get_finetune_tables_and_plots.py

import os
import pickle

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from rliable import library as rly, metrics, plot_utils

dataframe = pd.read_csv("runs_tables/finetune_urls.csv")
with open("bin/finetune_scores.pickle", "rb") as handle:
    full_scores = pickle.load(handle)

os.makedirs("./out", exist_ok=True)


def get_average_scores(scores):
    avg_scores = {algo: {ds: None for ds in scores[algo]} for algo in scores}
    stds = {algo: {ds: None for ds in scores[algo]} for algo in scores}
    for algo in scores:
        for data in scores[algo]:
            sc = scores[algo][data]
            if len(sc) > 0:
                ml = min(map(len, sc))
                sc = [s[:ml] for s in sc]
                scores[algo][data] = sc
                avg_scores[algo][data] = np.mean(sc, axis=0)
                stds[algo][data] = np.std(sc, axis=0)

    return avg_scores, stds


def get_max_scores(scores):
    avg_scores = {algo: {ds: None for ds in scores[algo]} for algo in scores}
    stds = {algo: {ds: None for ds in scores[algo]} for algo in scores}
    for algo in scores:
        for data in scores[algo]:
            sc = scores[algo][data]
            if len(sc) > 0:
                ml = min(map(len, sc))
                sc = [s[:ml] for s in sc]
                scores[algo][data] = sc
                max_scores = np.max(sc, axis=1)
                avg_scores[algo][data] = np.mean(max_scores)
                stds[algo][data] = np.std(max_scores)

    return avg_scores, stds


def get_last_scores(avg_scores, avg_stds):
    last_scores = {
        algo: {
            ds: avg_scores[algo][ds][-1] if avg_scores[algo][ds] is not None else None
            for ds in avg_scores[algo]
        }
        for algo in avg_scores
    }
    stds = {
        algo: {
            ds: avg_stds[algo][ds][-1] if avg_stds[algo][ds] is not None else None
            for ds in avg_scores[algo]
        }
        for algo in avg_scores
    }
    return last_scores, stds


full_offline_scores = {
    algo: {data: None for data in full_scores[algo]} for algo in full_scores
}
full_online_scores = {
    algo: {data: None for data in full_scores[algo]} for algo in full_scores
}
regrets = {algo: {data: None for data in full_scores[algo]} for algo in full_scores}
regrets_std = {algo: {data: None for data in full_scores[algo]} for algo in full_scores}

for algo in full_offline_scores:
    for data in full_offline_scores[algo]:
        full_offline_scores[algo][data] = [s[0] for s in full_scores[algo][data]]
        full_online_scores[algo][data] = [s[1] for s in full_scores[algo][data]]
        regrets[algo][data] = np.mean([s[2] for s in full_scores[algo][data]])
        regrets_std[algo][data] = np.std([s[2] for s in full_scores[algo][data]])

avg_offline_scores, avg_offline_stds = get_average_scores(full_offline_scores)
max_offline_scores, max_offline_stds = get_max_scores(full_offline_scores)
last_offline_scores, last_offline_stds = get_last_scores(
    avg_offline_scores, avg_offline_stds
)

avg_online_scores, avg_online_stds = get_average_scores(full_online_scores)
max_online_scores, max_online_stds = get_max_scores(full_online_scores)
last_online_scores, last_online_stds = get_last_scores(
    avg_online_scores, avg_online_stds
)


def add_domains_avg(scores):
    for algo in scores:
        antmaze = [
            scores[algo][data]
            for data in [
                "antmaze-umaze-v2",
                "antmaze-umaze-diverse-v2",
                "antmaze-medium-play-v2",
                "antmaze-medium-diverse-v2",
                "antmaze-large-play-v2",
                "antmaze-large-diverse-v2",
            ]
        ]
        adroit = [
            scores[algo][data]
            for data in [
                "pen-cloned-v1",
                "door-cloned-v1",
                "hammer-cloned-v1",
                "relocate-cloned-v1",
            ]
        ]

        scores[algo]["antmaze avg"] = np.mean(antmaze)
        scores[algo]["adroit avg"] = np.mean(adroit)

        scores[algo]["total avg"] = np.mean(np.hstack((antmaze, adroit)))


add_domains_avg(last_offline_scores)
add_domains_avg(last_online_scores)
add_domains_avg(regrets)

algorithms = ["AWAC", "CQL", "IQL", "SPOT", "Cal-QL"]
datasets = dataframe["dataset"].unique()
ordered_datasets = [
    "antmaze-umaze-v2",
    "antmaze-umaze-diverse-v2",
    "antmaze-medium-play-v2",
    "antmaze-medium-diverse-v2",
    "antmaze-large-play-v2",
    "antmaze-large-diverse-v2",
    "antmaze avg",
    "pen-cloned-v1",
    "door-cloned-v1",
    "hammer-cloned-v1",
    "relocate-cloned-v1",
    "adroit avg",
    "total avg",
]


