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KeyNMF

KeyNMF is a topic model that relies on contextually sensitive embeddings for keyword retrieval and term importance estimation, while taking inspiration from classical matrix-decomposition approaches for extracting topics.

Schematic overview of KeyNMF

Here's an example of how you can fit and interpret a KeyNMF model in the easiest way.

from turftopic import KeyNMF

model = KeyNMF(10, top_n=6)
model.fit(corpus)

model.print_topics()

How does KeyNMF work?

Keyword Extraction

KeyNMF discovers topics based on the importances of keywords for a given document. This is done by embedding words in a document, and then extracting the cosine similarities of documents to words using a transformer-model. Only the top_n keywords with positive similarity are kept.

Click to see formula
    1. Let \(x_d\) be the document's embedding produced with the encoder model.
      1. Let \(v_w\) be the word's embedding produced with the encoder model.
      2. Calculate cosine similarity between word and document

      For each word \(w\) in the document \(d\):

      \[ \text{sim}(d, w) = \frac{x_d \cdot v_w}{||x_d|| \cdot ||v_w||} \]

    For each document \(d\):

    1. Let \(K_d\) be the set of \(N\) keywords with the highest cosine similarity to document \(d\).
    \[ K_d = \text{argmax}_{K^*} \sum_{w \in K^*}\text{sim}(d,w)\text{, where } |K_d| = N\text{, and } \\ w \in d \]

  • Arrange positive keyword similarities into a keyword matrix \(M\) where the rows represent documents, and columns represent unique keywords.

    \[ M_{dw} = \begin{cases} \text{sim}(d,w), & \text{if } w \in K_d \text{ and } \text{sim}(d,w) > 0 \\ 0, & \text{otherwise}. \end{cases} \]

You can do this step manually if you want to precompute the keyword matrix. Keywords are represented as dictionaries mapping words to keyword importances.

model.extract_keywords(["Cars are perhaps the most important invention of the last couple of centuries. They have revolutionized transportation in many ways."])

[{'transportation': 0.44713873,
  'invention': 0.560524,
  'cars': 0.5046208,
  'revolutionized': 0.3339205,
  'important': 0.21803442}]

A precomputed Keyword matrix can also be used to fit a model:

keyword_matrix = model.extract_keywords(corpus)
model.fit(None, keywords=keyword_matrix)

Topic Discovery

Topics in this matrix are then discovered using Non-negative Matrix Factorization. Essentially the model tries to discover underlying dimensions/factors along which most of the variance in term importance can be explained.

Click to see formula

  • Decompose \(M\) with non-negative matrix factorization: \(M \approx WH\), where \(W\) is the document-topic matrix, and \(H\) is the topic-term matrix. Non-negative Matrix Factorization is done with the coordinate-descent algorithm, minimizing square loss:

    \[ L(W,H) = ||M - WH||^2 \]

You can fit KeyNMF on the raw corpus, with precomputed embeddings or with precomputed keywords.

# Fitting just on the corpus
model.fit(corpus)

# Fitting with precomputed embeddings
from sentence_transformers import SentenceTransformer

trf = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = trf.encode(corpus)

model = KeyNMF(10, encoder=trf)
model.fit(corpus, embeddings=embeddings)

# Fitting with precomputed keyword matrix
keyword_matrix = model.extract_keywords(corpus)
model.fit(None, keywords=keyword_matrix)

Asymmetric and Instruction-tuned Embedding Models

Some embedding models can be used together with prompting, or encode queries and passages differently. This is important for KeyNMF, as it is explicitly based on keyword retrieval, and its performance can be substantially enhanced by using asymmetric or prompted embeddings. Microsoft's E5 models are, for instance, all prompted by default, and it would be detrimental to performance not to do so yourself.

In these cases, you're better off NOT passing a string to Turftopic models, but explicitly loading the model using sentence-transformers.

Here's an example of using instruct models for keyword retrieval with KeyNMF. In this case, documents will serve as the queries and words as the passages:

from turftopic import KeyNMF
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer(
    "intfloat/multilingual-e5-large-instruct",
    prompts={
        "query": "Instruct: Retrieve relevant keywords from the given document. Query: "
        "passage": "Passage: "
    },
    # Make sure to set default prompt to query!
    default_prompt_name="query",
)
model = KeyNMF(10, encoder=encoder)

And a regular, asymmetric example:

encoder = SentenceTransformer(
    "intfloat/e5-large-v2",
    prompts={
        "query": "query: "
        "passage": "passage: "
    },
    # Make sure to set default prompt to query!
    default_prompt_name="query",
)
model = KeyNMF(10, encoder=encoder)

Setting the default prompt to query is especially important, when you are precomputing embeddings, as query should always be your default prompt to embed documents with.

