liquidSVM for Python

Welcome to the Python bindings for liquidSVM.

Summary:

Install it using any of the following variants:

pip install --user --upgrade liquidSVM
easy_install --user --upgrade liquidSVM

If you want to compile liquidSVM for your machine download liquidSVM-python.tar.gz. For Windows there are binaries at liquidSVM-python.win-amd64.zip, for Mac at liquidSVM-python.macosx.tar.gz

Then to try it out issue on the command line

python -m liquidSVM covtype.1000 mc --display=1

NOTE: it might be possible that there is a problem with the last line if there are files called liquidSVM* in the current directory, so change to some other or a newly created one.

Or use it in an interactive shell

from liquidSVM import *
model = mcSVM(iris, iris_labs, display=1,threads=2)
result, err = model.test(iris, iris_labs)
result = model.predict(iris)

reg = LiquidData('reg-1d')
model = lsSVM(reg.test, display=1)
result, err = model.test(reg.test)

More Information can be found in the demo [jupyter notebook] and in

from liquidSVM import *
help(SVM)
help(doc.configuration)

Both liquidSVM and these bindings are provided under the AGPL 3.0 license.

Native Library Compilation

liquidSVM is implemented in C++ therefore a native library needs to be compiled and included in the Python process. Binaries for Windows are included, however if it is possible for you, we recommend you compile it for every machine to get full performance.

To set compiler options use the the environment variable LIQUIDSVM_CONFIGURE_ARGS. The first word in it can be any of the following:

native: usually the fastest, but the resulting library is usually not portable to other machines.
generic: should be portable to most machines, yet slower (factor 2 to 4?)
debug: compiles with debugging activated (can be debugged e.g. with gdb)
empty: No special compilation options activated.

The remainder of the environment variable will be passed to the compiler. Extract liquidSVM-python.tar.gz and change into the directory. On Linux and MacOS X command line use for instance:

LIQUIDSVM_CONFIGURE_ARGS="native -mavx2" python setup.py bdist
LIQUIDSVM_CONFIGURE_ARGS=generic python setup.py bdist

MacOS:

Install Xcode and then the optional command line tools are installed from therein.

Windows:

If you have VisualStudio installed then you should have an environment variable like %VS90COMNTOOLS% (for VisualStudio 2015). Still it seems that setup.py needs to have this information in %VS90COMNTOOLS% so copy that environment variable or use for example:

set VS90COMNTOOLS=%VS140COMNTOOLS%

Note: At the moment the Visual Studio for Python only gives Version 9.0 and this is too old for compilation.

Overview of Configuration Parameters

display

This parameter determines the amount of output of you see at the screen: The larger its value is, the more you see. This can help as a progress indication.

scale

If set to a true value then for every feature in the training data a scaling is calculated so that its values lie in the interval [0, 1]. The training then is performed using these scaled values and any testing data is scaled transparently as well.

Because SVMs are not scale-invariant any data should be scaled for two main reasons: First that all features have the same weight, and second to assure that the default gamma parameters that liquidSVM provide remain meaningful.

If you do not have scaled the data previously this is an easy option.

threads

This parameter determines the number of cores used for computing the kernel matrices, the validation error, and the test error.

threads=0 (default) means that all physical cores of your CPU run one thread.
threads=-1 means that all but one physical cores of your CPU run one thread.

partition_choice

This parameter determines the way the input space is partitioned. This allows larger data sets for which the kernel matrix does not fit into memory.

partition_choice=0 (default) disables partitioning.
partition_choice=6 gives usually highest speed.
partition_choice=5 gives usually the best test error.

grid_choice

This parameter determines the size of the hyper- parameter grid used during the training phase. Larger values correspond to larger grids. By default, a 10x10 grid is used. Exact descriptions are given in the next section.

adaptivity_control

This parameter determines, whether an adaptive grid search heuristic is employed. Larger values lead to more aggressive strategies. The default adaptivity_control = 0 disables the heuristic.

random_seed

This parameter determines the seed for the random generator. random_seed = -1 uses the internal timer create the seed. All other values lead to repeatable behavior of the svm.

folds

How many folds should be used.

