'Refactored by Sourcery'

2023/machine_learning/mglearn/plot_interactive_tree.py

@@ -39,7 +39,7 @@ def tree_image(tree, fout=None):
     if fout is None:
         fout = "tmp"
     graph.render(fout)
-    return imread(fout + ".png")
+    return imread(f"{fout}.png")
 
 
 def plot_tree_progressive():

2023/machine_learning/mglearn/plot_tree_nonmonotonous.py

@@ -22,5 +22,5 @@ def plot_tree_not_monotone():
     export_graphviz(tree, out_file="mytree.dot", impurity=False, filled=True)
     with open("mytree.dot") as f:
         dot_graph = f.read()
-    print("Feature importances: %s" % tree.feature_importances_)
+    print(f"Feature importances: {tree.feature_importances_}")
     return graphviz.Source(dot_graph)

2023/machine_learning/mglearn/tools.py

@@ -39,10 +39,7 @@ def heatmap(values, xlabel, ylabel, xticklabels, yticklabels, cmap=None,
 
     for p, color, value in zip(img.get_paths(), img.get_facecolors(), img.get_array()):
         x, y = p.vertices[:-2, :].mean(0)
-        if np.mean(color[:3]) > 0.5:
-            c = 'k'
-        else:
-            c = 'w'
+        c = 'k' if np.mean(color[:3]) > 0.5 else 'w'
         ax.text(x, y, fmt % value, color=c, ha="center", va="center")
     return img