feat(bokeh): implement calibration-curve (#2359)

## Implementation: `calibration-curve` - bokeh

Implements the **bokeh** version of `calibration-curve`.

**File:** `plots/calibration-curve/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528203324)*

---------

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

Author: github-actions[bot]

plots/calibration-curve/implementations/bokeh.py

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+""" pyplots.ai
+calibration-curve: Calibration 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, save
+from bokeh.models import ColumnDataSource, Label
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+
+
+# Data - Simulate binary classification predictions with realistic calibration
+np.random.seed(42)
+n_samples = 5000
+
+# Generate predicted probabilities uniformly across [0, 1]
+y_prob = np.random.uniform(0, 1, n_samples)
+
+# Generate true labels based on probabilities with slight miscalibration
+# Simulating a model that's slightly overconfident (sigmoid distortion)
+calibration_factor = 1.3  # >1 means overconfident
+adjusted_prob = 1 / (1 + np.exp(-calibration_factor * (np.log(y_prob / (1 - y_prob + 1e-10)))))
+adjusted_prob = np.clip(adjusted_prob, 0.01, 0.99)
+y_true = (np.random.uniform(0, 1, n_samples) < adjusted_prob).astype(int)
+
+# Calculate calibration curve (binned)
+n_bins = 10
+bin_edges = np.linspace(0, 1, n_bins + 1)
+bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+fraction_of_positives = []
+mean_predicted_value = []
+bin_counts = []
+
+for i in range(n_bins):
+    mask = (y_prob >= bin_edges[i]) & (y_prob < bin_edges[i + 1])
+    if i == n_bins - 1:  # Include right edge for last bin
+        mask = (y_prob >= bin_edges[i]) & (y_prob <= bin_edges[i + 1])
+
+    if mask.sum() > 0:
+        fraction_of_positives.append(y_true[mask].mean())
+        mean_predicted_value.append(y_prob[mask].mean())
+        bin_counts.append(mask.sum())
+    else:
+        fraction_of_positives.append(np.nan)
+        mean_predicted_value.append(bin_centers[i])
+        bin_counts.append(0)
+
+# Filter out empty bins for plotting
+valid_mask = np.array(bin_counts) > 0
+mean_pred_valid = np.array(mean_predicted_value)[valid_mask]
+frac_pos_valid = np.array(fraction_of_positives)[valid_mask]
+counts_valid = np.array(bin_counts)[valid_mask]
+
+# Calculate Brier score
+brier_score = np.mean((y_prob - y_true) ** 2)
+
+# Calculate Expected Calibration Error (ECE)
+ece = 0
+total_samples = sum(bin_counts)
+for i in range(len(bin_counts)):
+    if bin_counts[i] > 0:
+        ece += (bin_counts[i] / total_samples) * abs(fraction_of_positives[i] - mean_predicted_value[i])
+
+# Create main calibration plot
+p = figure(
+    width=4800,
+    height=2700,
+    title="calibration-curve · bokeh · pyplots.ai",
+    x_axis_label="Mean Predicted Probability",
+    y_axis_label="Fraction of Positives",
+    x_range=(-0.02, 1.02),
+    y_range=(-0.02, 1.02),
+)
+
+# Add diagonal reference line (perfect calibration)
+p.line([0, 1], [0, 1], line_color="#888888", line_dash="dashed", line_width=4, legend_label="Perfect Calibration")
+
+# Create source for calibration curve
+source = ColumnDataSource(data={"x": mean_pred_valid, "y": frac_pos_valid, "count": counts_valid})
+
+# Plot calibration curve with markers
+p.line("x", "y", source=source, line_color="#306998", line_width=5, legend_label="Classifier")
+p.scatter("x", "y", source=source, size=25, color="#306998", fill_alpha=0.9, line_color="#1a3d5c", line_width=3)
+
+# Add metrics annotation
+metrics_text = f"Brier Score: {brier_score:.3f}\nECE: {ece:.3f}"
+metrics_label = Label(
+    x=0.05,
+    y=0.88,
+    x_units="data",
+    y_units="data",
+    text=metrics_text,
+    text_font_size="28pt",
+    text_color="#333333",
+    background_fill_color="white",
+    background_fill_alpha=0.9,
+)
+p.add_layout(metrics_label)
+
+# Style the plot
+p.title.text_font_size = "36pt"
+p.title.text_font_style = "bold"
+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"
+
+# Legend styling
+p.legend.label_text_font_size = "24pt"
+p.legend.location = "bottom_right"
+p.legend.background_fill_alpha = 0.9
+p.legend.border_line_width = 2
+p.legend.padding = 15
+p.legend.spacing = 10
+
+# Axis styling
+p.xaxis.axis_line_width = 2
+p.yaxis.axis_line_width = 2
+p.xaxis.major_tick_line_width = 2
+p.yaxis.major_tick_line_width = 2
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+
+# Save outputs
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="Calibration Curve")

plots/calibration-curve/metadata/bokeh.yaml

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+library: bokeh
+specification_id: calibration-curve
+created: '2025-12-26T19:37:16Z'
+updated: '2025-12-26T19:44:08Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528203324
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Correct implementation of calibration curve with all required elements (diagonal
+    reference, binned probabilities, metrics)
+  - Excellent text sizing for the 4800x2700 canvas with clear readability
+  - Good use of Bokeh ColumnDataSource pattern and Label annotation for metrics
+  - Clean simple code structure following KISS principles
+  - Appropriate data simulation showing realistic classifier miscalibration behavior
+  weaknesses:
+  - Legend appears small and positioned far from the main plot area
+  - Could leverage Bokeh interactive features like HoverTool to show bin counts on
+    hover
+  - Axis labels lack explicit context