Dynamic Topic Modeling

If you want to examine the evolution of topics over time, you will need a dynamic topic model.

Note that regular static models can also be used to study the evolution of topics and information dynamics, but they can't capture changes in the topics themselves.

Models

In Turftopic you can currently use three different topic models for modeling topics over time:

1. ClusteringTopicModel, where an overall model is fitted on the whole corpus, and then term importances are estimated over time slices.
2. GMM, similarly to clustering models, term importances are reestimated per time slice
3. KeyNMF, an overall decomposition is done, then using coordinate descent, topic-term-matrices are recalculated based on document-topic importances in the given time slice.
4. SemanticSignalSeparation, a global model is fitted and then local models are inferred using linear regression from embeddings and document-topic signals in a given time-slice.

Usage

Dynamic topic models in Turftopic have a unified interface. To fit a dynamic topic model you will need a corpus, that has been annotated with timestamps. The timestamps need to be Python datetime objects, but pandas Timestamp object are also supported.

Models that have dynamic modeling capabilities (KeyNMF, GMM, SemanticSignalSeparation and ClusteringTopicModel) have a fit_transform_dynamic() method, that fits the model on the corpus over time.

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
2014 10 14 - 2015 09 18	ethiopia, brew, coffee, birthplace, anderson	climate, sutter, countries, snapchat, injustice	women, guatemala, murder, country, worst	cnn, climate, oklahoma, women, topics	sweden, parental, dads, advantage, leave
2015 09 18 - 2016 08 22	snow, ice, winter, storm, pets	climate, crisis, drought, outbreaks, syrian	women, vulnerabilities, frontlines, countries, marcelas	cnn, warming, climate, sutter, theresa	sutter, band, paris, fans, crowd
2016 08 22 - 2017 07 26	derby, epsom, sporting, race, spectacle	overdoses, heroin, deaths, macron, emmanuel	fear, died, indigenous, people, arthur	siblings, amnesia, palombo, racial, mh370	bobbi, measles, raped, camp, rape
2017 07 26 - 2018 06 30	her, percussionist, drums, she, deported	novichok, hurricane, hospital, deaths, breathing	women, day, celebrate, taliban, international	abuse, harassment, cnn, women, pilgrimage	maradona, argentina, history, jadon, rape
2018 06 30 - 2019 06 03	athletes, teammates, celtics, white, racism	pope, archbishop, francis, vigano, resignation	racism, athletes, teammates, celtics, white	golf, iceland, volcanoes, atlantic, ocean	rape, sudanese, racist, women, soldiers
2019 06 03 - 2020 05 07	esports, climate, ice, racers, culver	esports, coronavirus, pandemic, football, teams	racers, women, compete, zone, bery	serena, stadium, sasha, final, naomi	kobe, bryant, greatest, basketball, influence
2020 05 07 - 2021 04 10	olympics, beijing, xinjiang, ioc, boycott	covid, vaccine, coronavirus, pandemic, vaccination	olympic, japan, medalist, canceled, tokyo	djokovic, novak, tennis, federer, masterclass	ronaldo, cristiano, messi, juventus, barcelona
2021 04 10 - 2022 03 16	olympics, tokyo, athletes, beijing, medal	covid, pandemic, vaccine, vaccinated, coronavirus	olympic, athletes, ioc, medal, athlete	djokovic, novak, tennis, wimbledon, federer	ronaldo, cristiano, messi, manchester, scored

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()

Topics over time in a Dynamic KeyNMF model.

API reference

All dynamic topic models have a temporal_components_ attribute, which contains the topic-term matrices for each time slice, along with a temporal_importance_ attribute, which contains the importance of each topic in each time slice.

turftopic.dynamic.DynamicTopicModel

Bases: ABC

Source code in turftopic/dynamic.py

class DynamicTopicModel(ABC):
    @staticmethod
    def bin_timestamps(
        timestamps: list[datetime], bins: Union[int, list[datetime]] = 10
    ) -> tuple[np.ndarray, list[datetime]]:
        """Bins timestamps based on given bins.

        Parameters
        ----------
        timestamps: list[datetime]
            List of timestamps for documents.
        bins: int or list[datetime], default 10
            Time bins to use.
            If the bins are an int (N), N equally sized bins are used.
            Otherwise they should be bin edges, including the last and first edge.
            Bins are inclusive at the lower end and exclusive at the upper (lower <= timestamp < upper).

