napkinXC can also be built and used as an executable that can be used to train and evaluate models and make a prediction.
To build napkinXC, first clone the project repository and run the following commands in the root directory of the project. It requires modern C++17 compiler, CMake and Git installed. Set CXX and CC environmental variables before running cmake
command if you want to build with the specific C++ compiler.
cmake .
make
-B
options can be passed to CMake command to specify other build directory. After successful compilation, nxc
executable should appear in the root or specified build directory.
napkinXC supports multi-label svmlight/libsvm like-format (less strict) and format of datasets from The Extreme Classification Repository, which has an additional header line with a number of data points, features, and labels.
The format is text-based. Each line contains an instance and is ended by a \n
character.
<label>,<label>,... <feature>(:<value>) <feature>(:<value>) ...
<label>
and <feature>
are indexes that should be positive integers. Unlike to normal svmlight/libsvm format, labels and features do not have to be sorted in ascending order. The :<value>
can be omitted after <feature>
, to assume value = 1.
nxc
executable needs command, i.e. train, test, predict as a first argument. -i
/--input
and -o
/--output
arguments needs to be always provided.
nxc <command> -i <path to dataset> -o <path to model directory> <args> ...
Usage: nxc <command> <args>
Commands:
train Train model on given input data
test Test model on given input data
predict Predict for given data
ofo Use online f-measure optimization
version Print napkinXC version
help Print help
Args:
General:
-i, --input Input dataset, required
-o, --output Output (model) dir, required
-m, --model Model type (default = plt):
Models: ovr, br, hsm, plt, oplt, svbopFull, svbopHf, brMips, svbopMips
--ensemble Number of models in ensemble (default = 1)
-t, --threads Number of threads to use (default = 0)
Note: -1 to use #cpus - 1, 0 to use #cpus
--hash Size of features space (default = 0)
Note: 0 to disable hashing
--featuresThreshold Prune features below given threshold (default = 0.0)
--seed Seed (default = system time)
--verbose Verbose level (default = 2)
Base classifiers:
--optimizer Optimizer used for training binary classifiers (default = libliner)
Optimizers: liblinear, sgd, adagrad, fobos
--bias Value of the bias features (default = 1)
--inbalanceLabelsWeighting Increase the weight of minority labels in base classifiers (default = 1)
--weightsThreshold Threshold value for pruning models weights (default = 0.1)
LIBLINEAR: (more about LIBLINEAR: https://github.com/cjlin1/liblinear)
-s, --liblinearSolver LIBLINEAR solver (default for log loss = L2R_LR_DUAL, for l2 loss = L2R_L2LOSS_SVC_DUAL)
Supported solvers: L2R_LR_DUAL, L2R_LR, L1R_LR,
L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, L1R_L2LOSS_SVC
-c, --liblinearC LIBLINEAR cost co-efficient, inverse of regularization strength, must be a positive float,
smaller values specify stronger regularization (default = 10.0)
--eps, --liblinearEps LIBLINEAR tolerance of termination criterion (default = 0.1)
SGD/AdaGrad:
-l, --lr, --eta Step size (learning rate) for online optimizers (default = 1.0)
--epochs Number of training epochs for online optimizers (default = 1)
--adagradEps Defines starting step size for AdaGrad (default = 0.001)
Tree:
-a, --arity Arity of tree nodes (default = 2)
--maxLeaves Maximum degree of pre-leaf nodes. (default = 100)
--tree File with tree structure
--treeType Type of a tree to build if file with structure is not provided
tree types: hierarchicalKmeans, huffman, completeKaryInOrder, completeKaryRandom,
balancedInOrder, balancedRandom, onlineComplete
K-Means tree:
--kmeansEps Tolerance of termination criterion of the k-means clustering
used in hierarchical k-means tree building procedure (default = 0.001)
--kmeansBalanced Use balanced K-Means clustering (default = 1)
Prediction:
--topK Predict top-k labels (default = 5)
--threshold Predict labels with probability above the threshold (default = 0)
--thresholds Path to a file with threshold for each label
--setUtility Type of set-utility function for prediction using svbopFull, svbopHf, svbopMips models.
Set-utility functions: uP, uF1, uAlfa, uAlfaBeta, uDeltaGamma
See: https://arxiv.org/abs/1906.08129
Set-Utility:
--alpha
--beta
--delta
--gamma
Test:
--measures Evaluate test using set of measures (default = "p@1,r@1,c@1,p@3,r@3,c@3,p@5,r@5,c@5")
Measures: acc (accuracy), p (precision), r (recall), c (coverage), hl (hamming loos)
p@k (precision at k), r@k (recall at k), c@k (coverage at k), s (prediction size)