SEARCHING AN OPTIMAL COMBINATION OF HYPERPARAMETERS FOR A MACHINE LEARNING MODEL
According to one aspect, a method for searching, using a computer, for an optimal combination of hyperparameters allows an automatic learning model to be defined. The method includes several hyperparameter combination tests, each hyperparameter combination test including cross-validation, using a validation data set, the cross-validation defining several performance tests, each hyperparameter combination test being stopped if a performance test score is lower than a best score, the cross-validation further including updating the best score when all of the performance scores computed for this cross-validation are higher than the best score, the updated best score then corresponding to the lowest performance score from among the set of performance scores computed for this cross-validation.
1 . A method for searching, using a computer, for an optimal combination of hyperparameters allowing an automatic learning model to be defined, the method comprising:
performing several hyperparameter combination tests, each hyperparameter combination test including cross-validation using a validation data set, the cross-validation defining several performance tests on at least one validation data subset, each performance test including:
computing a performance score;
comparing the computed performance score with a best score; and
stopping testing of this hyperparameter combination in response to the computed performance score being lower than the best score;
each cross-validation further comprising updating the best score in response to all of the performance scores computed for this cross-validation being higher than the best score, the updated best score then corresponding to a lowest performance score from among the performance scores computed for this cross-validation; and
defining the optimal combination of hyperparameters for the automatic learning model, the optimal combination of hyperparameters for the automatic learning model corresponding to the optimal combination of hyperparameters that defined a last best score once all of the hyperparameter combination tests have been carried out.
2 . The method according to claim 1 , further comprising:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
3 . The method according to claim 1 , wherein the automatic learning model is selected from among a linear model, a decision tree, or an artificial neural network.
4 . The method according to claim 3 , wherein each hyperparameter combination is sought using a grid search, a random search, or a Bayesian search.
5 . The method according to claim 3 , further comprising:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
6 . The method according to claim 1 , wherein each hyperparameter combination is sought using a grid search, a random search, or a Bayesian search.
7 . The method according to claim 6 , further comprising:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
8 . A non-transitory computer-readable media storing computer instructions for searching for an optimal combination of hyperparameters allowing an automatic learning model to be defined that, when executed by a processor, cause the processor to perform the steps of:
performing several hyperparameter combination tests, each hyperparameter combination test including cross-validation using a validation data set, the cross-validation defining several performance tests on at least one validation data subset, each performance test including:
computing a performance score;
comparing the computed performance score with a best score; and
stopping testing of this hyperparameter combination in response to the computed performance score being lower than the best score;
each cross-validation further comprising updating the best score in response to all of the performance scores computed for this cross-validation being higher than the best score, the updated best score then corresponding to a lowest performance score from among the performance scores computed for this cross-validation; and
defining the optimal combination of hyperparameters for the automatic learning model, the optimal combination of hyperparameters for the automatic learning model corresponding to the optimal combination of hyperparameters that defined a last best score once all of the hyperparameter combination tests have been carried out.
9 . The non-transitory computer-readable media according to claim 8 , storing further computer instructions that cause the processor to perform the steps of:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
10 . The non-transitory computer-readable media according to claim 8 , storing further computer instructions that cause the processor to select the automatic learning model from among a linear model, a decision tree, or an artificial neural network.
11 . The non-transitory computer-readable media according to claim 10 , storing further computer instructions that cause the processor to perform the steps of seeking each hyperparameter combination using a grid search, a random search, or a Bayesian search.
12 . The non-transitory computer-readable media according to claim 10 , storing further computer instructions that cause the processor to perform the steps of:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
13 . The non-transitory computer-readable media according to claim 8 , storing further computer instructions that cause the processor to perform the steps of seeking each hyperparameter combination using a grid search, a random search, or a Bayesian search.
14 . The non-transitory computer-readable media according to claim 13 , storing further computer instructions that cause the processor to perform the steps of:
carrying out the performance tests defined by the cross-validation in a given order; and
defining an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
15 . A computer system configured to search for an optimal combination of hyperparameters allowing an automatic learning model to be defined, the computer system comprising:
a non-transitory memory comprising instructions; and
a processor in communication with the non-transitory memory, wherein the processor executes the instructions to:
perform several hyperparameter combination tests, each hyperparameter combination test including cross-validation using a validation data set, the cross-validation defining several performance tests on at least one validation data subset, each performance test including:
computing a performance score;
comparing the computed performance score with a best score; and
stopping testing of this hyperparameter combination in response to the computed performance score being lower than the best score;
each cross-validation further comprising updating the best score in response to all of the performance scores computed for this cross-validation being higher than the best score, the updated best score then corresponding to a lowest performance score from among the performance scores computed for this cross-validation; and
define the optimal combination of hyperparameters for the automatic learning model, the optimal combination of hyperparameters for the automatic learning model corresponding to the optimal combination of hyperparameters that defined a last best score once all of the hyperparameter combination tests have been carried out.
16 . The computer system according to claim 15 , wherein the processor executes the instructions to:
carry out the performance tests defined by the cross-validation in a given order; and
define an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
17 . The computer system according to claim 15 , wherein the processor executes the instructions to select the automatic learning model from among a linear model, a decision tree, or an artificial neural network.
18 . The computer system according to claim 17 , wherein the processor executes the instructions to seek each hyperparameter combination using a grid search, a random search, or a Bayesian search.
19 . The computer system according to claim 17 , wherein the processor executes the instructions to:
carry out the performance tests defined by the cross-validation in a given order; and
define an order for the performance tests each time the best score is updated, with the defined order thus corresponding to an ascending order of the scores of the cross-validations used to update the best score.
20 . The computer system according to claim 15 , wherein the processor executes the instructions to seek each hyperparameter combination using a grid search, a random search, or a Bayesian search.