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This toolkit consists of ABCBoost, the implementation of Fast ABCBoost (Fast Adaptive Base Class Boost).
Run the following commands to build ABCBoost from source:
git clone https://github.com/pltrees/abcboost.git
cd abcboost
mkdir build
cd build
cmake ..
make
cd ..
This will create three executables (abcboost_train, abcboost_predict, and abcboost_clean) in the abcboost directory.
abcboost_train is the executable to train models.
abcboost_predict is the executable to validate and inference using trained models.
abcboost_clean is the executable to clean csv data.
The default setting builds ABCBoost as a single-thread program. To build ABCBoost with multi-thread support OpenMP (OpenMP comes with the system GCC toolchains on Linux), turn on the multi-thread option:
cmake -DOMP=ON ..
make clean
make
Note that the default g++ on Mac may not support OpenMP.
If we set -DNATIVE=ON, the compiler may better optimize the code according to specific native CPU instructions:
cmake -DOMP=ON -DNATIVE=ON ..
make clean
make
Again, we do not recommend to turn on this option on Mac.
To build ABCBoost with GPU support, install NVIDIA CUDA Toolkits and set the option CUDA=ON:
cmake -DOMP=ON -DNATIVE=ON -DCUDA=ON ..
make clean
make
Five datasets are provided under data/ folder:
comp_cpu for regression, in both CSV and libsvm formats: comp_cpu.train.libsvm, comp_cpu.train.csv, comp_cpu.test.libsvm, comp_cpu.test.csv. Note that other tree platforms may not support the CSV format.
ijcnn1 for binary classification.
covtype for multi-class classification. Note that ABCBoost package does not require class labels to start from 0 while other platforms may require so.
mslr10k for ranking. Only a small subset is provided here.
Census-Income (KDD) Data Set to illustrate data cleaning and categorical feature processing.
L2 regression (p=2) is the default, although in some datasets using p>2 achieves lower errors. To train Lp regression on the provided comp_cpu dataset, with p=2:
./abcboost_train -method regression -lp 2 -data data/comp_cpu.train.csv -J 20 -v 0.1 -iter 1000
This will generate a model named comp_cpu.train.libsvm_regression_J10_v0.1_p2.model on the current directory. For testing, execute
./abcboost_predict -data data/comp_cpu.test.csv -model comp_cpu.train.csv_regression_J20_v0.1_p2.model
which outputs two files: (1) comp_cpu.test.csv_regression_J20_v0.1_p2.testlog which stores the history of test L1 and L2 loss for all the iterations; and (2) comp_cpu.test.csv_regression_J20_v0.1_p2.prediction which stores the predictions for all testing data points.
We support both Robust LogitBoost and MART. Because Robust LogitBoost uses second-order information to compute the gain for tree plits, it often improves MART.
./abcboost_train -method robustlogit -data data/ijcnn1.train.csv -J 20 -v 0.1 -iter 1000
./abcboost_predict -data data/ijcnn1.test.csv -model ijcnn1.train.csv_robustlogit_J20_v0.1.model
Users can replace robustlogit by mart to test different algorithms.
We support four training methods: robustlogit, abcrobustlogit, mart, and abcmart. The following example is for abcrobustlogit on covtype dataset which has 7 classes. In order to identify the base class, we need to specify the -search parameter (between 1 and 7 for this dataset) and -gap parameter (5 by default):
./abcboost_train -method abcrobustlogit -data data/covtype.train.csv -J 20 -v 0.1 -iter 1000 -search 2 -gap 10
./abcboost_predict -data data/covtype.test.csv -model covtype.train.csv_abcrobustlogit2g10_J20_v0.1_w0.model
In this example, the _w0 in the model name means we set -warmup_iter to be 0 by default. Note that if the -search parameter is set to be 0, then the exhaustive strategy is adopted (which is the same as -search 7 in this example). We choose this design convention so that readers do not have to know the number of classes of the dataset.
In practice, the test data would not have labels. The following example outputs only the predicted class labels:
./abcboost_predict -data data/covtype.nolabel.test.csv -no_label 1 -model covtype.train.csv_abcrobustlogit2g10_J20_v0.1_w0.model
in covtype.nolabel.test.csv_abcrobustlogit2g10_J20_v0.1_w0.prediction file. In many scenarios, practitioners are often more interested in the predicted class probabilities. The next example
./abcboost_predict -data data/covtype.nolabel.test.csv -no_label 1 -save_prob 1 -model covtype.train.csv_abcrobustlogit2g10_J20_v0.1_w0.model
outputs an additional file covtype.nolabel.test.csv_abcrobustlogit2g10_J20_v0.1_w0.probability.
