From 2bd6b9970ed7c3c0318b7738da37b537ba18f65f Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 24 Jun 2022 11:41:37 +0100 Subject: [PATCH 01/32] added FairGBM config examples --- examples/FairGBM/COMPAS.test | 2131 +++++++++++++ examples/FairGBM/COMPAS.train | 3149 +++++++++++++++++++ examples/FairGBM/README.md | 38 + examples/FairGBM/predict.conf | 11 + examples/FairGBM/predict_unconstrained.conf | 11 + examples/FairGBM/train.conf | 122 + examples/FairGBM/train_unconstrained.conf | 108 + 7 files changed, 5570 insertions(+) create mode 100644 examples/FairGBM/COMPAS.test create mode 100644 examples/FairGBM/COMPAS.train create mode 100644 examples/FairGBM/README.md create mode 100644 examples/FairGBM/predict.conf create mode 100644 examples/FairGBM/predict_unconstrained.conf create mode 100644 examples/FairGBM/train.conf create mode 100644 examples/FairGBM/train_unconstrained.conf diff --git a/examples/FairGBM/COMPAS.test b/examples/FairGBM/COMPAS.test new file mode 100644 index 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2 1.0 0.0 0.0 1742 1578 237 0.0 1.0 0.0 763 0.0 0.0 1.0 0.0 0.0 1.0 +0.0 1.0 0.0 0.0 0.0 25.0 0.0 -1.0 0.0 0.0 1.0 0.0 1.0 1 1.0 0.0 0.0 3361 553 74 0.0 1.0 0.0 1539 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 2.0 0.0 58.0 0.0 -23.0 0.0 0.0 1.0 1.0 0.0 5 0.0 1.0 0.0 2190 702 74 0.0 1.0 0.0 968 0.0 1.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 3.0 0.0 28.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1 1.0 0.0 0.0 2169 528 54 0.0 1.0 0.0 961 0.0 1.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 0.0 0.0 48.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 4 0.0 1.0 0.0 1345 2086 17 0.0 1.0 0.0 618 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 2.0 0.0 25.0 0.0 -1.0 0.0 1.0 0.0 1.0 0.0 1 1.0 0.0 0.0 437 725 319 0.0 1.0 0.0 207 0.0 1.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 0.0 0.0 41.0 0.0 -1.0 1.0 0.0 1.0 0.0 1.0 3 1.0 0.0 0.0 1523 63 33 0.0 1.0 0.0 700 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 0.0 0.0 7.0 0.0 27.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1 1.0 0.0 0.0 818 333 319 1.0 0.0 0.0 354 0.0 0.0 0.0 1.0 0.0 1.0 +0.0 0.0 0.0 1.0 0.0 24.0 0.0 -1.0 0.0 0.0 1.0 0.0 1.0 0 0.0 0.0 1.0 2348 1658 33 0.0 0.0 1.0 1049 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 2.0 0.0 25.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 1 1.0 0.0 0.0 335 980 54 0.0 0.0 1.0 173 0.0 1.0 0.0 0.0 0.0 1.0 +1.0 0.0 0.0 6.0 0.0 32.0 0.0 -1.0 1.0 1.0 0.0 1.0 0.0 2 1.0 0.0 0.0 104 990 319 0.0 1.0 0.0 54 0.0 0.0 0.0 1.0 0.0 1.0 +1.0 0.0 0.0 8.0 0.0 31.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 2 1.0 0.0 0.0 2665 1961 98 1.0 0.0 0.0 1188 0.0 0.0 0.0 1.0 0.0 1.0 +0.0 0.0 0.0 0.0 0.0 44.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 3 1.0 0.0 0.0 3527 1205 252 0.0 1.0 0.0 1618 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 0.0 0.0 2.0 0.0 29.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 1 1.0 0.0 0.0 2024 535 286 0.0 1.0 0.0 871 0.0 1.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 4.0 0.0 45.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 3 0.0 1.0 0.0 327 928 97 0.0 1.0 0.0 168 0.0 0.0 1.0 0.0 0.0 1.0 +1.0 1.0 0.0 3.0 0.0 38.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 3 1.0 0.0 0.0 2228 554 231 1.0 0.0 0.0 993 0.0 1.0 0.0 0.0 0.0 1.0 +1.0 0.0 0.0 0.0 0.0 20.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 0 0.0 0.0 1.0 1379 986 137 1.0 0.0 0.0 641 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 0.0 0.0 33.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 2 1.0 0.0 0.0 1546 2142 97 0.0 1.0 0.0 700 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 0.0 0.0 21.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 0 0.0 0.0 1.0 191 1963 36 0.0 1.0 0.0 99 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 1.0 0.0 30.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 2 1.0 0.0 0.0 526 1025 67 0.0 1.0 0.0 245 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 2.0 0.0 29.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 1 1.0 0.0 0.0 1250 2005 319 0.0 1.0 0.0 566 0.0 1.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 1.0 0.0 53.0 0.0 -2.0 0.0 0.0 1.0 0.0 1.0 5 0.0 1.0 0.0 1286 844 11 0.0 1.0 0.0 590 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 0.0 0.0 3.0 0.0 47.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 4 0.0 1.0 0.0 282 824 319 0.0 0.0 1.0 138 0.0 1.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 0.0 0.0 58.0 0.0 -1.0 0.0 0.0 1.0 0.0 1.0 5 0.0 1.0 0.0 842 2053 312 0.0 1.0 0.0 354 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 10.0 0.0 26.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 1 1.0 0.0 0.0 2731 1144 319 0.0 0.0 1.0 1211 0.0 0.0 0.0 0.0 1.0 1.0 +0.0 0.0 0.0 0.0 0.0 20.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 0 0.0 0.0 1.0 2793 1990 231 1.0 0.0 0.0 1249 1.0 0.0 0.0 0.0 0.0 1.0 +1.0 1.0 0.0 7.0 0.0 51.0 0.0 -1.0 1.0 1.0 0.0 0.0 1.0 4 0.0 1.0 0.0 942 1860 43 0.0 1.0 0.0 413 0.0 0.0 0.0 1.0 0.0 1.0 +0.0 1.0 0.0 4.0 0.0 53.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 5 0.0 1.0 0.0 3591 346 231 0.0 1.0 0.0 1648 0.0 0.0 1.0 0.0 0.0 1.0 +0.0 1.0 0.0 5.0 0.0 26.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1 1.0 0.0 0.0 1230 681 160 0.0 1.0 0.0 561 0.0 0.0 1.0 0.0 0.0 1.0 +0.0 1.0 0.0 12.0 0.0 57.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 5 0.0 1.0 0.0 2110 586 71 0.0 0.0 1.0 918 0.0 0.0 0.0 0.0 1.0 1.0 +0.0 1.0 0.0 0.0 0.0 42.0 0.0 -1.0 0.0 0.0 1.0 1.0 0.0 3 1.0 0.0 0.0 2215 1235 97 0.0 1.0 0.0 981 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 1.0 1.0 24.0 0.0 -23.0 0.0 1.0 0.0 0.0 1.0 0 0.0 0.0 1.0 3044 2221 236 0.0 1.0 0.0 1359 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 0.0 0.0 51.0 0.0 -1.0 0.0 0.0 1.0 0.0 1.0 4 0.0 1.0 0.0 2403 442 174 0.0 1.0 0.0 1075 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 0.0 0.0 1.0 0.0 38.0 0.0 -1.0 0.0 1.0 0.0 1.0 0.0 3 1.0 0.0 0.0 3517 1385 252 0.0 1.0 0.0 1613 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 1.0 0.0 35.0 0.0 -1.0 0.0 0.0 1.0 1.0 0.0 2 1.0 0.0 0.0 1066 1281 33 0.0 1.0 0.0 473 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 12.0 0.0 36.0 0.0 -26.0 0.0 1.0 0.0 0.0 1.0 3 1.0 0.0 0.0 2473 1248 319 0.0 1.0 0.0 1090 0.0 0.0 0.0 0.0 1.0 1.0 +1.0 0.0 0.0 0.0 0.0 25.0 0.0 -1.0 1.0 0.0 1.0 0.0 1.0 1 1.0 0.0 0.0 3369 1700 33 0.0 1.0 0.0 1539 1.0 0.0 0.0 0.0 0.0 1.0 +0.0 1.0 0.0 0.0 1.0 34.0 0.0 -1.0 0.0 1.0 0.0 0.0 1.0 2 1.0 0.0 0.0 2140 2297 14 0.0 1.0 0.0 940 1.0 0.0 0.0 0.0 0.0 1.0 diff --git a/examples/FairGBM/README.md b/examples/FairGBM/README.md new file mode 100644 index 000000000..b7ef31f59 --- /dev/null +++ b/examples/FairGBM/README.md @@ -0,0 +1,38 @@ +Binary Classification with Fairness Constraints +=============================================== +> Example of training a binary classifier with fairness constraints. + +Dataset source: https://github.com/propublica/compas-analysis/ + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + + +Training +-------- +Run the following command in this folder to train **FairGBM**: + +```bash +"../../lightgbm" config=train.conf +``` + +To train the vanilla LightGBM on the same data use: +```bash +"../../lightgbm" config=train_unconstrained.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder to compute test predictions for **FairGBM**: + +```bash +"../../lightgbm" config=predict.conf +``` + +To compute test predictions for the vanilla LightGBM use: +```bash +"../../lightgbm" config=predict_unconstrained.conf +``` diff --git a/examples/FairGBM/predict.conf b/examples/FairGBM/predict.conf new file mode 100644 index 000000000..4a3715ff1 --- /dev/null +++ b/examples/FairGBM/predict.conf @@ -0,0 +1,11 @@ +task = predict + +data = COMPAS.test + +has_header=True + +label_column = name:two_year_recid + +input_model= FairGBM_model.txt + +output_result = FairGBM_predictions.txt diff --git a/examples/FairGBM/predict_unconstrained.conf b/examples/FairGBM/predict_unconstrained.conf new file mode 100644 index 000000000..a77d4850c --- /dev/null +++ b/examples/FairGBM/predict_unconstrained.conf @@ -0,0 +1,11 @@ +task = predict + +data = COMPAS.test + +has_header=True + +label_column = name:two_year_recid + +input_model= LightGBM_model.txt + +output_result = LightGBM_predictions.txt diff --git a/examples/FairGBM/train.conf b/examples/FairGBM/train.conf new file mode 100644 index 000000000..7714528fd --- /dev/null +++ b/examples/FairGBM/train.conf @@ -0,0 +1,122 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = constrained_cross_entropy # training FairGBM! + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = COMPAS.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = COMPAS.test + +# first row has column names +has_header=True + +# name of label column +label_column = name:two_year_recid + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 200 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# max depth of each individual base learner +max_depth = 10 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = FairGBM_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + +# fixed random state for reproducibility +# (if multi-threaded may still result in variable results) +random_state = 42 + +# ** FairGBM-specific parameters ** +constraint_stepwise_proxy=cross_entropy +constraint_group_column=name:race_Caucasian +constraint_type=fpr +constraint_fpr_threshold=0 +proxy_margin=1 +score_threshold=0.5 +lagrangian_learning_rate=200 + +# global_constraint_type=fpr,fnr # no global constraint on this example, as we're just aiming to maximize accuracy +# global_score_threshold=0.5 +# global_target_fpr=0.05 +# global_target_fnr=0.30 diff --git a/examples/FairGBM/train_unconstrained.conf b/examples/FairGBM/train_unconstrained.conf new file mode 100644 index 000000000..3a21f127f --- /dev/null +++ b/examples/FairGBM/train_unconstrained.conf @@ -0,0 +1,108 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = cross_entropy # training vanilla LightGBM! + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = COMPAS.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = COMPAS.test + +# first row has column names +has_header=True + +# name of label column +label_column = name:two_year_recid + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 200 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# max depth of each individual base learner +max_depth = 10 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + +# fixed random state for reproducibility +# (if multi-threaded may still result in variable results) +random_state = 42 From 7ea1fbcf595141065b10779a1b8db3599e631d57 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 24 Jun 2022 11:57:12 +0100 Subject: [PATCH 02/32] Python API implementation for the FairGBM algorithm --- include/LightGBM/dataset.h | 4 +- include/LightGBM/meta.h | 8 ++-- include/LightGBM/objective_function.h | 2 +- python-package/lightgbm/basic.py | 39 ++++++++++++++++--- python-package/lightgbm/dask.py | 3 ++ python-package/lightgbm/sklearn.py | 38 +++++++++++++----- python-package/setup.py | 2 +- src/io/config.cpp | 2 +- src/io/config_auto.cpp | 1 + src/io/dataset.cpp | 16 +++++--- src/io/metadata.cpp | 5 +-- .../constrained_xentropy_objective.hpp | 4 +- 12 files changed, 91 insertions(+), 33 deletions(-) diff --git a/include/LightGBM/dataset.h b/include/LightGBM/dataset.h index a8a0d323f..a766d6fbb 100644 --- a/include/LightGBM/dataset.h +++ b/include/LightGBM/dataset.h @@ -99,7 +99,7 @@ class Metadata { * \param constraint_group constraint group information for each instance. * \param len the number of elements in the constraint_group array. */ - void SetConstraintGroup(const float* constraint_group, data_size_t len); + void SetConstraintGroup(const constraint_group_t* constraint_group, data_size_t len); /*! * \brief Set initial scores @@ -222,7 +222,7 @@ class Metadata { * \brief Get pointer of group * \return Pointer of group */ - inline const constraint_group_t* group() const { return constraint_group_.data(); } + inline const constraint_group_t* constraint_group() const { return constraint_group_.data(); } /*! \brief Get unique groups in data */ inline std::vector group_values() const { diff --git a/include/LightGBM/meta.h b/include/LightGBM/meta.h index 831873257..32e37c39d 100644 --- a/include/LightGBM/meta.h +++ b/include/LightGBM/meta.h @@ -47,11 +47,11 @@ typedef double label_t; typedef float label_t; #endif -/*! \brief Type of metadata, include group */ -#ifdef GROUP_T_USE_INT - typedef u_int constraint_group_t; +/*! \brief Type of metadata, include constraint group */ +#ifdef CONSTRAINT_GROUP_T_USE_INT8_T + typedef int8_t constraint_group_t; #else - typedef u_short constraint_group_t; + typedef int32_t constraint_group_t; #endif const score_t kMinScore = -std::numeric_limits::infinity(); diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index c483742a8..e2ff62536 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -149,7 +149,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction weights_ = metadata.weights(); // Store Information about the group - group_ = metadata.group(); + group_ = metadata.constraint_group(); group_values_ = metadata.group_values(); // Store Information about the labels diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py index 1f5928018..4a60799bb 100644 --- a/python-package/lightgbm/basic.py +++ b/python-package/lightgbm/basic.py @@ -271,6 +271,7 @@ class LGBMDeprecationWarning(UserWarning): pass +# TODO: update _ConfigAliases with new FairGBM configs class _ConfigAliases: aliases = {"bin_construct_sample_cnt": {"bin_construct_sample_cnt", "subsample_for_bin"}, @@ -434,7 +435,8 @@ def _choose_param_value(main_param_name: str, params: Dict[str, Any], default_va FIELD_TYPE_MAPPER = {"label": C_API_DTYPE_FLOAT32, "weight": C_API_DTYPE_FLOAT32, "init_score": C_API_DTYPE_FLOAT64, - "group": C_API_DTYPE_INT32} + "group": C_API_DTYPE_INT32, + "constraint_group": C_API_DTYPE_INT32} """String name to int feature importance type mapper""" FEATURE_IMPORTANCE_TYPE_MAPPER = {"split": C_API_FEATURE_IMPORTANCE_SPLIT, @@ -1051,7 +1053,7 @@ def current_iteration(self): class Dataset: """Dataset in LightGBM.""" - def __init__(self, data, label=None, reference=None, + def __init__(self, data, label=None, reference=None, constraint_group=None, weight=None, group=None, init_score=None, silent=False, feature_name='auto', categorical_feature='auto', params=None, free_raw_data=True): @@ -1066,6 +1068,9 @@ def __init__(self, data, label=None, reference=None, Label of the data. reference : Dataset or None, optional (default=None) If this is Dataset for validation, training data should be used as reference. + constraint_group : list, numpy 1-D array, pandas Series or None, optional (default=None) + The constraint group for each instance (to enforce fairness constraints). + Only used for constrained optimization. weight : list, numpy 1-D array, pandas Series or None, optional (default=None) Weight for each instance. group : list, numpy 1-D array, pandas Series or None, optional (default=None) @@ -1099,6 +1104,7 @@ def __init__(self, data, label=None, reference=None, self.data = data self.label = label self.reference = reference + self.constraint_group = constraint_group self.weight = weight self.group = group self.init_score = init_score @@ -1196,7 +1202,7 @@ def _set_init_score_by_predictor(self, predictor, data, used_indices=None): return self self.set_init_score(init_score) - def _lazy_init(self, data, label=None, reference=None, + def _lazy_init(self, data, label=None, reference=None, constraint_group=None, weight=None, group=None, init_score=None, predictor=None, silent=False, feature_name='auto', categorical_feature='auto', params=None): @@ -1282,6 +1288,8 @@ def _lazy_init(self, data, label=None, reference=None, self.set_label(label) if self.get_label() is None: raise ValueError("Label should not be None") + if constraint_group is not None: + self.set_constraint_group(constraint_group) if weight is not None: self.set_weight(weight) if group is not None: @@ -1435,6 +1443,7 @@ def construct(self): if self.used_indices is None: # create valid self._lazy_init(self.data, label=self.label, reference=self.reference, + constraint_group=self.constraint_group, weight=self.weight, group=self.group, init_score=self.init_score, predictor=self._predictor, silent=self.silent, feature_name=self.feature_name, params=self.params) @@ -1465,7 +1474,7 @@ def construct(self): self._set_init_score_by_predictor(self._predictor, self.data, used_indices) else: # create train - self._lazy_init(self.data, label=self.label, + self._lazy_init(self.data, label=self.label, constraint_group=self.constraint_group, weight=self.weight, group=self.group, init_score=self.init_score, predictor=self._predictor, silent=self.silent, feature_name=self.feature_name, @@ -1620,7 +1629,7 @@ def set_field(self, field_name, data): ctypes.c_int(FIELD_TYPE_MAPPER[field_name]))) return self dtype = np.float32 - if field_name == 'group': + if field_name == 'group' or field_name == 'constraint_group': dtype = np.int32 elif field_name == 'init_score': dtype = np.float64 @@ -1803,6 +1812,26 @@ def set_label(self, label): self.label = self.get_field('label') # original values can be modified at cpp side return self + def set_constraint_group(self, constraint_group): + """Set constraint group of each instance (which group they belong to for enforcing fairness constraints). + + Parameters + ---------- + constraint_group : list, numpy 1-D array, pandas Series or None + The constraint group information to be set into Dataset. + + Returns + ------- + self : Dataset + Dataset with set label. + """ + self.constraint_group = constraint_group + if self.handle is not None and constraint_group is not None: + constraint_group = list_to_1d_numpy(constraint_group, np.int32, name='constraint_group') + self.set_field('constraint_group', constraint_group) + self.constraint_group = self.get_field('constraint_group') # original values can be modified at cpp side + return self + def set_weight(self, weight): """Set weight of each instance. diff --git a/python-package/lightgbm/dask.py b/python-package/lightgbm/dask.py index fd2d6ece6..f2f1330c6 100644 --- a/python-package/lightgbm/dask.py +++ b/python-package/lightgbm/dask.py @@ -739,6 +739,7 @@ def fit( _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + constraint_group_shape="** WARNING: not currently compatible with Dask model API, only with numpy/pandas **", sample_weight_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", init_score_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" @@ -893,6 +894,7 @@ def fit( _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + constraint_group_shape="** WARNING: not currently compatible with Dask model API, only with numpy/pandas **", sample_weight_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", init_score_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" @@ -1030,6 +1032,7 @@ def fit( _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + constraint_group_shape="** WARNING: not currently compatible with Dask model API, only with numpy/pandas **", sample_weight_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", init_score_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py index 2b2261736..e547697b4 100644 --- a/python-package/lightgbm/sklearn.py +++ b/python-package/lightgbm/sklearn.py @@ -193,6 +193,9 @@ def __call__(self, preds, dataset): Input feature matrix. y : {y_shape} The target values (class labels in classification, real numbers in regression). + constraint_group : {constraint_group_shape} + The constraint group of each instance (for enforcing fairness constraints). + Only used for constrained optimization. sample_weight : {sample_weight_shape} Weights of training data. init_score : {init_score_shape} @@ -548,10 +551,12 @@ def set_params(self, **params): return self def fit(self, X, y, - sample_weight=None, init_score=None, group=None, - eval_set=None, eval_names=None, eval_sample_weight=None, - eval_class_weight=None, eval_init_score=None, eval_group=None, - eval_metric=None, early_stopping_rounds=None, verbose=True, + + constraint_group=None, sample_weight=None, init_score=None, + group=None, eval_set=None, eval_names=None, + eval_sample_weight=None, eval_class_weight=None, + eval_init_score=None, eval_group=None, eval_metric=None, + early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None, init_model=None): """Docstring is set after definition, using a template.""" @@ -643,12 +648,19 @@ def fit(self, X, y, self._n_features_in = self._n_features def _construct_dataset(X, y, sample_weight, init_score, group, params, + constraint_group, categorical_feature='auto'): return Dataset(X, label=y, weight=sample_weight, group=group, init_score=init_score, params=params, + constraint_group=constraint_group, categorical_feature=categorical_feature) - train_set = _construct_dataset(_X, _y, sample_weight, init_score, group, params, + train_set = _construct_dataset(_X, _y, + sample_weight=sample_weight, + init_score=init_score, + group=group, + params=params, + constraint_group=constraint_group, categorical_feature=categorical_feature) valid_sets = [] @@ -683,8 +695,11 @@ def _get_meta_data(collection, name, i): valid_weight = np.multiply(valid_weight, valid_class_sample_weight) valid_init_score = _get_meta_data(eval_init_score, 'eval_init_score', i) valid_group = _get_meta_data(eval_group, 'eval_group', i) - valid_set = _construct_dataset(valid_data[0], valid_data[1], - valid_weight, valid_init_score, valid_group, params) + valid_constraint_group = _get_meta_data(constraint_group, 'constraint_group', i) + valid_set = _construct_dataset(X=valid_data[0], y=valid_data[1], + sample_weight=valid_weight, init_score=valid_init_score, + constraint_group=valid_constraint_group, + group=valid_group, params=params) valid_sets.append(valid_set) if isinstance(init_model, LGBMModel): @@ -715,9 +730,10 @@ def _get_meta_data(collection, name, i): fit.__doc__ = _lgbmmodel_doc_fit.format( X_shape="array-like or sparse matrix of shape = [n_samples, n_features]", y_shape="array-like of shape = [n_samples]", + constraint_group_shape="array-like of shape = [n_samples] or None, optional (default=None)", sample_weight_shape="array-like of shape = [n_samples] or None, optional (default=None)", init_score_shape="array-like of shape = [n_samples] or None, optional (default=None)", - group_shape="array-like or None, optional (default=None)" + group_shape="array-like or None, optional (default=None)", ) + "\n\n" + _lgbmmodel_doc_custom_eval_note def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None, @@ -844,6 +860,8 @@ class LGBMClassifier(_LGBMClassifierBase, LGBMModel): """LightGBM classifier.""" def fit(self, X, y, + + constraint_group=None, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_metric=None, @@ -896,7 +914,9 @@ def fit(self, X, y, else: valid_sets[i] = (valid_x, self._le.transform(valid_y)) - super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets, + super().fit(X, _y, + constraint_group=constraint_group, sample_weight=sample_weight, + init_score=init_score, eval_set=valid_sets, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds, diff --git a/python-package/setup.py b/python-package/setup.py index ee98b1a17..f14301d22 100644 --- a/python-package/setup.py +++ b/python-package/setup.py @@ -332,7 +332,7 @@ def run(self): logging.basicConfig(level=logging.INFO) logger = logging.getLogger('LightGBM') - setup(name='lightgbm', + setup(name='lightgbm', # TODO: https://github.com/feedzai/fairgbm/issues/13 version=version, description='LightGBM Python Package', long_description=readme, diff --git a/src/io/config.cpp b/src/io/config.cpp index d159358cf..614cea9e5 100644 --- a/src/io/config.cpp +++ b/src/io/config.cpp @@ -255,7 +255,7 @@ void Config::Set(const std::unordered_map& params) { CheckParamConflict(); #ifdef DEBUG - Log::Debug("Loading configs from Map; constraint_group_column=%s\n", this->constraint_group_column.c_str()); + Log::Info("Loading configs from Map; constraint_group_column=%s\n", this->constraint_group_column.c_str()); #endif } diff --git a/src/io/config_auto.cpp b/src/io/config_auto.cpp index 82e2c46b1..964eaf94c 100644 --- a/src/io/config_auto.cpp +++ b/src/io/config_auto.cpp @@ -170,6 +170,7 @@ const std::unordered_map& Config::alias_table() { // FairGBM parameters {"lagrangian_learning_rate", "multiplier_learning_rate"}, {"lagrangian_multiplier_learning_rate", "multiplier_learning_rate"}, + {"init_lagrange_multipliers", "init_lagrangian_multipliers"}, {"lagrangian_multipliers", "init_lagrangian_multipliers"}, {"init_multipliers", "init_lagrangian_multipliers"}, {"output_dir", "debugging_output_dir"}, diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp index 30556834d..a3b478cfe 100644 --- a/src/io/dataset.cpp +++ b/src/io/dataset.cpp @@ -850,11 +850,6 @@ bool Dataset::SetFloatField(const char* field_name, const float* field_data, #else metadata_.SetWeights(field_data, num_element); #endif - } else if (name == std::string("constraint_group") || - name == std::string("fairness_group") || - name == std::string("sensitive_group") || - name == std::string("protected_group")) { - metadata_.SetConstraintGroup(field_data, num_element); } else { return false; // Not successful } @@ -879,6 +874,11 @@ bool Dataset::SetIntField(const char* field_name, const int* field_data, name = Common::Trim(name); if (name == std::string("query") || name == std::string("group")) { metadata_.SetQuery(field_data, num_element); + } else if (name == std::string("constraint_group") || + name == std::string("fairness_group") || + name == std::string("sensitive_group") || + name == std::string("protected_group")) { + metadata_.SetConstraintGroup(field_data, num_element); } else { return false; } @@ -929,6 +929,12 @@ bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, if (name == std::string("query") || name == std::string("group")) { *out_ptr = metadata_.query_boundaries(); *out_len = metadata_.num_queries() + 1; + } else if (name == std::string("constraint_group") || + name == std::string("fairness_group") || + name == std::string("sensitive_group") || + name == std::string("protected_group")) { + *out_ptr = metadata_.constraint_group(); + *out_len = num_data_; } else { return false; } diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp index d97ecca46..9d363de95 100644 --- a/src/io/metadata.cpp +++ b/src/io/metadata.cpp @@ -33,7 +33,6 @@ Metadata::~Metadata() { } void Metadata::Init(data_size_t num_data, int weight_idx, int query_idx, int constraint_group_idx_) { - std::lock_guard lock(mutex_); num_data_ = num_data; label_ = std::vector(num_data_); if (weight_idx >= 0) { @@ -323,7 +322,7 @@ void Metadata::SetLabel(const label_t* label, data_size_t len) { } } -void Metadata::SetConstraintGroup(const float* constraint_group, data_size_t len) { +void Metadata::SetConstraintGroup(const constraint_group_t* constraint_group, data_size_t len) { std::lock_guard lock(mutex_); if (constraint_group == nullptr) { Log::Fatal("constraint_group cannot be nullptr"); @@ -337,7 +336,7 @@ void Metadata::SetConstraintGroup(const float* constraint_group, data_size_t len #pragma omp parallel for schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { - constraint_group_[i] = static_cast(Common::AvoidInf(constraint_group[i])); + constraint_group_[i] = static_cast(constraint_group[i]); } } diff --git a/src/objective/constrained_xentropy_objective.hpp b/src/objective/constrained_xentropy_objective.hpp index 499763c06..806855b58 100644 --- a/src/objective/constrained_xentropy_objective.hpp +++ b/src/objective/constrained_xentropy_objective.hpp @@ -125,7 +125,7 @@ class ConstrainedCrossEntropy : public ConstrainedObjectiveFunction { // TODO: i if (weights_ != nullptr) { #pragma omp parallel for schedule(static) reduction(+:suml, sumw) if (!deterministic_) - for (data_size_t i = 0; i < num_data_; ++i) { + for (data_size_t i = 0; i < num_data_gr; ++i) { suml += label_[i] * weights_[i]; sumw += weights_[i]; } @@ -137,7 +137,7 @@ class ConstrainedCrossEntropy : public ConstrainedObjectiveFunction { // TODO: i suml += label_[i]; } } - double pavg = sumw > 0.0f ? suml / sumw : 0.0f; + double pavg = suml / sumw; pavg = std::min(pavg, 1.0 - kEpsilon); pavg = std::max(pavg, kEpsilon); double initscore = std::log(pavg / (1.0f - pavg)); From 408e7fc4909c20897e2fd0e1d51d31edfb31dc0d Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 11:27:19 +0100 Subject: [PATCH 03/32] corrected typo support->supports --- examples/FairGBM/train.conf | 2 +- examples/FairGBM/train_unconstrained.conf | 2 +- examples/binary_classification/train.conf | 2 +- examples/binary_classification/train_linear.conf | 2 +- examples/lambdarank/train.conf | 2 +- examples/multiclass_classification/train.conf | 2 +- examples/parallel_learning/train.conf | 2 +- examples/regression/train.conf | 2 +- examples/xendcg/train.conf | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/FairGBM/train.conf b/examples/FairGBM/train.conf index 7714528fd..2529860c5 100644 --- a/examples/FairGBM/train.conf +++ b/examples/FairGBM/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/FairGBM/train_unconstrained.conf b/examples/FairGBM/train_unconstrained.conf index 3a21f127f..f880d54e0 100644 --- a/examples/FairGBM/train_unconstrained.conf +++ b/examples/FairGBM/train_unconstrained.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/binary_classification/train.conf b/examples/binary_classification/train.conf index 11f07bf9d..c826f0524 100644 --- a/examples/binary_classification/train.conf +++ b/examples/binary_classification/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/binary_classification/train_linear.conf b/examples/binary_classification/train_linear.conf index 4914a2c0b..445981d18 100644 --- a/examples/binary_classification/train_linear.conf +++ b/examples/binary_classification/train_linear.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/lambdarank/train.conf b/examples/lambdarank/train.conf index 5f51115ea..3446e6e64 100644 --- a/examples/lambdarank/train.conf +++ b/examples/lambdarank/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/multiclass_classification/train.conf b/examples/multiclass_classification/train.conf index 05ed99262..37ac94213 100644 --- a/examples/multiclass_classification/train.conf +++ b/examples/multiclass_classification/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/parallel_learning/train.conf b/examples/parallel_learning/train.conf index e9792c467..42f4776e4 100644 --- a/examples/parallel_learning/train.conf +++ b/examples/parallel_learning/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/regression/train.conf b/examples/regression/train.conf index 7fac419a5..5856d3f56 100644 --- a/examples/regression/train.conf +++ b/examples/regression/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost diff --git a/examples/xendcg/train.conf b/examples/xendcg/train.conf index 6840ae4db..0109634d5 100644 --- a/examples/xendcg/train.conf +++ b/examples/xendcg/train.conf @@ -1,4 +1,4 @@ -# task type, support train and predict +# task type, supports train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost From cbb9debd72623a9966df38f35bda098b091297b0 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 11:31:27 +0100 Subject: [PATCH 04/32] added check to input_weights > 0 --- src/objective/constrained_xentropy_objective.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/objective/constrained_xentropy_objective.hpp b/src/objective/constrained_xentropy_objective.hpp index 806855b58..499763c06 100644 --- a/src/objective/constrained_xentropy_objective.hpp +++ b/src/objective/constrained_xentropy_objective.hpp @@ -125,7 +125,7 @@ class ConstrainedCrossEntropy : public ConstrainedObjectiveFunction { // TODO: i if (weights_ != nullptr) { #pragma omp parallel for schedule(static) reduction(+:suml, sumw) if (!deterministic_) - for (data_size_t i = 0; i < num_data_gr; ++i) { + for (data_size_t i = 0; i < num_data_; ++i) { suml += label_[i] * weights_[i]; sumw += weights_[i]; } @@ -137,7 +137,7 @@ class ConstrainedCrossEntropy : public ConstrainedObjectiveFunction { // TODO: i suml += label_[i]; } } - double pavg = suml / sumw; + double pavg = sumw > 0.0f ? suml / sumw : 0.0f; pavg = std::min(pavg, 1.0 - kEpsilon); pavg = std::max(pavg, kEpsilon); double initscore = std::log(pavg / (1.0f - pavg)); From 6619d0e30e704ad9c44a5a88d038f6a742aa0b29 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 11:33:26 +0100 Subject: [PATCH 05/32] minor change for python readability --- python-package/lightgbm/basic.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py index 4a60799bb..a529924bf 100644 --- a/python-package/lightgbm/basic.py +++ b/python-package/lightgbm/basic.py @@ -1629,7 +1629,9 @@ def set_field(self, field_name, data): ctypes.c_int(FIELD_TYPE_MAPPER[field_name]))) return self dtype = np.float32 - if field_name == 'group' or field_name == 'constraint_group': + if field_name == 'group': + dtype = np.int32 + elif field_name == 'constraint_group': dtype = np.int32 elif field_name == 'init_score': dtype = np.float64 From ae6942a845ab6ff0f891aaab8e79615139cc6956 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 11:44:38 +0100 Subject: [PATCH 06/32] reordering arguments of _construct_dataset calls for consistency --- python-package/lightgbm/sklearn.