feat(matplotlib): implement pdp-basic

plots/pdp-basic/implementations/matplotlib.py

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+""" pyplots.ai
+pdp-basic: Partial Dependence Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.datasets import make_regression
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.inspection import partial_dependence
+
+
+# Data: Train a gradient boosting model and compute partial dependence
+np.random.seed(42)
+X, y = make_regression(n_samples=500, n_features=5, noise=15, random_state=42)
+
+# Train model
+model = GradientBoostingRegressor(n_estimators=100, max_depth=4, random_state=42)
+model.fit(X, y)
+
+# Compute partial dependence for feature 0
+feature_idx = 0
+
+# Get partial dependence using sklearn
+pd_result = partial_dependence(model, X, features=[feature_idx], kind="both", grid_resolution=80)
+pdp_values = pd_result["average"][0]
+ice_lines = pd_result["individual"][0]
+grid_values = pd_result["grid_values"][0]
+
+# Calculate confidence interval (mean ± std of ICE lines)
+ice_mean = pdp_values
+ice_std = np.std(ice_lines, axis=0)
+
+# Create plot (4800x2700 px)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot ICE lines (faint individual lines)
+for i in range(0, len(ice_lines), 10):  # Sample every 10th line for clarity
+    ax.plot(grid_values, ice_lines[i], color="#306998", alpha=0.1, linewidth=1)
+
+# Plot confidence band
+ax.fill_between(
+    grid_values,
+    ice_mean - 1.96 * ice_std,
+    ice_mean + 1.96 * ice_std,
+    alpha=0.25,
+    color="#306998",
+    label="95% Confidence Interval",
+)
+
+# Plot main PDP line
+ax.plot(grid_values, pdp_values, color="#306998", linewidth=4, label="Partial Dependence")
+
+# Add rug plot showing data distribution
+rug_y = ax.get_ylim()[0]
+ax.scatter(
+    X[:, feature_idx], np.full(len(X), rug_y), marker="|", color="#FFD43B", alpha=0.4, s=100, label="Data Distribution"
+)
+
+# Labels and styling
+ax.set_xlabel("Feature Value", fontsize=20)
+ax.set_ylabel("Partial Dependence (Predicted Value)", fontsize=20)
+ax.set_title("pdp-basic · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=16, loc="upper left")
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/pdp-basic/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: pdp-basic
+created: '2025-12-31T05:34:35Z'
+updated: '2025-12-31T05:46:31Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612797918
+issue: 2922
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/pdp-basic/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of PDP with ICE lines using sklearn partial_dependence
+    function with kind=both
+  - Proper confidence band visualization using fill_between with mean ± 1.96*std
+  - Rug plot effectively shows training data distribution along x-axis
+  - Clean, readable code that follows KISS principles
+  - Good colorblind-safe color scheme with Python blue and yellow
+  - Appropriate text sizing for 4800x2700 canvas
+  weaknesses:
+  - Legend in upper left slightly overlaps with ICE lines - consider frameon=True
+    with white background
+  - Rug plot markers (s=100) could be larger (s=150-200) for better visibility on
+    high-resolution canvas
+  - Y-axis range dominated by wide confidence band making PDP line variation appear
+    minimal