code: def evaluate(self, model, x_test, y_test, terms):
      report = {}
      predictions = model.predict(x_test, verbose=0)
      tp = 0
      fp = 0
      tn = 0
      fn = 0
  
      for i, prediction in enumerate(predictions):
          y_true = y_test[i][0]
  
          if prediction[0] > self.threshold:
              y_pred = 1
          else:
              y_pred = 0
  
          report[terms[i]] = (prediction[0], y_pred, y_true)
  
          if y_pred == 1:
              if y_true == 1:
                  tp = tp + 1
              else:
                  fp = fp + 1
          elif y_true == 0:
              tn = tn + 1
          else:
              fn = fn + 1
  
      prec = 100 * tp / (tp + fp)
      rec = 100 * tp / (tp + fn)
      f1 = 2 * (prec * rec) / (prec + rec)
  
      return (prec, rec, f1), report
  
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 12 for calling the RerankTerms obj's underlying member method code with example usage: obj.evaluate(model, x_test, y_test, terms) and returns: prec,  rec,  f1,  report