def get_table(
    scores,
    stds,
    pm="$\\pm$",
    delim=" & ",
    row_delim="\\midrule",
    row_end=" \\\\",
    row_begin="",
    scores2=None,
    stds2=None,
    scores_delim=" $\\to$ ",
):
    rows = [row_begin + delim.join(["Task Name"] + algorithms) + row_end]
    prev_env = "halfcheetah"
    for data in ordered_datasets:
        env = data.split("-")[0]
        if env != prev_env:
            if len(row_delim) > 0:
                rows.append(row_delim)
            prev_env = env

        row = [data]

        for algo in algorithms:
            if data in stds[algo]:
                row.append(
                    f"{scores[algo][data]:.2f} {pm} {stds[algo][data]:.2f}"
                    + (
                        ""
                        if scores2 is None
                        else f"{scores_delim} {scores2[algo][data]:.2f} {pm} {stds2[algo][data]:.2f}"  # noqa
                    )
                )
            else:
                row.append(
                    f"{scores[algo][data]:.2f}"
                    + (
                        ""
                        if scores2 is None
                        else f"{scores_delim} {scores2[algo][data]:.2f}"
                    )
                )
        rows.append(row_begin + delim.join(row) + row_end)
    return "\n".join(rows)


print(
    get_table(
        last_offline_scores,
        last_offline_stds,
        scores2=last_online_scores,
        stds2=last_online_stds,
    )
)
print()
print(get_table(regrets, regrets_std, "$\\pm$"))
print()
print(
    get_table(
        last_offline_scores,
        last_offline_stds,
        "±",
        "|",
        "",
        "|",
        "|",
        scores2=last_online_scores,
        stds2=last_online_stds,
        scores_delim=" -> ",
    )
)
print()
print(get_table(regrets, regrets_std, "±", "|", "", "|", "|"))

"""# Tunning plots"""

plt.rcParams["figure.figsize"] = (15, 8)
plt.rcParams["figure.dpi"] = 300
sns.set(style="ticks", font_scale=1.5)
# plt.rcParams.update({
#     "font.family": "serif",
#     "font.serif": "Times New Roman"
# })
# sns.set_palette("tab19")

linestyles = [
    ("solid", "solid"),
    ("dotted", (0, (1, 1))),
    ("long dash with offset", (5, (10, 3))),
    ("densely dashed", (0, (5, 1))),
    ("densely dashdotted", (0, (3, 1, 1, 1))),
    ("densely dashdotdotted", (0, (3, 1, 1, 1, 1, 1))),
]

for data in datasets:
    min_score = 1e6
    max_score = -1e6
    for i, algo in enumerate(algorithms):
        if avg_online_scores[algo][data] is not None:
            to_draw = avg_online_scores[algo][data]
            std_draw = avg_online_stds[algo][data]
            if len(to_draw) == 600 or len(to_draw) == 601:
                to_draw = to_draw[::3]
                std_draw = std_draw[::3]
            if len(to_draw) == 1000:
                to_draw = to_draw[::5]
                std_draw = std_draw[::5]
            if len(to_draw) == 3000:
                to_draw = to_draw[::15]
                std_draw = std_draw[::15]
            steps = np.linspace(0, 1, len(to_draw))
            min_score = min(min_score, np.min(to_draw))
            max_score = max(max_score, np.max(to_draw))
            plt.plot(
                steps, to_draw, label=algo, linestyle=linestyles[i % len(linestyles)][1]
            )
            plt.fill_between(steps, to_draw - std_draw, to_draw + std_draw, alpha=0.1)

    plt.title(data)
    plt.xlabel("Fraction of total tuning steps")
    plt.ylabel("Normalized score")
    plt.ylim([min_score - 3, max_score + 3])
    plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))
    plt.grid()
    plt.savefig(f"out/tuning_{data}.pdf", dpi=300, bbox_inches="tight")
    # plt.show()
    plt.close()


def convert_dataset_name(name):
    name = name.replace("v2", "")
    name = name.replace("v1", "")
    name = name.replace("v0", "")
    name = name.replace("medium-", "m-")
    name = name.replace("umaze-", "u-")
    name = name.replace("large-", "l-")
    name = name.replace("replay-", "re-")
    name = name.replace("random-", "ra-")
    name = name.replace("expert-", "e-")
    name = name.replace("play-", "p-")
    name = name.replace("diverse-", "d-")
    name = name.replace("human-", "h-")
    name = name.replace("cloned-", "c-")
    return name[:-1]


