Interactive DataMapPlot (#1853) and deprecate non-chat OpenAI models,…

.gitignore

@@ -75,6 +75,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+*.lock
 
 # Artifacts
 .idea

README.md

@@ -161,7 +161,7 @@ from bertopic.representation import OpenAI
 
 # Fine-tune topic representations with GPT
 client = openai.OpenAI(api_key="sk-...")
-representation_model = OpenAI(client, model="gpt-3.5-turbo", chat=True)
+representation_model = OpenAI(client, model="gpt-4o-mini", chat=True)
 topic_model = BERTopic(representation_model=representation_model)
 ```
 

bertopic/_bertopic.py

@@ -2532,38 +2532,51 @@ def visualize_documents(
 
     def visualize_document_datamap(
         self,
-        docs: List[str],
+        docs: List[str] = None,
         topics: List[int] = None,
         embeddings: np.ndarray = None,
         reduced_embeddings: np.ndarray = None,
         custom_labels: Union[bool, str] = False,
         title: str = "Documents and Topics",
         sub_title: Union[str, None] = None,
         width: int = 1200,
-        height: int = 1200,
-        **datamap_kwds,
+        height: int = 750,
+        interactive: bool = False,
+        enable_search: bool = False,
+        topic_prefix: bool = False,
+        datamap_kwds: dict = {},
+        int_datamap_kwds: dict = {},
     ):
         """Visualize documents and their topics in 2D as a static plot for publication using
         DataMapPlot. This works best if there are between 5 and 60 topics. It is therefore best
         to use a sufficiently large `min_topic_size` or set `nr_topics` when building the model.
 
         Arguments:
             topic_model:  A fitted BERTopic instance.
-            docs: The documents you used when calling either `fit` or `fit_transform`
+            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]. Documents not in these topics will be shown as noise points.
+                    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]`. Documents not in these topics will be shown
+                    as noise points.
             embeddings:  The embeddings of all documents in `docs`.
             reduced_embeddings:  The 2D reduced embeddings of all documents in `docs`.
             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".
+                        `topic_model.set_topic_labels`.
+                        If `str`, it uses labels from other aspects, e.g., "Aspect1".
             title: Title of the plot.
             sub_title: Sub-title of the plot.
             width: The width of the figure.
             height: The height of the figure.
-            **datamap_kwds:  All further keyword args will be passed on to DataMapPlot's
-                             `create_plot` function. See the DataMapPlot documentation
-                             for more details.
+            interactive: Whether to create an interactive plot using DataMapPlot's `create_interactive_plot`.
+            enable_search: Whether to enable search in the interactive plot. Only works if `interactive=True`.
+            topic_prefix: Prefix to add to the topic number when displaying the topic name.
+            datamap_kwds:  Keyword args be passed on to DataMapPlot's `create_plot` function
+                        if you are not using the interactive version.
+                        See the DataMapPlot documentation for more details.
+            int_datamap_kwds:  Keyword args be passed on to DataMapPlot's `create_interactive_plot` function
+                            if you are using the interactive version.
+                            See the DataMapPlot documentation for more details.
 
         Returns:
             figure: A Matplotlib Figure object.
@@ -2610,7 +2623,6 @@ def visualize_document_datamap(
         ```
         """
         check_is_fitted(self)
-        check_documents_type(docs)
         return plotting.visualize_document_datamap(
             self,
             docs,
@@ -2622,7 +2634,11 @@ def visualize_document_datamap(
             sub_title,
             width,
             height,
-            **datamap_kwds,
+            interactive,
+            enable_search,
+            topic_prefix,
+            datamap_kwds,
+            int_datamap_kwds,
         )
 
     def visualize_hierarchical_documents(

bertopic/plotting/_datamap.py

@@ -17,23 +17,27 @@ class Figure(object):
 
 def visualize_document_datamap(
     topic_model,
-    docs: List[str],
+    docs: List[str] = None,
     topics: List[int] = None,
     embeddings: np.ndarray = None,
     reduced_embeddings: np.ndarray = None,
     custom_labels: Union[bool, str] = False,
     title: str = "Documents and Topics",
     sub_title: Union[str, None] = None,
     width: int = 1200,
-    height: int = 1200,
-    **datamap_kwds,
+    height: int = 750,
+    interactive: bool = False,
+    enable_search: bool = False,
+    topic_prefix: bool = False,
+    datamap_kwds: dict = {},
+    int_datamap_kwds: dict = {},
 ) -> Figure:
     """Visualize documents and their topics in 2D as a static plot for publication using
     DataMapPlot.
 
     Arguments:
         topic_model:  A fitted BERTopic instance.
-        docs: The documents you used when calling either `fit` or `fit_transform`
+        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:
@@ -48,9 +52,15 @@ def visualize_document_datamap(
         sub_title: Sub-title of the plot.
         width: The width of the figure.
         height: The height of the figure.
-        **datamap_kwds:  All further keyword args will be passed on to DataMapPlot's
-                         `create_plot` function. See the DataMapPlot documentation
-                         for more details.
+        interactive: Whether to create an interactive plot using DataMapPlot's `create_interactive_plot`.
+        enable_search: Whether to enable search in the interactive plot. Only works if `interactive=True`.
+        topic_prefix: Prefix to add to the topic number when displaying the topic name.
+        datamap_kwds:  Keyword args be passed on to DataMapPlot's `create_plot` function
+                       if you are not using the interactive version.
+                       See the DataMapPlot documentation for more details.
+        int_datamap_kwds:  Keyword args be passed on to DataMapPlot's `create_interactive_plot` function
+                           if you are using the interactive version.
+                           See the DataMapPlot documentation for more details.
 
     Returns:
         figure: A Matplotlib Figure object.
@@ -127,10 +137,13 @@ def visualize_document_datamap(
     elif topic_model.custom_labels_ is not None and custom_labels:
         names = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in unique_topics]
     else:
-        names = [
-            f"Topic-{topic}: " + " ".join([word for word, value in topic_model.get_topic(topic)][:3])
-            for topic in unique_topics
-        ]
+        if topic_prefix:
+            names = [
+                f"Topic-{topic}: " + " ".join([word for word, value in topic_model.get_topic(topic)][:3])
+                for topic in unique_topics
+            ]
+        else:
+            names = [" ".join([word for word, value in topic_model.get_topic(topic)][:3]) for topic in unique_topics]
 
     topic_name_mapping = {topic_num: topic_name for topic_num, topic_name in zip(unique_topics, names)}
     topic_name_mapping[-1] = "Unlabelled"
@@ -145,14 +158,25 @@ def visualize_document_datamap(
     # Map in topic names and plot
     named_topic_per_doc = pd.Series(topic_per_doc).map(topic_name_mapping).values
 
-    figure, axes = datamapplot.create_plot(
-        embeddings_2d,
-        named_topic_per_doc,
-        figsize=(width / 100, height / 100),
-        dpi=100,
-        title=title,
-        sub_title=sub_title,
-        **datamap_kwds,
-    )
+    if interactive:
+        figure = datamapplot.create_interactive_plot(
+            embeddings_2d,
+            named_topic_per_doc,
+            hover_text=docs,
+            enable_search=enable_search,
+            width=width,
+            height=height,
+            **int_datamap_kwds,
+        )
+    else:
+        figure, _ = datamapplot.create_plot(
+            embeddings_2d,
+            named_topic_per_doc,
+            figsize=(width / 100, height / 100),
+            dpi=100,
+            title=title,
+            sub_title=sub_title,
+            **datamap_kwds,
+        )
 
     return figure