add plot script

scripts/plot.py

@@ -0,0 +1,73 @@
+import argparse
+import re
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Plot training metrics",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "-i",
+        "--input-path",
+        required=True,
+        help="path to input log file",
+    )
+    parser.add_argument(
+        "-t",
+        "--title",
+        help="figure title",
+    )
+    parser.add_argument(
+        "-o",
+        "--output-path",
+        help="path to output png file",
+    )
+    args = parser.parse_args()
+    return args
+
+
+def read_metrics(filename: str) -> pd.DataFrame:
+    data = []
+
+    with open(filename, "r", encoding="utf-8") as file:
+        for line in file:
+            if "mean score" not in line:
+                continue
+
+            episode, winning_rate, mean_score, max_tile = re.search(
+                (
+                    r"episode (\d+): winning rate = (\d+\.\d+), "
+                    r"mean score = (\d+\.\d+), max tile = (\d+)"
+                ),
+                line,
+            ).groups()
+
+            data.append(
+                {
+                    "episode": int(episode),
+                    "winning_rate": float(winning_rate),
+                    "mean_score": float(mean_score),
+                    "max_tile": int(max_tile),
+                }
+            )
+
+    return pd.DataFrame(data)
+
+
+def plot() -> None:
+    args = parse_args()
+    metrics = read_metrics(filename=args.input_path)
+    metrics.set_index("episode", inplace=True)
+    metrics.plot(subplots=True, title=args.title, grid=True)
+    plt.tight_layout()
+    plt.show()
+    if args.output_path is not None:
+        plt.savefig(args.output_path)
+
+
+if __name__ == "__main__":
+    plot()