BERTopic¶
BERTopic is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.
The default embedding model is all-MiniLM-L6-v2 when selecting language="english"
and paraphrase-multilingual-MiniLM-L12-v2 when selecting language="multilingual".
Attributes:
| Name | Type | Description |
|---|---|---|
topics_ |
List[int]) |
The topics that are generated for each document after training or updating the topic model. The most recent topics are tracked. |
probabilities_ |
List[float] |
The probability of the assigned topic per document. These are
only calculated if a HDBSCAN model is used for the clustering step.
When |
topic_sizes_ |
Mapping[int, int]) |
The size of each topic |
topic_mapper_ |
TopicMapper) |
A class for tracking topics and their mappings anytime they are merged, reduced, added, or removed. |
topic_representations_ |
Mapping[int, Tuple[int, float]]) |
The top n terms per topic and their respective c-TF-IDF values. |
c_tf_idf_ |
csr_matrix) |
The topic-term matrix as calculated through c-TF-IDF. To access its respective
words, run |
topic_labels_ |
Mapping[int, str]) |
The default labels for each topic. |
custom_labels_ |
List[str]) |
Custom labels for each topic. |
topic_embeddings_ |
np.ndarray) |
The embeddings for each topic. It is calculated by taking the weighted average of word embeddings in a topic based on their c-TF-IDF values. |
representative_docs_ |
Mapping[int, str]) |
The representative documents for each topic. |
Examples:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic()
topics, probabilities = topic_model.fit_transform(docs)
If you want to use your own embedding model, use it as follows:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
topic_model = BERTopic(embedding_model=sentence_model)
Due to the stochastisch nature of UMAP, the results from BERTopic might differ and the quality can degrade. Using your own embeddings allows you to try out BERTopic several times until you find the topics that suit you best.
Source code in bertopic\_bertopic.py
class BERTopic:
"""BERTopic is a topic modeling technique that leverages BERT embeddings and
c-TF-IDF to create dense clusters allowing for easily interpretable topics
whilst keeping important words in the topic descriptions.
The default embedding model is `all-MiniLM-L6-v2` when selecting `language="english"`
and `paraphrase-multilingual-MiniLM-L12-v2` when selecting `language="multilingual"`.
Attributes:
topics_ (List[int]) : The topics that are generated for each document after training or updating
the topic model. The most recent topics are tracked.
probabilities_ (List[float]): The probability of the assigned topic per document. These are
only calculated if a HDBSCAN model is used for the clustering step.
When `calculate_probabilities=True`, then it is the probabilities
of all topics per document.
topic_sizes_ (Mapping[int, int]) : The size of each topic
topic_mapper_ (TopicMapper) : A class for tracking topics and their mappings anytime they are
merged, reduced, added, or removed.
topic_representations_ (Mapping[int, Tuple[int, float]]) : The top n terms per topic and their respective
c-TF-IDF values.
c_tf_idf_ (csr_matrix) : The topic-term matrix as calculated through c-TF-IDF. To access its respective
words, run `.vectorizer_model.get_feature_names()` or
`.vectorizer_model.get_feature_names_out()`
topic_labels_ (Mapping[int, str]) : The default labels for each topic.
custom_labels_ (List[str]) : Custom labels for each topic.
topic_embeddings_ (np.ndarray) : The embeddings for each topic. It is calculated by taking the
weighted average of word embeddings in a topic based on their c-TF-IDF values.
representative_docs_ (Mapping[int, str]) : The representative documents for each topic.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic()
topics, probabilities = topic_model.fit_transform(docs)
```
If you want to use your own embedding model, use it as follows:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
topic_model = BERTopic(embedding_model=sentence_model)
```
Due to the stochastisch nature of UMAP, the results from BERTopic might differ
and the quality can degrade. Using your own embeddings allows you to
try out BERTopic several times until you find the topics that suit
you best.
"""
def __init__(self,
language: str = "english",
top_n_words: int = 10,
n_gram_range: Tuple[int, int] = (1, 1),
min_topic_size: int = 10,
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,
hdbscan_model: hdbscan.HDBSCAN = None,
vectorizer_model: CountVectorizer = None,
ctfidf_model: TfidfTransformer = None,
verbose: bool = False,
):
"""BERTopic initialization
Arguments:
language: The main language used in your documents. The default sentence-transformers
model for "english" is `all-MiniLM-L6-v2`. For a full overview of
supported languages see bertopic.backend.languages. Select
"multilingual" to load in the `paraphrase-multilingual-MiniLM-L12-v2`
sentence-tranformers model that supports 50+ languages.
top_n_words: The number of words per topic to extract. Setting this
too high can negatively impact topic embeddings as topics
are typically best represented by at most 10 words.
n_gram_range: The n-gram range for the CountVectorizer.
Advised to keep high values between 1 and 3.
More would likely lead to memory issues.
NOTE: This param will not be used if you pass in your own
CountVectorizer.
min_topic_size: The minimum size of the topic. Increasing this value will lead
to a lower number of clusters/topics.
NOTE: This param will not be used if you are not using HDBSCAN.
nr_topics: Specifying the number of topics will reduce the initial
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 using HDBSCAN.
low_memory: Sets UMAP low memory to True to make sure less memory is used.
NOTE: This is only used in UMAP. For example, if you use PCA instead of UMAP
this parameter will not be used.
calculate_probabilities: Whether to calculate the probabilities of all topics
per document instead of the probability of the assigned
topic per document. This could slow down the extraction
of topics if you have many documents (> 100_000). Set this
only to True if you have a low amount of documents or if
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.
embedding_model: Use a custom embedding model.
The following backends are currently supported
* SentenceTransformers
* Flair
* Spacy
* Gensim
* USE (TF-Hub)
You can also pass in a string that points to one of the following
sentence-transformers models:
* https://www.sbert.net/docs/pretrained_models.html
umap_model: Pass in a UMAP model to be used instead of the default.
NOTE: You can also pass in any dimensionality reduction algorithm as long
as it has `.fit` and `.transform` functions.
hdbscan_model: Pass in a hdbscan.HDBSCAN model to be used instead of the default
NOTE: You can also pass in any clustering algorithm as long as it has
`.fit` and `.predict` functions along with the `.labels_` variable.
vectorizer_model: Pass in a custom `CountVectorizer` instead of the default model.
ctfidf_model: Pass in a custom ClassTfidfTransformer instead of the default model.
"""
# Topic-based parameters
if top_n_words > 30:
raise ValueError("top_n_words should be lower or equal to 30. The preferred value is 10.")
self.top_n_words = top_n_words
self.min_topic_size = min_topic_size
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
# Embedding model
self.language = language if not embedding_model else None
self.embedding_model = embedding_model
# Vectorizer
self.n_gram_range = n_gram_range
self.vectorizer_model = vectorizer_model or CountVectorizer(ngram_range=self.n_gram_range)
self.ctfidf_model = ctfidf_model or ClassTfidfTransformer()
# UMAP or another algorithm that has .fit and .transform functions
self.umap_model = umap_model or UMAP(n_neighbors=15,
n_components=5,
min_dist=0.0,
metric='cosine',
low_memory=self.low_memory)
# HDBSCAN or another clustering algorithm that has .fit and .predict functions and
# the .labels_ variable to extract the labels
self.hdbscan_model = hdbscan_model or hdbscan.HDBSCAN(min_cluster_size=self.min_topic_size,
metric='euclidean',
cluster_selection_method='eom',
prediction_data=True)
# Public attributes
self.topics_ = None
self.probabilities_ = None
self.topic_sizes_ = None
self.topic_mapper_ = None
self.topic_representations_ = None
self.topic_embeddings_ = None
self.topic_labels_ = None
self.custom_labels_ = None
self.representative_docs_ = {}
self.c_tf_idf_ = None
# Private attributes for internal tracking purposes
self._outliers = 1
self._merged_topics = None
if verbose:
logger.set_level("DEBUG")
def fit(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None):
""" Fit the models (Bert, UMAP, and, HDBSCAN) on a collection of documents and generate topics
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
```
If you want to use your own embeddings, use it as follows:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
```
"""
self.fit_transform(documents, embeddings, y)
return self
def fit_transform(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None) -> Tuple[List[int],
Union[np.ndarray, None]]:
""" Fit the models on a collection of documents, generate topics, and return the docs with topics
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Returns:
predictions: Topic predictions for each documents
probabilities: The probability of the assigned topic per document.
If `calculate_probabilities` in BERTopic is set to True, then
it calculates the probabilities of all topics across all documents
instead of only the assigned topic. This, however, slows down
computation and may increase memory usage.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
```
If you want to use your own embeddings, use it as follows:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs, embeddings)
```
"""
check_documents_type(documents)
check_embeddings_shape(embeddings, documents)
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": None})
# Extract embeddings
if embeddings is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
embeddings = self._extract_embeddings(documents.Document,
method="document",
verbose=self.verbose)
logger.info("Transformed documents to Embeddings")
else:
if self.embedding_model is not None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
# Reduce dimensionality
if self.seed_topic_list is not None and self.embedding_model is not None:
y, embeddings = self._guided_topic_modeling(embeddings)
umap_embeddings = self._reduce_dimensionality(embeddings, y)
# Cluster reduced embeddings
documents, probabilities = self._cluster_embeddings(umap_embeddings, documents, y=y)
# Sort and Map Topic IDs by their frequency
if not self.nr_topics:
documents = self._sort_mappings_by_frequency(documents)
# Extract topics by calculating c-TF-IDF
self._extract_topics(documents)
# Reduce topics
if self.nr_topics:
documents = self._reduce_topics(documents)
if isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
self._map_representative_docs(original_topics=True)
else:
self._save_representative_docs(documents)
self.probabilities_ = self._map_probabilities(probabilities, original_topics=True)
predictions = documents.Topic.to_list()
return predictions, self.probabilities_
def transform(self,
documents: Union[str, List[str]],
embeddings: np.ndarray = None) -> Tuple[List[int], np.ndarray]:
""" After having fit a model, use transform to predict new instances
Arguments:
documents: A single document or a list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model.
Returns:
predictions: Topic predictions for each documents
probabilities: The topic probability distribution which is returned by default.
If `calculate_probabilities` in BERTopic is set to False, then the
probabilities are not calculated to speed up computation and
decrease memory usage.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
topics, probs = topic_model.transform(docs)
```
If you want to use your own embeddings:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
topics, probs = topic_model.transform(docs, embeddings)
```
"""
check_is_fitted(self)
check_embeddings_shape(embeddings, documents)
if isinstance(documents, str):
documents = [documents]
if embeddings is None:
embeddings = self._extract_embeddings(documents,
method="document",
verbose=self.verbose)
umap_embeddings = self.umap_model.transform(embeddings)
logger.info("Reduced dimensionality")
# Extract predictions and probabilities if it is a HDBSCAN-like model
if is_supported_hdbscan(self.hdbscan_model):
predictions, probabilities = hdbscan_delegator(self.hdbscan_model, "approximate_predict", umap_embeddings)
# Calculate probabilities
if self.calculate_probabilities and isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
probabilities = hdbscan.membership_vector(self.hdbscan_model, umap_embeddings)
logger.info("Calculated probabilities with HDBSCAN")
else:
predictions = self.hdbscan_model.predict(umap_embeddings)
probabilities = None
logger.info("Predicted clusters")
# Map probabilities and predictions
probabilities = self._map_probabilities(probabilities, original_topics=True)
predictions = self._map_predictions(predictions)
return predictions, probabilities
def partial_fit(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None):
""" Fit BERTopic on a subset of the data and perform online learning
with batch-like data.
Online topic modeling in BERTopic is performed by using dimensionality
reduction and cluster algorithms that support a `partial_fit` method
in order to incrementally train the topic model.
Likewise, the `bertopic.vectorizers.OnlineCountVectorizer` is used
to dynamically update its vocabulary when presented with new data.
It has several parameters for modeling decay and updating the
representations.
In other words, although the main algorithm stays the same, the training
procedure now works as follows:
For each subset of the data:
1. Generate embeddings with a pre-traing language model
2. Incrementally update the dimensionality reduction algorithm with `partial_fit`
3. Incrementally update the cluster algorithm with `partial_fit`
4. Incrementally update the OnlineCountVectorizer and apply some form of decay
Note that it is advised to use `partial_fit` with batches and
not single documents for the best performance.
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Examples:
```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic
# Prepare documents
docs = fetch_20newsgroups(subset=subset, remove=('headers', 'footers', 'quotes'))["data"]
# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)
topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model)
# Incrementally fit the topic model by training on 1000 documents at a time
for index in range(0, len(docs), 1000):
topic_model.partial_fit(docs[index: index+1000])
```
"""
# Checks
check_embeddings_shape(embeddings, documents)
if not hasattr(self.hdbscan_model, "partial_fit"):
raise ValueError("In order to use `.partial_fit`, the cluster model should have "
"a `.partial_fit` function.")
# Prepare documents
if isinstance(documents, str):
documents = [documents]
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": None})
# Extract embeddings
if embeddings is None:
if self.topic_representations_ is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
embeddings = self._extract_embeddings(documents.Document,
method="document",
verbose=self.verbose)
else:
if self.embedding_model is not None and self.topic_representations_ is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
# Reduce dimensionality
if self.seed_topic_list is not None and self.embedding_model is not None:
y, embeddings = self._guided_topic_modeling(embeddings)
umap_embeddings = self._reduce_dimensionality(embeddings, y, partial_fit=True)
# Cluster reduced embeddings
documents, self.probabilities_ = self._cluster_embeddings(umap_embeddings, documents, partial_fit=True)
topics = documents.Topic.to_list()
# Map and find new topics
if not self.topic_mapper_:
self.topic_mapper_ = TopicMapper(topics)
mappings = self.topic_mapper_.get_mappings()
new_topics = set(topics).difference(set(mappings.keys()))
new_topic_ids = {topic: max(mappings.values()) + index + 1 for index, topic in enumerate(new_topics)}
self.topic_mapper_.add_new_topics(new_topic_ids)
updated_mappings = self.topic_mapper_.get_mappings()
updated_topics = [updated_mappings[topic] for topic in topics]
documents["Topic"] = updated_topics
# Add missing topics (topics that were originally created but are now missing)
if self.topic_representations_:
missing_topics = set(self.topic_representations_.keys()).difference(set(updated_topics))
for missing_topic in missing_topics:
documents.loc[len(documents), :] = [" ", len(documents), missing_topic]
else:
missing_topics = {}
# Prepare documents
documents_per_topic = documents.sort_values("Topic").groupby(['Topic'], as_index=False)
updated_topics = documents_per_topic.first().Topic.astype(int)
documents_per_topic = documents_per_topic.agg({'Document': ' '.join})
# Update topic representations
self.c_tf_idf_, updated_words = self._c_tf_idf(documents_per_topic, partial_fit=True)
self.topic_representations_ = self._extract_words_per_topic(updated_words, self.c_tf_idf_, labels=updated_topics)
self._create_topic_vectors()
self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
for key, values in self.topic_representations_.items()}
# Update topic sizes
if len(missing_topics) > 0:
documents = documents.iloc[:-len(missing_topics)]
if self.topic_sizes_ is None:
self._update_topic_size(documents)
else:
sizes = documents.groupby(['Topic'], as_index=False).count()
for _, row in sizes.iterrows():
topic = int(row.Topic)
if self.topic_sizes_.get(topic) is not None and topic not in missing_topics:
self.topic_sizes_[topic] += int(row.Document)
elif self.topic_sizes_.get(topic) is None:
self.topic_sizes_[topic] = int(row.Document)
self.topics_ = documents.Topic.astype(int).tolist()
return self
def topics_over_time(self,
docs: List[str],
timestamps: Union[List[str],
List[int]],
nr_bins: int = None,
datetime_format: str = None,
evolution_tuning: bool = True,
global_tuning: bool = True) -> pd.DataFrame:
""" Create topics over time
To create the topics over time, BERTopic needs to be already fitted once.
From the fitted models, the c-TF-IDF representations are calculate at
each timestamp t. Then, the c-TF-IDF representations at timestamp t are
averaged with the global c-TF-IDF representations in order to fine-tune the
local representations.
NOTE:
Make sure to use a limited number of unique timestamps (<100) as the
c-TF-IDF representation will be calculated at each single unique timestamp.
Having a large number of unique timestamps can take some time to be calculated.
Moreover, there aren't many use-cased where you would like to see the difference
in topic representations over more than 100 different timestamps.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
timestamps: The timestamp of each document. This can be either a list of strings or ints.
If it is a list of strings, then the datetime format will be automatically
inferred. If it is a list of ints, then the documents will be ordered by
ascending order.
nr_bins: The number of bins you want to create for the timestamps. The left interval will
be chosen as the timestamp. An additional column will be created with the
entire interval.
datetime_format: The datetime format of the timestamps if they are strings, eg “%d/%m/%Y”.
Set this to None if you want to have it automatically detect the format.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
evolution_tuning: Fine-tune each topic representation at timestamp *t* by averaging its
c-TF-IDF matrix with the c-TF-IDF matrix at timestamp *t-1*. This creates
evolutionary topic representations.
global_tuning: Fine-tune each topic representation at timestamp *t* by averaging its c-TF-IDF matrix
with the global c-TF-IDF matrix. Turn this off if you want to prevent words in
topic representations that could not be found in the documents at timestamp *t*.
Returns:
topics_over_time: A dataframe that contains the topic, words, and frequency of topic
at timestamp *t*.
Examples:
The timestamps variable represent the timestamp of each document. If you have over
100 unique timestamps, it is advised to bin the timestamps as shown below:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_over_time = topic_model.topics_over_time(docs, timestamps, nr_bins=20)
```
"""
check_is_fitted(self)
check_documents_type(docs)
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_, "Timestamps": timestamps})
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm='l1', copy=False)
all_topics = sorted(list(documents.Topic.unique()))
all_topics_indices = {topic: index for index, topic in enumerate(all_topics)}
if isinstance(timestamps[0], str):
infer_datetime_format = True if not datetime_format else False
documents["Timestamps"] = pd.to_datetime(documents["Timestamps"],
infer_datetime_format=infer_datetime_format,
format=datetime_format)
if nr_bins:
documents["Bins"] = pd.cut(documents.Timestamps, bins=nr_bins)
documents["Timestamps"] = documents.apply(lambda row: row.Bins.left, 1)
# Sort documents in chronological order
documents = documents.sort_values("Timestamps")
timestamps = documents.Timestamps.unique()
if len(timestamps) > 100:
warnings.warn(f"There are more than 100 unique timestamps (i.e., {len(timestamps)}) "
"which significantly slows down the application. Consider setting `nr_bins` "
"to a value lower than 100 to speed up calculation. ")
# For each unique timestamp, create topic representations
topics_over_time = []
for index, timestamp in tqdm(enumerate(timestamps), disable=not self.verbose):
# Calculate c-TF-IDF representation for a specific timestamp
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, fit=False)
if global_tuning or evolution_tuning:
c_tf_idf = normalize(c_tf_idf, axis=1, norm='l1', copy=False)
# Fine-tune the c-TF-IDF matrix at timestamp t by averaging it with the c-TF-IDF
# matrix at timestamp t-1
if evolution_tuning and index != 0:
current_topics = sorted(list(documents_per_topic.Topic.values))
overlapping_topics = sorted(list(set(previous_topics).intersection(set(current_topics))))
current_overlap_idx = [current_topics.index(topic) for topic in overlapping_topics]
previous_overlap_idx = [previous_topics.index(topic) for topic in overlapping_topics]
c_tf_idf.tolil()[current_overlap_idx] = ((c_tf_idf[current_overlap_idx] +
previous_c_tf_idf[previous_overlap_idx]) / 2.0).tolil()
# Fine-tune the timestamp c-TF-IDF representation based on the global c-TF-IDF representation
# by simply taking the average of the two
if global_tuning:
selected_topics = [all_topics_indices[topic] for topic in documents_per_topic.Topic.values]
c_tf_idf = (global_c_tf_idf[selected_topics] + c_tf_idf) / 2.0
# Extract the words per topic
labels = sorted(list(documents_per_topic.Topic.unique()))
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels)
topic_frequency = pd.Series(documents_per_topic.Timestamps.values,
index=documents_per_topic.Topic).to_dict()
# Fill dataframe with results
topics_at_timestamp = [(topic,
", ".join([words[0] for words in values][:5]),
topic_frequency[topic],
timestamp) for topic, values in words_per_topic.items()]
topics_over_time.extend(topics_at_timestamp)
if evolution_tuning:
previous_topics = sorted(list(documents_per_topic.Topic.values))
previous_c_tf_idf = c_tf_idf.copy()
return pd.DataFrame(topics_over_time, columns=["Topic", "Words", "Frequency", "Timestamp"])
def topics_per_class(self,
docs: List[str],
classes: Union[List[int], List[str]],
global_tuning: bool = True) -> pd.DataFrame:
""" Create topics per class
To create the topics per class, BERTopic needs to be already fitted once.
From the fitted models, the c-TF-IDF representations are calculate at
each class c. Then, the c-TF-IDF representations at class c are
averaged with the global c-TF-IDF representations in order to fine-tune the
local representations. This can be turned off if the pure representation is
needed.
NOTE:
Make sure to use a limited number of unique classes (<100) as the
c-TF-IDF representation will be calculated at each single unique class.
Having a large number of unique classes can take some time to be calculated.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
classes: The class of each document. This can be either a list of strings or ints.
global_tuning: Fine-tune each topic representation for class c t by averaging its c-TF-IDF matrix
with the global c-TF-IDF matrix. Turn this off if you want to prevent words in
topic representations that could not be found in the documents for class c.
Returns:
topics_per_class: A dataframe that contains the topic, words, and frequency of topics
for each class.
Examples:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_per_class = topic_model.topics_per_class(docs, classes)
```
"""
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_, "Class": classes})
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm='l1', copy=False)
# For each unique timestamp, create topic representations
topics_per_class = []
for _, class_ in tqdm(enumerate(set(classes)), disable=not self.verbose):
# Calculate c-TF-IDF representation for a specific timestamp
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, 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
if global_tuning:
c_tf_idf = normalize(c_tf_idf, axis=1, norm='l1', copy=False)
c_tf_idf = (global_c_tf_idf[documents_per_topic.Topic.values + self._outliers] + c_tf_idf) / 2.0
# Extract the words per topic
labels = sorted(list(documents_per_topic.Topic.unique()))
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels)
topic_frequency = pd.Series(documents_per_topic.Class.values,
index=documents_per_topic.Topic).to_dict()
# Fill dataframe with results
topics_at_class = [(topic,
", ".join([words[0] for words in values][:5]),
topic_frequency[topic],
class_) for topic, values in words_per_topic.items()]
topics_per_class.extend(topics_at_class)
topics_per_class = pd.DataFrame(topics_per_class, columns=["Topic", "Words", "Frequency", "Class"])
return topics_per_class
def hierarchical_topics(self,
docs: List[int],
linkage_function: Callable[[csr_matrix], np.ndarray] = None,
distance_function: Callable[[csr_matrix], csr_matrix] = None) -> pd.DataFrame:
""" Create a hierarchy of topics
To create this hierarchy, BERTopic needs to be already fitted once.
Then, a hierarchy is calculated on the distance matrix of the c-TF-IDF
representation using `scipy.cluster.hierarchy.linkage`.
Based on that hierarchy, we calculate the topic representation at each
merged step. This is a local representation, as we only assume that the
chosen step is merged and not all others which typically improves the
topic representation.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
linkage_function: The linkage function to use. Default is:
`lambda x: sch.linkage(x, 'ward', optimal_ordering=True)`
distance_function: The distance function to use on the c-TF-IDF matrix. Default is:
`lambda x: 1 - cosine_similarity(x)`
Returns:
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children
Examples:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
```
A custom linkage function can be used as follows:
```python
from scipy.cluster import hierarchy as sch
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
# Hierarchical topics
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
hierarchical_topics = topic_model.hierarchical_topics(docs, linkage_function=linkage_function)
```
"""
if distance_function is None:
distance_function = lambda x: 1 - cosine_similarity(x)
if linkage_function is None:
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
# Calculate distance
embeddings = self.c_tf_idf_[self._outliers:]
X = distance_function(embeddings)
# Make sure it is the 1-D condensed distance matrix with zeros on the diagonal
np.fill_diagonal(X, 0)
X = squareform(X)
# Use the 1-D condensed distance matrix as an input instead of the raw distance matrix
Z = linkage_function(X)
# Calculate basic bag-of-words to be iteratively merged later
documents = pd.DataFrame({"Document": docs,
"ID": range(len(docs)),
"Topic": self.topics_})
documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
documents_per_topic = documents_per_topic.loc[documents_per_topic.Topic != -1, :]
documents = self._preprocess_text(documents_per_topic.Document.values)
# Scikit-Learn Deprecation: get_feature_names is deprecated in 1.0
# and will be removed in 1.2. Please use get_feature_names_out instead.
if version.parse(sklearn_version) >= version.parse("1.0.0"):
words = self.vectorizer_model.get_feature_names_out()
else:
words = self.vectorizer_model.get_feature_names()
bow = self.vectorizer_model.transform(documents)
# Extract clusters
hier_topics = pd.DataFrame(columns=["Parent_ID", "Parent_Name", "Topics",
"Child_Left_ID", "Child_Left_Name",
"Child_Right_ID", "Child_Right_Name"])
for index in tqdm(range(len(Z))):
# Find clustered documents
clusters = sch.fcluster(Z, t=Z[index][2], criterion='distance') - self._outliers
cluster_df = pd.DataFrame({"Topic": range(len(clusters)), "Cluster": clusters})
cluster_df = cluster_df.groupby("Cluster").agg({'Topic': lambda x: list(x)}).reset_index()
nr_clusters = len(clusters)
# Extract first topic we find to get the set of topics in a merged topic
topic = None
val = Z[index][0]
while topic is None:
if val - len(clusters) < 0:
topic = int(val)
else:
val = Z[int(val - len(clusters))][0]
clustered_topics = [i for i, x in enumerate(clusters) if x == clusters[topic]]
# Group bow per cluster, calculate c-TF-IDF and extract words
grouped = csr_matrix(bow[clustered_topics].sum(axis=0))
c_tf_idf = self.ctfidf_model.transform(grouped)
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels=[0])
# Extract parent's name and ID
parent_id = index + len(clusters)
parent_name = "_".join([x[0] for x in words_per_topic[0]][:5])
# Extract child's name and ID
Z_id = Z[index][0]
child_left_id = Z_id if Z_id - nr_clusters < 0 else Z_id - nr_clusters
if Z_id - nr_clusters < 0:
child_left_name = "_".join([x[0] for x in self.get_topic(Z_id)][:5])
else:
child_left_name = hier_topics.iloc[int(child_left_id)].Parent_Name
# Extract child's name and ID
Z_id = Z[index][1]
child_right_id = Z_id if Z_id - nr_clusters < 0 else Z_id - nr_clusters
if Z_id - nr_clusters < 0:
child_right_name = "_".join([x[0] for x in self.get_topic(Z_id)][:5])
else:
child_right_name = hier_topics.iloc[int(child_right_id)].Parent_Name
# Save results
hier_topics.loc[len(hier_topics), :] = [parent_id, parent_name,
clustered_topics,
int(Z[index][0]), child_left_name,
int(Z[index][1]), child_right_name]
hier_topics["Distance"] = Z[:, 2]
hier_topics = hier_topics.sort_values("Parent_ID", ascending=False)
hier_topics[["Parent_ID", "Child_Left_ID", "Child_Right_ID"]] = hier_topics[["Parent_ID", "Child_Left_ID", "Child_Right_ID"]].astype(str)
return hier_topics
def approximate_distribution(self,
documents: Union[str, List[str]],
window: int = 4,
stride: int = 1,
min_similarity: float = 0.1,
batch_size: int = 1000,
padding: bool = False,
use_embedding_model: bool = False,
calculate_tokens: bool = False,
separator: str = " ") -> Tuple[np.ndarray,
Union[List[np.ndarray], None]]:
""" A post-hoc approximation of topic distributions across documents.
