feat(bokeh): implement pdp-basic (#2971)

## Implementation: `pdp-basic` - bokeh

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

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

**Parent Issue:** #2922

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612798508)*

---------

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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/bokeh.py

@@ -0,0 +1,143 @@
+""" pyplots.ai
+pdp-basic: Partial Dependence Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import Band, ColumnDataSource, Span
+from bokeh.plotting import figure
+from sklearn.datasets import make_friedman1
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.inspection import partial_dependence
+
+
+# Data - Train a model and compute partial dependence
+np.random.seed(42)
+
+# Use Friedman #1 dataset which has known non-linear relationships
+X, y = make_friedman1(n_samples=500, n_features=5, noise=0.5, random_state=42)
+
+# Train a gradient boosting model
+model = GradientBoostingRegressor(n_estimators=100, max_depth=4, random_state=42)
+model.fit(X, y)
+
+# Compute partial dependence for feature 0 (has sin relationship)
+feature_idx = 0
+grid_resolution = 100
+
+# Compute partial dependence using sklearn
+pdp_results = partial_dependence(model, X, features=[feature_idx], kind="both", grid_resolution=grid_resolution)
+
+# Extract values
+avg_predictions = pdp_results["average"][0]
+individual_predictions = pdp_results["individual"][0]  # ICE lines
+grid_values = pdp_results["grid_values"][0]
+
+# Calculate confidence interval (percentiles of ICE lines)
+lower_bound = np.percentile(individual_predictions, 10, axis=0)
+upper_bound = np.percentile(individual_predictions, 90, axis=0)
+
+# Center partial dependence at zero for easier interpretation
+center_val = avg_predictions.mean()
+avg_centered = avg_predictions - center_val
+lower_centered = lower_bound - center_val
+upper_centered = upper_bound - center_val
+
+# Get training data distribution for rug plot
+rug_x = X[:, feature_idx]
+
+# Create data source for main line and band
+source = ColumnDataSource(data={"x": grid_values, "y": avg_centered, "lower": lower_centered, "upper": upper_centered})
+
+# Create data source for rug plot - position at bottom of plot area
+y_min = lower_centered.min() - 1.5
+rug_source = ColumnDataSource(data={"x": rug_x, "y": np.full_like(rug_x, y_min + 0.3)})
+
+# Create figure with proper sizing
+p = figure(
+    width=4800,
+    height=2700,
+    title="pdp-basic · bokeh · pyplots.ai",
+    x_axis_label="Feature X₀ Value",
+    y_axis_label="Partial Dependence (centered)",
+)
+
+# Add confidence band
+band = Band(
+    base="x",
+    lower="lower",
+    upper="upper",
+    source=source,
+    fill_color="#306998",
+    fill_alpha=0.25,
+    line_color="#306998",
+    line_alpha=0.4,
+)
+p.add_layout(band)
+
+# Add horizontal line at y=0 for reference
+zero_line = Span(location=0, dimension="width", line_color="#555555", line_width=3, line_dash="dashed", line_alpha=0.6)
+p.add_layout(zero_line)
+
+# Add invisible patch for confidence band legend entry
+p.patch([], [], fill_color="#306998", fill_alpha=0.25, line_color="#306998", line_alpha=0.4, legend_label="80% CI")
+
+# Add main PDP line
+p.line("x", "y", source=source, line_width=5, line_color="#306998", legend_label="Average PD")
+
+# Add rug plot for data distribution
+p.scatter(
+    "x",
+    "y",
+    source=rug_source,
+    size=25,
+    color="#FFD43B",
+    alpha=0.6,
+    line_width=3,
+    angle=1.5708,
+    marker="dash",
+    legend_label="Data Distribution",
+)
+
+# Text styling - scaled for 4800x2700 px canvas
+p.title.text_font_size = "56pt"
+p.title.text_font_style = "bold"
+p.xaxis.axis_label_text_font_size = "42pt"
+p.yaxis.axis_label_text_font_size = "42pt"
+p.xaxis.major_label_text_font_size = "32pt"
+p.yaxis.major_label_text_font_size = "32pt"
+
+# Axis styling
+p.xaxis.axis_line_width = 3
+p.yaxis.axis_line_width = 3
+p.xaxis.major_tick_line_width = 3
+p.yaxis.major_tick_line_width = 3
+p.xaxis.minor_tick_line_width = 2
+p.yaxis.minor_tick_line_width = 2
+
+# Grid styling
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = "dashed"
+p.ygrid.grid_line_dash = "dashed"
+
+# Legend styling
+p.legend.location = "bottom_right"
+p.legend.label_text_font_size = "32pt"
+p.legend.background_fill_alpha = 0.9
+p.legend.border_line_alpha = 0.5
+p.legend.border_line_width = 2
+p.legend.glyph_height = 50
+p.legend.glyph_width = 50
+p.legend.spacing = 20
+p.legend.padding = 25
+p.legend.margin = 40
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "white"
+
+# Save
+export_png(p, filename="plot.png")

plots/pdp-basic/metadata/bokeh.yaml

@@ -0,0 +1,26 @@
+library: bokeh
+specification_id: pdp-basic
+created: '2025-12-31T05:37:00Z'
+updated: '2025-12-31T05:50:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612798508
+issue: 2922
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of all spec requirements including confidence band, rug
+    plot, and zero-centering
+  - Uses real ML workflow with sklearn partial_dependence function and GradientBoostingRegressor
+  - Clean, well-structured code with proper reproducibility (random seeds)
+  - Good use of Bokeh Band model for the confidence interval visualization
+  - Appropriate text scaling for the 4800x2700 canvas size
+  weaknesses:
+  - Legend placement in bottom-right could be improved; top_right or top_left would
+    be safer
+  - Axis labels lack units (though partial dependence is unitless, could add context
+    like standardized units)