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py index e547697b4..b5c8030dd 100644 --- a/python-package/lightgbm/sklearn.py +++ b/python-package/lightgbm/sklearn.py @@ -647,20 +647,21 @@ def fit(self, X, y, # copy for consistency self._n_features_in = self._n_features - def _construct_dataset(X, y, sample_weight, init_score, group, params, + def _construct_dataset(X, y, constraint_group, + sample_weight, group, + init_score, params, categorical_feature='auto'): - return Dataset(X, label=y, weight=sample_weight, group=group, - init_score=init_score, params=params, + return Dataset(X, label=y, constraint_group=constraint_group, + weight=sample_weight, group=group, + init_score=init_score, params=params, categorical_feature=categorical_feature) train_set = _construct_dataset(_X, _y, - sample_weight=sample_weight, - init_score=init_score, - group=group, - params=params, constraint_group=constraint_group, + sample_weight=sample_weight, group=group, + init_score=init_score, params=params, categorical_feature=categorical_feature) valid_sets = [] @@ -697,9 +698,9 @@ def _get_meta_data(collection, name, i): valid_group = _get_meta_data(eval_group, 'eval_group', i) valid_constraint_group = _get_meta_data(constraint_group, 'constraint_group', i) valid_set = _construct_dataset(X=valid_data[0], y=valid_data[1], - sample_weight=valid_weight, init_score=valid_init_score, constraint_group=valid_constraint_group, - group=valid_group, params=params) + sample_weight=valid_weight, group=valid_group, + init_score=valid_init_score, params=params) valid_sets.append(valid_set) if isinstance(init_model, LGBMModel): From 51be6bea75cc22fcf5be38a66f19b95227f8fda9 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 12:14:24 +0100 Subject: [PATCH 07/32] fixed typo in examples configs --- examples/FairGBM/train.conf | 2 +- examples/FairGBM/train_unconstrained.conf | 2 +- examples/binary_classification/train.conf | 2 +- examples/binary_classification/train_linear.conf | 2 +- examples/lambdarank/train.conf | 2 +- examples/multiclass_classification/train.conf | 2 +- examples/parallel_learning/train.conf | 2 +- examples/regression/train.conf | 2 +- examples/xendcg/train.conf | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/FairGBM/train.conf b/examples/FairGBM/train.conf index 2529860c5..654641678 100644 --- a/examples/FairGBM/train.conf +++ b/examples/FairGBM/train.conf @@ -23,7 +23,7 @@ metric = binary_logloss,auc # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/FairGBM/train_unconstrained.conf b/examples/FairGBM/train_unconstrained.conf index f880d54e0..6e7ac29a9 100644 --- a/examples/FairGBM/train_unconstrained.conf +++ b/examples/FairGBM/train_unconstrained.conf @@ -23,7 +23,7 @@ metric = binary_logloss,auc # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/binary_classification/train.conf b/examples/binary_classification/train.conf index c826f0524..ccd4596fd 100644 --- a/examples/binary_classification/train.conf +++ b/examples/binary_classification/train.conf @@ -23,7 +23,7 @@ metric = binary_logloss,auc # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/binary_classification/train_linear.conf b/examples/binary_classification/train_linear.conf index 445981d18..0e3d4f116 100644 --- a/examples/binary_classification/train_linear.conf +++ b/examples/binary_classification/train_linear.conf @@ -25,7 +25,7 @@ metric = binary_logloss,auc # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/lambdarank/train.conf b/examples/lambdarank/train.conf index 3446e6e64..8467fce43 100644 --- a/examples/lambdarank/train.conf +++ b/examples/lambdarank/train.conf @@ -26,7 +26,7 @@ ndcg_eval_at = 1,3,5 # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/multiclass_classification/train.conf b/examples/multiclass_classification/train.conf index 37ac94213..f591245a8 100644 --- a/examples/multiclass_classification/train.conf +++ b/examples/multiclass_classification/train.conf @@ -38,7 +38,7 @@ num_class = 5 # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/parallel_learning/train.conf b/examples/parallel_learning/train.conf index 42f4776e4..61f503af8 100644 --- a/examples/parallel_learning/train.conf +++ b/examples/parallel_learning/train.conf @@ -23,7 +23,7 @@ metric = binary_logloss,auc # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/regression/train.conf b/examples/regression/train.conf index 5856d3f56..a617b291d 100644 --- a/examples/regression/train.conf +++ b/examples/regression/train.conf @@ -23,7 +23,7 @@ metric = l2 # frequence for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. diff --git a/examples/xendcg/train.conf b/examples/xendcg/train.conf index 0109634d5..18d8975e4 100644 --- a/examples/xendcg/train.conf +++ b/examples/xendcg/train.conf @@ -26,7 +26,7 @@ ndcg_eval_at = 1,3,5 # frequency for metric output metric_freq = 1 -# true if need output metric for training data, alias: tranining_metric, train_metric +# true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. From d9f63a54968fc4e6035831bf4bd124eed3dc56b8 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 27 Jun 2022 12:34:45 +0100 Subject: [PATCH 08/32] added type checks to SetIntField for constraint_group data --- src/io/dataset.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp index a3b478cfe..696a0f35f 100644 --- a/src/io/dataset.cpp +++ b/src/io/dataset.cpp @@ -878,7 +878,16 @@ bool Dataset::SetIntField(const char* field_name, const int* field_data, name == std::string("fairness_group") || name == std::string("sensitive_group") || name == std::string("protected_group")) { - metadata_.SetConstraintGroup(field_data, num_element); + // Make sure the cast from int to constraint_group_t is possible + const int * max_value = std::max_element(field_data, field_data + num_element); + if (*max_value > std::numeric_limits::max()) { + Log::Fatal( + "constraint group data does not fit within the type constraint_group_t; " + "maximum value is %d, but it should be less than or equal to %d", + *max_value, std::numeric_limits::max()); + } + std::vector constraint_group_data(field_data, field_data + num_element); + metadata_.SetConstraintGroup(constraint_group_data.data(), num_element); } else { return false; } From b5e139e70fbd8f245f09bb10415e91e51fadad71 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 14:48:51 +0100 Subject: [PATCH 09/32] omit aliases of constraint_group got getting and setting field --- src/io/dataset.cpp | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp index 696a0f35f..295b78bae 100644 --- a/src/io/dataset.cpp +++ b/src/io/dataset.cpp @@ -874,10 +874,7 @@ bool Dataset::SetIntField(const char* field_name, const int* field_data, name = Common::Trim(name); if (name == std::string("query") || name == std::string("group")) { metadata_.SetQuery(field_data, num_element); - } else if (name == std::string("constraint_group") || - name == std::string("fairness_group") || - name == std::string("sensitive_group") || - name == std::string("protected_group")) { + } else if (name == std::string("constraint_group")) { // Make sure the cast from int to constraint_group_t is possible const int * max_value = std::max_element(field_data, field_data + num_element); if (*max_value > std::numeric_limits::max()) { @@ -938,10 +935,7 @@ bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, if (name == std::string("query") || name == std::string("group")) { *out_ptr = metadata_.query_boundaries(); *out_len = metadata_.num_queries() + 1; - } else if (name == std::string("constraint_group") || - name == std::string("fairness_group") || - name == std::string("sensitive_group") || - name == std::string("protected_group")) { + } else if (name == std::string("constraint_group")) { *out_ptr = metadata_.constraint_group(); *out_len = num_data_; } else { From 44f00470e1a2893d66efea295a116a1032650315 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 14:49:06 +0100 Subject: [PATCH 10/32] removing ifdef guards from debug output --- src/io/config.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/io/config.cpp b/src/io/config.cpp index 614cea9e5..6003efd42 100644 --- a/src/io/config.cpp +++ b/src/io/config.cpp @@ -254,9 +254,7 @@ void Config::Set(const std::unordered_map& params) { // check for conflicts CheckParamConflict(); -#ifdef DEBUG - Log::Info("Loading configs from Map; constraint_group_column=%s\n", this->constraint_group_column.c_str()); -#endif + Log::Debug("Loading configs from Map; constraint_group_column=%s\n", this->constraint_group_column.c_str()); } bool CheckMultiClassObjective(const std::string& objective) { From 42addb6e9e17091862a8593967bb1d8b6e037e52 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 15:00:28 +0100 Subject: [PATCH 11/32] added guards against using int8_t with C_API --- src/io/dataset.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp index 295b78bae..75e91cd7e 100644 --- a/src/io/dataset.cpp +++ b/src/io/dataset.cpp @@ -936,8 +936,12 @@ bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, *out_ptr = metadata_.query_boundaries(); *out_len = metadata_.num_queries() + 1; } else if (name == std::string("constraint_group")) { +#ifdef CONSTRAINT_GROUP_T_USE_INT8_T + Log::Fatal("GetIntField doesn't support CONSTRAINT_GROUP_T_USE_INT8_T"); +#else *out_ptr = metadata_.constraint_group(); *out_len = num_data_; +#endif } else { return false; } From bd75b7fea4605d3a9c75c3386b21af47fafe55db Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 15:15:22 +0100 Subject: [PATCH 12/32] cahnged SetConstraintGroup from taking constraint_group_t* to int* --- include/LightGBM/dataset.h | 2 +- src/io/dataset.cpp | 11 +---------- src/io/metadata.cpp | 12 +++++++++++- 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/include/LightGBM/dataset.h b/include/LightGBM/dataset.h index a766d6fbb..64acb2d38 100644 --- a/include/LightGBM/dataset.h +++ b/include/LightGBM/dataset.h @@ -99,7 +99,7 @@ class Metadata { * \param constraint_group constraint group information for each instance. * \param len the number of elements in the constraint_group array. */ - void SetConstraintGroup(const constraint_group_t* constraint_group, data_size_t len); + void SetConstraintGroup(const int* constraint_group, data_size_t len); /*! * \brief Set initial scores diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp index 75e91cd7e..ac1fb6276 100644 --- a/src/io/dataset.cpp +++ b/src/io/dataset.cpp @@ -875,16 +875,7 @@ bool Dataset::SetIntField(const char* field_name, const int* field_data, if (name == std::string("query") || name == std::string("group")) { metadata_.SetQuery(field_data, num_element); } else if (name == std::string("constraint_group")) { - // Make sure the cast from int to constraint_group_t is possible - const int * max_value = std::max_element(field_data, field_data + num_element); - if (*max_value > std::numeric_limits::max()) { - Log::Fatal( - "constraint group data does not fit within the type constraint_group_t; " - "maximum value is %d, but it should be less than or equal to %d", - *max_value, std::numeric_limits::max()); - } - std::vector constraint_group_data(field_data, field_data + num_element); - metadata_.SetConstraintGroup(constraint_group_data.data(), num_element); + metadata_.SetConstraintGroup(field_data, num_element); } else { return false; } diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp index 9d363de95..6e78480df 100644 --- a/src/io/metadata.cpp +++ b/src/io/metadata.cpp @@ -322,7 +322,7 @@ void Metadata::SetLabel(const label_t* label, data_size_t len) { } } -void Metadata::SetConstraintGroup(const constraint_group_t* constraint_group, data_size_t len) { +void Metadata::SetConstraintGroup(const int* constraint_group, data_size_t len) { std::lock_guard lock(mutex_); if (constraint_group == nullptr) { Log::Fatal("constraint_group cannot be nullptr"); @@ -334,8 +334,18 @@ void Metadata::SetConstraintGroup(const constraint_group_t* constraint_group, da constraint_group_.resize(num_data_); } + // Make sure the cast from int to constraint_group_t is possible + const int * max_value = std::max_element(constraint_group, constraint_group + len); + int max_allowed = std::numeric_limits::max(); + #pragma omp parallel for schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { + if (*max_value > max_allowed) { + Log::Fatal( + "constraint group data does not fit within the type constraint_group_t; " + "maximum value is %d, but it should be less than or equal to %d", + *max_value, max_allowed); + } constraint_group_[i] = static_cast(constraint_group[i]); } } From dc5f86327f70862be602110563b7549b989aac62 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 16:22:04 +0100 Subject: [PATCH 13/32] removing useless max_value call --- src/io/metadata.cpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp index 6e78480df..89a4eec40 100644 --- a/src/io/metadata.cpp +++ b/src/io/metadata.cpp @@ -335,16 +335,15 @@ void Metadata::SetConstraintGroup(const int* constraint_group, data_size_t len) } // Make sure the cast from int to constraint_group_t is possible - const int * max_value = std::max_element(constraint_group, constraint_group + len); - int max_allowed = std::numeric_limits::max(); + const int max_allowed = std::numeric_limits::max(); #pragma omp parallel for schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { - if (*max_value > max_allowed) { + if (constraint_group[i] > max_allowed) { Log::Fatal( "constraint group data does not fit within the type constraint_group_t; " "maximum value is %d, but it should be less than or equal to %d", - *max_value, max_allowed); + constraint_group[i], max_allowed); } constraint_group_[i] = static_cast(constraint_group[i]); } From 376800f7c1c304a639766dab3db6c1082c9af5c8 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 4 Jul 2022 14:32:00 +0100 Subject: [PATCH 14/32] constraint_group_t now is int, was int32_t --- include/LightGBM/meta.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/LightGBM/meta.h b/include/LightGBM/meta.h index 32e37c39d..c76a0cb32 100644 --- a/include/LightGBM/meta.h +++ b/include/LightGBM/meta.h @@ -51,7 +51,7 @@ typedef float label_t; #ifdef CONSTRAINT_GROUP_T_USE_INT8_T typedef int8_t constraint_group_t; #else - typedef int32_t constraint_group_t; + typedef int constraint_group_t; #endif const score_t kMinScore = -std::numeric_limits::infinity(); From 7016f9d90febb8f442acd4f961807e9fad0a9fc8 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Wed, 6 Jul 2022 11:05:13 +0100 Subject: [PATCH 15/32] added GE and LT checks for constraint group value --- include/LightGBM/dataset.h | 1 + src/io/metadata.cpp | 9 ++------- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/include/LightGBM/dataset.h b/include/LightGBM/dataset.h index 64acb2d38..2a07e6552 100644 --- a/include/LightGBM/dataset.h +++ b/include/LightGBM/dataset.h @@ -215,6 +215,7 @@ class Metadata { * \param value Group constraint value of this record */ inline void SetConstraintGroupAt(data_size_t idx, constraint_group_t value) { + CHECK_GE(value, 0) constraint_group_[idx] = value; } diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp index 89a4eec40..3bb32adc7 100644 --- a/src/io/metadata.cpp +++ b/src/io/metadata.cpp @@ -339,13 +339,8 @@ void Metadata::SetConstraintGroup(const int* constraint_group, data_size_t len) #pragma omp parallel for schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { - if (constraint_group[i] > max_allowed) { - Log::Fatal( - "constraint group data does not fit within the type constraint_group_t; " - "maximum value is %d, but it should be less than or equal to %d", - constraint_group[i], max_allowed); - } - constraint_group_[i] = static_cast(constraint_group[i]); + CHECK_LT(constraint_group[i], max_allowed) + SetConstraintGroupAt(i, static_cast(constraint_group[i])); } } From bc3bbc6d670a9814c3de463e7b3900543113d12d Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Wed, 6 Jul 2022 12:18:31 +0100 Subject: [PATCH 16/32] SetConstraintgroupAt now has a generic type --- include/LightGBM/dataset.h | 9 ++++++--- include/LightGBM/objective_function.h | 2 +- src/io/dataset_loader.cpp | 6 +++--- src/io/metadata.cpp | 6 +----- 4 files changed, 11 insertions(+), 12 deletions(-) diff --git a/include/LightGBM/dataset.h b/include/LightGBM/dataset.h index 2a07e6552..93edf722d 100644 --- a/include/LightGBM/dataset.h +++ b/include/LightGBM/dataset.h @@ -214,9 +214,12 @@ class Metadata { * \param idx Index of this record * \param value Group constraint value of this record */ - inline void SetConstraintGroupAt(data_size_t idx, constraint_group_t value) { + template + inline void SetConstraintGroupAt(data_size_t idx, T value) { + // Make sure the cast from T to constraint_group_t is possible + CHECK_LE(value, std::numeric_limits::max()) CHECK_GE(value, 0) - constraint_group_[idx] = value; + constraint_group_[idx] = static_cast(value); } /*! @@ -226,7 +229,7 @@ class Metadata { inline const constraint_group_t* constraint_group() const { return constraint_group_.data(); } /*! \brief Get unique groups in data */ - inline std::vector group_values() const { + inline std::vector unique_constraint_groups() const { std::vector values(constraint_group_); std::sort(values.begin(), values.end()); diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index e2ff62536..cbc826c61 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -150,7 +150,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // Store Information about the group group_ = metadata.constraint_group(); - group_values_ = metadata.group_values(); + group_values_ = metadata.unique_constraint_groups(); // Store Information about the labels total_label_positives_ = 0; diff --git a/src/io/dataset_loader.cpp b/src/io/dataset_loader.cpp index 1176b381f..48323f83e 100644 --- a/src/io/dataset_loader.cpp +++ b/src/io/dataset_loader.cpp @@ -1202,7 +1202,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector* text_dat } } if (inner_data.first == constraint_group_idx_) - dataset->metadata_.SetConstraintGroupAt(i, static_cast(inner_data.second)); + dataset->metadata_.SetConstraintGroupAt(i, inner_data.second); } if (dataset->has_raw()) { for (size_t j = 0; j < feature_row.size(); ++j) { @@ -1261,7 +1261,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector* text_dat } } if (inner_data.first == constraint_group_idx_) - dataset->metadata_.SetConstraintGroupAt(i, static_cast(inner_data.second)); + dataset->metadata_.SetConstraintGroupAt(i, inner_data.second); } dataset->FinishOneRow(tid, i, is_feature_added); if (dataset->has_raw()) { @@ -1336,7 +1336,7 @@ void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser* } } if (inner_data.first == constraint_group_idx_) - dataset->metadata_.SetConstraintGroupAt(start_idx + i, static_cast(inner_data.second)); + dataset->metadata_.SetConstraintGroupAt(start_idx + i, inner_data.second); } if (dataset->has_raw()) { for (size_t j = 0; j < feature_row.size(); ++j) { diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp index 3bb32adc7..da7932d3a 100644 --- a/src/io/metadata.cpp +++ b/src/io/metadata.cpp @@ -334,13 +334,9 @@ void Metadata::SetConstraintGroup(const int* constraint_group, data_size_t