plotting/plottable.py

"""

This file is to plot the MDP and POMDP results separately.

"""

import jax.numpy as jnp  # Import JAX
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from jax import lax  # Import lax for cummax
from scipy.interpolate import interp1d

import wandb


def f(name):
    WINDOW_SIZE = 100
    SIGMA = 100
    INTERP_POINTS = 1000
    NORMALIZING_FACTOR = 200

    ENV_MAX_STEPS = {
        "CountRecallEasy": 2e7,
        "CountRecallMedium": 2e7,
        "CountRecallHard": 2e7,
        "BattleShipEasy": 2e7,
        "BattleShipMedium": 2e7,
        "BattleShipHard": 2e7,
        # other environments with default max steps 1e7
    }
    AXIS_FONT = {"fontsize": 9, "labelpad": 8}
    TICK_FONT = {"labelsize": 8}

    api = wandb.Api()
    runs = api.runs("bolt-um/Arcade-RLC")
    filtered_runs = [run for run in runs if run.state == "finished"]
    print(f"Total runs: {len(runs)}, Completed runs: {len(filtered_runs)}")

    METRIC_MAPPING = {
        "PQN": {"return_col": "returned_episode_returns", "time_col": "env_step"},
        "PQN_RNN": {"return_col": "returned_episode_returns", "time_col": "env_step"},
        "default": {"return_col": "episodic return", "time_col": "TOTAL_TIMESTEPS"},
    }

    def process_run(run):
        """Process individual W&B run with dynamic max steps per environment"""
        try:
            config = {k: v for k, v in run.config.items() if not k.startswith("_")}
            env_name = config.get("ENV_NAME", "UnknownEnv")
            partial_status = str(config.get("PARTIAL", False))

            if env_name in ENV_MAX_STEPS:
                env_max_step = ENV_MAX_STEPS[env_name]
            else:
                env_max_step = 1e7

            alg_name = config.get("ALG_NAME", "").upper()
            memory_type = "MLP"
            if alg_name == "PQN_RNN":
                memory_type = config.get("MEMORY_TYPE", "Unknown").capitalize()

            metric_map = METRIC_MAPPING.get(alg_name, METRIC_MAPPING["default"])
            # history = run.scan_history(keys=[metric_map["return_col"], metric_map["time_col"]])
            history = list(
                run.scan_history(
                    keys=[metric_map["return_col"], metric_map["time_col"]]
                )
            )
            history = pd.DataFrame(
                history, columns=[metric_map["return_col"], metric_map["time_col"]]
            )

            history["true_steps"] = history[metric_map["time_col"]].clip(
                upper=env_max_step
            )
            history = history.sort_values(metric_map["time_col"]).drop_duplicates(
                subset=["true_steps"]
            )

            if len(history) < 2:
                print(f"Skipping {run.name} due to insufficient data points")
                return None

            # Get first and last values for extrapolation
            first_return = history[metric_map["return_col"]].iloc[0]
            last_return = history[metric_map["return_col"]].iloc[-1]

            # Create unified interpolation grid for this environment
            unified_steps = np.linspace(0, env_max_step, INTERP_POINTS)
            unified_steps = np.round(unified_steps, decimals=5)
            scale_factor = NORMALIZING_FACTOR / env_max_step

            # Interpolate returns to uniform grid
            interp_func = interp1d(
                history["true_steps"],
                history[metric_map["return_col"]],
                kind="linear",
                bounds_error=False,
                fill_value=(first_return, last_return),
            )
            interpolated_returns = interp_func(unified_steps)

            smoothed_returns = (
                pd.Series(interpolated_returns)
                .ewm(span=100, adjust=False, min_periods=1)
                .mean()
                .values
            )
            # smoothed_returns = pd.Series(interpolated_returns).rolling(window=WINDOW_SIZE, min_periods=1).mean().values

            # Compute cumulative maximum using JAX
            cummax_returns = lax.cummax(jnp.array(smoothed_returns))

            return pd.DataFrame(
                {
                    "Algorithm": f"{alg_name} ({memory_type})",
                    "Return": interpolated_returns,
                    "Smoothed Return": smoothed_returns,
                    "Cummax Return": np.array(cummax_returns),  # Convert back to NumPy
                    "True Steps": unified_steps,
                    "EnvName": env_name,
                    "Partial": partial_status,
                    "Seed": str(config.get("SEED", 0)),
                    "run_id": run.id,
                    "StepsNormalized": unified_steps * scale_factor,
                    "EnvMaxStep": env_max_step,
                    "ScaleFactor": scale_factor,
                }
            )

        except Exception as e:
            print(f"Error processing {run.name}: {str(e)}")
        return None

    # # Process all runs and combine data
    # all_data = [df for run in filtered_runs if (df := process_run(run)) is not None]
    # if not all_data:
    #     print("No valid data to process")
    #     exit()
    # runs_df = pd.concat(all_data, ignore_index=True)
    # # save the data
    # runs_df.to_csv("data.csv")

    # load the data
    runs_df = pd.read_csv("F:/desktop/env_group.csv")

    # runs_df['FinalReturn'] = runs_df['Cummax Return'].astype(float)

    # # First aggregate across seeds within each environment for each model and Partial status:
    # # For each (Algorithm, Partial, EnvName) group, take the maximum final return across seeds.
    # seedgroup = runs_df.groupby(['Algorithm', 'Partial', 'EnvName', 'run_id', 'Seed'])['FinalReturn'].max().reset_index()

    # seedgroup.to_csv("seedgroup.csv")
    # env_group = seedgroup.groupby(['Algorithm', 'Partial', 'EnvName'])['FinalReturn'].agg(['mean', 'std']).reset_index()
    # # max for each seed then aggregate

    # # env_group.to_csv("env_group.csv")

    # # Now aggregate across the environments and difficults: compute mean and std for each (Algorithm, Partial)
    # model_group = env_group.groupby(['Algorithm', 'Partial']).agg(
    #     mean=('mean', 'mean'),
    #     std=('std', 'mean')
    # ).reset_index()
    # # model_group.to_csv("model_group.csv")

    # Pivot the table so that rows = Model and columns for Partial outcomes.
    # This will produce MultiIndex columns; then we rename them.
    pivot = {}
    for algo, group in runs_df.groupby("Algorithm"):
        table = group.pivot(index="EnvName", columns="Partial", values=["mean", "std"])
        pivot[algo] = table
        # Rename columns so that "False" becomes "MDP" and "True" becomes "POMDP".
        table.columns = table.columns.map(
            lambda x: (
                "MDP"
                if (x[0] == "mean" and str(x[1]) == "False")
                else (
                    "POMDP"
                    if (x[0] == "mean" and str(x[1]) == "True")
                    else (
                        "MDP_std"
                        if (x[0] == "std" and str(x[1]) == "False")
                        else (
                            "POMDP_std"
                            if (x[0] == "std" and str(x[1]) == "True")
                            else f"{x[0]}_{x[1]}"
                        )
                    )
                )
            )
        )

        # Compute the overall performance (MDP+POMDP) for the mean as the average of the two
        table["MDP+POMDP"] = table[["MDP", "POMDP"]].mean(axis=1)

        # Optionally, compute a combined variance (average the variances, here approximated via std)
        table["MDP+POMDP_std"] = table[["MDP_std", "POMDP_std"]].mean(axis=1)
    for algo, table in pivot.items():
        print(f"\n{algo}")
        print(table)
        # Print or save the table
        # print(table)
        table.to_csv(f"{algo}.csv", index=True)


for i in range(1):
    f(f"plot_{i}")