        Returns
        -------
        time_labels: ndarray of int
            Labels for time slice in each document.
        bin_edges: list[datetime]
            List of edges for time bins.
        """
        return bin_timestamps(timestamps, bins)

    @abstractmethod
    def fit_transform_dynamic(
        self,
        raw_documents,
        timestamps: list[datetime],
        embeddings: Optional[np.ndarray] = None,
        bins: Union[int, list[datetime]] = 10,
    ) -> np.ndarray:
        """Fits a dynamic topic model on the corpus and returns document-topic-importances.

        Parameters
        ----------
        raw_documents
            Documents to fit the model on.
        timestamps: list[datetime]
            Timestamp for each document in `datetime` format.
        embeddings: np.ndarray, default None
            Document embeddings produced by an embedding model.
        bins: int or list[datetime], default 10
            Specifies how to bin timestamps in to time slices.
            When an `int`, the corpus will be divided into N equal time slices.
            When a list, it describes the edges of each time slice including the starting
            and final edges of the slices.

        Returns
        -------
        ndarray of shape (n_documents, n_topics)
            Document-topic importance matrix.
        """
        pass

    def fit_dynamic(
        self,
        raw_documents,
        timestamps: list[datetime],
        embeddings: Optional[np.ndarray] = None,
        bins: Union[int, list[datetime]] = 10,
    ):
        """Fits a dynamic topic model on the corpus and returns document-topic-importances.

        Parameters
        ----------
        raw_documents
            Documents to fit the model on.
        timestamps: list[datetime]
            Timestamp for each document in `datetime` format.
        embeddings: np.ndarray, default None
            Document embeddings produced by an embedding model.
        bins: int or list[datetime], default 10
            Specifies how to bin timestamps in to time slices.
            When an `int`, the corpus will be divided into N equal time slices.
            When a list, it describes the edges of each time slice including the starting
            and final edges of the slices.

            Note: The final edge is not included. You might want to add one day to
            the last bin edge if it equals the last timestamp.
        """
        self.fit_transform_dynamic(raw_documents, timestamps, embeddings, bins)
        return self

    def get_time_slices(self) -> list[tuple[datetime, datetime]]:
        """Returns starting and ending datetime of
        each timeslice in the model."""
        bins = self.time_bin_edges
        res = []
        for i_bin, slice_end in enumerate(bins[1:]):
            res.append((bins[i_bin], slice_end))
        return res

    def get_topics_over_time(
        self, top_k: int = 10
    ) -> list[list[tuple[Any, list[tuple[str, float]]]]]:
        """Returns high-level topic representations in form of the top K words
        in each topic.

        Parameters
        ----------
        top_k: int, default 10
            Number of top words to return for each topic.

        Returns
        -------
        list[list[tuple]]
            List of topics over each time slice in the dynamic model.
            Each time slice is a list of topics.
            Each topic is a tuple of topic ID and the top k words.
            Top k words are a list of (word, word_importance) pairs.
        """
        n_topics = self.temporal_components_.shape[1]
        try:
            classes = self.classes_
        except AttributeError:
            classes = list(range(n_topics))
        res = []
        for components in self.temporal_components_:
            highest = np.argpartition(-components, top_k)[:, :top_k]
            vocab = self.get_vocab()
            top = []
            score = []
            for component, high in zip(components, highest):
                importance = component[high]
                high = high[np.argsort(-importance)]
                score.append(component[high])
                top.append(vocab[high])
            topics = []
            for topic, words, scores in zip(classes, top, score):
                topic_data = (topic, list(zip(words, scores)))
                topics.append(topic_data)
            res.append(topics)
        return res

    def _topics_over_time(
        self,
        top_k: int = 5,
        show_scores: bool = False,
        date_format: str = "%Y %m %d",
    ) -> list[list[str]]:
        temporal_components = self.temporal_components_
        slices = self.get_time_slices()
        slice_names = []
        for start_dt, end_dt in slices:
            start_str = start_dt.strftime(date_format)
            end_str = end_dt.strftime(date_format)
            slice_names.append(f"{start_str} - {end_str}")
        n_topics = self.temporal_components_.shape[1]
        try:
            topic_names = self.topic_names
        except AttributeError:
            topic_names = [f"Topic {i}" for i in range(n_topics)]
        columns = []
        rows = []
        columns.append("Time Slice")
        for topic in topic_names:
            columns.append(topic)
        for slice_name, components in zip(slice_names, temporal_components):
            fields = []
            fields.append(slice_name)
            highest = np.argpartition(-components, top_k)[:, :top_k]
            vocab = self.get_vocab()
            for component, high in zip(components, highest):
                if np.all(component == 0) or np.all(np.isnan(component)):
                    fields.append("Topic not present.")
                    continue
                importance = component[high]
                high = high[np.argsort(-importance)]
                high = high[importance != 0]
                scores = component[high]
                words = vocab[high]
                if show_scores:
                    concat_words = ", ".join(
                        [
                            f"{word}({importance:.2f})"
                            for word, importance in zip(words, scores)
                        ]
                    )
                else:
                    concat_words = ", ".join([word for word in words])
                fields.append(concat_words)
            rows.append(fields)
        return [columns, *rows]