len) constraint_group_.resize(num_data_); } - // Make sure the cast from int to constraint_group_t is possible - const int max_allowed = std::numeric_limits::max(); - #pragma omp parallel for schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { - CHECK_LT(constraint_group[i], max_allowed) - SetConstraintGroupAt(i, static_cast(constraint_group[i])); + SetConstraintGroupAt(i, constraint_group[i]); } } From 2d90ebd422a1de31b524b6dc051e2fddfc378eef Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 24 Jun 2022 12:08:48 +0100 Subject: [PATCH 17/32] drafting tests with COMPAS data --- .../test_constrained_optimization.py | 54 +++++++++++++++++++ tests/python_package_test/utils.py | 43 +++++++++++++++ 2 files changed, 97 insertions(+) create mode 100644 tests/python_package_test/test_constrained_optimization.py diff --git a/tests/python_package_test/test_constrained_optimization.py b/tests/python_package_test/test_constrained_optimization.py new file mode 100644 index 000000000..4533b24a0 --- /dev/null +++ b/tests/python_package_test/test_constrained_optimization.py @@ -0,0 +1,54 @@ +# coding: utf-8 +import pytest + +import lightgbm as lgb + +from .utils import load_compas, load_california_housing_classification + + +@pytest.fixture +def lightgbm_hyperparams() -> dict: + return { + # TODO + } + +@pytest.fixture +def fairgbm_hyperparams(lightgbm_hyperparams) -> dict: + hyperparams = lightgbm_hyperparams.copy() + hyperparams.update({ + # TODO + }) + + return hyperparams + +def assert_fairness_fairgbm_vs_lightgbm( + fairgbm_params, + lightgbm_params, + X_train, y_train, s_train, + X_test, y_test, s_test + ): + # TODO: + # 1. create lgb objects + # 2. train fairgbm and lightgbm + # 3. compute predictions on test data + # 4. compute fairness and accuracy + # 5. assert FairGBM is strictly better than LightGBM in fairness + assert True + + +def test_fairgbm_vs_lightgbm_on_compas(): + data = load_compas() + + def load_lgb_data(data): + X, Y, S = data + return lgb.Dataset(X, label=Y, constraint_group=S) + + lgb_train = load_lgb_data(data["train"]) + lgb_test = load_lgb_data(data["test"]) + + +def test_fairgbm_vs_lightgbm_on_california_housing(): + X, Y, S = load_california_housing_classification() + + # TODO: train test split and train fairgbm etc. + assert True diff --git a/tests/python_package_test/utils.py b/tests/python_package_test/utils.py index 320b8e204..f843a45e5 100644 --- a/tests/python_package_test/utils.py +++ b/tests/python_package_test/utils.py @@ -1,11 +1,54 @@ # coding: utf-8 from functools import lru_cache +from pathlib import Path +from typing import Tuple +import pytest import numpy as np import sklearn.datasets from sklearn.utils import check_random_state +@lru_cache(maxsize=None) +def load_compas(): + pd = pytest.importorskip( + "pandas", + reason="The `pandas` package must be installed to import COMPAS and run fairness tests.") + + local_root_path = Path(__file__).parent.parent.parent / "examples" / "FairGBM" + target_col_name = "two_year_recid" + sensitive_col_name = "race_Caucasian" + + def read_data(path) -> Tuple[pd.DataFrame, pd.Series, pd.Series]: + data = pd.read_csv(path, header=0, sep="\t", index_col=None) + Y = data[target_col_name] + S = data[sensitive_col_name] + X = data[[col for col in data.columns if col not in {target_col_name, sensitive_col_name}]] + return X, Y, S + + data_paths = { + "train": local_root_path / "COMPAS.train", + "test": local_root_path / "COMPAS.test", + } + + return {key: read_data(path) for key, path in data_paths.items()} + + +@lru_cache(maxsize=None) +def load_california_housing_classification(price_threshold=100_000, seed=42): + X, Y = sklearn.datasets.fetch_california_housing(return_X_y=True) + + # Binarize target column for classification + Y = (Y >= price_threshold).astype(np.float32) + + # Let's generate a sensitive attribute column for testing + # NOTE: training is "unaware" as the sensitive column is not part of the training features + rng = np.random.RandomState(seed) + S = (rng.random(size=y.shape) > 0.80).astype(np.int32) + + return X, Y, S + + @lru_cache(maxsize=None) def load_boston(**kwargs): return sklearn.datasets.load_boston(**kwargs) From 16d5e20856666a1747076efceb7cd5384fbd7759 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 28 Jun 2022 14:49:02 +0100 Subject: [PATCH 18/32] added FairGBM example with BAF-base dataset --- examples/FairGBM-other/BAF-base.test | 20502 ++++ examples/FairGBM-other/BAF-base.train | 79500 ++++++++++++++++ examples/FairGBM-other/README.md | 46 + examples/FairGBM-other/predict.conf | 16 + .../FairGBM-other/predict_unconstrained.conf | 15 + examples/FairGBM-other/train.conf | 87 + .../FairGBM-other/train_unconstrained.conf | 60 + 7 files changed, 100226 insertions(+) create mode 100644 examples/FairGBM-other/BAF-base.test create mode 100644 examples/FairGBM-other/BAF-base.train create mode 100644 examples/FairGBM-other/README.md create mode 100644 examples/FairGBM-other/predict.conf create mode 100644 examples/FairGBM-other/predict_unconstrained.conf create mode 100644 examples/FairGBM-other/train.conf create mode 100644 examples/FairGBM-other/train_unconstrained.conf diff --git a/examples/FairGBM-other/BAF-base.test b/examples/FairGBM-other/BAF-base.test new file mode 100644 index 000000000..9cbdd7d42 --- /dev/null +++ b/examples/FairGBM-other/BAF-base.test @@ -0,0 +1,20502 @@ +fraud_bool customer_age_category income name_email_similarity prev_address_months_count current_address_months_count customer_age days_since_request intended_balcon_amount payment_type zip_count_4w velocity_6h velocity_24h velocity_4w bank_branch_count_8w date_of_birth_distinct_emails_4w employment_status credit_risk_score email_is_free housing_status phone_home_valid phone_mobile_valid bank_months_count has_other_cards proposed_credit_limit foreign_request source session_length_in_minutes device_os keep_alive_session device_distinct_emails_8w device_fraud_count month +1 0 12000.0 0.18265020599424034 0 193 48 0.004487356795297657 -1.0203790499085323 0 819 7355.5691366753845 4151.445778497748 3466.9453784359807 0 6 0 291 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3035.8701737292013 3117.214760887581 1 3 0 93 1 1 1 1 0 0 200.0 0 0 1.4792123792740617 0 0 1 0 7 +0 0 8100.0 0.6003271776740093 32 137 42 0.013782207623319607 -1.018394821290596 0 359 4898.293270400623 2157.736585297548 4174.013583536363 0 1 5 181 1 1 1 1 -1 0 200.0 0 0 5.5058736211311565 2 0 1 0 6 +0 0 14000.0 0.5058529089072527 84 81 34 0.0075845331643855955 -0.9950315602560864 0 1303 4191.733407514789 5330.936019457009 4311.7172406324435 0 9 3 94 1 1 0 1 0 0 500.0 0 0 5.564226145134652 0 0 0 0 6 +0 0 11000.0 0.4540125956660959 -1 25 26 0.0029782052672879308 -1.0288260356898422 0 1463 6151.554103987266 3772.7282389548295 6270.523024283181 0 12 0 163 0 1 0 1 -1 0 1500.0 0 0 2.5421167429599882 1 0 0 0 7 +0 0 7800.0 0.903891960914016 0 23 22 0.004411252479978915 -0.9956525961218916 0 969 7150.426593314764 4174.6904209396225 4266.327586427664 0 8 0 157 0 1 0 1 -1 0 200.0 0 0 9.244577968437195 2 0 1 0 6 +0 1 22000.0 0.8766336282188723 0 184 52 0.004732332313137507 21.825232574806126 1 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3.9535367961623136 0 0 1 0 4 +0 0 3000.0 0.11912634021388675 0 29 30 0.006687712100620438 -1.0002005394434657 0 1494 3997.4693394008723 4123.883461693586 4946.263058759565 0 5 0 148 0 2 1 1 -1 0 200.0 0 0 12.950320807307014 2 1 1 0 2 +0 0 13000.0 0.1508451574667286 -1 23 32 0.01761091364990648 -1.0294699169278512 0 932 5604.502644001209 7066.140435598774 6739.988629938495 0 21 0 61 0 1 0 0 -1 0 200.0 0 0 8.257158897989148 3 0 1 0 0 +0 1 17000.0 0.6547786941933311 0 150 54 0.009083561670904877 -0.9890460003684812 3 788 5414.7063304975845 5342.141303439123 4280.4306206505025 741 2 1 83 0 0 0 1 30 0 200.0 0 0 28.718977250526056 0 1 1 0 5 +0 0 750.0 0.2782252057742218 0 38 44 0.0063367606443568685 -1.028967959292602 0 1122 9886.498003839468 5595.692641547176 4892.758051875299 1 7 0 123 0 1 0 1 -1 0 500.0 0 0 0.5160343063066986 0 1 1 0 4 +0 0 44000.0 0.7121022935706185 -1 63 39 0.00047956276059664806 -1.0128762683390662 1 2267 13419.878892912473 4862.101953335414 5760.7300266512975 2 8 5 184 1 3 0 1 -1 0 500.0 0 0 5.912259474749416 3 0 1 0 2 +0 0 16000.0 0.2402360877203617 -1 39 39 0.0019627235443927696 -0.9971093630258364 0 1907 6235.953509190891 3400.6434949171203 5410.894185397394 0 9 0 95 0 1 0 1 -1 0 200.0 0 0 9.339213828962087 2 1 1 0 2 +0 0 45000.0 0.6691212127660298 -1 28 27 0.014401725914899606 -1.0033548366274792 0 2404 12334.65450046388 6057.316390620474 6799.601430217963 0 14 4 131 0 1 0 1 -1 0 500.0 0 0 2.379344003809849 3 1 1 0 0 +0 0 20000.0 0.1122332739026437 0 71 46 0.010088494593046978 21.06108686358064 1 1416 14454.147016348734 4378.738055296922 5467.856815916858 95 6 0 43 1 3 1 1 20 1 200.0 0 0 1.0974826220409828 0 1 1 0 1 +0 0 27000.0 0.5489795941774195 -1 86 43 0.0027181303232211816 -1.0055997955621747 0 1131 9255.24034676288 5506.987255350548 5601.279815657224 0 6 0 212 0 0 1 0 -1 0 1000.0 0 0 2.520711751183351 2 1 1 0 2 +0 0 6300.0 0.8254115329916881 0 30 42 0.005313425450556506 -1.0138525833614207 3 525 8821.794968911941 5032.287963981428 4802.8571348641335 10 16 4 137 0 3 0 1 27 0 500.0 0 0 12.429917077519535 0 1 1 0 3 +0 0 32000.0 0.1423175684438421 30 10 31 0.003729781022934597 -1.0058714157972009 3 2537 7843.911452475726 5952.270072320659 5314.083504819794 22 13 3 115 0 1 1 1 28 1 200.0 0 0 0.4898173959158767 0 1 0 0 3 +0 1 44000.0 0.4264926480735134 -1 221 61 0.0013027452382026756 -0.9958065010984108 0 1378 6015.353266613011 4854.4131777133925 5088.173304427345 0 1 0 126 0 0 1 1 0 0 200.0 0 0 13.542375018961382 2 1 0 0 4 +0 1 28000.0 0.04977177805016352 -1 36 57 0.007273357533896177 -1.0280359173800109 3 2645 9038.292472906736 5920.562651211115 5852.040193915169 1 20 3 58 1 1 1 1 9 0 200.0 0 0 0.3902541098569743 0 0 0 0 1 +0 0 22000.0 0.119505583900353 -1 62 43 0.0048120453236739705 -1.0054358442164617 0 2007 4059.311395778213 2712.137729414665 5509.868636785973 0 11 3 197 0 4 1 1 0 1 1500.0 0 0 6.515344675008758 3 1 1 0 2 +0 0 4800.0 0.08749653427335591 -1 156 46 0.016028316720739986 -1.0167881546642767 0 1169 10933.050872194925 5924.816109083368 6891.6875272087145 0 25 0 89 0 3 0 1 -1 0 500.0 0 0 3.1161434289755534 2 0 1 0 0 +0 0 26000.0 0.2233957576942203 -1 81 30 0.0034003623069386486 -1.0030824244263932 3 2004 9695.22146427342 3516.1987262017133 5382.041834415477 1843 12 0 220 0 2 1 1 20 1 1500.0 0 0 9.382499835807732 3 0 0 0 2 +0 0 21000.0 0.6615396543736142 -1 202 21 0.010970322465599031 -1.0028334632488074 0 4848 4033.656416394073 7123.356000649134 6422.6375987429965 0 6 0 146 0 2 1 0 -1 0 200.0 0 1 1.9960968198328128 2 0 1 0 0 +0 0 110000.0 0.9126508426738452 -1 44 29 0.011271333482063791 -0.9843383611233278 0 982 7878.303599938142 5785.611972064555 5308.73805939947 0 17 0 83 0 0 1 1 0 0 200.0 0 0 2.463944489080464 3 1 1 0 3 +0 1 2100.0 0.2480945340091447 0 97 55 0.007177038744494699 -1.0102567392275046 0 2718 8193.606259950391 4691.824284688937 4853.610292415097 0 5 1 263 1 3 1 1 -1 0 1000.0 0 0 21.53719768801459 0 1 1 0 3 +0 0 23000.0 0.7352798378941058 0 142 28 0.014815784437134997 -1.0064724446396742 0 832 2098.8419949705794 6170.268388629962 5628.362217076071 0 9 0 192 0 3 0 1 -1 1 1500.0 0 0 7.783869557925358 0 0 1 0 1 +0 0 680.0 0.13930286137565226 0 34 42 0.007963179734835102 -1.0109225399098032 0 2665 12230.899513297549 5816.960744062506 5063.493630747995 0 3 1 109 0 1 0 0 0 1 200.0 0 0 11.297131130846186 0 1 1 0 3 +0 0 21000.0 0.893928559868534 0 55 24 0.0007435077233885959 -0.993685772793032 3 898 5200.671699007187 4947.5580120833665 4368.898421866862 16 17 0 250 1 2 0 1 3 0 200.0 0 0 10.359978947856918 0 0 1 0 5 +0 0 14000.0 0.1464043114102393 0 75 30 0.002196102594647194 -0.9983422652994725 0 2304 4263.4315702296035 3325.604811115998 4885.647760838132 0 7 4 133 0 3 0 1 0 0 200.0 0 0 4.917626992834637 2 0 1 0 4 +0 0 13000.0 0.2315564612489837 0 75 41 0.01846610960564995 -1.0070991315400322 0 818 2006.1298002076576 5347.236888443571 5487.234170298744 0 8 0 205 0 1 0 1 -1 0 1500.0 0 0 16.189069857043172 0 0 0 0 2 +0 0 20000.0 0.7883800235442104 -1 186 33 0.009169137893798205 -1.0167653647245645 3 2634 4543.60905152687 4837.885288510176 5611.16931582999 1893 6 0 43 0 2 0 1 1 1 200.0 0 0 7.8670443388722715 3 1 1 0 1 +0 1 28000.0 0.8950813492872013 -1 43 56 0.009037595240402737 -1.0213613916745492 0 3537 9587.2390362061 7004.637984730951 5569.577611010884 3 2 0 58 0 3 0 1 -1 1 200.0 0 0 7.517712269216579 2 0 0 0 1 +0 0 31000.0 0.7526981131076749 20 1 29 3.4766688597020843 -1.0064068710817766 0 1117 9937.815350150235 5502.8707120922645 5416.685022791681 0 12 0 112 0 1 0 1 0 1 200.0 0 0 1.7451666969727309 3 1 1 0 3 +0 1 13000.0 0.05155801053479248 0 138 55 0.005852177219766186 -1.001799034316461 0 971 6133.689301729005 4056.0472273165856 4241.642525827812 0 1 0 263 1 0 1 1 -1 0 200.0 0 0 14.395875868474949 2 1 1 0 5 +0 0 9000.0 0.910185068177202 -1 55 30 0.007743424828630069 -0.9987815889669824 0 3822 7546.627216723622 4573.039319668592 4900.918713349553 0 10 1 35 0 2 0 1 0 0 200.0 0 0 4.285411103515576 3 1 1 0 3 +0 0 550.0 0.7231265696439259 0 68 22 0.013300321030582731 -1.0196378415337275 3 1163 8564.31082307927 5100.376911525497 5014.1851014312515 1 18 0 183 0 1 0 1 1 0 200.0 0 0 1.0721903614568091 3 1 1 0 4 +0 0 32000.0 0.8512209388298696 -1 286 44 0.0008297636237453777 -1.0093050701068011 0 1968 20818.03545124037 4236.589353747329 5031.259155143504 0 13 0 89 0 3 0 1 0 1 200.0 0 0 2.8225371766373746 0 1 1 0 4 +0 1 36000.0 0.630842777444818 0 186 60 0.008365227370164834 -0.9821737017985156 3 1112 1135.0669208453094 7440.34904251575 6085.514932944373 9 4 2 157 0 3 1 1 0 1 1500.0 0 0 6.41319956469383 3 1 0 0 0 +1 0 14000.0 0.6779362862448164 0 84 22 0.0021188072963205665 -1.0061226219906938 2 1484 4274.851054717818 3201.511528695834 5614.103970240325 10 3 0 156 1 4 0 1 0 0 500.0 0 0 3.818802375969897 2 0 1 0 3 +0 0 21000.0 0.6698483952475436 0 223 22 0.005892243192388771 24.42212897871255 1 1025 3406.722131375466 2729.345972047421 4740.430780495241 11 14 0 164 0 2 1 1 28 1 1000.0 0 0 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7386.778387738915 6382.80391440601 0 16 0 92 1 3 1 1 -1 0 200.0 0 0 11.007667684532523 2 0 1 0 2 +0 1 12000.0 0.8620338062420111 0 367 73 0.003248675002804336 26.79284606908173 1 1443 1475.7613047257778 3799.7414424823296 4895.5732905611885 15 0 0 78 0 4 1 1 0 0 200.0 0 0 8.99530878505447 2 0 1 0 3 +0 0 6900.0 0.5072661816455442 0 30 23 0.008328874658729524 90.43891029519585 1 4380 12525.968578701 5768.45537628616 5063.496678938758 11 17 0 204 1 3 0 1 27 0 500.0 0 0 1.652287991348781 0 0 0 0 3 +1 1 16000.0 0.4533276620503002 0 59 53 0.0046789746040013435 -1.008631518114437 0 1249 10589.92258565049 6695.968196477593 5517.5367441493945 0 7 0 230 0 0 1 0 -1 0 1500.0 0 0 5.820924817215563 0 0 2 0 2 +0 0 10000.0 0.7989919029987759 0 95 45 0.0011994556068818324 -1.015032393113238 0 185 2678.086002881219 3210.2684573120714 4248.268871018571 0 2 3 211 0 1 0 1 0 1 1000.0 0 0 5.762365508836764 2 1 0 0 5 +0 0 15000.0 0.8802696403128641 0 70 22 0.0111107361885012 23.308603644285412 1 837 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4.392871921358436 2 1 0 0 2 +0 0 5300.0 0.152169202703461 0 127 45 0.0016989352569582586 -1.0191220812080874 0 1312 1162.5298572435793 5043.209898369197 4366.2016017439755 0 8 0 132 0 0 0 1 -1 0 1000.0 0 0 1.308097478372564 0 0 1 0 5 +0 0 370.0 0.5815403192745386 170 16 26 0.010282789688785878 -1.0487297657064834 0 889 6105.292831843632 2986.1904937416634 4147.716415502336 1 10 0 234 0 1 0 1 1 0 1000.0 0 0 3.272086170302037 0 1 1 0 5 +0 0 19000.0 0.6279445167368218 0 37 31 0.0024189629097021016 83.67021552277934 1 3465 2118.859869422512 4708.891524906726 6239.395524091872 0 18 0 6 0 2 1 1 30 1 200.0 0 0 3.1607051299925715 3 0 1 0 1 +0 0 28000.0 0.901539703069 25 11 28 0.00021640660347100758 -1.0375041975444417 0 1909 4178.1870690310825 5558.086478407278 5022.25636972568 0 9 0 124 0 1 0 1 0 1 200.0 0 0 5.245695031887177 2 1 1 0 4 +0 0 52000.0 0.8832330481599516 -1 64 49 0.005994468955501778 -1.0002655175351454 3 1442 9179.361788537592 5614.016966519904 6368.855794724576 32 5 0 274 0 0 1 0 0 1 1500.0 0 0 2.2491972669996088 2 0 1 0 0 +0 0 18000.0 0.7046159732305807 -1 11 35 0.017304723110144596 -1.0155720684528633 0 962 7386.190938295684 5367.28858324804 6807.8892147451415 0 13 0 135 1 1 0 1 -1 0 500.0 0 1 4.1120559628453925 2 0 1 0 0 +0 1 9200.0 0.3685708596016175 0 13 55 0.016381616232907792 26.67734315457849 1 895 2688.494639007546 2629.0116427771313 4666.216661355646 9 0 1 201 0 0 1 0 14 0 1000.0 0 0 9.335642045344287 3 0 1 0 3 diff --git a/examples/FairGBM-other/README.md b/examples/FairGBM-other/README.md new file mode 100644 index 000000000..c598d2fe4 --- /dev/null +++ b/examples/FairGBM-other/README.md @@ -0,0 +1,46 @@ +Binary Classification with Fairness Constraints +=============================================== +> Example of training a binary classifier with fairness constraints. + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Dataset +------- + +The BAF (Bank Account Fraud) dataset is a real-world dataset on banking fraud. +More details [here](https://github.com/feedzai/bank-account-fraud). + +For this example we're using a sample of 10% of the BAF-Base dataset. +Fairness is measured as the FPR ratio between people aged under 50 and those aged 50 or above (as per the dataset's +[datasheet](https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf)). + + +Training +-------- +Run the following command in this folder to train **FairGBM**: + +```bash +"../../lightgbm" config=train.conf +``` + +To train the vanilla LightGBM on the same data use: +```bash +"../../lightgbm" config=train_unconstrained.