feat(highcharts): implement bar-feature-importance (#2325)

## Implementation: `bar-feature-importance` - highcharts

Implements the **highcharts** version of `bar-feature-importance`.

**File:** `plots/bar-feature-importance/implementations/highcharts.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526656090)*

---------

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/bar-feature-importance/implementations/highcharts.py

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+""" pyplots.ai
+bar-feature-importance: Feature Importance Bar Chart
+Library: highcharts unknown | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import json
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support import expected_conditions as EC
+from selenium.webdriver.support.ui import WebDriverWait
+
+
+# Data - Feature importances from a Random Forest classifier (house price prediction)
+features = [
+    "Square Footage",
+    "Number of Bedrooms",
+    "Location Score",
+    "Year Built",
+    "Lot Size",
+    "Number of Bathrooms",
+    "Garage Capacity",
+    "School District Rating",
+    "Distance to City Center",
+    "Property Tax Rate",
+    "HOA Fees",
+    "Neighborhood Crime Rate",
+    "Nearby Amenities Count",
+    "Public Transit Access",
+    "Energy Efficiency Score",
+]
+
+# Importance scores (from ensemble averaging)
+importance = [0.215, 0.142, 0.128, 0.089, 0.078, 0.068, 0.062, 0.055, 0.048, 0.041, 0.028, 0.019, 0.014, 0.009, 0.004]
+
+# Sort by importance (already sorted, but make explicit)
+sorted_data = sorted(zip(features, importance, strict=True), key=lambda x: x[1], reverse=True)
+features_sorted = [x[0] for x in sorted_data]
+importance_sorted = [x[1] for x in sorted_data]
+
+# Reverse for horizontal bar chart (highest importance at top in Highcharts bar)
+features_sorted = features_sorted[::-1]
+importance_sorted = importance_sorted[::-1]
+
+# Create gradient colors based on importance (light to dark blue)
+max_imp = max(importance_sorted)
+min_imp = min(importance_sorted)
+
+
+def importance_to_color(imp):
+    """Map importance to color from light (#a8d5f2) to dark (#306998)."""
+    ratio = (imp - min_imp) / (max_imp - min_imp) if max_imp != min_imp else 0.5
+    r = int(168 + (48 - 168) * ratio)
+    g = int(213 + (105 - 213) * ratio)
+    b = int(242 + (152 - 242) * ratio)
+    return f"#{r:02x}{g:02x}{b:02x}"
+
+
+# Build data with colors
+bar_data = []
+for imp in importance_sorted:
+    bar_data.append({"y": imp, "color": importance_to_color(imp)})
+
+# Download Highcharts JS
+highcharts_url = "https://code.highcharts.com/highcharts.js"
+with urllib.request.urlopen(highcharts_url, timeout=30) as response:
+    highcharts_js = response.read().decode("utf-8")
+
+# Build chart options as a dictionary
+chart_options = {
+    "chart": {
+        "type": "bar",
+        "width": 4800,
+        "height": 2700,
+        "backgroundColor": "#ffffff",
+        "marginLeft": 380,
+        "marginRight": 180,
+        "marginTop": 150,
+        "marginBottom": 120,
+        "animation": False,
+    },
+    "title": {
+        "text": "bar-feature-importance \u00b7 highcharts \u00b7 pyplots.ai",
+        "style": {"fontSize": "48px", "fontWeight": "bold"},
+    },
+    "subtitle": {
+        "text": "House Price Prediction - Random Forest Feature Importances",
+        "style": {"fontSize": "32px", "color": "#666666"},
+    },
+    "xAxis": {
+        "categories": features_sorted,
+        "title": {"text": None},
+        "labels": {"style": {"fontSize": "26px"}},
+        "lineWidth": 0,
+        "tickWidth": 0,
+    },
+    "yAxis": {
+        "title": {"text": "Importance Score", "style": {"fontSize": "32px"}},
+        "labels": {"style": {"fontSize": "24px"}},
+        "min": 0,
+        "max": 0.25,
+        "gridLineWidth": 1,
+        "gridLineColor": "#e0e0e0",
+    },
+    "legend": {"enabled": False},
+    "tooltip": {"enabled": False},
+    "plotOptions": {
+        "bar": {
+            "dataLabels": {
+                "enabled": True,
+                "format": "{point.y:.3f}",
+                "style": {"fontSize": "22px", "fontWeight": "normal", "textOutline": "none"},
+                "align": "left",
+                "x": 10,
+            },
+            "pointPadding": 0.1,
+            "groupPadding": 0.05,
+            "borderWidth": 0,
+            "animation": False,
+        }
+    },
+    "credits": {"enabled": False},
+    "series": [{"name": "Feature Importance", "data": bar_data, "animation": False}],
+}
+
+# Convert to JSON for embedding
+chart_json = json.dumps(chart_options)
+
+# Generate HTML with inline scripts
+html_content = f"""<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="utf-8">
+    <script>{highcharts_js}</script>
+</head>
+<body style="margin:0; padding:0; background-color: #ffffff;">
+    <div id="container" style="width: 4800px; height: 2700px;"></div>
+    <script>
+        Highcharts.chart('container', {chart_json});
+    </script>
+</body>
+</html>"""
+
+# Save HTML for interactive version
+with open("plot.html", "w", encoding="utf-8") as f:
+    f.write(html_content)
+
+# Take screenshot with Selenium
+with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
+    f.write(html_content)
+    temp_path = f.name
+
+chrome_options = Options()
+chrome_options.add_argument("--headless=new")
+chrome_options.add_argument("--no-sandbox")
+chrome_options.add_argument("--disable-dev-shm-usage")
+chrome_options.add_argument("--disable-gpu")
+chrome_options.add_argument("--window-size=5000,3000")
+chrome_options.add_argument("--force-device-scale-factor=1")
+
+driver = webdriver.Chrome(options=chrome_options)
+driver.set_window_size(5000, 3000)
+driver.get(f"file://{temp_path}")
+
+# Wait for Highcharts chart to render
+try:
+    WebDriverWait(driver, 15).until(EC.presence_of_element_located((By.CSS_SELECTOR, ".highcharts-root")))
+    time.sleep(3)
+except Exception:
+    time.sleep(10)
+
+# Get the container element and screenshot it
+container = driver.find_element(By.ID, "container")
+container.screenshot("plot.png")
+driver.quit()
+
+Path(temp_path).unlink()

plots/bar-feature-importance/metadata/highcharts.yaml

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+library: highcharts
+specification_id: bar-feature-importance
+created: '2025-12-26T17:45:30Z'
+updated: '2025-12-26T17:53:37Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526656090
+issue: 0
+python_version: 3.13.11
+library_version: unknown
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/highcharts/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/highcharts/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/highcharts/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the feature importance bar chart with proper sorting
+    (highest at top)
+  - Clear gradient color mapping that visually emphasizes feature importance levels
+  - Data labels positioned cleanly at the end of each bar for precise value reading
+  - Realistic house price prediction scenario with meaningful feature names
+  - Proper title format following pyplots.ai conventions
+  - Clean, professional appearance suitable for publication
+  weaknesses:
+  - X-axis tick labels are overly dense (increments of 0.005 create visual clutter)
+  - Contains a helper function importance_to_color() which deviates slightly from
+    KISS structure
+  - Does not use the highcharts-core Python library as recommended in library rules
+  - Error bars not included despite spec mentioning them as valuable for ensemble
+    methods