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:
      predict_func (str): path to predict function call pickle
      train_data (str): path to training sample data
      feature_meta (str): path to a meta json file
      method: (str, Optional) interpreter method, default = 'lime'
      num_features (integer, Optional): number of features to show in the explanation, default 10
  
  Example:
      "component": {
          "package": "xai",
          "module": "compiler",
          "class": "ModelAgnosticExplainer",
          "attr": {
              "predict_func": "./sample_input/func.pkl",
              "train_data": "./sample_input/feature.csv",
              "feature_meta": "./sample_input/feature_meta.json",
              "method": "lime"
              "num_features": 10
          }
      }
docstr: Init
  
  Args:
      dictionary (dict): attribute to set
docstr_tok: ["Init","Args",":","dictionary","(","dict",")",":","attribute","to","set"]
code: def __init__(self, dictionary):
      """
      Init
  
      Args:
          dictionary (dict): attribute to set
      """
      super(ModelAgnosticExplainer, self).__init__(
          dictionary, schema=self.schema
      )
  
file_path: contextual-ai/xai/compiler/explainer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import xai
  from xai.compiler.base import Dict2Obj
  from xai.explainer import ExplainerFactory
  from xai.explainer.constants import OUTPUT
  from xai.explainer.helper import parse_feature_meta_tabular
  from xai.formatter import Report
label_desc: Use image node_id 1 to create a new ModelAgnosticExplainer object from inherited base classes: Dict2Obj with example: obj = ModelAgnosticExplainer(dictionary)