feat(matplotlib): implement bar-feature-importance

plots/bar-feature-importance/implementations/matplotlib.py

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+""" pyplots.ai
+bar-feature-importance: Feature Importance Bar Chart
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 94/100 | Created: 2025-12-26
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Feature importances from a RandomForest model
+np.random.seed(42)
+features = [
+    "Income",
+    "Credit Score",
+    "Age",
+    "Employment Years",
+    "Debt Ratio",
+    "Number of Accounts",
+    "Payment History",
+    "Loan Amount",
+    "Education Level",
+    "Home Ownership",
+    "Marital Status",
+    "Number of Dependents",
+]
+
+# Generate realistic importance values (sorted descending)
+importance = np.array([0.182, 0.156, 0.124, 0.098, 0.089, 0.078, 0.072, 0.065, 0.051, 0.042, 0.028, 0.015])
+std = np.array([0.025, 0.022, 0.018, 0.015, 0.014, 0.012, 0.011, 0.010, 0.008, 0.007, 0.005, 0.003])
+
+# Sort by importance (highest at top for horizontal bar chart)
+sorted_indices = np.argsort(importance)
+features_sorted = [features[i] for i in sorted_indices]
+importance_sorted = importance[sorted_indices]
+std_sorted = std[sorted_indices]
+
+# Create color gradient based on importance values
+cmap = plt.cm.Blues
+colors = cmap(0.3 + 0.6 * (importance_sorted / importance_sorted.max()))
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+bars = ax.barh(
+    features_sorted,
+    importance_sorted,
+    xerr=std_sorted,
+    color=colors,
+    edgecolor="#306998",
+    linewidth=1.5,
+    capsize=5,
+    error_kw={"elinewidth": 2, "capthick": 2, "alpha": 0.7},
+)
+
+# Add value annotations at the end of bars
+for bar, val, err in zip(bars, importance_sorted, std_sorted, strict=True):
+    ax.text(
+        val + err + 0.008,
+        bar.get_y() + bar.get_height() / 2,
+        f"{val:.3f}",
+        va="center",
+        ha="left",
+        fontsize=14,
+        color="#333333",
+    )
+
+# Labels and styling
+ax.set_xlabel("Importance Score", fontsize=20)
+ax.set_ylabel("Feature", fontsize=20)
+ax.set_title("bar-feature-importance · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.set_xlim(0, importance_sorted.max() + std_sorted.max() + 0.05)
+ax.grid(True, axis="x", alpha=0.3, linestyle="--")
+
+# Add subtle spine styling
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/bar-feature-importance/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: bar-feature-importance
+created: '2025-12-26T17:37:25Z'
+updated: '2025-12-26T17:45:28Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526602154
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 94
+review:
+  strengths:
+  - 'Excellent implementation of all spec requirements: sorted bars, color gradient,
+    error bars, and value annotations'
+  - Clean code structure following KISS principles with proper seed for reproducibility
+  - Professional styling with hidden top/right spines and subtle grid
+  - Realistic credit scoring context with appropriate feature names and importance
+    values
+  - Good use of Blues colormap for sequential color gradient mapped to importance
+    values
+  weaknesses:
+  - Axis labels could include units (e.g., "Importance Score (normalized)" or similar)
+  - Could leverage matplotlib annotate() for more styled value labels with arrows
+    or backgrounds