pytorch
7b7775be - feature_segmented_histogram_binning_calibration

Commit
3 years ago
feature_segmented_histogram_binning_calibration Summary: We implement a hierarchical fine grained binning structure, with the top level corresponding to different feature segments and bottom level corresponding to different range of ECTR. The model is designed to be general enough to perform segmented calibration on any useful feature Test Plan: buck test dper3/dper3/modules/calibration/tests:calibration_test -- test_histogram_binning_calibration_by_feature buck test dper3/dper3_models/ads_ranking/model_impl/mtml/tests:mtml_lib_test -- test_multi_label_dependent_task_with_histogram_binning_calibration_by_feature e2e test: buck test dper3/dper3_models/ads_ranking/tests:model_paradigm_e2e_tests -- test_sparse_nn_histogram_binning_calibration_by_feature buck test dper3/dper3_models/ads_ranking/tests:model_paradigm_e2e_tests -- test_mtml_with_dependent_task_histogram_binning_calibration_by_feature All tests passed Canary packages: Backend -> aml.dper2.canary:e0cd05ac9b9e4797a94e930426d76d18 Frontend -> ads_dper3.canary:55819413dd0f4aa1a47362e7869f6b1f Test FBL jobs: **SparseNN** ctr mbl feed f255676727 inline cvr f255677216 **MTML regular task** offsite cvr f255676719 **MTML dependent task** mobile cvr f255677551 **DSNN for AI models** ai oc f255730905 **MIMO for both AI DSNN part and AF SNN part** mimo ig f255683062 Reviewed By: zhongyx12 Differential Revision: D25043060 fbshipit-source-id: 8237cad41db66a09412beb301bc45231e1444d6b
Author
Tiehang Tim Duan
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