In order to perform this approximation, each document is split into tokens
according to the provided tokenizer in the `CountVectorizer`. Then, a
sliding window is applied on each document creating subsets of the document.
For example, with a window size of 3 and stride of 1, the sentence:
`Solving the right problem is difficult.`
can be split up into `solving the right`, `the right problem`, `right problem is`,
and `problem is difficult`. These are called tokensets. For each of these
tokensets, we calculate their c-TF-IDF representation and find out
how similar they are to the previously generated topics. Then, the
similarities to the topics for each tokenset are summed in order to
create a topic distribution for the entire document.
We can also dive into this a bit deeper by then splitting these tokensets
up into individual tokens and calculate how much a word, in a specific sentence,
contributes to the topics found in that document. This can be enabled by
setting `calculate_tokens=True` which can be used for visualization purposes
in `topic_model.visualize_approximate_distribution`.
The main output, `topic_distributions`, can also be used directly in
`.visualize_distribution(topic_distributions[index])` by simply selecting
a single distribution.
Arguments:
documents: A single document or a list of documents for which we
approximate their topic distributions
window: Size of the moving window which indicates the number of
tokens being considered.
stride: How far the window should move at each step.
min_similarity: The minimum similarity of a document's tokenset
with respect to the topics.
batch_size: The number of documents to process at a time. If None,
then all documents are processed at once.
NOTE: With a large number of documents, it is not
advised to process all documents at once.
padding: Whether to pad the beginning and ending of a document with
empty tokens.
use_embedding_model: Whether to use the topic model's embedding
model to calculate the similarity between
tokensets and topics instead of using c-TF-IDF.
calculate_tokens: Calculate the similarity of tokens with all topics.
NOTE: This is computation-wise more expensive and
can require more memory. Using this over batches of
documents might be preferred.
separator: The separator used to merge tokens into tokensets.
Returns:
topic_distributions: A `n` x `m` matrix containing the topic distributions
for all input documents with `n` being the documents
and `m` the topics.
topic_token_distributions: A list of `t` x `m` arrays with `t` being the
number of tokens for the respective document
and `m` the topics.
Examples:
After fitting the model, the topic distributions can be calculated regardless
of the clustering model and regardless of whether the documents were previously
seen or not:
```python
topic_distr, _ = topic_model.approximate_distribution(docs)
```
As a result, the topic distributions are calculated in `topic_distr` for the
entire document based on token set with a specific window size and stride.
If you want to calculate the topic distributions on a token-level:
```python
topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)
```
The `topic_token_distr` then contains, for each token, the best fitting topics.
As with `topic_distr`, it can contain multiple topics for a single token.
"""
if isinstance(documents, str):
documents = [documents]
if batch_size is None:
batch_size = len(documents)
batches = 1
else:
batches = math.ceil(len(documents)/batch_size)
topic_distributions = []
topic_token_distributions = []
for i in tqdm(range(batches), disable=not self.verbose):
doc_set = documents[i*batch_size: (i+1) * batch_size]
# Extract tokens
analyzer = self.vectorizer_model.build_tokenizer()
tokens = [analyzer(document) for document in doc_set]
# Extract token sets
all_sentences = []
all_indices = [0]
all_token_sets_ids = []
for tokenset in tokens:
if len(tokenset) < window:
token_sets = [tokenset]
token_sets_ids = [list(range(len(tokenset)))]
else:
# Extract tokensets using window and stride parameters
stride_indices = list(range(len(tokenset)))[::stride]
token_sets = []
token_sets_ids = []
for stride_index in stride_indices:
selected_tokens = tokenset[stride_index: stride_index+window]
if padding or len(selected_tokens) == window:
token_sets.append(selected_tokens)
token_sets_ids.append(list(range(stride_index, stride_index+len(selected_tokens))))
# Add empty tokens at the beginning and end of a document
if padding:
padded = []
padded_ids = []
t = math.ceil(window / stride) - 1
for i in range(math.ceil(window / stride) - 1):
padded.append(tokenset[:window - ((t-i) * stride)])
padded_ids.append(list(range(0, window - ((t-i) * stride))))
token_sets = padded + token_sets
token_sets_ids = padded_ids + token_sets_ids
# Join the tokens
sentences = [separator.join(token) for token in token_sets]
all_sentences.extend(sentences)
all_token_sets_ids.extend(token_sets_ids)
all_indices.append(all_indices[-1] + len(sentences))
# Calculate similarity between embeddings of token sets and the topics
if use_embedding_model:
embeddings = self._extract_embeddings(all_sentences, method="document", verbose=True)
similarity = cosine_similarity(embeddings, self.topic_embeddings_[self._outliers:])
# Calculate similarity between c-TF-IDF of token sets and the topics
else:
bow_doc = self.vectorizer_model.transform(all_sentences)
c_tf_idf_doc = self.ctfidf_model.transform(bow_doc)
similarity = cosine_similarity(c_tf_idf_doc, self.c_tf_idf_[self._outliers:])
# Only keep similarities that exceed the minimum
similarity[similarity < min_similarity] = 0
# Aggregate results on an individual token level
if calculate_tokens:
topic_distribution = []
topic_token_distribution = []
for index, token in enumerate(tokens):
start = all_indices[index]
end = all_indices[index+1]
if start == end:
end = end + 1
# Assign topics to individual tokens
token_id = [i for i in range(len(token))]
token_val = {index: [] for index in token_id}
for sim, token_set in zip(similarity[start:end], all_token_sets_ids[start:end]):
for token in token_set:
if token in token_val:
token_val[token].append(sim)
matrix = []
for _, value in token_val.items():
matrix.append(np.add.reduce(value))
# Take empty documents into account
matrix = np.array(matrix)
if len(matrix.shape) == 1:
matrix = np.zeros((1, len(self.topic_labels_) - self._outliers))
topic_token_distribution.append(np.array(matrix))
topic_distribution.append(np.add.reduce(matrix))
topic_distribution = normalize(topic_distribution, norm='l1', axis=1)
# Aggregate on a tokenset level indicated by the window and stride
else:
topic_distribution = []
for index in range(len(all_indices)-1):
start = all_indices[index]
end = all_indices[index+1]
if start == end:
end = end + 1
group = similarity[start:end].sum(axis=0)
topic_distribution.append(group)
topic_distribution = normalize(np.array(topic_distribution), norm='l1', axis=1)
topic_token_distribution = None
# Combine results
topic_distributions.append(topic_distribution)
if topic_token_distribution is None:
topic_token_distributions = None
else:
topic_token_distributions.extend(topic_token_distribution)
topic_distributions = np.vstack(topic_distributions)
return topic_distributions, topic_token_distributions
def find_topics(self,
search_term: str,
top_n: int = 5) -> Tuple[List[int], List[float]]:
""" Find topics most similar to a search_term
Creates an embedding for search_term and compares that with
the topic embeddings. The most similar topics are returned
along with their similarity values.
The search_term can be of any size but since it compares
with the topic representation it is advised to keep it
below 5 words.
Arguments:
search_term: the term you want to use to search for topics
top_n: the number of topics to return
Returns:
similar_topics: the most similar topics from high to low
similarity: the similarity scores from high to low
Examples:
You can use the underlying embedding model to find topics that
best represent the search term:
```python
topics, similarity = topic_model.find_topics("sports", top_n=5)
```
Note that the search query is typically more accurate if the
search_term consists of a phrase or multiple words.
"""
if self.embedding_model is None:
raise Exception("This method can only be used if you did not use custom embeddings.")
topic_list = list(self.topic_representations_.keys())
topic_list.sort()
# Extract search_term embeddings and compare with topic embeddings
search_embedding = self._extract_embeddings([search_term],
method="word",
verbose=False).flatten()
sims = cosine_similarity(search_embedding.reshape(1, -1), self.topic_embeddings_).flatten()
# Extract topics most similar to search_term
ids = np.argsort(sims)[-top_n:]
similarity = [sims[i] for i in ids][::-1]
similar_topics = [topic_list[index] for index in ids][::-1]
return similar_topics, similarity
def update_topics(self,
docs: List[str],
topics: List[int] = None,
top_n_words: int = 10,
n_gram_range: Tuple[int, int] = None,
diversity: float = None,
vectorizer_model: CountVectorizer = None,
ctfidf_model: ClassTfidfTransformer = None):
""" Updates the topic representation by recalculating c-TF-IDF with the new
parameters as defined in this function.
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. This function allows you
to update the topic representation after they have been formed.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
topics: A list of topics where each topic is related to a document in `docs`.
Use this variable to change or map the topics.
NOTE: Using a custom list of topic assignments may lead to errors if
topic reduction techniques are used afterwards. Make sure that
manually assigning topics is the last step in the pipeline
top_n_words: The number of words per topic to extract. Setting this
too high can negatively impact topic embeddings as topics
are typically best represented by at most 10 words.
n_gram_range: The n-gram range for the CountVectorizer.
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.
vectorizer_model: Pass in your own CountVectorizer from scikit-learn
ctfidf_model: Pass in your own c-TF-IDF model to update the representations
Examples:
In order to update the topic representation, you will need to first fit the topic
model and extract topics from them. Based on these, you can update the representation:
```python
topic_model.update_topics(docs, n_gram_range=(2, 3))
```
You can also use a custom vectorizer to update the representation:
```python
from sklearn.feature_extraction.text import CountVectorizer
vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")
topic_model.update_topics(docs, vectorizer_model=vectorizer_model)
```
You can also use this function to change or map the topics to something else.
You can update them as follows:
```python
topic_model.update_topics(docs, my_updated_topics)
```
"""
check_is_fitted(self)
if not n_gram_range:
n_gram_range = self.n_gram_range
if top_n_words > 30:
raise ValueError("top_n_words should be lower or equal to 30. The preferred value is 10.")
self.top_n_words = top_n_words
self.diversity = diversity
self.vectorizer_model = vectorizer_model or CountVectorizer(ngram_range=n_gram_range)
self.ctfidf_model = ctfidf_model or ClassTfidfTransformer()
if topics is None:
topics = self.topics_
labels = None
else:
labels = sorted(list(set(topics)))
warnings.warn("Using a custom list of topic assignments may lead to errors if "
"topic reduction techniques are used afterwards. Make sure that "
"manually assigning topics is the last step in the pipeline.")
# Extract words
documents = pd.DataFrame({"Document": docs, "Topic": topics})
documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
self.topic_representations_ = self._extract_words_per_topic(words, labels=labels)
self._create_topic_vectors()
self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
for key, values in
self.topic_representations_.items()}
self._update_topic_size(documents)
def get_topics(self) -> Mapping[str, Tuple[str, float]]:
""" Return topics with top n words and their c-TF-IDF score
Returns:
self.topic_representations_: The top n words per topic and the corresponding c-TF-IDF score
Examples:
```python
all_topics = topic_model.get_topics()
```
"""
check_is_fitted(self)
return self.topic_representations_
def get_topic(self, topic: int) -> Union[Mapping[str, Tuple[str, float]], bool]:
""" Return top n words for a specific topic and their c-TF-IDF scores
Arguments:
topic: A specific topic for which you want its representation
Returns:
The top n words for a specific word and its respective c-TF-IDF scores
Examples:
```python
topic = topic_model.get_topic(12)
```
"""
check_is_fitted(self)
if topic in self.topic_representations_:
return self.topic_representations_[topic]
else:
return False
def get_topic_info(self, topic: int = None) -> pd.DataFrame:
""" Get information about each topic including its ID, frequency, and name.
Arguments:
topic: A specific topic for which you want the frequency
Returns:
info: The information relating to either a single topic or all topics
Examples:
```python
info_df = topic_model.get_topic_info()
```
"""
check_is_fitted(self)
info = pd.DataFrame(self.topic_sizes_.items(), columns=["Topic", "Count"]).sort_values("Topic")
info["Name"] = info.Topic.map(self.topic_labels_)
if self.custom_labels_ is not None:
if len(self.custom_labels_) == len(info):
labels = {topic - self._outliers: label for topic, label in enumerate(self.custom_labels_)}
info["CustomName"] = info["Topic"].map(labels)
if topic is not None:
info = info.loc[info.Topic == topic, :]
return info.reset_index(drop=True)
def get_topic_freq(self, topic: int = None) -> Union[pd.DataFrame, int]:
""" Return the the size of topics (descending order)
Arguments:
topic: A specific topic for which you want the frequency
Returns:
Either the frequency of a single topic or dataframe with
the frequencies of all topics
Examples:
To extract the frequency of all topics:
```python
frequency = topic_model.get_topic_freq()
```
To get the frequency of a single topic:
```python
frequency = topic_model.get_topic_freq(12)
```
"""
check_is_fitted(self)
if isinstance(topic, int):
return self.topic_sizes_[topic]
else:
return pd.DataFrame(self.topic_sizes_.items(), columns=['Topic', 'Count']).sort_values("Count",
ascending=False)
def get_document_info(self,
docs: List[str],
df: pd.DataFrame = None,
metadata: Mapping[str, Any] = None) -> pd.DataFrame:
""" Get information about the documents on which the topic was trained
including the documents themselves, their respective topics, the name
of each topic, the top n words of each topic, whether it is a
representative document, and probability of the clustering if the cluster
model supports it.
There are also options to include other meta data, such as the topic
distributions or the x and y coordinates of the reduced embeddings.
Arguments:
docs: The documents on which the topic model was trained.
df: A dataframe containing the metadata and the documents on which
the topic model was originally trained on.
metadata: A dictionary with meta data for each document in the form
of column name (key) and the respective values (value).
Returns:
document_info: A dataframe with several statistics regarding
the documents on which the topic model was trained.
Usage:
To get the document info, you will only need to pass the documents on which
the topic model was trained:
```python
document_info = topic_model.get_document_info(docs)
```
There are additionally options to include meta data, such as the topic
distributions. Moreover, we can pass the original dataframe that contains
the documents and extend it with the information retrieved from BERTopic:
```python
from sklearn.datasets import fetch_20newsgroups
# The original data in a dataframe format to include the target variable
data= fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
df = pd.DataFrame({"Document": data['data'], "Class": data['target']})
# Add information about the percentage of the document that relates to the topic
topic_distr, _ = topic_model.approximate_distribution(docs, batch_size=1000)
distributions = [distr[topic] if topic != -1 else 0 for topic, distr in zip(topics, topic_distr)]
# Create our documents dataframe using the original dataframe and meta data about
# the topic distributions
document_info = topic_model.get_document_info(docs, df=df,
metadata={"Topic_distribution": distributions})
```
"""
if df is not None:
document_info = df.copy()
document_info["Document"] = docs
document_info["Topic"] = self.topics_
else:
document_info = pd.DataFrame({"Document": docs, "Topic": self.topics_})
# Add topic info through `.get_topic_info()`
topic_info = self.get_topic_info().drop("Count", axis=1)
document_info = pd.merge(document_info, topic_info, on="Topic", how="left")
# Add top n words
top_n_words = {topic: " - ".join(list(zip(*self.get_topic(topic)))[0]) for topic in set(self.topics_)}
document_info["Top_n_words"] = document_info.Topic.map(top_n_words)
# Add flat probabilities
if self.probabilities_ is not None:
if len(self.probabilities_.shape) == 1:
document_info["Probability"] = self.probabilities_
else:
document_info["Probability"] = [max(probs) if topic != -1 else 1-sum(probs)
for topic, probs in zip(self.topics_, self.probabilities_)]
# Add representative document labels
repr_docs = [repr_doc for repr_docs in self.representative_docs_.values() for repr_doc in repr_docs]
document_info["Representative_document"] = False
document_info.loc[document_info.Document.isin(repr_docs), "Representative_document"] = True
# Add custom meta data provided by the user
if metadata is not None:
for column, values in metadata.items():
document_info[column] = values
return document_info
def get_representative_docs(self, topic: int = None) -> List[str]:
""" Extract the best representing documents per topic.
NOTE:
This does not extract all documents per topic as all documents
are not saved within BERTopic. To get all documents, please
run the following:
```python
# When you used `.fit_transform`:
df = pd.DataFrame({"Document": docs, "Topic": topic})
# When you used `.fit`:
df = pd.DataFrame({"Document": docs, "Topic": topic_model.topics_})
```
Arguments:
topic: A specific topic for which you want
the representative documents
Returns:
Representative documents of the chosen topic
Examples:
To extract the representative docs of all topics:
```python
representative_docs = topic_model.get_representative_docs()
```
To get the representative docs of a single topic:
```python
representative_docs = topic_model.get_representative_docs(12)
```
"""
check_is_fitted(self)
if isinstance(topic, int):
if self.representative_docs_.get(topic):
return self.representative_docs_[topic]
else:
return None
else:
return self.representative_docs_
@staticmethod
def get_topic_tree(hier_topics: pd.DataFrame,
max_distance: float = None,
tight_layout: bool = False) -> str:
""" Extract the topic tree such that it can be printed
Arguments:
hier_topics: A dataframe containing the structure of the topic tree.
This is the output of `topic_model.hierachical_topics()`
max_distance: The maximum distance between two topics. This value is
based on the Distance column in `hier_topics`.
tight_layout: Whether to use a tight layout (narrow width) for
easier readability if you have hundreds of topics.
Returns:
A tree that has the following structure when printed:
.
.
└─health_medical_disease_patients_hiv
├─patients_medical_disease_candida_health
│ ├─■──candida_yeast_infection_gonorrhea_infections ── Topic: 48
│ └─patients_disease_cancer_medical_doctor
│ ├─■──hiv_medical_cancer_patients_doctor ── Topic: 34
│ └─■──pain_drug_patients_disease_diet ── Topic: 26
└─■──health_newsgroup_tobacco_vote_votes ── Topic: 9
The blocks (■) indicate that the topic is one you can directly access
from `topic_model.get_topic`. In other words, they are the original un-grouped topics.
Examples:
```python
# Train model
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Print topic tree
tree = topic_model.get_topic_tree(hierarchical_topics)
print(tree)
```
"""
width = 1 if tight_layout else 4
if max_distance is None:
max_distance = hier_topics.Distance.max() + 1
max_original_topic = hier_topics.Parent_ID.astype(int).min() - 1
# Extract mapping from ID to name
topic_to_name = dict(zip(hier_topics.Child_Left_ID, hier_topics.Child_Left_Name))
topic_to_name.update(dict(zip(hier_topics.Child_Right_ID, hier_topics.Child_Right_Name)))
topic_to_name = {topic: name[:100] for topic, name in topic_to_name.items()}
# Create tree
tree = {str(row[1].Parent_ID): [str(row[1].Child_Left_ID), str(row[1].Child_Right_ID)]
for row in hier_topics.iterrows()}
def get_tree(start, tree):
""" Based on: https://stackoverflow.com/a/51920869/10532563 """
def _tree(to_print, start, parent, tree, grandpa=None, indent=""):
# Get distance between merged topics
distance = hier_topics.loc[(hier_topics.Child_Left_ID == parent) |
(hier_topics.Child_Right_ID == parent), "Distance"]
distance = distance.values[0] if len(distance) > 0 else 10
if parent != start:
if grandpa is None:
to_print += topic_to_name[parent]
else:
if int(parent) <= max_original_topic:
# Do not append topic ID if they are not merged
if distance < max_distance:
to_print += "■──" + topic_to_name[parent] + f" ── Topic: {parent}" + "\n"
else:
to_print += "O \n"
else:
to_print += topic_to_name[parent] + "\n"
if parent not in tree:
return to_print
for child in tree[parent][:-1]:
to_print += indent + "├" + "─"
to_print = _tree(to_print, start, child, tree, parent, indent + "│" + " " * width)
child = tree[parent][-1]
to_print += indent + "└" + "─"
to_print = _tree(to_print, start, child, tree, parent, indent + " " * (width+1))
return to_print
to_print = "." + "\n"
to_print = _tree(to_print, start, start, tree)
return to_print
start = str(hier_topics.Parent_ID.astype(int).max())
return get_tree(start, tree)
def set_topic_labels(self, topic_labels: Union[List[str], Mapping[int, str]]) -> None:
""" Set custom topic labels in your fitted BERTopic model
Arguments:
topic_labels: If a list of topic labels, it should contain the same number
of labels as there are topics. This must be ordered
from the topic with the lowest ID to the highest ID,
including topic -1 if it exists.
If a dictionary of `topic ID`: `topic_label`, it can have
any number of topics as it will only map the topics found
in the dictionary.
Examples:
First, we define our topic labels with `.get_topic_labels` in which
we can customize our topic labels:
```python
topic_labels = topic_model.get_topic_labels(nr_words=2,
topic_prefix=True,
word_length=10,
separator=", ")
```
Then, we pass these `topic_labels` to our topic model which
can be accessed at any time with `.custom_labels_`:
```python
topic_model.set_topic_labels(topic_labels)
topic_model.custom_labels_
```
You might want to change only a few topic labels instead of all of them.
To do so, you can pass a dictionary where the keys are the topic IDs and
its keys the topic labels:
```python
topic_model.set_topic_labels({0: "Space", 1: "Sports", 2: "Medicine"})
topic_model.custom_labels_
```
"""
unique_topics = sorted(set(self.topics_))
if isinstance(topic_labels, dict):
if self.custom_labels_ is not None:
original_labels = {topic: label for topic, label in zip(unique_topics, self.custom_labels_)}
else:
info = self.get_topic_info()
original_labels = dict(zip(info.Topic, info.Name))
custom_labels = [topic_labels.get(topic) if topic_labels.get(topic) else original_labels[topic] for topic in unique_topics]
elif isinstance(topic_labels, list):
if len(topic_labels) == len(unique_topics):
custom_labels = topic_labels
else:
raise ValueError("Make sure that `topic_labels` contains the same number "
"of labels as that there are topics.")
self.custom_labels_ = custom_labels
def generate_topic_labels(self,
nr_words: int = 3,
topic_prefix: bool = True,
word_length: int = None,
separator: str = "_") -> List[str]:
""" Get labels for each topic in a user-defined format
Arguments:
original_labels:
nr_words: Top `n` words per topic to use
topic_prefix: Whether to use the topic ID as a prefix.
If set to True, the topic ID will be separated
using the `separator`
word_length: The maximum length of each word in the topic label.
Some words might be relatively long and setting this
value helps to make sure that all labels have relatively
similar lengths.
separator: The string with which the words and topic prefix will be
separated. Underscores are the default but a nice alternative
is `", "`.
Returns:
topic_labels: A list of topic labels sorted from the lowest topic ID to the highest.
If the topic model was trained using HDBSCAN, the lowest topic ID is -1,
otherwise it is 0.
Examples:
To create our custom topic labels, usage is rather straightforward:
```python
topic_labels = topic_model.get_topic_labels(nr_words=2, separator=", ")
```
"""
unique_topics = sorted(set(self.topics_))
topic_labels = []
for topic in unique_topics:
words, _ = zip(*self.get_topic(topic))
if word_length:
words = [word[:word_length] for word in words][:nr_words]
else:
words = list(words)[:nr_words]
if topic_prefix:
topic_label = f"{topic}{separator}" + separator.join(words)
else:
topic_label = separator.join(words)
topic_labels.append(topic_label)
return topic_labels
def merge_topics(self,
docs: List[str],
topics_to_merge: List[Union[Iterable[int], int]]) -> None:
"""
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
topics_to_merge: Either a list of topics or a list of list of topics
to merge. For example:
[1, 2, 3] will merge topics 1, 2 and 3
[[1, 2], [3, 4]] will merge topics 1 and 2, and
separately merge topics 3 and 4.