Specialized configuration parameters

Parameters for regression (least-squares, quantile, and expectile)

clipping: This parameter determines whether the decision functions should be clipped at the specified value. The value clipping = -1.0 leads to an adaptive clipping value, whereas clipping = 0 disables clipping.

Parameter for multiclass classification determine the multiclass strategy: mc-type=0 : AvA with hinge loss. mc-type=1 : OvA with least squares loss. mc-type=2 : OvA with hinge loss. mc-type=3 : AvA with least squares loss.

Parameters for Neyman-Pearson Learning

class: The class, the constraint is enforced on.
constraint: The constraint on the false alarm rate. The script actually considers a couple of values around the value of constraint to give the user an informed choice.

Hyperparameter Grid

For Support Vector Machines two hyperparameters need to be determined:

gamma the bandwith of the kernel
lambda has to be chosen such that neither over- nor underfitting happen. lambda values are the classical regularization parameter in front of the norm term.

liquidSVM has a built-in a cross-validation scheme to calculate validation errors for many values of these hyperparameters and then to choose the best pair. Since there are two parameters this means we consider a two-dimensional grid.

For both parameters either specific values can be given or a geometrically spaced grid can be specified.

gamma_steps, min_gamma, max_gamma: specifies in the interval between min_gamma and max_gamma there should be gamma_steps many values
gammas: e.g. gammas=c(0.1,1,10,100) will do these four gamma values
lambda_steps, min_lambda, max_lambda: specifies in the interval between min_lambda and max_lambda there should be lambda_steps many values
lambdas: e.g. lambdas=c(0.1,1,10,100) will do these four lambda values
c_values: the classical term in front of the empirical error term, e.g. c_values=c(0.1,1,10,100) will do these four cost values (basically inverse of lambdas)

Note the min and max values are scaled according the the number of samples, the dimensionality of the data sets, the number of folds used, and the estimated diameter of the data set.

Using grid_choice allows for some general choices of these parameters

`grid_choice`	0	1	2
`gamma_steps`	10	15	20
`lambda_steps`	10	15	20
`min_gamma`	0.2	0.1	0.05
`max_gamma`	5.0	10.0	20.0
`min_lambda`	0.001	0.0001	0.00001
`max_lambda`	0.01	0.01	0.01

Using negative values of grid_choice we create a grid with listed gamma and lambda values:

`grid_choice`	-1
`gammas`	`c(10.0, 5.0, 2.0, 1.0, 0.5, 0.25, 0.1, 0.05)`
`lambdas`	`c(1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001, 0.0000001)`

`grid_choice`	-2
`gammas`	`c(10.0, 5.0, 2.0, 1.0, 0.5, 0.25, 0.1, 0.05)`
`c_values`	`c(0.01, 0.1, 1, 10, 100, 1000, 10000)`

Adaptive Grid

An adaptive grid search can be activated. The higher the values of MAX_LAMBDA_INCREASES and MAX_NUMBER_OF_WORSE_GAMMAS are set the more conservative the search strategy is. The values can be freely modified.

`ADAPTIVITY_CONTROL`	1	2
`MAX_LAMBDA_INCREASES`	4	3
`MAX_NUMBER_OF_WORSE_GAMMAS`	4	3

Cells

A major issue with SVMs is that for larger sample sizes the kernel matrix does not fit into the memory any more. Classically this gives an upper limit for the class of problems that traditional SVMs can handle without significant runtime increase. Furthermore also the time complexity is at least O(n²).

liquidSVM implements two major concepts to circumvent these issues. One is random chunks which is known well in the literature. However we prefer the new alternative of splitting the space into spatial cells and use local SVMs on every cell.

If you specify useCells=TRUE then the sample space X gets partitioned into a number of cells. The training is done first for cell 1 then for cell 2 and so on. Now, to predict the label for a value x ∈ X liquidSVM first finds out to which cell this x belongs and then uses the SVM of that cell to predict a label for it.

If you run into memory issues turn cells on: useCells=TRUE

This is quite performant, since the complexity in both time and memore are both O(CELLSIZE × n) and this holds both for training as well as testing! It also can be shown that the quality of the solution is comparable, at least for moderate dimensions.

The cells can be configured using the partition_choice:

This gives a partition into random chunks of size 2000

VORONOI=c(1, 2000)
This gives a partition into 10 random chunks

VORONOI=c(2, 10)
This gives a Voronoi partition into cells with radius not larger than 1.0. For its creation a subsample containing at most 50.000 samples is used.

VORONOI=c(3, 1.0, 50000)
This gives a Voronoi partition into cells with at most 2000 samples (approximately). For its creation a subsample containing at most 50.000 samples is used. A shrinking heuristic is used to reduce the number of cells.

VORONOI=c(4, 2000, 1, 50000)
This gives a overlapping regions with at most 2000 samples (approximately). For its creation a subsample containing at most 50.000 samples is used. A stopping heuristic is used to stop the creation of regions if 0.5 * 2000 samples have not been assigned to a region, yet.

VORONOI=c(5, 2000, 0.5, 50000, 1)
This splits the working sets into Voronoi like with PARTITION_TYPE=4. Unlike that case, the centers for the Voronoi partition are found by a recursive tree approach, which in many cases may be faster.

VORONOI=c(6, 2000, 1, 50000, 2.0, 20, 4,)

The first parameter values correspond to NO_PARTITION, RANDOM_CHUNK_BY_SIZE, RANDOM_CHUNK_BY_NUMBER, VORONOI_BY_RADIUS, VORONOI_BY_SIZE, OVERLAP_BY_SIZE

Weights

qt, ex: Here the number of considered tau-quantiles/expectiles as well as the considered tau-values are defined. You can freely change these values but notice that the list of tau-values is space-separated!
npl, roc: Here, you define, which weighted classification problems will be considered. The choice is usually a bit tricky. Good luck ...

NPL:
WEIGHT_STEPS=10
MIN_WEIGHT=0.001
MAX_WEIGHT=0.5
GEO_WEIGHTS=1

ROC:
WEIGHT_STEPS=9
MAX_WEIGHT=0.9
MIN_WEIGHT=0.1
GEO_WEIGHTS=0

More Advanced Parameters

The following parameters should only employed by experienced users and are self-explanatory for these:

KERNEL: specifies the kernel to use, at the moment either GAUSS_RBF or POISSON
RETRAIN_METHOD: After training on grids and folds there are only solutions on folds. In order to construct a global solution one can either retrain on the whole training data (SELECT_ON_ENTIRE_TRAIN_SET) or the (partial) solutions from the training are kept and combined using voting (SELECT_ON_EACH_FOLD default)
store_solutions_internally: If this is true (default in all applicable cases) then the solutions of the train phase are stored and can be just reused in the select phase. If you slowly run out of memory during the train phase maybe disable this. However then in the select phase the best models have to be trained again.

For completeness here are some values that usually get set by the learning scenario

SVM_TYPE: KERNEL_RULE, SVM_LS_2D, SVM_HINGE_2D, SVM_QUANTILE, SVM_EXPECTILE_2D, SVM_TEMPLATE
LOSS_TYPE: CLASSIFICATION_LOSS, MULTI_CLASS_LOSS, LEAST_SQUARES_LOSS, WEIGHTED_LEAST_SQUARES_LOSS, PINBALL_LOSS, TEMPLATE_LOSS
VOTE_SCENARIO: VOTE_CLASSIFICATION, VOTE_REGRESSION, VOTE_NPL
KERNEL_MEMORY_MODEL: LINE_BY_LINE, BLOCK, CACHE, EMPTY
FOLDS_KIND: FROM_FILE, BLOCKS, ALTERNATING, RANDOM, STRATIFIED, RANDOM_SUBSET
WS_TYPE: FULL_SET, MULTI_CLASS_ALL_VS_ALL, MULTI_CLASS_ONE_VS_ALL, BOOT_STRAP