Merge pull request #42 from x-tabdeveloping/color_scheme

Added option to use custom color scheme in figures API

Author: x-tabdeveloping

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "topic-wizard"
-version = "1.1.1"
+version = "1.1.2"
 description = "Pretty and opinionated topic model visualization in Python."
 authors = ["Márton Kardos <[email protected]>"]
 license = "MIT"
@@ -13,7 +13,7 @@ dash = "^2.7.1"
 dash-extensions = "^1.0.4"
 dash-mantine-components = "~0.12.1"
 dash-iconify = "~0.1.2"
-joblib = "~1.2.0"
+joblib = "^1.2.0"
 scikit-learn = "^1.2.0"
 scipy = ">=1.8.0"
 umap-learn = ">=0.5.3"

topicwizard/figures/documents.py

@@ -1,4 +1,5 @@
 """External API for creating self-contained figures for documents."""
+
 from typing import List, Optional, Union
 
 import numpy as np
@@ -56,7 +57,10 @@ def document_map(
 
 
 def document_topic_distribution(
-    topic_data: TopicData, documents: Union[List[str], str], top_n: int = 8
+    topic_data: TopicData,
+    documents: Union[List[str], str],
+    top_n: int = 8,
+    color_scheme: str = "Portland",
 ) -> go.Figure:
     """Displays topic distribution on a bar plot for a document
     or a set of documents.
@@ -69,6 +73,8 @@ def document_topic_distribution(
         Documents to display topic distribution for.
     top_n: int, default 8
         Number of topics to display at most.
+    color_scheme: str, default 'Portland'
+        Name of the Plotly color scheme to use for the plot.
     """
     transform = topic_data["transform"]
     if transform is None:
@@ -80,7 +86,7 @@ def document_topic_distribution(
     topic_importances = prepare.document_topic_importances(transform(documents))
     topic_importances = topic_importances.groupby(["topic_id"]).sum().reset_index()
     n_topics = topic_data["document_topic_matrix"].shape[-1]
-    twilight = colors.get_colorscale("Portland")
+    twilight = colors.get_colorscale(color_scheme)
     topic_colors = colors.sample_colorscale(twilight, np.arange(n_topics) / n_topics)
     topic_colors = np.array(topic_colors)
     return plots.document_topic_barplot(
@@ -89,7 +95,11 @@ def document_topic_distribution(
 
 
 def document_topic_timeline(
-    topic_data: TopicData, document: str, window_size: int = 10, step_size: int = 1
+    topic_data: TopicData,
+    document: str,
+    window_size: int = 10,
+    step_size: int = 1,
+    color_scheme: str = "Portland",
 ) -> go.Figure:
     """Projects documents into 2d space and displays them on a scatter plot.
 
@@ -103,6 +113,8 @@ def document_topic_timeline(
         The windows over which topic inference should be run.
     step_size: int, default 1
         Size of the steps for the rolling window.
+    color_scheme: str, default 'Portland'
+        Name of Plotly color scheme to use for the plot.
     """
     timeline = prepare.calculate_timeline(
         doc_id=0,
@@ -113,7 +125,7 @@ def document_topic_timeline(
     )
     topic_names = topic_data["topic_names"]
     n_topics = len(topic_names)
-    twilight = colors.get_colorscale("Portland")
+    twilight = colors.get_colorscale(color_scheme)
     topic_colors = colors.sample_colorscale(twilight, np.arange(n_topics) / n_topics)
     topic_colors = np.array(topic_colors)
     return plots.document_timeline(timeline, topic_names, topic_colors)

topicwizard/figures/groups.py

@@ -1,4 +1,5 @@
 """External API for creating self-contained figures for groups."""
+
 from typing import List
 
 import numpy as np
@@ -68,7 +69,11 @@ def group_map(topic_data: TopicData, group_labels: List[str]) -> go.Figure:
 
 
 def group_topic_barcharts(
-    topic_data: TopicData, group_labels: List[str], top_n: int = 5, n_columns: int = 4
+    topic_data: TopicData,
+    group_labels: List[str],
+    top_n: int = 5,
+    n_columns: int = 4,
+    color_scheme: str = "Portland",
 ):
     """Displays the most important topics for each group.
 