Examples:
If you want to merge topics 1, 2, and 3:
```python
topics_to_merge = [1, 2, 3]
topic_model.merge_topics(docs, topics_to_merge)
```
or if you want to merge topics 1 and 2, and separately
merge topics 3 and 4:
```python
topics_to_merge = [[1, 2]
[3, 4]]
topic_model.merge_topics(docs, topics_to_merge)
```
"""
check_is_fitted(self)
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_})
mapping = {topic: topic for topic in set(self.topics_)}
if isinstance(topics_to_merge[0], int):
for topic in sorted(topics_to_merge):
mapping[topic] = topics_to_merge[0]
elif isinstance(topics_to_merge[0], Iterable):
for topic_group in sorted(topics_to_merge):
for topic in topic_group:
mapping[topic] = topic_group[0]
else:
raise ValueError("Make sure that `topics_to_merge` is either"
"a list of topics or a list of list of topics.")
documents.Topic = documents.Topic.map(mapping)
self.topic_mapper_.add_mappings(mapping)
documents = self._sort_mappings_by_frequency(documents)
self._extract_topics(documents)
self._update_topic_size(documents)
self._map_representative_docs()
self.probabilities_ = self._map_probabilities(self.probabilities_)
def reduce_topics(self,
docs: List[str],
nr_topics: int = 20) -> None:
""" Further reduce the number of topics to nr_topics.
The number of topics is further reduced by calculating the c-TF-IDF matrix
of the documents and then reducing them by iteratively merging the least
frequent topic with the most similar one based on their c-TF-IDF matrices.
The topics, their sizes, and representations are updated.
Arguments:
docs: The docs you used when calling either `fit` or `fit_transform`
nr_topics: The number of topics you want reduced to
Updates:
topics_ : Assigns topics to their merged representations.
probabilities_ : Assigns probabilities to their merged representations.
Examples:
You can further reduce the topics by passing the documents with its
topics and probabilities (if they were calculated):
```python
topic_model.reduce_topics(docs, nr_topics=30)
```
You can then access the updated topics and probabilities with:
```python
topics = topic_model.topics_
probabilities = topic_model.probabilities_
```
"""
check_is_fitted(self)
self.nr_topics = nr_topics
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_})
# Reduce number of topics
documents = self._reduce_topics(documents)
self._merged_topics = None
self._map_representative_docs()
# Map probabilities
self.probabilities_ = self._map_probabilities(self.probabilities_)
return self
def reduce_outliers(self,
documents: List[str],
topics: List[int],
strategy: str = "distributions",
probabilities: np.ndarray = None,
threshold: int = 0,
embeddings: np.ndarray = None,
distributions_params: Mapping[str, Any] = {}) -> List[int]:
""" Reduce outliers by merging them with their nearest topic according
to one of several strategies.
When using HDBSCAN, DBSCAN, or OPTICS, a number of outlier documents might be created
that do not fall within any of the created topics. These are labeled as -1.
This function allows the user to match outlier documents with their nearest topic
using one of the following strategies using the `strategy` parameter:
* "probabilities"
This uses the soft-clustering as performed by HDBSCAN to find the
best matching topic for each outlier document. To use this, make
sure to calculate the `probabilities` beforehand by instantiating
BERTopic with `calculate_probabilities=True`.
* "distributions"
Use the topic distributions, as calculated with `.approximate_distribution`
to find the most frequent topic in each outlier document. You can use the
`distributions_params` variable to tweak the parameters of
`.approximate_distribution`.
* "c-tf-idf"
Calculate the c-TF-IDF representation for each outlier document and
find the best matching c-TF-IDF topic representation using
cosine similarity.
* "embeddings"
Using the embeddings of each outlier documents, find the best
matching topic embedding using cosine similarity.
Arguments:
documents: A list of documents for which we reduce or remove the outliers.
topics: The topics that correspond to the documents
strategy: The strategy used for reducing outliers.
Options:
* "probabilities"
This uses the soft-clustering as performed by HDBSCAN
to find the best matching topic for each outlier document.
* "distributions"
Use the topic distributions, as calculated with `.approximate_distribution`
to find the most frequent topic in each outlier document.
* "c-tf-idf"
Calculate the c-TF-IDF representation for outlier documents and
find the best matching c-TF-IDF topic representation.
* "embeddings"
Calculate the embeddings for outlier documents and
find the best matching topic embedding.
threshold: The threshold for assigning topics to outlier documents. This value
represents the minimum probability when `strategy="probabilities"`.
For all other strategies, it represents the minimum similarity.
embeddings: The pre-computed embeddings to be used when `strategy="embeddings"`.
If this is None, then it will compute the embeddings for the outlier documents.
distributions_params: The parameters used in `.approximate_distribution` when using
the strategy `"distributions"`.
Returns:
new_topics: The updated topics
Usage:
The default settings uses the `"distributions"` strategy:
```python
new_topics = topic_model.reduce_outliers(docs, topics)
```
When you use the `"probabilities"` strategy, make sure to also pass the probabilities
as generated through HDBSCAN:
```python
from bertopic import BERTopic
topic_model = BERTopic(calculate_probabilities=True)
topics, probs = topic_model.fit_transform(docs)
new_topics = topic_model.reduce_outliers(docs, topics, probabilities=probs, strategy="probabilities")
```
"""
# Check correct use of parameters
if strategy.lower() == "probabilities" and probabilities is None:
raise ValueError("Make sure to pass in `probabilities` in order to use the probabilities strategy")
# Reduce outliers by extracting most likely topics through the topic-term probability matrix
if strategy.lower() == "probabilities":
new_topics = [np.argmax(prob) if max(prob) >= threshold and topic == -1 else topic
for topic, prob in zip(topics, probabilities)]
# Reduce outliers by extracting most frequent topics through calculating of Topic Distributions
elif strategy.lower() == "distributions":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
topic_distr, _ = self.approximate_distribution(outlier_docs, min_similarity=threshold, **distributions_params)
outlier_topics = iter([np.argmax(prob) if sum(prob) > 0 else -1 for prob in topic_distr])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
# Reduce outliers by finding the most similar c-TF-IDF representations
elif strategy.lower() == "c-tf-idf":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
# Calculate c-TF-IDF of outlier documents with all topics
bow_doc = self.vectorizer_model.transform(outlier_docs)
c_tf_idf_doc = self.ctfidf_model.transform(bow_doc)
similarity = cosine_similarity(c_tf_idf_doc, self.c_tf_idf_[self._outliers:])
# Update topics
similarity[similarity < threshold] = 0
outlier_topics = iter([np.argmax(sim) if sum(sim) > 0 else -1 for sim in similarity])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
# Reduce outliers by finding the most similar topic embeddings
elif strategy.lower() == "embeddings":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
# Extract or calculate embeddings for outlier documents
if embeddings is not None:
outlier_embeddings = np.array([embeddings[index] for index in outlier_ids])
else:
outlier_embeddings = self.embedding_model.embed_documents(outlier_docs)
similarity = cosine_similarity(outlier_embeddings, self.topic_embeddings_[self._outliers:])
# Update topics
similarity[similarity < threshold] = 0
outlier_topics = iter([np.argmax(sim) if sum(sim) > 0 else -1 for sim in similarity])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
return new_topics
def visualize_topics(self,
topics: List[int] = None,
top_n_topics: int = None,
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
technique typically reserved for LDA.
Arguments:
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.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_topics()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics(self,
topics=topics,
top_n_topics=top_n_topics,
width=width,
height=height)
def visualize_documents(self,
docs: List[str],
topics: List[int] = None,
embeddings: np.ndarray = None,
reduced_embeddings: np.ndarray = None,
sample: float = None,
hide_annotations: bool = False,
hide_document_hover: bool = False,
custom_labels: bool = False,
width: int = 1200,
height: int = 750) -> go.Figure:
""" Visualize documents and their topics in 2D
Arguments:
topic_model: A fitted BERTopic instance.
docs: The documents you used when calling either `fit` or `fit_transform`
topics: A selection of topics to visualize.
Not to be confused with the topics that you get from `.fit_transform`.
For example, if you want to visualize only topics 1 through 5:
`topics = [1, 2, 3, 4, 5]`.
embeddings: The embeddings of all documents in `docs`.
reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
sample: The percentage of documents in each topic that you would like to keep.
Value can be between 0 and 1. Setting this value to, for example,
0.1 (10% of documents in each topic) makes it easier to visualize
millions of documents as a subset is chosen.
hide_annotations: Hide the names of the traces on top of each cluster.
hide_document_hover: Hide the content of the documents when hovering over
specific points. Helps to speed up generation of visualization.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_documents(docs)
```
Do note that this re-calculates the embeddings and reduces them to 2D.
The advised and prefered pipeline for using this function is as follows:
```python
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic
topic_model = BERTopic().fit(docs, embeddings)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_documents(docs, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/documents.html"
style="width:1000px; height: 800px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_documents(self,
docs=docs,
topics=topics,
embeddings=embeddings,
reduced_embeddings=reduced_embeddings,
sample=sample,
hide_annotations=hide_annotations,
hide_document_hover=hide_document_hover,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_hierarchical_documents(self,
docs: List[str],
hierarchical_topics: pd.DataFrame,
topics: List[int] = None,
embeddings: np.ndarray = None,
reduced_embeddings: np.ndarray = None,
sample: Union[float, int] = None,
hide_annotations: bool = False,
hide_document_hover: bool = True,
nr_levels: int = 10,
custom_labels: bool = False,
width: int = 1200,
height: int = 750) -> go.Figure:
""" Visualize documents and their topics in 2D at different levels of hierarchy
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children
topics: A selection of topics to visualize.
Not to be confused with the topics that you get from `.fit_transform`.
For example, if you want to visualize only topics 1 through 5:
`topics = [1, 2, 3, 4, 5]`.
embeddings: The embeddings of all documents in `docs`.
reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
sample: The percentage of documents in each topic that you would like to keep.
Value can be between 0 and 1. Setting this value to, for example,
0.1 (10% of documents in each topic) makes it easier to visualize
millions of documents as a subset is chosen.
hide_annotations: Hide the names of the traces on top of each cluster.
hide_document_hover: Hide the content of the documents when hovering over
specific points. Helps to speed up generation of visualizations.
nr_levels: The number of levels to be visualized in the hierarchy. First, the distances
in `hierarchical_topics.Distance` are split in `nr_levels` lists of distances with
equal length. Then, for each list of distances, the merged topics are selected that
have a distance less or equal to the maximum distance of the selected list of distances.
NOTE: To get all possible merged steps, make sure that `nr_levels` is equal to
the length of `hierarchical_topics`.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
NOTE: Custom labels are only generated for the original
un-merged topics.
width: The width of the figure.
height: The height of the figure.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics)
```
Do note that this re-calculates the embeddings and reduces them to 2D.
The advised and prefered pipeline for using this function is as follows:
```python
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic and extract hierarchical topics
topic_model = BERTopic().fit(docs, embeddings)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/hierarchical_documents.html"
style="width:1000px; height: 770px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_hierarchical_documents(self,
docs=docs,
hierarchical_topics=hierarchical_topics,
topics=topics,
embeddings=embeddings,
reduced_embeddings=reduced_embeddings,
sample=sample,
hide_annotations=hide_annotations,
hide_document_hover=hide_document_hover,
nr_levels=nr_levels,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_term_rank(self,
topics: List[int] = None,
log_scale: bool = False,
custom_labels: bool = False,
width: int = 800,
height: int = 500) -> go.Figure:
""" Visualize the ranks of all terms across all topics
Each topic is represented by a set of words. These words, however,
do not all equally represent the topic. This visualization shows
how many words are needed to represent a topic and at which point
the beneficial effect of adding words starts to decline.
Arguments:
topics: A selection of topics to visualize. These will be colored
red where all others will be colored black.
log_scale: Whether to represent the ranking on a log scale
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Examples:
To visualize the ranks of all words across
all topics simply run:
```python
topic_model.visualize_term_rank()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_term_rank()
fig.write_html("path/to/file.html")
```
Reference:
This visualization was heavily inspired by the
"Term Probability Decline" visualization found in an
analysis by the amazing [tmtoolkit](https://tmtoolkit.readthedocs.io/).
Reference to that specific analysis can be found
[here](https://wzbsocialsciencecenter.github.io/tm_corona/tm_analysis.html).
"""
check_is_fitted(self)
return plotting.visualize_term_rank(self,
topics=topics,
log_scale=log_scale,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_topics_over_time(self,
topics_over_time: pd.DataFrame,
top_n_topics: int = None,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
width: int = 1250,
height: int = 450) -> go.Figure:
""" Visualize topics over time
Arguments:
topics_over_time: The topics you would like to be visualized with the
corresponding topic representation
top_n_topics: To visualize the most frequent topics instead of all
topics: Select which topics you would like to be visualized
normalize_frequency: Whether to normalize each topic's frequency individually
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
A plotly.graph_objects.Figure including all traces
Examples:
To visualize the topics over time, simply run:
```python
topics_over_time = topic_model.topics_over_time(docs, timestamps)
topic_model.visualize_topics_over_time(topics_over_time)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics_over_time(topics_over_time)
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics_over_time(self,
topics_over_time=topics_over_time,
top_n_topics=top_n_topics,
topics=topics,
normalize_frequency=normalize_frequency,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_topics_per_class(self,
topics_per_class: pd.DataFrame,
top_n_topics: int = 10,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
width: int = 1250,
height: int = 900) -> go.Figure:
""" Visualize topics per class
Arguments:
topics_per_class: The topics you would like to be visualized with the
corresponding topic representation
top_n_topics: To visualize the most frequent topics instead of all
topics: Select which topics you would like to be visualized
normalize_frequency: Whether to normalize each topic's frequency individually
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
A plotly.graph_objects.Figure including all traces
Examples:
To visualize the topics per class, simply run:
```python
topics_per_class = topic_model.topics_per_class(docs, classes)
topic_model.visualize_topics_per_class(topics_per_class)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics_per_class(topics_per_class)
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics_per_class(self,
topics_per_class=topics_per_class,
top_n_topics=top_n_topics,
topics=topics,
normalize_frequency=normalize_frequency,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_distribution(self,
probabilities: np.ndarray,
min_probability: float = 0.015,
custom_labels: bool = False,
width: int = 800,
height: int = 600) -> go.Figure:
""" Visualize the distribution of topic probabilities
Arguments:
probabilities: An array of probability scores
min_probability: The minimum probability score to visualize.
All others are ignored.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Examples:
Make sure to fit the model before and only input the
probabilities of a single document:
```python
topic_model.visualize_distribution(topic_model.probabilities_[0])
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_distribution(topic_model.probabilities_[0])
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_distribution(self,
probabilities=probabilities,
min_probability=min_probability,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_approximate_distribution(self,
document: str,
topic_token_distribution: np.ndarray,
normalize: bool = False):
""" Visualize the topic distribution calculated by `.approximate_topic_distribution`
on a token level. Thereby indicating the extend to which a certain word or phrases belong
to a specific topic. The assumption here is that a single word can belong to multiple
similar topics and as such give information about the broader set of topics within
a single document.
Arguments:
topic_model: A fitted BERTopic instance.
document: The document for which you want to visualize
the approximated topic distribution.
topic_token_distribution: The topic-token distribution of the document as
extracted by `.approximate_topic_distribution`
normalize: Whether to normalize, between 0 and 1 (summing to 1), the
topic distribution values.
Returns:
df: A stylized dataframe indicating the best fitting topics
for each token.
Examples:
```python
# Calculate the topic distributions on a token level
# Note that we need to have `calculate_token_level=True`
topic_distr, topic_token_distr = topic_model.approximate_distribution(
docs, calculate_token_level=True
)
# Visualize the approximated topic distributions
df = topic_model.visualize_approximate_distribution(docs[0], topic_token_distr[0])
df
```
To revert this stylized dataframe back to a regular dataframe,
you can run the following:
```python
df.data.columns = [column.strip() for column in df.data.columns]
df = df.data
```
"""
check_is_fitted(self)
return plotting.visualize_approximate_distribution(self,
document=document,
topic_token_distribution=topic_token_distribution,
normalize=normalize)
def visualize_hierarchy(self,
orientation: str = "left",
topics: List[int] = None,
top_n_topics: int = None,
custom_labels: bool = False,
width: int = 1000,
height: int = 600,
hierarchical_topics: pd.DataFrame = None,
linkage_function: Callable[[csr_matrix], np.ndarray] = None,
distance_function: Callable[[csr_matrix], csr_matrix] = None,
color_threshold: int = 1) -> go.Figure:
""" Visualize a hierarchical structure of the topics
A ward linkage function is used to perform the
hierarchical clustering based on the cosine distance
matrix between topic embeddings.
Arguments:
topic_model: A fitted BERTopic instance.
orientation: The orientation of the figure.
Either 'left' or 'bottom'
topics: A selection of topics to visualize
top_n_topics: Only select the top n most frequent topics
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
NOTE: Custom labels are only generated for the original
un-merged topics.
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'
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children.
NOTE: The hierarchical topic names are only visualized
if both `topics` and `top_n_topics` are not set.
linkage_function: The linkage function to use. Default is:
`lambda x: sch.linkage(x, 'ward', optimal_ordering=True)`
NOTE: Make sure to use the same `linkage_function` as used
in `topic_model.hierarchical_topics`.
distance_function: The distance function to use on the c-TF-IDF matrix. Default is:
`lambda x: 1 - cosine_similarity(x)`
NOTE: Make sure to use the same `distance_function` as used
in `topic_model.hierarchical_topics`.
color_threshold: Value at which the separation of clusters will be made which
will result in different colors for different clusters.
A higher value will typically lead in less colored clusters.
Returns:
fig: A plotly figure
Examples:
To visualize the hierarchical structure of
topics simply run:
```python
topic_model.visualize_hierarchy()
```
If you also want the labels visualized of hierarchical topics,
run the following:
```python
# Extract hierarchical topics and their representations
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Visualize these representations
topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)
```
If you want to save the resulting figure:
```python
fig = topic_model.visualize_hierarchy()
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/hierarchy.html"
style="width:1000px; height: 680px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_hierarchy(self,
orientation=orientation,
topics=topics,
top_n_topics=top_n_topics,
custom_labels=custom_labels,
width=width,
height=height,
hierarchical_topics=hierarchical_topics,
linkage_function=linkage_function,
distance_function=distance_function,
color_threshold=color_threshold
)
def visualize_heatmap(self,
topics: List[int] = None,
top_n_topics: int = None,
n_clusters: int = None,
custom_labels: bool = False,
width: int = 800,
height: int = 800) -> go.Figure:
""" Visualize a heatmap of the topic's similarity matrix
Based on the cosine similarity matrix between topic embeddings,
a heatmap is created showing the similarity between topics.
Arguments:
topics: A selection of topics to visualize.
top_n_topics: Only select the top n most frequent topics.
n_clusters: Create n clusters and order the similarity
matrix by those clusters.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Examples:
To visualize the similarity matrix of
topics simply run:
```python
topic_model.visualize_heatmap()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_heatmap()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_heatmap(self,
topics=topics,
top_n_topics=top_n_topics,
n_clusters=n_clusters,
custom_labels=custom_labels,
width=width,
height=height)
def visualize_barchart(self,
topics: List[int] = None,
top_n_topics: int = 8,
n_words: int = 5,
custom_labels: bool = False,
title: str = "Topic Word Scores",
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
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
title: Title of the plot.
width: The width of each figure.
height: The height of each figure.
Returns:
fig: A plotly figure
Examples:
To visualize the barchart of selected topics
simply run:
```python
topic_model.visualize_barchart()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_barchart()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_barchart(self,
topics=topics,
top_n_topics=top_n_topics,
n_words=n_words,
custom_labels=custom_labels,
title=title,
width=width,
height=height)
def save(self,
path: str,
save_embedding_model: bool = True) -> None:
""" Saves the model to the specified path
When saving the model, make sure to also keep track of the versions
of dependencies and Python used. Loading and saving the model should
be done using the same dependencies and Python. Moreover, models
saved in one version of BERTopic should not be loaded in other versions.
Arguments:
path: the location and name of the file you want to save
save_embedding_model: Whether to save the embedding model in this class
as you might have selected a local model or one that
is downloaded automatically from the cloud.
Examples:
```python
topic_model.save("my_model")
```
or if you do not want the embedding_model to be saved locally:
```python
topic_model.save("my_model", save_embedding_model=False)
```
"""
with open(path, 'wb') as file:
# This prevents the vectorizer from being too large in size if `min_df` was
# set to a value higher than 1
self.vectorizer_model.stop_words_ = None
if not save_embedding_model:
embedding_model = self.embedding_model
self.embedding_model = None
joblib.dump(self, file)
self.embedding_model = embedding_model
else:
joblib.dump(self, file)
@classmethod
def load(cls,
path: str,
embedding_model=None):
""" Loads the model from the specified path
Arguments:
path: the location and name of the BERTopic file you want to load
embedding_model: If the embedding_model was not saved to save space or to load
it in from the cloud, you can load it in by specifying it here.
Examples:
```python
BERTopic.load("my_model")
```
or if you did not save the embedding model:
```python
BERTopic.load("my_model", embedding_model="all-MiniLM-L6-v2")
```
"""
with open(path, 'rb') as file:
if embedding_model:
topic_model = joblib.load(file)
topic_model.embedding_model = select_backend(embedding_model)
else:
topic_model = joblib.load(file)
return topic_model
def get_params(self, deep: bool = False) -> Mapping[str, Any]:
""" Get parameters for this estimator.
Adapted from:
https://github.com/scikit-learn/scikit-learn/blob/b3ea3ed6a/sklearn/base.py#L178
Arguments:
deep: bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns:
out: Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def _extract_embeddings(self,
documents: Union[List[str], str],
method: str = "document",
verbose: bool = None) -> np.ndarray:
""" Extract sentence/document embeddings through pre-trained embeddings
For an overview of pre-trained models: https://www.sbert.net/docs/pretrained_models.html
Arguments:
documents: Dataframe with documents and their corresponding IDs
method: Whether to extract document or word-embeddings, options are "document" and "word"
verbose: Whether to show a progressbar demonstrating the time to extract embeddings
Returns:
embeddings: The extracted embeddings.
"""
if isinstance(documents, str):
documents = [documents]
if method == "word":
embeddings = self.embedding_model.embed_words(documents, verbose)
elif method == "document":
embeddings = self.embedding_model.embed_documents(documents, verbose)
else:
raise ValueError("Wrong method for extracting document/word embeddings. "
"Either choose 'word' or 'document' as the method. ")
return embeddings
def _map_predictions(self, predictions: List[int]) -> List[int]:
""" Map predictions to the correct topics if topics were reduced """
mappings = self.topic_mapper_.get_mappings(original_topics=True)
mapped_predictions = [mappings[prediction]
if prediction in mappings
else -1
for prediction in predictions]
return mapped_predictions
def _reduce_dimensionality(self,
embeddings: Union[np.ndarray, csr_matrix],
y: Union[List[int], np.ndarray] = None,
partial_fit: bool = False) -> np.ndarray:
""" Reduce dimensionality of embeddings using UMAP and train a UMAP model
Arguments:
embeddings: The extracted embeddings using the sentence transformer module.
y: The target class for (semi)-supervised dimensionality reduction
partial_fit: Whether to run `partial_fit` for online learning
Returns:
umap_embeddings: The reduced embeddings
"""
# Partial fit
if partial_fit:
if hasattr(self.umap_model, "partial_fit"):
self.umap_model = self.umap_model.partial_fit(embeddings)
elif self.topic_representations_ is None:
self.umap_model.fit(embeddings)
# Regular fit
else:
try:
self.umap_model.fit(embeddings, y=y)
except TypeError:
logger.info("The dimensionality reduction algorithm did not contain the `y` parameter and"
" therefore the `y` parameter was not used")
self.umap_model.fit(embeddings)
umap_embeddings = self.umap_model.transform(embeddings)
logger.info("Reduced dimensionality")
return np.nan_to_num(umap_embeddings)
def _cluster_embeddings(self,
umap_embeddings: np.ndarray,
documents: pd.DataFrame,
partial_fit: bool = False,
y: np.ndarray = None) -> Tuple[pd.DataFrame,
np.ndarray]:
""" Cluster UMAP embeddings with HDBSCAN
Arguments:
umap_embeddings: The reduced sentence embeddings with UMAP
documents: Dataframe with documents and their corresponding IDs
partial_fit: Whether to run `partial_fit` for online learning
Returns:
documents: Updated dataframe with documents and their corresponding IDs
and newly added Topics
probabilities: The distribution of probabilities
"""
if partial_fit:
self.hdbscan_model = self.hdbscan_model.partial_fit(umap_embeddings)
labels = self.hdbscan_model.labels_
documents['Topic'] = labels
self.topics_ = labels
else:
try:
self.hdbscan_model.fit(umap_embeddings, y=y)
except TypeError:
self.hdbscan_model.fit(umap_embeddings)
try:
labels = self.hdbscan_model.labels_
except AttributeError:
labels = y
documents['Topic'] = labels
self._update_topic_size(documents)
# Some algorithms have outlier labels (-1) that can be tricky to work
# with if you are slicing data based on that labels. Therefore, we
# track if there are outlier labels and act accordingly when slicing.
self._outliers = 1 if -1 in set(labels) else 0
# Save representative docs
if isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
self._save_representative_docs(documents)
# Extract probabilities
probabilities = None
if hasattr(self.hdbscan_model, "probabilities_"):
probabilities = self.hdbscan_model.probabilities_
if self.calculate_probabilities and is_supported_hdbscan(self.hdbscan_model):
probabilities = hdbscan_delegator(self.hdbscan_model, "all_points_membership_vectors")
if not partial_fit:
self.topic_mapper_ = TopicMapper(self.topics_)
logger.info("Clustered reduced embeddings")
return documents, probabilities
def _guided_topic_modeling(self, embeddings: np.ndarray) -> Tuple[List[int], np.array]:
""" Apply Guided Topic Modeling
We transform the seeded topics to embeddings using the
same embedder as used for generating document embeddings.
Then, we apply cosine similarity between the embeddings
and set labels for documents that are more similar to
one of the topics, then the average document.
If a document is more similar to the average document
than any of the topics, it gets the -1 label and is
thereby not included in UMAP.
