AttributeError: 'numpy.random.mtrand.RandomState' object has no attribute 'integers' #284

AttributeError                            Traceback (most recent call last)
<ipython-input-6-54024aaf20f0> in <module>()
      3                                         algo= tpe.suggest, max_evals= 5,
      4                                         trials= Trials(),
----> 5                                         notebook_name='Deep learning GridSearch')
      6     xtr, ytr, xte, yte= data()
      7 

~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp)
     67                                      notebook_name=notebook_name,
     68                                      verbose=verbose,
---> 69                                      keep_temp=keep_temp)
     70 
     71     best_model = None

~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp)
    137              trials=trials,
    138              rstate=np.random.RandomState(rseed),
--> 139              return_argmin=True),
    140         get_space()
    141     )

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
    553             show_progressbar=show_progressbar,
    554             early_stop_fn=early_stop_fn,
--> 555             trials_save_file=trials_save_file,
    556         )
    557 

~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)
    686             show_progressbar=show_progressbar,
    687             early_stop_fn=early_stop_fn,
--> 688             trials_save_file=trials_save_file,
    689         )
    690 

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
    584 
    585     # next line is where the fmin is actually executed
--> 586     rval.exhaust()
    587 
    588     if return_argmin:

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in exhaust(self)
    362     def exhaust(self):
    363         n_done = len(self.trials)
--> 364         self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
    365         self.trials.refresh()
    366         return self

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in run(self, N, block_until_done)
    277                     # processes orchestration
    278                     new_trials = algo(
--> 279                         new_ids, self.domain, trials, self.rstate.integers(2 ** 31 - 1)
    280                     )
    281                     assert len(new_ids) >= len(new_trials)

AttributeError: 'numpy.random.mtrand.RandomState' object has no attribute 'integers'

def data():
    (xtr, ytr), (xte, yte)= mnist.load_data()
    xtr= xtr.reshape(60000, 784); xtr= xtr.astype('float32')
    xte= xte.reshape(10000, 784); xte= xte.astype('float32')
    xtr/= 255; xte/= 255
    nb_classes= 10
    ytr= np_utils.to_categorical(ytr, nb_classes)
    yte= np_utils.to_categorical(yte, nb_classes)
    return xtr, ytr, xte, yte

def create_model(xtr, ytr, xte, yte):
    # returns a dictionary of loss, status and model
    model= Sequential()
    # 1st layer:
    model.add(Dense(512, input_shape= (784,), activation= 'relu'))  # equivalently input_dim= 784    
    model.add(Dropout({{uniform(0,1)}}))
    
    # 2nd layer:
    # hyperparameter = {{choice([...])}}
    model.add(Dense(units= {{choice([256,5125,1024])}}, 
                    activation= {{choice(['relu', 'sigmoid'])}}))
    model.add(Dropout({{uniform(0,1)}}))
    
    # 3rd layer:
    if {{choice(['three','four'])}} == 'four':
        model.add(Dense(100))
        # choice between 2 different types of Dense(100) layers:
        model.add({{choice([Dropout(0.5), Activation('linear')])}})
        model.add(Activation('relu'))
    
    # 4th layer:
    model.add(Dense(10, activation= 'softmax'))
    
    model.compile(loss='categorical_crossentropy', 
                  optimizer= {{choice(['rmsprop', 'adam', 'SGD'])}},
                 metrics=['accuracy'])
    
    # Model fit:
    result= model.fit(xtr, ytr, batch_size= {{choice([64, 128])}},
                     epochs= 2, verbose= 2, validation_split= 0.1)
    validation_acc= np.amax(result.history['val_acc'])
    print('Best validation accuracy of epoch:', validation_acc)
    return {'Loss:', -validation_acc, 'status:', STATUS_OK, 'model:',model}

if __name__ == '__main__':
    best_run, best_model= optim.minimize(model= create_model, data= data,
                                        algo= tpe.suggest, max_evals= 5,
                                        trials= Trials(),
                                        notebook_name='Deep learning GridSearch')
    xtr, ytr, xte, yte= data()
    
    print('Evaluation of best performing model:')
    print(best_model.evaluate(xte, yte))
    
    print('Optimal hyperparameter choice:')
    print(best_run)