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)

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)

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)

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')

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')

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)

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)

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