Ranking tasks are supported by using -method lambdarank. Note that the query/group file need to be specified (the query file tells us how many instances in the data for each query):
./abcboost_train -method lambdarank -data data/mslr10k.train -query data/mslr10k.train.query -J 20 -v 0.1 -iter 100
./abcboost_predict -data data/mslr10k.test -query data/mslr10k.test.query -model mslr10k.train_lambdarank_J20_v0.1.model
Before the training stage, each feature is preprocessed to be integers between 0 and MaxBin-1 where we set the up limit by -data_max_n_bins MaxBin. Smaller MaxBin results in faster training, but it may hurt the accuracy if MaxBin is set to be too small. The default value of -data_max_n_bins is 1000. The following example changes this parameter to 500:
./abcboost_train -method robustlogit -data data/ijcnn1.train.csv -J 20 -v 0.1 -iter 1000 -data_max_n_bins 500`
If the executables are compiled with GPU support, we can specify the GPU device from the command line:
CUDA_VISIBLE_DEVICES=0 ./abcboost_train -method robustlogit -data data/ijcnn1.train.csv -J 20 -v 0.1 -iter 1000
Here we specify GPU 0 as the device. (Use nvidia-smi to find out available GPUs)
Here we illustrate some common parameters and provide some examples:
-iternumber of iterations (default 1000)-Jnumber of leaves in a tree (default 20)-vlearning rate (default 0.1)-searchsearching size for the base class (default 2: we greedily choose the base classes according to the training loss). For example, 2 means we try the class with the greatest loss and the class with the second greatest loss as base class and pick the one with lower loss as the base class for the current iteration.-n_threadsnumber of threads (default 1) It can only be used when multi-thread is enabled. (Compile the code with-DOMP=ONin cmake.)-additional_filesusing other files to do bin quantization besides the training data. File names are separated by,without additional spaces, e.g.,-additional_files file1,file2,file3.-additional_files_no_labelusing other unlabeled files to do bin quantization besides the training data. File names are separated by,without additional spaces, e.g.,-additional_files_no_label file1,file2,file3.
To train the model with 2000 iterations, 16 leaves per tree and 0.08 learning rate:
./abcboost_train -data data/covtype.train.csv -J 16 -v 0.08 -iter 2000
To train the model with 2000 iterations, 16 leaves per tree, 0.08 learning rate and enable the exhaustive base class searching:
./abcboost_train -data data/covtype.train.csv -J 16 -v 0.08 -iter 2000 -search 0
Note that the exhaustive searching produces better-generalized model while requiring substantially more time. For the covtype dataset (which has 7 classes), using -search 0 is effectively equivalent to -search 7.
The labels in the specified additional files are not used in the training. Only the feature values are used to generate (potentially) better quantization. Better testing results may be obtained when using additional files
-data_use_mean_as_missing-data_min_bin_sizeminimum size of the bin-data_sparsity_threshold-data_max_n_binsmax number of bins (default 1000)-data_path, -datapath to train/test data
-tree_clip_valuegradient clip (default 50)-tree_damping_factor, regularization on denominator (default 1e-100)-tree_max_n_leaves, -J (default 20)-tree_min_node_size(default 10)
-model_use_logit, whether use logitboost-model_data_sample_rate(default 1.0)-model_feature_sample_rate(default 1.0)-model_shrinkage,-shrinkage,-v, the learning rate (default 0.1)-model_n_iterations,-iter(default 1000)-model_save_every,-save(default 100)-model_eval_every,-eval(default 1)-model_name,-methodregression/lambdarank/mart/abcmart/robustlogit/abcrobustlogit (default abcrobustlogit)-model_pretrained_path,-model
