| import numpy as np |
| from typing import List, Union |
| import plotly.graph_objects as go |
|
|
|
|
| def visualize_term_rank(topic_model, |
| topics: List[int] = None, |
| log_scale: bool = False, |
| custom_labels: Union[bool, str] = False, |
| title: str = "<b>Term score decline per Topic</b>", |
| 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: |
| topic_model: A fitted BERTopic instance. |
| 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: If bool, whether to use custom topic labels that were defined using |
| `topic_model.set_topic_labels`. |
| If `str`, it uses labels from other aspects, e.g., "Aspect1". |
| title: Title of the plot. |
| 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") |
| ``` |
| |
| <iframe src="../../getting_started/visualization/term_rank.html" |
| style="width:1000px; height: 530px; border: 0px;""></iframe> |
| |
| <iframe src="../../getting_started/visualization/term_rank_log.html" |
| style="width:1000px; height: 530px; border: 0px;""></iframe> |
| |
| 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). |
| """ |
|
|
| topics = [] if topics is None else topics |
|
|
| topic_ids = topic_model.get_topic_info().Topic.unique().tolist() |
| topic_words = [topic_model.get_topic(topic) for topic in topic_ids] |
|
|
| values = np.array([[value[1] for value in values] for values in topic_words]) |
| indices = np.array([[value + 1 for value in range(len(values))] for values in topic_words]) |
|
|
| |
| lines = [] |
| for topic, x, y in zip(topic_ids, indices, values): |
| if not any(y > 1.5): |
|
|
| |
| if isinstance(custom_labels, str): |
| label = f"{topic}_" + "_".join(list(zip(*topic_model.topic_aspects_[custom_labels][topic]))[0][:3]) |
| elif topic_model.custom_labels_ is not None and custom_labels: |
| label = topic_model.custom_labels_[topic + topic_model._outliers] |
| else: |
| label = f"<b>Topic {topic}</b>:" + "_".join([word[0] for word in topic_model.get_topic(topic)]) |
| label = label[:50] |
|
|
| |
| color = "red" if topic in topics else "black" |
| opacity = 1 if topic in topics else .1 |
| if any(y == 0): |
| y[y == 0] = min(values[values > 0]) |
| y = np.log10(y, out=y, where=y > 0) if log_scale else y |
|
|
| line = go.Scatter(x=x, y=y, |
| name="", |
| hovertext=label, |
| mode="lines+lines", |
| opacity=opacity, |
| line=dict(color=color, width=1.5)) |
| lines.append(line) |
|
|
| fig = go.Figure(data=lines) |
|
|
| |
| fig.update_xaxes(range=[0, len(indices[0])], tick0=1, dtick=2) |
| fig.update_layout( |
| showlegend=False, |
| template="plotly_white", |
| title={ |
| 'text': f"{title}", |
| 'y': .9, |
| 'x': 0.5, |
| 'xanchor': 'center', |
| 'yanchor': 'top', |
| 'font': dict( |
| size=22, |
| color="Black") |
| }, |
| width=width, |
| height=height, |
| hoverlabel=dict( |
| bgcolor="white", |
| font_size=16, |
| font_family="Rockwell" |
| ), |
| ) |
|
|
| fig.update_xaxes(title_text='Term Rank') |
| if log_scale: |
| fig.update_yaxes(title_text='c-TF-IDF score (log scale)') |
| else: |
| fig.update_yaxes(title_text='c-TF-IDF score') |
|
|
| return fig |
|
|
