class_docstr: Assigns weights to each type in the column type tree and attempts to traverse it using a weight percentage threshold.
class_docstr_tok: ["Assigns","weights","to","each","type","in","the","column","type","tree","and","attempts","to","traverse","it","using","a","weight","percentage","threshold","."]
docstr: The default weighter function.
  
  It assigns weights 1, 2, 4 and 8 for LOW, MEDIUM, HIGH and VERY_HIGH confidences, respectively. It then sums
  the weights of all provided confidence scores.
docstr_tok: ["The","default","weighter","function",".","It","assigns","weights","1",",","2",",","4","and","8","for","LOW",",","MEDIUM",",","HIGH","and","VERY_HIGH","confidences",",","respectively",".","It","then","sums","the","weights","of","all","provided","confidence","scores","."]
code: def default_weighter_function(
      confidence: InferenceSignalConfidence,
  ) -> int:
      """The default weighter function.
  
      It assigns weights 1, 2, 4 and 8 for LOW, MEDIUM, HIGH and VERY_HIGH confidences, respectively. It then sums
      the weights of all provided confidence scores.
      """
      confidence_weight_map = {
          InferenceSignalConfidence.LOW: 1,
          InferenceSignalConfidence.MEDIUM: 2,
          InferenceSignalConfidence.HIGH: 4,
          InferenceSignalConfidence.VERY_HIGH: 8,
      }
  
      return confidence_weight_map[confidence]
  
file_path: metricflow/metricflow/inference/solver/weighted_tree.py
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import Callable, Dict, List, Optional
  from metricflow.dataflow.sql_column import SqlColumn
  from metricflow.inference.models import InferenceResult, InferenceSignal, InferenceSignalConfidence, InferenceSignalNode, InferenceSignalType
  from metricflow.inference.solver.base import InferenceSolver
label_desc: Use image node_id 1 for calling the WeightedTypeTreeInferenceSolver obj's underlying member method code with example usage: obj.default_weighter_function(confidence) and returns: confidence_weight_map