    def print_topics_over_time(
        self,
        top_k: int = 5,
        show_scores: bool = False,
        date_format: str = "%Y %m %d",
    ):
        """Pretty prints topics in the model in a table.

        Parameters
        ----------
        top_k: int, default 10
            Number of top words to return for each topic.
        show_scores: bool, default False
            Indicates whether to show importance scores for each word.
        """
        columns, *rows = self._topics_over_time(
            top_k, show_scores, date_format
        )
        table = Table(show_lines=True)
        for column in columns:
            table.add_column(column)
        for row in rows:
            table.add_row(*row)
        console = Console()
        console.print(table)

    def export_topics_over_time(
        self,
        top_k: int = 5,
        show_scores: bool = False,
        date_format: str = "%Y %m %d",
        format="csv",
    ) -> str:
        """Pretty prints topics in the model in a table.

        Parameters
        ----------
        top_k: int, default 10
            Number of top words to return for each topic.
        show_scores: bool, default False
            Indicates whether to show importance scores for each word.
        format: 'csv', 'latex' or 'markdown'
            Specifies which format should be used.
            'csv', 'latex' and 'markdown' are supported.
        """
        table = self._topics_over_time(top_k, show_scores, date_format)
        return export_table(table, format=format)

    def plot_topics_over_time(
        self,
        top_k: int = 6,
        color_discrete_sequence: Optional[Iterable[str]] = None,
        color_discrete_map: Optional[dict[str, str]] = None,
    ):
        """Displays topics over time in the fitted dynamic model on a dynamic HTML figure.

        > You will need to `pip install plotly` to use this method.

        Parameters
        ----------
        top_k: int, default 6
            Number of top words per topic to display on the figure.
        color_discrete_sequence: Iterable[str], default None
            Color palette to use in the plot.
            Example:

            ```python
            import plotly.express as px
            model.plot_topics_over_time(color_discrete_sequence=px.colors.qualitative.Light24)
            ```

        color_discrete_map: dict[str, str], default None
            Topic names mapped to the colors that should
            be associated with them.

        Returns
        -------
        go.Figure
            Plotly graph objects Figure, that can be displayed or exported as
            HTML or static image.
        """
        try:
            import plotly.express as px
            import plotly.graph_objects as go
        except (ImportError, ModuleNotFoundError) as e:
            raise ModuleNotFoundError(
                "Please install plotly if you intend to use plots in Turftopic."
            ) from e
        if color_discrete_sequence is not None:
            topic_colors = itertools.cycle(color_discrete_sequence)
        elif color_discrete_map is not None:
            topic_colors = [
                color_discrete_map[topic_name]
                for topic_name in self.topic_names
            ]
        else:
            topic_colors = px.colors.qualitative.Dark24
        fig = go.Figure()
        vocab = self.get_vocab()
        n_topics = self.temporal_components_.shape[1]
        try:
            topic_names = self.topic_names
        except AttributeError:
            topic_names = [f"Topic {i}" for i in range(n_topics)]
        for trace_color, (i_topic, topic_imp_t) in zip(
            topic_colors, enumerate(self.temporal_importance_.T)
        ):
            component_over_time = self.temporal_components_[:, i_topic, :]
            name_over_time = []
            for component in component_over_time:
                high = np.argpartition(-component, top_k)[:top_k]
                values = component[high]
                if np.all(values == 0) or np.all(np.isnan(values)):
                    name_over_time.append("<not present>")
                    continue
                high = high[np.argsort(-values)]
                name_over_time.append(", ".join(vocab[high]))
            times = self.time_bin_edges[:-1]
            fig.add_trace(
                go.Scatter(
                    x=times,
                    y=topic_imp_t,
                    mode="markers+lines",
                    text=name_over_time,
                    name=topic_names[i_topic],
                    hovertemplate="<b>%{text}</b>",
                    marker=dict(
                        line=dict(width=2, color="black"),
                        size=14,
                        color=trace_color,
                    ),
                    line=dict(width=3),
                )
            )
        fig.update_layout(
            template="plotly_white",
            hoverlabel=dict(font_size=16, bgcolor="white"),
            hovermode="x",
        )
        fig.update_xaxes(title="Time Slice Start")
        fig.update_yaxes(title="Topic Importance")
        return fig

