pwollstadt / IDTxl

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory.
http://pwollstadt.github.io/IDTxl/
GNU General Public License v3.0
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Discrete Data Issue #58

Closed Xirailuyo closed 3 years ago

Xirailuyo commented 3 years ago

I seem to be getting the following error given a 0,1 numpy 2d array. I have verified that the data is a 463x14 shaped numpy array, with only 0s and 1s. I put it through the data class given by IDTxl and receive this error when running network_analysis. Thank you for the assistance.


AssertionError Traceback (most recent call last)

in () 1 # IDTxl analysis ----> 2 results = network_analysis.analyse_network(settings=settings, data=idtxl_data) /home/xirailuyo/Desktop/Network/IDTxl/idtxl/bivariate_te.py in analyse_network(self, settings, data, targets, sources) 153 .format(t, targets)) 154 res_single = self.analyse_single_target( --> 155 settings, data, targets[t], sources[t]) 156 results.combine_results(res_single) 157 /home/xirailuyo/Desktop/Network/IDTxl/idtxl/bivariate_te.py in analyse_single_target(self, settings, data, target, sources) 273 # Main algorithm. 274 print('\n---------------------------- (1) include target candidates') --> 275 self._include_target_candidates(data) 276 print('\n---------------------------- (2) include source candidates') 277 self._include_source_candidates(data) /home/xirailuyo/Desktop/Network/IDTxl/idtxl/network_inference.py in _include_target_candidates(self, data) 426 -self.settings['tau_target']).tolist() 427 candidates = self._define_candidates(procs, samples) --> 428 sources_found = self._include_candidates(candidates, data) 429 430 # If no candidates were found in the target's past, add at least one /home/xirailuyo/Desktop/Network/IDTxl/idtxl/network_inference.py in _include_candidates(self, candidate_set, data) 118 var1=cand_real, 119 var2=self._current_value_realisations, --> 120 conditional=self._selected_vars_realisations) 121 except ex.AlgorithmExhaustedError as aee: 122 # The algorithm cannot continue here, so /home/xirailuyo/Desktop/Network/IDTxl/idtxl/estimator.py in estimate_parallel(self, n_chunks, re_use, **data) 328 v, data[v].shape[0], chunk_size)) 329 chunk_data[v] = data[v] --> 330 results[i] = self.estimate(**chunk_data) 331 idx_1 = idx_2 332 idx_2 += chunk_size /home/xirailuyo/Desktop/Network/IDTxl/idtxl/estimators_jidt.py in estimate(self, var1, var2, conditional, return_calc) 568 # Return value will be just the estimate if return_calc is False, 569 # or estimate plus the JIDT MI calculator if return_calc is True: --> 570 return self.mi_calc.estimate(var1, var2, return_calc) 571 else: 572 assert(conditional.size != 0), 'Conditional Array is empty.' /home/xirailuyo/Desktop/Network/IDTxl/idtxl/estimators_jidt.py in estimate(self, var1, var2, return_calc) 744 745 # Discretise variables if requested. --> 746 var1, var2 = self._discretise_vars(var1, var2) 747 748 # Then collapse any mulitvariates into univariate arrays: /home/xirailuyo/Desktop/Network/IDTxl/idtxl/estimators_jidt.py in _discretise_vars(self, var1, var2, conditional) 220 elif self.settings['discretise_method'] == 'none': 221 assert issubclass(var1.dtype.type, np.integer), ( --> 222 'Var1 is not an integer numpy array. ' 223 'Discretise data to use this estimator.') 224 assert issubclass(var2.dtype.type, np.integer), ( AssertionError: Var1 is not an integer numpy array. Discretise data to use this estimator.
jlizier commented 3 years ago

It looks like python is not recognising that you're supplying an array of integers. I would assume your array is typed as floats instead, even though you're assigning 0's and 1's. Could you check that please? Then try the following:

  1. Try forcing the type of the array data to be integers. That is the recommended solution.
  2. Otherwise a more hacky fix would be to set 'discretise_method' to 'equal', 'alph1' to 2 and 'alph2' to 2, and IDTxl will make a conversion which should keep the right integer values (so long as each variable has samples of both 0's and 1's)
  3. Finally, send a minimal example to reproduce this error.

--joe

Xirailuyo commented 3 years ago

Thank you for the response. I needed to turn normalization off with the Data function.

jlizier commented 3 years ago

Ok, thanks @Xirailuyo - from what I gather, your use of this option was changing your discrete data into floats (before passing to analyse_network) without you realising it. Closing, not a bug.