How to use get_num_tests method in Slash

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compare_ppo_dqn.py

Source:compare_ppo_dqn.py

...64#######################################################################'''\65        .format(test_no))66        test_once()67#  Visualisation {{{1 #68def get_num_tests(keys, measurement_name):69    return len([None for k in keys if k.startswith(measurement_name)])70#  Generalisation {{{2 #71def visualise_generalisation(f):72    import matplotlib.pyplot as plt73    # Generalisation loss74    fig, axs = plt.subplots(2, 2)75    for measurement_template, i in (76            ('35fixed-{}-new-', 0),77            ('35random-{}-cont.-', 1),):78        # DQN79        legend = 'loss'80        measurement_name = measurement_template.format('DQN')81        for t in range(get_num_tests(f.keys(),measurement_name)):82            axs[i, 0].plot(f[measurement_name + str(t)]['loss'],83                           '-', color = (0, 0, 1, 0.1),84                           label = legend)85            legend = '_'86        axs[i, 0].grid(True)87        axs[i, 0].set_title(['DQN baseline',88                             'DQN generalisation (random)'][i])89        axs[i, 0].legend()90        # PPO91        legend_prefix = ''92        measurement_name = measurement_template.format('PPO')93        for t in range(get_num_tests(f.keys(), measurement_name)):94            ppo_loss = f[measurement_name + str(t)]['loss']95            axs[i, 1].plot(ppo_loss[0::3], '-', color = (0, 0, 1, 0.1),96                           label = legend_prefix + 'clip')97            axs[i, 1].plot(ppo_loss[1::3], '-', color = (0, 1, 0, 0.1),98                           label = legend_prefix + 'vf')99            axs[i, 1].plot(ppo_loss[2::3], '-', color = (1, 0, 0, 0.1),100                           label = legend_prefix + 'entropy')101            legend_prefix = '_'102        axs[i, 1].grid(True)103        axs[i, 1].set_title(['PPO baseline',104                             'PPO generalisation (random)'][i])105        axs[i, 1].legend()106    fig.tight_layout()107    plt.show()108    # Generalisation reward prediction error109    fig, axs = plt.subplots(2, 2)110    for measurement_template, i in (111            ('35fixed-{}-new-', 0),112            ('35random-{}-cont.-', 1),):113        # DQN fixed114        legend = 'reward prediction error'115        measurement_name = measurement_template.format('DQN')116        for t in range(get_num_tests(f.keys(),measurement_name)):117            g = f[measurement_name + str(t)]118            num_episodes = g['num_episodes'][0]119            rp_error = g['reward_prediction'][:num_episodes] - \120                g['episode_rewards'][:num_episodes]121            axs[i, 0].plot(rp_error, 'r.', label = legend)122            legend = '_'123        axs[i, 0].grid(True)124        axs[i, 0].set_title(['DQN baseline',125                             'DQN generalisation (random start)'][i])126        axs[i, 0].legend()127        # PPO fixed128        legend = 'reward prediction error'129        measurement_name = measurement_template.format('PPO')130        for t in range(get_num_tests(f.keys(), measurement_name)):131            g = f[measurement_name + str(t)]132            num_episodes = g['num_episodes'][0]133            rp_error = g['reward_prediction'][:num_episodes] - \134                g['episode_rewards'][:num_episodes]135            episode_ends = g['episode_ends'][:num_episodes]136            axs[i, 1].plot(137                episode_ends, rp_error, 'r.',138                label = legend)139            legend = '_'140        axs[i, 1].grid(True)141        axs[i, 1].set_title(['PPO baseline',142                             'PPO generalisation (random start)'][i])143    fig.tight_layout()144    plt.show()145#  2}}} #146def visualise_fixed(f):147    import matplotlib.pyplot as plt148    # FIXME determine this dynamically149    TEST_REWARD_INTERVAL = 100150    # Loss151    fig, axs = plt.subplots(len(STARTING_HEIGHTS), 2)152    for i, starting_height in enumerate(STARTING_HEIGHTS):153        # DQN154        legend = 'loss'155        measurement_name = '{}fixed-DQN-new-'.format(starting_height)156        for t in range(get_num_tests(f.keys(), measurement_name)):157            axs[i, 0].plot(f[measurement_name + str(t)]['loss'],158                           '-', color = (0, 0, 1, 0.1),159                           label = legend)160            legend = '_'161        axs[i, 0].set_ylim([-1, 10])162        axs[i, 0].grid(True)163        axs[i, 0].set_title('DQN started from ' + str(starting_height))164        axs[i, 0].legend()165        # PPO166        legend_prefix = ''167        measurement_name = '{}fixed-PPO-new-'.format(starting_height)168        for t in range(get_num_tests(f.keys(), measurement_name)):169            ppo_loss = f[measurement_name + str(t)]['loss']170            axs[i, 1].plot(ppo_loss[0::3], '-', color = (0, 0, 1, 0.1),171                           label = legend_prefix + 'clip')172            axs[i, 1].plot(ppo_loss[1::3], '-', color = (0, 1, 0, 0.1),173                           label = legend_prefix + 'vf')174            axs[i, 1].plot(ppo_loss[2::3], '-', color = (1, 0, 0, 0.1),175                           label = legend_prefix + 'entropy')176            legend_prefix = '_'177        axs[i, 1].set_ylim([-3, 20])178        axs[i, 1].grid(True)179        axs[i, 1].set_title('PPO started from ' + str(starting_height))180        axs[i, 1].legend()181    fig.tight_layout()182    plt.show()183    # Metrics184    fig, axs = plt.subplots(len(STARTING_HEIGHTS), 2)185    for i, starting_height in enumerate(STARTING_HEIGHTS):186        # DQN187        legend_prefix = ''188        measurement_name = '{}fixed-DQN-new-'.format(starting_height)189        for t in range(get_num_tests(f.keys(), measurement_name)):190            g = f[measurement_name + str(t)]191            num_episodes = g['num_episodes'][0]192            episode_ends = g['episode_ends'][:num_episodes]193            episode_rewards = g['episode_rewards'][:num_episodes]194            # xx = episode_ends + \195            #     np.random.random(episode_ends.shape) * 0.5 - 0.25196            # axs[i, 0].plot(xx, episode_rewards, '.',197            #          label = legend_prefix + 'episode rewards')198            reward_prediction = g['reward_prediction'][:num_episodes]199            xx = episode_ends + \200                np.random.random(episode_ends.shape) * 0.5 - 0.25201            # axs[i, 0].plot(xx, reward_prediction, 'x',202            #          label = legend_prefix + 'reward prediction')203            axs[i, 0].plot(xx, reward_prediction - episode_rewards, 'b.',204                           label = legend_prefix + 'reward prediction error')205            test_episodes = g['test_episode_rewards'][206                :num_episodes // TEST_REWARD_INTERVAL]207            xx = episode_ends[TEST_REWARD_INTERVAL - 1::TEST_REWARD_INTERVAL]208            xx = xx + np.random.random(*xx.shape) * 0.5 - 0.25209            axs[i, 0].plot(xx, test_episodes,210                     'ro', label = legend_prefix + 'test rewards')211            legend_prefix = '_'212        axs[i, 0].grid(True)213        axs[i, 0].set_title('DQN started from ' + str(starting_height))214        axs[i, 0].legend()215        legend_prefix = ''216        measurement_name = '{}fixed-PPO-new-'.format(starting_height)217        for t in range(get_num_tests(f.keys(), measurement_name)):218            g = f[measurement_name + str(t)]219            num_episodes = g['num_episodes'][0]220            episode_ends = g['episode_ends'][:num_episodes]221            episode_rewards = g['episode_rewards'][:num_episodes]222            # xx = episode_ends + \223            #     np.random.random(episode_ends.shape) * 0.5 - 0.25224            # axs[i, 1].plot(xx, episode_rewards, '.',225            #          label = legend_prefix + 'episode rewards')226            reward_prediction = g['reward_prediction'][:num_episodes]227            xx = episode_ends + \228                np.random.random(episode_ends.shape) * 0.5 - 0.25229            # axs[i, 1].plot(xx, reward_prediction, 'x',230            #          label = legend_prefix + 'reward prediction')231            axs[i, 1].plot(xx, reward_prediction - episode_rewards, 'b.',...