bin_timestamps(timestamps, bins=10) staticmethod

Bins timestamps based on given bins.

Parameters:

Name	Type	Description	Default
timestamps	list[datetime]	List of timestamps for documents.	required
bins	Union[int, list[datetime]]	Time bins to use. If the bins are an int (N), N equally sized bins are used. Otherwise they should be bin edges, including the last and first edge. Bins are inclusive at the lower end and exclusive at the upper (lower <= timestamp < upper).	10

Returns:

Name	Type	Description
time_labels	ndarray of int	Labels for time slice in each document.
bin_edges	list[datetime]	List of edges for time bins.

Source code in turftopic/dynamic.py

@staticmethod
def bin_timestamps(
    timestamps: list[datetime], bins: Union[int, list[datetime]] = 10
) -> tuple[np.ndarray, list[datetime]]:
    """Bins timestamps based on given bins.

    Parameters
    ----------
    timestamps: list[datetime]
        List of timestamps for documents.
    bins: int or list[datetime], default 10
        Time bins to use.
        If the bins are an int (N), N equally sized bins are used.
        Otherwise they should be bin edges, including the last and first edge.
        Bins are inclusive at the lower end and exclusive at the upper (lower <= timestamp < upper).

    Returns
    -------
    time_labels: ndarray of int
        Labels for time slice in each document.
    bin_edges: list[datetime]
        List of edges for time bins.
    """
    return bin_timestamps(timestamps, bins)

export_topics_over_time(top_k=5, show_scores=False, date_format='%Y %m %d', format='csv')

Pretty prints topics in the model in a table.

Parameters:

Name	Type	Description	Default
top_k	int	Number of top words to return for each topic.	5
show_scores	bool	Indicates whether to show importance scores for each word.	False
format		Specifies which format should be used. 'csv', 'latex' and 'markdown' are supported.	'csv'

Source code in turftopic/dynamic.py

def export_topics_over_time(
    self,
    top_k: int = 5,
    show_scores: bool = False,
    date_format: str = "%Y %m %d",
    format="csv",
) -> str:
    """Pretty prints topics in the model in a table.

    Parameters
    ----------
    top_k: int, default 10
        Number of top words to return for each topic.
    show_scores: bool, default False
        Indicates whether to show importance scores for each word.
    format: 'csv', 'latex' or 'markdown'
        Specifies which format should be used.
        'csv', 'latex' and 'markdown' are supported.
    """
    table = self._topics_over_time(top_k, show_scores, date_format)
    return export_table(table, format=format)

fit_dynamic(raw_documents, timestamps, embeddings=None, bins=10)

Fits a dynamic topic model on the corpus and returns document-topic-importances.

Parameters:

Name	Type	Description	Default
raw_documents		Documents to fit the model on.	required
timestamps	list[datetime]	Timestamp for each document in datetime format.	required
embeddings	Optional[ndarray]	Document embeddings produced by an embedding model.	None
bins	Union[int, list[datetime]]	Specifies how to bin timestamps in to time slices. When an int, the corpus will be divided into N equal time slices. When a list, it describes the edges of each time slice including the starting and final edges of the slices. Note: The final edge is not included. You might want to add one day to the last bin edge if it equals the last timestamp.	10

Source code in turftopic/dynamic.py

def fit_dynamic(
    self,
    raw_documents,
    timestamps: list[datetime],
    embeddings: Optional[np.ndarray] = None,
    bins: Union[int, list[datetime]] = 10,
):
    """Fits a dynamic topic model on the corpus and returns document-topic-importances.

    Parameters
    ----------
    raw_documents
        Documents to fit the model on.
    timestamps: list[datetime]
        Timestamp for each document in `datetime` format.
    embeddings: np.ndarray, default None
        Document embeddings produced by an embedding model.
    bins: int or list[datetime], default 10
        Specifies how to bin timestamps in to time slices.
        When an `int`, the corpus will be divided into N equal time slices.
        When a list, it describes the edges of each time slice including the starting
        and final edges of the slices.