Arguments:
embeddings: The document embeddings
Returns
y: The labels for each seeded topic
embeddings: Updated embeddings
"""
# Create embeddings from the seeded topics
seed_topic_list = [" ".join(seed_topic) for seed_topic in self.seed_topic_list]
seed_topic_embeddings = self._extract_embeddings(seed_topic_list, verbose=self.verbose)
seed_topic_embeddings = np.vstack([seed_topic_embeddings, embeddings.mean(axis=0)])
# Label documents that are most similar to one of the seeded topics
sim_matrix = cosine_similarity(embeddings, seed_topic_embeddings)
y = [np.argmax(sim_matrix[index]) for index in range(sim_matrix.shape[0])]
y = [val if val != len(seed_topic_list) else -1 for val in y]
# Average the document embeddings related to the seeded topics with the
# embedding of the seeded topic to force the documents in a cluster
for seed_topic in range(len(seed_topic_list)):
indices = [index for index, topic in enumerate(y) if topic == seed_topic]
embeddings[indices] = np.average([embeddings[indices], seed_topic_embeddings[seed_topic]], weights=[3, 1])
return y, embeddings
def _extract_topics(self, documents: pd.DataFrame):
""" Extract topics from the clusters using a class-based TF-IDF
Arguments:
documents: Dataframe with documents and their corresponding IDs
Returns:
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)
self.topic_representations_ = self._extract_words_per_topic(words)
self._create_topic_vectors()
self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
for key, values in
self.topic_representations_.items()}
def _save_representative_docs(self, documents: pd.DataFrame):
""" Save the most representative docs (3) per topic
The most representative docs are extracted by taking
the exemplars from the HDBSCAN-generated clusters.
Full instructions can be found here:
https://hdbscan.readthedocs.io/en/latest/soft_clustering_explanation.html
Arguments:
documents: Dataframe with documents and their corresponding IDs
"""
smallest_cluster_size = min(self.topic_sizes_.items(), key=lambda x: x[1])[1]
if smallest_cluster_size < 3:
top_n_representative_docs = smallest_cluster_size
else:
top_n_representative_docs = 3
if isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
# Prepare the condensed tree and luf clusters beneath a given cluster
condensed_tree = self.hdbscan_model.condensed_tree_
raw_tree = condensed_tree._raw_tree
clusters = sorted(condensed_tree._select_clusters())
cluster_tree = raw_tree[raw_tree['child_size'] > 1]
# Find the points with maximum lambda value in each leaf
representative_docs = {}
for topic in documents['Topic'].unique():
if topic != -1:
leaves = hdbscan.plots._recurse_leaf_dfs(cluster_tree, clusters[topic])
result = np.array([])
for leaf in leaves:
max_lambda = raw_tree['lambda_val'][raw_tree['parent'] == leaf].max()
points = raw_tree['child'][(raw_tree['parent'] == leaf) & (raw_tree['lambda_val'] == max_lambda)]
result = np.hstack((result, points))
representative_docs[topic] = list(np.random.choice(result, top_n_representative_docs, replace=False).astype(int))
# Convert indices to documents
self.representative_docs_ = {topic: [documents.iloc[doc_id].Document for doc_id in doc_ids]
for topic, doc_ids in
representative_docs.items()}
else:
documents_per_topic = documents.groupby('Topic').sample(n=500, replace=True, random_state=42).drop_duplicates()
self.representative_docs_ = {}
for topic in documents['Topic'].unique():
# Calculate similarity
selected_docs = documents_per_topic.loc[documents_per_topic.Topic == topic, "Document"].values
bow = self.vectorizer_model.transform(selected_docs)
ctfidf = self.ctfidf_model.transform(bow)
sim_matrix = cosine_similarity(ctfidf, self.c_tf_idf_[topic + self._outliers])
# Extract top 3 most representative documents
indices = np.argpartition(sim_matrix.reshape(1, -1)[0],
-top_n_representative_docs)[-top_n_representative_docs:]
self.representative_docs_[topic] = [selected_docs[index] for index in indices]
def _map_representative_docs(self, original_topics: bool = False):
""" Map the representative docs per topic to the correct topics
If topics were reduced, remove documents from topics that were
merged into larger topics as we assume that the documents from
larger topics are better representative of the entire merged
topic.
Arguments:
original_topics: Whether we want to map from the
original topics to the most recent topics
or from the second-most recent topics.
"""
mappings = self.topic_mapper_.get_mappings(original_topics)
if self.representative_docs_ is not None:
representative_docs = self.representative_docs_.copy()
else:
representative_docs = {}
# Update the representative documents
updated_representative_docs = {mappings[old_topic]: []
for old_topic, _ in representative_docs.items()}
for old_topic, docs in representative_docs.items():
new_topic = mappings[old_topic]
updated_representative_docs[new_topic].extend(docs)
self.representative_docs_ = updated_representative_docs
self.representative_docs_.pop(-1, None)
def _create_topic_vectors(self):
""" Creates embeddings per topics based on their topic representation
We start by creating embeddings out of the topic representation. This
results in a number of embeddings per topic. Then, we take the weighted
average of embeddings in a topic by their c-TF-IDF score. This will put
more emphasis to words that represent a topic best.
Only allow topic vectors to be created if there are no custom embeddings and therefore
a sentence-transformer model to be used or there are custom embeddings but it is allowed
to use a different multi-lingual sentence-transformer model
"""
if self.embedding_model is not None and type(self.embedding_model) is not BaseEmbedder:
topic_list = list(self.topic_representations_.keys())
topic_list.sort()
n = self.top_n_words
# Extract embeddings for all words in all topics
topic_words = [self.get_topic(topic) for topic in topic_list]
topic_words = [word[0] for topic in topic_words for word in topic]
embeddings = self._extract_embeddings(topic_words,
method="word",
verbose=False)
# Take the weighted average of word embeddings in a topic based on their c-TF-IDF value
# The embeddings var is a single numpy matrix and therefore slicing is necessary to
# access the words per topic
topic_embeddings = []
for i, topic in enumerate(topic_list):
word_importance = [val[1] for val in self.get_topic(topic)]
if sum(word_importance) == 0:
word_importance = [1 for _ in range(len(self.get_topic(topic)))]
topic_embedding = np.average(embeddings[i * n: n + (i * n)], weights=word_importance, axis=0)
topic_embeddings.append(topic_embedding)
self.topic_embeddings_ = topic_embeddings
def _c_tf_idf(self,
documents_per_topic: pd.DataFrame,
fit: bool = True,
partial_fit: bool = False) -> Tuple[csr_matrix, List[str]]:
""" Calculate a class-based TF-IDF where m is the number of total documents.
Arguments:
documents_per_topic: The joined documents per topic such that each topic has a single
string made out of multiple documents
m: The total number of documents (unjoined)
fit: Whether to fit a new vectorizer or use the fitted self.vectorizer_model
partial_fit: Whether to run `partial_fit` for online learning
Returns:
tf_idf: The resulting matrix giving a value (importance score) for each word per topic
words: The names of the words to which values were given
"""
documents = self._preprocess_text(documents_per_topic.Document.values)
if partial_fit:
X = self.vectorizer_model.partial_fit(documents).update_bow(documents)
elif fit:
self.vectorizer_model.fit(documents)
X = self.vectorizer_model.transform(documents)
else:
X = self.vectorizer_model.transform(documents)
# Scikit-Learn Deprecation: get_feature_names is deprecated in 1.0
# and will be removed in 1.2. Please use get_feature_names_out instead.
if version.parse(sklearn_version) >= version.parse("1.0.0"):
words = self.vectorizer_model.get_feature_names_out()
else:
words = self.vectorizer_model.get_feature_names()
if self.seed_topic_list:
seed_topic_list = [seed for seeds in self.seed_topic_list for seed in seeds]
multiplier = np.array([1.2 if word in seed_topic_list else 1 for word in words])
else:
multiplier = None
if fit:
self.ctfidf_model = self.ctfidf_model.fit(X, multiplier=multiplier)
c_tf_idf = self.ctfidf_model.transform(X)
return c_tf_idf, words
def _update_topic_size(self, documents: pd.DataFrame):
""" Calculate the topic sizes
Arguments:
documents: Updated dataframe with documents and their corresponding IDs and newly added Topics
"""
sizes = documents.groupby(['Topic']).count().sort_values("Document", ascending=False).reset_index()
self.topic_sizes_ = dict(zip(sizes.Topic, sizes.Document))
self.topics_ = documents.Topic.astype(int).tolist()
def _extract_words_per_topic(self,
words: List[str],
c_tf_idf: csr_matrix = None,
labels: List[int] = None) -> Mapping[str,
List[Tuple[str, float]]]:
""" Based on tf_idf scores per topic, extract the top n words per topic
If the top words per topic need to be extracted, then only the `words` parameter
needs to be passed. If the top words per topic in a specific timestamp, then it
is important to pass the timestamp-based c-TF-IDF matrix and its corresponding
labels.
Arguments:
words: List of all words (sorted according to tf_idf matrix position)
c_tf_idf: A c-TF-IDF matrix from which to calculate the top words
labels: A list of topic labels
Returns:
topics: The top words per topic
"""
if c_tf_idf is None:
c_tf_idf = self.c_tf_idf_
if labels is None:
labels = sorted(list(self.topic_sizes_.keys()))
# Get the top 30 indices and values per row in a sparse c-TF-IDF matrix
indices = self._top_n_idx_sparse(c_tf_idf, 30)
scores = self._top_n_values_sparse(c_tf_idf, indices)
sorted_indices = np.argsort(scores, 1)
indices = np.take_along_axis(indices, sorted_indices, axis=1)
scores = np.take_along_axis(scores, sorted_indices, axis=1)
# Get top 30 words per topic based on c-TF-IDF score
topics = {label: [(words[word_index], score)
if word_index is not None and score > 0
else ("", 0.00001)
for word_index, score in zip(indices[index][::-1], scores[index][::-1])
]
for index, label in enumerate(labels)}
# 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.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=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
def _reduce_topics(self, documents: pd.DataFrame) -> pd.DataFrame:
""" Reduce topics to self.nr_topics
Arguments:
documents: Dataframe with documents and their corresponding IDs and Topics
Returns:
documents: Updated dataframe with documents and the reduced number of Topics
"""
initial_nr_topics = len(self.get_topics())
if isinstance(self.nr_topics, int):
if self.nr_topics < initial_nr_topics:
documents = self._reduce_to_n_topics(documents)
elif isinstance(self.nr_topics, str):
documents = self._auto_reduce_topics(documents)
else:
raise ValueError("nr_topics needs to be an int or 'auto'! ")
logger.info(f"Reduced number of topics from {initial_nr_topics} to {len(self.get_topic_freq())}")
return documents
def _reduce_to_n_topics(self, documents: pd.DataFrame) -> pd.DataFrame:
""" Reduce topics to self.nr_topics
Arguments:
documents: Dataframe with documents and their corresponding IDs and Topics
Returns:
documents: Updated dataframe with documents and the reduced number of Topics
"""
# Track which topics where originally merged
if not self._merged_topics:
self._merged_topics = []
# Create topic similarity matrix
similarities = cosine_similarity(self.c_tf_idf_)
np.fill_diagonal(similarities, 0)
# Find most similar topic to least common topic
topics = documents.Topic.tolist().copy()
mapped_topics = {}
while len(self.get_topic_freq()) > self.nr_topics + self._outliers:
topic_to_merge = self.get_topic_freq().iloc[-1].Topic
topic_to_merge_into = np.argmax(similarities[topic_to_merge + self._outliers]) - self._outliers
similarities[:, topic_to_merge + self._outliers] = -self._outliers
self._merged_topics.append(topic_to_merge)
# Update Topic labels
documents.loc[documents.Topic == topic_to_merge, "Topic"] = topic_to_merge_into
mapped_topics[topic_to_merge] = topic_to_merge_into
self._update_topic_size(documents)
# Map topics
mapped_topics = {from_topic: to_topic for from_topic, to_topic in zip(topics, documents.Topic.tolist())}
self.topic_mapper_.add_mappings(mapped_topics)
# Update representations
documents = self._sort_mappings_by_frequency(documents)
self._extract_topics(documents)
self._update_topic_size(documents)
return documents
def _auto_reduce_topics(self, documents: pd.DataFrame) -> pd.DataFrame:
""" Reduce the number of topics automatically using HDBSCAN
Arguments:
documents: Dataframe with documents and their corresponding IDs and Topics
Returns:
documents: Updated dataframe with documents and the reduced number of Topics
"""
topics = documents.Topic.tolist().copy()
unique_topics = sorted(list(documents.Topic.unique()))[self._outliers:]
max_topic = unique_topics[-1]
# Find similar topics
if self.topic_embeddings_ is not None:
embeddings = np.array(self.topic_embeddings_)
else:
embeddings = self.c_tf_idf_.toarray()
norm_data = normalize(embeddings, norm='l2')
predictions = hdbscan.HDBSCAN(min_cluster_size=2,
metric='euclidean',
cluster_selection_method='eom',
prediction_data=True).fit_predict(norm_data[self._outliers:])
# Map similar topics
mapped_topics = {unique_topics[index]: prediction + max_topic
for index, prediction in enumerate(predictions)
if prediction != -1}
documents.Topic = documents.Topic.map(mapped_topics).fillna(documents.Topic).astype(int)
mapped_topics = {from_topic: to_topic for from_topic, to_topic in zip(topics, documents.Topic.tolist())}
# Update documents and topics
self.topic_mapper_.add_mappings(mapped_topics)
documents = self._sort_mappings_by_frequency(documents)
self._extract_topics(documents)
self._update_topic_size(documents)
return documents
def _sort_mappings_by_frequency(self, documents: pd.DataFrame) -> pd.DataFrame:
""" Reorder mappings by their frequency.
For example, if topic 88 was mapped to topic
5 and topic 5 turns out to be the largest topic,
then topic 5 will be topic 0. The second largest,
will be topic 1, etc.
If there are no mappings since no reduction of topics
took place, then the topics will simply be ordered
by their frequency and will get the topic ids based
on that order.
This means that -1 will remain the outlier class, and
that the rest of the topics will be in descending order
of ids and frequency.
Arguments:
documents: Dataframe with documents and their corresponding IDs and Topics
Returns:
documents: Updated dataframe with documents and the mapped
and re-ordered topic ids
"""
self._update_topic_size(documents)
# Map topics based on frequency
df = pd.DataFrame(self.topic_sizes_.items(), columns=["Old_Topic", "Size"]).sort_values("Size", ascending=False)
df = df[df.Old_Topic != -1]
sorted_topics = {**{-1: -1}, **dict(zip(df.Old_Topic, range(len(df))))}
self.topic_mapper_.add_mappings(sorted_topics)
# Map documents
documents.Topic = documents.Topic.map(sorted_topics).fillna(documents.Topic).astype(int)
self._update_topic_size(documents)
return documents
def _map_probabilities(self,
probabilities: Union[np.ndarray, None],
original_topics: bool = False) -> Union[np.ndarray, None]:
""" Map the probabilities to the reduced topics.
This is achieved by adding the probabilities together
of all topics that were mapped to the same topic. Then,
the topics that were mapped from were set to 0 as they
were reduced.
Arguments:
probabilities: An array containing probabilities
original_topics: Whether we want to map from the
original topics to the most recent topics
or from the second-most recent topics.
Returns:
mapped_probabilities: Updated probabilities
"""
mappings = self.topic_mapper_.get_mappings(original_topics)
# Map array of probabilities (probability for assigned topic per document)
if probabilities is not None:
if len(probabilities.shape) == 2 and self.get_topic(-1):
mapped_probabilities = np.zeros((probabilities.shape[0],
len(set(mappings.values())) - 1))
for from_topic, to_topic in mappings.items():
if to_topic != -1 and from_topic != -1:
mapped_probabilities[:, to_topic] += probabilities[:, from_topic]
return mapped_probabilities
return probabilities
def _preprocess_text(self, documents: np.ndarray) -> List[str]:
""" Basic preprocessing of text
Steps:
* Replace \n and \t with whitespace
* Only keep alpha-numerical characters
"""
cleaned_documents = [doc.replace("\n", " ") for doc in documents]
cleaned_documents = [doc.replace("\t", " ") for doc in cleaned_documents]
if self.language == "english":
cleaned_documents = [re.sub(r'[^A-Za-z0-9 ]+', '', doc) for doc in cleaned_documents]
cleaned_documents = [doc if doc != "" else "emptydoc" for doc in cleaned_documents]
return cleaned_documents
@staticmethod
def _top_n_idx_sparse(matrix: csr_matrix, n: int) -> np.ndarray:
""" Return indices of top n values in each row of a sparse matrix
Retrieved from:
https://stackoverflow.com/questions/49207275/finding-the-top-n-values-in-a-row-of-a-scipy-sparse-matrix
Arguments:
matrix: The sparse matrix from which to get the top n indices per row
n: The number of highest values to extract from each row
Returns:
indices: The top n indices per row
"""
indices = []
for le, ri in zip(matrix.indptr[:-1], matrix.indptr[1:]):
n_row_pick = min(n, ri - le)
values = matrix.indices[le + np.argpartition(matrix.data[le:ri], -n_row_pick)[-n_row_pick:]]
values = [values[index] if len(values) >= index + 1 else None for index in range(n)]
indices.append(values)
return np.array(indices)
@staticmethod
def _top_n_values_sparse(matrix: csr_matrix, indices: np.ndarray) -> np.ndarray:
""" Return the top n values for each row in a sparse matrix
Arguments:
matrix: The sparse matrix from which to get the top n indices per row
indices: The top n indices per row
Returns:
top_values: The top n scores per row
"""
top_values = []
for row, values in enumerate(indices):
scores = np.array([matrix[row, value] if value is not None else 0 for value in values])
top_values.append(scores)
return np.array(top_values)
@classmethod
def _get_param_names(cls):
"""Get parameter names for the estimator
Adapted from:
https://github.com/scikit-learn/scikit-learn/blob/b3ea3ed6a/sklearn/base.py#L178
"""
init_signature = inspect.signature(cls.__init__)
parameters = sorted([p.name for p in init_signature.parameters.values()
if p.name != 'self' and p.kind != p.VAR_KEYWORD])
return parameters
def __str__(self):
"""Get a string representation of the current object.
Returns:
str: Human readable representation of the most important model parameters.
The parameters that represent models are ignored due to their length.
"""
parameters = ""
for parameter, value in self.get_params().items():
value = str(value)
if "(" in value and value[0] != "(":
value = value.split("(")[0] + "(...)"
parameters += f"{parameter}={value}, "
return f"BERTopic({parameters[:-2]})"
__init__(self, language='english', top_n_words=10, n_gram_range=(1, 1), min_topic_size=10, nr_topics=None, low_memory=False, calculate_probabilities=False, diversity=None, seed_topic_list=None, embedding_model=None, umap_model=None, hdbscan_model=None, vectorizer_model=None, ctfidf_model=None, verbose=False)
special
¶
BERTopic initialization
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
language |
str |
The main language used in your documents. The default sentence-transformers
model for "english" is |
'english' |
top_n_words |
int |
The number of words per topic to extract. Setting this too high can negatively impact topic embeddings as topics are typically best represented by at most 10 words. |
10 |
n_gram_range |
Tuple[int, int] |
The n-gram range for the CountVectorizer. Advised to keep high values between 1 and 3. More would likely lead to memory issues. NOTE: This param will not be used if you pass in your own CountVectorizer. |
(1, 1) |
min_topic_size |
int |
The minimum size of the topic. Increasing this value will lead to a lower number of clusters/topics. NOTE: This param will not be used if you are not using HDBSCAN. |
10 |
nr_topics |
Union[int, str] |
Specifying the number of topics will reduce the initial 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 using HDBSCAN. |
None |
low_memory |
bool |
Sets UMAP low memory to True to make sure less memory is used. NOTE: This is only used in UMAP. For example, if you use PCA instead of UMAP this parameter will not be used. |
False |
calculate_probabilities |
bool |
Whether to calculate the probabilities of all topics
per document instead of the probability of the assigned
topic per document. This could slow down the extraction
of topics if you have many documents (> 100_000). Set this
only to True if you have a low amount of documents or if
you do not mind more computation time.
NOTE: If false you cannot use the corresponding
visualization method |
False |
diversity |
float |
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. |
None |
seed_topic_list |
List[List[str]] |
A list of seed words per topic to converge around |
None |
verbose |
bool |
Changes the verbosity of the model, Set to True if you want to track the stages of the model. |
False |
embedding_model |
Use a custom embedding model. The following backends are currently supported * SentenceTransformers * Flair * Spacy * Gensim * USE (TF-Hub) You can also pass in a string that points to one of the following sentence-transformers models: * https://www.sbert.net/docs/pretrained_models.html |
None |
|
umap_model |
UMAP |
Pass in a UMAP model to be used instead of the default.
NOTE: You can also pass in any dimensionality reduction algorithm as long
as it has |
None |
hdbscan_model |
HDBSCAN |
Pass in a hdbscan.HDBSCAN model to be used instead of the default
NOTE: You can also pass in any clustering algorithm as long as it has
|
None |
vectorizer_model |
CountVectorizer |
Pass in a custom |
None |
ctfidf_model |
TfidfTransformer |
Pass in a custom ClassTfidfTransformer instead of the default model. |
None |
Source code in bertopic\_bertopic.py
def __init__(self,
language: str = "english",
top_n_words: int = 10,
n_gram_range: Tuple[int, int] = (1, 1),
min_topic_size: int = 10,
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,
hdbscan_model: hdbscan.HDBSCAN = None,
vectorizer_model: CountVectorizer = None,
ctfidf_model: TfidfTransformer = None,
verbose: bool = False,
):
"""BERTopic initialization
Arguments:
language: The main language used in your documents. The default sentence-transformers
model for "english" is `all-MiniLM-L6-v2`. For a full overview of
supported languages see bertopic.backend.languages. Select
"multilingual" to load in the `paraphrase-multilingual-MiniLM-L12-v2`
sentence-tranformers model that supports 50+ languages.
top_n_words: The number of words per topic to extract. Setting this
too high can negatively impact topic embeddings as topics
are typically best represented by at most 10 words.
n_gram_range: The n-gram range for the CountVectorizer.
Advised to keep high values between 1 and 3.
More would likely lead to memory issues.
NOTE: This param will not be used if you pass in your own
CountVectorizer.
min_topic_size: The minimum size of the topic. Increasing this value will lead
to a lower number of clusters/topics.
NOTE: This param will not be used if you are not using HDBSCAN.
nr_topics: Specifying the number of topics will reduce the initial
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 using HDBSCAN.
low_memory: Sets UMAP low memory to True to make sure less memory is used.
NOTE: This is only used in UMAP. For example, if you use PCA instead of UMAP
this parameter will not be used.
calculate_probabilities: Whether to calculate the probabilities of all topics
per document instead of the probability of the assigned
topic per document. This could slow down the extraction
of topics if you have many documents (> 100_000). Set this
only to True if you have a low amount of documents or if
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.
embedding_model: Use a custom embedding model.
The following backends are currently supported
* SentenceTransformers
* Flair
* Spacy
* Gensim
* USE (TF-Hub)
You can also pass in a string that points to one of the following
sentence-transformers models:
* https://www.sbert.net/docs/pretrained_models.html
umap_model: Pass in a UMAP model to be used instead of the default.
NOTE: You can also pass in any dimensionality reduction algorithm as long
as it has `.fit` and `.transform` functions.
hdbscan_model: Pass in a hdbscan.HDBSCAN model to be used instead of the default
NOTE: You can also pass in any clustering algorithm as long as it has
`.fit` and `.predict` functions along with the `.labels_` variable.
vectorizer_model: Pass in a custom `CountVectorizer` instead of the default model.
ctfidf_model: Pass in a custom ClassTfidfTransformer instead of the default model.
"""
# Topic-based parameters
if top_n_words > 30:
raise ValueError("top_n_words should be lower or equal to 30. The preferred value is 10.")
self.top_n_words = top_n_words
self.min_topic_size = min_topic_size
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
# Embedding model
self.language = language if not embedding_model else None
self.embedding_model = embedding_model
# Vectorizer
self.n_gram_range = n_gram_range
self.vectorizer_model = vectorizer_model or CountVectorizer(ngram_range=self.n_gram_range)
self.ctfidf_model = ctfidf_model or ClassTfidfTransformer()
# UMAP or another algorithm that has .fit and .transform functions
self.umap_model = umap_model or UMAP(n_neighbors=15,
n_components=5,
min_dist=0.0,
metric='cosine',
low_memory=self.low_memory)
# HDBSCAN or another clustering algorithm that has .fit and .predict functions and
# the .labels_ variable to extract the labels
self.hdbscan_model = hdbscan_model or hdbscan.HDBSCAN(min_cluster_size=self.min_topic_size,
metric='euclidean',
cluster_selection_method='eom',
prediction_data=True)
# Public attributes
self.topics_ = None
self.probabilities_ = None
self.topic_sizes_ = None
self.topic_mapper_ = None
self.topic_representations_ = None
self.topic_embeddings_ = None
self.topic_labels_ = None
self.custom_labels_ = None
self.representative_docs_ = {}
self.c_tf_idf_ = None
# Private attributes for internal tracking purposes
self._outliers = 1
self._merged_topics = None
if verbose:
logger.set_level("DEBUG")
__str__(self)
special
¶
Get a string representation of the current object.
Returns:
| Type | Description |
|---|---|
str |
Human readable representation of the most important model parameters. The parameters that represent models are ignored due to their length. |
Source code in bertopic\_bertopic.py
def __str__(self):
"""Get a string representation of the current object.
Returns:
str: Human readable representation of the most important model parameters.
The parameters that represent models are ignored due to their length.
"""
parameters = ""
for parameter, value in self.get_params().items():
value = str(value)
if "(" in value and value[0] != "(":
value = value.split("(")[0] + "(...)"
parameters += f"{parameter}={value}, "
return f"BERTopic({parameters[:-2]})"
approximate_distribution(self, documents, window=4, stride=1, min_similarity=0.1, batch_size=1000, padding=False, use_embedding_model=False, calculate_tokens=False, separator=' ')
¶
A post-hoc approximation of topic distributions across documents.
In order to perform this approximation, each document is split into tokens
according to the provided tokenizer in the CountVectorizer. Then, a
sliding window is applied on each document creating subsets of the document.