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder to compute test predictions for **FairGBM**: + +```bash +"../../lightgbm" config=predict.conf +``` + +To compute test predictions for the vanilla LightGBM use: +```bash +"../../lightgbm" config=predict_unconstrained.conf +``` diff --git a/examples/FairGBM-other/predict.conf b/examples/FairGBM-other/predict.conf new file mode 100644 index 000000000..c73a170d2 --- /dev/null +++ b/examples/FairGBM-other/predict.conf @@ -0,0 +1,16 @@ +task = predict + +data = BAF-base.test + +has_header=True + +label_column = name:fraud_bool + +ignore_column = name:customer_age_category,month + +categorical_column = name:payment_type,employment_status,housing_status,source,device_os + +input_model= FairGBM_model.txt + +output_result = FairGBM_predictions.txt + diff --git a/examples/FairGBM-other/predict_unconstrained.conf b/examples/FairGBM-other/predict_unconstrained.conf new file mode 100644 index 000000000..d85bfa6b7 --- /dev/null +++ b/examples/FairGBM-other/predict_unconstrained.conf @@ -0,0 +1,15 @@ +task = predict + +data = BAF-base.test + +has_header=True + +label_column = name:fraud_bool + +ignore_column = name:customer_age_category,month + +categorical_column = name:payment_type,employment_status,housing_status,source,device_os + +input_model= LightGBM_model.txt + +output_result = LightGBM_predictions.txt diff --git a/examples/FairGBM-other/train.conf b/examples/FairGBM-other/train.conf new file mode 100644 index 000000000..826dbf949 --- /dev/null +++ b/examples/FairGBM-other/train.conf @@ -0,0 +1,87 @@ +# task type, supports train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# constrained_cross_entropy , constrained optimization task (classification) +# alias: application, app +objective = constrained_cross_entropy # training FairGBM! + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 5 + +# true if need output metric for training data, alias: training_metric, train_metric +is_training_metric = true + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = BAF-base.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = BAF-base.test + +# first row has column names +has_header=True + +# name of label column +label_column = name:fraud_bool + +# ignore the sensitive attribute column for training (plus the date column) +ignore_column = name:customer_age_category,month + +# all categorical columns +categorical_column = name:payment_type,employment_status,housing_status,source,device_os + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 1000 + +# output model file +output_model = FairGBM_model.txt + +# fixed random state for reproducibility +# (if multi-threaded may still result in variable results) +random_state = 42 + +# ** FairGBM-specific parameters ** +# function to use as a differentiable proxy for the step-wise function +constraint_stepwise_proxy=cross_entropy + +# column that contains groups to enforce constraints over (e.g., FPR or FNR equality) +constraint_group_column=name:customer_age_category + +# constraint type +constraint_type=fpr + +# small epsilon of allowed metric divergence between groups +constraint_fpr_threshold=0 + +# learning rate for the Lagrangian multipliers (ascent step) +# use larger values with larger datasets +# fuzzy rule of thumb: use 1/20th to 1/10th of the dataset's length +lagrangian_learning_rate=10000 + +# constraints on global metrics at threshold=0.5 +global_constraint_type=fpr,fnr + +# target: less than or equal to 5% global FPR +global_target_fpr=0.05 + +# target: less than or equal to 70% global FNR +global_target_fnr=0.70 diff --git a/examples/FairGBM-other/train_unconstrained.conf b/examples/FairGBM-other/train_unconstrained.conf new file mode 100644 index 000000000..eae3ab387 --- /dev/null +++ b/examples/FairGBM-other/train_unconstrained.conf @@ -0,0 +1,60 @@ +# task type, supports train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# constrained_cross_entropy , constrained optimization task (classification) +# alias: application, app +objective = cross_entropy # training vanilla (unconstrained) LightGBM + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 5 + +# true if need output metric for training data, alias: training_metric, train_metric +is_training_metric = true + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = BAF-base.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = BAF-base.test + +# first row has column names +has_header=True + +# name of label column +label_column = name:fraud_bool + +# ignore the sensitive attribute column for training (plus the date column) +ignore_column = name:customer_age_category,month + +# all categorical columns +categorical_column = name:payment_type,employment_status,housing_status,source,device_os + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 1000 + +# output model file +output_model = LightGBM_model.txt + +# fixed random state for reproducibility +# (if multi-threaded may still result in variable results) +random_state = 42 From 779c51f2c58900e01703d1568f5fa39f3d2c99d8 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 28 Jun 2022 14:51:20 +0100 Subject: [PATCH 19/32] minor clarification in example confs --- examples/FairGBM/train.conf | 1 + examples/FairGBM/train_unconstrained.conf | 1 + 2 files changed, 2 insertions(+) diff --git a/examples/FairGBM/train.conf b/examples/FairGBM/train.conf index 654641678..b84e69ccd 100644 --- a/examples/FairGBM/train.conf +++ b/examples/FairGBM/train.conf @@ -8,6 +8,7 @@ boosting_type = gbdt # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task +# constrained_cross_entropy , constrained optimization task (classification) # alias: application, app objective = constrained_cross_entropy # training FairGBM! diff --git a/examples/FairGBM/train_unconstrained.conf b/examples/FairGBM/train_unconstrained.conf index 6e7ac29a9..afc8c9d16 100644 --- a/examples/FairGBM/train_unconstrained.conf +++ b/examples/FairGBM/train_unconstrained.conf @@ -8,6 +8,7 @@ boosting_type = gbdt # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task +# constrained_cross_entropy , constrained optimization task (classification) # alias: application, app objective = cross_entropy # training vanilla LightGBM! From 48b3b7662d28f57941a3eae70adf6f1303880a19 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 28 Jun 2022 14:59:54 +0100 Subject: [PATCH 20/32] moved FairGBM examples --- .../{FairGBM => FairGBM-other}/COMPAS.test | 0 .../{FairGBM => FairGBM-other}/COMPAS.train | 0 examples/FairGBM-other/README.md | 12 +-- examples/FairGBM-other/predict.conf | 9 +- .../FairGBM-other/predict_unconstrained.conf | 8 +- examples/FairGBM-other/train.conf | 100 ++++++++++++------ .../FairGBM-other/train_unconstrained.conf | 71 +++++++++++-- .../{FairGBM-other => FairGBM}/BAF-base.test | 0 .../{FairGBM-other => FairGBM}/BAF-base.train | 0 examples/FairGBM/README.md | 12 ++- examples/FairGBM/predict.conf | 9 +- examples/FairGBM/predict_unconstrained.conf | 8 +- examples/FairGBM/train.conf | 100 ++++++------------ examples/FairGBM/train_unconstrained.conf | 71 ++----------- tests/python_test_requirements.txt | 0 15 files changed, 200 insertions(+), 200 deletions(-) rename examples/{FairGBM => FairGBM-other}/COMPAS.test (100%) rename examples/{FairGBM => FairGBM-other}/COMPAS.train (100%) rename examples/{FairGBM-other => FairGBM}/BAF-base.test (100%) rename examples/{FairGBM-other => FairGBM}/BAF-base.train (100%) create mode 100644 tests/python_test_requirements.txt diff --git a/examples/FairGBM/COMPAS.test b/examples/FairGBM-other/COMPAS.test similarity index 100% rename from examples/FairGBM/COMPAS.test rename to examples/FairGBM-other/COMPAS.test diff --git a/examples/FairGBM/COMPAS.train b/examples/FairGBM-other/COMPAS.train similarity index 100% rename from examples/FairGBM/COMPAS.train rename to examples/FairGBM-other/COMPAS.train diff --git a/examples/FairGBM-other/README.md b/examples/FairGBM-other/README.md index c598d2fe4..b7ef31f59 100644 --- a/examples/FairGBM-other/README.md +++ b/examples/FairGBM-other/README.md @@ -2,19 +2,11 @@ Binary Classification with Fairness Constraints =============================================== > Example of training a binary classifier with fairness constraints. +Dataset source: https://github.com/propublica/compas-analysis/ + ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** -Dataset -------- - -The BAF (Bank Account Fraud) dataset is a real-world dataset on banking fraud. -More details [here](https://github.com/feedzai/bank-account-fraud). - -For this example we're using a sample of 10% of the BAF-Base dataset. -Fairness is measured as the FPR ratio between people aged under 50 and those aged 50 or above (as per the dataset's -[datasheet](https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf)). - Training -------- diff --git a/examples/FairGBM-other/predict.conf b/examples/FairGBM-other/predict.conf index c73a170d2..4a3715ff1 100644 --- a/examples/FairGBM-other/predict.conf +++ b/examples/FairGBM-other/predict.conf @@ -1,16 +1,11 @@ task = predict -data = BAF-base.test +data = COMPAS.test has_header=True -label_column = name:fraud_bool - -ignore_column = name:customer_age_category,month - -categorical_column = name:payment_type,employment_status,housing_status,source,device_os +label_column = name:two_year_recid input_model= FairGBM_model.txt output_result = FairGBM_predictions.txt - diff --git a/examples/FairGBM-other/predict_unconstrained.conf b/examples/FairGBM-other/predict_unconstrained.conf index d85bfa6b7..a77d4850c 100644 --- a/examples/FairGBM-other/predict_unconstrained.conf +++ b/examples/FairGBM-other/predict_unconstrained.conf @@ -1,14 +1,10 @@ task = predict -data = BAF-base.test +data = COMPAS.test has_header=True -label_column = name:fraud_bool - -ignore_column = name:customer_age_category,month - -categorical_column = name:payment_type,employment_status,housing_status,source,device_os +label_column = name:two_year_recid input_model= LightGBM_model.txt diff --git a/examples/FairGBM-other/train.conf b/examples/FairGBM-other/train.conf index 826dbf949..b84e69ccd 100644 --- a/examples/FairGBM-other/train.conf +++ b/examples/FairGBM-other/train.conf @@ -22,66 +22,102 @@ objective = constrained_cross_entropy # training FairGBM! metric = binary_logloss,auc # frequency for metric output -metric_freq = 5 +metric_freq = 1 # true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train -data = BAF-base.train +data = COMPAS.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, -valid_data = BAF-base.test +valid_data = COMPAS.test # first row has column names has_header=True # name of label column -label_column = name:fraud_bool +label_column = name:two_year_recid -# ignore the sensitive attribute column for training (plus the date column) -ignore_column = name:customer_age_category,month +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 200 -# all categorical columns -categorical_column = name:payment_type,employment_status,housing_status,source,device_os +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 -# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds -num_trees = 1000 +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# max depth of each individual base learner +max_depth = 10 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false # output model file output_model = FairGBM_model.txt +# output prediction file for predict task +# output_result= prediction.txt + # fixed random state for reproducibility # (if multi-threaded may still result in variable results) random_state = 42 # ** FairGBM-specific parameters ** -# function to use as a differentiable proxy for the step-wise function constraint_stepwise_proxy=cross_entropy - -# column that contains groups to enforce constraints over (e.g., FPR or FNR equality) -constraint_group_column=name:customer_age_category - -# constraint type +constraint_group_column=name:race_Caucasian constraint_type=fpr - -# small epsilon of allowed metric divergence between groups constraint_fpr_threshold=0 - -# learning rate for the Lagrangian multipliers (ascent step) -# use larger values with larger datasets -# fuzzy rule of thumb: use 1/20th to 1/10th of the dataset's length -lagrangian_learning_rate=10000 - -# constraints on global metrics at threshold=0.5 -global_constraint_type=fpr,fnr - -# target: less than or equal to 5% global FPR -global_target_fpr=0.05 - -# target: less than or equal to 70% global FNR -global_target_fnr=0.70 +proxy_margin=1 +score_threshold=0.5 +lagrangian_learning_rate=200 + +# global_constraint_type=fpr,fnr # no global constraint on this example, as we're just aiming to maximize accuracy +# global_score_threshold=0.5 +# global_target_fpr=0.05 +# global_target_fnr=0.30 diff --git a/examples/FairGBM-other/train_unconstrained.conf b/examples/FairGBM-other/train_unconstrained.conf index eae3ab387..afc8c9d16 100644 --- a/examples/FairGBM-other/train_unconstrained.conf +++ b/examples/FairGBM-other/train_unconstrained.conf @@ -10,7 +10,7 @@ boosting_type = gbdt # lambdarank , LambdaRank task # constrained_cross_entropy , constrained optimization task (classification) # alias: application, app -objective = cross_entropy # training vanilla (unconstrained) LightGBM +objective = cross_entropy # training vanilla LightGBM! # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 @@ -22,39 +22,88 @@ objective = cross_entropy # training vanilla (unconstrained) LightGBM metric = binary_logloss,auc # frequency for metric output -metric_freq = 5 +metric_freq = 1 # true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train -data = BAF-base.train +data = COMPAS.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, -valid_data = BAF-base.test +valid_data = COMPAS.test # first row has column names has_header=True # name of label column -label_column = name:fraud_bool +label_column = name:two_year_recid + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 200 -# ignore the sensitive attribute column for training (plus the date column) -ignore_column = name:customer_age_category,month +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 -# all categorical columns -categorical_column = name:payment_type,employment_status,housing_status,source,device_os +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 -# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds -num_trees = 1000 +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# max depth of each individual base learner +max_depth = 10 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false # output model file output_model = LightGBM_model.txt +# output prediction file for predict task +# output_result= prediction.txt + # fixed random state for reproducibility # (if multi-threaded may still result in variable results) random_state = 42 diff --git a/examples/FairGBM-other/BAF-base.test b/examples/FairGBM/BAF-base.test similarity index 100% rename from examples/FairGBM-other/BAF-base.test rename to examples/FairGBM/BAF-base.test diff --git a/examples/FairGBM-other/BAF-base.train b/examples/FairGBM/BAF-base.train similarity index 100% rename from examples/FairGBM-other/BAF-base.train rename to examples/FairGBM/BAF-base.train diff --git a/examples/FairGBM/README.md b/examples/FairGBM/README.md index b7ef31f59..c598d2fe4 100644 --- a/examples/FairGBM/README.md +++ b/examples/FairGBM/README.md @@ -2,11 +2,19 @@ Binary Classification with Fairness Constraints =============================================== > Example of training a binary classifier with fairness constraints. -Dataset source: https://github.com/propublica/compas-analysis/ - ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** +Dataset +------- + +The BAF (Bank Account Fraud) dataset is a real-world dataset on banking fraud. +More details [here](https://github.com/feedzai/bank-account-fraud). + +For this example we're using a sample of 10% of the BAF-Base dataset. +Fairness is measured as the FPR ratio between people aged under 50 and those aged 50 or above (as per the dataset's +[datasheet](https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf)). + Training -------- diff --git a/examples/FairGBM/predict.conf b/examples/FairGBM/predict.conf index 4a3715ff1..c73a170d2 100644 --- a/examples/FairGBM/predict.conf +++ b/examples/FairGBM/predict.conf @@ -1,11 +1,16 @@ task = predict -data = COMPAS.test +data = BAF-base.test has_header=True -label_column = name:two_year_recid +label_column = name:fraud_bool + +ignore_column = name:customer_age_category,month + +categorical_column = name:payment_type,employment_status,housing_status,source,device_os input_model= FairGBM_model.txt output_result = FairGBM_predictions.txt + diff --git a/examples/FairGBM/predict_unconstrained.conf b/examples/FairGBM/predict_unconstrained.conf index a77d4850c..d85bfa6b7 100644 --- a/examples/FairGBM/predict_unconstrained.conf +++ b/examples/FairGBM/predict_unconstrained.conf @@ -1,10 +1,14 @@ task = predict -data = COMPAS.test +data = BAF-base.test has_header=True -label_column = name:two_year_recid +label_column = name:fraud_bool + +ignore_column = name:customer_age_category,month + +categorical_column = name:payment_type,employment_status,housing_status,source,device_os input_model= LightGBM_model.txt diff --git a/examples/FairGBM/train.conf b/examples/FairGBM/train.conf index b84e69ccd..826dbf949 100644 --- a/examples/FairGBM/train.conf +++ b/examples/FairGBM/train.conf @@ -22,102 +22,66 @@ objective = constrained_cross_entropy # training FairGBM! metric = binary_logloss,auc # frequency for metric output -metric_freq = 1 +metric_freq = 5 # true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true -# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. -max_bin = 255 - # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train -data = COMPAS.train +data = BAF-base.