Dynamic Topic Modeling

KeyNMF is also capable of modeling topics over time. This happens by fitting a KeyNMF model first on the entire corpus, then fitting individual topic-term matrices using coordinate descent based on the document-topic and document-term matrices in the given time slices.

Click to see formula
  1. Compute keyword matrix \(M\) for the whole corpus.
  2. Decompose \(M\) with non-negative matrix factorization: \(M \approx WH\).
    1. Let \(W_t\) be the document-topic proportions for documents in time slice \(t\), and \(M_t\) be the keyword matrix for words in time slice \(t\).
    2. Obtain the topic-term matrix for the time slice, by minimizing square loss using coordinate descent and fixing \(W_t\):

    For each time slice \(t\):

    \[ H_t = \text{argmin}_{H^{*}} ||M_t - W_t H^{*}||^2 \]

Here's an example of using KeyNMF in a dynamic modeling setting:

from datetime import datetime

from turftopic import KeyNMF

corpus: list[str] = []
timestamps: list[datetime] = []

model = KeyNMF(5, top_n=5, random_state=42)
document_topic_matrix = model.fit_transform_dynamic(
    corpus, timestamps=timestamps, bins=10
)

You can use the print_topics_over_time() method for producing a table of the topics over the generated time slices.

This example uses CNN news data.

model.print_topics_over_time()

Time Slice 0_olympics_tokyo_athletes_beijing 1_covid_vaccine_pandemic_coronavirus 2_olympic_athletes_ioc_athlete 3_djokovic_novak_tennis_federer 4_ronaldo_cristiano_messi_manchester
2012 12 06 - 2013 11 10 genocide, yugoslavia, karadzic, facts, cnn cnn, russia, chechnya, prince, merkel france, cnn, francois, hollande, bike tennis, tournament, wimbledon, grass, courts beckham, soccer, retired, david, learn
2013 11 10 - 2014 10 14 keith, stones, richards, musician, author georgia, russia, conflict, 2008, cnn civil, rights, hear, why, should cnn, kidneys, traffickers, organ, nepal ronaldo, cristiano, goalscorer, soccer, player
...
2020 05 07 - 2021 04 10 olympics, beijing, xinjiang, ioc, boycott covid, vaccine, coronavirus, pandemic, vaccination olympic, japan, medalist, canceled, tokyo djokovic, novak, tennis, federer, masterclass ronaldo, cristiano, messi, juventus, barcelona
2021 04 10 - 2022 03 16 olympics, tokyo, athletes, beijing, medal covid, pandemic, vaccine, vaccinated, coronavirus olympic, athletes, ioc, medal, athlete djokovic, novak, tennis, wimbledon, federer ronaldo, cristiano, messi, manchester, scored

You can also display the topics over time on an interactive HTML figure. The most important words for topics get revealed by hovering over them.

You will need to install Plotly for this to work.

pip install plotly

model.plot_topics_over_time(top_k=5)
Topics over time on a Figure

Online Topic Modeling

KeyNMF can also be fitted in an online manner. This is done by fitting NMF with batches of data instead of the whole dataset at once.

Use Cases:

  1. You can use online fitting when you have very large corpora at hand, and it would be impractical to fit a model on it at once.
  2. You have new data flowing in constantly, and need a model that can morph the topics based on the incoming data. You can also do this in a dynamic fashion.
  3. You need to finetune an already fitted topic model to novel data.

Batch Fitting

We will use the batching function from the itertools recipes to produce batches.

In newer versions of Python (>=3.12) you can just from itertools import batched

def batched(iterable, n: int):
    "Batch data into lists of length n. The last batch may be shorter."
    if n < 1:
        raise ValueError("n must be at least one")
    it = iter(iterable)
    while batch := tuple(itertools.islice(it, n)):
        yield batch

You can fit a KeyNMF model to a very large corpus in batches like so:

from turftopic import KeyNMF

model = KeyNMF(10, top_n=5)

corpus = ["some string", "etc", ...]
for batch in batched(corpus, 200):
    batch = list(batch)
    model.partial_fit(batch)

Precomputing the Keyword Matrix

If you desire the best results, it might make sense for you to go over the corpus in multiple epochs:

for epoch in range(5):
    for batch in batched(corpus, 200):
        model.partial_fit(batch)

This is mildly inefficient, however, as the texts need to be encoded on every epoch, and keywords need to be extracted. In such scenarios you might want to precompute and maybe even save the extracted keywords to disk using the extract_keywords() method.

