docstr: load candidate terms with their basic features, Y labels and polarities from feature file
  
  Args:
      filename: feature table file path
  Returns:
      x: feature vector
      terms: candidate terms
      polarities: opinion polarity per term
code: def load_terms_and_generate_features(self, filename):
      """
      load candidate terms with their basic features, Y labels and polarities from feature file
  
      Args:
          filename: feature table file path
      Returns:
          x: feature vector
          terms: candidate terms
          polarities: opinion polarity per term
      """
      print("\nLoading feature table...\n")
  
      terms, polarities = self.load_terms_and_polarities(filename)
  
      x, terms, polarities = self.generate_features(terms, polarities)
  
      return x, terms, polarities
  
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 9 for calling the RerankTerms obj's underlying member method code with example usage: obj.load_terms_and_generate_features(filename) and returns: x,  terms,  polarities