Update: seaborn plot dependency optional

setup.py

@@ -1,6 +1,6 @@
 from setuptools import setup, find_packages
 
-VERSION = '0.42.9'
+VERSION = '0.42.10'
 DESCRIPTION = 'Machine Learning project startup utilities'
 LONG_DESCRIPTION = 'My commonly used utilities for machine learning projects'
 
@@ -13,7 +13,7 @@
     description=DESCRIPTION,
     long_description=LONG_DESCRIPTION,
     url='https://github.com/StefanHeng/stef-util',
-    download_url='https://github.com/StefanHeng/stef-util/archive/refs/tags/v0.42.9Y.tar.gz',
+    download_url='https://github.com/StefanHeng/stef-util/archive/refs/tags/v0.42.10.tar.gz',
     packages=find_packages(),
     include_package_data=True,
     install_requires=[
@@ -24,7 +24,8 @@
     ],
     extras_require={
         'legacy_styling': ['sty', 'colorama'],
-        'plot':  ['matplotlib', 'seaborn'],
+        'plot':  ['matplotlib'],
+        'plot-optional': ['seaborn'],
         'machine_learning': ['scikit-learn'],
         'deep_learning': ['spacy', 'torch', 'transformers>=4.33.2', 'sentence-transformers', 'tensorboard'],
         'optional': ['pygments', 'pyinstrument']

stefutil/packaging.py

@@ -16,7 +16,8 @@
     'installed_packages'
 ]
 
-_PKGS_PLT = ['matplotlib', 'seaborn']
+# _PKGS_PLT = ['matplotlib', 'seaborn']  # make seaborn optional
+_PKGS_PLT = ['matplotlib']
 _PKGS_ML = ['scikit-learn']
 _PKGS_DL = ['torch', 'tensorboard', 'transformers', 'sentence-transformers', 'spacy']
 

stefutil/plot.py

@@ -12,7 +12,7 @@
 
 from stefutil.prettier import s, ca, get_logger, Timer
 from stefutil.container import df_col2cat_col
-from stefutil.packaging import _use_plot, _use_ml
+from stefutil.packaging import installed_packages, _use_plot, _use_ml
 
 
 __all__ = []
@@ -25,7 +25,6 @@
     import numpy as np
     import pandas as pd
     import matplotlib.pyplot as plt
-    import seaborn as sns
     from matplotlib.patches import Ellipse
     from matplotlib.colors import to_rgba
     import matplotlib.colors as colors
@@ -46,8 +45,10 @@ def set_plot_style():
             r'\usepackage{sansmath}',  # render math sans-serif
             r'\sansmath'
         ]))
-        sns.set_style('darkgrid')
-        sns.set_context(rc={'grid.linewidth': 0.5})
+        if 'seaborn' in installed_packages():
+            import seaborn as sns
+            sns.set_style('darkgrid')
+            sns.set_context(rc={'grid.linewidth': 0.5})
 
     LN_KWARGS = dict(marker='o', ms=0.3, lw=0.25)  # matplotlib line plot default args
 
@@ -77,6 +78,7 @@ def vals2colors(
             If given, reduce visual spread/difference of colors
             Intended for a less drastic color at the extremes
         """
+        import seaborn as sns
         vals = np.asarray(vals)
         cmap = sns.color_palette(color_palette, as_cmap=True)
         mi, ma = np.min(vals), np.max(vals)
@@ -117,6 +119,8 @@ def barplot(
             show: bool = True,
             **kwargs
     ):
+        import seaborn as sns
+
         ca(bar_orient=orient)
         if data is not None:
             df = data
@@ -192,21 +196,23 @@ def vector_projection_plot(
             Given vectors grouped by key, plot projections of vectors into 2D space
                 Intended for plotting embedding space of SBert sentence representations
 
-            :param name2vectors: 2D vectors grouped by setup name
-            :param tsne_args: Arguments for TSNE dimensionality reduction
-            :param tight_fig_size: If true, resize the figure to fit the axis range
-            :param key_name: column name for setup in the internal dataframe
-            :param ellipse: If true, plot confidence ellipse for each setup
-            :param ellipse_std: Number of standard deviations for ellipse
-            :param scatter_ms: Base marker size for scatter plot
-                Will be scaled by number of samples in each group
-            :param scatter_kwargs: arguments for scatter plot
-            :param ax: matplotlib axes object to plot on
-            :param title: plot title
-            :param verbose: If true, prints status to logger
-            :param logger: logger
+            :param name2vectors: 2D vectors grouped by setup name.
+            :param tsne_args: Arguments for TSNE dimensionality reduction.
+            :param tight_fig_size: If true, resize the figure to fit the axis range.
+            :param key_name: column name for setup in the internal dataframe.
+            :param ellipse: If true, plot confidence ellipse for each setup.
+            :param ellipse_std: Number of standard deviations for ellipse.
+            :param scatter_ms: Base marker size for scatter plot.
+                Will be scaled by number of samples in each group.
+            :param scatter_kwargs: arguments for scatter plot.
+            :param ax: matplotlib axes object to plot on.
+            :param title: plot title.
+            :param verbose: If true, prints status to logger.
+            :param logger: logger.
             """
             from sklearn.manifold import TSNE  # lazy import to save time
+            import seaborn as sns
+
             vects = np.concatenate(list(name2vectors.values()), axis=0)
             tsne_args_ = dict(n_components=2, perplexity=50, random_state=42)
             tsne_args_.update(tsne_args or dict())