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import numpy as np
import time
import torch
from MyDecisionTransformer import MyDecisionTransformer
from citylearn.citylearn import CityLearnEnv
"""
This file is used to evaluate a decision transformer loaded form https://huggingface.co/TobiTob/model_name
"""
class Constants:
"""Environment Constants"""
episodes = 1 # amount of environment resets
state_dim = 28 # size of state space
action_dim = 1 # size of action space
schema_path = './data/citylearn_challenge_2022_phase_1/schema.json'
"""Model Constants"""
load_model = "TobiTob/decision_transformer_2"
force_download = False
device = "cpu"
TARGET_RETURN = -2500 # vllt Vector aus 5 Werten
# mean and std computed from training dataset these are available in the model card for each model.
state_mean = np.array(
[6.525973284621532, 3.9928073981048064, 12.498801233017467, 16.836990550577212, 16.837287388159297,
16.83684213167729, 16.837161803003287, 73.00388172165772, 73.00331088023746, 73.00445256307798,
73.00331088023746, 208.30597100125584, 208.30597100125584, 208.20287704075807, 208.30597100125584,
201.25448110514898, 201.25448110514898, 201.16189062678387, 201.25448110514898, 0.15652765849893777,
1.0663012570140091, 0.6994348432433195, 0.5023924181838172, 0.49339119658209996, 0.2731373418679261,
0.2731373418679261, 0.2731373418679261, 0.2731373418679261])
state_std = np.array(
[3.448045414453991, 2.0032677368929734, 6.921673394725967, 3.564552828057008, 3.5647828974724476,
3.5643565817901974, 3.564711987899257, 16.480221141108398, 16.480030755727572, 16.480238315742053,
16.480030755727565, 292.79094956097464, 292.79094956097464, 292.70528837855596, 292.79094956097543,
296.18549714910006, 296.18549714910023, 296.1216266457902, 296.18549714910006, 0.035369600587780235,
0.8889958578862672, 1.0171468928300462, 0.40202104980478576, 2.6674362928093682, 0.11780233435944305,
0.11780233435944333, 0.11780233435944351, 0.11780233435944402])
def preprocess_states(state_list_of_lists, amount_buildings):
for bi in range(amount_buildings):
for si in range(Constants.state_dim):
state_list_of_lists[bi][si] = (state_list_of_lists[bi][si] - Constants.state_mean[si]) / Constants.state_std[si]
return state_list_of_lists
def evaluate():
print("========================= Start Evaluation ========================")
print("==> Model:", Constants.load_model)
print()
env = CityLearnEnv(schema=Constants.schema_path)
agent = MyDecisionTransformer(load_from=Constants.load_model, force_download=Constants.force_download,
device=Constants.device)
context_length = agent.model.config.max_length
amount_buildings = len(env.buildings)
scale = 1000.0 # normalization for rewards/returns
target_return = Constants.TARGET_RETURN / scale
print("Target Return:", Constants.TARGET_RETURN)
print("Context Length:", context_length)
# Initialize Tensors
episode_return = np.zeros(amount_buildings)
state_list_of_lists = env.reset()
state_list_of_lists = preprocess_states(state_list_of_lists, amount_buildings)
state_list_of_tensors = []
target_return_list_of_tensors = []
action_list_of_tensors = []
reward_list_of_tensors = []
for bi in range(amount_buildings):
state_bi = torch.from_numpy(np.array(state_list_of_lists[bi])).reshape(1, Constants.state_dim).to(
device=Constants.device,
dtype=torch.float32)
target_return_bi = torch.tensor(target_return, device=Constants.device, dtype=torch.float32).reshape(1, 1)
action_bi = torch.zeros((0, Constants.action_dim), device=Constants.device, dtype=torch.float32)
reward_bi = torch.zeros(0, device=Constants.device, dtype=torch.float32)
state_list_of_tensors.append(state_bi)
target_return_list_of_tensors.append(target_return_bi)
action_list_of_tensors.append(action_bi)
reward_list_of_tensors.append(reward_bi)
timesteps = torch.tensor(0, device=Constants.device, dtype=torch.long).reshape(1, 1)
# print(state_list_of_tensors) Liste mit 5 Tensoren, jeder Tensor enthält einen State s der Länge 28
# print(action_list_of_tensors) Liste mit 5 leeren Tensoren mit size (0,1)
# print(reward_list_of_tensors) Liste mit 5 leeren Tensoren ohne size
# print(target_return_list_of_tensors) Liste mit 5 leeren Tensoren, jeder Tensor enthält den target_return / scale
# print(timesteps) enthält einen Tensor mit 0: tensor([[0]])
episodes_completed = 0
num_steps = 0
t = 0
agent_time_elapsed = 0
episode_metrics = []
while True:
next_actions = []
