The objective in extreme multi-label learning is to build classifiers that can annotate a data point with the subset of relevant labels from an extremely large label set. Extreme classification has, thus far, only been studied in the context of predicting labels for novel test points. SwiftXML is useful for solving extreme classification problem when predictions need to be made on training points with partially revealed labels. This allows the reformulation of warm-start tagging, ranking and recommendation problems as extreme multi-label learning with each item to be ranked/recommended being mapped onto a separate label. SwiftXML can be significantly more accurate as compared to leading extreme classifiers such as FastXML and PfastreXML as well as compared to classical recommendation algorithms on warm-start extreme classification tasks. Please refer to the research paper  for more details.
This code is made available as is for non-commercial research purposes. Please make sure that you have read the license agreement in LICENSE.doc/pdf. Please do not install or use SwiftXML unless you agree to the terms of the license.Download SwiftXML source code in C++ and Matlab as well as precompiled Windows/Linux binaries.
The code for SwiftXML is written in C++ and should compile on 64 bit Windows/Linux machines using a C++11 enabled compiler. Matlab wrappers have also been provided with the code. Installation and usage instructions are provided below.
Please contact Yashoteja Prabhu and Manik Varma if you have any questions or feedback.
Please visit the Extreme Classification Repository to download the benchmark datasets and compare SwiftXML's performance to baseline algorithms. Please download the label (or item) features for the benchmark datasets on the Repository as well as user features and labels for new benchmark datasets used in the paper from here. For more information about item features, please refer to the research paper.
trn_X_Xf.txt, trn_X_Y.txt, trn_item_X_Xf.txt, tst_X_Xf.txt, inc_tst_X_Y.txt, exc_tst_X_Y.txt, tst_item_X_Xf.txt, inv_prop.txtPreprocessing requires Perl and Matlab. Please refer to sample_run.sh/sample_run.bat for better understanding.
./swiftXML_train [user feature file name] [item feature file name] [label file name] [inverse propensity file name] [model folder name] -S 0 -T 1 -s 0 -t 50 -b 1.0 -c 1.0 -m 10 -l 100 -g 30 -a 0.8 -q 1 -N [number of original training points]Matlab:
swiftXML_train([user feature matrix], [item feature matrix], [input label matrix], [inverse propensity score vector], [output model folder name], param);where:
-S = param.pfswitch : PfastXML switch, setting this to 1 omits tail classifiers, thus leading to PfastXML algorithm. default=0 -T = param.num_thread : Number of threads to use. default=1 -s = param.start_tree : Starting tree index. default=0 -t = param.num_tree : Number of trees to be grown. default=50 -b = param.bias : Feature bias value, extre feature value to be appended. default=1.0 -c = param.log_loss_coeff : log-loss weight co-efficient for separator in user feature space. default=1.0 -m = param.max_leaf : Maximum allowed instances in a leaf node. Larger nodes are attempted to be split, and on failure converted to leaves. default=10 -l = param.lbl_per_leaf : Number of label-probability pairs to retain in a leaf. default=100 -g = param.gamma : gamma parameter appearing in tail label classifiers. default=30 -a = param.alpha : Trade-off parameter between PfastXML and tail label classifiers. default=0.8 -q = param.quiet : Quiet option (0/1). default=0 -ic = param.item_log_loss_coeff : Log-loss weight co-efficient for separator in item feature space. default=1.0 -f = param.feat_imp : Relative importance of user and item feature separators during classification. default=0.5 -N = param.num_trn_X : Number of original training instances in dataset. Note that test points are also used during training and are not counted here.The fine-tuned hyperparameter settings for the benchmark datasets used in the  are available from "hyperparameters.txt" file in the SwiftXML's code folder. For C++, the feature and label input files are expected to be in sparse matrix text format (refer to Miscellaneous section). For Matlab, the feature and label matrices are Matlab's sparse matrices.
./swiftXML_test [user feature file name] [item feature file name] [score file name] [model folder name] -S 0 -T 1 -s 0 -t 50 -n 1000 -q 1Matlab:
output_score_mat = parabel_predict( [user feature file name], [item feature file name], [input model folder name], param );where:
-S = param.pfswitch : PfastXML switch, setting this to 1 omits tail classifiers, thus leading to PfastXML algorithm. default=[value saved in trained model] -T = param.num_thread : Number of threads to use. default=[value saved in trained model] -s = param.start_tree : Starting tree index. default=[value saved in trained model] -t = param.num_tree : Number of trees to be grown. default=[value saved in trained model] -n = param.actlbl : Number of predicted scores per test instance. Lower value means quicker prediction. default=1000 -q = param.quiet : quiet option (0/1). default=[value saved in trained model]For C++, the feature and score files are expected to be in sparse matrix text format (refer to Miscellaneous section). For Matlab, the feature and score matrices are Matlab's sparse matrices.
swiftXML_evaluate_predictions( [test score matrix], [revealed test label matrix], [held-out test label matrix], [inverse label propensity vector] );
[matrix] = read_text_mat([text matrix name]);To write a Matlab matrix into text format:
write_text_mat([Matlab sparse matrix], [text matrix name to be written to]);
[weights vector] = inv_propensity([training label matrix],A,B);A,B are the parameters of the inverse propensity model. Following values are to be used over the benchmark datasets:
Wikipedia-LSHTC: A=0.5, B=0.4 Amazon: A=0.6, B=2.6 Other: A=0.55, B=1.5