For example, with a window size of 3 and stride of 1, the sentence:
Solving the right problem is difficult.
can be split up into solving the right, the right problem, right problem is,
and problem is difficult. These are called tokensets. For each of these
tokensets, we calculate their c-TF-IDF representation and find out
how similar they are to the previously generated topics. Then, the
similarities to the topics for each tokenset are summed in order to
create a topic distribution for the entire document.
We can also dive into this a bit deeper by then splitting these tokensets
up into individual tokens and calculate how much a word, in a specific sentence,
contributes to the topics found in that document. This can be enabled by
setting calculate_tokens=True which can be used for visualization purposes
in topic_model.visualize_approximate_distribution.
The main output, topic_distributions, can also be used directly in
.visualize_distribution(topic_distributions[index]) by simply selecting
a single distribution.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
Union[str, List[str]] |
A single document or a list of documents for which we approximate their topic distributions |
required |
window |
int |
Size of the moving window which indicates the number of tokens being considered. |
4 |
stride |
int |
How far the window should move at each step. |
1 |
min_similarity |
float |
The minimum similarity of a document's tokenset with respect to the topics. |
0.1 |
batch_size |
int |
The number of documents to process at a time. If None, then all documents are processed at once. NOTE: With a large number of documents, it is not advised to process all documents at once. |
1000 |
padding |
bool |
Whether to pad the beginning and ending of a document with empty tokens. |
False |
use_embedding_model |
bool |
Whether to use the topic model's embedding model to calculate the similarity between tokensets and topics instead of using c-TF-IDF. |
False |
calculate_tokens |
bool |
Calculate the similarity of tokens with all topics. NOTE: This is computation-wise more expensive and can require more memory. Using this over batches of documents might be preferred. |
False |
separator |
str |
The separator used to merge tokens into tokensets. |
' ' |
Returns:
| Type | Description |
|---|---|
topic_distributions |
A |
Examples:
After fitting the model, the topic distributions can be calculated regardless of the clustering model and regardless of whether the documents were previously seen or not:
topic_distr, _ = topic_model.approximate_distribution(docs)
As a result, the topic distributions are calculated in topic_distr for the
entire document based on token set with a specific window size and stride.
If you want to calculate the topic distributions on a token-level:
topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)
The topic_token_distr then contains, for each token, the best fitting topics.
As with topic_distr, it can contain multiple topics for a single token.
Source code in bertopic\_bertopic.py
def approximate_distribution(self,
documents: Union[str, List[str]],
window: int = 4,
stride: int = 1,
min_similarity: float = 0.1,
batch_size: int = 1000,
padding: bool = False,
use_embedding_model: bool = False,
calculate_tokens: bool = False,
separator: str = " ") -> Tuple[np.ndarray,
Union[List[np.ndarray], None]]:
""" A post-hoc approximation of topic distributions across documents.
In order to perform this approximation, each document is split into tokens
according to the provided tokenizer in the `CountVectorizer`. Then, a
sliding window is applied on each document creating subsets of the document.
For example, with a window size of 3 and stride of 1, the sentence:
`Solving the right problem is difficult.`
can be split up into `solving the right`, `the right problem`, `right problem is`,
and `problem is difficult`. These are called tokensets. For each of these
tokensets, we calculate their c-TF-IDF representation and find out
how similar they are to the previously generated topics. Then, the
similarities to the topics for each tokenset are summed in order to
create a topic distribution for the entire document.
We can also dive into this a bit deeper by then splitting these tokensets
up into individual tokens and calculate how much a word, in a specific sentence,
contributes to the topics found in that document. This can be enabled by
setting `calculate_tokens=True` which can be used for visualization purposes
in `topic_model.visualize_approximate_distribution`.
The main output, `topic_distributions`, can also be used directly in
`.visualize_distribution(topic_distributions[index])` by simply selecting
a single distribution.
Arguments:
documents: A single document or a list of documents for which we
approximate their topic distributions
window: Size of the moving window which indicates the number of
tokens being considered.
stride: How far the window should move at each step.
min_similarity: The minimum similarity of a document's tokenset
with respect to the topics.
batch_size: The number of documents to process at a time. If None,
then all documents are processed at once.
NOTE: With a large number of documents, it is not
advised to process all documents at once.
padding: Whether to pad the beginning and ending of a document with
empty tokens.
use_embedding_model: Whether to use the topic model's embedding
model to calculate the similarity between
tokensets and topics instead of using c-TF-IDF.
calculate_tokens: Calculate the similarity of tokens with all topics.
NOTE: This is computation-wise more expensive and
can require more memory. Using this over batches of
documents might be preferred.
separator: The separator used to merge tokens into tokensets.
Returns:
topic_distributions: A `n` x `m` matrix containing the topic distributions
for all input documents with `n` being the documents
and `m` the topics.
topic_token_distributions: A list of `t` x `m` arrays with `t` being the
number of tokens for the respective document
and `m` the topics.
Examples:
After fitting the model, the topic distributions can be calculated regardless
of the clustering model and regardless of whether the documents were previously
seen or not:
```python
topic_distr, _ = topic_model.approximate_distribution(docs)
```
As a result, the topic distributions are calculated in `topic_distr` for the
entire document based on token set with a specific window size and stride.
If you want to calculate the topic distributions on a token-level:
```python
topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)
```
The `topic_token_distr` then contains, for each token, the best fitting topics.
As with `topic_distr`, it can contain multiple topics for a single token.
"""
if isinstance(documents, str):
documents = [documents]
if batch_size is None:
batch_size = len(documents)
batches = 1
else:
batches = math.ceil(len(documents)/batch_size)
topic_distributions = []
topic_token_distributions = []
for i in tqdm(range(batches), disable=not self.verbose):
doc_set = documents[i*batch_size: (i+1) * batch_size]
# Extract tokens
analyzer = self.vectorizer_model.build_tokenizer()
tokens = [analyzer(document) for document in doc_set]
# Extract token sets
all_sentences = []
all_indices = [0]
all_token_sets_ids = []
for tokenset in tokens:
if len(tokenset) < window:
token_sets = [tokenset]
token_sets_ids = [list(range(len(tokenset)))]
else:
# Extract tokensets using window and stride parameters
stride_indices = list(range(len(tokenset)))[::stride]
token_sets = []
token_sets_ids = []
for stride_index in stride_indices:
selected_tokens = tokenset[stride_index: stride_index+window]
if padding or len(selected_tokens) == window:
token_sets.append(selected_tokens)
token_sets_ids.append(list(range(stride_index, stride_index+len(selected_tokens))))
# Add empty tokens at the beginning and end of a document
if padding:
padded = []
padded_ids = []
t = math.ceil(window / stride) - 1
for i in range(math.ceil(window / stride) - 1):
padded.append(tokenset[:window - ((t-i) * stride)])
padded_ids.append(list(range(0, window - ((t-i) * stride))))
token_sets = padded + token_sets
token_sets_ids = padded_ids + token_sets_ids
# Join the tokens
sentences = [separator.join(token) for token in token_sets]
all_sentences.extend(sentences)
all_token_sets_ids.extend(token_sets_ids)
all_indices.append(all_indices[-1] + len(sentences))
# Calculate similarity between embeddings of token sets and the topics
if use_embedding_model:
embeddings = self._extract_embeddings(all_sentences, method="document", verbose=True)
similarity = cosine_similarity(embeddings, self.topic_embeddings_[self._outliers:])
# Calculate similarity between c-TF-IDF of token sets and the topics
else:
bow_doc = self.vectorizer_model.transform(all_sentences)
c_tf_idf_doc = self.ctfidf_model.transform(bow_doc)
similarity = cosine_similarity(c_tf_idf_doc, self.c_tf_idf_[self._outliers:])
# Only keep similarities that exceed the minimum
similarity[similarity < min_similarity] = 0
# Aggregate results on an individual token level
if calculate_tokens:
topic_distribution = []
topic_token_distribution = []
for index, token in enumerate(tokens):
start = all_indices[index]
end = all_indices[index+1]
if start == end:
end = end + 1
# Assign topics to individual tokens
token_id = [i for i in range(len(token))]
token_val = {index: [] for index in token_id}
for sim, token_set in zip(similarity[start:end], all_token_sets_ids[start:end]):
for token in token_set:
if token in token_val:
token_val[token].append(sim)
matrix = []
for _, value in token_val.items():
matrix.append(np.add.reduce(value))
# Take empty documents into account
matrix = np.array(matrix)
if len(matrix.shape) == 1:
matrix = np.zeros((1, len(self.topic_labels_) - self._outliers))
topic_token_distribution.append(np.array(matrix))
topic_distribution.append(np.add.reduce(matrix))
topic_distribution = normalize(topic_distribution, norm='l1', axis=1)
# Aggregate on a tokenset level indicated by the window and stride
else:
topic_distribution = []
for index in range(len(all_indices)-1):
start = all_indices[index]
end = all_indices[index+1]
if start == end:
end = end + 1
group = similarity[start:end].sum(axis=0)
topic_distribution.append(group)
topic_distribution = normalize(np.array(topic_distribution), norm='l1', axis=1)
topic_token_distribution = None
# Combine results
topic_distributions.append(topic_distribution)
if topic_token_distribution is None:
topic_token_distributions = None
else:
topic_token_distributions.extend(topic_token_distribution)
topic_distributions = np.vstack(topic_distributions)
return topic_distributions, topic_token_distributions
find_topics(self, search_term, top_n=5)
¶
Find topics most similar to a search_term
Creates an embedding for search_term and compares that with the topic embeddings. The most similar topics are returned along with their similarity values.
The search_term can be of any size but since it compares with the topic representation it is advised to keep it below 5 words.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
search_term |
str |
the term you want to use to search for topics |
required |
top_n |
int |
the number of topics to return |
5 |
Returns:
| Type | Description |
|---|---|
similar_topics |
the most similar topics from high to low similarity: the similarity scores from high to low |
Examples:
You can use the underlying embedding model to find topics that best represent the search term:
topics, similarity = topic_model.find_topics("sports", top_n=5)
Note that the search query is typically more accurate if the search_term consists of a phrase or multiple words.
Source code in bertopic\_bertopic.py
def find_topics(self,
search_term: str,
top_n: int = 5) -> Tuple[List[int], List[float]]:
""" Find topics most similar to a search_term
Creates an embedding for search_term and compares that with
the topic embeddings. The most similar topics are returned
along with their similarity values.
The search_term can be of any size but since it compares
with the topic representation it is advised to keep it
below 5 words.
Arguments:
search_term: the term you want to use to search for topics
top_n: the number of topics to return
Returns:
similar_topics: the most similar topics from high to low
similarity: the similarity scores from high to low
Examples:
You can use the underlying embedding model to find topics that
best represent the search term:
```python
topics, similarity = topic_model.find_topics("sports", top_n=5)
```
Note that the search query is typically more accurate if the
search_term consists of a phrase or multiple words.
"""
if self.embedding_model is None:
raise Exception("This method can only be used if you did not use custom embeddings.")
topic_list = list(self.topic_representations_.keys())
topic_list.sort()
# Extract search_term embeddings and compare with topic embeddings
search_embedding = self._extract_embeddings([search_term],
method="word",
verbose=False).flatten()
sims = cosine_similarity(search_embedding.reshape(1, -1), self.topic_embeddings_).flatten()
# Extract topics most similar to search_term
ids = np.argsort(sims)[-top_n:]
similarity = [sims[i] for i in ids][::-1]
similar_topics = [topic_list[index] for index in ids][::-1]
return similar_topics, similarity
fit(self, documents, embeddings=None, y=None)
¶
Fit the models (Bert, UMAP, and, HDBSCAN) on a collection of documents and generate topics
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
List[str] |
A list of documents to fit on |
required |
embeddings |
ndarray |
Pre-trained document embeddings. These can be used instead of the sentence-transformer model |
None |
y |
Union[List[int], numpy.ndarray] |
The target class for (semi)-supervised modeling. Use -1 if no class for a specific instance is specified. |
None |
Examples:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
If you want to use your own embeddings, use it as follows:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
Source code in bertopic\_bertopic.py
def fit(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None):
""" Fit the models (Bert, UMAP, and, HDBSCAN) on a collection of documents and generate topics
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
```
If you want to use your own embeddings, use it as follows:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
```
"""
self.fit_transform(documents, embeddings, y)
return self
fit_transform(self, documents, embeddings=None, y=None)
¶
Fit the models on a collection of documents, generate topics, and return the docs with topics
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
List[str] |
A list of documents to fit on |
required |
embeddings |
ndarray |
Pre-trained document embeddings. These can be used instead of the sentence-transformer model |
None |
y |
Union[List[int], numpy.ndarray] |
The target class for (semi)-supervised modeling. Use -1 if no class for a specific instance is specified. |
None |
Returns:
| Type | Description |
|---|---|
predictions |
Topic predictions for each documents
probabilities: The probability of the assigned topic per document.
If |
Examples:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
If you want to use your own embeddings, use it as follows:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs, embeddings)
Source code in bertopic\_bertopic.py
def fit_transform(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None) -> Tuple[List[int],
Union[np.ndarray, None]]:
""" Fit the models on a collection of documents, generate topics, and return the docs with topics
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Returns:
predictions: Topic predictions for each documents
probabilities: The probability of the assigned topic per document.
If `calculate_probabilities` in BERTopic is set to True, then
it calculates the probabilities of all topics across all documents
instead of only the assigned topic. This, however, slows down
computation and may increase memory usage.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
```
If you want to use your own embeddings, use it as follows:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs, embeddings)
```
"""
check_documents_type(documents)
check_embeddings_shape(embeddings, documents)
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": None})
# Extract embeddings
if embeddings is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
embeddings = self._extract_embeddings(documents.Document,
method="document",
verbose=self.verbose)
logger.info("Transformed documents to Embeddings")
else:
if self.embedding_model is not None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
# Reduce dimensionality
if self.seed_topic_list is not None and self.embedding_model is not None:
y, embeddings = self._guided_topic_modeling(embeddings)
umap_embeddings = self._reduce_dimensionality(embeddings, y)
# Cluster reduced embeddings
documents, probabilities = self._cluster_embeddings(umap_embeddings, documents, y=y)
# Sort and Map Topic IDs by their frequency
if not self.nr_topics:
documents = self._sort_mappings_by_frequency(documents)
# Extract topics by calculating c-TF-IDF
self._extract_topics(documents)
# Reduce topics
if self.nr_topics:
documents = self._reduce_topics(documents)
if isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
self._map_representative_docs(original_topics=True)
else:
self._save_representative_docs(documents)
self.probabilities_ = self._map_probabilities(probabilities, original_topics=True)
predictions = documents.Topic.to_list()
return predictions, self.probabilities_
generate_topic_labels(self, nr_words=3, topic_prefix=True, word_length=None, separator='_')
¶
Get labels for each topic in a user-defined format
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
original_labels |
required | ||
nr_words |
int |
Top |
3 |
topic_prefix |
bool |
Whether to use the topic ID as a prefix.
If set to True, the topic ID will be separated
using the |
True |
word_length |
int |
The maximum length of each word in the topic label. Some words might be relatively long and setting this value helps to make sure that all labels have relatively similar lengths. |
None |
separator |
str |
The string with which the words and topic prefix will be
separated. Underscores are the default but a nice alternative
is |
'_' |
Returns:
| Type | Description |
|---|---|
topic_labels |
A list of topic labels sorted from the lowest topic ID to the highest. If the topic model was trained using HDBSCAN, the lowest topic ID is -1, otherwise it is 0. |
Examples:
To create our custom topic labels, usage is rather straightforward:
topic_labels = topic_model.get_topic_labels(nr_words=2, separator=", ")
Source code in bertopic\_bertopic.py
def generate_topic_labels(self,
nr_words: int = 3,
topic_prefix: bool = True,
word_length: int = None,
separator: str = "_") -> List[str]:
""" Get labels for each topic in a user-defined format
Arguments:
original_labels:
nr_words: Top `n` words per topic to use
topic_prefix: Whether to use the topic ID as a prefix.
If set to True, the topic ID will be separated
using the `separator`
word_length: The maximum length of each word in the topic label.
Some words might be relatively long and setting this
value helps to make sure that all labels have relatively
similar lengths.
separator: The string with which the words and topic prefix will be
separated. Underscores are the default but a nice alternative
is `", "`.
Returns:
topic_labels: A list of topic labels sorted from the lowest topic ID to the highest.
If the topic model was trained using HDBSCAN, the lowest topic ID is -1,
otherwise it is 0.
Examples:
To create our custom topic labels, usage is rather straightforward:
```python
topic_labels = topic_model.get_topic_labels(nr_words=2, separator=", ")
```
"""
unique_topics = sorted(set(self.topics_))
topic_labels = []
for topic in unique_topics:
words, _ = zip(*self.get_topic(topic))
if word_length:
words = [word[:word_length] for word in words][:nr_words]
else:
words = list(words)[:nr_words]
if topic_prefix:
topic_label = f"{topic}{separator}" + separator.join(words)
else:
topic_label = separator.join(words)
topic_labels.append(topic_label)
return topic_labels
get_document_info(self, docs, df=None, metadata=None)
¶
Get information about the documents on which the topic was trained including the documents themselves, their respective topics, the name of each topic, the top n words of each topic, whether it is a representative document, and probability of the clustering if the cluster model supports it.
There are also options to include other meta data, such as the topic distributions or the x and y coordinates of the reduced embeddings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents on which the topic model was trained. |
required |
df |
DataFrame |
A dataframe containing the metadata and the documents on which the topic model was originally trained on. |
None |
metadata |
Mapping[str, Any] |
A dictionary with meta data for each document in the form of column name (key) and the respective values (value). |
None |
Returns:
| Type | Description |
|---|---|
document_info |
A dataframe with several statistics regarding the documents on which the topic model was trained. |
Usage:
To get the document info, you will only need to pass the documents on which the topic model was trained:
document_info = topic_model.get_document_info(docs)
There are additionally options to include meta data, such as the topic distributions. Moreover, we can pass the original dataframe that contains the documents and extend it with the information retrieved from BERTopic:
from sklearn.datasets import fetch_20newsgroups
# The original data in a dataframe format to include the target variable
data= fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
df = pd.DataFrame({"Document": data['data'], "Class": data['target']})
# Add information about the percentage of the document that relates to the topic
topic_distr, _ = topic_model.approximate_distribution(docs, batch_size=1000)
distributions = [distr[topic] if topic != -1 else 0 for topic, distr in zip(topics, topic_distr)]
# Create our documents dataframe using the original dataframe and meta data about
# the topic distributions
document_info = topic_model.get_document_info(docs, df=df,
metadata={"Topic_distribution": distributions})
Source code in bertopic\_bertopic.py
def get_document_info(self,
docs: List[str],
df: pd.DataFrame = None,
metadata: Mapping[str, Any] = None) -> pd.DataFrame:
""" Get information about the documents on which the topic was trained
including the documents themselves, their respective topics, the name
of each topic, the top n words of each topic, whether it is a
representative document, and probability of the clustering if the cluster
model supports it.
There are also options to include other meta data, such as the topic
distributions or the x and y coordinates of the reduced embeddings.
Arguments:
docs: The documents on which the topic model was trained.
df: A dataframe containing the metadata and the documents on which
the topic model was originally trained on.
metadata: A dictionary with meta data for each document in the form
of column name (key) and the respective values (value).
Returns:
document_info: A dataframe with several statistics regarding
the documents on which the topic model was trained.
Usage:
To get the document info, you will only need to pass the documents on which
the topic model was trained:
```python
document_info = topic_model.get_document_info(docs)
```
There are additionally options to include meta data, such as the topic
distributions. Moreover, we can pass the original dataframe that contains
the documents and extend it with the information retrieved from BERTopic:
```python
from sklearn.datasets import fetch_20newsgroups
# The original data in a dataframe format to include the target variable
data= fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
df = pd.DataFrame({"Document": data['data'], "Class": data['target']})
# Add information about the percentage of the document that relates to the topic
topic_distr, _ = topic_model.approximate_distribution(docs, batch_size=1000)
distributions = [distr[topic] if topic != -1 else 0 for topic, distr in zip(topics, topic_distr)]
# Create our documents dataframe using the original dataframe and meta data about
# the topic distributions
document_info = topic_model.get_document_info(docs, df=df,
metadata={"Topic_distribution": distributions})
```
"""
if df is not None:
document_info = df.copy()
document_info["Document"] = docs
document_info["Topic"] = self.topics_
else:
document_info = pd.DataFrame({"Document": docs, "Topic": self.topics_})
# Add topic info through `.get_topic_info()`
topic_info = self.get_topic_info().drop("Count", axis=1)
document_info = pd.merge(document_info, topic_info, on="Topic", how="left")
# Add top n words
top_n_words = {topic: " - ".join(list(zip(*self.get_topic(topic)))[0]) for topic in set(self.topics_)}
document_info["Top_n_words"] = document_info.Topic.map(top_n_words)
# Add flat probabilities
if self.probabilities_ is not None:
if len(self.probabilities_.shape) == 1:
document_info["Probability"] = self.probabilities_
else:
document_info["Probability"] = [max(probs) if topic != -1 else 1-sum(probs)
for topic, probs in zip(self.topics_, self.probabilities_)]
# Add representative document labels
repr_docs = [repr_doc for repr_docs in self.representative_docs_.values() for repr_doc in repr_docs]
document_info["Representative_document"] = False
document_info.loc[document_info.Document.isin(repr_docs), "Representative_document"] = True
# Add custom meta data provided by the user
if metadata is not None:
for column, values in metadata.items():
document_info[column] = values
return document_info
get_params(self, deep=False)
¶
Get parameters for this estimator.
Adapted from: https://github.com/scikit-learn/scikit-learn/blob/b3ea3ed6a/sklearn/base.py#L178
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
deep |
bool |
bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. |
False |
Returns:
| Type | Description |
|---|---|
out |
Parameter names mapped to their values. |
Source code in bertopic\_bertopic.py
def get_params(self, deep: bool = False) -> Mapping[str, Any]:
""" Get parameters for this estimator.
Adapted from:
https://github.com/scikit-learn/scikit-learn/blob/b3ea3ed6a/sklearn/base.py#L178
Arguments:
deep: bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns:
out: Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
get_representative_docs(self, topic=None)
¶
Extract the best representing documents per topic.
Note
This does not extract all documents per topic as all documents are not saved within BERTopic. To get all documents, please run the following:
# When you used `.fit_transform`:
df = pd.DataFrame({"Document": docs, "Topic": topic})
# When you used `.fit`:
df = pd.DataFrame({"Document": docs, "Topic": topic_model.topics_})
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic |
int |
A specific topic for which you want the representative documents |
None |
Returns:
| Type | Description |
|---|---|
List[str] |
Representative documents of the chosen topic |
Examples:
To extract the representative docs of all topics:
representative_docs = topic_model.get_representative_docs()
To get the representative docs of a single topic:
representative_docs = topic_model.get_representative_docs(12)
Source code in bertopic\_bertopic.py
def get_representative_docs(self, topic: int = None) -> List[str]:
""" Extract the best representing documents per topic.
NOTE:
This does not extract all documents per topic as all documents
are not saved within BERTopic. To get all documents, please
run the following:
```python
# When you used `.fit_transform`:
df = pd.DataFrame({"Document": docs, "Topic": topic})
# When you used `.fit`:
df = pd.DataFrame({"Document": docs, "Topic": topic_model.topics_})
```
Arguments:
topic: A specific topic for which you want
the representative documents
Returns:
Representative documents of the chosen topic
Examples:
To extract the representative docs of all topics:
```python
representative_docs = topic_model.get_representative_docs()
```
To get the representative docs of a single topic:
```python
representative_docs = topic_model.get_representative_docs(12)
```
"""
check_is_fitted(self)
if isinstance(topic, int):
if self.representative_docs_.get(topic):
return self.representative_docs_[topic]
else:
return None
else:
return self.representative_docs_
get_topic(self, topic)
¶
Return top n words for a specific topic and their c-TF-IDF scores
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic |
int |
A specific topic for which you want its representation |
required |
Returns:
| Type | Description |
|---|---|
Union[Mapping[str, Tuple[str, float]], bool] |
The top n words for a specific word and its respective c-TF-IDF scores |
Examples:
topic = topic_model.get_topic(12)
Source code in bertopic\_bertopic.py
def get_topic(self, topic: int) -> Union[Mapping[str, Tuple[str, float]], bool]:
""" Return top n words for a specific topic and their c-TF-IDF scores
Arguments:
topic: A specific topic for which you want its representation
Returns:
The top n words for a specific word and its respective c-TF-IDF scores
Examples:
```python
topic = topic_model.get_topic(12)
```
"""
check_is_fitted(self)
if topic in self.topic_representations_:
return self.topic_representations_[topic]
else:
return False
get_topic_freq(self, topic=None)
¶
Return the the size of topics (descending order)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic |
int |
A specific topic for which you want the frequency |
None |
Returns:
| Type | Description |
|---|---|
Union[pandas.core.frame.DataFrame, int] |
Either the frequency of a single topic or dataframe with the frequencies of all topics |
Examples:
To extract the frequency of all topics:
frequency = topic_model.get_topic_freq()
To get the frequency of a single topic:
frequency = topic_model.get_topic_freq(12)
Source code in bertopic\_bertopic.py
def get_topic_freq(self, topic: int = None) -> Union[pd.DataFrame, int]:
""" Return the the size of topics (descending order)
Arguments:
topic: A specific topic for which you want the frequency
Returns:
Either the frequency of a single topic or dataframe with
the frequencies of all topics
Examples:
To extract the frequency of all topics:
```python
frequency = topic_model.get_topic_freq()
```
To get the frequency of a single topic:
```python
frequency = topic_model.get_topic_freq(12)
```
"""
check_is_fitted(self)
if isinstance(topic, int):
return self.topic_sizes_[topic]
else:
return pd.DataFrame(self.topic_sizes_.items(), columns=['Topic', 'Count']).sort_values("Count",
ascending=False)
get_topic_info(self, topic=None)
¶
Get information about each topic including its ID, frequency, and name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic |
int |
A specific topic for which you want the frequency |
None |
Returns:
| Type | Description |
|---|---|
info |
The information relating to either a single topic or all topics |
Examples:
info_df = topic_model.get_topic_info()
Source code in bertopic\_bertopic.py
def get_topic_info(self, topic: int = None) -> pd.DataFrame:
""" Get information about each topic including its ID, frequency, and name.