-model_base_candidate_size,base_candidates_size,-search(default 2) base class searching size in abcmart/abcrobustlogit-model_gap,-gap(default 5) the gap between two base class searchings. For example,-model_gap 2means we will do the base class searching in iteration 1, 4, 6, ...-model_warmup_iter,-warmup_iter(default 0) the number of iterations that use normal boosting before ABC method kicks in. It might be helpful for datasets with a large number of classes when we only have a limited base class searching parameter (-search)-model_warmup_use_logit,-warmup_use_logit0/1 (default 1) whether use logitboost in warmup iterations.-model_abc_sample_rate,-abc_sample_rate(default 1.0) the sample rate used for the base class searching-model_abc_sample_min_data-abc_sample_min_data(default 2000) the minimum sampled data for base class selection. This parameter only takes into effect when-abc_sample_rateis less than1.0
-regression_lp_loss,-lp(default 2.0) whether use Lp norm instead of L2 norm. p (p >= 1.0) has to be specified-regression_test_lp,-test_lp(default none) display Lp norm as an additional column in test log. p (p >= 1.0) has to be specified-regression_use_hessian0/1 (default 1) whether use second-order derivatives in the regression. This parameter only takes into effect when-regression_lp_loss pis set andpis greater than2.-regression_huber_loss,-huber0/1 (default 0) whether use huber loss-regression_huber_delta,-huber_deltathe delta parameter for huber loss. This parameter only takes into effect when-regression_huber_loss 1is set
-n_threads,-threads(default 1)-use_gpu0/1 (default 1 if compiled with CUDA) whether use GPU to train models. This parameter only takes into effect when the executable is complied with CUDA (i.e., the flag-DCUDA=onis enabled incmake).
-save_log, 0/1 (default 0) whether save the runtime log to file-save_model, 0/1 (default 1)-no_label, 0/1 (default 0) It should only be enabled to output prediction file when the testing data has no label in test-test_auc, 0/1 (default 0) whether compute AUC in test-stop_tolerance(default 2e-14) It works for all non-regression tasks, e.g., classification. The training will stop when the total training loss is less than the stop tolerance.-regression_stop_factor(default 1e-6) The auto stopping criterion is different from the classification task because the scale of the regression target is unknown. We adaptively set the regression stop tolerate toregression_stop_factor * total_loss / sum(y^p), whereyis the regression targets andpis the value specified in-regression_lp_loss.-regression_auto_clip_value0/1 (default 1) whether use our adaptive clipping value computation for the predict value on terminal nodes. When enabled, the adaptive clipping value is computed astree_clip_value * max_y - min_ywheretree_clip_valueis set via-tree_clip_value,max_yandmin_yare the maximum and minimum regression target value, respectively.-gbrank_tau(default 0.1) The tau parameter for gbrank.
We provide an R library to enable calling ABCBoost subroutines from R.
To build and install the library, type the following command in abcboost/:
cd ..
R CMD build abcboost
For users' convience, we also provide, under R/, the pre-built abcboost_1.0.0.tar.gz and abcboost_1.0.0_mult.tar.gz, for single-thread version and multi-thread version, respectively. To install the (single-thread) package, in R console, type
install.packages('R/abcboost_1.0.0.tar.gz', repos = NULL, type = 'source')
One can use setwd to change the current working directory. Note that we should first remove the package (remove.packages('abcboost')) if we hope to replace the single-thread version with the multi-thread version.
Function description (no need to copy to console):
# No need to copy to console
# abcboost_train: (train_Y,train_X,model_name,iter,leaves,shinkage,params=NULL)
# abcboost_test: (test_Y,test_X,model,params=NULL)
# abcboost_predict: (test_X,model,params=NULL)
# abcboost_save_model: function(model,path)
# abcboost_load_model: function(path)
Here we show an example of training and testing:
library(abcboost)
data <- read.csv(file='data/covtype.train.csv',header=FALSE)
X <- data[,-1]
Y <- data[,1]
data <- read.csv(file='data/covtype.test.csv',header=FALSE)
testX <- data[,-1]
testY <- data[,1]
