module ml.competitions
#
Short summary#
module ensae_projects.ml.competitions
Compute metrics in for a competition
Functions#
function |
truncated documentation |
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Compute the AUC. |
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Compute the AUC. |
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Compute the AUC. |
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adapt the tempate available at evaluate.py … |
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adapt the tempate available at evaluate.py … |
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Wraps the function following the guidelines User_Building a Scoring Program for a Competition. … |
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Wraps the function following the guidelines User_Building a Scoring Program for a Competition. … |
Documentation#
Compute metrics in for a competition
- ensae_projects.ml.competitions.AUC(answers, scores)#
Compute the AUC.
- Parameters:
answers – expected answers 0 (false), 1 (true)
scores – score obtained for class 1
- Returns:
number
- ensae_projects.ml.competitions.AUC_multi(answers, scores, ignored=None)#
Compute the AUC.
- Parameters:
answers – expected answers class as a string
scores – prediction and score (class, score)
ignored – ignored class
- Returns:
number
- ensae_projects.ml.competitions.AUC_multi_multi(nb, answers, scores, ignored=None)#
Compute the AUC.
- Parameters:
nb – number of observations
answers – expected answers, list of tuple of classes as a string
scores – prediction and score (class, score)
ignored – ignored class
- Returns:
number
Dummy expected classes (both classes):
endettement 4.0 surendettement 4.0 surendettement 4.0 surendettement 4.0
Dummy predicted answers:
2.0 endettement 0.48775936896183714 0.5033579692108108 5.0 microcredit social 0.16592396695909017 0.8643847837801871 5.0 microcredit personnel 0.7962830470795325 0.6233706526012659 3.0 impayes 0.17370233487556486 0.779432954126955
- ensae_projects.ml.competitions.main_codalab_wrapper_binary_classification(fct, metric_name, argv, truth_file='truth.txt', submission_file='answer.txt', output_file='scores.txt')#
adapt the tempate available at evaluate.py
- ensae_projects.ml.competitions.main_codalab_wrapper_multi_classification(fct, variables_name, argv, truth_file='truth.txt', submission_file='answer.txt', output_file='scores.txt')#
adapt the tempate available at evaluate.py
- ensae_projects.ml.competitions.private_codalab_wrapper_binary_classification(fct, metric_name, fold1, fold2, f1='answer.txt', f2='answer.txt', output='scores.txt', use_print=False)#
Wraps the function following the guidelines User_Building a Scoring Program for a Competition. It replicates the example available at competition-examples/hello_world.
- Parameters:
fct – function to wrap
metric_name – metric name
fold1 – folder which contains the data for folder containing the truth
fold2 – folder which contains the data for folder containing the data
f1 – filename for the truth
f2 – filename for the produced answers
output – produces an output with the expected results
use_print – display intermediate results
- Returns:
metric
- ensae_projects.ml.competitions.private_codalab_wrapper_multi_classification(fct, variables_name, fold1, fold2, f1='answer.txt', f2='answer.txt', output='scores.txt', use_print=False, ignored=None)#
Wraps the function following the guidelines User_Building a Scoring Program for a Competition. It replicates the example available at competition-examples/hello_world.
- Parameters:
fct – function to wrap
variables_name – variables names
fold1 – folder which contains the data for folder containing the truth
fold2 – folder which contains the data for folder containing the data
f1 – filename for the truth
f2 – filename for the produced answers
output – produces an output with the expected results
use_print – display intermediate results
ignored – ignored labels
- Returns:
metric