feat(letsplot): implement calibration-curve (#2358)

## Implementation: `calibration-curve` - letsplot

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528205405)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/calibration-curve/implementations/letsplot.py

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+""" pyplots.ai
+calibration-curve: Calibration Curve
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data - Generate realistic binary classification predictions
+np.random.seed(42)
+n_samples = 1000
+
+# Create true labels with imbalanced classes (60/40 split)
+y_true = np.concatenate([np.zeros(600), np.ones(400)])
+
+# Generate predicted probabilities with realistic calibration issues
+# Model tends to be slightly overconfident (probabilities pushed toward extremes)
+y_prob = np.zeros(n_samples)
+
+# For true negatives: mostly low probabilities with some mid-range
+y_prob[:600] = np.clip(np.random.beta(2, 5, 600) * 0.6 + np.random.normal(0, 0.05, 600), 0, 1)
+# For true positives: mostly high probabilities but with spread
+y_prob[600:] = np.clip(np.random.beta(5, 2, 400) * 0.6 + 0.35 + np.random.normal(0, 0.08, 400), 0, 1)
+
+# Shuffle the data
+shuffle_idx = np.random.permutation(n_samples)
+y_true = y_true[shuffle_idx]
+y_prob = y_prob[shuffle_idx]
+
+# Calculate calibration curve with 10 bins
+n_bins = 10
+bin_edges = np.linspace(0, 1, n_bins + 1)
+bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+mean_predicted = []
+fraction_positive = []
+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:
+        mean_predicted.append(y_prob[mask].mean())
+        fraction_positive.append(y_true[mask].mean())
+        bin_counts.append(mask.sum())
+    else:
+        mean_predicted.append(bin_centers[i])
+        fraction_positive.append(np.nan)
+        bin_counts.append(0)
+
+# 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(n_bins):
+    if bin_counts[i] > 0:
+        ece += (bin_counts[i] / total_samples) * abs(fraction_positive[i] - mean_predicted[i])
+
+# Create dataframe for calibration curve
+df_calibration = pd.DataFrame(
+    {"mean_predicted": mean_predicted, "fraction_positive": fraction_positive, "bin_count": bin_counts}
+)
+df_calibration = df_calibration.dropna()
+
+# Create dataframe for diagonal (perfect calibration)
+df_diagonal = pd.DataFrame({"x": [0, 1], "y": [0, 1]})
+
+# Create dataframe for histogram of predictions
+hist_bins = 20
+hist_counts, hist_edges = np.histogram(y_prob, bins=hist_bins, range=(0, 1))
+hist_centers = (hist_edges[:-1] + hist_edges[1:]) / 2
+df_histogram = pd.DataFrame(
+    {
+        "prob_center": hist_centers,
+        "count": hist_counts / hist_counts.max(),  # Normalize for subplot
+    }
+)
+
+# Plot
+plot = (
+    ggplot()
+    # Perfect calibration diagonal line
+    + geom_line(aes(x="x", y="y"), data=df_diagonal, color="#888888", size=1.5, linetype="dashed")
+    # Calibration curve
+    + geom_line(aes(x="mean_predicted", y="fraction_positive"), data=df_calibration, color="#306998", size=2)
+    + geom_point(
+        aes(x="mean_predicted", y="fraction_positive"), data=df_calibration, color="#306998", size=5, alpha=0.9
+    )
+    # Histogram bars at bottom showing prediction distribution
+    + geom_bar(
+        aes(x="prob_center", y="count"), data=df_histogram, stat="identity", fill="#FFD43B", alpha=0.6, width=0.045
+    )
+    # Labels and styling
+    + labs(
+        x="Mean Predicted Probability",
+        y="Fraction of Positives",
+        title=f"calibration-curve · letsplot · pyplots.ai\nBrier Score: {brier_score:.4f} | ECE: {ece:.4f}",
+    )
+    + scale_x_continuous(limits=[0, 1], breaks=[0, 0.2, 0.4, 0.6, 0.8, 1.0])
+    + scale_y_continuous(limits=[0, 1], breaks=[0, 0.2, 0.4, 0.6, 0.8, 1.0])
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=22),
+        axis_title=element_text(size=18),
+        axis_text=element_text(size=14),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save outputs
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")

plots/calibration-curve/metadata/letsplot.yaml

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+library: letsplot
+specification_id: calibration-curve
+created: '2025-12-26T19:37:18Z'
+updated: '2025-12-26T19:43:58Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528205405
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/calibration-curve/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of all spec requirements including diagonal reference
+    line, 10 bins, both Brier score and ECE metrics
+  - Clean integration of histogram showing prediction distribution at the bottom without
+    requiring a separate subplot
+  - Good color scheme with blue calibration curve, gray reference diagonal, and yellow
+    histogram bars
+  - Proper title format following pyplots.ai conventions
+  - Well-structured code following KISS principles with good reproducibility
+  weaknesses:
+  - The calibration curve is quite extreme - fraction of positives jumps from near
+    0 to near 1 very sharply around the 0.3-0.6 probability range, making the miscalibration
+    pattern less gradual than typical real-world examples
+  - Histogram bars extend quite high (normalized to max) which visually competes with
+    the calibration curve; could use smaller scale factor