- #OPTIMAL LATIN HYPERCUBE SAMPLING UPDATE THE MODEL#
- #OPTIMAL LATIN HYPERCUBE SAMPLING HOW TO FIND WHICH#
OptimumLHS( n 10, k 2, maxSweeps 2, eps 0.1, verbose FALSE)Before joining SAS, I was unaware of the possibilities around hyperparameter autotuning. This function uses the Columnwise Pairwise ( CP) algorithm to generate an optimal design with respect to the S optimality criterion. Draws a Latin Hypercube Sample from a set of uniform distributions for use in creating a Latin Hypercube Design.
Hyperparameter autotuning is now one of my favorite machine learning capabilities ( right next to feature machines). While SAS searches the hyperparameter space in the background, I am free to pursue other work. In SAS Visual Data Mining and Machine Learning, adding hyperparameter autotuning only requires an extra line of code or a button click. This effort created a better performing model, but it still took up a large chunk of my time.
Optimal Latin Hypercube Sampling How To Find Which
Distribution is not optimal, for our purposes.Training and tuning a model are two different tasks. This com-bination resulted in a much faster calculation of optimized ushing congurations in aSample the factorial design, using an implementation of LHS-MDU in SAS/IML. As a result of an approximation using latin hypercube sampling and universal Kriging metamodeling with quadratic polynomial, the number of simulations could be greatly decreased by a factor of 106, compared with a full factorial design. In general, they found that the.rations. What are hyperparameters?They included a fixed maximin Latin hypercube design at the full sample size as a comparator to the sequential strategy designs. Therefore today, I will review what hyperparameters are, how to find which combination of hyperparameters to use, and how to get started using hyperparameter autotuning on SAS Viya.
Grid searchGrid search was my pre-SAS approach to hyperparameter autotuning. These methods include a grid search, a random search, Latin Hypercube Sampling (LHS), a Genetic Algorithm (GA), and a Bayesian search. How can I find which combination of hyperparameters to use?There are various methods used to search the hyperparameter space for improved model performance. For example, maximum tree depth is a hyperparameter in a decision tree model and the number of hidden layers is a hyperparameter in a neural network. In model tuning, you strive to create the best model configuration for the task at hand. For instance, coefficient values are the parameters in a regression and splitting rules are parameters in a decision tree.
In cross-over events, various hyperparameter combinations are created from the hyperparameter values of the parents. Next these configurations can experience cross-over or random mutations to create new configurations. By default, the number of Latin Hypercube Sampling evaluations used to initialize the Genetic Algorithm is fewer than the number of evaluations used when Latin Hypercube Sampling is used as a search method.
Optimal Latin Hypercube Sampling Update The Model
The surrogate models generate new configurations to evaluate, which are then used to iteratively update the model. These are probabilistic statistical models that measure the similarity between points to predict unseen points within a finite collection of variables that have a multivariate normal distribution. Latin Hypercube Sampling is used to initialize a Kriging surrogate model (also known as a Gaussian process surrogate regression) to approximate the objective function. Bayesian searchBayesian search uses a surrogate model to improve upon Latin Hypercube Sampling. Following, the new configurations are evaluated and the best configurations can serve as parents for a new generation of configurations in this iterative process. An additional Generating Set Search (GSS) step performs local perturbations of the best hyperparameter combinations to add more potential high-performing configurations.
A system that can run all of the evaluations in parallel would find that grid search, random search, and Latin Hypercube Sampling would run the fastest. With the Genetic Algorithm and Bayesian search, the configuration evaluations within each iteration can be done in parallel, but better configurations can be achieved through learning across sequential iterations.Ultimately which algorithm runs fastest depends on the resources used to run the algorithm. It is important to note that for grid search, random search, and Latin Hypercube Sampling, all configuration evaluations can be done in parallel, but no learning occurs.
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