Arguments:
topic: A specific topic for which you want the frequency
Returns:
info: The information relating to either a single topic or all topics
Examples:
```python
info_df = topic_model.get_topic_info()
```
"""
check_is_fitted(self)
info = pd.DataFrame(self.topic_sizes_.items(), columns=["Topic", "Count"]).sort_values("Topic")
info["Name"] = info.Topic.map(self.topic_labels_)
if self.custom_labels_ is not None:
if len(self.custom_labels_) == len(info):
labels = {topic - self._outliers: label for topic, label in enumerate(self.custom_labels_)}
info["CustomName"] = info["Topic"].map(labels)
if topic is not None:
info = info.loc[info.Topic == topic, :]
return info.reset_index(drop=True)
get_topic_tree(hier_topics, max_distance=None, tight_layout=False)
staticmethod
¶
Extract the topic tree such that it can be printed
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
hier_topics |
DataFrame |
A dataframe containing the structure of the topic tree.
This is the output of |
required |
max_distance |
float |
The maximum distance between two topics. This value is
based on the Distance column in |
None |
tight_layout |
bool |
Whether to use a tight layout (narrow width) for easier readability if you have hundreds of topics. |
False |
Returns:
| Type | Description |
|---|---|
A tree that has the following structure when printed |
. . └─health_medical_disease_patients_hiv ├─patients_medical_disease_candida_health │ ├─■──candida_yeast_infection_gonorrhea_infections ── Topic: 48 │ └─patients_disease_cancer_medical_doctor │ ├─■──hiv_medical_cancer_patients_doctor ── Topic: 34 │ └─■──pain_drug_patients_disease_diet ── Topic: 26 └─■──health_newsgroup_tobacco_vote_votes ── Topic: 9 The blocks (■) indicate that the topic is one you can directly access
from |
Examples:
# Train model
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Print topic tree
tree = topic_model.get_topic_tree(hierarchical_topics)
print(tree)
Source code in bertopic\_bertopic.py
@staticmethod
def get_topic_tree(hier_topics: pd.DataFrame,
max_distance: float = None,
tight_layout: bool = False) -> str:
""" Extract the topic tree such that it can be printed
Arguments:
hier_topics: A dataframe containing the structure of the topic tree.
This is the output of `topic_model.hierachical_topics()`
max_distance: The maximum distance between two topics. This value is
based on the Distance column in `hier_topics`.
tight_layout: Whether to use a tight layout (narrow width) for
easier readability if you have hundreds of topics.
Returns:
A tree that has the following structure when printed:
.
.
└─health_medical_disease_patients_hiv
├─patients_medical_disease_candida_health
│ ├─■──candida_yeast_infection_gonorrhea_infections ── Topic: 48
│ └─patients_disease_cancer_medical_doctor
│ ├─■──hiv_medical_cancer_patients_doctor ── Topic: 34
│ └─■──pain_drug_patients_disease_diet ── Topic: 26
└─■──health_newsgroup_tobacco_vote_votes ── Topic: 9
The blocks (■) indicate that the topic is one you can directly access
from `topic_model.get_topic`. In other words, they are the original un-grouped topics.
Examples:
```python
# Train model
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Print topic tree
tree = topic_model.get_topic_tree(hierarchical_topics)
print(tree)
```
"""
width = 1 if tight_layout else 4
if max_distance is None:
max_distance = hier_topics.Distance.max() + 1
max_original_topic = hier_topics.Parent_ID.astype(int).min() - 1
# Extract mapping from ID to name
topic_to_name = dict(zip(hier_topics.Child_Left_ID, hier_topics.Child_Left_Name))
topic_to_name.update(dict(zip(hier_topics.Child_Right_ID, hier_topics.Child_Right_Name)))
topic_to_name = {topic: name[:100] for topic, name in topic_to_name.items()}
# Create tree
tree = {str(row[1].Parent_ID): [str(row[1].Child_Left_ID), str(row[1].Child_Right_ID)]
for row in hier_topics.iterrows()}
def get_tree(start, tree):
""" Based on: https://stackoverflow.com/a/51920869/10532563 """
def _tree(to_print, start, parent, tree, grandpa=None, indent=""):
# Get distance between merged topics
distance = hier_topics.loc[(hier_topics.Child_Left_ID == parent) |
(hier_topics.Child_Right_ID == parent), "Distance"]
distance = distance.values[0] if len(distance) > 0 else 10
if parent != start:
if grandpa is None:
to_print += topic_to_name[parent]
else:
if int(parent) <= max_original_topic:
# Do not append topic ID if they are not merged
if distance < max_distance:
to_print += "■──" + topic_to_name[parent] + f" ── Topic: {parent}" + "\n"
else:
to_print += "O \n"
else:
to_print += topic_to_name[parent] + "\n"
if parent not in tree:
return to_print
for child in tree[parent][:-1]:
to_print += indent + "├" + "─"
to_print = _tree(to_print, start, child, tree, parent, indent + "│" + " " * width)
child = tree[parent][-1]
to_print += indent + "└" + "─"
to_print = _tree(to_print, start, child, tree, parent, indent + " " * (width+1))
return to_print
to_print = "." + "\n"
to_print = _tree(to_print, start, start, tree)
return to_print
start = str(hier_topics.Parent_ID.astype(int).max())
return get_tree(start, tree)
get_topics(self)
¶
Return topics with top n words and their c-TF-IDF score
Returns:
| Type | Description |
|---|---|
self.topic_representations_ |
The top n words per topic and the corresponding c-TF-IDF score |
Examples:
all_topics = topic_model.get_topics()
Source code in bertopic\_bertopic.py
def get_topics(self) -> Mapping[str, Tuple[str, float]]:
""" Return topics with top n words and their c-TF-IDF score
Returns:
self.topic_representations_: The top n words per topic and the corresponding c-TF-IDF score
Examples:
```python
all_topics = topic_model.get_topics()
```
"""
check_is_fitted(self)
return self.topic_representations_
hierarchical_topics(self, docs, linkage_function=None, distance_function=None)
¶
Create a hierarchy of topics
To create this hierarchy, BERTopic needs to be already fitted once.
Then, a hierarchy is calculated on the distance matrix of the c-TF-IDF
representation using scipy.cluster.hierarchy.linkage.
Based on that hierarchy, we calculate the topic representation at each merged step. This is a local representation, as we only assume that the chosen step is merged and not all others which typically improves the topic representation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[int] |
The documents you used when calling either |
required |
linkage_function |
Callable[[scipy.sparse.csr.csr_matrix], numpy.ndarray] |
The linkage function to use. Default is:
|
None |
distance_function |
Callable[[scipy.sparse.csr.csr_matrix], scipy.sparse.csr.csr_matrix] |
The distance function to use on the c-TF-IDF matrix. Default is:
|
None |
Returns:
| Type | Description |
|---|---|
hierarchical_topics |
A dataframe that contains a hierarchy of topics represented by their parents and their children |
Examples:
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
A custom linkage function can be used as follows:
from scipy.cluster import hierarchy as sch
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
# Hierarchical topics
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
hierarchical_topics = topic_model.hierarchical_topics(docs, linkage_function=linkage_function)
Source code in bertopic\_bertopic.py
def hierarchical_topics(self,
docs: List[int],
linkage_function: Callable[[csr_matrix], np.ndarray] = None,
distance_function: Callable[[csr_matrix], csr_matrix] = None) -> pd.DataFrame:
""" Create a hierarchy of topics
To create this hierarchy, BERTopic needs to be already fitted once.
Then, a hierarchy is calculated on the distance matrix of the c-TF-IDF
representation using `scipy.cluster.hierarchy.linkage`.
Based on that hierarchy, we calculate the topic representation at each
merged step. This is a local representation, as we only assume that the
chosen step is merged and not all others which typically improves the
topic representation.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
linkage_function: The linkage function to use. Default is:
`lambda x: sch.linkage(x, 'ward', optimal_ordering=True)`
distance_function: The distance function to use on the c-TF-IDF matrix. Default is:
`lambda x: 1 - cosine_similarity(x)`
Returns:
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children
Examples:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
hierarchical_topics = topic_model.hierarchical_topics(docs)
```
A custom linkage function can be used as follows:
```python
from scipy.cluster import hierarchy as sch
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
# Hierarchical topics
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
hierarchical_topics = topic_model.hierarchical_topics(docs, linkage_function=linkage_function)
```
"""
if distance_function is None:
distance_function = lambda x: 1 - cosine_similarity(x)
if linkage_function is None:
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
# Calculate distance
embeddings = self.c_tf_idf_[self._outliers:]
X = distance_function(embeddings)
# Make sure it is the 1-D condensed distance matrix with zeros on the diagonal
np.fill_diagonal(X, 0)
X = squareform(X)
# Use the 1-D condensed distance matrix as an input instead of the raw distance matrix
Z = linkage_function(X)
# Calculate basic bag-of-words to be iteratively merged later
documents = pd.DataFrame({"Document": docs,
"ID": range(len(docs)),
"Topic": self.topics_})
documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
documents_per_topic = documents_per_topic.loc[documents_per_topic.Topic != -1, :]
documents = self._preprocess_text(documents_per_topic.Document.values)
# Scikit-Learn Deprecation: get_feature_names is deprecated in 1.0
# and will be removed in 1.2. Please use get_feature_names_out instead.
if version.parse(sklearn_version) >= version.parse("1.0.0"):
words = self.vectorizer_model.get_feature_names_out()
else:
words = self.vectorizer_model.get_feature_names()
bow = self.vectorizer_model.transform(documents)
# Extract clusters
hier_topics = pd.DataFrame(columns=["Parent_ID", "Parent_Name", "Topics",
"Child_Left_ID", "Child_Left_Name",
"Child_Right_ID", "Child_Right_Name"])
for index in tqdm(range(len(Z))):
# Find clustered documents
clusters = sch.fcluster(Z, t=Z[index][2], criterion='distance') - self._outliers
cluster_df = pd.DataFrame({"Topic": range(len(clusters)), "Cluster": clusters})
cluster_df = cluster_df.groupby("Cluster").agg({'Topic': lambda x: list(x)}).reset_index()
nr_clusters = len(clusters)
# Extract first topic we find to get the set of topics in a merged topic
topic = None
val = Z[index][0]
while topic is None:
if val - len(clusters) < 0:
topic = int(val)
else:
val = Z[int(val - len(clusters))][0]
clustered_topics = [i for i, x in enumerate(clusters) if x == clusters[topic]]
# Group bow per cluster, calculate c-TF-IDF and extract words
grouped = csr_matrix(bow[clustered_topics].sum(axis=0))
c_tf_idf = self.ctfidf_model.transform(grouped)
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels=[0])
# Extract parent's name and ID
parent_id = index + len(clusters)
parent_name = "_".join([x[0] for x in words_per_topic[0]][:5])
# Extract child's name and ID
Z_id = Z[index][0]
child_left_id = Z_id if Z_id - nr_clusters < 0 else Z_id - nr_clusters
if Z_id - nr_clusters < 0:
child_left_name = "_".join([x[0] for x in self.get_topic(Z_id)][:5])
else:
child_left_name = hier_topics.iloc[int(child_left_id)].Parent_Name
# Extract child's name and ID
Z_id = Z[index][1]
child_right_id = Z_id if Z_id - nr_clusters < 0 else Z_id - nr_clusters
if Z_id - nr_clusters < 0:
child_right_name = "_".join([x[0] for x in self.get_topic(Z_id)][:5])
else:
child_right_name = hier_topics.iloc[int(child_right_id)].Parent_Name
# Save results
hier_topics.loc[len(hier_topics), :] = [parent_id, parent_name,
clustered_topics,
int(Z[index][0]), child_left_name,
int(Z[index][1]), child_right_name]
hier_topics["Distance"] = Z[:, 2]
hier_topics = hier_topics.sort_values("Parent_ID", ascending=False)
hier_topics[["Parent_ID", "Child_Left_ID", "Child_Right_ID"]] = hier_topics[["Parent_ID", "Child_Left_ID", "Child_Right_ID"]].astype(str)
return hier_topics
load(path, embedding_model=None)
classmethod
¶
Loads the model from the specified path
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
str |
the location and name of the BERTopic file you want to load |
required |
embedding_model |
If the embedding_model was not saved to save space or to load it in from the cloud, you can load it in by specifying it here. |
None |
Examples:
BERTopic.load("my_model")
or if you did not save the embedding model:
BERTopic.load("my_model", embedding_model="all-MiniLM-L6-v2")
Source code in bertopic\_bertopic.py
@classmethod
def load(cls,
path: str,
embedding_model=None):
""" Loads the model from the specified path
Arguments:
path: the location and name of the BERTopic file you want to load
embedding_model: If the embedding_model was not saved to save space or to load
it in from the cloud, you can load it in by specifying it here.
Examples:
```python
BERTopic.load("my_model")
```
or if you did not save the embedding model:
```python
BERTopic.load("my_model", embedding_model="all-MiniLM-L6-v2")
```
"""
with open(path, 'rb') as file:
if embedding_model:
topic_model = joblib.load(file)
topic_model.embedding_model = select_backend(embedding_model)
else:
topic_model = joblib.load(file)
return topic_model
merge_topics(self, docs, topics_to_merge)
¶
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents you used when calling either |
required |
topics_to_merge |
List[Union[Iterable[int], int]] |
Either a list of topics or a list of list of topics to merge. For example: [1, 2, 3] will merge topics 1, 2 and 3 [[1, 2], [3, 4]] will merge topics 1 and 2, and separately merge topics 3 and 4. |
required |
Examples:
If you want to merge topics 1, 2, and 3:
topics_to_merge = [1, 2, 3]
topic_model.merge_topics(docs, topics_to_merge)
or if you want to merge topics 1 and 2, and separately merge topics 3 and 4:
topics_to_merge = [[1, 2]
[3, 4]]
topic_model.merge_topics(docs, topics_to_merge)
Source code in bertopic\_bertopic.py
def merge_topics(self,
docs: List[str],
topics_to_merge: List[Union[Iterable[int], int]]) -> None:
"""
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
topics_to_merge: Either a list of topics or a list of list of topics
to merge. For example:
[1, 2, 3] will merge topics 1, 2 and 3
[[1, 2], [3, 4]] will merge topics 1 and 2, and
separately merge topics 3 and 4.
Examples:
If you want to merge topics 1, 2, and 3:
```python
topics_to_merge = [1, 2, 3]
topic_model.merge_topics(docs, topics_to_merge)
```
or if you want to merge topics 1 and 2, and separately
merge topics 3 and 4:
```python
topics_to_merge = [[1, 2]
[3, 4]]
topic_model.merge_topics(docs, topics_to_merge)
```
"""
check_is_fitted(self)
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_})
mapping = {topic: topic for topic in set(self.topics_)}
if isinstance(topics_to_merge[0], int):
for topic in sorted(topics_to_merge):
mapping[topic] = topics_to_merge[0]
elif isinstance(topics_to_merge[0], Iterable):
for topic_group in sorted(topics_to_merge):
for topic in topic_group:
mapping[topic] = topic_group[0]
else:
raise ValueError("Make sure that `topics_to_merge` is either"
"a list of topics or a list of list of topics.")
documents.Topic = documents.Topic.map(mapping)
self.topic_mapper_.add_mappings(mapping)
documents = self._sort_mappings_by_frequency(documents)
self._extract_topics(documents)
self._update_topic_size(documents)
self._map_representative_docs()
self.probabilities_ = self._map_probabilities(self.probabilities_)
partial_fit(self, documents, embeddings=None, y=None)
¶
Fit BERTopic on a subset of the data and perform online learning with batch-like data.
Online topic modeling in BERTopic is performed by using dimensionality
reduction and cluster algorithms that support a partial_fit method
in order to incrementally train the topic model.
Likewise, the bertopic.vectorizers.OnlineCountVectorizer is used
to dynamically update its vocabulary when presented with new data.
It has several parameters for modeling decay and updating the
representations.
In other words, although the main algorithm stays the same, the training procedure now works as follows:
For each subset of the data:
- Generate embeddings with a pre-traing language model
- Incrementally update the dimensionality reduction algorithm with
partial_fit - Incrementally update the cluster algorithm with
partial_fit - Incrementally update the OnlineCountVectorizer and apply some form of decay
Note that it is advised to use partial_fit with batches and
not single documents for the best performance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
List[str] |
A list of documents to fit on |
required |
embeddings |
ndarray |
Pre-trained document embeddings. These can be used instead of the sentence-transformer model |
None |
y |
Union[List[int], numpy.ndarray] |
The target class for (semi)-supervised modeling. Use -1 if no class for a specific instance is specified. |
None |
Examples:
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic
# Prepare documents
docs = fetch_20newsgroups(subset=subset, remove=('headers', 'footers', 'quotes'))["data"]
# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)
topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model)
# Incrementally fit the topic model by training on 1000 documents at a time
for index in range(0, len(docs), 1000):
topic_model.partial_fit(docs[index: index+1000])
Source code in bertopic\_bertopic.py
def partial_fit(self,
documents: List[str],
embeddings: np.ndarray = None,
y: Union[List[int], np.ndarray] = None):
""" Fit BERTopic on a subset of the data and perform online learning
with batch-like data.
Online topic modeling in BERTopic is performed by using dimensionality
reduction and cluster algorithms that support a `partial_fit` method
in order to incrementally train the topic model.
Likewise, the `bertopic.vectorizers.OnlineCountVectorizer` is used
to dynamically update its vocabulary when presented with new data.
It has several parameters for modeling decay and updating the
representations.
In other words, although the main algorithm stays the same, the training
procedure now works as follows:
For each subset of the data:
1. Generate embeddings with a pre-traing language model
2. Incrementally update the dimensionality reduction algorithm with `partial_fit`
3. Incrementally update the cluster algorithm with `partial_fit`
4. Incrementally update the OnlineCountVectorizer and apply some form of decay
Note that it is advised to use `partial_fit` with batches and
not single documents for the best performance.
Arguments:
documents: A list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model
y: The target class for (semi)-supervised modeling. Use -1 if no class for a
specific instance is specified.
Examples:
```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic
# Prepare documents
docs = fetch_20newsgroups(subset=subset, remove=('headers', 'footers', 'quotes'))["data"]
# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)
topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model)
# Incrementally fit the topic model by training on 1000 documents at a time
for index in range(0, len(docs), 1000):
topic_model.partial_fit(docs[index: index+1000])
```
"""
# Checks
check_embeddings_shape(embeddings, documents)
if not hasattr(self.hdbscan_model, "partial_fit"):
raise ValueError("In order to use `.partial_fit`, the cluster model should have "
"a `.partial_fit` function.")
# Prepare documents
if isinstance(documents, str):
documents = [documents]
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": None})
# Extract embeddings
if embeddings is None:
if self.topic_representations_ is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
embeddings = self._extract_embeddings(documents.Document,
method="document",
verbose=self.verbose)
else:
if self.embedding_model is not None and self.topic_representations_ is None:
self.embedding_model = select_backend(self.embedding_model,
language=self.language)
# Reduce dimensionality
if self.seed_topic_list is not None and self.embedding_model is not None:
y, embeddings = self._guided_topic_modeling(embeddings)
umap_embeddings = self._reduce_dimensionality(embeddings, y, partial_fit=True)
# Cluster reduced embeddings
documents, self.probabilities_ = self._cluster_embeddings(umap_embeddings, documents, partial_fit=True)
topics = documents.Topic.to_list()
# Map and find new topics
if not self.topic_mapper_:
self.topic_mapper_ = TopicMapper(topics)
mappings = self.topic_mapper_.get_mappings()
new_topics = set(topics).difference(set(mappings.keys()))
new_topic_ids = {topic: max(mappings.values()) + index + 1 for index, topic in enumerate(new_topics)}
self.topic_mapper_.add_new_topics(new_topic_ids)
updated_mappings = self.topic_mapper_.get_mappings()
updated_topics = [updated_mappings[topic] for topic in topics]
documents["Topic"] = updated_topics
# Add missing topics (topics that were originally created but are now missing)
if self.topic_representations_:
missing_topics = set(self.topic_representations_.keys()).difference(set(updated_topics))
for missing_topic in missing_topics:
documents.loc[len(documents), :] = [" ", len(documents), missing_topic]
else:
missing_topics = {}
# Prepare documents
documents_per_topic = documents.sort_values("Topic").groupby(['Topic'], as_index=False)
updated_topics = documents_per_topic.first().Topic.astype(int)
documents_per_topic = documents_per_topic.agg({'Document': ' '.join})
# Update topic representations
self.c_tf_idf_, updated_words = self._c_tf_idf(documents_per_topic, partial_fit=True)
self.topic_representations_ = self._extract_words_per_topic(updated_words, self.c_tf_idf_, labels=updated_topics)
self._create_topic_vectors()
self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
for key, values in self.topic_representations_.items()}
# Update topic sizes
if len(missing_topics) > 0:
documents = documents.iloc[:-len(missing_topics)]
if self.topic_sizes_ is None:
self._update_topic_size(documents)
else:
sizes = documents.groupby(['Topic'], as_index=False).count()
for _, row in sizes.iterrows():
topic = int(row.Topic)
if self.topic_sizes_.get(topic) is not None and topic not in missing_topics:
self.topic_sizes_[topic] += int(row.Document)
elif self.topic_sizes_.get(topic) is None:
self.topic_sizes_[topic] = int(row.Document)
self.topics_ = documents.Topic.astype(int).tolist()
return self
reduce_outliers(self, documents, topics, strategy='distributions', probabilities=None, threshold=0, embeddings=None, distributions_params={})
¶
Reduce outliers by merging them with their nearest topic according to one of several strategies.
When using HDBSCAN, DBSCAN, or OPTICS, a number of outlier documents might be created
that do not fall within any of the created topics. These are labeled as -1.
This function allows the user to match outlier documents with their nearest topic
using one of the following strategies using the strategy parameter:
* "probabilities"
This uses the soft-clustering as performed by HDBSCAN to find the
best matching topic for each outlier document. To use this, make
sure to calculate the probabilities beforehand by instantiating
BERTopic with calculate_probabilities=True.
* "distributions"
Use the topic distributions, as calculated with .approximate_distribution
to find the most frequent topic in each outlier document. You can use the
distributions_params variable to tweak the parameters of
.approximate_distribution.
* "c-tf-idf"
Calculate the c-TF-IDF representation for each outlier document and
find the best matching c-TF-IDF topic representation using
cosine similarity.
* "embeddings"
Using the embeddings of each outlier documents, find the best
matching topic embedding using cosine similarity.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
List[str] |
A list of documents for which we reduce or remove the outliers. |
required |
topics |
List[int] |
The topics that correspond to the documents |
required |
strategy |
str |
The strategy used for reducing outliers. Options: * "probabilities" This uses the soft-clustering as performed by HDBSCAN to find the best matching topic for each outlier document. |
'distributions' |
threshold |
int |
The threshold for assigning topics to outlier documents. This value
represents the minimum probability when |
0 |
embeddings |
ndarray |
The pre-computed embeddings to be used when |
None |
distributions_params |
Mapping[str, Any] |
The parameters used in |
{} |
Returns:
| Type | Description |
|---|---|
new_topics |
The updated topics |
Usage:
The default settings uses the "distributions" strategy:
new_topics = topic_model.reduce_outliers(docs, topics)
When you use the "probabilities" strategy, make sure to also pass the probabilities
as generated through HDBSCAN:
from bertopic import BERTopic
topic_model = BERTopic(calculate_probabilities=True)
topics, probs = topic_model.fit_transform(docs)
new_topics = topic_model.reduce_outliers(docs, topics, probabilities=probs, strategy="probabilities")
Source code in bertopic\_bertopic.py
def reduce_outliers(self,
documents: List[str],
topics: List[int],
strategy: str = "distributions",
probabilities: np.ndarray = None,
threshold: int = 0,
embeddings: np.ndarray = None,
distributions_params: Mapping[str, Any] = {}) -> List[int]:
""" Reduce outliers by merging them with their nearest topic according
to one of several strategies.
When using HDBSCAN, DBSCAN, or OPTICS, a number of outlier documents might be created
that do not fall within any of the created topics. These are labeled as -1.
This function allows the user to match outlier documents with their nearest topic
using one of the following strategies using the `strategy` parameter:
* "probabilities"
This uses the soft-clustering as performed by HDBSCAN to find the
best matching topic for each outlier document. To use this, make
sure to calculate the `probabilities` beforehand by instantiating
BERTopic with `calculate_probabilities=True`.
* "distributions"
Use the topic distributions, as calculated with `.approximate_distribution`
to find the most frequent topic in each outlier document. You can use the
`distributions_params` variable to tweak the parameters of
`.approximate_distribution`.
* "c-tf-idf"
Calculate the c-TF-IDF representation for each outlier document and
find the best matching c-TF-IDF topic representation using
cosine similarity.
* "embeddings"
Using the embeddings of each outlier documents, find the best
matching topic embedding using cosine similarity.
Arguments:
documents: A list of documents for which we reduce or remove the outliers.
topics: The topics that correspond to the documents
strategy: The strategy used for reducing outliers.
Options:
* "probabilities"
This uses the soft-clustering as performed by HDBSCAN
to find the best matching topic for each outlier document.
* "distributions"
Use the topic distributions, as calculated with `.approximate_distribution`
to find the most frequent topic in each outlier document.
* "c-tf-idf"
Calculate the c-TF-IDF representation for outlier documents and
find the best matching c-TF-IDF topic representation.
* "embeddings"
Calculate the embeddings for outlier documents and
find the best matching topic embedding.
threshold: The threshold for assigning topics to outlier documents. This value
represents the minimum probability when `strategy="probabilities"`.
For all other strategies, it represents the minimum similarity.
embeddings: The pre-computed embeddings to be used when `strategy="embeddings"`.
If this is None, then it will compute the embeddings for the outlier documents.
distributions_params: The parameters used in `.approximate_distribution` when using
the strategy `"distributions"`.