for bi in range(amount_buildings):
action_list_of_tensors[bi] = torch.cat(
[action_list_of_tensors[bi], torch.zeros((1, Constants.action_dim), device=Constants.device)], dim=0)
reward_list_of_tensors[bi] = torch.cat(
[reward_list_of_tensors[bi], torch.zeros(1, device=Constants.device)])
# get actions for all buildings
step_start = time.perf_counter()
action_bi = agent.get_action(
state_list_of_tensors[bi],
action_list_of_tensors[bi],
reward_list_of_tensors[bi],
target_return_list_of_tensors[bi],
timesteps,
)
agent_time_elapsed += time.perf_counter() - step_start
action_list_of_tensors[bi][-1] = action_bi
action_bi = action_bi.detach().cpu().numpy()
next_actions.append(action_bi)
# Interaction with the environment
state_list_of_lists, reward_list_of_lists, done, _ = env.step(next_actions)
state_list_of_lists = preprocess_states(state_list_of_lists, amount_buildings)
if done:
episodes_completed += 1
metrics_t = env.evaluate()
metrics = {"price_cost": metrics_t[0], "emmision_cost": metrics_t[1], "grid_cost": metrics_t[2]}
if np.any(np.isnan(metrics_t)):
raise ValueError("Episode metrics are nan, please contant organizers")
episode_metrics.append(metrics)
print(f"Episode complete: {episodes_completed} | Latest episode metrics: {metrics}", )
print("Episode Return:", episode_return)
# new Initialization and env Reset
t = 0
episode_return = np.zeros(amount_buildings)
state_list_of_lists = env.reset()
state_list_of_lists = preprocess_states(state_list_of_lists, amount_buildings)
state_list_of_tensors = []
target_return_list_of_tensors = []
action_list_of_tensors = []
reward_list_of_tensors = []
for bi in range(amount_buildings):
state_bi = torch.from_numpy(np.array(state_list_of_lists[bi])).reshape(1, Constants.state_dim).to(
device=Constants.device, dtype=torch.float32)
target_return_bi = torch.tensor(target_return, device=Constants.device, dtype=torch.float32).reshape(1,
1)
action_bi = torch.zeros((0, Constants.action_dim), device=Constants.device, dtype=torch.float32)
reward_bi = torch.zeros(0, device=Constants.device, dtype=torch.float32)
state_list_of_tensors.append(state_bi)
target_return_list_of_tensors.append(target_return_bi)
action_list_of_tensors.append(action_bi)
reward_list_of_tensors.append(reward_bi)
timesteps = torch.tensor(0, device=Constants.device, dtype=torch.long).reshape(1, 1)
else:
# Process data for next step
for bi in range(amount_buildings):
cur_state = torch.from_numpy(np.array(state_list_of_lists[bi])).to(device=Constants.device).reshape(1,
Constants.state_dim)
state_list_of_tensors[bi] = torch.cat([state_list_of_tensors[bi], cur_state], dim=0)
reward_list_of_tensors[bi][-1] = reward_list_of_lists[bi]
pred_return = target_return_list_of_tensors[bi][0, -1] - (reward_list_of_lists[bi] / scale)
target_return_list_of_tensors[bi] = torch.cat(
[target_return_list_of_tensors[bi], pred_return.reshape(1, 1)], dim=1)
episode_return[bi] += reward_list_of_lists[bi]
timesteps = torch.cat([timesteps, torch.ones((1, 1), device=Constants.device, dtype=torch.long) * (t + 1)],
dim=1)
if timesteps.size(dim=1) > context_length:
# Store only the last values according to context_length
timesteps = timesteps[:, -context_length:]
for bi in range(amount_buildings):
state_list_of_tensors[bi] = state_list_of_tensors[bi][-context_length:]
action_list_of_tensors[bi] = action_list_of_tensors[bi][-context_length:]
reward_list_of_tensors[bi] = reward_list_of_tensors[bi][-context_length:]
target_return_list_of_tensors[bi] = target_return_list_of_tensors[bi][:, -context_length:]
num_steps += 1
t += 1
if num_steps % 100 == 0:
print(f"Num Steps: {num_steps}, Num episodes: {episodes_completed}")
if episodes_completed >= Constants.episodes:
break
print("========================= Evaluation Done ========================")
print("Total number of steps:", num_steps)
if len(episode_metrics) > 0:
price_cost = np.mean([e['price_cost'] for e in episode_metrics])
emission_cost = np.mean([e['emmision_cost'] for e in episode_metrics])
grid_cost = np.mean([e['grid_cost'] for e in episode_metrics])
print("Average Price Cost:", price_cost)
print("Average Emission Cost:", emission_cost)
print("Average Grid Cost:", grid_cost)
print("==>", (price_cost+emission_cost+grid_cost)/3)
print(f"Total time taken by agent: {agent_time_elapsed}s")
if __name__ == '__main__':
evaluate()
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