offlinedv2/dreamerv2/common/plot.py

import argparse
import collections
import functools
import itertools
import json
import multiprocessing as mp
import os
import pathlib
import re
import subprocess
import warnings

os.environ['NO_AT_BRIDGE'] = '1'  # Hide X org false warning.

import matplotlib

matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd

np.set_string_function(lambda x: f'<np.array shape={x.shape} dtype={x.dtype}>')

Run = collections.namedtuple('Run', 'task method seed xs ys')

PALETTES = dict(
    discrete=(
        '#377eb8', '#4daf4a', '#984ea3', '#e41a1c', '#ff7f00', '#a65628',
        '#f781bf', '#888888', '#a6cee3', '#b2df8a', '#cab2d6', '#fb9a99',
    ),
    contrast=(
        '#0022ff', '#33aa00', '#ff0011', '#ddaa00', '#cc44dd', '#0088aa',
        '#001177', '#117700', '#990022', '#885500', '#553366', '#006666',
    ),
    gradient=(
        '#fde725', '#a0da39', '#4ac16d', '#1fa187', '#277f8e', '#365c8d',
        '#46327e', '#440154',
    ),
    baselines=(
        '#222222', '#666666', '#aaaaaa', '#cccccc',
    ),
)

LEGEND = dict(
    fontsize='medium', numpoints=1, labelspacing=0, columnspacing=1.2,
    handlelength=1.5, handletextpad=0.5, loc='lower center')

DEFAULT_BASELINES = ['d4pg', 'rainbow_sticky', 'human_gamer', 'impala']


def find_keys(args):
    filenames = []
    for indir in args.indir:
        task = next(indir.iterdir())  # First only.
        for method in task.iterdir():
            seed = next(indir.iterdir())  # First only.
            filenames += list(seed.glob('**/*.jsonl'))
    keys = set()
    for filename in filenames:
        keys |= set(load_jsonl(filename).columns)
    print(f'Keys      ({len(keys)}):', ', '.join(keys), flush=True)


def load_runs(args):
    total, toload = [], []
    for indir in args.indir:
        filenames = list(indir.glob('**/*.jsonl'))
        total += filenames
        for filename in filenames:
            task, method, seed = filename.relative_to(indir).parts[:-1]
            if not any(p.search(task) for p in args.tasks):
                continue
            if not any(p.search(method) for p in args.methods):
                continue
            toload.append((filename, indir))
    print(f'Loading {len(toload)} of {len(total)} runs...')
    jobs = [functools.partial(load_run, f, i, args) for f, i in toload]
    # Disable async data loading:
    # runs = [j() for j in jobs]
    with mp.Pool(10) as pool:
        promises = [pool.apply_async(j) for j in jobs]
        runs = [p.get() for p in promises]
    runs = [r for r in runs if r is not None]
    return runs


def load_run(filename, indir, args):
    task, method, seed = filename.relative_to(indir).parts[:-1]
    prefix = f'indir{args.indir.index(indir) + 1}_'
    if task == 'atari_jamesbond':
        task = 'atari_james_bond'
    seed = prefix + seed
    if args.prefix:
        method = prefix + method
    df = load_jsonl(filename)
    if df is None:
        print('Skipping empty run')
        return
    try:
        df = df[[args.xaxis, args.yaxis]].dropna()
        if args.maxval:
            df = df.replace([+np.inf], +args.maxval)
            df = df.replace([-np.inf], -args.maxval)
            df[args.yaxis] = df[args.yaxis].clip(-args.maxval, +args.maxval)
    except KeyError:
        return
    xs = df[args.xaxis].to_numpy()
    if args.xmult != 1:
        xs = xs.astype(np.float32) * args.xmult
    ys = df[args.yaxis].to_numpy()
    bins = {
        'atari': 1e6,
        'dmc': 1e4,
        'crafter': 1e4,
    }.get(task.split('_')[0], 1e5) if args.bins == -1 else args.bins
    if bins:
        borders = np.arange(0, xs.max() + 1e-8, bins)
        xs, ys = bin_scores(xs, ys, borders)
    if not len(xs):
        print('Skipping empty run', task, method, seed)
        return
    return Run(task, method, seed, xs, ys)


