feat(pygal): implement learning-curve-basic (#2301)

## Implementation: `learning-curve-basic` - pygal

Implements the **pygal** version of `learning-curve-basic`.

**File:** `plots/learning-curve-basic/implementations/pygal.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526602804)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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/learning-curve-basic/implementations/pygal.py

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+""" pyplots.ai
+learning-curve-basic: Model Learning Curve
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Simulating sklearn's learning_curve output
+np.random.seed(42)
+
+# Training set sizes (as percentages converted to actual samples)
+n_samples_total = 1000
+train_sizes_pct = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
+train_sizes = (train_sizes_pct * n_samples_total).astype(int)
+
+# Simulate cross-validation folds (5 folds)
+n_folds = 5
+n_sizes = len(train_sizes)
+
+# Training scores: start high, remain high (slight overfitting pattern)
+train_scores_base = 0.95 - 0.05 * np.exp(-train_sizes / 200)
+train_scores_std_vals = 0.02 * np.exp(-train_sizes / 300)
+train_scores = np.array(
+    [train_scores_base[i] + np.random.randn(n_folds) * train_scores_std_vals[i] for i in range(n_sizes)]
+).T  # Shape: (n_folds, n_sizes)
+
+# Validation scores: start lower, converge towards training (gap shows variance)
+val_scores_base = 0.65 + 0.25 * (1 - np.exp(-train_sizes / 400))
+val_scores_std_vals = 0.04 * np.exp(-train_sizes / 500) + 0.01
+val_scores = np.array(
+    [val_scores_base[i] + np.random.randn(n_folds) * val_scores_std_vals[i] for i in range(n_sizes)]
+).T  # Shape: (n_folds, n_sizes)
+
+# Calculate means and standard deviations
+train_mean = np.mean(train_scores, axis=0)
+train_std = np.std(train_scores, axis=0)
+val_mean = np.mean(val_scores, axis=0)
+val_std = np.std(val_scores, axis=0)
+
+# Custom style for pyplots - scaled for 4800x2700 canvas
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333",
+    foreground_strong="#333",
+    foreground_subtle="#666",
+    colors=("#306998", "#FFD43B", "#306998", "#306998", "#FFD43B", "#FFD43B"),
+    title_font_size=56,
+    label_font_size=36,
+    major_label_font_size=32,
+    legend_font_size=36,
+    value_font_size=28,
+    stroke_width=5,
+    opacity=0.9,
+    opacity_hover=1.0,
+)
+
+# Create XY chart for learning curve
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="learning-curve-basic · pygal · pyplots.ai",
+    x_title="Training Set Size (samples)",
+    y_title="Accuracy Score",
+    show_dots=True,
+    dots_size=12,
+    stroke_style={"width": 5},
+    show_x_guides=False,
+    show_y_guides=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=2,
+    truncate_legend=-1,
+    range=(0.5, 1.02),
+    xrange=(50, 1050),
+    x_labels=[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000],
+    margin=50,
+)
+
+# Prepare data points as (x, y) tuples
+train_points = [(int(train_sizes[i]), round(train_mean[i], 3)) for i in range(n_sizes)]
+val_points = [(int(train_sizes[i]), round(val_mean[i], 3)) for i in range(n_sizes)]
+
+# Add upper/lower bounds for confidence bands (±1 std)
+train_upper = [(int(train_sizes[i]), round(train_mean[i] + train_std[i], 3)) for i in range(n_sizes)]
+train_lower = [(int(train_sizes[i]), round(train_mean[i] - train_std[i], 3)) for i in range(n_sizes)]
+val_upper = [(int(train_sizes[i]), round(val_mean[i] + val_std[i], 3)) for i in range(n_sizes)]
+val_lower = [(int(train_sizes[i]), round(val_mean[i] - val_std[i], 3)) for i in range(n_sizes)]
+
+# Add main learning curves with larger markers
+chart.add("Training Score (±1σ band)", train_points, stroke_style={"width": 6})
+chart.add("Validation Score (±1σ band)", val_points, stroke_style={"width": 6})
+
+# Add confidence bounds as secondary lines (thinner, dashed, no legend)
+chart.add(None, train_upper, show_dots=False, stroke_style={"width": 2, "dasharray": "8, 4"})
+chart.add(None, train_lower, show_dots=False, stroke_style={"width": 2, "dasharray": "8, 4"})
+chart.add(None, val_upper, show_dots=False, stroke_style={"width": 2, "dasharray": "8, 4"})
+chart.add(None, val_lower, show_dots=False, stroke_style={"width": 2, "dasharray": "8, 4"})
+
+# Save as HTML (interactive) and PNG
+chart.render_to_file("plot.html")
+chart.render_to_png("plot.png")

plots/learning-curve-basic/metadata/pygal.yaml

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+library: pygal
+specification_id: learning-curve-basic
+created: '2025-12-26T17:38:14Z'
+updated: '2025-12-26T17:47:21Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526602804
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/pygal/plot.html
+quality_score: 90
+review:
+  strengths:
+  - Clear visualization of learning curve with appropriate training vs validation
+    gap
+  - Confidence bands shown as dashed lines effectively communicate uncertainty
+  - Custom style properly scaled for 4800x2700 canvas
+  - Colorblind-safe color palette (blue and gold)
+  - Proper KISS code structure with fixed random seed
+  weaknesses:
+  - Legend at bottom appears slightly clipped/truncated, reducing readability
+  - Lower portion of the plot (y < 0.7) is mostly empty white space
+  - Does not fully leverage pygal interactive SVG features (tooltips, hover effects)