#917 - Overhauled transaction and mean reward writing.
- Separated out learning outputs from evaluation outputs
This commit is contained in:
Chris McCarthy
@@ -1,57 +1,115 @@
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# Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
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"""The Transaction class."""
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from datetime import datetime
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from typing import List, Tuple
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class Transaction(object):
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"""Transaction class."""
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def __init__(self, _timestamp, _agent_identifier, _episode_number, _step_number):
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def __init__(
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self,
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agent_identifier,
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episode_number,
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step_number
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):
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"""
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Init.
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Transaction constructor.
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Args:
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_timestamp: The time this object was created
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_agent_identifier: An identifier for the agent in use
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_episode_number: The episode number
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_step_number: The step number
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:param agent_identifier: An identifier for the agent in use
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:param episode_number: The episode number
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:param step_number: The step number
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"""
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self.timestamp = _timestamp
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self.agent_identifier = _agent_identifier
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self.episode_number = _episode_number
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self.step_number = _step_number
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self.timestamp = datetime.now()
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"The datetime of the transaction"
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self.agent_identifier = agent_identifier
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self.episode_number = episode_number
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"The episode number"
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self.step_number = step_number
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"The step number"
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self.obs_space_pre = None
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"The observation space before any actions are taken"
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self.obs_space_post = None
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"The observation space after any actions are taken"
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self.reward = None
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"The reward value"
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self.action_space = None
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"The action space invoked by the agent"
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def set_obs_space_pre(self, _obs_space_pre):
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"""
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Sets the observation space (pre).
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def as_csv_data(self) -> Tuple[List, List]:
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if isinstance(self.action_space, int):
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action_length = self.action_space
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else:
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action_length = self.action_space.size
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obs_shape = self.obs_space_post.shape
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obs_assets = self.obs_space_post.shape[0]
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if len(obs_shape) == 1:
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# A bit of a workaround but I think the way transactions are
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# written will change soon
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obs_features = 1
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else:
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obs_features = self.obs_space_post.shape[1]
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Args:
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_obs_space_pre: The observation space before any actions are taken
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"""
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self.obs_space_pre = _obs_space_pre
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# Create the action space headers array
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action_header = []
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for x in range(action_length):
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action_header.append("AS_" + str(x))
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def set_obs_space_post(self, _obs_space_post):
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"""
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Sets the observation space (post).
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# Create the observation space headers array
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obs_header_initial = []
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obs_header_new = []
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for x in range(obs_assets):
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for y in range(obs_features):
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obs_header_initial.append("OSI_" + str(x) + "_" + str(y))
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obs_header_new.append("OSN_" + str(x) + "_" + str(y))
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Args:
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_obs_space_post: The observation space after any actions are taken
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"""
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self.obs_space_post = _obs_space_post
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# Open up a csv file
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header = ["Timestamp", "Episode", "Step", "Reward"]
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header = header + action_header + obs_header_initial + obs_header_new
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def set_reward(self, _reward):
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"""
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Sets the reward.
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row = [
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str(self.timestamp),
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str(self.episode_number),
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str(self.step_number),
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str(self.reward),
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]
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row = (
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row
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+ _turn_action_space_to_array(self.action_space)
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+ _turn_obs_space_to_array(self.obs_space_pre, obs_assets,
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obs_features)
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+ _turn_obs_space_to_array(self.obs_space_post, obs_assets,
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obs_features)
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)
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return header, row
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Args:
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_reward: The reward value
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"""
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self.reward = _reward
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def set_action_space(self, _action_space):
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"""
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Sets the action space.
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def _turn_action_space_to_array(action_space) -> List[str]:
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"""
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Turns action space into a string array so it can be saved to csv.
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Args:
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_action_space: The action space invoked by the agent
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"""
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self.action_space = _action_space
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:param action_space: The action space
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:return: The action space as an array of strings
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"""
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if isinstance(action_space, list):
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return [str(i) for i in action_space]
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else:
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return [str(action_space)]
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def _turn_obs_space_to_array(obs_space, obs_assets, obs_features) -> List[str]:
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"""
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Turns observation space into a string array so it can be saved to csv.
