USER
In this below code i am randomly generating the targets for testing and training process in the function 'generate_random_targets' and saving and loading all the necessary data's relating to the testing and training target data and number of target data's with in the training and testing loop. now i need to modify the code as, instead of generating random targets with in the ranges, i need to choose the targets from the .xlsx file (path: 'D:\OPAMP CIRCUITS\RLGNN_LTSpice\Dataset_Predict.xlsx'), where the excel file having four columns with header 'gain', 'bandwidth', 'unitygainfrequency', 'phasemargin', excluding the first row (header), the excel data has 355 rows, each row are the corresponding values of the target data which required for the below training and testing process. so i need you to read all the row data and shuffle it in row wise and select 300 row datas as training target data and 35 row data as testing target data, these training and testing data are need to be keep saved and loaded in the training and testing loop, it datas are need to be fetch from save state while it is in loading, only at the first time it is need to be setch from excel file and seperate it s test and training data (the consistancy should be maintained) no duplicate must be present between testing and trainning data, it should not be fetch from excel on every time loading. while selecting training datas and testing datas (the selection of data is row 'x' column1, row 'x' column2, row 'x' column3, row 'x' column4, 'x' is the row number the column data must be choosen from the same row for the corresponding target data.
# Define Circuit Environment
class CircuitEnvironment:
PERFORMANCE_METRICS_TARGET_LOW = np.array([45, 10e3, 5e6, 60])
PERFORMANCE_METRICS_TARGET_HIGH = np.array([55, 30e3, 10e6, 75])
def __init__(self, bounds_low, bounds_high):
# Initialization code: setup bounds
@staticmethod
def generate_random_targets(num_targets, seed):
np.random.seed(seed)
gain_targets = np.random.choice(np.arange(45, 56), num_targets)
bw_targets = np.random.choice(np.arange(10e3, 30.1e3, 1e3), num_targets)
ugf_targets = np.random.choice(np.arange(5e6, 10.1e6, 1e6), num_targets)
pm_targets = np.random.choice(np.arange(60, 76), num_targets)
random_targets = np.vstack((gain_targets, bw_targets, ugf_targets, pm_targets)).T
return random_targets
# save and load training process
def save_training_state(agent, episode, rewards_log, losses_log, metrics_log, train_targets, test_targets, filepath='training_state_TrainTest7.pt'):
state = {
'episode': episode,
'actor_state_dict': agent.actor.state_dict(),
'critic_state_dict': agent.critic.state_dict(),
'optimizer_actor_state_dict': agent.optimizer_actor.state_dict(),
'optimizer_critic_state_dict': agent.optimizer_critic.state_dict(),
'rewards_log': rewards_log,
'losses_log': losses_log,
'metrics_log': metrics_log,
'train_targets': train_targets,
'test_targets': test_targets
}
torch.save(state, filepath)
def load_training_state(filepath='training_state_TrainTest7.pt'):
if os.path.isfile(filepath):
state = torch.load(filepath)
return state
else:
return None
# Training Function
def train(env, agent, num_episodes, max_timesteps):
num_targets = 100
train_targets = CircuitEnvironment.generate_random_targets(num_targets=num_targets, seed=0)
test_targets = CircuitEnvironment.generate_random_targets(num_targets=20, seed=1)
# Initialize the episode to start from and log lists
start_episode = 0
rewards_log = []
losses_log = []
metrics_log = []
episode_lengths = []
# Attempt to load the saved state
saved_state = load_training_state()
if saved_state:
start_episode = saved_state['episode'] + 1 # Start from the next episode
agent.actor.load_state_dict(saved_state['actor_state_dict'])
agent.critic.load_state_dict(saved_state['critic_state_dict'])
agent.optimizer_actor.load_state_dict(saved_state['optimizer_actor_state_dict'])
agent.optimizer_critic.load_state_dict(saved_state['optimizer_critic_state_dict'])
rewards_log = saved_state['rewards_log']
losses_log = saved_state['losses_log']
metrics_log = saved_state['metrics_log']
train_targets = saved_state['train_targets']
test_targets = saved_state['test_targets']
print(f"Resuming from episode {start_episode}")
for episode in range(start_episode, num_episodes):
if episode >= num_targets:
break # Exit the loop if exhausted the number of targets
target = train_targets[episode % len(train_targets)]
node_features_tensor, edge_index, performance_metrics = env.reset(target)
state = (node_features_tensor, edge_index)
# Initialize storage for episode data
episode_rewards = []
states = []
actions = []
log_probs = []
values = []
masks = []
perf_metrics_log = []
for t in range(max_timesteps):
action, log_prob, perf_metrics = agent.select_action(state, performance_metrics)
next_node_features_tensor, next_edge_index, reward, done, previous_metrics = env.step(action, perf_metrics, target)
next_state = (next_node_features_tensor, next_edge_index)
