docstr: Score each reasoning chain.
docstr_tok: ["Score","each","reasoning","chain","."]
code: def evaluate(
      self, score_types: Optional[List[str]] = None
  ) -> Dict[str, Any]:
      """
      Score each reasoning chain.
      """
      score_types = (
          score_types if score_types is not None else self.score_types
      )
      # scores[score_type] = [score_chain1, score_chain2, ...]
      scores = defaultdict(list)
  
      if contains_embedding_scores(score_types):
          # Pre-compute chain, step, and word embeddings for embedding-based metrics
          self.precompute_embeddings(
              hypos=self.hypos,
              contexts=self.context,
              references=self.references,
          )
  
      for i in tqdm(range(len(self.hypos)), desc="Scoring chains ... "):
          if len(self.hypos[i].chain) == 0:
              for score in score_types:
                  scores[score].append("N/A")
              continue
  
          if contains_embedding_scores(score_types):
              # Calculate all embedding-based scores
              scores = self.compute_embedding_scores(
                  hypo=self.hypos[i],
                  context=self.context[i],
                  reference=self.references[i]
                  if self.references
                  else None,
                  score_types=score_types,
                  scores=scores,
              )
  
          if contains_nli_scores(score_types):
              scores = self.compute_nli_scores(
                  hypo=self.hypos[i],
                  context=self.context[i],
                  score_types=score_types,
                  scores=scores,
              )
  
          if contains_ppl_scores(score_types):
              ppl_scores = self.compute_ppl_scores(
                  hypo=self.hypos[i],
                  score_types=score_types,
              )
              for k, v in ppl_scores.items():
                  scores[k].append(v)
  
          # grammar acceptability classifier models
          if contains_grammar_scores(score_types):
              grammar_scores = self.compute_grammar_scores(
                  hypo=self.hypos[i],
                  score_types=score_types,
              )
              for k, v in grammar_scores.items():
                  scores[k].append(v)
  
      return scores
  
imports: from collections import defaultdict
  import itertools
  import math
  import numpy
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from tqdm import tqdm
  from projects.roscoe.utils import cosine_similarity_scaled, embedding_alignment, al_mean
label_desc: Use image node_id 30 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate(score_types) and returns: scores