def load_baselines(patterns, prefix=False):
    runs = []
    directory = pathlib.Path(__file__).parent.parent / 'scores'
    for filename in directory.glob('**/*_baselines.json'):
        for task, methods in json.loads(filename.read_text()).items():
            for method, score in methods.items():
                if prefix:
                    method = f'baseline_{method}'
                if not any(p.search(method) for p in patterns):
                    continue
                runs.append(Run(task, method, None, None, score))
    return runs


def stats(runs, baselines):
    tasks = sorted(set(r.task for r in runs))
    methods = sorted(set(r.method for r in runs))
    seeds = sorted(set(r.seed for r in runs))
    baseline = sorted(set(r.method for r in baselines))
    print('Loaded', len(runs), 'runs.')
    print(f'Tasks     ({len(tasks)}):', ', '.join(tasks))
    print(f'Methods   ({len(methods)}):', ', '.join(methods))
    print(f'Seeds     ({len(seeds)}):', ', '.join(seeds))
    print(f'Baselines ({len(baseline)}):', ', '.join(baseline))


def order_methods(runs, baselines, args):
    methods = []
    for pattern in args.methods:
        for method in sorted(set(r.method for r in runs)):
            if pattern.search(method):
                if method not in methods:
                    methods.append(method)
                if method not in args.colors:
                    index = len(args.colors) % len(args.palette)
                    args.colors[method] = args.palette[index]
    non_baseline_colors = len(args.colors)
    for pattern in args.baselines:
        for method in sorted(set(r.method for r in baselines)):
            if pattern.search(method):
                if method not in methods:
                    methods.append(method)
                if method not in args.colors:
                    index = len(args.colors) - non_baseline_colors
                    index = index % len(PALETTES['baselines'])
                    args.colors[method] = PALETTES['baselines'][index]
    return methods


def figure(runs, methods, args):
    tasks = sorted(set(r.task for r in runs if r.xs is not None))
    rows = int(np.ceil((len(tasks) + len(args.add)) / args.cols))
    figsize = args.size[0] * args.cols, args.size[1] * rows
    fig, axes = plt.subplots(rows, args.cols, figsize=figsize, squeeze=False)
    for task, ax in zip(tasks, axes.flatten()):
        relevant = [r for r in runs if r.task == task]
        plot(task, ax, relevant, methods, args)
    for name, ax in zip(args.add, axes.flatten()[len(tasks):]):
        ax.set_facecolor((0.9, 0.9, 0.9))
        if name == 'median':
            plot_combined(
                'combined_median', ax, runs, methods, args,
                agg=lambda x: np.nanmedian(x, -1))
        elif name == 'mean':
            plot_combined(
                'combined_mean', ax, runs, methods, args,
                agg=lambda x: np.nanmean(x, -1))
        elif name == 'gamer_median':
            plot_combined(
                'combined_gamer_median', ax, runs, methods, args,
                lo='random', hi='human_gamer',
                agg=lambda x: np.nanmedian(x, -1))
        elif name == 'gamer_mean':
            plot_combined(
                'combined_gamer_mean', ax, runs, methods, args,
                lo='random', hi='human_gamer',
                agg=lambda x: np.nanmean(x, -1))
        elif name == 'record_mean':
            plot_combined(
                'combined_record_mean', ax, runs, methods, args,
                lo='random', hi='record',
                agg=lambda x: np.nanmean(x, -1))
        elif name == 'clip_record_mean':
            plot_combined(
                'combined_clipped_record_mean', ax, runs, methods, args,
                lo='random', hi='record', clip=True,
                agg=lambda x: np.nanmean(x, -1))
        elif name == 'seeds':
            plot_combined(
                'combined_seeds', ax, runs, methods, args,
                agg=lambda x: np.isfinite(x).sum(-1))
        elif name == 'human_above':
            plot_combined(
                'combined_above_human_gamer', ax, runs, methods, args,
                agg=lambda y: (y >= 1.0).astype(float).sum(-1))
        elif name == 'human_below':
            plot_combined(
                'combined_below_human_gamer', ax, runs, methods, args,
                agg=lambda y: (y <= 1.0).astype(float).sum(-1))
        else:
            raise NotImplementedError(name)
    if args.xlim:
        for ax in axes[:-1].flatten():
            ax.xaxis.get_offset_text().set_visible(False)
    if args.xlabel:
        for ax in axes[-1]:
            ax.set_xlabel(args.xlabel)
    if args.ylabel:
        for ax in axes[:, 0]:
            ax.set_ylabel(args.ylabel)
    for ax in axes.flatten()[len(tasks) + len(args.add):]:
        ax.axis('off')
    legend(fig, args.labels, ncol=args.legendcols, **LEGEND)
    return fig