save_training_state(agent, episode, rewards_log, losses_log, metrics_log, train_targets, test_targets)
agent.save_checkpoint('final')
# save and load testing process
def save_test_state(test_targets, rewards_log, metrics_log, current_target_index, filepath='test_state_TrainTest7.pt'):
state = {
'test_targets': test_targets,
'rewards_log': rewards_log,
'metrics_log': metrics_log,
'current_target_index': current_target_index
}
torch.save(state, filepath)
def load_test_state(filepath='test_state_TrainTest7.pt'):
if os.path.isfile(filepath):
state = torch.load(filepath)
return state
else:
return None
# Test Function
def test(env, agent, num_targets, max_timesteps_per_target=300, test_targets=None):
rewards_log = []
metrics_log = []
# Load test state if exists
saved_test_state = load_test_state()
if saved_test_state:
test_targets = saved_test_state['test_targets']
rewards_log = saved_test_state['rewards_log']
metrics_log = saved_test_state['metrics_log']
start_target_index = saved_test_state['current_target_index'] + 1
print(f"Resuming from target index {start_target_index}")
else:
start_target_index = 0
if not test_targets:
saved_state = load_training_state()
if saved_state and 'test_targets' in saved_state:
test_targets = saved_state['test_targets']
else:
test_targets = CircuitEnvironment.generate_random_targets(num_targets=num_targets, seed=1)
for i in range(start_target_index, num_targets):
target = test_targets[i]
node_features_tensor, edge_index, performance_metrics = env.reset(target)
state = (node_features_tensor, edge_index)
episode_rewards = []
states = []
actions = []
values = []
masks = []
perf_metrics_log = []
done = False
t = 0
while not done and t < max_timesteps_per_target:
action, log_prob, perf_metrics = agent.select_action(state, performance_metrics)
next_node_features_tensor, next_edge_index, reward, done, previous_metrics = env.step(action, perf_metrics, target)
state = (next_node_features_tensor, next_edge_index)
# Save the test state after each target
save_test_state(test_targets, rewards_log, metrics_log, i, filepath='test_state_TrainTest7.pt')
if __name__ == "__main__":
# Initialize environment parameters
# Define the values for bounds, and target metrics
env = CircuitEnvironment(bounds_low, bounds_high)
agent = PPOAgent(state_dim, action_space, lr_actor, lr_critic, gamma, gae_lambda, epsilon, policy_clip, epochs, entropy_coef)
num_episodes = 1000
max_timesteps = 300
train(env, agent, num_episodes, max_timesteps)
trained_state = load_training_state()
if trained_state:
agent.actor.load_state_dict(trained_state['actor_state_dict'])
agent.critic.load_state_dict(trained_state['critic_state_dict'])
num_test_targets = 20
test(env, agent, num_test_targets, max_timesteps_per_target=300)ASSISTANT
To modify the provided code so that it reads target values from the specified Excel file, shuffles them, splits them into training and testing datasets, and ensures consistent fetching from a saved state, follow these steps:
1. **Install necessary libraries for Excel handling**:
Ensure that you have `pandas` and `openpyxl` installed. If not, install them using:
```sh
pip install pandas openpyxl
```
2. **Modify the `generate_random_targets` method**:
Replace it with a method to read the Excel file, shuffle the rows, and split them into training and testing datasets.
3. **Adjust the `train` and `test` functions**:
Load the targets from the saved states, if available.
Below is the modified code:
```python
import numpy as np
import pandas as pd
import torch
import os
class CircuitEnvironment:
PERFORMANCE_METRICS_TARGET_LOW = np.array([45, 10e3, 5e6, 60])
PERFORMANCE_METRICS_TARGET_HIGH = np.array([55, 30e3, 10e6, 75])
def __init__(self, bounds_low, bounds_high):
# Initialization code: setup bounds
pass # Replace with actual initialization
@staticmethod
def load_and_prepare_targets(filepath, seed=0):
df = pd.read_excel(filepath)
np.random.seed(seed)
data = df.to_numpy()
np.random.shuffle(data)
train_targets = data[:300]
test_targets = data[300:]
return train_targets, test_targets
# Save and load training process
def save_training_state(agent, episode, rewards_log, losses_log, metrics_log, train_targets, test_targets, filepath='training_state_TrainTest7.pt'):
state = {
'episode': episode,
'actor_state_dict': agent.actor.state_dict(),
'critic_state_dict': agent.critic.state_dict(),
'optimizer_actor_state_dict': agent.optimizer_actor.state_dict(),
'optimizer_critic_state_dict': agent.optimizer_critic.state_dict(),
'rewards_log': rewards_log,
'losses_log': losses_log,
'metrics_log': metrics_log,
'train_targets': train_targets,
'test_targets': test_targets
}
torch.save(state, filepath)
def load_training_state(filepath='training_state_TrainTest7.pt'):
if os.path.isfile(filepath):
state = torch.load(filepath)
return state
else:
return None
# Training Function
def train(env, agent, num_episodes, max_timesteps, target_filepath):
saved_state = load_training_state()
if saved_state:
train_targets = saved_state['train_targets']