        Note: The final edge is not included. You might want to add one day to
        the last bin edge if it equals the last timestamp.
    """
    self.fit_transform_dynamic(raw_documents, timestamps, embeddings, bins)
    return self

fit_transform_dynamic(raw_documents, timestamps, embeddings=None, bins=10) abstractmethod

Fits a dynamic topic model on the corpus and returns document-topic-importances.

Parameters:

Name	Type	Description	Default
raw_documents		Documents to fit the model on.	required
timestamps	list[datetime]	Timestamp for each document in datetime format.	required
embeddings	Optional[ndarray]	Document embeddings produced by an embedding model.	None
bins	Union[int, list[datetime]]	Specifies how to bin timestamps in to time slices. When an int, the corpus will be divided into N equal time slices. When a list, it describes the edges of each time slice including the starting and final edges of the slices.	10

Returns:

Type	Description
ndarray of shape (n_documents, n_topics)	Document-topic importance matrix.

Source code in turftopic/dynamic.py

@abstractmethod
def fit_transform_dynamic(
    self,
    raw_documents,
    timestamps: list[datetime],
    embeddings: Optional[np.ndarray] = None,
    bins: Union[int, list[datetime]] = 10,
) -> np.ndarray:
    """Fits a dynamic topic model on the corpus and returns document-topic-importances.

    Parameters
    ----------
    raw_documents
        Documents to fit the model on.
    timestamps: list[datetime]
        Timestamp for each document in `datetime` format.
    embeddings: np.ndarray, default None
        Document embeddings produced by an embedding model.
    bins: int or list[datetime], default 10
        Specifies how to bin timestamps in to time slices.
        When an `int`, the corpus will be divided into N equal time slices.
        When a list, it describes the edges of each time slice including the starting
        and final edges of the slices.

    Returns
    -------
    ndarray of shape (n_documents, n_topics)
        Document-topic importance matrix.
    """
    pass

get_time_slices()

Returns starting and ending datetime of each timeslice in the model.

Source code in turftopic/dynamic.py

def get_time_slices(self) -> list[tuple[datetime, datetime]]:
    """Returns starting and ending datetime of
    each timeslice in the model."""
    bins = self.time_bin_edges
    res = []
    for i_bin, slice_end in enumerate(bins[1:]):
        res.append((bins[i_bin], slice_end))
    return res

get_topics_over_time(top_k=10)

Returns high-level topic representations in form of the top K words in each topic.

Parameters:

Name	Type	Description	Default
top_k	int	Number of top words to return for each topic.	10

Returns:

Type	Description
list[list[tuple]]	List of topics over each time slice in the dynamic model. Each time slice is a list of topics. Each topic is a tuple of topic ID and the top k words. Top k words are a list of (word, word_importance) pairs.

Source code in turftopic/dynamic.py

def get_topics_over_time(
    self, top_k: int = 10
) -> list[list[tuple[Any, list[tuple[str, float]]]]]:
    """Returns high-level topic representations in form of the top K words
    in each topic.

    Parameters
    ----------
    top_k: int, default 10
        Number of top words to return for each topic.

    Returns
    -------
    list[list[tuple]]
        List of topics over each time slice in the dynamic model.
        Each time slice is a list of topics.
        Each topic is a tuple of topic ID and the top k words.
        Top k words are a list of (word, word_importance) pairs.
    """
    n_topics = self.temporal_components_.shape[1]
    try:
        classes = self.classes_
    except AttributeError:
        classes = list(range(n_topics))
    res = []
    for components in self.temporal_components_:
        highest = np.argpartition(-components, top_k)[:, :top_k]
        vocab = self.get_vocab()
        top = []
        score = []
        for component, high in zip(components, highest):
            importance = component[high]
            high = high[np.argsort(-importance)]
            score.append(component[high])
            top.append(vocab[high])
        topics = []
        for topic, words, scores in zip(classes, top, score):
            topic_data = (topic, list(zip(words, scores)))
            topics.append(topic_data)
        res.append(topics)
    return res

plot_topics_over_time(top_k=6, color_discrete_sequence=None, color_discrete_map=None)

Displays topics over time in the fitted dynamic model on a dynamic HTML figure.

You will need to pip install plotly to use this method.