def plot(task, ax, runs, methods, args):
    assert runs
    try:
        title = task.split('_', 1)[1].replace('_', ' ').title()
    except IndexError:
        title = task.title()
    ax.set_title(title)
    xlim = [+np.inf, -np.inf]
    for index, method in enumerate(methods):
        relevant = [r for r in runs if r.method == method]
        if not relevant:
            continue
        if any(r.xs is None for r in relevant):
            baseline(index, method, ax, relevant, args)
        else:
            if args.agg == 'none':
                xs, ys = curve_lines(index, task, method, ax, relevant, args)
            else:
                xs, ys = curve_area(index, task, method, ax, relevant, args)
            if len(xs) == len(ys) == 0:
                print(f'Skipping empty: {task} {method}')
                continue
            xlim = [min(xlim[0], np.nanmin(xs)), max(xlim[1], np.nanmax(xs))]
    ax.ticklabel_format(axis='x', style='sci', scilimits=(0, 0))
    steps = [1, 2, 2.5, 5, 10]
    ax.xaxis.set_major_locator(ticker.MaxNLocator(args.xticks, steps=steps))
    ax.yaxis.set_major_locator(ticker.MaxNLocator(args.yticks, steps=steps))
    if np.isfinite(xlim).all():
        ax.set_xlim(args.xlim or xlim)
    if args.xlim:
        ticks = sorted({*ax.get_xticks(), *args.xlim})
        ticks = [x for x in ticks if args.xlim[0] <= x <= args.xlim[1]]
        ax.set_xticks(ticks)
    if args.ylim:
        ax.set_ylim(args.ylim)
        if args.ylimticks:
            ticks = sorted({*ax.get_yticks(), *args.ylim})
            ticks = [x for x in ticks if args.ylim[0] <= x <= args.ylim[1]]
            ax.set_yticks(ticks)