Class.py

Source:Class.py

...2223    def get_n_correct(self):24        return self.n_correct2526    def get_num_tests(self):27        return self.num_tests2829    def get_prediction(self):30        return self.prediction3132    def set_prediction(self, support_set, targets):33        # to be overridden by subclasses34        self.prediction = 03536    def update_score(self, support_set, targets, verbose):37        self.set_prediction(support_set, targets)38        if targets[self.get_prediction()] == 1:39            self.n_correct += 140        self.num_tests += 14142        if verbose and self.get_num_tests() < 10:43            print('TEST {}'.format(self.get_num_tests()))44            print('{} GUESSED PAIR {}'.format(self.get_name(), self.get_prediction() + 1))45    46            if targets[self.get_prediction()] == 1:47                print("CORRECT")48            else:49                print("INCORRECT")50            print('\n')5152    def calc_accuracy(self, N, K):53        acc = round((self.n_correct/self.num_tests) * 100, 2)54        print("{} Model achieved {}% accuracy on {} {}-way {}-shot tests".format(self.get_name(), acc, self.get_num_tests(), N, K))55        return acc56   5758class FSL(Predictor):5960    def __init__(self, model, name='Few Shot Learning', use_training_set=False, convert_2_rgb=False):61        super().__init__(name)62        self.model = model63        self.name = name64        self.probs = None65        self.use_training_set = use_training_set66        self.convert_2_rgb = convert_2_rgb67        self.fsl = True68        
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