def plot_bars(scores, save_name):
    agg_l = []

    for env in ["antmaze", "pen", "door", "hammer", "relocate"]:
        if env in ["halfcheetah", "hopper", "walker2d"]:
            datas = ["medium-v2", "medium-expert-v2", "medium-replay-v2"]
        elif "maze2d" in env:
            datas = ["umaze-v1", "medium-v1", "large-v1"]
        elif "antmaze" in env:
            datas = [
                "umaze-v2",
                "umaze-diverse-v2",
                "medium-play-v2",
                "medium-diverse-v2",
                "large-play-v2",
                "large-diverse-v2",
            ]
        else:
            datas = ["cloned-v1"]
        for data in datas:
            line = convert_dataset_name(f"{env}-{data}")
            for algo in algorithms:
                agg_l.append([algo, line, scores[algo][f"{env}-{data}"]])
    df_agg = pd.DataFrame(agg_l, columns=["Algorithm", "Dataset", "Normalized Score"])

    sns.set(style="ticks", font_scale=2)
    plt.rcParams["figure.figsize"] = (20, 10)  # (10, 6)

    b = sns.barplot(
        data=df_agg[df_agg.Dataset.apply(lambda x: "ant" in x)],
        x="Dataset",
        y="Normalized Score",
        hue="Algorithm",
    )
    # plt.tight_layout()
    plt.xticks(fontsize=30)
    plt.yticks(fontsize=30)
    plt.legend(fontsize=10)
    plt.xticks(rotation=45)
    sns.move_legend(b, "upper left", bbox_to_anchor=(1, 1))
    plt.grid()

    plt.savefig(f"out/bars_{save_name}_ant.pdf", dpi=300, bbox_inches="tight")
    # plt.show()
    plt.close()

    b = sns.barplot(
        data=df_agg[
            df_agg.Dataset.apply(
                lambda x: "pen" in x or "hammer" in x or "door" in x or "relocate" in x
            )
        ],
        x="Dataset",
        y="Normalized Score",
        hue="Algorithm",
    )
    plt.grid()
    # plt.tight_layout()
    plt.xticks(fontsize=30)
    plt.yticks(fontsize=30)
    plt.legend(fontsize=10)
    plt.xticks(rotation=45)
    sns.move_legend(b, "upper left", bbox_to_anchor=(1, 1))
    plt.savefig(f"out/bars_{save_name}_adroit.pdf", dpi=300, bbox_inches="tight")
    # plt.show()
    plt.close()


plot_bars(last_online_scores, "last_online")

plt.rcParams["figure.figsize"] = (10, 6)
plt.rcParams["figure.dpi"] = 300
sns.set(style="ticks", font_scale=0.5)
plt.rcParams.update(
    {
        # "font.family": "serif",
        "font.serif": "Times New Roman"
    }
)


def flatten(data):
    res = {}
    for algo in data:
        flat = []
        for env in data[algo]:
            if "avg" not in env:
                env_list = np.array(data[algo][env])[:, -1]
                flat.append(env_list)
        res[algo] = np.array(flat).T
    return res


flat = flatten(full_online_scores)

algorithms = list(flat)

normalized_score_dict = flat

# Human normalized score thresholds
thresholds = np.linspace(-5.0, 150.0, 31)
score_distributions, score_distributions_cis = rly.create_performance_profile(
    normalized_score_dict, thresholds
)
# Plot score distributions
fig, ax = plt.subplots(ncols=1, figsize=(7, 5))
# plt.legend()
plot_utils.plot_performance_profiles(
    score_distributions,
    thresholds,
    performance_profile_cis=score_distributions_cis,
    colors=dict(zip(algorithms, sns.color_palette("colorblind"))),
    xlabel=r"D4RL Normalized Score $(\tau)$",
    ax=ax,
    legend=True,
)
plt.savefig("out/perf_profiles_online.pdf", dpi=300, bbox_inches="tight")

algorithm_pairs = {}
sns.set(style="ticks", font_scale=0.5)
algs = [
    "SPOT",
    "CQL",
    "IQL",
    "AWAC",
]
for a1 in ["Cal-QL"]:
    for a2 in algs:
        algorithm_pairs[f"{a1},{a2}"] = (flat[a1], flat[a2])
average_probabilities, average_prob_cis = rly.get_interval_estimates(
    algorithm_pairs, metrics.probability_of_improvement, reps=200
)
ax = plot_utils.plot_probability_of_improvement(average_probabilities, average_prob_cis)
# ax.set_xlim(0.5, 0.8)
plt.savefig("out/improvement_probability_online.pdf", dpi=300, bbox_inches="tight")