mrosol / nonlincausality Goto Github PK

Hi!

Thank you for sharing the code, this is really impressive.
Is it possible to extract or look into the assumed coefficients for each lag used within fitting the model?

Best regards

In your paper, you showed results for AR and GRBF NN models.
I was wondering if the GRBF architecture is included in this library? Based on NN_architecture function, it seems like only LSTMs GRU or standard MLP (dense with ReLU) are available.

Hi Maciej,

thanks for interesting package. There is an error when any of example usage is run:

TypeError: run_nonlincausality() missing 3 required positional arguments: 'verbose', 'plot', and 'functin_type'

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__}.'

The three argument is not aligned to the definition of run_nonlincausality and will cause the following issue:

TypeError: run_nonlincausality() missing 3 required positional arguments: 'verbose', 'plot', and 'functin_type'

Suggestion:

verbose=verbose,
plot=plot,
function_type='LSTM'

The same thing should be fixed in the definitions of other neural network types.

I was trying the example code. But I got this error.

run_nonlincausality() missing 2 required positional arguments: 'plot' and 'functin_type'

Hi, thanks for open sourcing this. I am trying to understand how to use your code by running the example.py but getting an error. Seems something is broken in the function definitions/usage. Please check.

TypeError Traceback (most recent call last)
Cell In [68], line 41
38 data_train = data[:7000, :]
39 data_test = data[7000:, :]
---> 41 results = nlc.nonlincausalityMLP(
42 x=data_train,
43 maxlag=lags,
44 Dense_layers=2,
45 Dense_neurons=[100, 100],
46 x_test=data_test,
47 run=1,
48 add_Dropout=True,
49 Dropout_rate=0.01,
50 epochs_num=[50, 100],
51 learning_rate=[0.001, 0.0001],
52 batch_size_num=128,
53 verbose=True,
54 plot=True,
55 )
57 #%% Example of obtaining the results
58 for lag in lags:

File ~/anaconda3/envs/nlctest/lib/python3.8/site-packages/nonlincausality/nonlincausality.py:817, in nonlincausalityMLP(x, maxlag, Dense_layers, Dense_neurons, x_test, run, z, z_test, add_Dropout, Dropout_rate, epochs_num, learning_rate, batch_size_num, verbose, plot)
743 def nonlincausalityMLP(
744 x,
745 maxlag,
(...)
758 plot=False,
759 ):
760 """
761 Parameters
762 ----------
(...)
815
816 """
--> 817 results = run_nonlincausality(
818 MLP_architecture,
819 x,
820 maxlag,
821 None,
822 None,
823 Dense_layers,
824 Dense_neurons,
825 x_test,
826 run,
827 z,
828 z_test,
829 add_Dropout,
830 Dropout_rate,
831 epochs_num,
832 learning_rate,
833 batch_size_num,
834 verbose,
835 plot,
836 "MLP",
837 )
839 return results

TypeError: run_nonlincausality() missing 3 required positional arguments: 'verbose', 'plot', and 'functin_type'

Hi,

Base on the example, I want to add Z into X's generation, making Y & Z causing X at the same time.

How do I use more than 2 columns (X,Y,Z) to predict X, testing whether Y & Z are the compouned causality of X?

Thank you.