def plot_combined(
        name, ax, runs, methods, args, agg, lo=None, hi=None, clip=False):
    tasks = sorted(set(run.task for run in runs if run.xs is not None))
    seeds = list(set(run.seed for run in runs))
    runs = [r for r in runs if r.task in tasks]  # Discard unused baselines.
    # Bin all runs onto the same X steps.
    borders = sorted(
        [r.xs for r in runs if r.xs is not None],
        key=lambda x: np.nanmax(x))[-1]
    for index, run in enumerate(runs):
        if run.xs is None:
            continue
        xs, ys = bin_scores(run.xs, run.ys, borders, fill='last')
        runs[index] = run._replace(xs=xs, ys=ys)
    # Per-task normalization by low and high baseline.
    if lo or hi:
        mins = collections.defaultdict(list)
        maxs = collections.defaultdict(list)
        [mins[r.task].append(r.ys) for r in load_baselines([re.compile(lo)])]
        [maxs[r.task].append(r.ys) for r in load_baselines([re.compile(hi)])]
        mins = {task: min(ys) for task, ys in mins.items() if task in tasks}
        maxs = {task: max(ys) for task, ys in maxs.items() if task in tasks}
        missing_baselines = []
        for task in tasks:
            if task not in mins or task not in maxs:
                missing_baselines.append(task)
        if set(missing_baselines) == set(tasks):
            print(f'No baselines found to normalize any tasks in {name} plot.')
        else:
            for task in missing_baselines:
                print(f'No baselines found to normalize {task} in {name} plot.')
        for index, run in enumerate(runs):
            if run.task not in mins or run.task not in maxs:
                continue
            ys = (run.ys - mins[run.task]) / (maxs[run.task] - mins[run.task])
            if clip:
                ys = np.minimum(ys, 1.0)
            runs[index] = run._replace(ys=ys)
    # Aggregate across tasks but not methods or seeds.
    combined = []
    for method, seed in itertools.product(methods, seeds):
        relevant = [r for r in runs if r.method == method and r.seed == seed]
        if not relevant:
            continue
        if relevant[0].xs is None:
            xs, ys = None, np.array([r.ys for r in relevant])
        else:
            xs, ys = stack_scores(*zip(*[(r.xs, r.ys) for r in relevant]))
        with warnings.catch_warnings():  # Ignore empty slice warnings.
            warnings.simplefilter('ignore', category=RuntimeWarning)
            combined.append(Run('combined', method, seed, xs, agg(ys)))
    plot(name, ax, combined, methods, args)


def curve_lines(index, task, method, ax, runs, args):
    zorder = 10000 - 10 * index - 1
    for run in runs:
        color = args.colors[method]
        ax.plot(run.xs, run.ys, label=method, color=color, zorder=zorder)
    xs, ys = stack_scores(*zip(*[(r.xs, r.ys) for r in runs]))
    return xs, ys


def curve_area(index, task, method, ax, runs, args):
    xs, ys = stack_scores(*zip(*[(r.xs, r.ys) for r in runs]))
    with warnings.catch_warnings():  # NaN buckets remain NaN.
        warnings.simplefilter('ignore', category=RuntimeWarning)
        if args.agg == 'std1':
            mean, std = np.nanmean(ys, -1), np.nanstd(ys, -1)
            lo, mi, hi = mean - std, mean, mean + std
        elif args.agg == 'per0':
            lo, mi, hi = [np.nanpercentile(ys, k, -1) for k in (0, 50, 100)]
        elif args.agg == 'per5':
            lo, mi, hi = [np.nanpercentile(ys, k, -1) for k in (5, 50, 95)]
        elif args.agg == 'per25':
            lo, mi, hi = [np.nanpercentile(ys, k, -1) for k in (25, 50, 75)]
        else:
            raise NotImplementedError(args.agg)
    color = args.colors[method]
    kw = dict(color=color, zorder=1000 - 10 * index, alpha=0.1, linewidths=0)
    mask = ~np.isnan(mi)
    xs, lo, mi, hi = xs[mask], lo[mask], mi[mask], hi[mask]
    ax.fill_between(xs, lo, hi, **kw)
    ax.plot(xs, mi, label=method, color=color, zorder=10000 - 10 * index - 1)
    return xs, mi


def baseline(index, method, ax, runs, args):
    assert all(run.xs is None for run in runs)
    ys = np.array([run.ys for run in runs])
    mean, std = ys.mean(), ys.std()
    color = args.colors[method]
    kw = dict(color=color, zorder=500 - 20 * index - 1, alpha=0.1, linewidths=0)
    ax.fill_between([-np.inf, np.inf], [mean - std] * 2, [mean + std] * 2, **kw)
    kw = dict(ls='--', color=color, zorder=5000 - 10 * index - 1)
    ax.axhline(mean, label=method, **kw)