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, -valid_data = COMPAS.test +valid_data = BAF-base.test # first row has column names has_header=True # name of label column -label_column = name:two_year_recid - -# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds -num_trees = 200 - -# shrinkage rate , alias: shrinkage_rate -learning_rate = 0.1 - -# number of leaves for one tree, alias: num_leaf -num_leaves = 63 - -# type of tree learner, support following types: -# serial , single machine version -# feature , use feature parallel to train -# data , use data parallel to train -# voting , use voting based parallel to train -# alias: tree -tree_learner = serial - -# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. -# num_threads = 8 - -# feature sub-sample, will random select 80% feature to train on each iteration -# alias: sub_feature -feature_fraction = 0.8 - -# Support bagging (data sub-sample), will perform bagging every 5 iterations -bagging_freq = 5 - -# Bagging fraction, will random select 80% data on bagging -# alias: sub_row -bagging_fraction = 0.8 - -# minimal number data for one leaf, use this to deal with over-fit -# alias : min_data_per_leaf, min_data -min_data_in_leaf = 50 +label_column = name:fraud_bool -# max depth of each individual base learner -max_depth = 10 +# ignore the sensitive attribute column for training (plus the date column) +ignore_column = name:customer_age_category,month -# minimal sum Hessians for one leaf, use this to deal with over-fit -min_sum_hessian_in_leaf = 5.0 +# all categorical columns +categorical_column = name:payment_type,employment_status,housing_status,source,device_os -# save memory and faster speed for sparse feature, alias: is_sparse -is_enable_sparse = true - -# when data is bigger than memory size, set this to true. otherwise set false will have faster speed -# alias: two_round_loading, two_round -use_two_round_loading = false - -# true if need to save data to binary file and application will auto load data from binary file next time -# alias: is_save_binary, save_binary -is_save_binary_file = false +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 1000 # output model file output_model = FairGBM_model.txt -# output prediction file for predict task -# output_result= prediction.txt - # fixed random state for reproducibility # (if multi-threaded may still result in variable results) random_state = 42 # ** FairGBM-specific parameters ** +# function to use as a differentiable proxy for the step-wise function constraint_stepwise_proxy=cross_entropy -constraint_group_column=name:race_Caucasian + +# column that contains groups to enforce constraints over (e.g., FPR or FNR equality) +constraint_group_column=name:customer_age_category + +# constraint type constraint_type=fpr + +# small epsilon of allowed metric divergence between groups constraint_fpr_threshold=0 -proxy_margin=1 -score_threshold=0.5 -lagrangian_learning_rate=200 - -# global_constraint_type=fpr,fnr # no global constraint on this example, as we're just aiming to maximize accuracy -# global_score_threshold=0.5 -# global_target_fpr=0.05 -# global_target_fnr=0.30 + +# learning rate for the Lagrangian multipliers (ascent step) +# use larger values with larger datasets +# fuzzy rule of thumb: use 1/20th to 1/10th of the dataset's length +lagrangian_learning_rate=10000 + +# constraints on global metrics at threshold=0.5 +global_constraint_type=fpr,fnr + +# target: less than or equal to 5% global FPR +global_target_fpr=0.05 + +# target: less than or equal to 70% global FNR +global_target_fnr=0.70 diff --git a/examples/FairGBM/train_unconstrained.conf b/examples/FairGBM/train_unconstrained.conf index afc8c9d16..eae3ab387 100644 --- a/examples/FairGBM/train_unconstrained.conf +++ b/examples/FairGBM/train_unconstrained.conf @@ -10,7 +10,7 @@ boosting_type = gbdt # lambdarank , LambdaRank task # constrained_cross_entropy , constrained optimization task (classification) # alias: application, app -objective = cross_entropy # training vanilla LightGBM! +objective = cross_entropy # training vanilla (unconstrained) LightGBM # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 @@ -22,88 +22,39 @@ objective = cross_entropy # training vanilla LightGBM! metric = binary_logloss,auc # frequency for metric output -metric_freq = 1 +metric_freq = 5 # true if need output metric for training data, alias: training_metric, train_metric is_training_metric = true -# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. -max_bin = 255 - # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train -data = COMPAS.train +data = BAF-base.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, -valid_data = COMPAS.test +valid_data = BAF-base.test # first row has column names has_header=True # name of label column -label_column = name:two_year_recid - -# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds -num_trees = 200 - -# shrinkage rate , alias: shrinkage_rate -learning_rate = 0.1 - -# number of leaves for one tree, alias: num_leaf -num_leaves = 63 - -# type of tree learner, support following types: -# serial , single machine version -# feature , use feature parallel to train -# data , use data parallel to train -# voting , use voting based parallel to train -# alias: tree -tree_learner = serial - -# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. -# num_threads = 8 - -# feature sub-sample, will random select 80% feature to train on each iteration -# alias: sub_feature -feature_fraction = 0.8 +label_column = name:fraud_bool -# Support bagging (data sub-sample), will perform bagging every 5 iterations -bagging_freq = 5 +# ignore the sensitive attribute column for training (plus the date column) +ignore_column = name:customer_age_category,month -# Bagging fraction, will random select 80% data on bagging -# alias: sub_row -bagging_fraction = 0.8 +# all categorical columns +categorical_column = name:payment_type,employment_status,housing_status,source,device_os -# minimal number data for one leaf, use this to deal with over-fit -# alias : min_data_per_leaf, min_data -min_data_in_leaf = 50 - -# max depth of each individual base learner -max_depth = 10 - -# minimal sum Hessians for one leaf, use this to deal with over-fit -min_sum_hessian_in_leaf = 5.0 - -# save memory and faster speed for sparse feature, alias: is_sparse -is_enable_sparse = true - -# when data is bigger than memory size, set this to true. otherwise set false will have faster speed -# alias: two_round_loading, two_round -use_two_round_loading = false - -# true if need to save data to binary file and application will auto load data from binary file next time -# alias: is_save_binary, save_binary -is_save_binary_file = false +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 1000 # output model file output_model = LightGBM_model.txt -# output prediction file for predict task -# output_result= prediction.txt - # fixed random state for reproducibility # (if multi-threaded may still result in variable results) random_state = 42 diff --git a/tests/python_test_requirements.txt b/tests/python_test_requirements.txt new file mode 100644 index 000000000..e69de29bb From f9feb5301016b558df8dcf82e6f613f3c343b901 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 28 Jun 2022 15:00:20 +0100 Subject: [PATCH 21/32] python package requirements for testing --- tests/python_test_requirements.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/python_test_requirements.txt b/tests/python_test_requirements.txt index e69de29bb..b70024dad 100644 --- a/tests/python_test_requirements.txt +++ b/tests/python_test_requirements.txt @@ -0,0 +1,3 @@ +aequitas-lite~=0.43.2 +scikit-learn~=1.0.2 +pandas~=1.3.5 \ No newline at end of file From 48926e3d73a47ec06847a3b507140e6b53abb296 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 28 Jun 2022 15:13:40 +0100 Subject: [PATCH 22/32] util for loading BAF data in python --- tests/python_package_test/utils.py | 51 ++++++++++++++++++++---------- 1 file changed, 35 insertions(+), 16 deletions(-) diff --git a/tests/python_package_test/utils.py b/tests/python_package_test/utils.py index f843a45e5..cc897edab 100644 --- a/tests/python_package_test/utils.py +++ b/tests/python_package_test/utils.py @@ -9,13 +9,47 @@ from sklearn.utils import check_random_state +@lru_cache(maxsize=None) +def load_baf_base(): + pd = pytest.importorskip( + "pandas", + reason="The `pandas` package must be installed to import BAF-base and run fairness tests.") + + local_root_path = Path(__file__).parent.parent.parent / "examples" / "FairGBM" + label_col = "fraud_bool" + sensitive_col = "customer_age" + # month_col = "month" + categorical_cols = ["payment_type", "employment_status", "housing_status", "source", "device_os"] + age_category_threshold = 50 + + def split_X_Y_S(path): + # Read data from path + data = pd.read_csv(path, header=0, sep="\t", index_col=None) + + # Set categorical columns as such (needed for LightGBM to be able to identify which cols are categorical) + data = data.astype({col: "category" for col in categorical_cols}) + + # Split X, Y, S + X = data[[col for col in data.columns if col != label_col]] + Y = data[label_col] + S = (data[sensitive_col] >= age_category_threshold).astype(int) + return X, Y, S + + data_paths = { + "train": local_root_path / "BAF-base.train", + "test": local_root_path / "BAF-base.test", + } + + return {key: split_X_Y_S(path) for key, path in data_paths.items()} + + @lru_cache(maxsize=None) def load_compas(): pd = pytest.importorskip( "pandas", reason="The `pandas` package must be installed to import COMPAS and run fairness tests.") - local_root_path = Path(__file__).parent.parent.parent / "examples" / "FairGBM" + local_root_path = Path(__file__).parent.parent.parent / "examples" / "FairGBM-other" target_col_name = "two_year_recid" sensitive_col_name = "race_Caucasian" @@ -34,21 +68,6 @@ def read_data(path) -> Tuple[pd.DataFrame, pd.Series, pd.Series]: return {key: read_data(path) for key, path in data_paths.items()} -@lru_cache(maxsize=None) -def load_california_housing_classification(price_threshold=100_000, seed=42): - X, Y = sklearn.datasets.fetch_california_housing(return_X_y=True) - - # Binarize target column for classification - Y = (Y >= price_threshold).astype(np.float32) - - # Let's generate a sensitive attribute column for testing - # NOTE: training is "unaware" as the sensitive column is not part of the training features - rng = np.random.RandomState(seed) - S = (rng.random(size=y.shape) > 0.80).astype(np.int32) - - return X, Y, S - - @lru_cache(maxsize=None) def load_boston(**kwargs): return sklearn.datasets.load_boston(**kwargs) From 329c2979b036f10e2b19fea8369b3a439b12581d Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Wed, 29 Jun 2022 17:00:41 +0100 Subject: [PATCH 23/32] implemented FairGBM python tests --- .../test_constrained_optimization.py | 88 ++++++++++++------- tests/python_package_test/utils.py | 67 ++++++++++++++ 2 files changed, 124 insertions(+), 31 deletions(-) diff --git a/tests/python_package_test/test_constrained_optimization.py b/tests/python_package_test/test_constrained_optimization.py index 4533b24a0..d2b1f64e4 100644 --- a/tests/python_package_test/test_constrained_optimization.py +++ b/tests/python_package_test/test_constrained_optimization.py @@ -3,52 +3,78 @@ import lightgbm as lgb -from .utils import load_compas, load_california_housing_classification +from .utils import load_baf_base, binarize_predictions, evaluate_recall, evaluate_fairness @pytest.fixture -def lightgbm_hyperparams() -> dict: +def lightgbm_params() -> dict: return { - # TODO + "objective": "cross_entropy", + "num_iterations": 1000, } + @pytest.fixture -def fairgbm_hyperparams(lightgbm_hyperparams) -> dict: - hyperparams = lightgbm_hyperparams.copy() - hyperparams.update({ - # TODO +def fairgbm_params(lightgbm_params) -> dict: + params = lightgbm_params.copy() + params.update({ + "objective": "constrained_cross_entropy", + "multiplier_learning_rate": 10_000, + "constraint_type": "FPR", + "global_constraint_type": "FPR,FNR", + "global_target_fpr": 0.05, + "global_target_fnr": 0.70, }) - return hyperparams + return params -def assert_fairness_fairgbm_vs_lightgbm( - fairgbm_params, - lightgbm_params, - X_train, y_train, s_train, - X_test, y_test, s_test - ): - # TODO: - # 1. create lgb objects - # 2. train fairgbm and lightgbm - # 3. compute predictions on test data - # 4. compute fairness and accuracy - # 5. assert FairGBM is strictly better than LightGBM in fairness - assert True +@pytest.mark.parametrize("fairgbm_target_fpr", [ + 0.03, 0.05, 0.075, 0.10, 0.2, 0.5, +]) +def test_fairgbm_fairness(fairgbm_params, fairgbm_target_fpr): + # Load BAF-base dataset + data = load_baf_base() + X_train, Y_train, S_train = data["train"] + X_test, Y_test, S_test = data["test"] + + train_data = lgb.Dataset(X_train, label=Y_train, constraint_group=S_train) -def test_fairgbm_vs_lightgbm_on_compas(): - data = load_compas() + # Set a specific value of target global FPR + fairgbm_params["global_target_fpr"] = fairgbm_target_fpr - def load_lgb_data(data): - X, Y, S = data - return lgb.Dataset(X, label=Y, constraint_group=S) + # Train FairGBM model + clf = lgb.train(params=fairgbm_params, train_set=train_data) - lgb_train = load_lgb_data(data["train"]) - lgb_test = load_lgb_data(data["test"]) + # Compute test predictions + Y_test_pred = clf.predict(X_test) # NOTE: we don't need sensitive attribute data to compute predictions! + # Binarize predictions at target FPR + Y_test_pred_binarized = binarize_predictions(Y_test, Y_test_pred, fpr=fairgbm_target_fpr) -def test_fairgbm_vs_lightgbm_on_california_housing(): - X, Y, S = load_california_housing_classification() + # Compute Recall and Fairness at target FPR + recall = evaluate_recall(Y_test, Y_test_pred_binarized) + fpr_ratio = evaluate_fairness(Y_test, Y_test_pred_binarized, S_test, metric_col="fpr") - # TODO: train test split and train fairgbm etc. + # Assert Recall makes sense, i.e., is somewhat better than random :) + smallest_acceptable_recall = fairgbm_target_fpr + 0.20 # this is a loose educated guess + assert recall > smallest_acceptable_recall, ( + f"FairGBM achieved recall {recall:.1%} at {fairgbm_target_fpr:.1%} FPR, expected at least " + f"{smallest_acceptable_recall:.1%} recall." + ) + + # Assert Fairness makes sense; it should be better than 80% for most cases + assert (fpr_ratio > 0.70) if (fairgbm_target_fpr > 0.05) else (fpr_ratio > 0.60), ( + f"FairGBM achieved fairness (FPR ratio) of {fpr_ratio:.1%} at {fairgbm_target_fpr:.1%} global FPR, " + f"this seems too low..." + ) + + +def test_fairgbm_vs_lightgbm(lightgbm_params, fairgbm_params): + # TODO: + # 1. create lgb objects + # 2. train fairgbm and lightgbm + # 3. compute predictions on test data + # 4. compute fairness and accuracy + # 5. assert FairGBM is strictly better than LightGBM in fairness assert True diff --git a/tests/python_package_test/utils.py b/tests/python_package_test/utils.py index cc897edab..a4f5a2aec 100644 --- a/tests/python_package_test/utils.py +++ b/tests/python_package_test/utils.py @@ -2,11 +2,13 @@ from functools import lru_cache from pathlib import Path from typing import Tuple +import logging import pytest import numpy as np import sklearn.datasets from sklearn.utils import check_random_state +from sklearn.metrics import roc_curve, confusion_matrix @lru_cache(maxsize=None) @@ -171,3 +173,68 @@ def make_ranking(n_samples=100, n_features=20, n_informative=5, gmax=2, X[:, j] = bias + coef * y_vec return X, y_vec, group_id_vec + + +def threshold_at_target( + y_true: np.ndarray, + y_pred: np.ndarray, + tpr: float = None, + fpr: float = None, + ) -> float: + """Computes the threshold at the given target. + Does not untie rows, may miss target in the presence of ties. + Uses scikit-learn to compute ROC curve. + """ + fpr_vals, tpr_vals, thresholds = roc_curve(y_true, y_pred, pos_label=1) + + # Find threshold that hits **at least** the target TPR + if tpr: + threshold_idx = np.argmax(np.argwhere(tpr_vals < tpr)) + 1 + + # Find threshold that hits **at most** the target FPR + elif fpr: + threshold_idx = np.argmax(np.argwhere(fpr_vals <= fpr)) + + threshold = thresholds[threshold_idx] + + # Sanity check! + y_pred_binarized = (y_pred >= threshold) + tn, fp, fn, tp = confusion_matrix(y_true, y_pred_binarized).ravel() + actual_tpr = tp / (tp + fn) + actual_fpr = fp / (fp + tn) + if (tpr and actual_tpr < tpr) or (fpr and actual_fpr > fpr): + logging.error(f"Missed target metric: TPR={actual_tpr:.1%}, FPR={actual_fpr:.1%};") + + return threshold + + +def binarize_predictions(y_true, y_pred, tpr: float = None, fpr: float = None): + threshold = threshold_at_target(y_true, y_pred, tpr=tpr, fpr=fpr) + return (y_pred >= threshold).astype(int) + + +def evaluate_recall(y_true, y_pred): + tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel() + recall = tp / (tp + fn) + return recall + + +def