liblinear-official 2.46.1

Python binding of LIBLINEAR

-------------------------------------

--- Python interface of LIBLINEAR ---

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Table of Contents

=================



- Introduction

- Installation via PyPI

- Installation via Sources

- Quick Start

- Quick Start with Scipy

- Design Description

- Data Structures

- Utility Functions

- Additional Information



Introduction

============



Python (http://www.python.org/) is a programming language suitable for rapid

development. This tool provides a simple Python interface to LIBLINEAR, a library

for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/liblinear). The

interface is very easy to use as the usage is the same as that of LIBLINEAR. The

interface is developed with the built-in Python library "ctypes."



Installation via PyPI

=====================



To install the interface from PyPI, execute the following command:



> pip install -U liblinear-official



Installation via Sources

========================



Alternatively, you may install the interface from sources by

generating the LIBLINEAR shared library.



Depending on your use cases, you can choose between local-directory

and system-wide installation.



- Local-directory installation:



    On Unix systems, type



    > make



    This generates a .so file in the LIBLINEAR main directory and you

    can run the interface in the current python directory.

    

    For Windows, the shared library liblinear.dll is ready in the

    directory `..\windows' and you can directly run the interface in

    the current python directory. You can copy liblinear.dll to the

    system directory (e.g., `C:\WINDOWS\system32\') to make it

    system-widely available. To regenerate liblinear.dll, please

    follow the instruction of building Windows binaries in LIBLINEAR

    README.



- System-wide installation:



    Type



    > pip install -e .



    or



    > pip install --user -e .



    The option --user would install the package in the home directory

    instead of the system directory, and thus does not require the

    root privilege.



    Please note that you must keep the sources after the installation.



    For Windows, to run the above command, Microsoft Visual C++ and

    other tools are needed.



    In addition, DON'T use the following FAILED commands



    > python setup.py install (failed to run at the python directory)

    > pip install .



Quick Start

===========



"Quick Start with Scipy" is in the next section.



There are two levels of usage. The high-level one uses utility

functions in liblinearutil.py and commonutil.py (shared with LIBSVM

and imported by svmutil.py). The usage is the same as the LIBLINEAR

MATLAB interface.



>>> from liblinear.liblinearutil import *

# Read data in LIBSVM format

>>> y, x = svm_read_problem('../heart_scale')

>>> m = train(y[:200], x[:200], '-c 4')

>>> p_label, p_acc, p_val = predict(y[200:], x[200:], m)



# Construct problem in python format

# Dense data

>>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]

# Sparse data

>>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]

>>> prob  = problem(y, x)

>>> param = parameter('-s 0 -c 4 -B 1')

>>> m = train(prob, param)



# Other utility functions

>>> save_model('heart_scale.model', m)

>>> m = load_model('heart_scale.model')

>>> p_label, p_acc, p_val = predict(y, x, m, '-b 1')

>>> ACC, MSE, SCC = evaluations(y, p_label)



# Getting online help

>>> help(train)



The low-level use directly calls C interfaces imported by liblinear.py. Note that

all arguments and return values are in ctypes format. You need to handle them

carefully.



>>> from liblinear.liblinear import *

>>> prob = problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])

>>> param = parameter('-c 4')

>>> m = liblinear.train(prob, param) # m is a ctype pointer to a model

# Convert a Python-format instance to feature_nodearray, a ctypes structure

>>> x0, max_idx = gen_feature_nodearray({1:1, 3:1})

>>> label = liblinear.predict(m, x0)



Quick Start with Scipy

======================



Make sure you have Scipy installed to proceed in this section.

If numba (http://numba.pydata.org) is installed, some operations will be much faster.



There are two levels of usage. The high-level one uses utility functions

in liblinearutil.py and the usage is the same as the LIBLINEAR MATLAB interface.



