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import csv |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import matplotlib |
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from math import ceil |
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from constants import ENV_NAMES |
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import seaborn |
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np.random.seed(1024) |
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COLORS = [(np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) for x in range(20)] |
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def switch_to_outer_plot(fig): |
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ax0 = fig.add_subplot(111, frame_on=False) |
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ax0.set_xticks([]) |
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ax0.set_yticks([]) |
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return ax0 |
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def ema(data_in, smoothing=0): |
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data_out = np.zeros_like(data_in) |
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curr = np.nan |
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for i in range(len(data_in)): |
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x = data_in[i] |
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if np.isnan(curr): |
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curr = x |
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else: |
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curr = (1 - smoothing) * x + smoothing * curr |
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data_out[i] = curr |
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return data_out |
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def plot_data_mean_std(ax, data_y, color_idx=0, data_x=None, x_scale=1, smoothing=0, first_valid=0, label=None): |
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color = COLORS[color_idx] |
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hexcolor = '#%02x%02x%02x' % color |
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data_y = data_y[:, first_valid:] |
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nx, num_datapoint = np.shape(data_y) |
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if smoothing > 0: |
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for i in range(nx): |
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data_y[i, ...] = ema(data_y[i, ...], smoothing) |
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if data_x is None: |
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data_x = (np.array(range(num_datapoint)) + first_valid) * x_scale |
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data_mean = np.mean(data_y, axis=0) |
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data_std = np.std(data_y, axis=0, ddof=1) |
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ax.plot(data_x, data_mean, color=hexcolor, label=label, linestyle='solid', alpha=1, rasterized=True) |
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ax.fill_between(data_x, data_mean - data_std, data_mean + data_std, color=hexcolor, alpha=.25, linewidth=0.0, |
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rasterized=True) |
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def read_csv(filename, key_name): |
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with open(filename) as csv_file: |
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csv_reader = csv.reader(csv_file, delimiter=',') |
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key_index = -1 |
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values = [] |
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for line_num, row in enumerate(csv_reader): |
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row = [x.lower() for x in row] |
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if line_num == 0: |
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idxs = [i for i, val in enumerate(row) if val == key_name] |
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key_index = idxs[0] |
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else: |
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values.append(row[key_index]) |
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return np.array(values, dtype=np.float32) |
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def plot_values(ax, all_values, title=None, max_x=0, label=None, **kwargs): |
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if max_x > 0: |
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all_values = all_values[..., :max_x] |
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if ax is not None: |
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plot_data_mean_std(ax, all_values, label=label, **kwargs) |
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ax.set_title(title, fontsize=20) |
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return all_values |
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def plot_experiment(env_name, run_directory_prefix, titles=None, suffixes=[''], normalization_ranges=None, |
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key_name='eprewmean', **kwargs): |
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run_folders = [f'{run_directory_prefix}_{0}_{2020 + x}' for x in range(3)] |
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sppo_run_folders = ['sppo-' + rf for rf in run_folders] |
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ppo_run_folders = ['ppo-' + rf for rf in run_folders] |
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run_folders = [sppo_run_folders, ppo_run_folders] |
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run_names = ['SPPO', 'PPO'] |
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num_envs = 1 |
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will_normalize_and_reduce = normalization_ranges is not None |
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if will_normalize_and_reduce: |
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num_visible_plots = 1 |
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f, axarr = plt.subplots() |
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else: |
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num_visible_plots = num_envs |
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dimx = dimy = ceil(np.sqrt(num_visible_plots)) |
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f, axarr = plt.subplots(dimx, dimy, sharex=True) |
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color_idx = 0 |
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for rf in range(len(run_folders)): |
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for suffix in suffixes: |
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all_values = [] |
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game_weights = [1] * num_envs |
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if len(suffixes) == 1: |
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label = run_names[rf] |
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else: |
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if suffix == '': |
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label = run_names[rf] + ' train' |
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else: |
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label = run_names[rf] + ' test' |
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print(f'loading results from {env_name}...') |
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if num_visible_plots == 1: |
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ax = axarr |
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else: |
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dimy = len(axarr[0]) |
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ax = axarr[0 // dimy][0 % dimy] |
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csv_files = [f"checkpoints/{resid}/progress{'-' if len(suffix) > 0 else ''}{suffix}.csv" for resid in |
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run_folders[rf]] |
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curr_ax = None if will_normalize_and_reduce else ax |
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raw_data = np.array([read_csv(file, key_name) for file in csv_files]) |
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values = plot_values(curr_ax, raw_data, title=env_name, color_idx=color_idx, label=label, **kwargs) |
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if will_normalize_and_reduce: |
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game_range = normalization_ranges[env_name] |
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game_min = game_range[0] |
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game_max = game_range[1] |
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game_delta = game_max - game_min |
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sub_values = game_weights[0] * (np.array(values) - game_min) / (game_delta) |
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all_values.append(sub_values) |
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if will_normalize_and_reduce: |
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normalized_data = np.sum(all_values, axis=0) |
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normalized_data = normalized_data / np.sum(game_weights) |
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title = 'Mean Normalized Score' |
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plot_values(ax, normalized_data, title=None, color_idx=color_idx, label=suffix, **kwargs) |
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color_idx += 1 |
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if num_visible_plots == 1: |
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ax.legend(loc='lower right') |
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else: |
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f.legend(loc='lower right', bbox_to_anchor=(.5, 0, .5, 1)) |
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return f, axarr |
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