Returns:
new_topics: The updated topics
Usage:
The default settings uses the `"distributions"` strategy:
```python
new_topics = topic_model.reduce_outliers(docs, topics)
```
When you use the `"probabilities"` strategy, make sure to also pass the probabilities
as generated through HDBSCAN:
```python
from bertopic import BERTopic
topic_model = BERTopic(calculate_probabilities=True)
topics, probs = topic_model.fit_transform(docs)
new_topics = topic_model.reduce_outliers(docs, topics, probabilities=probs, strategy="probabilities")
```
"""
# Check correct use of parameters
if strategy.lower() == "probabilities" and probabilities is None:
raise ValueError("Make sure to pass in `probabilities` in order to use the probabilities strategy")
# Reduce outliers by extracting most likely topics through the topic-term probability matrix
if strategy.lower() == "probabilities":
new_topics = [np.argmax(prob) if max(prob) >= threshold and topic == -1 else topic
for topic, prob in zip(topics, probabilities)]
# Reduce outliers by extracting most frequent topics through calculating of Topic Distributions
elif strategy.lower() == "distributions":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
topic_distr, _ = self.approximate_distribution(outlier_docs, min_similarity=threshold, **distributions_params)
outlier_topics = iter([np.argmax(prob) if sum(prob) > 0 else -1 for prob in topic_distr])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
# Reduce outliers by finding the most similar c-TF-IDF representations
elif strategy.lower() == "c-tf-idf":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
# Calculate c-TF-IDF of outlier documents with all topics
bow_doc = self.vectorizer_model.transform(outlier_docs)
c_tf_idf_doc = self.ctfidf_model.transform(bow_doc)
similarity = cosine_similarity(c_tf_idf_doc, self.c_tf_idf_[self._outliers:])
# Update topics
similarity[similarity < threshold] = 0
outlier_topics = iter([np.argmax(sim) if sum(sim) > 0 else -1 for sim in similarity])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
# Reduce outliers by finding the most similar topic embeddings
elif strategy.lower() == "embeddings":
outlier_ids = [index for index, topic in enumerate(topics) if topic == -1]
outlier_docs = [documents[index] for index in outlier_ids]
# Extract or calculate embeddings for outlier documents
if embeddings is not None:
outlier_embeddings = np.array([embeddings[index] for index in outlier_ids])
else:
outlier_embeddings = self.embedding_model.embed_documents(outlier_docs)
similarity = cosine_similarity(outlier_embeddings, self.topic_embeddings_[self._outliers:])
# Update topics
similarity[similarity < threshold] = 0
outlier_topics = iter([np.argmax(sim) if sum(sim) > 0 else -1 for sim in similarity])
new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]
return new_topics
reduce_topics(self, docs, nr_topics=20)
¶
Further reduce the number of topics to nr_topics.
The number of topics is further reduced by calculating the c-TF-IDF matrix of the documents and then reducing them by iteratively merging the least frequent topic with the most similar one based on their c-TF-IDF matrices. The topics, their sizes, and representations are updated.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The docs you used when calling either |
required |
nr_topics |
int |
The number of topics you want reduced to |
20 |
Updates
topics_ : Assigns topics to their merged representations. probabilities_ : Assigns probabilities to their merged representations.
Examples:
You can further reduce the topics by passing the documents with its topics and probabilities (if they were calculated):
topic_model.reduce_topics(docs, nr_topics=30)
You can then access the updated topics and probabilities with:
topics = topic_model.topics_
probabilities = topic_model.probabilities_
Source code in bertopic\_bertopic.py
def reduce_topics(self,
docs: List[str],
nr_topics: int = 20) -> None:
""" Further reduce the number of topics to nr_topics.
The number of topics is further reduced by calculating the c-TF-IDF matrix
of the documents and then reducing them by iteratively merging the least
frequent topic with the most similar one based on their c-TF-IDF matrices.
The topics, their sizes, and representations are updated.
Arguments:
docs: The docs you used when calling either `fit` or `fit_transform`
nr_topics: The number of topics you want reduced to
Updates:
topics_ : Assigns topics to their merged representations.
probabilities_ : Assigns probabilities to their merged representations.
Examples:
You can further reduce the topics by passing the documents with its
topics and probabilities (if they were calculated):
```python
topic_model.reduce_topics(docs, nr_topics=30)
```
You can then access the updated topics and probabilities with:
```python
topics = topic_model.topics_
probabilities = topic_model.probabilities_
```
"""
check_is_fitted(self)
self.nr_topics = nr_topics
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_})
# Reduce number of topics
documents = self._reduce_topics(documents)
self._merged_topics = None
self._map_representative_docs()
# Map probabilities
self.probabilities_ = self._map_probabilities(self.probabilities_)
return self
save(self, path, save_embedding_model=True)
¶
Saves the model to the specified path
When saving the model, make sure to also keep track of the versions of dependencies and Python used. Loading and saving the model should be done using the same dependencies and Python. Moreover, models saved in one version of BERTopic should not be loaded in other versions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
str |
the location and name of the file you want to save |
required |
save_embedding_model |
bool |
Whether to save the embedding model in this class as you might have selected a local model or one that is downloaded automatically from the cloud. |
True |
Examples:
topic_model.save("my_model")
or if you do not want the embedding_model to be saved locally:
topic_model.save("my_model", save_embedding_model=False)
Source code in bertopic\_bertopic.py
def save(self,
path: str,
save_embedding_model: bool = True) -> None:
""" Saves the model to the specified path
When saving the model, make sure to also keep track of the versions
of dependencies and Python used. Loading and saving the model should
be done using the same dependencies and Python. Moreover, models
saved in one version of BERTopic should not be loaded in other versions.
Arguments:
path: the location and name of the file you want to save
save_embedding_model: Whether to save the embedding model in this class
as you might have selected a local model or one that
is downloaded automatically from the cloud.
Examples:
```python
topic_model.save("my_model")
```
or if you do not want the embedding_model to be saved locally:
```python
topic_model.save("my_model", save_embedding_model=False)
```
"""
with open(path, 'wb') as file:
# This prevents the vectorizer from being too large in size if `min_df` was
# set to a value higher than 1
self.vectorizer_model.stop_words_ = None
if not save_embedding_model:
embedding_model = self.embedding_model
self.embedding_model = None
joblib.dump(self, file)
self.embedding_model = embedding_model
else:
joblib.dump(self, file)
set_topic_labels(self, topic_labels)
¶
Set custom topic labels in your fitted BERTopic model
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic_labels |
Union[List[str], Mapping[int, str]] |
If a list of topic labels, it should contain the same number
of labels as there are topics. This must be ordered
from the topic with the lowest ID to the highest ID,
including topic -1 if it exists.
If a dictionary of |
required |
Examples:
First, we define our topic labels with .get_topic_labels in which
we can customize our topic labels:
topic_labels = topic_model.get_topic_labels(nr_words=2,
topic_prefix=True,
word_length=10,
separator=", ")
Then, we pass these topic_labels to our topic model which
can be accessed at any time with .custom_labels_:
topic_model.set_topic_labels(topic_labels)
topic_model.custom_labels_
You might want to change only a few topic labels instead of all of them. To do so, you can pass a dictionary where the keys are the topic IDs and its keys the topic labels:
topic_model.set_topic_labels({0: "Space", 1: "Sports", 2: "Medicine"})
topic_model.custom_labels_
Source code in bertopic\_bertopic.py
def set_topic_labels(self, topic_labels: Union[List[str], Mapping[int, str]]) -> None:
""" Set custom topic labels in your fitted BERTopic model
Arguments:
topic_labels: If a list of topic labels, it should contain the same number
of labels as there are topics. This must be ordered
from the topic with the lowest ID to the highest ID,
including topic -1 if it exists.
If a dictionary of `topic ID`: `topic_label`, it can have
any number of topics as it will only map the topics found
in the dictionary.
Examples:
First, we define our topic labels with `.get_topic_labels` in which
we can customize our topic labels:
```python
topic_labels = topic_model.get_topic_labels(nr_words=2,
topic_prefix=True,
word_length=10,
separator=", ")
```
Then, we pass these `topic_labels` to our topic model which
can be accessed at any time with `.custom_labels_`:
```python
topic_model.set_topic_labels(topic_labels)
topic_model.custom_labels_
```
You might want to change only a few topic labels instead of all of them.
To do so, you can pass a dictionary where the keys are the topic IDs and
its keys the topic labels:
```python
topic_model.set_topic_labels({0: "Space", 1: "Sports", 2: "Medicine"})
topic_model.custom_labels_
```
"""
unique_topics = sorted(set(self.topics_))
if isinstance(topic_labels, dict):
if self.custom_labels_ is not None:
original_labels = {topic: label for topic, label in zip(unique_topics, self.custom_labels_)}
else:
info = self.get_topic_info()
original_labels = dict(zip(info.Topic, info.Name))
custom_labels = [topic_labels.get(topic) if topic_labels.get(topic) else original_labels[topic] for topic in unique_topics]
elif isinstance(topic_labels, list):
if len(topic_labels) == len(unique_topics):
custom_labels = topic_labels
else:
raise ValueError("Make sure that `topic_labels` contains the same number "
"of labels as that there are topics.")
self.custom_labels_ = custom_labels
topics_over_time(self, docs, timestamps, nr_bins=None, datetime_format=None, evolution_tuning=True, global_tuning=True)
¶
Create topics over time
To create the topics over time, BERTopic needs to be already fitted once. From the fitted models, the c-TF-IDF representations are calculate at each timestamp t. Then, the c-TF-IDF representations at timestamp t are averaged with the global c-TF-IDF representations in order to fine-tune the local representations.
Note
Make sure to use a limited number of unique timestamps (<100) as the c-TF-IDF representation will be calculated at each single unique timestamp. Having a large number of unique timestamps can take some time to be calculated. Moreover, there aren't many use-cased where you would like to see the difference in topic representations over more than 100 different timestamps.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents you used when calling either |
required |
timestamps |
Union[List[str], List[int]] |
The timestamp of each document. This can be either a list of strings or ints. If it is a list of strings, then the datetime format will be automatically inferred. If it is a list of ints, then the documents will be ordered by ascending order. |
required |
nr_bins |
int |
The number of bins you want to create for the timestamps. The left interval will be chosen as the timestamp. An additional column will be created with the entire interval. |
None |
datetime_format |
str |
The datetime format of the timestamps if they are strings, eg “%d/%m/%Y”. Set this to None if you want to have it automatically detect the format. See strftime documentation for more information on choices: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior. |
None |
evolution_tuning |
bool |
Fine-tune each topic representation at timestamp t by averaging its c-TF-IDF matrix with the c-TF-IDF matrix at timestamp t-1. This creates evolutionary topic representations. |
True |
global_tuning |
bool |
Fine-tune each topic representation at timestamp t by averaging its c-TF-IDF matrix with the global c-TF-IDF matrix. Turn this off if you want to prevent words in topic representations that could not be found in the documents at timestamp t. |
True |
Returns:
| Type | Description |
|---|---|
topics_over_time |
A dataframe that contains the topic, words, and frequency of topic at timestamp t. |
Examples:
The timestamps variable represent the timestamp of each document. If you have over 100 unique timestamps, it is advised to bin the timestamps as shown below:
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_over_time = topic_model.topics_over_time(docs, timestamps, nr_bins=20)
Source code in bertopic\_bertopic.py
def topics_over_time(self,
docs: List[str],
timestamps: Union[List[str],
List[int]],
nr_bins: int = None,
datetime_format: str = None,
evolution_tuning: bool = True,
global_tuning: bool = True) -> pd.DataFrame:
""" Create topics over time
To create the topics over time, BERTopic needs to be already fitted once.
From the fitted models, the c-TF-IDF representations are calculate at
each timestamp t. Then, the c-TF-IDF representations at timestamp t are
averaged with the global c-TF-IDF representations in order to fine-tune the
local representations.
NOTE:
Make sure to use a limited number of unique timestamps (<100) as the
c-TF-IDF representation will be calculated at each single unique timestamp.
Having a large number of unique timestamps can take some time to be calculated.
Moreover, there aren't many use-cased where you would like to see the difference
in topic representations over more than 100 different timestamps.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
timestamps: The timestamp of each document. This can be either a list of strings or ints.
If it is a list of strings, then the datetime format will be automatically
inferred. If it is a list of ints, then the documents will be ordered by
ascending order.
nr_bins: The number of bins you want to create for the timestamps. The left interval will
be chosen as the timestamp. An additional column will be created with the
entire interval.
datetime_format: The datetime format of the timestamps if they are strings, eg “%d/%m/%Y”.
Set this to None if you want to have it automatically detect the format.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
evolution_tuning: Fine-tune each topic representation at timestamp *t* by averaging its
c-TF-IDF matrix with the c-TF-IDF matrix at timestamp *t-1*. This creates
evolutionary topic representations.
global_tuning: Fine-tune each topic representation at timestamp *t* by averaging its c-TF-IDF matrix
with the global c-TF-IDF matrix. Turn this off if you want to prevent words in
topic representations that could not be found in the documents at timestamp *t*.
Returns:
topics_over_time: A dataframe that contains the topic, words, and frequency of topic
at timestamp *t*.
Examples:
The timestamps variable represent the timestamp of each document. If you have over
100 unique timestamps, it is advised to bin the timestamps as shown below:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_over_time = topic_model.topics_over_time(docs, timestamps, nr_bins=20)
```
"""
check_is_fitted(self)
check_documents_type(docs)
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_, "Timestamps": timestamps})
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm='l1', copy=False)
all_topics = sorted(list(documents.Topic.unique()))
all_topics_indices = {topic: index for index, topic in enumerate(all_topics)}
if isinstance(timestamps[0], str):
infer_datetime_format = True if not datetime_format else False
documents["Timestamps"] = pd.to_datetime(documents["Timestamps"],
infer_datetime_format=infer_datetime_format,
format=datetime_format)
if nr_bins:
documents["Bins"] = pd.cut(documents.Timestamps, bins=nr_bins)
documents["Timestamps"] = documents.apply(lambda row: row.Bins.left, 1)
# Sort documents in chronological order
documents = documents.sort_values("Timestamps")
timestamps = documents.Timestamps.unique()
if len(timestamps) > 100:
warnings.warn(f"There are more than 100 unique timestamps (i.e., {len(timestamps)}) "
"which significantly slows down the application. Consider setting `nr_bins` "
"to a value lower than 100 to speed up calculation. ")
# For each unique timestamp, create topic representations
topics_over_time = []
for index, timestamp in tqdm(enumerate(timestamps), disable=not self.verbose):
# Calculate c-TF-IDF representation for a specific timestamp
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, fit=False)
if global_tuning or evolution_tuning:
c_tf_idf = normalize(c_tf_idf, axis=1, norm='l1', copy=False)
# Fine-tune the c-TF-IDF matrix at timestamp t by averaging it with the c-TF-IDF
# matrix at timestamp t-1
if evolution_tuning and index != 0:
current_topics = sorted(list(documents_per_topic.Topic.values))
overlapping_topics = sorted(list(set(previous_topics).intersection(set(current_topics))))
current_overlap_idx = [current_topics.index(topic) for topic in overlapping_topics]
previous_overlap_idx = [previous_topics.index(topic) for topic in overlapping_topics]
c_tf_idf.tolil()[current_overlap_idx] = ((c_tf_idf[current_overlap_idx] +
previous_c_tf_idf[previous_overlap_idx]) / 2.0).tolil()
# Fine-tune the timestamp c-TF-IDF representation based on the global c-TF-IDF representation
# by simply taking the average of the two
if global_tuning:
selected_topics = [all_topics_indices[topic] for topic in documents_per_topic.Topic.values]
c_tf_idf = (global_c_tf_idf[selected_topics] + c_tf_idf) / 2.0
# Extract the words per topic
labels = sorted(list(documents_per_topic.Topic.unique()))
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels)
topic_frequency = pd.Series(documents_per_topic.Timestamps.values,
index=documents_per_topic.Topic).to_dict()
# Fill dataframe with results
topics_at_timestamp = [(topic,
", ".join([words[0] for words in values][:5]),
topic_frequency[topic],
timestamp) for topic, values in words_per_topic.items()]
topics_over_time.extend(topics_at_timestamp)
if evolution_tuning:
previous_topics = sorted(list(documents_per_topic.Topic.values))
previous_c_tf_idf = c_tf_idf.copy()
return pd.DataFrame(topics_over_time, columns=["Topic", "Words", "Frequency", "Timestamp"])
topics_per_class(self, docs, classes, global_tuning=True)
¶
Create topics per class
To create the topics per class, BERTopic needs to be already fitted once. From the fitted models, the c-TF-IDF representations are calculate at each class c. Then, the c-TF-IDF representations at class c are averaged with the global c-TF-IDF representations in order to fine-tune the local representations. This can be turned off if the pure representation is needed.
Note
Make sure to use a limited number of unique classes (<100) as the c-TF-IDF representation will be calculated at each single unique class. Having a large number of unique classes can take some time to be calculated.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents you used when calling either |
required |
classes |
Union[List[int], List[str]] |
The class of each document. This can be either a list of strings or ints. |
required |
global_tuning |
bool |
Fine-tune each topic representation for class c t by averaging its c-TF-IDF matrix with the global c-TF-IDF matrix. Turn this off if you want to prevent words in topic representations that could not be found in the documents for class c. |
True |
Returns:
| Type | Description |
|---|---|
topics_per_class |
A dataframe that contains the topic, words, and frequency of topics for each class. |
Examples:
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_per_class = topic_model.topics_per_class(docs, classes)
Source code in bertopic\_bertopic.py
def topics_per_class(self,
docs: List[str],
classes: Union[List[int], List[str]],
global_tuning: bool = True) -> pd.DataFrame:
""" Create topics per class
To create the topics per class, BERTopic needs to be already fitted once.
From the fitted models, the c-TF-IDF representations are calculate at
each class c. Then, the c-TF-IDF representations at class c are
averaged with the global c-TF-IDF representations in order to fine-tune the
local representations. This can be turned off if the pure representation is
needed.
NOTE:
Make sure to use a limited number of unique classes (<100) as the
c-TF-IDF representation will be calculated at each single unique class.
Having a large number of unique classes can take some time to be calculated.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
classes: The class of each document. This can be either a list of strings or ints.
global_tuning: Fine-tune each topic representation for class c t by averaging its c-TF-IDF matrix
with the global c-TF-IDF matrix. Turn this off if you want to prevent words in
topic representations that could not be found in the documents for class c.
Returns:
topics_per_class: A dataframe that contains the topic, words, and frequency of topics
for each class.
Examples:
```python
from bertopic import BERTopic
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_per_class = topic_model.topics_per_class(docs, classes)
```
"""
documents = pd.DataFrame({"Document": docs, "Topic": self.topics_, "Class": classes})
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm='l1', copy=False)
# For each unique timestamp, create topic representations
topics_per_class = []
for _, class_ in tqdm(enumerate(set(classes)), disable=not self.verbose):
# Calculate c-TF-IDF representation for a specific timestamp
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, 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
if global_tuning:
c_tf_idf = normalize(c_tf_idf, axis=1, norm='l1', copy=False)
c_tf_idf = (global_c_tf_idf[documents_per_topic.Topic.values + self._outliers] + c_tf_idf) / 2.0
# Extract the words per topic
labels = sorted(list(documents_per_topic.Topic.unique()))
words_per_topic = self._extract_words_per_topic(words, c_tf_idf, labels)
topic_frequency = pd.Series(documents_per_topic.Class.values,
index=documents_per_topic.Topic).to_dict()
# Fill dataframe with results
topics_at_class = [(topic,
", ".join([words[0] for words in values][:5]),
topic_frequency[topic],
class_) for topic, values in words_per_topic.items()]
topics_per_class.extend(topics_at_class)
topics_per_class = pd.DataFrame(topics_per_class, columns=["Topic", "Words", "Frequency", "Class"])
return topics_per_class
transform(self, documents, embeddings=None)
¶
After having fit a model, use transform to predict new instances
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
documents |
Union[str, List[str]] |
A single document or a list of documents to fit on |
required |
embeddings |
ndarray |
Pre-trained document embeddings. These can be used instead of the sentence-transformer model. |
None |
Returns:
| Type | Description |
|---|---|
predictions |
Topic predictions for each documents
probabilities: The topic probability distribution which is returned by default.
If |
Examples:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
topics, probs = topic_model.transform(docs)
If you want to use your own embeddings:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
topics, probs = topic_model.transform(docs, embeddings)
Source code in bertopic\_bertopic.py
def transform(self,
documents: Union[str, List[str]],
embeddings: np.ndarray = None) -> Tuple[List[int], np.ndarray]:
""" After having fit a model, use transform to predict new instances
Arguments:
documents: A single document or a list of documents to fit on
embeddings: Pre-trained document embeddings. These can be used
instead of the sentence-transformer model.
Returns:
predictions: Topic predictions for each documents
probabilities: The topic probability distribution which is returned by default.
If `calculate_probabilities` in BERTopic is set to False, then the
probabilities are not calculated to speed up computation and
decrease memory usage.
Examples:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all')['data']
topic_model = BERTopic().fit(docs)
topics, probs = topic_model.transform(docs)
```
If you want to use your own embeddings:
```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
# Create embeddings
docs = fetch_20newsgroups(subset='all')['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=True)
# Create topic model
topic_model = BERTopic().fit(docs, embeddings)
topics, probs = topic_model.transform(docs, embeddings)
```
"""
check_is_fitted(self)
check_embeddings_shape(embeddings, documents)
if isinstance(documents, str):
documents = [documents]
if embeddings is None:
embeddings = self._extract_embeddings(documents,
method="document",
verbose=self.verbose)
umap_embeddings = self.umap_model.transform(embeddings)
logger.info("Reduced dimensionality")
# Extract predictions and probabilities if it is a HDBSCAN-like model
if is_supported_hdbscan(self.hdbscan_model):
predictions, probabilities = hdbscan_delegator(self.hdbscan_model, "approximate_predict", umap_embeddings)
# Calculate probabilities
if self.calculate_probabilities and isinstance(self.hdbscan_model, hdbscan.HDBSCAN):
probabilities = hdbscan.membership_vector(self.hdbscan_model, umap_embeddings)
logger.info("Calculated probabilities with HDBSCAN")
else:
predictions = self.hdbscan_model.predict(umap_embeddings)
probabilities = None
logger.info("Predicted clusters")
# Map probabilities and predictions
probabilities = self._map_probabilities(probabilities, original_topics=True)
predictions = self._map_predictions(predictions)
return predictions, probabilities
update_topics(self, docs, topics=None, top_n_words=10, n_gram_range=None, diversity=None, vectorizer_model=None, ctfidf_model=None)
¶
Updates the topic representation by recalculating c-TF-IDF with the new parameters as defined in this function.
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. This function allows you to update the topic representation after they have been formed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents you used when calling either |
required |
topics |
List[int] |
A list of topics where each topic is related to a document in |
None |
top_n_words |
int |
The number of words per topic to extract. Setting this too high can negatively impact topic embeddings as topics are typically best represented by at most 10 words. |
10 |
n_gram_range |
Tuple[int, int] |
The n-gram range for the CountVectorizer. |
None |
diversity |
float |
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. |
None |
vectorizer_model |
CountVectorizer |
Pass in your own CountVectorizer from scikit-learn |
None |
ctfidf_model |
ClassTfidfTransformer |
Pass in your own c-TF-IDF model to update the representations |
None |
Examples:
In order to update the topic representation, you will need to first fit the topic model and extract topics from them. Based on these, you can update the representation:
topic_model.update_topics(docs, n_gram_range=(2, 3))
You can also use a custom vectorizer to update the representation:
from sklearn.feature_extraction.text import CountVectorizer
vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")
topic_model.update_topics(docs, vectorizer_model=vectorizer_model)
You can also use this function to change or map the topics to something else. You can update them as follows:
topic_model.update_topics(docs, my_updated_topics)
Source code in bertopic\_bertopic.py
def update_topics(self,
docs: List[str],
topics: List[int] = None,
top_n_words: int = 10,
n_gram_range: Tuple[int, int] = None,
diversity: float = None,
vectorizer_model: CountVectorizer = None,
ctfidf_model: ClassTfidfTransformer = None):
""" Updates the topic representation by recalculating c-TF-IDF with the new
parameters as defined in this function.
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. This function allows you
to update the topic representation after they have been formed.
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
topics: A list of topics where each topic is related to a document in `docs`.
Use this variable to change or map the topics.
NOTE: Using a custom list of topic assignments may lead to errors if
topic reduction techniques are used afterwards. Make sure that
manually assigning topics is the last step in the pipeline
top_n_words: The number of words per topic to extract. Setting this
too high can negatively impact topic embeddings as topics
are typically best represented by at most 10 words.
n_gram_range: The n-gram range for the CountVectorizer.
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.
vectorizer_model: Pass in your own CountVectorizer from scikit-learn
ctfidf_model: Pass in your own c-TF-IDF model to update the representations
Examples:
In order to update the topic representation, you will need to first fit the topic
model and extract topics from them. Based on these, you can update the representation:
```python
topic_model.update_topics(docs, n_gram_range=(2, 3))
```
You can also use a custom vectorizer to update the representation:
```python
from sklearn.feature_extraction.text import CountVectorizer
vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")
topic_model.update_topics(docs, vectorizer_model=vectorizer_model)
```
You can also use this function to change or map the topics to something else.
You can update them as follows:
```python
topic_model.update_topics(docs, my_updated_topics)
```
"""
check_is_fitted(self)
if not n_gram_range:
n_gram_range = self.n_gram_range
if top_n_words > 30:
raise ValueError("top_n_words should be lower or equal to 30. The preferred value is 10.")
self.top_n_words = top_n_words
self.diversity = diversity
self.vectorizer_model = vectorizer_model or CountVectorizer(ngram_range=n_gram_range)
self.ctfidf_model = ctfidf_model or ClassTfidfTransformer()
if topics is None:
topics = self.topics_
labels = None
else:
labels = sorted(list(set(topics)))
warnings.warn("Using a custom list of topic assignments may lead to errors if "
"topic reduction techniques are used afterwards. Make sure that "
"manually assigning topics is the last step in the pipeline.")