Keywords are represented as dictionaries mapping words to keyword importances.

model.extract_keywords(["Cars are perhaps the most important invention of the last couple of centuries. They have revolutionized transportation in many ways."])

[{'transportation': 0.44713873,
  'invention': 0.560524,
  'cars': 0.5046208,
  'revolutionized': 0.3339205,
  'important': 0.21803442}]

You can extract keywords in batches and save them to disk to a file format of your choice. In this example I will use NDJSON because of its simplicity.

import json
from pathlib import Path
from typing import Iterable

# Here we are saving keywords to a JSONL/NDJSON file
with Path("keywords.jsonl").open("w") as keyword_file:
    # Doing this in batches is much more efficient than individual texts because
    # of the encoding.
    for batch in batched(corpus, 200):
        batch_keywords = model.extract_keywords(batch)
        # We serialize each
        for keywords in batch_keywords:
            keyword_file.write(json.dumps(keywords) + "\n")

def stream_keywords() -> Iterable[dict[str, float]]:
    """This function streams keywords from the file."""
    with Path("keywords.jsonl").open() as keyword_file:
        for line in keyword_file:
            yield json.loads(line.strip())

for epoch in range(5):
    keyword_stream = stream_keywords()
    for keyword_batch in batched(keyword_stream, 200):
        model.partial_fit(keywords=keyword_batch)

Dynamic Online Topic Modeling

KeyNMF can be online fitted in a dynamic manner as well. This is useful when you have large corpora of text over time, or when you want to fit the model on future information flowing in and want to analyze the topics' changes over time.

When using dynamic online topic modeling you have to predefine the time bins that you will use, as the model can't infer these from the data.

from datetime import datetime

# We will bin by years in a period of 2020-2030
bins = [datetime(year=y, month=1, day=1) for y in range(2020, 2030 + 2, 1)]

You can then online fit a dynamic topic model with partial_fit_dynamic().

model = KeyNMF(5, top_n=10)

corpus: list[str] = [...]
timestamps: list[datetime] = [...]

for batch in batched(zip(corpus, timestamps)):
    text_batch, ts_batch = zip(*batch)
    model.partial_fit_dynamic(text_batch, timestamps=ts_batch, bins=bins)

Hierarchical Topic Modeling

When you suspect that subtopics might be present in the topics you find with the model, KeyNMF can be used to discover topics further down the hierarchy.

This is done by utilising a special case of weighted NMF, where documents are weighted by how high they score on the parent topic.

Click to see formula
  1. Decompose keyword matrix \(M \approx WH\)
  2. To find subtopics in topic \(j\), define document weights \(w\) as the \(j\)th column of \(W\).
  3. Estimate subcomponents with wNMF \(M \approx \mathring{W} \mathring{H}\) with document weight \(w\)
    1. Initialise \(\mathring{H}\) and \(\mathring{W}\) randomly.
    2. Perform multiplicative updates until convergence.
      \(\mathring{W}^T = \mathring{W}^T \odot \frac{\mathring{H} \cdot (M^T \odot w)}{\mathring{H} \cdot \mathring{H}^T \cdot (\mathring{W}^T \odot w)}\)
      \(\mathring{H}^T = \mathring{H}^T \odot \frac{ (M^T \odot w)\cdot \mathring{W}}{\mathring{H}^T \cdot (\mathring{W}^T \odot w) \cdot \mathring{W}}\)
  4. To sufficiently differentiate the subcomponents from each other a pseudo-c-tf-idf weighting scheme is applied to \(\mathring{H}\):
    1. \(\mathring{H} = \mathring{H}_{ij} \odot ln(1 + \frac{A}{1+\sum_k \mathring{H}_{kj}})\), where \(A\) is the average of all elements in \(\mathring{H}\)

To create a hierarchical model, you can use the hierarchy property of the model.

# This divides each of the topics in the model to 3 subtopics.
model.hierarchy.divide_children(n_subtopics=3)
print(model.hierarchy)
Root
├── 0: windows, dos, os, disk, card, drivers, file, pc, files, microsoft
│ ├── 0.0: dos, file, disk, files, program, windows, disks, shareware, norton, memory
│ ├── 0.1: os, unix, windows, microsoft, apps, nt, ibm, ms, os2, platform
│ └── 0.2: card, drivers, monitor, driver, vga, ram, motherboard, cards, graphics, ati
└── 1: atheism, atheist, atheists, religion, christians, religious, belief, christian, god, beliefs
. ├── 1.0: atheism, alt, newsgroup, reading, faq, islam, questions, read, newsgroups, readers
. ├── 1.1: atheists, atheist, belief, theists, beliefs, religious, religion, agnostic, gods, religions
. └── 1.2: morality, bible, christian, christians, moral, christianity, biblical, immoral, god, religion

For a detailed tutorial on hierarchical modeling click here.

