feat(letsplot): implement contour-decision-boundary

plots/contour-decision-boundary/implementations/letsplot.py

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+""" pyplots.ai
+contour-decision-boundary: Decision Boundary Classifier Visualization
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2026-01-01
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_text,
+    geom_point,
+    geom_tile,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_color_manual,
+    scale_fill_manual,
+    scale_shape_manual,
+    theme,
+    theme_minimal,
+)
+from sklearn.datasets import make_moons
+from sklearn.neighbors import KNeighborsClassifier
+
+
+LetsPlot.setup_html()
+
+# Data - Create synthetic classification data
+np.random.seed(42)
+X, y = make_moons(n_samples=200, noise=0.25, random_state=42)
+
+# Train a KNN classifier
+classifier = KNeighborsClassifier(n_neighbors=5)
+classifier.fit(X, y)
+
+# Create mesh grid for decision boundary
+h = 0.02  # Step size
+x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
+y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
+xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+
+# Predict class for each point in mesh
+Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+# Create DataFrame for mesh grid
+mesh_df = pd.DataFrame({"X1": xx.ravel(), "X2": yy.ravel(), "Predicted": Z.ravel().astype(str)})
+
+# Create DataFrame for training points
+train_df = pd.DataFrame({"X1": X[:, 0], "X2": X[:, 1], "Class": y.astype(str)})
+
+# Add classification result (correct/incorrect) for training points
+predictions = classifier.predict(X)
+train_df["Correct"] = np.where(predictions == y, "Correct", "Incorrect")
+
+# Plot - Decision boundary with training points
+plot = (
+    ggplot()
+    # Decision regions as filled contour using tiles
+    + geom_tile(aes(x="X1", y="X2", fill="Predicted"), data=mesh_df, alpha=0.4)
+    # Training points
+    + geom_point(aes(x="X1", y="X2", color="Class", shape="Correct"), data=train_df, size=5, stroke=1.5)
+    # Color scales
+    + scale_fill_manual(values=["#306998", "#FFD43B"], name="Predicted Class")
+    + scale_color_manual(values=["#306998", "#FFD43B"], name="True Class")
+    + scale_shape_manual(values=[16, 4], name="Classification")  # Circle for correct, X for incorrect
+    # Labels
+    + labs(title="contour-decision-boundary · letsplot · pyplots.ai", x="Feature X1", y="Feature X2")
+    # Theme
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=14),
+        legend_position="right",
+    )
+    + ggsize(1600, 900)
+)
+
+# Save - use path parameter to specify exact location
+ggsave(plot, filename="plot.png", path=".", scale=3)
+
+# Save HTML for interactive version
+ggsave(plot, filename="plot.html", path=".")

plots/contour-decision-boundary/metadata/letsplot.yaml

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+library: letsplot
+specification_id: contour-decision-boundary
+created: '2026-01-01T21:31:34Z'
+updated: '2026-01-01T21:33:49Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20645829576
+issue: 2921
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual representation of the decision boundary using geom_tile for filled
+    regions
+  - Clear distinction between correctly and incorrectly classified points using shape
+    encoding
+  - Appropriate use of lets-plot grammar of graphics with proper layering (background
+    regions + foreground points)
+  - 'Good color scheme with Python colors (#306998 blue, #FFD43B yellow) that is colorblind-accessible'
+  - 'Comprehensive legend system showing all three aspects: predicted class, true
+    class, and classification accuracy'
+  weaknesses:
+  - Three separate legends consume significant horizontal space; could potentially
+    be consolidated
+  - Axis labels lack meaningful domain context (just Feature X1/X2 since using synthetic
+    data)