docstr: concatenate similarity scores to features
  
  Args:
      x: feature vector
      neg_sim: similarity scores between cand terms and neg opinion terms
      pos_sim: similarity scores between cand terms and pos opinion terms
  
  Returns:
      x: concatenated features and similarity scores
code: def concat_sim_scores_and_features(x, neg_sim, pos_sim):
      """
      concatenate similarity scores to features
  
      Args:
          x: feature vector
          neg_sim: similarity scores between cand terms and neg opinion terms
          pos_sim: similarity scores between cand terms and pos opinion terms
  
      Returns:
          x: concatenated features and similarity scores
      """
      neg = np.array(neg_sim)
      pos = np.array(pos_sim)
  
      neg_avg = np.mean(neg, axis=1, keepdims=True)
      neg_std = np.std(neg, axis=1, keepdims=True)
      neg_min = np.min(neg, axis=1, keepdims=True)
      neg_max = np.max(neg, axis=1, keepdims=True)
  
      pos_avg = np.mean(pos, axis=1, keepdims=True)
      pos_std = np.std(pos, axis=1, keepdims=True)
      pos_min = np.min(pos, axis=1, keepdims=True)
      pos_max = np.max(pos, axis=1, keepdims=True)
  
      print("\nAdding polarity similarity features...")
  
      res_x = np.concatenate(
          (
              neg_avg,
              neg_std,
              neg_min,
              neg_max,
              pos_avg,
              pos_std,
              pos_min,
              pos_max,
              x,
          ),
          1,
      )
  
      return res_x
  
imports: import csv
  import pickle
  import numpy
  import tensorflow
  from os import PathLike
  from pathlib import Path
  from nlp_architect.models.absa.utils import _read_generic_lex_for_similarity
  from nlp_architect.models.absa import TRAIN_OUT, TRAIN_LEXICONS, GENERIC_OP_LEX, LEXICONS_OUT
  from scipy.spatial.distance import cosine
  from sklearn.model_selection import StratifiedKFold
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.models import Sequential, load_model
  from nlp_architect.utils.embedding import load_word_embeddings
label_desc: Use image node_id 6 for calling the RerankTerms obj's underlying member method code with example usage: obj.concat_sim_scores_and_features(x, neg_sim, pos_sim) and returns: res_x