API Reference

turftopic.models.keynmf.KeyNMF

Bases: ContextualModel, DynamicTopicModel

Extracts keywords from documents based on semantic similarity of term encodings to document encodings. Topics are then extracted with non-negative matrix factorization from keywords' proximity to documents.

from turftopic import KeyNMF

corpus: list[str] = ["some text", "more text", ...]

model = KeyNMF(10, top_n=10).fit(corpus)
model.print_topics()

Parameters:

Name Type Description Default
n_components int

Number of topics.

 required
encoder Union[Encoder, str]

Model to encode documents/terms, all-MiniLM-L6-v2 is the default.

 'sentence-transformers/all-MiniLM-L6-v2'
vectorizer Optional[CountVectorizer]

Vectorizer used for term extraction. Can be used to prune or filter the vocabulary.

 None
top_n int

Number of keywords to extract for each document.

 25
random_state Optional[int]

Random state to use so that results are exactly reproducible.

 None
Source code in turftopic/models/keynmf.py 
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class KeyNMF(ContextualModel, DynamicTopicModel):
    """Extracts keywords from documents based on semantic similarity of
    term encodings to document encodings.
    Topics are then extracted with non-negative matrix factorization from
    keywords' proximity to documents.

    ```python
    from turftopic import KeyNMF

    corpus: list[str] = ["some text", "more text", ...]

    model = KeyNMF(10, top_n=10).fit(corpus)
    model.print_topics()
    ```

    Parameters
    ----------
    n_components: int
        Number of topics.
    encoder: str or SentenceTransformer
        Model to encode documents/terms, all-MiniLM-L6-v2 is the default.
    vectorizer: CountVectorizer, default None
        Vectorizer used for term extraction.
        Can be used to prune or filter the vocabulary.
    top_n: int, default 25
        Number of keywords to extract for each document.
    random_state: int, default None
        Random state to use so that results are exactly reproducible.
    """

    def __init__(
        self,
        n_components: int,
        encoder: Union[
            Encoder, str
        ] = "sentence-transformers/all-MiniLM-L6-v2",
        vectorizer: Optional[CountVectorizer] = None,
        top_n: int = 25,
        random_state: Optional[int] = None,
    ):
        self.random_state = random_state
        self.n_components = n_components
        self.top_n = top_n
        self.encoder = encoder
        self._has_custom_vectorizer = vectorizer is not None
        if isinstance(encoder, str):
            self.encoder_ = SentenceTransformer(encoder)
        else:
            self.encoder_ = encoder
        if vectorizer is None:
            self.vectorizer = default_vectorizer()
        else:
            self.vectorizer = vectorizer
        self.model = KeywordNMF(
            n_components=n_components, seed=random_state, top_n=self.top_n
        )
        self.extractor = SBertKeywordExtractor(
            top_n=self.top_n, vectorizer=self.vectorizer, encoder=self.encoder_
        )

    def extract_keywords(
        self,
        batch_or_document: Union[str, list[str]],
        embeddings: Optional[np.ndarray] = None,
    ) -> list[dict[str, float]]:
        """Extracts keywords from a document or a batch of documents.

        Parameters
        ----------
        batch_or_document: str | list[str]
            A single document or a batch of documents.
        embeddings: ndarray, optional
            Precomputed document embeddings.
        """
        if isinstance(batch_or_document, str):
            batch_or_document = [batch_or_document]
        return self.extractor.batch_extract_keywords(
            batch_or_document, embeddings=embeddings
        )

    def vectorize(
        self,
        raw_documents=None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ) -> spr.csr_array:
        """Creates document-term-matrix from documents."""
        if keywords is None:
            keywords = self.extract_keywords(
                raw_documents, embeddings=embeddings
            )
        return self.model.vectorize(keywords)