# The last argument of abcboost_train is optional.
# We use n_threads as an example
# All command line supported parameters can be passed via list:
# list(parameter1=value1, parameter2=value2,...)
model <- abcboost_train(Y,X,"abcrobustlogit",100,20,0.1,list(n_threads=1,search=2,gap=5))
# abcboost_save_model(model,'mymodel.model')
# model <- abcboost_load_model('mymodel.model')
res <- abcboost_test(testY,testX,model,list(test_auc=1))
# predict without label
res <- abcboost_predict(testX,model,list(test_auc=1))
# We also provide a method to read libsvm format data into sparse array
data <- abcboost_read_libsvm('data/covtype.train.libsvm')
X <- data$X
Y <- data$Y
data <- abcboost_read_libsvm('data/covtype.test.libsvm')
testX <- data$X
testY <- data$Y
# X can be a either a dense matrix or a sparse matrix
# The interface is the same as the dense case,
# but with better performance for sparse data
model <- abcboost_train(Y,X,"abcrobustlogit",100,20,0.1,list(n_threads=1,search=2,gap=5))
res <- abcboost_test(testY,testX,model)
We provide a Matlab wrapper to call ABCBoost subroutines from Matlab. To compile the Matlab mex files in Matlab:
cd src/matlab
compile % single-thread version
or
cd src/matlab
compile_mult_thread
One can use mex -setup cpp in a matlab console to check the specified C++ compiler.
For the convenience of users, we provide the compiled executables for both Linux and Windows under matlab/linux and matlab/windows respectively.
Assume the executables are stored in matlab/linux or matlab/windows. Here we show an example of training and testing:
tr = load('../../data/covtype.train.csv');
te = load('../../data/covtype.test.csv');
Y = tr(:,1);
X = tr(:,2:end);
testY = te(:,1);
testX = te(:,2:end);
params = struct;
params.n_threads = 1;
params.search = 2;
params.gap = 5;
% The params argument is optional.
% We use n_threads as an example
% All command line supported parameters can be passed via params: params.parameter_name = value
model = abcboost_train(Y,X,'abcrobustlogit',100,20,0.1,params);
% abcboost_save(model,'mymodel.model');
% model = abcboost_load('mymodel.model');
params.test_auc = 1;
res = abcboost_test(testY,testX,model,params);
% predict without label
res = abcboost_predict(testX,model,params);
% Sparse matlab matrix is also supported
% For example, we included the libsvmread.c from the LIBSVM package for data loading
[Y, X] = libsvmread('../../data/covtype.train.libsvm');
[testY, testX] = libsvmread('../../data/covtype.test.libsvm');
% Here X and testX are sparse matrices
model = abcboost_train(Y,X,'abcrobustlogit',100,20,0.1,params);
res = abcboost_test(testY,testX,model);
We provide the python support through pybind11.
Before the compilation, pybind11 should be installed:
python3 -m pip install pybind11
To compile the single-thread version on Linux (not Mac):
cd python/linux
bash compile_py.sh
After the compilation, a shared library abcboost.so is generated.
Analogously, there are two folders for Mac: python/mac_m1, python/mac_x86.
For windows, we provide the shared (python3.10) library abcboost.pyd under python/windows.
Make sure abcboost.so (or abcboost.pyd for Windows) is in the current directory.
Paste the following code in a python3 interactive shell:
import numpy as np
import abcboost
# We use a matrix-format sample data here
data = np.genfromtxt('../../data/covtype.train.csv',delimiter=',').astype(float)
#
Y = data[:,0]
X = data[:,1:]
data = np.genfromtxt('../../data/covtype.test.csv',delimiter=',').astype(float)
testY = data[:,0]
testX = data[:,1:]
model = abcboost.train(Y,X,'abcrobustlogit',100,20,0.1)
# All command line supported parameters can be passed as optional keyword arguments
# For example:
# model = abcboost.train(Y,X,'abcrobustlogit',100,20,0.1,search=2,gap=5,n_threads=1)
# abcboost.save(model,'mymodel.model')
# model = abcboost.load('mymodel.model')
res = abcboost.test(testY,testX,model,test_auc=1)
# predict without label
res = abcboost.predict(testX,model,test_auc=1)
# Alternatively, we also support libsvm-format sparse matrix
# We use sklearn to load libsvm format data as a scipy.sparse matrix
# sklearn can be installed as: python3 -m pip install scikit-learn
import sklearn
import sklearn.datasets
# X is a scipy.sparse matrix
[X, Y] = sklearn.datasets.load_svmlight_file('../../data/covtype.train.libsvm')
[testX, testY] = sklearn.datasets.load_svmlight_file('../../data/covtype.train.libsvm')
# The training and testing interfaces are unified for both dense and sparse matrices
model = abcboost.train(Y,X,'abcrobustlogit',100,20,0.1)
res = abcboost.test(testY,testX,model)
We provide an executable abcboost_clean for cleaning CSV files and detecting categorical feature. The categorical features will be encoded into one-hot representations and placed after the numerical features. The processed dataset will be stored by (by default) in the libsvm format but users also choose to specify the CSV format.
