class_docstr: Evaluate feature importance weights on data pages
class_docstr_tok: ["Evaluate","feature","importance","weights","on","data","pages"]
docstr: :param sorted_action_feature_start_indices: the starting index of each
      action feature in the action vector (need this because some features
      (e.g., one-hot encoding enum) may take multiple components)
  :param sorted_state_feature_start_indices: the starting index of each
      state feature in the state vector
docstr_tok: [":","param","sorted_action_feature_start_indices",":","the","starting","index","of","each","action","feature","in","the","action","vector","(","need","this","because","some","features","(","e.g.",",","one-hot","encoding","enum",")","may","take","multiple","components",")",":","param","sorted_state_feature_start_indices",":","the","starting","index","of","each","state","feature","in","the","state","vector"]
code: def __init__(
      self,
      trainer: MDNRNNTrainer,
      discrete_action: bool,
      state_feature_num: int,
      action_feature_num: int,
      sorted_action_feature_start_indices: List[int],
      sorted_state_feature_start_indices: List[int],
  ) -> None:
      """
      :param sorted_action_feature_start_indices: the starting index of each
          action feature in the action vector (need this because some features
          (e.g., one-hot encoding enum) may take multiple components)
      :param sorted_state_feature_start_indices: the starting index of each
          state feature in the state vector
      """
      self.trainer = trainer
      self.discrete_action = discrete_action
      self.state_feature_num = state_feature_num
      self.action_feature_num = action_feature_num
      self.sorted_action_feature_start_indices = (
          sorted_action_feature_start_indices
      )
      self.sorted_state_feature_start_indices = (
          sorted_state_feature_start_indices
      )
  
file_path: ReAgent/reagent/evaluation/world_model_evaluator.py
imports: import logging
  from typing import Dict, List
  import torch
  from reagent.core.types import FeatureData, MemoryNetworkInput
  from reagent.training.world_model.mdnrnn_trainer import MDNRNNTrainer
label_desc: Use image node_id 1 to create a new FeatureImportanceEvaluator object  with example: obj = FeatureImportanceEvaluator(trainer, discrete_action, state_feature_num, action_feature_num, sorted_action_feature_start_indices, sorted_state_feature_start_indices)