class_docstr: Compiler for Feature Importance Ranking
  
  Param:
      package (str, Optional): component package name
      module (str, Optional): component module name
      class (str): component class name
  
  Attr:
      trained_model (str): path to trained model pickle
      train_data (str): path to training sample data
      feature_names (str, Optional): array-list of feature names
      method: (str, Optional) interpreter method, default = 'default'
      threshold (number, Optional): importance threshold, default 0.005
  
  Example:
      "component": {
          "package": "xai",
          "module": "compiler",
          "class": "FeatureImportanceRanking",
          "attr": {
              "mode": "classification",
              "trained_model": "./sample_input/model.pkl",
              "train_data": "./sample_input/train.csv",
              "feature_names": "./sample_input/feature_name.csv",
              "method": "default"
              "threshold": 0.005
          }
      }
docstr: Init
  
  Args:
      dictionary (dict): attribute to set
code: def __init__(self, dictionary):
      """
      Init
  
      Args:
          dictionary (dict): attribute to set
      """
      super(FeatureImportanceRanking, self).__init__(
          dictionary, schema=self.schema
      )
  
file_path: contextual-ai/xai/compiler/features.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from pandas import DataFrame
  from xai import ALG, MODE
  from xai.compiler.base import Dict2Obj
  from xai.formatter import Report
  from xai.model.interpreter import FeatureInterpreter
label_desc: Use image node_id 1 to create a new FeatureImportanceRanking object from inherited base classes: Dict2Obj with example: obj = FeatureImportanceRanking(dictionary)