module ml.competitions

Short summary

module ensae_projects.ml.competitions

Compute metrics in for a competition

source on GitHub

Functions

function truncated documentation
AUC Compute the AUC.
AUC_multi Compute the AUC.
AUC_multi_multi Compute the AUC.
main_codalab_wrapper_binary_classification adapt the tempate available at evaluate.py
main_codalab_wrapper_multi_classification adapt the tempate available at evaluate.py
private_codalab_wrapper_binary_classification Wraps the function following the guidelines User_Building a Scoring Program for a Competition. …
private_codalab_wrapper_multi_classification Wraps the function following the guidelines User_Building a Scoring Program for a Competition. …

Documentation

Compute metrics in for a competition

source on GitHub

ensae_projects.ml.competitions.AUC(answers, scores)[source]

Compute the AUC.

Parameters:
  • answers – expected answers 0 (false), 1 (true)
  • scores – score obtained for class 1
Returns:

number

source on GitHub

ensae_projects.ml.competitions.AUC_multi(answers, scores, ignored=None)[source]

Compute the AUC.

Parameters:
  • answers – expected answers class as a string
  • scores – prediction and score (class, score)
  • ignored – ignored class
Returns:

number

source on GitHub

ensae_projects.ml.competitions.AUC_multi_multi(nb, answers, scores, ignored=None)[source]

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

source on GitHub

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')[source]

adapt the tempate available at evaluate.py

source on GitHub

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')[source]

adapt the tempate available at evaluate.py

source on GitHub

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)[source]

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

source on GitHub

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)[source]

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

source on GitHub