feat(pygal): implement bar-permutation-importance

plots/bar-permutation-importance/implementations/python/pygal.py

@@ -1,18 +1,36 @@
-""" pyplots.ai
+""" anyplot.ai
 bar-permutation-importance: Permutation Feature Importance Plot
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 83/100 | Created: 2025-12-31
+Library: pygal 3.1.0 | Python 3.13.13
+Quality: 86/100 | Updated: 2026-05-17
 """
 
+import os
+import sys
+
 import numpy as np
-import pygal
-from pygal.style import Style
 
 
-# Data - Simulated permutation importance results (realistic ML feature importance)
+# Temporarily remove current directory from path to avoid name collision with pygal module
+_cwd = sys.path[0] if sys.path[0] else "."
+if _cwd in sys.path:
+    sys.path.remove(_cwd)
+
+import pygal  # noqa: E402
+from pygal.style import Style  # noqa: E402
+
+
+# Restore path
+sys.path.insert(0, _cwd)
+
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+
+# Data - Simulated permutation importance results
 np.random.seed(42)
 
-# Feature names representing typical ML model features (sorted by importance, ascending)
 features = [
     "Year Built",
     "Bathrooms",
@@ -26,24 +44,19 @@
     "Overall Quality",
 ]
 
-# Mean importance values (mean decrease in R² score from permutation)
-# Sorted ascending for bottom-to-top display in horizontal bar
 importance_mean = np.array([0.002, 0.008, 0.015, 0.024, 0.032, 0.048, 0.067, 0.095, 0.128, 0.185])
-
-# Standard deviation across 10 permutation repetitions
 importance_std = np.array([0.003, 0.005, 0.008, 0.011, 0.014, 0.018, 0.022, 0.028, 0.035, 0.042])
 
-# Generate sequential color gradient (viridis-inspired) inline
+# Generate viridis color gradient for importance values
+# Inline color generation without helper function
 min_imp = importance_mean.min()
 max_imp = importance_mean.max()
 imp_range = max_imp - min_imp if max_imp != min_imp else 1.0
-
-# Viridis color stops: purple (0) -> teal (0.5) -> yellow (1)
 viridis_stops = [(0.0, 68, 1, 84), (0.25, 58, 82, 139), (0.5, 32, 144, 140), (0.75, 94, 201, 97), (1.0, 253, 231, 36)]
 
-
-def get_viridis_color(t):
-    """Interpolate viridis color for normalized value t in [0, 1]."""
+bar_colors = []
+for imp in importance_mean:
+    t = (imp - min_imp) / imp_range
     for j in range(len(viridis_stops) - 1):
         t0, r0, g0, b0 = viridis_stops[j]
         t1, r1, g1, b1 = viridis_stops[j + 1]
@@ -52,80 +65,59 @@ def get_viridis_color(t):
             r = int(r0 + (r1 - r0) * seg_t)
             g = int(g0 + (g1 - g0) * seg_t)
             b = int(b0 + (b1 - b0) * seg_t)
-            return f"#{r:02x}{g:02x}{b:02x}"
-    return "#fde724"
-
+            bar_colors.append(f"#{r:02x}{g:02x}{b:02x}")
+            break
+    else:
+        bar_colors.append("#fde724")
 
-bar_colors = [get_viridis_color((imp - min_imp) / imp_range) for imp in importance_mean]
-
-# Custom style with larger fonts for 4800x2700 canvas and subtle guides
+# Custom style with theme-adaptive colors and large fonts
 custom_style = Style(
-    background="white",
-    plot_background="white",
-    foreground="#333333",
-    foreground_strong="#333333",
-    foreground_subtle="#999999",
-    guide_stroke_color="#cccccc",
-    major_guide_stroke_color="#cccccc",
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_SOFT,
     colors=tuple(bar_colors),
-    title_font_size=84,
-    label_font_size=48,
-    major_label_font_size=48,
-    legend_font_size=44,
-    value_font_size=40,
-    stroke_width=2,
-    font_family="sans-serif",
-    opacity=0.95,
-    guide_stroke_dasharray="4,4",
+    title_font_size=28,
+    label_font_size=22,
+    major_label_font_size=18,
+    legend_font_size=16,
+    value_font_size=14,
+    stroke_width=3,
 )
 
-# Create horizontal stacked bar chart for error bar visualization
-# First stack: mean value (solid), Second stack: +std (semi-transparent for error bar effect)
-chart = pygal.HorizontalStackedBar(
+# Create horizontal bar chart
+chart = pygal.HorizontalBar(
     width=4800,
     height=2700,
     style=custom_style,
-    title="bar-permutation-importance · pygal · pyplots.ai",
+    title="bar-permutation-importance · pygal · anyplot.ai",
     x_title="Mean Decrease in R² Score",
-    show_legend=True,
-    legend_at_bottom=True,
-    legend_at_bottom_columns=2,
-    legend_box_size=36,
+    show_legend=False,
     print_values=True,
     print_values_position="top",
     show_y_guides=False,
     show_x_guides=True,
-    truncate_label=-1,
-    spacing=28,
-    margin=80,
-    margin_left=420,
-    margin_bottom=200,
-    range=(-0.02, importance_mean.max() + importance_std.max() + 0.02),
-    zero=0,
+    range=(0, importance_mean.max() + importance_std.max() + 0.02),
+    margin_bottom=120,
+    margin_left=360,
+    margin_right=80,
+    margin_top=80,
 )
 
-# Set feature labels on y-axis
+# Set feature labels
 chart.x_labels = features
 
-# Create data series: main bars (mean values) and error extensions (+std)
-mean_data = list(importance_mean)
-std_data = list(importance_std)
-
-# Add mean importance bars with viridis colors
+# Add mean importance bars with viridis gradient colors
 chart.add(
-    "Mean Importance",
-    [{"value": v, "color": c} for v, c in zip(mean_data, bar_colors, strict=True)],
+    "Importance",
+    [
+        {"value": mean, "color": color, "xlink": {"href": "#"}, "label": f"Mean: {mean:.3f} ± {std:.3f}"}
+        for mean, std, color in zip(importance_mean, importance_std, bar_colors, strict=True)
+    ],
     formatter=lambda x: f"{x:.3f}" if x else "",
 )
 
-# Add error bar extensions (std values) with semi-transparent styling
-error_color = "rgba(100, 100, 100, 0.35)"
-chart.add(
-    "± Std Dev (Error Range)",
-    [{"value": v, "color": error_color} for v in std_data],
-    formatter=lambda x: f"±{x:.3f}" if x else "",
-)
-
 # Save outputs
-chart.render_to_file("plot.html")
-chart.render_to_png("plot.png")
+chart.render_to_file(f"plot-{THEME}.html")
+chart.render_to_png(f"plot-{THEME}.png")