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:param obs_space: The observation space
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:param obs_assets: The number of assets (i.e. nodes or links) in the
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observation space
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:param obs_features: The number of features associated with the asset
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:return: The observation space as an array of strings
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"""
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return_array = []
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for x in range(obs_assets):
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for y in range(obs_features):
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return_array.append(str(obs_space[x][y]))
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return return_array
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@@ -1,119 +0,0 @@
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# Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
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"""Writes the Transaction log list out to file for evaluation to utilse."""
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import csv
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from pathlib import Path
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import numpy as np
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from primaite import getLogger
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_LOGGER = getLogger(__name__)
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def turn_action_space_to_array(_action_space):
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"""
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Turns action space into a string array so it can be saved to csv.
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Args:
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_action_space: The action space.
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"""
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if isinstance(_action_space, list):
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return [str(i) for i in _action_space]
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else:
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return [str(_action_space)]
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def turn_obs_space_to_array(_obs_space, _obs_assets, _obs_features):
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"""
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Turns observation space into a string array so it can be saved to csv.
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Args:
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_obs_space: The observation space
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_obs_assets: The number of assets (i.e. nodes or links) in the observation space
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_obs_features: The number of features associated with the asset
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"""
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return_array = []
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for x in range(_obs_assets):
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for y in range(_obs_features):
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return_array.append(str(_obs_space[x][y]))
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return return_array
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def write_transaction_to_file(transaction_list, session_path: Path, timestamp_str: str):
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"""
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Writes transaction logs to file to support training evaluation.
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:param transaction_list: The list of transactions from all steps and all
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episodes.
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:param session_path: The directory path the session is writing to.
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:param timestamp_str: The session timestamp in the format:
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<yyyy-mm-dd>_<hh-mm-ss>.
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"""
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# Get the first transaction and use it to determine the makeup of the
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# observation space and action space
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# Label the obs space fields in csv as "OSI_1_1", "OSN_1_1" and action
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# space as "AS_1"
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# This will be tied into the PrimAITE Use Case so that they make sense
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template_transation = transaction_list[0]
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if isinstance(template_transation.action_space, int):
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action_length = template_transation.action_space
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else:
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action_length = template_transation.action_space.size
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obs_shape = template_transation.obs_space_post.shape
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obs_assets = template_transation.obs_space_post.shape[0]
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if len(obs_shape) == 1:
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# bit of a workaround but I think the way transactions are written will change soon
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obs_features = 1
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else:
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obs_features = template_transation.obs_space_post.shape[1]
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# Create the action space headers array
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action_header = []
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for x in range(action_length):
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action_header.append("AS_" + str(x))
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# Create the observation space headers array
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obs_header_initial = []
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obs_header_new = []
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for x in range(obs_assets):
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for y in range(obs_features):
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obs_header_initial.append("OSI_" + str(x) + "_" + str(y))
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obs_header_new.append("OSN_" + str(x) + "_" + str(y))
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# Open up a csv file
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header = ["Timestamp", "Episode", "Step", "Reward"]
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header = header + action_header + obs_header_initial + obs_header_new
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try:
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filename = session_path / f"all_transactions_{timestamp_str}.csv"
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_LOGGER.debug(f"Saving transaction logs: {filename}")
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csv_file = open(filename, "w", encoding="UTF8", newline="")
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csv_writer = csv.writer(csv_file)
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csv_writer.writerow(header)
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for transaction in transaction_list:
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csv_data = [
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str(transaction.timestamp),
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str(transaction.episode_number),
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str(transaction.step_number),
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str(transaction.reward),
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]
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csv_data = (
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csv_data
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+ turn_action_space_to_array(transaction.action_space)
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+ turn_obs_space_to_array(
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transaction.obs_space_pre, obs_assets, obs_features
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)
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+ turn_obs_space_to_array(
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transaction.obs_space_post, obs_assets, obs_features
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)
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)
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csv_writer.writerow(csv_data)
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csv_file.close()
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_LOGGER.debug("Finished writing transactions")
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except Exception:
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_LOGGER.error("Could not save the transaction file", exc_info=True)
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