    def divide_topic(
        self,
        node: TopicNode,
        n_subtopics: int,
    ) -> list[TopicNode]:
        document_term_matrix = getattr(self, "document_term_matrix", None)
        if document_term_matrix is None:
            raise ValueError(
                "document_term_matrix is needed for computing hierarchies. Perhaps you fitted the model online?"
            )
        dtm = document_term_matrix
        subtopics = []
        weight = node.document_topic_vector
        subcomponents, sub_doc_topic = weighted_nmf(
            dtm, weight, n_subtopics, self.random_state, max_iter=200
        )
        subcomponents = subcomponents * np.log(
            1 + subcomponents.mean() / (subcomponents.sum(axis=0) + 1)
        )
        subcomponents = normalize(subcomponents, axis=1, norm="l2")
        for i, component, doc_topic_vector in zip(
            range(n_subtopics), subcomponents, sub_doc_topic.T
        ):
            sub = TopicNode(
                self,
                path=(*node.path, i),
                word_importance=component,
                document_topic_vector=doc_topic_vector,
                children=None,
            )
            subtopics.append(sub)
        return subtopics

    def fit_transform(
        self,
        raw_documents=None,
        y=None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ) -> np.ndarray:
        """Fits topic model and returns topic importances for documents.

        Parameters
        ----------
        raw_documents: iterable of str, optional
            Documents to fit the model on.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        keywords: list[dict[str, float]], optional
            Precomputed keyword dictionaries.

        Returns
        -------
        ndarray of shape (n_dimensions, n_topics)
            Document-topic matrix.
        """
        console = Console()
        with console.status("Running KeyNMF") as status:
            if keywords is None:
                status.update("Extracting keywords")
                keywords = self.extract_keywords(
                    raw_documents, embeddings=embeddings
                )
                console.log("Keyword extraction done.")
            status.update("Decomposing with NMF")
            try:
                doc_topic_matrix = self.model.transform(keywords)
            except (NotFittedError, AttributeError):
                doc_topic_matrix = self.model.fit_transform(keywords)
                self.components_ = self.model.components
            console.log("Model fitting done.")
        self.document_topic_matrix = doc_topic_matrix
        self.document_term_matrix = self.model.vectorize(keywords)
        self.hierarchy = TopicNode.create_root(
            self, self.components_, self.document_topic_matrix
        )
        return doc_topic_matrix

    def fit(
        self,
        raw_documents=None,
        y=None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ) -> np.ndarray:
        """Fits topic model and returns topic importances for documents.

        Parameters
        ----------
        raw_documents: iterable of str, optional
            Documents to fit the model on.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        keywords: list[dict[str, float]], optional
            Precomputed keyword dictionaries.
        """
        self.fit_transform(raw_documents, y, embeddings, keywords)
        return self

    def get_vocab(self) -> np.ndarray:
        return np.array(self.model.index_to_key)

    def transform(
        self,
        raw_documents=None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ) -> np.ndarray:
        """Infers topic importances for new documents based on a fitted model.

        Parameters
        ----------
        raw_documents: iterable of str
            Documents to fit the model on.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        keywords: list[dict[str, float]], optional
            Precomputed keyword dictionaries.

        Returns
        -------
        ndarray of shape (n_dimensions, n_topics)
            Document-topic matrix.
        """
        if keywords is None and raw_documents is None:
            raise ValueError(
                "You have to pass either keywords or raw_documents."
            )
        if keywords is None:
            keywords = self.extract_keywords(
                list(raw_documents), embeddings=embeddings
            )
        return self.model.transform(keywords)

    def partial_fit(
        self,
        raw_documents: Optional[list[str]] = None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ):
        """Online fits KeyNMF on a batch of documents.

        Parameters
        ----------
        raw_documents: iterable of str
            Documents to fit the model on.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        keywords: list[dict[str, float]], optional
            Precomputed keyword dictionaries.
        """
        if not self._has_custom_vectorizer:
            self.vectorizer = CountVectorizer(stop_words="english")
            self._has_custom_vectorizer = True
        min_df = self.vectorizer.min_df
        max_df = self.vectorizer.max_df
        if (min_df != 1) or (max_df != 1.0):
            warnings.warn(
                f"""When applying partial fitting, the vectorizer is fitted batch-wise in KeyNMF.
            You have a vectorizer with min_df={min_df}, and max_df={max_df}.
            If you continue with these settings, all tokens might get filtered out.
            We recommend setting min_df=1 and max_df=1.0 for online fitting.
            `model = KeyNMF(10, vectorizer=CountVectorizer(min_df=1, max_df=1.0)`
            """
            )
        if keywords is None and raw_documents is None:
            raise ValueError(
                "You have to pass either keywords or raw_documents."
            )
        if keywords is None:
            keywords = self.extract_keywords(
                raw_documents, embeddings=embeddings
            )
        self.model.partial_fit(keywords)
        self.components_ = self.model.components
        return self

