Add plot example.

LICENSE

@@ -0,0 +1,7 @@
+Copyright 2024 Erik Terpstra
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -51,8 +51,14 @@ I focused on small incremental improvements through separation of concerns.
 In the branch `extract-data-and-output-rendering-to-files`, I separated the
 data handling and output rendering. Subsequently, in the branch `extract-lib`,
 I isolated the core algorithm into a dedicated library file.
+In the branch `plotting` I added a script that plots the user's opinions on a
+2D plane.
 
 The full changes can be reviewed in the associated pull requests.
 
 I prefer small steps and improving abstraction before focusing more on
 feature completeness for the algorithm.
+
+## Plot example
+
+![Image of a the user's opinions on a 2D plane](plot_example.png)

plot_opinions.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+import yaml
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+from scipy.spatial import ConvexHull
+from matplotlib.patches import Polygon
+from polis_core import OpinionAnalyzer
+
+
+def load_from_yaml(filepath):
+    with open(filepath) as f:
+        data = yaml.safe_load(f)
+    vote_map = {"agree": 1, "disagree": -1}
+    votes = [
+        [vote_map.get(v, 0) for v in user_votes]
+        for user_votes in data["votes"].values()
+    ]
+    return data["statements"], np.array(votes), list(data["votes"].keys())
+
+
+def plot_opinion_clusters(points_2d, clusters, usernames, output_path=None):
+    plt.figure(figsize=(12, 8))
+
+    # Draw buffered convex hull for each cluster
+    for cluster_id in np.unique(clusters):
+        mask = clusters == cluster_id
+        cluster_points = points_2d[mask]
+
+        if len(cluster_points) >= 3:
+            hull = ConvexHull(cluster_points)
+            hull_points = cluster_points[hull.vertices]
+
+            # Buffer the hull
+            centroid = np.mean(hull_points, axis=0)
+            vectors = hull_points - centroid
+            lengths = np.sqrt(np.sum(vectors**2, axis=1))
+            normalized_vectors = vectors / lengths[:, np.newaxis]
+            buffered_points = hull_points + normalized_vectors * 0.5
+            buffered_points = np.vstack((buffered_points, buffered_points[0]))
+
+            # Draw hull
+            color = plt.cm.viridis(cluster_id / len(np.unique(clusters)))
+            plt.gca().add_patch(Polygon(buffered_points, alpha=0.2, facecolor=color))
+
+    # Plot points and labels
+    scatter = plt.scatter(
+        points_2d[:, 0], points_2d[:, 1], c=clusters, cmap="viridis", s=100, alpha=0.6
+    )
+
+    for i, user in enumerate(usernames):
+        plt.annotate(
+            user,
+            (points_2d[i, 0], points_2d[i, 1]),
+            xytext=(5, 5),
+            textcoords="offset points",
+        )
+
+    # Set view limits with padding
+    x_min, x_max = points_2d[:, 0].min(), points_2d[:, 0].max()
+    y_min, y_max = points_2d[:, 1].min(), points_2d[:, 1].max()
+    padding_x = (x_max - x_min) * 0.2
+    padding_y = (y_max - y_min) * 0.2
+    plt.xlim(x_min - padding_x, x_max + padding_x)
+    plt.ylim(y_min - padding_y, y_max + padding_y)
+
+    plt.title("Opinion Clusters")
+    plt.xlabel("First Principal Component")
+    plt.ylabel("Second Principal Component")
+    plt.legend(*scatter.legend_elements(), title="Clusters", loc="upper right")
+    plt.grid(True, linestyle="--", alpha=0.7)
+
+    if output_path:
+        plt.savefig(output_path, bbox_inches="tight", dpi=300)
+        print(f"Plot saved to {output_path}")
+    else:
+        plt.show()
+    plt.close()
+
+
+def main(yaml_file):
+    statements, votes, usernames = load_from_yaml(yaml_file)
+    analyzer = OpinionAnalyzer()
+    results = analyzer.analyze(votes, statements)
+
+    # Add jitter to separate overlapping points
+    points_2d = results["points_2d"]
+    jitter = np.random.normal(0, 0.1, points_2d.shape)
+    jittered_points = points_2d + jitter
+
+    output_path = Path(yaml_file).with_suffix(".png")
+    plot_opinion_clusters(jittered_points, results["clusters"], usernames, output_path)
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) != 2:
+        print("Usage: python plot_opinions.py input.yaml")
+        sys.exit(1)
+    main(sys.argv[1])