>>> import numpy as np

>>> import scipy

>>> from liblinear.liblinearutil import *

# Read data in LIBSVM format

>>> y, x = svm_read_problem('../heart_scale', return_scipy = True) # y: ndarray, x: csr_matrix

>>> m = train(y[:200], x[:200, :], '-c 4')

>>> p_label, p_acc, p_val = predict(y[200:], x[200:, :], m)



# Construct problem in Scipy format

# Dense data: numpy ndarray

>>> y, x = np.asarray([1,-1]), np.asarray([[1,0,1], [-1,0,-1]])

# Sparse data: scipy csr_matrix((data, (row_ind, col_ind))

>>> y, x = np.asarray([1,-1]), scipy.sparse.csr_matrix(([1, 1, -1, -1], ([0, 0, 1, 1], [0, 2, 0, 2])))

>>> prob  = problem(y, x)

>>> param = parameter('-s 0 -c 4 -B 1')

>>> m = train(prob, param)



# Apply data scaling in Scipy format

>>> y, x = svm_read_problem('../heart_scale', return_scipy=True)

>>> scale_param = csr_find_scale_param(x, lower=0)

>>> scaled_x = csr_scale(x, scale_param)



# Other utility functions

>>> save_model('heart_scale.model', m)

>>> m = load_model('heart_scale.model')

>>> p_label, p_acc, p_val = predict(y, x, m, '-b 1')

>>> ACC, MSE, SCC = evaluations(y, p_label)



# Getting online help

>>> help(train)



The low-level use directly calls C interfaces imported by liblinear.py. Note that

all arguments and return values are in ctypes format. You need to handle them

carefully.



>>> from liblinear.liblinear import *

>>> prob = problem(np.asarray([1,-1]), scipy.sparse.csr_matrix(([1, 1, -1, -1], ([0, 0, 1, 1], [0, 2, 0, 2]))))

>>> param = parameter('-c 4')

>>> m = liblinear.train(prob, param) # m is a ctype pointer to a model

# Convert a tuple of ndarray (index, data) to feature_nodearray, a ctypes structure

# Note that index starts from 0, though the following example will be changed to 1:1, 3:1 internally

>>> x0, max_idx = gen_feature_nodearray((np.asarray([0,2]), np.asarray([1,1])))

>>> label = liblinear.predict(m, x0)



Design Description

==================



There are two files liblinear.py and liblinearutil.py, which respectively correspond to

low-level and high-level use of the interface.



In liblinear.py, we adopt the Python built-in library "ctypes," so that

Python can directly access C structures and interface functions defined

in linear.h.



While advanced users can use structures/functions in liblinear.py, to

avoid handling ctypes structures, in liblinearutil.py we provide some easy-to-use

functions. The usage is similar to LIBLINEAR MATLAB interface.



Data Structures

===============



Three data structures derived from linear.h are node, problem, and

parameter. They all contain fields with the same names in

linear.h. Access these fields carefully because you directly use a C structure

instead of a Python object. The following description introduces additional

fields and methods.



Before using the data structures, execute the following command to load the

LIBLINEAR shared library:



    >>> from liblinear.liblinear import *



- class feature_node:



    Construct a feature_node.



    >>> node = feature_node(idx, val)



    idx: an integer indicates the feature index.



    val: a float indicates the feature value.



    Show the index and the value of a node.



    >>> print(node)



- Function: gen_feature_nodearray(xi [,feature_max=None])



    Generate a feature vector from a Python list/tuple/dictionary, numpy ndarray or tuple of (index, data):



    >>> xi_ctype, max_idx = gen_feature_nodearray({1:1, 3:1, 5:-2})



    xi_ctype: the returned feature_nodearray (a ctypes structure)



    max_idx: the maximal feature index of xi



    feature_max: if feature_max is assigned, features with indices larger than

                 feature_max are removed.



- class problem:



    Construct a problem instance



    >>> prob = problem(y, x [,bias=-1])



    y: a Python list/tuple/ndarray of l labels (type must be int/double).



    x: 1. a list/tuple of l training instances. Feature vector of

          each training instance is a list/tuple or dictionary.



       2. an l * n numpy ndarray or scipy spmatrix (n: number of features).



    bias: if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term

          added (default -1)



    You can also modify the bias value by



    >>> prob.set_bias(1)



    Note that if your x contains sparse data (i.e., dictionary), the internal

    ctypes data format is still sparse.