    def prepare_topic_data(
        self,
        corpus: list[str],
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
    ) -> TopicData:
        if keywords is None and corpus is None:
            raise ValueError(
                "You have to pass either keywords or raw_documents."
            )
        console = Console()
        with console.status("Running KeyNMF") as status:
            if embeddings is None:
                embeddings = self.encode_documents(corpus)
            if keywords is None:
                status.update("Extracting keywords")
                keywords = self.extract_keywords(corpus, embeddings=embeddings)
                console.log("Keyword extraction done.")
            if (corpus is not None) and (len(keywords) != len(corpus)):
                raise ValueError(
                    "length of keywords is not the same as length of the corpus"
                )
            status.update("Decomposing with NMF")
            try:
                doc_topic_matrix = self.model.transform(keywords)
            except (NotFittedError, AttributeError):
                doc_topic_matrix = self.model.fit_transform(keywords)
                self.components_ = self.model.components
            console.log("Model fitting done.")
            document_term_matrix = self.model.vectorize(keywords)
        self.document_topic_matrix = doc_topic_matrix
        self.document_term_matrix = document_term_matrix
        self.hierarchy = TopicNode.create_root(
            self, self.components_, self.document_topic_matrix
        )
        res: TopicData = {
            "corpus": corpus,
            "document_term_matrix": document_term_matrix,
            "vocab": self.get_vocab(),
            "document_topic_matrix": doc_topic_matrix,
            "document_representation": embeddings,
            "topic_term_matrix": self.components_,  # type: ignore
            "transform": getattr(self, "transform", None),
            "topic_names": self.topic_names,
        }
        return res

    def fit_transform_dynamic(
        self,
        raw_documents=None,
        timestamps: Optional[list[datetime]] = None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
        bins: Union[int, list[datetime]] = 10,
    ) -> np.ndarray:
        if timestamps is None:
            raise TypeError(
                "You have to pass timestamps when fitting a dynamic model."
            )
        if keywords is None and raw_documents is None:
            raise ValueError(
                "You have to pass either keywords or raw_documents."
            )
        if keywords is None:
            keywords = self.extract_keywords(
                raw_documents, embeddings=embeddings
            )
        time_labels, self.time_bin_edges = self.bin_timestamps(
            timestamps, bins
        )
        doc_topic_matrix = self.model.fit_transform_dynamic(
            keywords, time_labels, self.time_bin_edges
        )
        self.temporal_importance_ = (
            self.model.temporal_importance_.T
            / self.model.temporal_importance_.sum(axis=1)
        ).T
        self.temporal_components_ = self.model.temporal_components
        self.components_ = self.model.components
        self.document_topic_matrix = doc_topic_matrix
        self.document_term_matrix = self.model.vectorize(keywords)
        self.hierarchy = TopicNode.create_root(
            self, self.components_, self.document_topic_matrix
        )
        return doc_topic_matrix

    def partial_fit_dynamic(
        self,
        raw_documents=None,
        timestamps: Optional[list[datetime]] = None,
        embeddings: Optional[np.ndarray] = None,
        keywords: Optional[list[dict[str, float]]] = None,
        bins: Union[int, list[datetime]] = 10,
    ):
        """Online fits Dynamic KeyNMF on a batch of documents.

        Parameters
        ----------
        raw_documents: iterable of str
            Documents to fit the model on.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        keywords: list[dict[str, float]], optional
            Precomputed keyword dictionaries.
        timestamps: list[datetime], optional
            List of timestamps for the batch.
        bins: list[datetime]
            Explicit time bin edges for the dynamic model.
        """
        if timestamps is None:
            raise TypeError(
                "You have to pass timestamps when fitting a dynamic model."
            )
        if keywords is None and raw_documents is None:
            raise ValueError(
                "You have to pass either keywords or raw_documents."
            )
        time_bin_edges = getattr(self, "time_bin_edges", None)
        if time_bin_edges is None:
            if isinstance(bins, int):
                raise TypeError(
                    "You have to pass explicit time bins (list of time bin edges) when partial "
                    "fitting KeyNMF, at least at the first call."
                )
            else:
                self.time_bin_edges = bins
        time_labels, self.time_bin_edges = self.bin_timestamps(
            timestamps, self.time_bin_edges
        )
        if keywords is None:
            keywords = self.extract_keywords(
                raw_documents, embeddings=embeddings
            )
        self.model.partial_fit_dynamic(
            keywords, time_labels, self.time_bin_edges
        )
        self.temporal_importance_ = (
            self.model.temporal_importance_.T
            / self.model.temporal_importance_.sum(axis=1)
        ).T
        self.temporal_components_ = self.model.temporal_components
        self.components_ = self.model.components
        return self

extract_keywords(batch_or_document, embeddings=None)

Extracts keywords from a document or a batch of documents.

