feat(letsplot): implement pdp-basic (#2967)

## Implementation: `pdp-basic` - letsplot

Implements the **letsplot** version of `pdp-basic`.

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

**Parent Issue:** #2922

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612804067)*

---------

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

Author: github-actions[bot]

plots/pdp-basic/implementations/letsplot.py

@@ -0,0 +1,93 @@
+""" pyplots.ai
+pdp-basic: Partial Dependence Plot
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+from sklearn.datasets import make_regression
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.inspection import partial_dependence
+
+
+LetsPlot.setup_html()
+
+# Train a model for partial dependence
+np.random.seed(42)
+X, y = make_regression(n_samples=500, n_features=5, noise=20, random_state=42)
+feature_names = ["Temperature", "Humidity", "Pressure", "WindSpeed", "Altitude"]
+
+model = GradientBoostingRegressor(n_estimators=100, max_depth=4, random_state=42)
+model.fit(X, y)
+
+# Compute partial dependence for Temperature (feature 0)
+feature_idx = 0
+feature_name = feature_names[feature_idx]
+pdp_result = partial_dependence(model, X, features=[feature_idx], kind="both", grid_resolution=80)
+
+feature_values = pdp_result["grid_values"][0]
+avg_pd = pdp_result["average"][0]
+
+# Get individual conditional expectations (ICE) for uncertainty
+ice_lines = pdp_result["individual"][0]
+lower_bound = np.percentile(ice_lines, 10, axis=0)
+upper_bound = np.percentile(ice_lines, 90, axis=0)
+
+# Create DataFrame for plotting
+df_pdp = pd.DataFrame(
+    {"feature_value": feature_values, "partial_dependence": avg_pd, "lower": lower_bound, "upper": upper_bound}
+)
+
+# Sample ICE lines for visualization (show a subset)
+n_ice_lines = 50
+ice_indices = np.random.choice(ice_lines.shape[0], n_ice_lines, replace=False)
+ice_data = []
+for i, idx in enumerate(ice_indices):
+    for j, fv in enumerate(feature_values):
+        ice_data.append({"feature_value": fv, "ice_value": ice_lines[idx, j], "line_id": i})
+df_ice = pd.DataFrame(ice_data)
+
+# Get rug data (sample of training feature values for distribution)
+rug_sample = np.random.choice(X[:, feature_idx], size=100, replace=False)
+y_min = avg_pd.min() - (avg_pd.max() - avg_pd.min()) * 0.02
+y_max = avg_pd.min() + (avg_pd.max() - avg_pd.min()) * 0.02
+df_rug = pd.DataFrame(
+    {"x": rug_sample, "y_start": np.full(len(rug_sample), y_min), "y_end": np.full(len(rug_sample), y_max)}
+)
+
+# Create the partial dependence plot
+plot = (
+    ggplot()
+    # Confidence band (80% interval from ICE lines)
+    + geom_ribbon(aes(x="feature_value", ymin="lower", ymax="upper"), data=df_pdp, fill="#306998", alpha=0.2)
+    # ICE lines (individual conditional expectations)
+    + geom_line(
+        aes(x="feature_value", y="ice_value", group="line_id"), data=df_ice, color="#306998", alpha=0.15, size=0.5
+    )
+    # Main PDP line
+    + geom_line(aes(x="feature_value", y="partial_dependence"), data=df_pdp, color="#FFD43B", size=2.5)
+    # Rug plot showing data distribution (vertical segments at bottom)
+    + geom_segment(aes(x="x", y="y_start", xend="x", yend="y_end"), data=df_rug, color="#306998", alpha=0.4, size=0.8)
+    # Labels and title
+    + labs(
+        x=f"{feature_name} (standardized)",
+        y="Partial Dependence (predicted outcome)",
+        title="pdp-basic · letsplot · pyplots.ai",
+    )
+    # Theme for readability
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.3),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG and HTML
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")

plots/pdp-basic/metadata/letsplot.yaml

@@ -0,0 +1,27 @@
+library: letsplot
+specification_id: pdp-basic
+created: '2025-12-31T05:36:21Z'
+updated: '2025-12-31T05:49:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612804067
+issue: 2922
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of PDP with comprehensive visualization including ICE
+    lines, confidence band, and rug plot
+  - Clean code structure using sklearn partial_dependence for proper ML interpretation
+  - Professional color scheme with yellow main line contrasting against blue uncertainty
+    elements
+  - Proper use of lets-plot ggplot2-style grammar with layered geoms
+  - Good choice of realistic weather-related feature names for the regression context
+  weaknesses:
+  - Missing legend to explain the different plot elements (main PDP line, ICE lines,
+    confidence band)
+  - The rug plot is very subtle and may be hard to see; could use slightly taller
+    segments