公开(公告)号：US12299503B1

公开(公告)日：2025-05-13

申请号：US19000697

申请日：2024-12-24

Applicant: SAS Institute Inc.

Inventor： Xindian Long ,  Liping Cai ,  Xingqi Du ,  Steven Eric Krueger ,  Joshua David Griffin ,  Yan Xu ,  Scott Russell Pope ,  Lawrence Edmund Lewis

IPC: G06F9/54 ,  G06N20/00

Abstract: A system, method, and computer-program product includes receiving, by a worker process, a plurality of chunks of data from a client process; deriving, by the worker process, an input pattern for feeding the plurality of chunks of data to a machine learning model; caching, by the worker process, a subset of data elements of the plurality of chunks of data specified by the input pattern based on a data caching policy; and training the machine learning model by feeding the subset of data elements cached by the worker process and a remainder of data elements in the plurality of chunks of data when requested by the input pattern.

公开(公告)号：US20180240041A1

公开(公告)日：2018-08-23

申请号：US15822462

申请日：2017-11-27

Applicant: SAS Institute Inc.

Inventor： Patrick Nathan Koch ,  Brett Alan Wujek ,  Oleg Borisovich Golovidov ,  Steven Joseph Gardner ,  Joshua David Griffin ,  Scott Russell Pope ,  Yan Xu

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F16/24578 ,  G06N3/08 ,  G06N3/126 ,  G06N5/003 ,  G06N20/10

Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.

公开(公告)号：US20190258697A1

公开(公告)日：2019-08-22

申请号：US16398690

申请日：2019-04-30

Applicant: SAS Institute Inc.

Inventor： Xinmin Wu ,  Tao Wang ,  Scott Russell Pope

IPC: G06F17/18

Abstract: A computing device computes a quantile value for a variable value extracted from an event block object by computing a bin number for the variable value. If the computed bin number is between a before bin number and an after bin number computed for a quantile, the quantile is identified. Frequency data is updated to include the extracted variable value as a key value. A frequency value associated with the key value indicates a number of occurrences of the variable value in previously processed data. A cumulative rank value of the identified quantile is updated. A quantile adjustment value is computed based on a comparison between the variable value and a current quantile value of the identified quantile. An updated quantile value associated with the identified quantile is computed using the updated frequency data, the computed quantile adjustment value, and the updated cumulative rank value of the identified quantile.

公开(公告)号：US10360517B2

公开(公告)日：2019-07-23

申请号：US15822462

申请日：2017-11-27

Applicant: SAS Institute Inc.

Inventor： Patrick Nathan Koch ,  Brett Alan Wujek ,  Oleg Borisovich Golovidov ,  Steven Joseph Gardner ,  Joshua David Griffin ,  Scott Russell Pope ,  Yan Xu

IPC: G06N20/00 ,  G06F16/2457 ,  G06N3/08 ,  G06N3/12 ,  G06N5/00 ,  G06N20/10

Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.

公开(公告)号：US12282807B1

公开(公告)日：2025-04-22

申请号：US19000691

申请日：2024-12-23

Applicant: SAS Institute Inc.

Inventor： Xindian Long ,  Liping Cai ,  Xingqi Du ,  Steven Eric Krueger ,  Joshua David Griffin ,  Yan Xu ,  Scott Russell Pope ,  Lawrence Edmund Lewis

IPC: G06F9/54 ,  G06N20/00

Abstract: A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.

公开(公告)号：US12271795B1

公开(公告)日：2025-04-08

申请号：US19000713

申请日：2024-12-24

Applicant: SAS Institute Inc.

Inventor： Xindian Long ,  Liping Cai ,  Xingqi Du ,  Steven Eric Krueger ,  Joshua David Griffin ,  Yan Xu ,  Scott Russell Pope ,  Lawrence Edmund Lewis

IPC: G06N20/00

Abstract: A system, method, and computer-program product includes selecting, by a controller node, a plurality of hyperparameter search points from a hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models for a target number of epochs using the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models during the target number of epochs; and removing, by the controller node, one or more underperforming hyperparameter search points from the plurality of hyperparameter search points according to a pre-defined performance metric ranking criterion associated with the plurality of performance metrics.