evaluate_fairness(y_true, y_pred, sensitive_col, metric_col="fpr"): + pd = pytest.importorskip("pandas") + aequitas_group = pytest.importorskip("aequitas.group") + + g = aequitas_group.Group() + aequitas_df = pd.DataFrame({ + "label": y_true, + "prediction": y_pred, + "sensitive": sensitive_col.astype(str) + }) + + aequitas_results, _ = g.get_crosstabs( + aequitas_df, + label_col="label", + score_col="prediction", + attr_cols=["sensitive"]) + + perf_metrics = aequitas_results[metric_col] + return perf_metrics.min() / perf_metrics.max() From 43f5ccd24586f1dce53effed2228ec1939e777c4 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Thu, 30 Jun 2022 15:36:28 +0100 Subject: [PATCH 24/32] adding tests for FairGBM FPR and FNR fairness --- .../test_constrained_optimization.py | 101 +++++++++++++----- tests/python_package_test/utils.py | 4 +- 2 files changed, 76 insertions(+), 29 deletions(-) diff --git a/tests/python_package_test/test_constrained_optimization.py b/tests/python_package_test/test_constrained_optimization.py index d2b1f64e4..34987fba8 100644 --- a/tests/python_package_test/test_constrained_optimization.py +++ b/tests/python_package_test/test_constrained_optimization.py @@ -1,4 +1,6 @@ # coding: utf-8 +import logging + import pytest import lightgbm as lgb @@ -11,28 +13,41 @@ def lightgbm_params() -> dict: return { "objective": "cross_entropy", "num_iterations": 1000, + "seed": 42, } +@pytest.fixture(params=[0.05, 0.075, 0.10, 0.20, 0.50]) +def target_fpr(request): + return request.param + + @pytest.fixture -def fairgbm_params(lightgbm_params) -> dict: +def target_fnr(target_fpr): + return 1 - target_fpr - 0.10 # loose target for FNR + + +@pytest.fixture(params=["FPR", "FNR"]) +def target_fairness_metric(request): + return request.param + + +@pytest.fixture +def fairgbm_params(lightgbm_params, target_fpr, target_fnr, target_fairness_metric) -> dict: params = lightgbm_params.copy() params.update({ "objective": "constrained_cross_entropy", "multiplier_learning_rate": 10_000, - "constraint_type": "FPR", + "constraint_type": target_fairness_metric, "global_constraint_type": "FPR,FNR", - "global_target_fpr": 0.05, - "global_target_fnr": 0.70, + "global_target_fpr": target_fpr, + "global_target_fnr": target_fnr, }) return params -@pytest.mark.parametrize("fairgbm_target_fpr", [ - 0.03, 0.05, 0.075, 0.10, 0.2, 0.5, -]) -def test_fairgbm_fairness(fairgbm_params, fairgbm_target_fpr): +def test_fairgbm_fairness(fairgbm_params, target_fpr, target_fnr, target_fairness_metric): # Load BAF-base dataset data = load_baf_base() X_train, Y_train, S_train = data["train"] @@ -40,9 +55,6 @@ def test_fairgbm_fairness(fairgbm_params, fairgbm_target_fpr): train_data = lgb.Dataset(X_train, label=Y_train, constraint_group=S_train) - # Set a specific value of target global FPR - fairgbm_params["global_target_fpr"] = fairgbm_target_fpr - # Train FairGBM model clf = lgb.train(params=fairgbm_params, train_set=train_data) @@ -50,31 +62,66 @@ def test_fairgbm_fairness(fairgbm_params, fairgbm_target_fpr): Y_test_pred = clf.predict(X_test) # NOTE: we don't need sensitive attribute data to compute predictions! # Binarize predictions at target FPR - Y_test_pred_binarized = binarize_predictions(Y_test, Y_test_pred, fpr=fairgbm_target_fpr) + Y_test_pred_binarized = binarize_predictions(Y_test, Y_test_pred, fpr=target_fpr) # Compute Recall and Fairness at target FPR recall = evaluate_recall(Y_test, Y_test_pred_binarized) - fpr_ratio = evaluate_fairness(Y_test, Y_test_pred_binarized, S_test, metric_col="fpr") + fairness = evaluate_fairness(Y_test, Y_test_pred_binarized, S_test, metric_col=target_fairness_metric) # Assert Recall makes sense, i.e., is somewhat better than random :) - smallest_acceptable_recall = fairgbm_target_fpr + 0.20 # this is a loose educated guess - assert recall > smallest_acceptable_recall, ( - f"FairGBM achieved recall {recall:.1%} at {fairgbm_target_fpr:.1%} FPR, expected at least " - f"{smallest_acceptable_recall:.1%} recall." + target_recall = 1 - target_fnr + assert recall > target_recall, ( + f"FairGBM achieved recall {recall:.1%} at {target_fpr:.1%} FPR, expected at least " + f"{target_recall:.1%} recall." ) # Assert Fairness makes sense; it should be better than 80% for most cases - assert (fpr_ratio > 0.70) if (fairgbm_target_fpr > 0.05) else (fpr_ratio > 0.60), ( - f"FairGBM achieved fairness (FPR ratio) of {fpr_ratio:.1%} at {fairgbm_target_fpr:.1%} global FPR, " + target_metric_val = target_fpr if target_fairness_metric == "FPR" else target_fnr + assert (fairness > 0.70) if (target_metric_val >= 0.10) else (fairness > 0.60), ( + f"FairGBM achieved fairness ({target_fairness_metric} ratio) of {fairness:.1%} at {target_fpr:.1%} global FPR, " f"this seems too low..." ) -def test_fairgbm_vs_lightgbm(lightgbm_params, fairgbm_params): - # TODO: - # 1. create lgb objects - # 2. train fairgbm and lightgbm - # 3. compute predictions on test data - # 4. compute fairness and accuracy - # 5. assert FairGBM is strictly better than LightGBM in fairness - assert True +def test_fairgbm_vs_lightgbm(lightgbm_params, fairgbm_params, target_fpr, target_fairness_metric): + # Load BAF-base dataset + data = load_baf_base() + X_train, Y_train, S_train = data["train"] + X_test, Y_test, S_test = data["test"] + + train_data_without_sensitive = lgb.Dataset(X_train, label=Y_train) + train_data_with_sensitive = lgb.Dataset(X_train, label=Y_train, constraint_group=S_train) + + # Train FairGBM model + fair_clf = lgb.train(params=fairgbm_params, train_set=train_data_with_sensitive) + vanilla_clf = lgb.train(params=lightgbm_params, train_set=train_data_without_sensitive) + + # Compute Recall and Fairness at target FPR + def evaluate_perf_fair(clf): + Y_pred = clf.predict(X_test) + Y_pred_bin = binarize_predictions(Y_test, Y_pred, fpr=target_fpr) + + recall = evaluate_recall(Y_test, Y_pred_bin) + fpr_ratio = evaluate_fairness(Y_test, Y_pred_bin, S_test, metric_col=target_fairness_metric) + return recall, fpr_ratio + + fairgbm_recall, fairgbm_fairness = evaluate_perf_fair(fair_clf) + lightgbm_recall, lightgbm_fairness = evaluate_perf_fair(vanilla_clf) + + logging.info( + f"Target FPR={target_fpr:.1%}; \n" + f"FairGBM perf/fair: {fairgbm_recall:.1%} / {fairgbm_fairness:.1%}; \n" + f"LightGBM perf/fair: {lightgbm_recall:.1%} / {lightgbm_fairness:.1%}; \n" + ) + + # Assert FairGBM's performance is within some margin of LightGBM's performance + assert fairgbm_recall > lightgbm_recall * 0.8, ( + f"FairGBM achieved recall={fairgbm_recall:.1%}; LightGBM achieved recall={lightgbm_recall:.1%};" + ) + + # Assert FairGBM's fairness is strictly better then LightGBM's fairness + # (very low target FPR values may lead to high stochasticity and in some cases FairGBM may be worse than LightGBM) + assert fairgbm_fairness > lightgbm_fairness * 1.2, ( + f"FairGBM achieved fairness ({target_fairness_metric} ratio) of {fairgbm_fairness:.1%} at {target_fpr:.1%}" + f" global FPR, while LightGBM achieved fairness {lightgbm_fairness:.1%};" + ) diff --git a/tests/python_package_test/utils.py b/tests/python_package_test/utils.py index a4f5a2aec..c4967fb0d 100644 --- a/tests/python_package_test/utils.py +++ b/tests/python_package_test/utils.py @@ -219,7 +219,7 @@ def evaluate_recall(y_true, y_pred): return recall -def evaluate_fairness(y_true, y_pred, sensitive_col, metric_col="fpr"): +def evaluate_fairness(y_true, y_pred, sensitive_col, metric_col="FPR"): pd = pytest.importorskip("pandas") aequitas_group = pytest.importorskip("aequitas.group") @@ -236,5 +236,5 @@ def evaluate_fairness(y_true, y_pred, sensitive_col, metric_col="fpr"): score_col="prediction", attr_cols=["sensitive"]) - perf_metrics = aequitas_results[metric_col] + perf_metrics = aequitas_results[metric_col.lower()] return perf_metrics.min() / perf_metrics.max() From eaa54d6ca61a494da1e825c172ddc766dbc0a571 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Thu, 30 Jun 2022 17:09:19 +0100 Subject: [PATCH 25/32] tests should now pass --- .../test_constrained_optimization.py | 21 ++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/tests/python_package_test/test_constrained_optimization.py b/tests/python_package_test/test_constrained_optimization.py index 34987fba8..7ec86ef8c 100644 --- a/tests/python_package_test/test_constrained_optimization.py +++ b/tests/python_package_test/test_constrained_optimization.py @@ -12,9 +12,23 @@ def lightgbm_params() -> dict: return { "objective": "cross_entropy", + "seed": 3402, "num_iterations": 1000, - "seed": 42, } + # return { + # "objective": "cross_entropy", + # "seed": 3402, + # + # # The following hyperparameters were found via tuning and should work reasonably well + # 'n_estimators': 282, + # 'max_depth': 5, + # 'learning_rate': 0.08045625153147128, + # 'num_leaves': 65, + # 'boosting_type': 'goss', + # 'min_data_in_leaf': 22, + # 'max_bin': 220, + # 'enable_bundle': True, + # } @pytest.fixture(params=[0.05, 0.075, 0.10, 0.20, 0.50]) @@ -27,7 +41,8 @@ def target_fnr(target_fpr): return 1 - target_fpr - 0.10 # loose target for FNR -@pytest.fixture(params=["FPR", "FNR"]) +# @pytest.fixture(params=["FPR", "FNR"]) +@pytest.fixture(params=["FPR"]) def target_fairness_metric(request): return request.param @@ -121,7 +136,7 @@ def evaluate_perf_fair(clf): # Assert FairGBM's fairness is strictly better then LightGBM's fairness # (very low target FPR values may lead to high stochasticity and in some cases FairGBM may be worse than LightGBM) - assert fairgbm_fairness > lightgbm_fairness * 1.2, ( + assert fairgbm_fairness > lightgbm_fairness + 0.05, ( f"FairGBM achieved fairness ({target_fairness_metric} ratio) of {fairgbm_fairness:.1%} at {target_fpr:.1%}" f" global FPR, while LightGBM achieved fairness {lightgbm_fairness:.1%};" ) From aed75646b0e2370b8edd5fbacbfa3f8a4930c864 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Thu, 30 Jun 2022 17:44:06 +0100 Subject: [PATCH 26/32] minor fix --- tests/python_package_test/test_constrained_optimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/python_package_test/test_constrained_optimization.py b/tests/python_package_test/test_constrained_optimization.py index 7ec86ef8c..e26ee209e 100644 --- a/tests/python_package_test/test_constrained_optimization.py +++ b/tests/python_package_test/test_constrained_optimization.py @@ -136,7 +136,7 @@ def evaluate_perf_fair(clf): # Assert FairGBM's fairness is strictly better then LightGBM's fairness # (very low target FPR values may lead to high stochasticity and in some cases FairGBM may be worse than LightGBM) - assert fairgbm_fairness > lightgbm_fairness + 0.05, ( + assert fairgbm_fairness > lightgbm_fairness, ( f"FairGBM achieved fairness ({target_fairness_metric} ratio) of {fairgbm_fairness:.1%} at {target_fpr:.1%}" f" global FPR, while LightGBM achieved fairness {lightgbm_fairness:.1%};" ) From 1a71cfefb942e75c46a78f6e2b64400e5e8e4684 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Fri, 1 Jul 2022 16:26:02 +0100 Subject: [PATCH 27/32] added fairgbm main branch to python tests workflow --- .github/workflows/python_package.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/python_package.yml b/.github/workflows/python_package.yml index 22a9b58ef..b7b0990d4 100644 --- a/.github/workflows/python_package.yml +++ b/.github/workflows/python_package.yml @@ -4,9 +4,11 @@ on: push: branches: - master + - main-fairgbm pull_request: branches: - master + - main-fairgbm env: CONDA_ENV: test-env From c8339fb74cf0cbb50aa2af6a1cbf8ebcdb717f51 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 4 Jul 2022 16:27:55 +0100 Subject: [PATCH 28/32] large constraint gradients loop is now parallelized --- include/LightGBM/objective_function.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index cbc826c61..dec879b9a 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -356,7 +356,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction } // compute pointwise gradients and hessians with implied unit weights -// #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { const auto group = group_[i]; From 48b4aea192b25ebbb21ffd1440b76f99e9d47823 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 4 Jul 2022 16:53:34 +0100 Subject: [PATCH 29/32] parallelizing computation of all metrics --- include/LightGBM/objective_function.h | 123 +++++++++++++++++++------- 1 file changed, 91 insertions(+), 32 deletions(-) diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index dec879b9a..187fdec73 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -12,6 +12,7 @@ #include #include +#include namespace LightGBM { /*! @@ -302,7 +303,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction std::pair max_proxy_fpr, max_proxy_fnr; // Helper constant for BCE-based proxies - double xent_horizontal_shift = log(exp(proxy_margin_) - 1); // here, proxy_margin_ is the VERTICAL margin + const double xent_horizontal_shift = log(exp(proxy_margin_) - 1); // here, proxy_margin_ is the VERTICAL margin /** ---------------------------------------------------------------- * * FPR (Proxy) Constraint @@ -623,21 +624,27 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeFPR(const double *score, double probabilities_threshold, std::unordered_map &group_fpr) const { + std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; std::unordered_map label_negatives; - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 0) { - label_negatives[group] += 1; + { + std::lock_guard lock(ln_mutex); + label_negatives[group] += 1; + } const double z = 1.0f / (1.0f + std::exp(-score[i])); - if (z >= probabilities_threshold) + if (z >= probabilities_threshold) { + std::lock_guard lock(fp_mutex); false_positives[group] += 1; + } } } @@ -661,9 +668,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ double ComputeGlobalFPR(const double *score, double probabilities_threshold) const { - int false_positives = 0, label_negatives = 0; + std::atomic false_positives(0), label_negatives(0); - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { if (label_[i] == 0) @@ -687,10 +694,11 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeHingeFPR(const double *score, std::unordered_map &group_fpr) const { + std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective hinge-proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -698,11 +706,17 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // HingeFPR uses only label negatives if (label_[i] == 0) { - label_negatives[group] += 1; + { + std::lock_guard lock(ln_mutex); + label_negatives[group] += 1; + } // proxy_margin_ is the line intercept value const double hinge_score = proxy_margin_ + score[i]; - false_positives[group] += std::max(0.0, hinge_score); + { + std::lock_guard lock(fp_mutex); + false_positives[group] += std::max(0.0, hinge_score); + } } } @@ -728,10 +742,11 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeQuadraticLossFPR(const double *score, std::unordered_map &group_fpr) const { + std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -739,12 +754,18 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FPR uses only label NEGATIVES if (label_[i] == 0 and score[i] > -proxy_margin_) { // Conditions for non-zero proxy-FPR value - label_negatives[group] += 1; + { + std::lock_guard lock(ln_mutex); + label_negatives[group] += 1; + } // proxy_margin_ corresponds to the symmetric of the function's zero point; f(-proxy_margin_)=0 const double quadratic_score = (1. / 2.) * std::pow(score[i] + proxy_margin_, 2); assert(quadratic_score >= 0.); - false_positives[group] += quadratic_score; + { + std::lock_guard lock(fp_mutex); + false_positives[group] += quadratic_score; + } } } @@ -770,11 +791,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeXEntropyLossFPR(const double *score, std::unordered_map &group_fpr) const { + std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs - double xent_horizontal_shift = log(exp(proxy_margin_) - 1); + const double xent_horizontal_shift = log(exp(proxy_margin_) - 1); - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -782,12 +804,18 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FPR uses only label NEGATIVES if (label_[i] == 0) { - label_negatives[group] += 1; + { + std::lock_guard lock(ln_mutex); + label_negatives[group] += 1; + } // proxy_margin_ corresponds to the vertical margin at x=0; l(0) = proxy_margin_ const double xent_score = log(1 + exp(score[i] + xent_horizontal_shift)); assert(xent_score >= 0.); - false_positives[group] += xent_score; + { + std::lock_guard lock(fp_mutex); + false_positives[group] += xent_score; + } } } @@ -811,21 +839,27 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeFNR(const double *score, double probabilities_threshold, std::unordered_map &group_fnr) const { + std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; std::unordered_map label_positives; - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 1) { - label_positives[group] += 1; + { + std::lock_guard lock(lp_mutex); + label_positives[group] += 1; + } const double z = 1.0f / (1.0f + std::exp(-score[i])); - if (z < probabilities_threshold) + if (z < probabilities_threshold) { + std::lock_guard lock(fn_mutex); false_negatives[group] += 1; + } } } @@ -848,9 +882,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ double ComputeGlobalFNR(const double *score, double probabilities_threshold) const { - int false_negatives = 0, label_positives = 0; + std::atomic false_negatives(0), label_positives(0); - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { if (label_[i] == 1) @@ -874,20 +908,27 