From a0cb89820ec08f2040bfaf4551c7fe5a7e63bcba Mon Sep 17 00:00:00 2001 From: VedantDave Date: Tue, 18 Jul 2023 16:12:08 +0200 Subject: [PATCH] Adding Graphs --- Dreamer/graph_plot.py | 211 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 211 insertions(+) create mode 100644 Dreamer/graph_plot.py diff --git a/Dreamer/graph_plot.py b/Dreamer/graph_plot.py new file mode 100644 index 0000000..920fe5e --- /dev/null +++ b/Dreamer/graph_plot.py @@ -0,0 +1,211 @@ +import os +import numpy as np +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker + + +def binning(xs, ys, bins, reducer): + binned_xs = np.arange(xs.min(), xs.max() + 1e-10, bins) + binned_ys = [] + for start, stop in zip([-np.inf] + list(binned_xs), binned_xs): + left = (xs <= start).sum() + right = (xs <= stop).sum() + binned_ys.append(reducer(ys[left:right])) + binned_ys = np.array(binned_ys) + return binned_xs, binned_ys + + +def plot_data(parent_dir, tag_filter="test/return", xaxis='step', value="AverageEpRet", condition="Condition1", smooth=1, bins=30000, xticks=5, yticks=5): + # List to store all DataFrames + data = [] + + # Traversing through each subfolder in the parent directory + for subfolder in os.listdir(parent_dir): + json_dir = os.path.join(parent_dir, subfolder) + if not os.path.isdir(json_dir): + continue + # Read each JSON file separately + for json_file in os.listdir(json_dir): + if not json_file.endswith('.jsonl'): + continue + # Read the data from the JSON file + df = pd.read_json(os.path.join(json_dir, json_file), lines=True) + + # Check if tag_filter exists in DataFrame + if tag_filter not in df.columns: + continue + + df = df[['step', tag_filter]].dropna().sort_values(by='step') + + # Apply binning + xs, ys = binning(df['step'].to_numpy(), df[tag_filter].to_numpy(), bins, np.nanmean) + + # Replace original data with binned data + df = pd.DataFrame({ 'step': xs, tag_filter: ys }) + + # Append the DataFrame to the list + data.append(df) + + # Combine all DataFrames + combined_df = pd.concat(data, ignore_index=True) + + # Plotting the combined DataFrame + sns.set(style="white", font_scale=1.5) + plot = sns.lineplot(data=combined_df, x=xaxis, y=tag_filter, errorbar='sd') + + ax = plot.axes + ax.ticklabel_format(axis="x", scilimits=(5, 5)) + steps = [1, 2, 2.5, 5, 10] + ax.xaxis.set_major_locator(ticker.MaxNLocator(xticks, steps=steps)) + ax.yaxis.set_major_locator(ticker.MaxNLocator(yticks, steps=steps)) + + xlim = [+np.inf, -np.inf] + xlim = [min(xlim[0], xs.min()), max(xlim[1], xs.max())] + ax.set_xlim(xlim) + #plt.xlim([0, max]) + + #plt.legend(loc='best').set_draggable(True) + plt.tight_layout(pad=0.5) + plt.show() + +# Call the function +plot_data('/media/vedant/cpsDataStorageWK/Vedant/tia_logs/dmc_cheetah_run_driving/tia/') + + +exit() + + + + + + + + + + + + + + + + + + +def plot_vanilla(parent_dir, tag_filter="train/return", smoothing=0.99): + # List to store all EMAs + emas = [] + + # Traversing through each subfolder in the parent directory + for subfolder in os.listdir(parent_dir): + json_dir = os.path.join(parent_dir, subfolder) + if not os.path.isdir(json_dir): + continue + # Read each JSON file separately + for json_file in os.listdir(json_dir): + if not json_file.endswith('.jsonl'): + continue + # Read the data from the JSON file + df = pd.read_json(os.path.join(json_dir, json_file), lines=True) + + # Check if tag_filter exists in DataFrame + if tag_filter not in df.columns: + continue + + df = df[['step', tag_filter]].sort_values(by='step') + + # Calculate exponential moving average for the smoothing value + df['EMA'] = df[tag_filter].ewm(alpha=smoothing, adjust=False).mean() + + # Append the EMA DataFrame to the emas list + emas.append(df) + + # Concatenate all EMAs into a single DataFrame and calculate mean and standard deviation + all_emas = pd.concat(emas).groupby('step')['EMA'] + mean_emas = all_emas.mean() + std_emas = all_emas.std() + + # Plotting begins here + sns.set_style("whitegrid", {'axes.grid' : True, 'axes.edgecolor':'black'}) + fig = plt.figure() + plt.clf() + ax = fig.gca() + + # Plot mean and standard deviation of EMAs + plt.plot(mean_emas.index, mean_emas, color='blue') + plt.fill_between(std_emas.index, (mean_emas-std_emas), (mean_emas+std_emas), color='blue', alpha=.1) + + plt.xlabel('Training Episodes $(\\times10^6)$', fontsize=22) + plt.ylabel('Average return', fontsize=22) + lgd=plt.legend(frameon=True, fancybox=True, prop={'weight':'bold', 'size':14}, loc="best") + #plt.title('Title', fontsize=14) + ax = plt.gca() + + plt.setp(ax.get_xticklabels(), fontsize=16) + plt.setp(ax.get_yticklabels(), fontsize=16) + sns.despine() + plt.tight_layout() + plt.show() + +# Call the function +plot_vanilla('/media/vedant/cpsDataStorageWK/Vedant/tia_logs/dmc_cheetah_run_driving/tia/') + + +""" +import os +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Set the path to the JSON file +parent_dir = '/media/vedant/cpsDataStorageWK/Vedant/tia_logs/dmc_cheetah_run_driving/tia/' + +# Specific tag to filter +tag_filter = "train/return" + +# Collect data from all JSON files +data = [] + +# Smoothing values +smoothing = 0.001 # Change num to set the number of smoothing values + +# List to store all EMAs +emas = [] + +# Traversing through each subfolder in the parent directory +for subfolder in os.listdir(parent_dir): + json_dir = os.path.join(parent_dir, subfolder) + if not os.path.isdir(json_dir): + continue + # Read each JSON file separately + for json_file in os.listdir(json_dir): + if not json_file.endswith('.jsonl'): + continue + # Read the data from the JSON file + df = pd.read_json(os.path.join(json_dir, json_file), lines=True) + + # Check if tag_filter exists in DataFrame + if tag_filter not in df.columns: + continue + + df = df[['step', tag_filter]].sort_values(by='step') + + # Calculate exponential moving average for the smoothing value + df['EMA'] = df[tag_filter].ewm(alpha=smoothing, adjust=False).mean() + + # Append the EMA DataFrame to the emas list + emas.append(df) + +# Concatenate all EMAs into a single DataFrame and calculate mean and standard deviation +all_emas = pd.concat(emas).groupby('step')['EMA'] +mean_emas = all_emas.mean() +std_emas = all_emas.std() + +# Plot mean and standard deviation of EMAs +plt.figure(figsize=(10, 6)) +plt.plot(mean_emas.index, mean_emas) +plt.fill_between(std_emas.index, (mean_emas-std_emas), (mean_emas+std_emas), color='b', alpha=.1) +plt.legend() +plt.show() +""" \ No newline at end of file