test_targets = saved_state['test_targets']
start_episode = saved_state['episode'] + 1
else:
train_targets, test_targets = CircuitEnvironment.load_and_prepare_targets(target_filepath)
start_episode = 0
rewards_log = []
losses_log = []
metrics_log = []
for episode in range(start_episode, num_episodes):
if episode >= len(train_targets):
break # Exit the loop if exhausted the number of targets
target = train_targets[episode % len(train_targets)]
node_features_tensor, edge_index, performance_metrics = env.reset(target)
state = (node_features_tensor, edge_index)
# Initialize storage for episode data
episode_rewards = []
states = []
actions = []
log_probs = []
values = []
masks = []
perf_metrics_log = []
for t in range(max_timesteps):
action, log_prob, perf_metrics = agent.select_action(state, performance_metrics)
next_node_features_tensor, next_edge_index, reward, done, previous_metrics = env.step(action, perf_metrics, target)
next_state = (next_node_features_tensor, next_edge_index)
save_training_state(agent, episode, rewards_log, losses_log, metrics_log, train_targets, test_targets)
agent.save_checkpoint('final')
# Save and load testing process
def save_test_state(test_targets, rewards_log, metrics_log, current_target_index, filepath='test_state_TrainTest7.pt'):
state = {
'test_targets': test_targets,
'rewards_log': rewards_log,
'metrics_log': metrics_log,
'current_target_index': current_target_index
}
torch.save(state, filepath)
def load_test_state(filepath='test_state_TrainTest7.pt'):
if os.path.isfile(filepath):
state = torch.load(filepath)
return state
else:
return None
# Test Function
def test(env, agent, num_targets, max_timesteps_per_target=300, target_filepath=None):
rewards_log = []
metrics_log = []
saved_test_state = load_test_state()
if saved_test_state:
test_targets = saved_test_state['test_targets']
rewards_log = saved_test_state['rewards_log']
metrics_log = saved_test_state['metrics_log']
start_target_index = saved_test_state['current_target_index'] + 1
else:
start_target_index = 0
if target_filepath:
_, test_targets = CircuitEnvironment.load_and_prepare_targets(target_filepath)
else:
saved_state = load_training_state()
if saved_state and 'test_targets' in saved_state:
test_targets = saved_state['test_targets']
else:
raise ValueError("Target filepath must be provided on first run")
for i in range(start_target_index, num_targets):
target = test_targets[i]
node_features_tensor, edge_index, performance_metrics = env.reset(target)
state = (node_features_tensor, edge_index)
episode_rewards = []
states = []
actions = []
values = []
masks = []
perf_metrics_log = []
done = False
t = 0
while not done and t < max_timesteps_per_target:
action, log_prob, perf_metrics = agent.select_action(state, performance_metrics)
next_node_features_tensor, next_edge_index, reward, done, previous_metrics = env.step(action, perf_metrics, target)
state = (next_node_features_tensor, next_edge_index)
save_test_state(test_targets, rewards_log, metrics_log, i, filepath='test_state_TrainTest7.pt')
if __name__ == "__main__":
# Initialize environment parameters
# Define the values for bounds, and target metrics
bounds_low = ...
bounds_high = ...
state_dim = ...
action_space = ...
lr_actor = ...
lr_critic = ...
gamma = ...
gae_lambda = ...
epsilon = ...
policy_clip = ...
epochs = ...
entropy_coef = ...
env = CircuitEnvironment(bounds_low, bounds_high)
agent = PPOAgent(state_dim, action_space, lr_actor, lr_critic, gamma, gae_lambda, epsilon, policy_clip, epochs, entropy_coef)
num_episodes = 1000
max_timesteps = 300
target_filepath = 'D:\\OPAMP CIRCUITS\\RLGNN_LTSpice\\Dataset_Predict.xlsx'
train(env, agent, num_episodes, max_timesteps, target_filepath)
trained_state = load_training_state()
if trained_state:
agent.actor.load_state_dict(trained_state['actor_state_dict'])
agent.critic.load_state_dict(trained_state['critic_state_dict'])
num_test_targets = 20
test(env, agent, num_test_targets, max_timesteps_per_target=300, target_filepath=target_filepath)
```
### Explanation:
1. **Loading and Preparing Targets**:
The static method `load_and_prepare_targets` reads the target data from the Excel file located at `target_filepath`, shuffles it, and splits it into training and testing datasets.
2. **Training and Testing Function Modifications**:
- During training, targets are loaded from a saved state if available, ensuring consistency. If no saved state is found, it reads the targets from the Excel file.
- During testing, the targets are loaded in the same manner, ensuring consistency between training and testing data.
3. **Maintaining State Consistency**:
Save and load methods for training and testing states are included, which store and fetch the targets along with other relevant data, ensuring consistent training and testing across multiple runs.
By following the above modifications, you can ensure that your training and testing procedures work with real target data from the provided Excel file while maintaining persistency across sessions.