- initial commit

Author: MathPhysSim

AE_dyna_results.py

@@ -0,0 +1,192 @@
+import os
+import pickle
+import matplotlib.pyplot as plt
+import numpy as np
+
+label = "ME-TRPO"
+label = "AE-DYNA"
+if label == "ME-TRPO":
+    # ME-TRPO results
+    project_directory = 'Data_Experiments/2020_10_06_ME_TRPO_stable@FERMI/run2/'
+else:
+    # AE-Dyna results
+    project_directory = 'Data_Experiments/2020_11_05_AE_Dyna@FERMI/-nr_steps_25-cr_lr-n_ep_13-m_bs_100-sim_steps_3000-m_iter_35-ensnr_3-init_200/'
+
+def read_rewards(rewards):
+    iterations_all = []
+    final_rews_all = []
+    mean_rews_all = []
+    stds = []
+
+
+
+    iterations = []
+    final_rews = []
+    mean_rews = []
+    for i in range(len(rewards)):
+        if len(rewards[i]) > 0:
+            final_rews.append(rewards[i][len(rewards[i]) - 1])
+            iterations.append(len(rewards[i]))
+            try:
+                mean_rews.append(np.sum(rewards[i][1:]))
+            except:
+                mean_rews.append([])
+            stds.append(np.std(rewards[i][1:]))
+
+    # iterations = np.mean(np.array(iterations_all), axis=0)
+    # final_rews = np.mean(np.array(final_rews_all), axis=0)
+    # mean_rews = np.mean(np.array(mean_rews_all), axis=0)
+
+    return np.array(iterations), np.array(final_rews), np.array(mean_rews), np.array(stds)
+
+
+
+def plot_results(data, label='Verification', **kwargs):
+        '''plotting'''
+        rewards = data['rews']
+        # iterations = []
+        # finals = []
+        # means = []
+        # stds = []
+        #
+        # for i in range(len(rewards)):
+        #     if (len(rewards[i]) > 1):
+        #         finals.append(rewards[i][-1])
+        #         means.append(np.mean(rewards[i][1:]))
+        #         stds.append(np.std(rewards[i][1:]))
+        #         iterations.append(len(rewards[i]))
+        #
+        # x = range(len(iterations))
+        # iterations = np.array(iterations)
+        # finals = np.array(finals)
+        # means = np.array(means)
+        # stds = np.array(stds)
+
+        iterations, finals, means, stds = read_rewards(rewards)
+        plot_suffix = label  # , Fermi time: {env.TOTAL_COUNTER / 600:.1f} h'
+
+        fig, axs = plt.subplots(2, 1, sharex=True)
+
+        ax = axs[0]
+        x = range(len(iterations))
+        ax.plot(x, iterations)
+        ax.set_ylabel('Iterations (1)')
+        ax.set_title(plot_suffix)
+        # fig.suptitle(label, fontsize=12)
+        if 'data_number' in kwargs:
+            ax1 = plt.twinx(ax)
+            color = 'lime'
+            ax1.set_ylabel('Mean reward', color=color)  # we already handled the x-label with ax1
+            ax1.tick_params(axis='y', labelcolor=color)
+            ax1.plot(x, kwargs.get('data_number'), color=color)
+
+        ax = axs[1]
+        color = 'blue'
+        ax.set_ylabel('Final reward', color=color)  # we already handled the x-label with ax1
+        ax.tick_params(axis='y', labelcolor=color)
+        ax.plot(x, finals, color=color)
+
+        ax.set_title('Final reward per episode')  # + plot_suffix)
+        ax.set_xlabel('Episodes (1)')
+
+        ax1 = plt.twinx(ax)
+        color = 'lime'
+        ax1.set_ylabel('Mean reward', color=color)  # we already handled the x-label with ax1
+        ax1.tick_params(axis='y', labelcolor=color)
+        ax1.fill_between(x, means - stds, means + stds,
+                         alpha=0.5, edgecolor=color, facecolor='#FF9848')
+        ax1.plot(x, means, color=color)
+        fig.align_labels()
+        # ax.set_ylim(ax1.get_ylim())
+        if 'save_name' in kwargs:
+            plt.savefig(kwargs.get('save_name') + '.pdf')
+            plt.savefig(kwargs.get('save_name') + '.png')
+        plt.show()
+
+def plot_observables(data, label='Experiment', **kwargs):
+    """plot observables during the test"""
+
+    sim_rewards_all = np.array(data.get('sim_rewards_all'))
+    step_counts_all = np.array(data.get('step_counts_all'))
+    batch_rews_all = np.array(data.get('batch_rews_all'))
+    tests_all = np.array(data.get('tests_all'))
+    length_all = object['entropy_all']
+
+    fig, axs = plt.subplots(2, 1, sharex=True)
+    x = np.arange(len(batch_rews_all[0]))
+    ax = axs[0]
+    ax.step(x, batch_rews_all[0])
+    ax.fill_between(x, batch_rews_all[0] - batch_rews_all[1], batch_rews_all[0] + batch_rews_all[1],
+                    alpha=0.5)
+    ax.set_ylabel('rews per batch')
+
+    ax.set_title(label)
+
+    ax2 = ax.twinx()
+
+    color = 'lime'
+    ax2.set_ylabel('data points', color=color)  # we already handled the x-label with ax1
+    ax2.tick_params(axis='y', labelcolor=color)
+    ax2.step(x, step_counts_all, color=color)
+
+    ax = axs[1]
+    ax.plot(sim_rewards_all[0], ls=':')
+    ax.fill_between(x, sim_rewards_all[0] - sim_rewards_all[1], sim_rewards_all[0] + sim_rewards_all[1],
+                    alpha=0.5)
+    try:
+        ax.plot(tests_all[0])
+        ax.fill_between(x, tests_all[0] - tests_all[1], tests_all[0] + tests_all[1],
+                        alpha=0.5)
+        ax.axhline(y=np.max(tests_all[0]), c='orange')
+    except:
+        pass
+    ax.set_ylabel('rewards tests')
+    # plt.tw
+    ax.grid(True)
+    if length_all:
+        ax2 = ax.twinx()
+        color = 'lime'
+        ax2.set_ylabel(r'- log(std($p_\pi$))', color=color)   # we already handled the x-label with ax1
+        ax2.tick_params(axis='y', labelcolor=color)
+        ax2.plot(length_all, color=color)
+    fig.align_labels()
+
+    if 'save_name' in kwargs:
+        plt.savefig(kwargs.get('save_name') + '.pdf')
+        plt.savefig(kwargs.get('save_name') + '.png')
+    plt.show()
+
+# plot verification
+
+filenames = []
+for file in os.listdir(project_directory):
+    if 'final' in file:
+        filenames.append(file)
+
+filenames.sort()
+
+filename = filenames[-1]
+print(filename)
+
+filehandler = open(project_directory + filename, 'rb')
+object = pickle.load(filehandler)
+save_name = 'Figures/' + label+'_verification'
+plot_results(object,label=label, save_name=save_name)
+
+# plot observables
+
+filenames = []
+for file in os.listdir(project_directory):
+    if 'training_observables' in file:
+        filenames.append(file)
+
+filenames.sort()
+
+filename = filenames[-1]
+print(filename)
+
+filehandler = open(project_directory + filename, 'rb')
+object = pickle.load(filehandler)
+save_name = 'Figures/' + label+'_observables'
+plot_observables(object, label=label, save_name=save_name)
+