@@ -82,6 +87,8 @@ def group_topic_barcharts(
         Maximum number of topics to display for each group.
     n_columns: int, default 4
         Indicates how many columns the faceted plot should have.
+    color_scheme: str, default 'Portland'
+        Name of the plotly color scheme to use for the figure.
     """
     # Factorizing group labels
     group_id_labels, group_names = pd.factorize(group_labels)
@@ -105,7 +112,7 @@ def group_topic_barcharts(
         horizontal_spacing=0.01,
     )
     n_topics = len(topic_data["topic_names"])
-    color_scheme = colors.get_colorscale("Portland")
+    color_scheme = colors.get_colorscale(color_scheme)
     topic_colors = colors.sample_colorscale(
         color_scheme, np.arange(n_topics) / n_topics, low=0.25, high=1.0
     )

topicwizard/figures/topics.py

@@ -110,6 +110,7 @@ def topic_wordclouds(
     topic_data: TopicData,
     top_n: int = 30,
     n_columns: int = 4,
+    color_scheme: str = "copper",
 ) -> go.Figure:
     """Plots most relevant words as word clouds for every topic.
 
@@ -121,6 +122,8 @@ def topic_wordclouds(
         Specifies the number of words to show for each topic.
     n_columns: int, default 4
         Number of columns in the subplot grid.
+    color_scheme: str, default 'copper'
+        Matplotlib color scheme to use for the wordcloud.
     """
     n_topics = topic_data["topic_term_matrix"].shape[0]
     (
@@ -147,7 +150,7 @@ def topic_wordclouds(
             components=topic_term_importances,
             vocab=topic_data["vocab"],
         )
-        subfig = plots.wordcloud(top_words)
+        subfig = plots.wordcloud(top_words, color_scheme=color_scheme)
         row, column = (topic_id // n_columns) + 1, (topic_id % n_columns) + 1
         fig.add_trace(subfig.data[0], row=row, col=column)
     fig.update_layout(

topicwizard/figures/words.py

@@ -18,6 +18,7 @@ def word_map(
     topic_data: TopicData,
     z_threshold: float = 2.0,
     topic_axes: Optional[Tuple[Union[str, int], Union[str, int]]] = None,
+    color_scheme: str = "tempo",
 ) -> go.Figure:
     """Plots words on a scatter plot based on UMAP projections
     of their importances in topics into 2D space or by two topic axes.
@@ -38,6 +39,8 @@ def word_map(
         The topic axes along which the words should be displayed.
         If not specified, the axes on the graph are going to be
         UMAP projections' dimensions.
+    color_scheme: str, default 'tempo'
+        Name of the Plotly color scheme to use for the plot.
     """
     topic_names = topic_data["topic_names"]
     if topic_axes is None:
@@ -58,7 +61,7 @@ def word_map(
     freq_z = zscore(word_frequencies)
     dominant_topic = prepare.dominant_topic(topic_data["topic_term_matrix"])
     dominant_topic = np.array(topic_data["topic_names"])[dominant_topic]
-    tempo = colors.get_colorscale("tempo")
+    tempo = colors.get_colorscale(color_scheme)
     n_topics = len(topic_data["topic_names"])
     topic_colors = colors.sample_colorscale(tempo, np.arange(n_topics) / n_topics)
     topic_colors = np.array(topic_colors)
@@ -99,6 +102,7 @@ def word_association_barchart(
     words: Union[List[str], str],
     n_association: int = 0,
     top_n: int = 20,
+    color_scheme: str = "Rainbow",
 ):
     """Plots bar chart of most important topics for the given words and their closest
     associations in topic space.
@@ -114,6 +118,8 @@ def word_association_barchart(
         None get displayed by default.
     top_n: int = 20
         Top N topics to display.
+    color_scheme: str, default 'Rainbow'
+        Name of the Plotly color scheme to use for the plot.
     """
     if isinstance(words, str):
         words = [words]
@@ -128,7 +134,7 @@ def word_association_barchart(
         word_ids, topic_data["topic_term_matrix"], n_association
     )
     n_topics = topic_data["topic_term_matrix"].shape[0]
-    tempo = colors.get_colorscale("Rainbow")
+    tempo = colors.get_colorscale(color_scheme)
     topic_colors = colors.sample_colorscale(tempo, np.arange(n_topics) / n_topics)
     topic_colors = np.array(topic_colors)
     top_topics = prepare.top_topics(

topicwizard/plots/topics.py

@@ -1,4 +1,5 @@
 """Module containing plotting utilities for topics."""
+
 from typing import List
 
 import numpy as np
@@ -139,7 +140,7 @@ def topic_plot(top_words: pd.DataFrame):
     return fig
 
 
-def wordcloud(top_words: pd.DataFrame) -> go.Figure:
+def wordcloud(top_words: pd.DataFrame, color_scheme: str = "copper") -> go.Figure:
     """Plots most relevant words for current topic as a worcloud."""
     top_dict = {
         word: importance
@@ -151,7 +152,7 @@ def wordcloud(top_words: pd.DataFrame) -> go.Figure:
         width=800,
         height=1060,
         background_color="white",
-        colormap="copper",
+        colormap=color_scheme,
         scale=4,
     ).generate_from_frequencies(top_dict)
     image = cloud.to_image()