- class parameter:



    Construct a parameter instance



    >>> param = parameter('training_options')



    If 'training_options' is empty, LIBLINEAR default values are applied.



    Set param to LIBLINEAR default values.



    >>> param.set_to_default_values()



    Parse a string of options.



    >>> param.parse_options('training_options')



    Show values of parameters.



    >>> print(param)



- class model:



    There are two ways to obtain an instance of model:



    >>> model_ = train(y, x)

    >>> model_ = load_model('model_file_name')



    Note that the returned structure of interface functions

    liblinear.train and liblinear.load_model is a ctypes pointer of

    model, which is different from the model object returned

    by train and load_model in liblinearutil.py. We provide a

    function toPyModel for the conversion:



    >>> model_ptr = liblinear.train(prob, param)

    >>> model_ = toPyModel(model_ptr)



    If you obtain a model in a way other than the above approaches,

    handle it carefully to avoid memory leak or segmentation fault.



    Some interface functions to access LIBLINEAR models are wrapped as

    members of the class model:



    >>> nr_feature =  model_.get_nr_feature()

    >>> nr_class = model_.get_nr_class()

    >>> class_labels = model_.get_labels()

    >>> is_prob_model = model_.is_probability_model()

    >>> is_regression_model = model_.is_regression_model()



    The decision function is W*x + b, where

        W is an nr_class-by-nr_feature matrix, and

        b is a vector of size nr_class.

    To access W_kj (i.e., coefficient for the k-th class and the j-th feature)

    and b_k (i.e., bias for the k-th class), use the following functions.



    >>> W_kj = model_.get_decfun_coef(feat_idx=j, label_idx=k)

    >>> b_k = model_.get_decfun_bias(label_idx=k)



    We also provide a function to extract w_k (i.e., the k-th row of W) and

    b_k directly as follows.



    >>> [w_k, b_k] = model_.get_decfun(label_idx=k)



    Note that w_k is a Python list of length nr_feature, which means that

        w_k[0] = W_k1.

    For regression models, W is just a vector of length nr_feature. Either

    set label_idx=0 or omit the label_idx parameter to access the coefficients.



    >>> W_j = model_.get_decfun_coef(feat_idx=j)

    >>> b = model_.get_decfun_bias()

    >>> [W, b] = model_.get_decfun()



    For one-class SVM models, label_idx is ignored and b=-rho is

    returned from get_decfun(). That is, the decision function is

    w*x+b = w*x-rho.



    >>> rho = model_.get_decfun_rho()

    >>> [W, b] = model_.get_decfun()



    Note that in get_decfun_coef, get_decfun_bias, and get_decfun, feat_idx

    starts from 1, while label_idx starts from 0. If label_idx is not in the

    valid range (0 to nr_class-1), then a NaN will be returned; and if feat_idx

    is not in the valid range (1 to nr_feature), then a zero value will be

    returned. For regression models, label_idx is ignored.



Utility Functions

=================



To use utility functions, type



    >>> from liblinear.liblinearutil import *



The above command loads

    train()            : train a linear model

    predict()          : predict testing data

    svm_read_problem() : read the data from a LIBSVM-format file.

    load_model()       : load a LIBLINEAR model.

    save_model()       : save model to a file.

    evaluations()      : evaluate prediction results.



- Function: train



    There are three ways to call train()



    >>> model = train(y, x [, 'training_options'])

    >>> model = train(prob [, 'training_options'])

    >>> model = train(prob, param)



    y: a list/tuple/ndarray of l training labels (type must be int/double).



    x: 1. a list/tuple of l training instances. Feature vector of

          each training instance is a list/tuple or dictionary.



       2. an l * n numpy ndarray or scipy spmatrix (n: number of features).



    training_options: a string in the same form as that for LIBLINEAR command

                      mode.



    prob: a problem instance generated by calling

          problem(y, x).



    param: a parameter instance generated by calling

           parameter('training_options')



    model: the returned model instance. See linear.h for details of this

           structure. If '-v' is specified, cross validation is

           conducted and the returned model is just a scalar: cross-validation

           accuracy for classification and mean-squared error for regression.



