feat(plotnine): implement pdp-basic (#2951)

## Implementation: `pdp-basic` - plotnine

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

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

**Parent Issue:** #2922

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612800365)*

---------

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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/plotnine.py

@@ -0,0 +1,91 @@
+""" pyplots.ai
+pdp-basic: Partial Dependence Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_blank,
+    element_line,
+    element_text,
+    geom_line,
+    geom_ribbon,
+    geom_segment,
+    ggplot,
+    labs,
+    theme,
+    theme_minimal,
+)
+from sklearn.datasets import make_regression
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.inspection import partial_dependence
+
+
+# Data - Train a model and compute partial dependence
+np.random.seed(42)
+
+# Generate synthetic data for a regression problem
+X, y = make_regression(n_samples=500, n_features=5, noise=10, random_state=42)
+feature_names = ["Temperature", "Humidity", "Pressure", "WindSpeed", "Elevation"]
+
+# Train a gradient boosting model
+model = GradientBoostingRegressor(n_estimators=100, max_depth=3, random_state=42)
+model.fit(X, y)
+
+# Compute partial dependence for the first feature (Temperature)
+feature_idx = 0
+
+# Get partial dependence values
+pd_results = partial_dependence(model, X, features=[feature_idx], kind="average", grid_resolution=80)
+pd_values = pd_results["average"][0]
+grid_actual = pd_results["grid_values"][0]
+
+# Compute ICE curves for confidence interval estimation
+pd_individual = partial_dependence(model, X, features=[feature_idx], kind="individual", grid_resolution=80)
+ice_values = pd_individual["individual"][0]
+
+# Calculate confidence interval (mean ± 1.96 * std for 95% CI)
+pd_mean = ice_values.mean(axis=0)
+pd_std = ice_values.std(axis=0)
+ci_lower = pd_mean - 1.96 * pd_std
+ci_upper = pd_mean + 1.96 * pd_std
+
+# Create DataFrame for plotting
+df = pd.DataFrame(
+    {"feature_value": grid_actual, "partial_dependence": pd_mean, "ci_lower": ci_lower, "ci_upper": ci_upper}
+)
+
+# Rug data - sample of training data for feature distribution at bottom of plot
+y_min = ci_lower.min()
+rug_height = (ci_upper.max() - ci_lower.min()) * 0.02
+rug_sample = pd.DataFrame({"x": X[:80, feature_idx], "yend": y_min, "y": y_min - rug_height})
+
+# Plot
+plot = (
+    ggplot(df, aes(x="feature_value", y="partial_dependence"))
+    + geom_ribbon(aes(ymin="ci_lower", ymax="ci_upper"), fill="#306998", alpha=0.25)
+    + geom_line(color="#306998", size=2)
+    + geom_segment(
+        data=rug_sample, mapping=aes(x="x", xend="x", y="y", yend="yend"), color="#FFD43B", alpha=0.7, size=0.8
+    )
+    + labs(
+        title="pdp-basic · plotnine · pyplots.ai",
+        x="Temperature (standardized)",
+        y="Partial Dependence (avg. prediction)",
+    )
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold", ha="left"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#cccccc", alpha=0.3),
+        panel_grid_minor=element_blank(),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/pdp-basic/metadata/plotnine.yaml

@@ -0,0 +1,24 @@
+library: plotnine
+specification_id: pdp-basic
+created: '2025-12-31T05:34:04Z'
+updated: '2025-12-31T05:45:09Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612800365
+issue: 2922
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of PDP using real sklearn partial_dependence computation
+  - Proper confidence interval calculation from ICE curves (mean ± 1.96*std)
+  - Clean rug plot implementation using geom_segment showing training data distribution
+  - Professional appearance with appropriate color scheme and typography
+  - Follows plotnine grammar of graphics idiomatically
+  weaknesses:
+  - Missing legend to explain what the shaded confidence band represents
+  - The confidence band is very wide, visually overwhelming the actual trend line
+  - Rug marks are positioned slightly below the plot area rather than at the axis