feat(bokeh): implement precision-recall (#2318)

## Implementation: `precision-recall` - bokeh

Implements the **bokeh** version of `precision-recall`.

**File:** `plots/precision-recall/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526596746)*

---------

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

Author: github-actions[bot]

plots/precision-recall/implementations/bokeh.py

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+""" pyplots.ai
+precision-recall: Precision-Recall Curve
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource, Legend
+from bokeh.plotting import figure
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import average_precision_score, precision_recall_curve
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+
+
+# Data - Generate imbalanced classification dataset
+np.random.seed(42)
+X, y_true = make_classification(
+    n_samples=2000,
+    n_features=20,
+    n_informative=10,
+    n_redundant=5,
+    n_classes=2,
+    weights=[0.7, 0.3],  # Imbalanced dataset
+    random_state=42,
+)
+
+# Split into train and test for realistic evaluation
+X_train, X_test, y_train, y_test = train_test_split(X, y_true, test_size=0.5, random_state=42, stratify=y_true)
+
+# Train two classifiers for comparison
+lr_model = LogisticRegression(random_state=42, max_iter=1000)
+nb_model = GaussianNB()
+
+lr_model.fit(X_train, y_train)
+nb_model.fit(X_train, y_train)
+
+# Get prediction probabilities on test set
+lr_scores = lr_model.predict_proba(X_test)[:, 1]
+nb_scores = nb_model.predict_proba(X_test)[:, 1]
+
+# Calculate precision-recall curves
+lr_precision, lr_recall, _ = precision_recall_curve(y_test, lr_scores)
+nb_precision, nb_recall, _ = precision_recall_curve(y_test, nb_scores)
+
+# Calculate Average Precision scores
+lr_ap = average_precision_score(y_test, lr_scores)
+nb_ap = average_precision_score(y_test, nb_scores)
+
+# Baseline (random classifier) - positive class ratio
+baseline = np.mean(y_test)
+
+# Create figure (16:9 aspect ratio at 4800x2700)
+p = figure(
+    width=4800,
+    height=2700,
+    title="precision-recall · bokeh · pyplots.ai",
+    x_axis_label="Recall",
+    y_axis_label="Precision",
+    x_range=(-0.02, 1.05),
+    y_range=(0, 1.08),
+)
+
+# Create data sources for stepped lines
+lr_source = ColumnDataSource(data={"recall": lr_recall, "precision": lr_precision})
+nb_source = ColumnDataSource(data={"recall": nb_recall, "precision": nb_precision})
+
+# Plot Precision-Recall curves with step style
+lr_line = p.step(x="recall", y="precision", source=lr_source, line_width=5, color="#306998", alpha=0.9, mode="after")
+
+nb_line = p.step(x="recall", y="precision", source=nb_source, line_width=5, color="#FFD43B", alpha=0.9, mode="after")
+
+# Baseline reference line (random classifier)
+baseline_source = ColumnDataSource(data={"x": [0, 1], "y": [baseline, baseline]})
+baseline_line = p.line(
+    x="x", y="y", source=baseline_source, line_width=4, line_dash="dashed", color="#666666", alpha=0.8
+)
+
+# Create legend with AP scores
+legend = Legend(
+    items=[
+        (f"Logistic Regression (AP = {lr_ap:.3f})", [lr_line]),
+        (f"Naive Bayes (AP = {nb_ap:.3f})", [nb_line]),
+        (f"Random Classifier (baseline = {baseline:.2f})", [baseline_line]),
+    ],
+    location="top_right",
+    label_text_font_size="28pt",
+    glyph_width=50,
+    glyph_height=30,
+    spacing=20,
+    padding=25,
+    background_fill_alpha=0.9,
+    background_fill_color="white",
+    border_line_color="#cccccc",
+    border_line_width=2,
+)
+
+p.add_layout(legend)
+
+# Style the plot - scaled for 4800x2700
+p.title.text_font_size = "36pt"
+p.title.align = "center"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "22pt"
+p.yaxis.major_label_text_font_size = "22pt"
+
+# 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"
+
+# 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
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "white"
+
+# Outline
+p.outline_line_width = 2
+p.outline_line_color = "#cccccc"
+
+# Min border for padding
+p.min_border_left = 100
+p.min_border_right = 100
+p.min_border_top = 80
+p.min_border_bottom = 100
+
+# Save as PNG
+export_png(p, filename="plot.png")

plots/precision-recall/metadata/bokeh.yaml

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+library: bokeh
+specification_id: precision-recall
+created: '2025-12-26T17:40:49Z'
+updated: '2025-12-26T17:51:43Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526596746
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/precision-recall/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/precision-recall/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/precision-recall/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of stepped line style (mode="after") which accurately represents
+    threshold-based PR curves
+  - Clean comparison of two classifiers with distinct, colorblind-safe colors
+  - Proper inclusion of baseline reference line showing random classifier performance
+  - Average Precision scores displayed prominently in legend
+  - Good font sizing scaled appropriately for 4800x2700 resolution
+  - Well-structured code following KISS principles with proper reproducibility
+  weaknesses:
+  - Missing optional iso-F1 curves mentioned in specification notes (would enhance
+    the visualization)
+  - Could add hover tooltips to show precision/recall values at specific points (Bokeh
+    strength)