We provide an illustrative example: Census-Income (KDD) Data Set. First of all, the binary labels are - 50000 and 50000+ in the last column, which will be converted to respectively 0 and 1 and be placed in the first column of the processed dataset. This dataset contains missing values and many categorical features represented by strings. Interestingly, on this dataset Census-Income (KDD) Data Set, we notice that MART slightly outperforms Robust LogitBoost.
Executing the following terminal command will generate cleaned csv files for both training data census-income.data and testing data census-income.test:
./abcboost_clean -data data/census-income.data -label_column -1 -cleaned_format csv -additional_files data/census-income.test
-label_column -1 indicate the last column is for the labels. We can replace csv by libsvm if we hope to store the data in a different format. The default choice is libsvm. The following is the end of the output of the above command:
Found non-numeric labels:
(50000+.)->1 (- 50000.)->0
Cleaning summary: | # data: 299285 | # numeric features 14 | # categorical features: 28 | # converted features: 401 | # classes: 2
In the above command, -additional_files data/census-income.test indicates that census-income.data and census-income.test will be internally combined as one file to process categorical features. Alternatively, we can also first clean census-income.data and then use the information stored in census-income.data.cleaninfo to separately process census-income.test:
./abcboost_clean -data data/census-income.data -label_column -1
./abcboost_clean -data data/census-income.test -cleaninfo data/census-income.data.cleaninfo -cleaned_format csv
Note that the above two ways may not necessarily generate the same results because the additional files may contain additional categories. In this particular example, one can check that the results are the same.
In summary, abcboost_clean is fairly powerful with many functionalities. In the following, we list the options and explanations.
-datathe data file to clean-ignore_columnsthe columns to ignore in the CSV file. Multiple columns can be separated by commas, e.g.,-ignore_columns 1,3,-2ignores the first, third, and the second last columns. The index is one-based. There should be no space between the comma and the column indices-ignore_rowsthe rows to ignore in the CSV file. Multiple rows can be separated by commas-label_column(default 1) the column contains the label-category_limit(default 10000) the limit of the categories in a feature. In the auto detection, we will consider the column as a numeric column and treat non-numeric values as missing if the detected categories in the feature exceed this limit. We can specify the-additional_categorical_columnsto bypass this limit-additional_categorical_columnsspecifies additional categorical columns (it will override the auto categorical feature detection result)-additional_numeric_columnsspecifies additional numeric columns (it will override the auto categorical feature detection result). All non-numeric values in those columns will be considered as missing-missing_values(default ne,na,nan,none,null,unknown,,?) specifies the possible missing values (case-insensitive).-missing_substitution(default 0) we will substitute all missing values with this specified number-cleaned_format(default libsvm) the output format of the cleaned data. It can be specified to csv or libsvm. We suggest to use libsvm for a compact representation of the one-hot encoded categorical values.-cleaninfospecifies the.cleaninfofile. If this is unspecified. We will generate a file with a.cleaninfosuffix that contains the cleaning information, e.g., label columns, categorical mapping, etc. Specifying-cleaninfoenables us to clean other data with the same mapping of the previous cleaning. For example, we clean the training data first. And later we can use the.cleaninfoof the training data to clean the testing data to ensure they have the same feature mapping. Note that the-ignore_rowsis not saved in the.cleaninfo.-additional_filesthe additional files to clean together with the-data. For example, we may clean the training, testing, validating dataset together by specifying the training data in-data, testing and validating data in the additional_files (file names are separated by comma with no space).
- Ping Li. ABC-Boost: Adaptive Base Class Boost for Multi-Class Classification. ICML 2009.
- Ping Li. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost. UAI 2010.
- Ping Li and Weijie Zhao. Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification. arXiv:2205.10927 2022.
- Ping Li and Weijie Zhao. Package for Fast ABC-Boost. arXiv:2207.08770, 2022.
- Ping Li and Weijie Zhao. pGMM Kernel Regression and Comparisons with Boosted Trees. arXiv:2207.08667, 2022.
- Lecture notes on trees & boosting (pages 14-77) www.stat.rutgers.edu/home/pingli/doc/PingLiTutorial.pdf
ABCBoost is provided under the Apache-2.0 license.
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