Update MAE analysis script (#274)

Update script

Author: Sukh-P

experiments/analysis.py experiments/mae_analysis.py

@@ -1,5 +1,8 @@
 """
-Script to generate a table comparing two run for MAE values for 48 hour 15 minute forecast
+Script to generate analysis of MAE values for multiple model forecasts
+
+Does this for 48 hour horizon forecasts with 15 minute granularity
+
 """
 
 import argparse
@@ -10,15 +13,21 @@
 import wandb
 
 
-def main(runs: list[str], run_names: list[str]) -> None:
+def main(project: str, runs: list[str], run_names: list[str]) -> None:
     """
-    Compare two runs for MAE values for 48 hour 15 minute forecast
+    Compare MAE values for multiple model forecasts for 48 hour horizon with 15 minute granularity
+
+    Args:
+            project: name of W&B project
+            runs: W&B ids of runs
+            run_names: user specified names for runs
+
     """
     api = wandb.Api()
     dfs = []
     epoch_num = []
     for run in runs:
-        run = api.run(f"openclimatefix/PROJECT/{run}")
+        run = api.run(f"openclimatefix/{project}/{run}")
 
         df = run.history(samples=run.lastHistoryStep + 1)
         # Get the columns that are in the format 'MAE_horizon/step_<number>/val`
@@ -88,36 +97,41 @@ def main(runs: list[str], run_names: list[str]) -> None:
     for idx, df in enumerate(dfs):
         print(f"{run_names[idx]}: {df.mean()*100:0.3f}")
 
-    # Plot the error on per timestep, and all timesteps
+    # Plot the error per timestep
     plt.figure()
     for idx, df in enumerate(dfs):
-        plt.plot(column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}")
+        plt.plot(
+            column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}", linestyle="-"
+        )
     plt.legend()
     plt.xlabel("Timestep (minutes)")
     plt.ylabel("MAE %")
     plt.title("MAE % for each timestep")
     plt.savefig("mae_per_timestep.png")
     plt.show()
 
-    # Plot the error on per timestep, and grouped timesteps
+    # Plot the error per grouped timestep
     plt.figure()
     for idx, run_name in enumerate(run_names):
-        plt.plot(groups_df[run_name], label=f"{run_name}, epoch: {epoch_num[idx]}")
+        plt.plot(
+            groups_df[run_name],
+            label=f"{run_name}, epoch: {epoch_num[idx]}",
+            marker="o",
+            linestyle="-",
+        )
     plt.legend()
     plt.xlabel("Timestep (minutes)")
     plt.ylabel("MAE %")
-    plt.title("MAE % for each timestep")
-    plt.savefig("mae_per_timestep.png")
+    plt.title("MAE % for each grouped timestep")
+    plt.savefig("mae_per_grouped_timestep.png")
     plt.show()
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    "5llq8iw6"
-    parser.add_argument("--first_run", type=str, default="xdlew7ib")
-    parser.add_argument("--second_run", type=str, default="v3mja33d")
+    parser.add_argument("--project", type=str, default="")
     # Add arguments that is a list of strings
     parser.add_argument("--list_of_runs", nargs="+")
     parser.add_argument("--run_names", nargs="+")
     args = parser.parse_args()
-    main(args.list_of_runs, args.run_names)
+    main(args.project, args.list_of_runs, args.run_names)