text: import numpy as np
  import keras.backend as K
  from utils import load_cache, group_label, shuffle_idxs, score_reshape, get_lr, set_lr
  
  from datagen import TrainingData
  from score import ScoreGen, FeatureGen
  from model import build_model 
      
  def test(clf, data, y_, n_splits=2):
      
      from sklearn.model_selection import StratifiedKFold
      from sklearn.metrics import f1_score, accuracy_score    
      
      skfold = StratifiedKFold(n_splits=n_splits)
      import pandas
      
      
      y_.index = list(range(len(y_)))
      for train_idx, test_idx in skfold.split(data, y_):
          y = clf.predict(data[test_idx])
          
          f1, acc = f1_score(y_[test_idx], y, average='weighted'), accuracy_score(y_[test_idx], y)
          print(f'f1: {f1}, acc: {acc}')    
      
  if __name__ == '__main__':
      train, y_, _, _ = load_cache('../../')
      model, branch_model, head_model = build_model(1e-6, 0)
      
      test(model, train, y_)
      
  