feat(letsplot): implement learning-curve-basic (#2327)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20527872881)*

---------

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

Author: github-actions[bot]

plots/learning-curve-basic/implementations/letsplot.py

@@ -0,0 +1,101 @@
+""" pyplots.ai
+learning-curve-basic: Model Learning Curve
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *  # noqa: F403
+from lets_plot import ggsave
+
+
+LetsPlot.setup_html()  # noqa: F405
+
+# Data - Simulate learning curve for a model showing slight overfitting pattern
+np.random.seed(42)
+
+# Training set sizes (10 different sizes)
+train_sizes = np.array([50, 100, 200, 400, 600, 800, 1000, 1200, 1400, 1600])
+
+# Simulate 5 cross-validation folds
+n_folds = 5
+n_sizes = len(train_sizes)
+
+# Training scores: Start high, stay high (model fits training data well)
+train_scores_mean = 0.99 - 0.15 * np.exp(-train_sizes / 200)
+train_scores = np.zeros((n_folds, n_sizes))
+for i in range(n_folds):
+    noise = np.random.randn(n_sizes) * 0.01
+    train_scores[i] = train_scores_mean + noise
+
+# Validation scores: Start lower, improve with more data (learning effect)
+# Show a gap with training that narrows as data increases
+validation_scores_mean = 0.65 + 0.20 * (1 - np.exp(-train_sizes / 500))
+validation_scores = np.zeros((n_folds, n_sizes))
+for i in range(n_folds):
+    noise = np.random.randn(n_sizes) * 0.02
+    validation_scores[i] = validation_scores_mean + noise
+
+# 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(validation_scores, axis=0)
+val_std = np.std(validation_scores, axis=0)
+
+# Create DataFrames for plotting
+df_train = pd.DataFrame(
+    {
+        "Training Set Size": train_sizes,
+        "Score": train_mean,
+        "Lower": train_mean - train_std,
+        "Upper": train_mean + train_std,
+        "Type": "Training Score",
+    }
+)
+
+df_val = pd.DataFrame(
+    {
+        "Training Set Size": train_sizes,
+        "Score": val_mean,
+        "Lower": val_mean - val_std,
+        "Upper": val_mean + val_std,
+        "Type": "Validation Score",
+    }
+)
+
+df = pd.concat([df_train, df_val], ignore_index=True)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="Training Set Size", y="Score", color="Type", fill="Type"))
+    + geom_ribbon(aes(ymin="Lower", ymax="Upper"), alpha=0.2, color="rgba(0,0,0,0)")
+    + geom_line(size=2)
+    + geom_point(size=4)
+    + scale_color_manual(values=["#306998", "#FFD43B"])
+    + scale_fill_manual(values=["#306998", "#FFD43B"])
+    + scale_y_continuous(limits=[0.55, 1.02])
+    + scale_x_continuous(limits=[0, 1700], breaks=list(range(0, 1800, 200)))
+    + labs(
+        x="Training Set Size (samples)",
+        y="Accuracy Score",
+        title="learning-curve-basic · letsplot · pyplots.ai",
+        color="",
+        fill="",
+    )
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_text=element_text(size=16),
+        legend_position="bottom",
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x = 4800 x 2700 px) and HTML
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")

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

@@ -0,0 +1,25 @@
+library: letsplot
+specification_id: learning-curve-basic
+created: '2025-12-26T19:13:26Z'
+updated: '2025-12-26T19:15:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20527872881
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/learning-curve-basic/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with well-chosen blue/yellow color scheme that provides
+    high contrast
+  - Perfect implementation of learning curve concept showing classic overfitting pattern
+  - Clean ggplot2 grammar usage with geom_ribbon for confidence bands
+  - Well-structured code following KISS principles
+  - Proper title format and legend placement
+  weaknesses:
+  - Y-axis label could include units/scale indicator (e.g., Accuracy Score 0-1)
+  - Could demonstrate more lets-plot specific features like tooltips or interactive
+    elements