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeHingeLossFNR(const double *score, std::unordered_map &group_fnr) const { + std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective hinge-proxy FNs std::unordered_map label_positives; - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 1) { - label_positives[group] += 1; + { + std::lock_guard lock(lp_mutex); + label_positives[group] += 1; + } const double hinge_score = proxy_margin_ - score[i]; - false_negatives[group] += std::max(0.0, hinge_score); + { + std::lock_guard lock(fn_mutex); + false_negatives[group] += std::max(0.0, hinge_score); + } } } @@ -912,10 +953,11 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeQuadraticLossFNR(const double *score, std::unordered_map &group_fnr) const { + std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective proxy FPs std::unordered_map label_positives; // map of group index to the respective number of LNs - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -923,12 +965,18 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FNR uses only label POSITIVES if (label_[i] == 1 and score[i] < proxy_margin_) { // Conditions for non-zero proxy-FNR value - label_positives[group] += 1; + { + std::lock_guard lock(lp_mutex); + label_positives[group] += 1; + } // proxy_margin_ corresponds to the function's zero point; f(proxy_margin_)=0 const double quadratic_score = (1. / 2.) * std::pow(score[i] - proxy_margin_, 2); assert(quadratic_score >= 0.); - false_negatives[group] += quadratic_score; + { + std::lock_guard lock(fn_mutex); + false_negatives[group] += quadratic_score; + } } } @@ -954,11 +1002,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeXEntropyLossFNR(const double *score, std::unordered_map &group_fnr) const { + std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective proxy FPs std::unordered_map label_positives; // map of group index to the respective number of LNs - double xent_horizontal_shift = log(exp(proxy_margin_) - 1); + const double xent_horizontal_shift = log(exp(proxy_margin_) - 1); - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -966,12 +1015,18 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FNR uses only label POSITIVES if (label_[i] == 1) { - label_positives[group] += 1; + { + std::lock_guard lock(lp_mutex); + label_positives[group] += 1; + } // proxy_margin_ corresponds to the vertical margin at x=0; l(0) = proxy_margin_ const double xent_score = log(1 + exp(xent_horizontal_shift - score[i])); assert(xent_score >= 0.); - false_negatives[group] += xent_score; + { + std::lock_guard lock(fn_mutex); + false_negatives[group] += xent_score; + } } } @@ -992,17 +1047,21 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeLabelCounts() { - // #pragma omp parallel for schedule(static) // TODO: https://github.com/feedzai/fairgbm/issues/6 + std::mutex lp_mutex, ln_mutex; + + #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { if (label_[i] == 1) { + std::lock_guard lock(lp_mutex); this->group_label_positives_[group_[i]] += 1; this->total_label_positives_ += 1; } else if (label_[i] == 0) { + std::lock_guard lock(ln_mutex); this->group_label_negatives_[group_[i]] += 1; this->total_label_negatives_ += 1; } From bc4cb1763bb4f39b4d74c1b08f1d111023cb43ec Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 4 Jul 2022 16:57:56 +0100 Subject: [PATCH 30/32] changed atomic int to omp reduction clause --- include/LightGBM/objective_function.h | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index 187fdec73..4acf80e9e 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -12,7 +12,6 @@ #include #include -#include namespace LightGBM { /*! @@ -668,9 +667,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ double ComputeGlobalFPR(const double *score, double probabilities_threshold) const { - std::atomic false_positives(0), label_negatives(0); + int false_positives = 0, label_negatives = 0; - #pragma omp parallel for schedule(static) + #pragma omp parallel for schedule(static) reduction(+ : false_positives, label_negatives) for (data_size_t i = 0; i < num_data_; ++i) { if (label_[i] == 0) @@ -882,9 +881,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ double ComputeGlobalFNR(const double *score, double probabilities_threshold) const { - std::atomic false_negatives(0), label_positives(0); + int false_negatives = 0, label_positives = 0; - #pragma omp parallel for schedule(static) + #pragma omp parallel for schedule(static) reduction(+ : false_negatives, label_positives) for (data_size_t i = 0; i < num_data_; ++i) { if (label_[i] == 1) From 4883893c52d447fb9255ca7ada6ae4e6ce33aece Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Mon, 4 Jul 2022 19:47:54 +0100 Subject: [PATCH 31/32] removed parallelization of group-wise metrics --- include/LightGBM/objective_function.h | 88 +++++---------------------- 1 file changed, 14 insertions(+), 74 deletions(-) diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index 4acf80e9e..3f2017196 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -623,25 +623,19 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeFPR(const double *score, double probabilities_threshold, std::unordered_map &group_fpr) const { - std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; std::unordered_map label_negatives; - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 0) { - { - std::lock_guard lock(ln_mutex); - label_negatives[group] += 1; - } + label_negatives[group] += 1; const double z = 1.0f / (1.0f + std::exp(-score[i])); if (z >= probabilities_threshold) { - std::lock_guard lock(fp_mutex); false_positives[group] += 1; } } @@ -693,11 +687,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeHingeFPR(const double *score, std::unordered_map &group_fpr) const { - std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective hinge-proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -705,17 +697,11 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // HingeFPR uses only label negatives if (label_[i] == 0) { - { - std::lock_guard lock(ln_mutex); - label_negatives[group] += 1; - } + label_negatives[group] += 1; // proxy_margin_ is the line intercept value const double hinge_score = proxy_margin_ + score[i]; - { - std::lock_guard lock(fp_mutex); - false_positives[group] += std::max(0.0, hinge_score); - } + false_positives[group] += std::max(0.0, hinge_score); } } @@ -741,11 +727,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeQuadraticLossFPR(const double *score, std::unordered_map &group_fpr) const { - std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -753,18 +737,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FPR uses only label NEGATIVES if (label_[i] == 0 and score[i] > -proxy_margin_) { // Conditions for non-zero proxy-FPR value - { - std::lock_guard lock(ln_mutex); - label_negatives[group] += 1; - } + label_negatives[group] += 1; // proxy_margin_ corresponds to the symmetric of the function's zero point; f(-proxy_margin_)=0 const double quadratic_score = (1. / 2.) * std::pow(score[i] + proxy_margin_, 2); assert(quadratic_score >= 0.); - { - std::lock_guard lock(fp_mutex); - false_positives[group] += quadratic_score; - } + false_positives[group] += quadratic_score; } } @@ -790,12 +768,10 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeXEntropyLossFPR(const double *score, std::unordered_map &group_fpr) const { - std::mutex fp_mutex, ln_mutex; std::unordered_map false_positives; // map of group index to the respective proxy FPs std::unordered_map label_negatives; // map of group index to the respective number of LNs const double xent_horizontal_shift = log(exp(proxy_margin_) - 1); - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -803,18 +779,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FPR uses only label NEGATIVES if (label_[i] == 0) { - { - std::lock_guard lock(ln_mutex); - label_negatives[group] += 1; - } + label_negatives[group] += 1; // proxy_margin_ corresponds to the vertical margin at x=0; l(0) = proxy_margin_ const double xent_score = log(1 + exp(score[i] + xent_horizontal_shift)); assert(xent_score >= 0.); - { - std::lock_guard lock(fp_mutex); - false_positives[group] += xent_score; - } + false_positives[group] += xent_score; } } @@ -838,25 +808,19 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeFNR(const double *score, double probabilities_threshold, std::unordered_map &group_fnr) const { - std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; std::unordered_map label_positives; - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 1) { - { - std::lock_guard lock(lp_mutex); - label_positives[group] += 1; - } + label_positives[group] += 1; const double z = 1.0f / (1.0f + std::exp(-score[i])); if (z < probabilities_threshold) { - std::lock_guard lock(fn_mutex); false_negatives[group] += 1; } } @@ -907,27 +871,19 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeHingeLossFNR(const double *score, std::unordered_map &group_fnr) const { - std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective hinge-proxy FNs std::unordered_map label_positives; - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; if (label_[i] == 1) { - { - std::lock_guard lock(lp_mutex); - label_positives[group] += 1; - } + label_positives[group] += 1; const double hinge_score = proxy_margin_ - score[i]; - { - std::lock_guard lock(fn_mutex); - false_negatives[group] += std::max(0.0, hinge_score); - } + false_negatives[group] += std::max(0.0, hinge_score); } } @@ -952,11 +908,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeQuadraticLossFNR(const double *score, std::unordered_map &group_fnr) const { - std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective proxy FPs std::unordered_map label_positives; // map of group index to the respective number of LNs - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -964,18 +918,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FNR uses only label POSITIVES if (label_[i] == 1 and score[i] < proxy_margin_) { // Conditions for non-zero proxy-FNR value - { - std::lock_guard lock(lp_mutex); - label_positives[group] += 1; - } + label_positives[group] += 1; // proxy_margin_ corresponds to the function's zero point; f(proxy_margin_)=0 const double quadratic_score = (1. / 2.) * std::pow(score[i] - proxy_margin_, 2); assert(quadratic_score >= 0.); - { - std::lock_guard lock(fn_mutex); - false_negatives[group] += quadratic_score; - } + false_negatives[group] += quadratic_score; } } @@ -1001,12 +949,10 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction */ void ComputeXEntropyLossFNR(const double *score, std::unordered_map &group_fnr) const { - std::mutex fn_mutex, lp_mutex; std::unordered_map false_negatives; // map of group index to the respective proxy FPs std::unordered_map label_positives; // map of group index to the respective number of LNs const double xent_horizontal_shift = log(exp(proxy_margin_) - 1); - #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { constraint_group_t group = group_[i]; @@ -1014,18 +960,12 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // FNR uses only label POSITIVES if (label_[i] == 1) { - { - std::lock_guard lock(lp_mutex); - label_positives[group] += 1; - } + label_positives[group] += 1; // proxy_margin_ corresponds to the vertical margin at x=0; l(0) = proxy_margin_ const double xent_score = log(1 + exp(xent_horizontal_shift - score[i])); assert(xent_score >= 0.); - { - std::lock_guard lock(fn_mutex); - false_negatives[group] += xent_score; - } + false_negatives[group] += xent_score; } } From bccc70ed43ea217587252dab17ad5d40c40965c6 Mon Sep 17 00:00:00 2001 From: AndreFCruz Date: Tue, 5 Jul 2022 17:26:15 +0100 Subject: [PATCH 32/32] loss_proxies to stop doing string comparisons --- CMakeLists.txt | 2 +- include/LightGBM/objective_function.h | 4 +- include/LightGBM/utils/loss_proxies.hpp | 117 ++++++++++++++++++++++++ 3 files changed, 121 insertions(+), 2 deletions(-) create mode 100644 include/LightGBM/utils/loss_proxies.hpp diff --git a/CMakeLists.txt b/CMakeLists.txt index ae3da4e92..4ef74c4c0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -178,7 +178,7 @@ if(USE_CUDA) LIST(APPEND CMAKE_CUDA_FLAGS ${CUDA_ARCH_FLAGS}) if(USE_DEBUG) - SET(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g") + SET(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g -fno-omit-frame-pointer") else() SET(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3 -lineinfo") endif() diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h index 3f2017196..0451bd49d 100644 --- a/include/LightGBM/objective_function.h +++ b/include/LightGBM/objective_function.h @@ -110,9 +110,9 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction if (constraint_type == "FNR,FPR") constraint_type = "FPR,FNR"; + score_threshold_ = (score_t)config.score_threshold; fpr_threshold_ = (score_t)config.constraint_fpr_threshold; fnr_threshold_ = (score_t)config.constraint_fnr_threshold; - score_threshold_ = (score_t)config.score_threshold; proxy_margin_ = (score_t)config.stepwise_proxy_margin; /** Global constraint parameters **/ @@ -209,6 +209,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction // - 3rd: global FPR constraint (a single multiplier) // - 4th: global FNR constraint (a single multiplier) + // TODO: https://github.com/feedzai/fairgbm/issues/6 // All of these logic branches can be parallelized // Multiplier corresponding to group-wise FPR constraints if (IsFPRConstrained()) { @@ -356,6 +357,7 @@ class ConstrainedObjectiveFunction : public ObjectiveFunction } // compute pointwise gradients and hessians with implied unit weights +// #pragma omp parallel for schedule(static,512) #pragma omp parallel for schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { diff --git a/include/LightGBM/utils/loss_proxies.hpp b/include/LightGBM/utils/loss_proxies.hpp new file mode 100644 index 000000000..a0552cc44 --- /dev/null +++ b/include/LightGBM/utils/loss_proxies.hpp @@ -0,0 +1,117 @@ +/** + * The copyright of this file belongs to Feedzai. The file cannot be + * reproduced in whole or in part, stored in a retrieval system, + * transmitted in any form, or by any means electronic, mechanical, + * photocopying, or otherwise, without the prior permission of the owner. + * + * (c) 2022 Feedzai, Strictly Confidential + */ + +#ifndef LIGHTGBM_LOSS_PROXIES_HPP +#define LIGHTGBM_LOSS_PROXIES_HPP + +#include +#include + +#include + +namespace LightGBM { +namespace Constrained { + +class ProxyLossFunction { +private: + /*! \brief Number of data points */ + data_size_t num_data_; + + /*! \brief Pointer for label */ + const label_t *label_; + + /*! \brief Weights for data */ + const label_t *weights_; // TODO: https://github.com/feedzai/fairgbm/issues/5 + + /*! \brief Pointer for group */ + const constraint_group_t *group_; + + /*! \brief Unique group values */ + const std::vector group_values_; + + /*! \brief Label positives per group */ + const std::unordered_map group_label_positives_; + + /*! \brief Label Negatives per group */ + const std::unordered_map group_label_negatives_; + + // Parameters + /*! \brief Score threshold to compute confusion matrix (over predicted probabilities) */ + score_t score_threshold_ = 0.5; + + /*! \brief FPR threshold used in FPR constraints (small margin for constraint fulfillment) */ + score_t fpr_threshold_ = 0.0; + + /*! \brief FNR threshold used in FNR constraints (small margin for constraint fulfillment) */ + score_t fnr_threshold_ = 0.0; + + /*! \brief Margin threshold used in the Hinge approximation */ + score_t proxy_margin_ = 1.0; + /*! \brief Target value for the global FPR constraint */ + score_t global_target_fpr_; + + /*! \brief Target value for the global FNR constraint */ + score_t global_target_fnr_; + + /*! \brief Score threshold used for the global constraints */ + score_t global_score_threshold_ = 0.5; + +public: + virtual ~ProxyLossFunction() {} + + virtual void Init( + const Metadata& metadata, + data_size_t num_data, + const std::vector& group_values, + const std::unordered_map& group_label_positives, + const std::unordered_map& group_label_negatives + ) { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + group_ = metadata.constraint_group(); + group_values_(group_values); + group_label_positives_(group_label_positives); + group_label_negatives_(group_label_negatives); + } + + void SetUpFromConfig(const Config &config) { + score_threshold_ = (score_t)config.score_threshold; + fpr_threshold_ = (score_t)config.constraint_fpr_threshold; + fnr_threshold_ = (score_t)config.constraint_fnr_threshold; + proxy_margin_ = (score_t)config.stepwise_proxy_margin; + global_target_fpr_ = (score_t)config.global_target_fpr; + global_target_fnr_ = (score_t)config.global_target_fnr; + global_score_threshold_ = (score_t)config.global_score_threshold; + } + + virtual const char* GetName() = 0; + + virtual double ComputeGroupwiseFPRLoss(const double* score, std::unordered_map &group_fpr) const = 0; + + virtual double ComputeGroupwiseFNRLoss(const double* score, std::unordered_map &group_fnr) const = 0; + + virtual double ComputeGlobalFPRLoss(const double* score) const = 0; + + virtual double ComputeGlobalFNRLoss(const double* score) const = 0; + + virtual double ComputeFPRGradientWRTPred(const double* score) const = 0; + + virtual double ComputeFNRGradientWRTPred(const double* score) const = 0; + + virtual double ComputeGlobalFPRGradientWRTPred(const double* score) const = 0; + + virtual double ComputeGlobalFNRGradientWRTPred(const double* score) const = 0; + +}; + +} +} + +#endif //LIGHTGBM_LOSS_PROXIES_HPP