Parameters:

Name Type Description Default
batch_or_document Union[str, list[str]]

A single document or a batch of documents.

 required
embeddings Optional[ndarray]

Precomputed document embeddings.

 None
Source code in turftopic/models/keynmf.py 
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def extract_keywords(
    self,
    batch_or_document: Union[str, list[str]],
    embeddings: Optional[np.ndarray] = None,
) -> list[dict[str, float]]:
    """Extracts keywords from a document or a batch of documents.

    Parameters
    ----------
    batch_or_document: str | list[str]
        A single document or a batch of documents.
    embeddings: ndarray, optional
        Precomputed document embeddings.
    """
    if isinstance(batch_or_document, str):
        batch_or_document = [batch_or_document]
    return self.extractor.batch_extract_keywords(
        batch_or_document, embeddings=embeddings
    )

fit(raw_documents=None, y=None, embeddings=None, keywords=None)

Fits topic model and returns topic importances for documents.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
keywords Optional[list[dict[str, float]]]

Precomputed keyword dictionaries.

 None
Source code in turftopic/models/keynmf.py 
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def fit(
    self,
    raw_documents=None,
    y=None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
) -> np.ndarray:
    """Fits topic model and returns topic importances for documents.

    Parameters
    ----------
    raw_documents: iterable of str, optional
        Documents to fit the model on.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    keywords: list[dict[str, float]], optional
        Precomputed keyword dictionaries.
    """
    self.fit_transform(raw_documents, y, embeddings, keywords)
    return self

fit_transform(raw_documents=None, y=None, embeddings=None, keywords=None)

Fits topic model and returns topic importances for documents.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
keywords Optional[list[dict[str, float]]]

Precomputed keyword dictionaries.

 None

Returns:

Type Description
ndarray of shape (n_dimensions, n_topics)

Document-topic matrix.
Source code in turftopic/models/keynmf.py 
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def fit_transform(
    self,
    raw_documents=None,
    y=None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
) -> np.ndarray:
    """Fits topic model and returns topic importances for documents.

    Parameters
    ----------
    raw_documents: iterable of str, optional
        Documents to fit the model on.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    keywords: list[dict[str, float]], optional
        Precomputed keyword dictionaries.

    Returns
    -------
    ndarray of shape (n_dimensions, n_topics)
        Document-topic matrix.
    """
    console = Console()
    with console.status("Running KeyNMF") as status:
        if keywords is None:
            status.update("Extracting keywords")
            keywords = self.extract_keywords(
                raw_documents, embeddings=embeddings
            )
            console.log("Keyword extraction done.")
        status.update("Decomposing with NMF")
        try:
            doc_topic_matrix = self.model.transform(keywords)
        except (NotFittedError, AttributeError):
            doc_topic_matrix = self.model.fit_transform(keywords)
            self.components_ = self.model.components
        console.log("Model fitting done.")
    self.document_topic_matrix = doc_topic_matrix
    self.document_term_matrix = self.model.vectorize(keywords)
    self.hierarchy = TopicNode.create_root(
        self, self.components_, self.document_topic_matrix
    )
    return doc_topic_matrix

partial_fit(raw_documents=None, embeddings=None, keywords=None)

Online fits KeyNMF on a batch of documents.

Parameters:

Name Type Description Default
raw_documents Optional[list[str]]

Documents to fit the model on.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
keywords Optional[list[dict[str, float]]]

Precomputed keyword dictionaries.

 None
Source code in turftopic/models/keynmf.py 
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def partial_fit(
    self,
    raw_documents: Optional[list[str]] = None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
):
    """Online fits KeyNMF on a batch of documents.