Parameters:

Name	Type	Description	Default
top_k	int	Number of top words per topic to display on the figure.	6
color_discrete_sequence	Optional[Iterable[str]]	Color palette to use in the plot. Example: import plotly.express as px model.plot_topics_over_time(color_discrete_sequence=px.colors.qualitative.Light24)	None
color_discrete_map	Optional[dict[str, str]]	Topic names mapped to the colors that should be associated with them.	None

Returns:

Type	Description
Figure	Plotly graph objects Figure, that can be displayed or exported as HTML or static image.

Source code in turftopic/dynamic.py

def plot_topics_over_time(
    self,
    top_k: int = 6,
    color_discrete_sequence: Optional[Iterable[str]] = None,
    color_discrete_map: Optional[dict[str, str]] = None,
):
    """Displays topics over time in the fitted dynamic model on a dynamic HTML figure.

    > You will need to `pip install plotly` to use this method.

    Parameters
    ----------
    top_k: int, default 6
        Number of top words per topic to display on the figure.
    color_discrete_sequence: Iterable[str], default None
        Color palette to use in the plot.
        Example:

        ```python
        import plotly.express as px
        model.plot_topics_over_time(color_discrete_sequence=px.colors.qualitative.Light24)
        ```

    color_discrete_map: dict[str, str], default None
        Topic names mapped to the colors that should
        be associated with them.

    Returns
    -------
    go.Figure
        Plotly graph objects Figure, that can be displayed or exported as
        HTML or static image.
    """
    try:
        import plotly.express as px
        import plotly.graph_objects as go
    except (ImportError, ModuleNotFoundError) as e:
        raise ModuleNotFoundError(
            "Please install plotly if you intend to use plots in Turftopic."
        ) from e
    if color_discrete_sequence is not None:
        topic_colors = itertools.cycle(color_discrete_sequence)
    elif color_discrete_map is not None:
        topic_colors = [
            color_discrete_map[topic_name]
            for topic_name in self.topic_names
        ]
    else:
        topic_colors = px.colors.qualitative.Dark24
    fig = go.Figure()
    vocab = self.get_vocab()
    n_topics = self.temporal_components_.shape[1]
    try:
        topic_names = self.topic_names
    except AttributeError:
        topic_names = [f"Topic {i}" for i in range(n_topics)]
    for trace_color, (i_topic, topic_imp_t) in zip(
        topic_colors, enumerate(self.temporal_importance_.T)
    ):
        component_over_time = self.temporal_components_[:, i_topic, :]
        name_over_time = []
        for component in component_over_time:
            high = np.argpartition(-component, top_k)[:top_k]
            values = component[high]
            if np.all(values == 0) or np.all(np.isnan(values)):
                name_over_time.append("<not present>")
                continue
            high = high[np.argsort(-values)]
            name_over_time.append(", ".join(vocab[high]))
        times = self.time_bin_edges[:-1]
        fig.add_trace(
            go.Scatter(
                x=times,
                y=topic_imp_t,
                mode="markers+lines",
                text=name_over_time,
                name=topic_names[i_topic],
                hovertemplate="<b>%{text}</b>",
                marker=dict(
                    line=dict(width=2, color="black"),
                    size=14,
                    color=trace_color,
                ),
                line=dict(width=3),
            )
        )
    fig.update_layout(
        template="plotly_white",
        hoverlabel=dict(font_size=16, bgcolor="white"),
        hovermode="x",
    )
    fig.update_xaxes(title="Time Slice Start")
    fig.update_yaxes(title="Topic Importance")
    return fig

print_topics_over_time(top_k=5, show_scores=False, date_format='%Y %m %d')

Pretty prints topics in the model in a table.

Parameters:

Name	Type	Description	Default
top_k	int	Number of top words to return for each topic.	5
show_scores	bool	Indicates whether to show importance scores for each word.	False

Source code in turftopic/dynamic.py

def print_topics_over_time(
    self,
    top_k: int = 5,
    show_scores: bool = False,
    date_format: str = "%Y %m %d",
):
    """Pretty prints topics in the model in a table.

    Parameters
    ----------
    top_k: int, default 10
        Number of top words to return for each topic.
    show_scores: bool, default False
        Indicates whether to show importance scores for each word.
    """
    columns, *rows = self._topics_over_time(
        top_k, show_scores, date_format
    )
    table = Table(show_lines=True)
    for column in columns:
        table.add_column(column)
    for row in rows:
        table.add_row(*row)
    console = Console()
    console.print(table)