# Extract words
documents = pd.DataFrame({"Document": docs, "Topic": topics})
documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
self.topic_representations_ = self._extract_words_per_topic(words, labels=labels)
self._create_topic_vectors()
self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
for key, values in
self.topic_representations_.items()}
self._update_topic_size(documents)
visualize_approximate_distribution(self, document, topic_token_distribution, normalize=False)
¶
Visualize the topic distribution calculated by .approximate_topic_distribution
on a token level. Thereby indicating the extend to which a certain word or phrases belong
to a specific topic. The assumption here is that a single word can belong to multiple
similar topics and as such give information about the broader set of topics within
a single document.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic_model |
A fitted BERTopic instance. |
required | |
document |
str |
The document for which you want to visualize the approximated topic distribution. |
required |
topic_token_distribution |
ndarray |
The topic-token distribution of the document as
extracted by |
required |
normalize |
bool |
Whether to normalize, between 0 and 1 (summing to 1), the topic distribution values. |
False |
Returns:
| Type | Description |
|---|---|
df |
A stylized dataframe indicating the best fitting topics for each token. |
Examples:
# Calculate the topic distributions on a token level
# Note that we need to have `calculate_token_level=True`
topic_distr, topic_token_distr = topic_model.approximate_distribution(
docs, calculate_token_level=True
)
# Visualize the approximated topic distributions
df = topic_model.visualize_approximate_distribution(docs[0], topic_token_distr[0])
df
To revert this stylized dataframe back to a regular dataframe, you can run the following:
df.data.columns = [column.strip() for column in df.data.columns]
df = df.data
Source code in bertopic\_bertopic.py
def visualize_approximate_distribution(self,
document: str,
topic_token_distribution: np.ndarray,
normalize: bool = False):
""" Visualize the topic distribution calculated by `.approximate_topic_distribution`
on a token level. Thereby indicating the extend to which a certain word or phrases belong
to a specific topic. The assumption here is that a single word can belong to multiple
similar topics and as such give information about the broader set of topics within
a single document.
Arguments:
topic_model: A fitted BERTopic instance.
document: The document for which you want to visualize
the approximated topic distribution.
topic_token_distribution: The topic-token distribution of the document as
extracted by `.approximate_topic_distribution`
normalize: Whether to normalize, between 0 and 1 (summing to 1), the
topic distribution values.
Returns:
df: A stylized dataframe indicating the best fitting topics
for each token.
Examples:
```python
# Calculate the topic distributions on a token level
# Note that we need to have `calculate_token_level=True`
topic_distr, topic_token_distr = topic_model.approximate_distribution(
docs, calculate_token_level=True
)
# Visualize the approximated topic distributions
df = topic_model.visualize_approximate_distribution(docs[0], topic_token_distr[0])
df
```
To revert this stylized dataframe back to a regular dataframe,
you can run the following:
```python
df.data.columns = [column.strip() for column in df.data.columns]
df = df.data
```
"""
check_is_fitted(self)
return plotting.visualize_approximate_distribution(self,
document=document,
topic_token_distribution=topic_token_distribution,
normalize=normalize)
visualize_barchart(self, topics=None, top_n_topics=8, n_words=5, custom_labels=False, title='Topic Word Scores', width=250, height=250)
¶
Visualize a barchart of selected topics
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics |
List[int] |
A selection of topics to visualize. |
None |
top_n_topics |
int |
Only select the top n most frequent topics. |
8 |
n_words |
int |
Number of words to show in a topic |
5 |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
title |
str |
Title of the plot. |
'Topic Word Scores' |
width |
int |
The width of each figure. |
250 |
height |
int |
The height of each figure. |
250 |
Returns:
| Type | Description |
|---|---|
fig |
A plotly figure |
Examples:
To visualize the barchart of selected topics simply run:
topic_model.visualize_barchart()
Or if you want to save the resulting figure:
fig = topic_model.visualize_barchart()
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_barchart(self,
topics: List[int] = None,
top_n_topics: int = 8,
n_words: int = 5,
custom_labels: bool = False,
title: str = "Topic Word Scores",
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
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
title: Title of the plot.
width: The width of each figure.
height: The height of each figure.
Returns:
fig: A plotly figure
Examples:
To visualize the barchart of selected topics
simply run:
```python
topic_model.visualize_barchart()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_barchart()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_barchart(self,
topics=topics,
top_n_topics=top_n_topics,
n_words=n_words,
custom_labels=custom_labels,
title=title,
width=width,
height=height)
visualize_distribution(self, probabilities, min_probability=0.015, custom_labels=False, width=800, height=600)
¶
Visualize the distribution of topic probabilities
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
probabilities |
ndarray |
An array of probability scores |
required |
min_probability |
float |
The minimum probability score to visualize. All others are ignored. |
0.015 |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
800 |
height |
int |
The height of the figure. |
600 |
Examples:
Make sure to fit the model before and only input the probabilities of a single document:
topic_model.visualize_distribution(topic_model.probabilities_[0])
Or if you want to save the resulting figure:
fig = topic_model.visualize_distribution(topic_model.probabilities_[0])
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_distribution(self,
probabilities: np.ndarray,
min_probability: float = 0.015,
custom_labels: bool = False,
width: int = 800,
height: int = 600) -> go.Figure:
""" Visualize the distribution of topic probabilities
Arguments:
probabilities: An array of probability scores
min_probability: The minimum probability score to visualize.
All others are ignored.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Examples:
Make sure to fit the model before and only input the
probabilities of a single document:
```python
topic_model.visualize_distribution(topic_model.probabilities_[0])
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_distribution(topic_model.probabilities_[0])
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_distribution(self,
probabilities=probabilities,
min_probability=min_probability,
custom_labels=custom_labels,
width=width,
height=height)
visualize_documents(self, docs, topics=None, embeddings=None, reduced_embeddings=None, sample=None, hide_annotations=False, hide_document_hover=False, custom_labels=False, width=1200, height=750)
¶
Visualize documents and their topics in 2D
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic_model |
A fitted BERTopic instance. |
required | |
docs |
List[str] |
The documents you used when calling either |
required |
topics |
List[int] |
A selection of topics to visualize.
Not to be confused with the topics that you get from |
None |
embeddings |
ndarray |
The embeddings of all documents in |
None |
reduced_embeddings |
ndarray |
The 2D reduced embeddings of all documents in |
None |
sample |
float |
The percentage of documents in each topic that you would like to keep. Value can be between 0 and 1. Setting this value to, for example, 0.1 (10% of documents in each topic) makes it easier to visualize millions of documents as a subset is chosen. |
None |
hide_annotations |
bool |
Hide the names of the traces on top of each cluster. |
False |
hide_document_hover |
bool |
Hide the content of the documents when hovering over specific points. Helps to speed up generation of visualization. |
False |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
1200 |
height |
int |
The height of the figure. |
750 |
Examples:
To visualize the topics simply run:
topic_model.visualize_documents(docs)
Do note that this re-calculates the embeddings and reduces them to 2D. The advised and prefered pipeline for using this function is as follows:
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic
topic_model = BERTopic().fit(docs, embeddings)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_documents(docs, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
Or if you want to save the resulting figure:
fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_documents(self,
docs: List[str],
topics: List[int] = None,
embeddings: np.ndarray = None,
reduced_embeddings: np.ndarray = None,
sample: float = None,
hide_annotations: bool = False,
hide_document_hover: bool = False,
custom_labels: bool = False,
width: int = 1200,
height: int = 750) -> go.Figure:
""" Visualize documents and their topics in 2D
Arguments:
topic_model: A fitted BERTopic instance.
docs: The documents you used when calling either `fit` or `fit_transform`
topics: A selection of topics to visualize.
Not to be confused with the topics that you get from `.fit_transform`.
For example, if you want to visualize only topics 1 through 5:
`topics = [1, 2, 3, 4, 5]`.
embeddings: The embeddings of all documents in `docs`.
reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
sample: The percentage of documents in each topic that you would like to keep.
Value can be between 0 and 1. Setting this value to, for example,
0.1 (10% of documents in each topic) makes it easier to visualize
millions of documents as a subset is chosen.
hide_annotations: Hide the names of the traces on top of each cluster.
hide_document_hover: Hide the content of the documents when hovering over
specific points. Helps to speed up generation of visualization.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_documents(docs)
```
Do note that this re-calculates the embeddings and reduces them to 2D.
The advised and prefered pipeline for using this function is as follows:
```python
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic
topic_model = BERTopic().fit(docs, embeddings)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_documents(docs, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/documents.html"
style="width:1000px; height: 800px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_documents(self,
docs=docs,
topics=topics,
embeddings=embeddings,
reduced_embeddings=reduced_embeddings,
sample=sample,
hide_annotations=hide_annotations,
hide_document_hover=hide_document_hover,
custom_labels=custom_labels,
width=width,
height=height)
visualize_heatmap(self, topics=None, top_n_topics=None, n_clusters=None, custom_labels=False, width=800, height=800)
¶
Visualize a heatmap of the topic's similarity matrix
Based on the cosine similarity matrix between topic embeddings, a heatmap is created showing the similarity between topics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics |
List[int] |
A selection of topics to visualize. |
None |
top_n_topics |
int |
Only select the top n most frequent topics. |
None |
n_clusters |
int |
Create n clusters and order the similarity matrix by those clusters. |
None |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
800 |
height |
int |
The height of the figure. |
800 |
Returns:
| Type | Description |
|---|---|
fig |
A plotly figure |
Examples:
To visualize the similarity matrix of topics simply run:
topic_model.visualize_heatmap()
Or if you want to save the resulting figure:
fig = topic_model.visualize_heatmap()
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_heatmap(self,
topics: List[int] = None,
top_n_topics: int = None,
n_clusters: int = None,
custom_labels: bool = False,
width: int = 800,
height: int = 800) -> go.Figure:
""" Visualize a heatmap of the topic's similarity matrix
Based on the cosine similarity matrix between topic embeddings,
a heatmap is created showing the similarity between topics.
Arguments:
topics: A selection of topics to visualize.
top_n_topics: Only select the top n most frequent topics.
n_clusters: Create n clusters and order the similarity
matrix by those clusters.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Examples:
To visualize the similarity matrix of
topics simply run:
```python
topic_model.visualize_heatmap()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_heatmap()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_heatmap(self,
topics=topics,
top_n_topics=top_n_topics,
n_clusters=n_clusters,
custom_labels=custom_labels,
width=width,
height=height)
visualize_hierarchical_documents(self, docs, hierarchical_topics, topics=None, embeddings=None, reduced_embeddings=None, sample=None, hide_annotations=False, hide_document_hover=True, nr_levels=10, custom_labels=False, width=1200, height=750)
¶
Visualize documents and their topics in 2D at different levels of hierarchy
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
docs |
List[str] |
The documents you used when calling either |
required |
hierarchical_topics |
DataFrame |
A dataframe that contains a hierarchy of topics represented by their parents and their children |
required |
topics |
List[int] |
A selection of topics to visualize.
Not to be confused with the topics that you get from |
None |
embeddings |
ndarray |
The embeddings of all documents in |
None |
reduced_embeddings |
ndarray |
The 2D reduced embeddings of all documents in |
None |
sample |
Union[float, int] |
The percentage of documents in each topic that you would like to keep. Value can be between 0 and 1. Setting this value to, for example, 0.1 (10% of documents in each topic) makes it easier to visualize millions of documents as a subset is chosen. |
None |
hide_annotations |
bool |
Hide the names of the traces on top of each cluster. |
False |
hide_document_hover |
bool |
Hide the content of the documents when hovering over specific points. Helps to speed up generation of visualizations. |
True |
nr_levels |
int |
The number of levels to be visualized in the hierarchy. First, the distances
in |
10 |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
1200 |
height |
int |
The height of the figure. |
750 |
Examples:
To visualize the topics simply run:
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics)
Do note that this re-calculates the embeddings and reduces them to 2D. The advised and prefered pipeline for using this function is as follows:
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic and extract hierarchical topics
topic_model = BERTopic().fit(docs, embeddings)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
Or if you want to save the resulting figure:
fig = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_hierarchical_documents(self,
docs: List[str],
hierarchical_topics: pd.DataFrame,
topics: List[int] = None,
embeddings: np.ndarray = None,
reduced_embeddings: np.ndarray = None,
sample: Union[float, int] = None,
hide_annotations: bool = False,
hide_document_hover: bool = True,
nr_levels: int = 10,
custom_labels: bool = False,
width: int = 1200,
height: int = 750) -> go.Figure:
""" Visualize documents and their topics in 2D at different levels of hierarchy
Arguments:
docs: The documents you used when calling either `fit` or `fit_transform`
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children
topics: A selection of topics to visualize.
Not to be confused with the topics that you get from `.fit_transform`.
For example, if you want to visualize only topics 1 through 5:
`topics = [1, 2, 3, 4, 5]`.
embeddings: The embeddings of all documents in `docs`.
reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
sample: The percentage of documents in each topic that you would like to keep.
Value can be between 0 and 1. Setting this value to, for example,
0.1 (10% of documents in each topic) makes it easier to visualize
millions of documents as a subset is chosen.
hide_annotations: Hide the names of the traces on top of each cluster.
hide_document_hover: Hide the content of the documents when hovering over
specific points. Helps to speed up generation of visualizations.
nr_levels: The number of levels to be visualized in the hierarchy. First, the distances
in `hierarchical_topics.Distance` are split in `nr_levels` lists of distances with
equal length. Then, for each list of distances, the merged topics are selected that
have a distance less or equal to the maximum distance of the selected list of distances.
NOTE: To get all possible merged steps, make sure that `nr_levels` is equal to
the length of `hierarchical_topics`.
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
NOTE: Custom labels are only generated for the original
un-merged topics.
width: The width of the figure.
height: The height of the figure.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics)
```
Do note that this re-calculates the embeddings and reduces them to 2D.
The advised and prefered pipeline for using this function is as follows:
```python
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# 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 BERTopic and extract hierarchical topics
topic_model = BERTopic().fit(docs, embeddings)
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/hierarchical_documents.html"
style="width:1000px; height: 770px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_hierarchical_documents(self,
docs=docs,
hierarchical_topics=hierarchical_topics,
topics=topics,
embeddings=embeddings,
reduced_embeddings=reduced_embeddings,
sample=sample,
hide_annotations=hide_annotations,
hide_document_hover=hide_document_hover,
nr_levels=nr_levels,
custom_labels=custom_labels,
width=width,
height=height)
visualize_hierarchy(self, orientation='left', topics=None, top_n_topics=None, custom_labels=False, width=1000, height=600, hierarchical_topics=None, linkage_function=None, distance_function=None, color_threshold=1)
¶
Visualize a hierarchical structure of the topics
A ward linkage function is used to perform the hierarchical clustering based on the cosine distance matrix between topic embeddings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic_model |
A fitted BERTopic instance. |
required | |
orientation |
str |
The orientation of the figure. Either 'left' or 'bottom' |
'left' |
topics |
List[int] |
A selection of topics to visualize |
None |
top_n_topics |
int |
Only select the top n most frequent topics |
None |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. Only works if orientation is set to 'left' |
1000 |
height |
int |
The height of the figure. Only works if orientation is set to 'bottom' |
600 |
hierarchical_topics |
DataFrame |
A dataframe that contains a hierarchy of topics
represented by their parents and their children.
NOTE: The hierarchical topic names are only visualized
if both |
None |
linkage_function |
Callable[[scipy.sparse.csr.csr_matrix], numpy.ndarray] |
The linkage function to use. Default is:
|
None |
distance_function |
Callable[[scipy.sparse.csr.csr_matrix], scipy.sparse.csr.csr_matrix] |
The distance function to use on the c-TF-IDF matrix. Default is:
|
None |
color_threshold |
int |
Value at which the separation of clusters will be made which will result in different colors for different clusters. A higher value will typically lead in less colored clusters. |
1 |
Returns:
| Type | Description |
|---|---|
fig |
A plotly figure |
Examples:
To visualize the hierarchical structure of topics simply run:
topic_model.visualize_hierarchy()
If you also want the labels visualized of hierarchical topics, run the following:
# Extract hierarchical topics and their representations
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Visualize these representations
topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)
If you want to save the resulting figure:
fig = topic_model.visualize_hierarchy()
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_hierarchy(self,
orientation: str = "left",
topics: List[int] = None,
top_n_topics: int = None,
custom_labels: bool = False,
width: int = 1000,
height: int = 600,
hierarchical_topics: pd.DataFrame = None,
linkage_function: Callable[[csr_matrix], np.ndarray] = None,
distance_function: Callable[[csr_matrix], csr_matrix] = None,
color_threshold: int = 1) -> go.Figure:
""" Visualize a hierarchical structure of the topics
A ward linkage function is used to perform the
hierarchical clustering based on the cosine distance
matrix between topic embeddings.
Arguments:
topic_model: A fitted BERTopic instance.
orientation: The orientation of the figure.
Either 'left' or 'bottom'
topics: A selection of topics to visualize
top_n_topics: Only select the top n most frequent topics
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
NOTE: Custom labels are only generated for the original
un-merged topics.
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'
hierarchical_topics: A dataframe that contains a hierarchy of topics
represented by their parents and their children.
NOTE: The hierarchical topic names are only visualized
if both `topics` and `top_n_topics` are not set.
linkage_function: The linkage function to use. Default is:
`lambda x: sch.linkage(x, 'ward', optimal_ordering=True)`
NOTE: Make sure to use the same `linkage_function` as used
in `topic_model.hierarchical_topics`.
distance_function: The distance function to use on the c-TF-IDF matrix. Default is:
`lambda x: 1 - cosine_similarity(x)`
NOTE: Make sure to use the same `distance_function` as used
in `topic_model.hierarchical_topics`.
color_threshold: Value at which the separation of clusters will be made which
will result in different colors for different clusters.
A higher value will typically lead in less colored clusters.
Returns:
fig: A plotly figure
Examples:
To visualize the hierarchical structure of
topics simply run:
```python
topic_model.visualize_hierarchy()
```
If you also want the labels visualized of hierarchical topics,
run the following:
```python
# Extract hierarchical topics and their representations
hierarchical_topics = topic_model.hierarchical_topics(docs)
# Visualize these representations
topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)
```
If you want to save the resulting figure:
```python
fig = topic_model.visualize_hierarchy()
fig.write_html("path/to/file.html")
```
<iframe src="../getting_started/visualization/hierarchy.html"
style="width:1000px; height: 680px; border: 0px;""></iframe>
"""
check_is_fitted(self)
return plotting.visualize_hierarchy(self,
orientation=orientation,
topics=topics,
top_n_topics=top_n_topics,
custom_labels=custom_labels,
width=width,
height=height,
hierarchical_topics=hierarchical_topics,
linkage_function=linkage_function,
distance_function=distance_function,
color_threshold=color_threshold
)
visualize_term_rank(self, topics=None, log_scale=False, custom_labels=False, width=800, height=500)
¶
Visualize the ranks of all terms across all topics
Each topic is represented by a set of words. These words, however, do not all equally represent the topic. This visualization shows how many words are needed to represent a topic and at which point the beneficial effect of adding words starts to decline.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics |
List[int] |
A selection of topics to visualize. These will be colored red where all others will be colored black. |
None |
log_scale |
bool |
Whether to represent the ranking on a log scale |
False |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
800 |
height |
int |
The height of the figure. |
500 |
Returns:
| Type | Description |
|---|---|
fig |
A plotly figure |
Examples:
To visualize the ranks of all words across all topics simply run:
topic_model.visualize_term_rank()
Or if you want to save the resulting figure:
fig = topic_model.visualize_term_rank()
fig.write_html("path/to/file.html")
Reference:
This visualization was heavily inspired by the "Term Probability Decline" visualization found in an analysis by the amazing tmtoolkit. Reference to that specific analysis can be found here.
Source code in bertopic\_bertopic.py
def visualize_term_rank(self,
topics: List[int] = None,
log_scale: bool = False,
custom_labels: bool = False,
width: int = 800,
height: int = 500) -> go.Figure:
""" Visualize the ranks of all terms across all topics
Each topic is represented by a set of words. These words, however,
do not all equally represent the topic. This visualization shows
how many words are needed to represent a topic and at which point
the beneficial effect of adding words starts to decline.
Arguments:
topics: A selection of topics to visualize. These will be colored
red where all others will be colored black.
log_scale: Whether to represent the ranking on a log scale
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Examples:
To visualize the ranks of all words across
all topics simply run:
```python
topic_model.visualize_term_rank()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_term_rank()
fig.write_html("path/to/file.html")
```
Reference:
This visualization was heavily inspired by the
"Term Probability Decline" visualization found in an
analysis by the amazing [tmtoolkit](https://tmtoolkit.readthedocs.io/).
Reference to that specific analysis can be found
[here](https://wzbsocialsciencecenter.github.io/tm_corona/tm_analysis.html).
"""
check_is_fitted(self)
return plotting.visualize_term_rank(self,
topics=topics,
log_scale=log_scale,
custom_labels=custom_labels,
width=width,
height=height)
visualize_topics(self, topics=None, top_n_topics=None, width=650, height=650)
¶
Visualize topics, their sizes, and their corresponding words
This visualization is highly inspired by LDAvis, a great visualization technique typically reserved for LDA.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics |
List[int] |
A selection of topics to visualize |
None |
top_n_topics |
int |
Only select the top n most frequent topics |
None |
width |
int |
The width of the figure. |
650 |
height |
int |
The height of the figure. |
650 |
Examples:
To visualize the topics simply run:
topic_model.visualize_topics()
Or if you want to save the resulting figure:
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_topics(self,
topics: List[int] = None,
top_n_topics: int = None,
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
technique typically reserved for LDA.
Arguments:
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.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_topics()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics(self,
topics=topics,
top_n_topics=top_n_topics,
width=width,
height=height)
visualize_topics_over_time(self, topics_over_time, top_n_topics=None, topics=None, normalize_frequency=False, custom_labels=False, width=1250, height=450)
¶
Visualize topics over time
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics_over_time |
DataFrame |
The topics you would like to be visualized with the corresponding topic representation |
required |
top_n_topics |
int |
To visualize the most frequent topics instead of all |
None |
topics |
List[int] |
Select which topics you would like to be visualized |
None |
normalize_frequency |
bool |
Whether to normalize each topic's frequency individually |
False |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
1250 |
height |
int |
The height of the figure. |
450 |
Returns:
| Type | Description |
|---|---|
Figure |
A plotly.graph_objects.Figure including all traces |
Examples:
To visualize the topics over time, simply run:
topics_over_time = topic_model.topics_over_time(docs, timestamps)
topic_model.visualize_topics_over_time(topics_over_time)
Or if you want to save the resulting figure:
fig = topic_model.visualize_topics_over_time(topics_over_time)
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_topics_over_time(self,
topics_over_time: pd.DataFrame,
top_n_topics: int = None,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
width: int = 1250,
height: int = 450) -> go.Figure:
""" Visualize topics over time
Arguments:
topics_over_time: The topics you would like to be visualized with the
corresponding topic representation
top_n_topics: To visualize the most frequent topics instead of all
topics: Select which topics you would like to be visualized
normalize_frequency: Whether to normalize each topic's frequency individually
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
A plotly.graph_objects.Figure including all traces
Examples:
To visualize the topics over time, simply run:
```python
topics_over_time = topic_model.topics_over_time(docs, timestamps)
topic_model.visualize_topics_over_time(topics_over_time)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics_over_time(topics_over_time)
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics_over_time(self,
topics_over_time=topics_over_time,
top_n_topics=top_n_topics,
topics=topics,
normalize_frequency=normalize_frequency,
custom_labels=custom_labels,
width=width,
height=height)
visualize_topics_per_class(self, topics_per_class, top_n_topics=10, topics=None, normalize_frequency=False, custom_labels=False, width=1250, height=900)
¶
Visualize topics per class
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topics_per_class |
DataFrame |
The topics you would like to be visualized with the corresponding topic representation |
required |
top_n_topics |
int |
To visualize the most frequent topics instead of all |
10 |
topics |
List[int] |
Select which topics you would like to be visualized |
None |
normalize_frequency |
bool |
Whether to normalize each topic's frequency individually |
False |
custom_labels |
bool |
Whether to use custom topic labels that were defined using
|
False |
width |
int |
The width of the figure. |
1250 |
height |
int |
The height of the figure. |
900 |
Returns:
| Type | Description |
|---|---|
Figure |
A plotly.graph_objects.Figure including all traces |
Examples:
To visualize the topics per class, simply run:
topics_per_class = topic_model.topics_per_class(docs, classes)
topic_model.visualize_topics_per_class(topics_per_class)
Or if you want to save the resulting figure:
fig = topic_model.visualize_topics_per_class(topics_per_class)
fig.write_html("path/to/file.html")
Source code in bertopic\_bertopic.py
def visualize_topics_per_class(self,
topics_per_class: pd.DataFrame,
top_n_topics: int = 10,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
width: int = 1250,
height: int = 900) -> go.Figure:
""" Visualize topics per class
Arguments:
topics_per_class: The topics you would like to be visualized with the
corresponding topic representation
top_n_topics: To visualize the most frequent topics instead of all
topics: Select which topics you would like to be visualized
normalize_frequency: Whether to normalize each topic's frequency individually
custom_labels: Whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
width: The width of the figure.
height: The height of the figure.
Returns:
A plotly.graph_objects.Figure including all traces
Examples:
To visualize the topics per class, simply run:
```python
topics_per_class = topic_model.topics_per_class(docs, classes)
topic_model.visualize_topics_per_class(topics_per_class)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics_per_class(topics_per_class)
fig.write_html("path/to/file.html")
```
"""
check_is_fitted(self)
return plotting.visualize_topics_per_class(self,
topics_per_class=topics_per_class,
top_n_topics=top_n_topics,
topics=topics,
normalize_frequency=normalize_frequency,
custom_labels=custom_labels,
width=width,
height=height)