           If the '-C' option is specified, best parameters are found

           by cross validation. The parameter selection utility is supported

           only by -s 0, -s 2 (for finding C) and -s 11 (for finding C, p).

           The returned structure is a triple with the best C, the best p,

           and the corresponding cross-validation accuracy or mean squared

           error. The returned best p for -s 0 and -s 2 is set to -1 because

           the p parameter is not used by classification models.





    To train the same data many times with different

    parameters, the second and the third ways should be faster..



    Examples:



    >>> y, x = svm_read_problem('../heart_scale')

    >>> prob = problem(y, x)

    >>> param = parameter('-s 3 -c 5 -q')

    >>> m = train(y, x, '-c 5')

    >>> m = train(prob, '-w1 5 -c 5')

    >>> m = train(prob, param)

    >>> CV_ACC = train(y, x, '-v 3')

    >>> best_C, best_p, best_rate = train(y, x, '-C -s 0') # best_p is only for -s 11

    >>> m = train(y, x, '-c {0} -s 0'.format(best_C)) # use the same solver: -s 0



- Function: predict



    To predict testing data with a model, use



    >>> p_labs, p_acc, p_vals = predict(y, x, model [,'predicting_options'])



    y: a list/tuple/ndarray of l true labels (type must be int/double).

       It is used for calculating the accuracy. Use [] if true labels are

       unavailable.



    x: 1. a list/tuple of l training instances. Feature vector of

          each training instance is a list/tuple or dictionary.



       2. an l * n numpy ndarray or scipy spmatrix (n: number of features).



    predicting_options: a string of predicting options in the same format as

                        that of LIBLINEAR.



    model: a model instance.



    p_labels: a list of predicted labels



    p_acc: a tuple including accuracy (for classification), mean

           squared error, and squared correlation coefficient (for

           regression).



    p_vals: a list of decision values or probability estimates (if '-b 1'

            is specified). If k is the number of classes, for decision values,

            each element includes results of predicting k binary-class

            SVMs. If k = 2 and solver is not MCSVM_CS, only one decision value

            is returned. For probabilities, each element contains k values

            indicating the probability that the testing instance is in each class.

            Note that the order of classes here is the same as 'model.label'

            field in the model structure.



    Example:



    >>> m = train(y, x, '-c 5')

    >>> p_labels, p_acc, p_vals = predict(y, x, m)



- Functions: svm_read_problem/load_model/save_model



    See the usage by examples:



    >>> y, x = svm_read_problem('data.txt')

    >>> m = load_model('model_file')

    >>> save_model('model_file', m)



- Function: evaluations



    Calculate some evaluations using the true values (ty) and the predicted

    values (pv):



    >>> (ACC, MSE, SCC) = evaluations(ty, pv, useScipy)



    ty: a list/tuple/ndarray of true values.



    pv: a list/tuple/ndarray of predicted values.



    useScipy: convert ty, pv to ndarray, and use scipy functions to do the evaluation



    ACC: accuracy.



    MSE: mean squared error.



    SCC: squared correlation coefficient.



- Function: csr_find_scale_parameter/csr_scale



    Scale data in csr format.



    >>> param = csr_find_scale_param(x [, lower=l, upper=u])

    >>> x = csr_scale(x, param)



    x: a csr_matrix of data.



    l: x scaling lower limit; default -1.



    u: x scaling upper limit; default 1.



    The scaling process is: x * diag(coef) + ones(l, 1) * offset'



    param: a dictionary of scaling parameters, where param['coef'] = coef and param['offset'] = offset.



    coef: a scipy array of scaling coefficients.



    offset: a scipy array of scaling offsets.



Additional Information

======================



This interface was originally written by Hsiang-Fu Yu from Department of Computer

Science, National Taiwan University. If you find this tool useful, please

cite LIBLINEAR as follows



R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.

LIBLINEAR: A Library for Large Linear Classification, Journal of

Machine Learning Research 9(2008), 1871-1874. Software available at

http://www.csie.ntu.edu.tw/~cjlin/liblinear



For any question, please contact Chih-Jen Lin <[e-mail]>,

or check the FAQ page:



http://www.csie.ntu.edu.tw/~cjlin/liblinear/faq.html