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How to use state_iterator method in autotest

Best Python code snippet using autotest_python

reinforcement_learning_module.py

Source:reinforcement_learning_module.py

1import pandas as pd2import enum34from tensorforce import Environment5from tensorforce.agents import Agent6from fe_module import features_file_path, flash_crash_event_starting_timestamp, flash_crash_event_ending_timestamp789class Decision(enum.Enum):10    NORMAL = 011    ABNORMAL = 1121314class ExchangeEnvironment(Environment):15    def __init__(self, environment_observations):16        super().__init__()17        self.state_iterator = 018        self.reward_var = environment_observations['REWARD']19        self.environment_observations = environment_observations.iloc[:, :-2]20        self.no_states = self.environment_observations.shape[0]21        self.current_state = {}2223    def states(self):24        state_dict = dict(type='float', shape=(7,))25        return state_dict2627    def actions(self):28        return {"DECISION": dict(type="int", num_values=2)}2930    def close(self):31        super().close()3233    def reset(self):34        self.state_iterator = 035        self.current_state = self.environment_observations.iloc[self.state_iterator]36        return self.current_state3738    def max_episode_timesteps(self):39        return self.no_states4041    def check_terminal_criteria(self, actions, all_decisions, timestamp, early_terminate=False):42        terminate = False43        cutoff_timestamp = '2022-05-02 07:58:20.119000+00:00'4445        if early_terminate:46            if pd.to_datetime(timestamp) < pd.to_datetime(flash_crash_event_starting_timestamp) and \47                    actions['DECISION'] == 1:48                terminate = True49            elif pd.to_datetime(timestamp) > pd.to_datetime(cutoff_timestamp) and \50                    sum(all_decisions) == 0:51                terminate = True52        if self.state_iterator == self.no_states - 1:53            terminate = True54        return terminate5556    def calculate_reward(self, reward_val, timestamp, decision, terminal):57        if terminal is True and pd.to_datetime(timestamp) < pd.to_datetime(flash_crash_event_ending_timestamp):58            reward = -20059        else:60            if pd.to_datetime(timestamp) < pd.to_datetime(flash_crash_event_starting_timestamp):61                if decision == Decision.NORMAL.value:62                    reward = reward_val63                else:64                    reward = 06566            elif pd.to_datetime(flash_crash_event_starting_timestamp) <= pd.to_datetime(timestamp) <= \67                    pd.to_datetime(flash_crash_event_ending_timestamp):68                if decision == Decision.NORMAL.value:69                    reward = -reward_val70                else:71                    reward = 07273            elif pd.to_datetime(timestamp) > pd.to_datetime(flash_crash_event_ending_timestamp):74                if decision == Decision.ABNORMAL.value:75                    reward = -3*reward_val76                else:77                    reward = 07879        return reward8081    def report_accuracy(self, all_decisions, y):82        y_before = y[:y.index(1)]83        y_during = y[y.index(1): y[y.index(1):].index(0)+len(y_before)]84        y_after = y[::-1][:y[::-1].index(1)]8586        accuracy = {}87        true_before = 088        true_during = 089        true_after = 09091        for i in range(len(y)):92            if all_decisions[i] == y[i]:93                if i <= len(y_before):94                    true_before += 195                elif len(y_before) < i < len(y_before) + len(y_during):96                    true_during += 197                elif i >= len(y_before) + len(y_during):98                    true_after += 199100        accuracy['before'] = true_before / len(y_before)*100101        accuracy['during'] = true_during / len(y_during)*100102        accuracy['after'] = true_after / len(y_after)*100103        accuracy['overall'] = (true_after+true_before+true_during) / len(y)*100104105        print('Accuracy before the crash: ', accuracy['before'])106        print('Accuracy during crash: ', accuracy['during'])107        print('Accuracy after the crash:', accuracy['after'])108        print('Overall Accuracy: ', accuracy['overall'])109110        return accuracy111112    def execute(self, all_decisions, actions):113        timestamp = self.environment_observations.index[self.state_iterator]114        self.state_iterator += 1115        reward_val = self.reward_var.iloc[self.state_iterator]116        next_state = self.environment_observations.iloc[self.state_iterator]117        terminal = self.check_terminal_criteria(118            actions, all_decisions, timestamp, early_terminate=False)119        reward = self.calculate_reward(reward_val,120                                       timestamp, actions['DECISION'], terminal)121122        return next_state, terminal, reward123124125class ExchangeAgent:126    def __init__(self, environment: ExchangeEnvironment):127128        self.market_evaluation = Agent.create(129            agent='dqn', environment=environment, batch_size=128, memory=len(y))130        self.episode_actions = {}131132133def execute_episode_batch(environment: ExchangeEnvironment, agent: ExchangeAgent, episodes):134    decision_reward_df = pd.DataFrame()135    for i in range(episodes):136        episode_length = 0137        state_vector = environment.reset()138        all_timestamps = []139        all_decisions = []140        all_rewards = []141        terminate = False142        while not terminate:143            episode_length += 1144            actions = agent.market_evaluation.act(states=state_vector)145            state_vector, terminate, reward = environment.execute(all_decisions,146                                                                  actions=actions)147            agent.market_evaluation.observe(148                terminal=terminate, reward=reward)149            all_decisions.append(actions['DECISION'])150            all_rewards.append(reward)151            all_timestamps.append(state_vector.name)152153        if episode_length == len(y):154            decision_reward_df['Timestamp'] = all_timestamps155            decision_reward_df[f'model/Decision_Episode{i}'] = all_decisions156            decision_reward_df[f'Rewards_Episode{i}'] = all_rewards157            accuracy = environment.report_accuracy(all_decisions, y)158            if accuracy['overall'] > 90:159                decision_reward_df.to_excel('Decision_reward.xlsx')160161            if accuracy['before'] == 100 and accuracy['during'] > 70 and accuracy['after'] == 100:162                decision_reward_df.to_excel('Decision_reward.xlsx')163                break164165166def simulator(environment: ExchangeEnvironment, agent: ExchangeAgent, episodes=100):167168    execute_episode_batch(environment, agent, episodes)169    environment.close()170171172if __name__ == "__main__":173174    environment_observations = pd.read_csv(175        features_file_path + 'df_environment.csv', index_col='Timestamp')176    exchange_environment = ExchangeEnvironment(177        environment_observations)178    y = environment_observations['LABEL'][1:].to_list()179    exchange_agent = ExchangeAgent(exchange_environment)
...

