + width=width, + yaxis=dict(tickmode="array", ticktext=new_labels)) + + # Fix empty space on the bottom of the graph + y_max = max([trace['y'].max()+5 for trace in fig['data']]) + y_min = min([trace['y'].min()-5 for trace in fig['data']]) + fig.update_layout(yaxis=dict(range=[y_min, y_max])) + else: - fig.update_layout(xaxis=dict(tickmode="array", + fig.update_layout(width=200+(15*len(topics)), + height=height, + xaxis=dict(tickmode="array", ticktext=new_labels)) return fig diff --git a/docs/changelog.md b/docs/changelog.md index f7c45b22..2f18395a 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -9,10 +9,11 @@ A number of fixes, documentation updates, and small features: * This also means that the topic embeddings could be improved or do not work properly with certain language models * Perhaps this could be improved by averaging out the exemplars but that still would not fully represent a topic * c-TF-IDF seems to hold the most information with respect to the topics -* Drop python 3.7 ([#333](https://github.com/MaartenGr/BERTopic/issues/333)) +* Added property to c-TF-IDF that all IDF values should be positive ([#351](https://github.com/MaartenGr/BERTopic/issues/351)) +* Improve stability of `.visualize_barchart()` and `.visualize_hierarchy()` * Major documentation overhaul (mkdocs, tutorials, FAQ, images, etc. ) ([#330](https://github.com/MaartenGr/BERTopic/issues/330)) -* Add property to c-TF-IDF that all IDF values should be positive ([#351](https://github.com/MaartenGr/BERTopic/issues/351)) - +* Drop python 3.7 ([#333](https://github.com/MaartenGr/BERTopic/issues/333)) + ## **Version 0.9.3** *Release date: 17 October, 2021* From 134d53ba39c535e08966c1f005d2a73ed1f047cc Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Wed, 8 Dec 2021 14:35:37 +0100 Subject: [PATCH 14/17] Update barchart --- bertopic/_bertopic.py | 2 +- bertopic/plotting/_barchart.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py index 5ef2d034..39f907ae 100644 --- a/bertopic/_bertopic.py +++ b/bertopic/_bertopic.py @@ -1193,7 +1193,7 @@ def visualize_heatmap(self, def visualize_barchart(self, topics: List[int] = None, - top_n_topics: int = 6, + top_n_topics: int = 8, n_words: int = 5, width: int = 250, height: int = 250) -> go.Figure: diff --git a/bertopic/plotting/_barchart.py b/bertopic/plotting/_barchart.py index cd859694..d637a6fc 100644 --- a/bertopic/plotting/_barchart.py +++ b/bertopic/plotting/_barchart.py @@ -8,7 +8,7 @@ def visualize_barchart(topic_model, topics: List[int] = None, - top_n_topics: int = 6, + top_n_topics: int = 8, n_words: int = 5, width: int = 250, height: int = 250) -> go.Figure: @@ -90,7 +90,7 @@ def visualize_barchart(topic_model, showlegend=False, title={ 'text': "Topic Word Scores", - 'x': .15, + 'x': .5, 'xanchor': 'center', 'yanchor': 'top', 'font': dict( From 42b41c29cc2b7640b42fb31cfd1c109decde7c1f Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 14 Dec 2021 08:14:58 +0100 Subject: [PATCH 15/17] Fix #88 --- bertopic/plotting/_topics.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/bertopic/plotting/_topics.py b/bertopic/plotting/_topics.py index 0d20cb1a..a6a37722 100644 --- a/bertopic/plotting/_topics.py +++ b/bertopic/plotting/_topics.py @@ -87,13 +87,13 @@ def get_color(topic_selected): # Plot topics fig = px.scatter(df, x="x", y="y", size="Size", size_max=40, template="simple_white", labels={"x": "", "y": ""}, - hover_data={"x": False, "y": False, "Topic": True, "Words": True, "Size": True}) + hover_data={"Topic": True, "Words": True, "Size": True, "x": False, "y": False}) fig.update_traces(marker=dict(color="#B0BEC5", line=dict(width=2, color='DarkSlateGrey'))) # Update hover order - fig.update_traces(hovertemplate="
".join(["Topic %{customdata[2]}", - "Words: %{customdata[3]}", - "Size: %{customdata[4]}"])) + fig.update_traces(hovertemplate="
".join(["Topic %{customdata[0]}", + "Words: %{customdata[1]}", + "Size: %{customdata[2]}"])) # Create a slider for topic selection steps = [dict(label=f"Topic {topic}", method="update", args=get_color(topic)) for topic in topic_list[1:]] From 8e9254dfc810f3497cbe05a63a59410756c999ff Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 14 Dec 2021 08:35:51 +0100 Subject: [PATCH 16/17] Relax plotly dependency --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index a3efd795..9073273f 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ "scikit-learn>=0.22.2.post1", "tqdm>=4.41.1", "sentence-transformers>=0.4.1", - "plotly>=4.7.0,<4.14.3", + "plotly>=4.7.0", "pyyaml<6.0" ] From 06fb49c9dc407c70fba49e87ad98196ff45ad115 Mon Sep 17 00:00:00 2001 From: MaartenGr Date: Tue, 14 Dec 2021 10:40:47 +0100 Subject: [PATCH 17/17] Update changelog --- docs/changelog.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 2f18395a..d6279e70 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,18 +1,18 @@ ## **Version 0.9.4** -*Release date: XXXXX, 2021* +*Release date: 14 December, 2021* A number of fixes, documentation updates, and small features: * Expose diversity parameter - * Use `diversity=0.1` to change how diverse the words in a topic representation are (ranges from 0 to 1) + * Use `BERTopic(diversity=0.1)` to change how diverse the words in a topic representation are (ranges from 0 to 1) * Improve stability of topic reduction by only computing the cosine similarity within c-TF-IDF and not the topic embeddings - * This also means that the topic embeddings could be improved or do not work properly with certain language models - * Perhaps this could be improved by averaging out the exemplars but that still would not fully represent a topic - * c-TF-IDF seems to hold the most information with respect to the topics * Added property to c-TF-IDF that all IDF values should be positive ([#351](https://github.com/MaartenGr/BERTopic/issues/351)) * Improve stability of `.visualize_barchart()` and `.visualize_hierarchy()` -* Major documentation overhaul (mkdocs, tutorials, FAQ, images, etc. ) ([#330](https://github.com/MaartenGr/BERTopic/issues/330)) -* Drop python 3.7 ([#333](https://github.com/MaartenGr/BERTopic/issues/333)) +* Major [documentation](https://maartengr.github.io/BERTopic/) overhaul (mkdocs, tutorials, FAQ, images, etc. ) ([#330](https://github.com/MaartenGr/BERTopic/issues/330)) +* Drop python 3.6 ([#333](https://github.com/MaartenGr/BERTopic/issues/333)) +* Relax plotly dependency ([#88](https://github.com/MaartenGr/BERTopic/issues/88)) +* Additional logging for `.transform` ([#356](https://github.com/MaartenGr/BERTopic/issues/356)) + ## **Version 0.9.3** *Release date: 17 October, 2021*