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Evaluating dataset:ricci
Sensitive attribute:Race
Algorithm: SVM
supported types: {'numerical', 'numerical-binsensitive'}
C:\Users\kevin\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\metrics_classification.py:1221: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
C:\Users\kevin\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\metrics_classification.py:1221: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use zero_division parameter to control this behavior.
C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmps24_t_kk"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmp86i67yum"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmp3q3uqt1g"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
The text was updated successfully, but these errors were encountered:
using python 3.7 on Windows 10. installed with pip3 utilized
Evaluating dataset:ricci
Sensitive attribute:Race
Algorithm: SVM
supported types: {'numerical', 'numerical-binsensitive'}
C:\Users\kevin\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\metrics_classification.py:1221: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use
zero_division
parameter to control this behavior._warn_prf(average, modifier, msg_start, len(result))
C:\Users\kevin\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\metrics_classification.py:1221: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use
zero_division
parameter to control this behavior.C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmps24_t_kk"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmp86i67yum"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
C:\Users\kevin\Documents\GitHub\fairness-comparison-master\fairness\algorithms\kamishima\KamishimaAlgorithm.py:99: UserWarning: loadtxt: Empty input file: "C:\Users\kevin\AppData\Local\Temp\tmp3q3uqt1g"
m = numpy.loadtxt(output_name)
run for parameters {} failed: too many indices for array
The text was updated successfully, but these errors were encountered: