nonLincausality is not running on CPU machine

[RakeshSharma21]

Hello mrosol,

I am trying to run the following code on the CPU machine. but getting the error pasted below the code. Please help to resolve this error. I am using python 3.7 and latest version of nonLincausality.

import nonlincausality as nlc
import numpy as np
lags = [10,20,30]
n_obs = 53
result_of_non_causality=[]
for col in columns_candidate_for_x:
initial_list=[col]
print(f'non-causality for {initial_list}')
initial_list.append('Nifty_Price')
col_nifty_df=sentiment_all_exch_rate_all_indices_normal_Df[initial_list]
df_train, df_test = col_nifty_df[0:-n_obs], col_nifty_df[-n_obs:]
results = nlc.nonlincausalityGRU(x=np.array(df_train), maxlag=lags, GRU_layers=2, GRU_neurons=[25,25], Dense_layers=2, Dense_neurons=[100, 100], x_test=np.array(df_test), run=2, add_Dropout=True, Dropout_rate=0.01, epochs_num=[100], learning_rate=[0.001], batch_size_num=128, verbose=False, plot=False)
for lag in lags:
single_record={}
single_record['X_value']=col

p_value = results[lag].p_value
test_statistic = results[lag].test_statistic

best_errors_X = results[lag].best_errors_X
best_errors_XY = results[lag].best_errors_XY
cohens_d = np.abs(
    (np.mean(np.abs(best_errors_X)) - np.mean(np.abs(best_errors_XY)))
    / np.std([best_errors_X, best_errors_XY])
)
single_record['lag']=lag
single_record['test_statistic']=test_statistic
single_record['p_value']=p_value
single_record['cohens_d']=cohens_d
result_of_non_causality.append(single_record)

del results

---

KeyError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_22704\273327593.py in
10 col_nifty_df=sentiment_all_exch_rate_all_indices_normal_Df[initial_list]
11 df_train, df_test = col_nifty_df[0:-n_obs], col_nifty_df[-n_obs:]
---> 12 results = nlc.nonlincausalityGRU(x=np.array(df_train), maxlag=lags, GRU_layers=2, GRU_neurons=[25,25], Dense_layers=2, Dense_neurons=[100, 100], x_test=np.array(df_test), run=2, add_Dropout=True, Dropout_rate=0.01, epochs_num=[100], learning_rate=[0.001], batch_size_num=128, verbose=False, plot=False)
13 for lag in lags:
14 single_record={}

~\Anaconda3\envs\thesispy37\lib\site-packages\nonlincausality\nonlincausality.py in nonlincausalityGRU(x, maxlag, GRU_layers, GRU_neurons, Dense_layers, Dense_neurons, x_test, run, z, z_test, add_Dropout, Dropout_rate, epochs_num, learning_rate, batch_size_num, regularization, reg_alpha, callbacks, verbose, plot)
713 input_layer = Input((data_shape[1], data_shape[2]))
714
--> 715 layers_dense = Dense(Dense_neurons[0], activation="relu")(input_layer)
716 # Adding Dropout
717 if add_Dropout:

~\Anaconda3\envs\thesispy37\lib\site-packages\nonlincausality\nonlincausality.py in run_nonlincausality(network_architecture, x, maxlag, Network_layers, Network_neurons, Dense_layers, Dense_neurons, x_test, run, z, z_test, add_Dropout, Dropout_rate, epochs_num, learning_rate, batch_size_num, regularization, reg_alpha, callbacks, verbose, plot, functin_type)
258 # Appending RSS, models, history of training and prediction errors to results object
259 result_lag.append_results(
--> 260 sum(error_X ** 2),
261 sum(error_XY ** 2),
262 model_X,

~\Anaconda3\envs\thesispy37\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb

~\Anaconda3\envs\thesispy37\lib\site-packages\keras\engine\training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False

KeyError: in user code:

File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\engine\training.py", line 1249, in train_function  *
    return step_function(self, iterator)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\engine\training.py", line 1233, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\engine\training.py", line 1222, in run_step  **
    outputs = model.train_step(data)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\engine\training.py", line 1027, in train_step
    self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 527, in minimize
    self.apply_gradients(grads_and_vars)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 1140, in apply_gradients
    return super().apply_gradients(grads_and_vars, name=name)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 634, in apply_gradients
    iteration = self._internal_apply_gradients(grads_and_vars)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 1169, in _internal_apply_gradients
    grads_and_vars,
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 1217, in _distributed_apply_gradients_fn
    var, apply_grad_to_update_var, args=(grad,), group=False
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 1213, in apply_grad_to_update_var  **
    return self._update_step(grad, var)
File "C:\Users\00006262\Anaconda3\envs\thesispy37\lib\site-packages\keras\optimizers\optimizer_experimental\optimizer.py", line 217, in _update_step
    f"The optimizer cannot recognize variable {variable.name}. "

KeyError: 'The optimizer cannot recognize variable gru_8/gru_cell_8/kernel:0. This usually means you are trying to call the optimizer to update different parts of the model separately. Please call `optimizer.build(variables)` with the full list of trainable variables before the training loop or use legacy optimizer `tf.keras.optimizers.legacy.{self.__class__.__name__}.'