    Parameters
    ----------
    raw_documents: iterable of str
        Documents to fit the model on.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    keywords: list[dict[str, float]], optional
        Precomputed keyword dictionaries.
    """
    if not self._has_custom_vectorizer:
        self.vectorizer = CountVectorizer(stop_words="english")
        self._has_custom_vectorizer = True
    min_df = self.vectorizer.min_df
    max_df = self.vectorizer.max_df
    if (min_df != 1) or (max_df != 1.0):
        warnings.warn(
            f"""When applying partial fitting, the vectorizer is fitted batch-wise in KeyNMF.
        You have a vectorizer with min_df={min_df}, and max_df={max_df}.
        If you continue with these settings, all tokens might get filtered out.
        We recommend setting min_df=1 and max_df=1.0 for online fitting.
        `model = KeyNMF(10, vectorizer=CountVectorizer(min_df=1, max_df=1.0)`
        """
        )
    if keywords is None and raw_documents is None:
        raise ValueError(
            "You have to pass either keywords or raw_documents."
        )
    if keywords is None:
        keywords = self.extract_keywords(
            raw_documents, embeddings=embeddings
        )
    self.model.partial_fit(keywords)
    self.components_ = self.model.components
    return self

partial_fit_dynamic(raw_documents=None, timestamps=None, embeddings=None, keywords=None, bins=10)

Online fits Dynamic KeyNMF on a batch of documents.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
keywords Optional[list[dict[str, float]]]

Precomputed keyword dictionaries.

 None
timestamps Optional[list[datetime]]

List of timestamps for the batch.

 None
bins Union[int, list[datetime]]

Explicit time bin edges for the dynamic model.

 10
Source code in turftopic/models/keynmf.py 
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def partial_fit_dynamic(
    self,
    raw_documents=None,
    timestamps: Optional[list[datetime]] = None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
    bins: Union[int, list[datetime]] = 10,
):
    """Online fits Dynamic KeyNMF on a batch of documents.

    Parameters
    ----------
    raw_documents: iterable of str
        Documents to fit the model on.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    keywords: list[dict[str, float]], optional
        Precomputed keyword dictionaries.
    timestamps: list[datetime], optional
        List of timestamps for the batch.
    bins: list[datetime]
        Explicit time bin edges for the dynamic model.
    """
    if timestamps is None:
        raise TypeError(
            "You have to pass timestamps when fitting a dynamic model."
        )
    if keywords is None and raw_documents is None:
        raise ValueError(
            "You have to pass either keywords or raw_documents."
        )
    time_bin_edges = getattr(self, "time_bin_edges", None)
    if time_bin_edges is None:
        if isinstance(bins, int):
            raise TypeError(
                "You have to pass explicit time bins (list of time bin edges) when partial "
                "fitting KeyNMF, at least at the first call."
            )
        else:
            self.time_bin_edges = bins
    time_labels, self.time_bin_edges = self.bin_timestamps(
        timestamps, self.time_bin_edges
    )
    if keywords is None:
        keywords = self.extract_keywords(
            raw_documents, embeddings=embeddings
        )
    self.model.partial_fit_dynamic(
        keywords, time_labels, self.time_bin_edges
    )
    self.temporal_importance_ = (
        self.model.temporal_importance_.T
        / self.model.temporal_importance_.sum(axis=1)
    ).T
    self.temporal_components_ = self.model.temporal_components
    self.components_ = self.model.components
    return self

transform(raw_documents=None, embeddings=None, keywords=None)

Infers topic importances for new documents based on a fitted model.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
keywords Optional[list[dict[str, float]]]

Precomputed keyword dictionaries.

 None

Returns:

Type Description
ndarray of shape (n_dimensions, n_topics)

Document-topic matrix.
Source code in turftopic/models/keynmf.py 
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def transform(
    self,
    raw_documents=None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
) -> np.ndarray:
    """Infers topic importances for new documents based on a fitted model.

    Parameters
    ----------
    raw_documents: iterable of str
        Documents to fit the model on.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    keywords: list[dict[str, float]], optional
        Precomputed keyword dictionaries.

    Returns
    -------
    ndarray of shape (n_dimensions, n_topics)
        Document-topic matrix.
    """
    if keywords is None and raw_documents is None:
        raise ValueError(
            "You have to pass either keywords or raw_documents."
        )
    if keywords is None:
        keywords = self.extract_keywords(
            list(raw_documents), embeddings=embeddings
        )
    return self.model.transform(keywords)

vectorize(raw_documents=None, embeddings=None, keywords=None)

Creates document-term-matrix from documents.

Source code in turftopic/models/keynmf.py 
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def vectorize(
    self,
    raw_documents=None,
    embeddings: Optional[np.ndarray] = None,
    keywords: Optional[list[dict[str, float]]] = None,
) -> spr.csr_array:
    """Creates document-term-matrix from documents."""
    if keywords is None:
        keywords = self.extract_keywords(
            raw_documents, embeddings=embeddings
        )
    return self.model.vectorize(keywords)