def legend(fig, mapping=None, **kwargs):
    entries = {}
    for ax in fig.axes:
        for handle, label in zip(*ax.get_legend_handles_labels()):
            if mapping and label in mapping:
                label = mapping[label]
            entries[label] = handle
    leg = fig.legend(entries.values(), entries.keys(), **kwargs)
    leg.get_frame().set_edgecolor('white')
    extent = leg.get_window_extent(fig.canvas.get_renderer())
    extent = extent.transformed(fig.transFigure.inverted())
    yloc, xloc = kwargs['loc'].split()
    y0 = dict(lower=extent.y1, center=0, upper=0)[yloc]
    y1 = dict(lower=1, center=1, upper=extent.y0)[yloc]
    x0 = dict(left=extent.x1, center=0, right=0)[xloc]
    x1 = dict(left=1, center=1, right=extent.x0)[xloc]
    fig.tight_layout(rect=[x0, y0, x1, y1], h_pad=0.5, w_pad=0.5)


def save(fig, args):
    args.outdir.mkdir(parents=True, exist_ok=True)
    filename = args.outdir / 'curves.png'
    fig.savefig(filename, dpi=args.dpi)
    print('Saved to', filename)
    filename = args.outdir / 'curves.pdf'
    fig.savefig(filename)
    try:
        subprocess.call(['pdfcrop', str(filename), str(filename)])
    except FileNotFoundError:
        print('Install texlive-extra-utils to crop PDF outputs.')


def bin_scores(xs, ys, borders, reducer=np.nanmean, fill='nan'):
    order = np.argsort(xs)
    xs, ys = xs[order], ys[order]
    binned = []
    with warnings.catch_warnings():  # Empty buckets become NaN.
        warnings.simplefilter('ignore', category=RuntimeWarning)
        for start, stop in zip(borders[:-1], borders[1:]):
            left = (xs <= start).sum()
            right = (xs <= stop).sum()
            if left < right:
                value = reducer(ys[left:right])
            elif binned:
                value = {'nan': np.nan, 'last': binned[-1]}[fill]
            else:
                value = np.nan
            binned.append(value)
    return borders[1:], np.array(binned)


def stack_scores(multiple_xs, multiple_ys, fill='last'):
    longest_xs = sorted(multiple_xs, key=lambda x: len(x))[-1]
    multiple_padded_ys = []
    for xs, ys in zip(multiple_xs, multiple_ys):
        assert (longest_xs[:len(xs)] == xs).all(), (list(xs), list(longest_xs))
        value = {'nan': np.nan, 'last': ys[-1]}[fill]
        padding = [value] * (len(longest_xs) - len(xs))
        padded_ys = np.concatenate([ys, padding])
        multiple_padded_ys.append(padded_ys)
    stacked_ys = np.stack(multiple_padded_ys, -1)
    return longest_xs, stacked_ys


def load_jsonl(filename):
    try:
        with filename.open() as f:
            lines = list(f.readlines())
        records = []
        for index, line in enumerate(lines):
            try:
                records.append(json.loads(line))
            except Exception:
                if index == len(lines) - 1:
                    continue  # Silently skip last line if it is incomplete.
                raise ValueError(
                    f'Skipping invalid JSON line ({index + 1}/{len(lines) + 1}) in'
                    f'{filename}: {line}')
        return pd.DataFrame(records)
    except ValueError as e:
        print('Invalid', filename, e)
        return None


def save_runs(runs, filename):
    filename.parent.mkdir(parents=True, exist_ok=True)
    records = []
    for run in runs:
        if run.xs is None:
            continue
        records.append(dict(
            task=run.task, method=run.method, seed=run.seed,
            xs=run.xs.tolist(), ys=run.ys.tolist()))
    runs = json.dumps(records)
    filename.write_text(runs)
    print('Saved', filename)