base.py

Source:base.py

...13        self.job = job14        self.finished = False15        self.line_buffer = status_lib.line_buffer()16        # create and prime the parser state machine17        self.state = self.state_iterator(self.line_buffer)18        self.state.next()19    def process_lines(self, lines):20        """ Feed 'lines' into the parser state machine, and return21        a list of all the new test results produced."""22        self.line_buffer.put_multiple(lines)23        try:24            return self.state.next()25        except StopIteration:26            msg = ("WARNING: parser was called to process status "27                   "lines after it was end()ed\n"28                   "Current traceback:\n" +29                   traceback.format_exc() +30                   "\nCurrent stack:\n" +31                   "".join(traceback.format_stack()))32            tko_utils.dprint(msg)33            return []34    def end(self, lines=[]):35        """ Feed 'lines' into the parser state machine, signal to the36        state machine that no more lines are forthcoming, and then37        return a list of all the new test results produced."""38        self.line_buffer.put_multiple(lines)39        # run the state machine to clear out the buffer40        self.finished = True41        try:42            return self.state.next()43        except StopIteration:44            msg = ("WARNING: parser was end()ed multiple times\n"45                   "Current traceback:\n" +46                   traceback.format_exc() +47                   "\nCurrent stack:\n" +48                   "".join(traceback.format_stack()))49            tko_utils.dprint(msg)50            return []51    @staticmethod52    def make_job(dir):53        """ Create a new instance of the job model used by the54        parser, given a results directory."""55        raise NotImplementedError56    def state_iterator(self, buffer):57        """ A generator method that implements the actual parser58        state machine. """...

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