def calculate_feature_importance(
gym_env: OpenAIGymEnvironment,
trainer: MDNRNNTrainer,
use_gpu: bool,
run_details: OpenAiRunDetails,
test_batch: rlt.PreprocessedTrainingBatch,
):
assert run_details.max_steps is not None
assert run_details.num_test_episodes is not None
assert run_details.seq_len is not None
feature_importance_evaluator = FeatureImportanceEvaluator(
trainer,
discrete_action=gym_env.action_type == EnvType.DISCRETE_ACTION,
state_feature_num=gym_env.state_dim,
action_feature_num=gym_env.action_dim,
sorted_action_feature_start_indices=list(range(gym_env.action_dim)),
sorted_state_feature_start_indices=list(range(gym_env.state_dim)),
)
feature_loss_vector = feature_importance_evaluator.evaluate(
test_batch)["feature_loss_increase"]
feature_importance_map = {}
for i in range(gym_env.action_dim):
print("action {}, feature importance: {}".format(
i, feature_loss_vector[i].item()))
feature_importance_map[f"action{i}"] = feature_loss_vector[i].item()
for i in range(gym_env.state_dim):
print("state {}, feature importance: {}".format(
i, feature_loss_vector[i + gym_env.action_dim].item()))
feature_importance_map[f"state{i}"] = feature_loss_vector[
i + gym_env.action_dim].item()
return feature_importance_map
def calculate_feature_importance(
gym_env: OpenAIGymEnvironment,
trainer: MDNRNNTrainer,
use_gpu: bool,
seq_len: int = 5,
num_test_episodes: int = 100,
max_steps: Optional[int] = None,
**kwargs,
):
feature_importance_evaluator = FeatureImportanceEvaluator(
trainer,
discrete_action=gym_env.action_type == EnvType.DISCRETE_ACTION,
state_feature_num=gym_env.state_dim,
action_feature_num=gym_env.action_dim,
sorted_action_feature_start_indices=list(range(gym_env.action_dim)),
sorted_state_feature_start_indices=list(range(gym_env.state_dim)),
)
test_replay_buffer = get_replay_buffer(num_test_episodes, seq_len,
max_steps, gym_env)
test_batch = test_replay_buffer.sample_memories(
test_replay_buffer.memory_size, use_gpu=use_gpu, batch_first=True)
feature_loss_vector = feature_importance_evaluator.evaluate(
test_batch)["feature_loss_increase"]
feature_importance_map = {}
for i in range(gym_env.action_dim):
print("action {}, feature importance: {}".format(
i, feature_loss_vector[i].item()))
feature_importance_map[f"action{i}"] = feature_loss_vector[i].item()
for i in range(gym_env.state_dim):
print("state {}, feature importance: {}".format(
i, feature_loss_vector[i + gym_env.action_dim].item()))
feature_importance_map[f"state{i}"] = feature_loss_vector[
i + gym_env.action_dim].item()
return feature_importance_map
def calculate_feature_importance(gym_env,
trainer,
seq_len=5,
num_test_episodes=100,
max_steps=None,
**kwargs):
feature_importance_evaluator = FeatureImportanceEvaluator(trainer)
test_replay_buffer = get_replay_buffer(num_test_episodes, seq_len,
max_steps, gym_env)
test_batch = test_replay_buffer.sample_memories(
test_replay_buffer.memory_size)
feature_loss_vector = feature_importance_evaluator.evaluate(
test_batch)["feature_loss_increase"]
for i in range(gym_env.action_dim):
print("action {}, feature importance: {}".format(
i, feature_loss_vector[i].item()))
for i in range(gym_env.state_dim):
print("state {}, feature importance: {}".format(
i, feature_loss_vector[i + gym_env.action_dim].item()))