def main(args):
    find_keys(args)
    runs = load_runs(args)
    save_runs(runs, args.outdir / 'runs.json')
    baselines = load_baselines(args.baselines, args.prefix)
    stats(runs, baselines)
    methods = order_methods(runs, baselines, args)
    if not runs:
        print('Noting to plot.')
        return
    # Adjust options based on loaded runs.
    tasks = set(r.task for r in runs)
    if 'auto' in args.add:
        index = args.add.index('auto')
        del args.add[index]
        atari = any(run.task.startswith('atari_') for run in runs)
        if len(tasks) < 2:
            pass
        elif atari:
            args.add[index:index] = [
                'gamer_median', 'gamer_mean', 'record_mean', 'clip_record_mean',
            ]
        else:
            args.add[index:index] = ['mean', 'median']
    args.cols = min(args.cols, len(tasks) + len(args.add))
    args.legendcols = min(args.legendcols, args.cols)
    print('Plotting...')
    fig = figure(runs + baselines, methods, args)
    save(fig, args)


def parse_args():
    boolean = lambda x: bool(['False', 'True'].index(x))
    parser = argparse.ArgumentParser()
    parser.add_argument('--indir', nargs='+', type=pathlib.Path, required=True)
    parser.add_argument('--indir-prefix', type=pathlib.Path)
    parser.add_argument('--outdir', type=pathlib.Path, required=True)
    parser.add_argument('--subdir', type=boolean, default=True)
    parser.add_argument('--xaxis', type=str, default='step')
    parser.add_argument('--yaxis', type=str, default='eval_return')
    parser.add_argument('--tasks', nargs='+', default=[r'.*'])
    parser.add_argument('--methods', nargs='+', default=[r'.*'])
    parser.add_argument('--baselines', nargs='+', default=DEFAULT_BASELINES)
    parser.add_argument('--prefix', type=boolean, default=False)
    parser.add_argument('--bins', type=float, default=-1)
    parser.add_argument('--agg', type=str, default='std1')
    parser.add_argument('--size', nargs=2, type=float, default=[2.5, 2.3])
    parser.add_argument('--dpi', type=int, default=80)
    parser.add_argument('--cols', type=int, default=6)
    parser.add_argument('--xlim', nargs=2, type=float, default=None)
    parser.add_argument('--ylim', nargs=2, type=float, default=None)
    parser.add_argument('--ylimticks', type=boolean, default=True)
    parser.add_argument('--xlabel', type=str, default=None)
    parser.add_argument('--ylabel', type=str, default=None)
    parser.add_argument('--xticks', type=int, default=6)
    parser.add_argument('--yticks', type=int, default=5)
    parser.add_argument('--xmult', type=float, default=1)
    parser.add_argument('--labels', nargs='+', default=None)
    parser.add_argument('--palette', nargs='+', default=['contrast'])
    parser.add_argument('--legendcols', type=int, default=4)
    parser.add_argument('--colors', nargs='+', default={})
    parser.add_argument('--maxval', type=float, default=0)
    parser.add_argument('--add', nargs='+', type=str, default=['auto', 'seeds'])
    args = parser.parse_args()
    if args.subdir:
        args.outdir /= args.indir[0].stem
    if args.indir_prefix:
        args.indir = [args.indir_prefix / indir for indir in args.indir]
    args.indir = [d.expanduser() for d in args.indir]
    args.outdir = args.outdir.expanduser()
    if args.labels:
        assert len(args.labels) % 2 == 0
        args.labels = {k: v for k, v in zip(args.labels[:-1], args.labels[1:])}
    if args.colors:
        assert len(args.colors) % 2 == 0
        args.colors = {k: v for k, v in zip(args.colors[:-1], args.colors[1:])}
    args.tasks = [re.compile(p) for p in args.tasks]
    args.methods = [re.compile(p) for p in args.methods]
    args.baselines = [re.compile(p) for p in args.baselines]
    if 'return' not in args.yaxis:
        args.baselines = []
    if args.prefix is None:
        args.prefix = len(args.indir) > 1
    if len(args.palette) == 1 and args.palette[0] in PALETTES:
        args.palette = 10 * PALETTES[args.palette[0]]
    if len(args.add) == 1 and args.add[0] == 'none':
        args.add = []
    return args


if __name__ == '__main__':
    main(parse_args())