liblinear-official 2.50.0

-------------------------------------
--- Python interface of LIBLINEAR ---
-------------------------------------

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, starting from version 2.48, we no longer provide the 
    pre-built shared library liblinear.dll. To run the interface in the 
    current python directory, please follow the instruction of building 
    Windows binaries in LIBLINEAR README. You can copy liblinear.dll to 
    the system directory (e.g., `C:\WINDOWS\system32\') to make it 
    system-widely available.

- 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')
>>> pred_labels, pred_metrics, pred_values = 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')
>>> pred_labels, pred_metrics, pred_values = predict(y, x, m, '-b 1')
>>> ACC, MSE, SCC = evaluations(y, pred_labels)

# 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')
>>> pred_labels, pred_metrics, pred_values = 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')
>>> pred_labels, pred_metrics, pred_values = predict(y, x, m, '-b 1')
>>> ACC, MSE, SCC = evaluations(y, pred_labels)

# 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('-s 1 -c 4')
# One may also direct assign the options after creating the parameter instance
>>> param = parameter()
>>> param.solver_type = 1
>>> param.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.

    Copy a problem instance.
    DON'T use this unless you know what it does.

    >>> prob_copy = prob.copy()

    The reason we need to copy a problem instance is because, for example,
    in multi-label tasks using the OVR setting, we need to train a binary
    classification problem for each label on data with/without that label.
    Since each training uses the same x but different y, simply looping
    over labels and creating a new problem instance for each training would
    introduce overhead either from repeatedly transforming x into feature_node
    or from allocating additional memory space for x during parallel training.

    With problem copying, suppose x represents data, y1 represents the label
    for class 1 and y2 represent the label for class 2.
    We can do:

    >>> class1_prob = problem(y1, x)
    >>> class2_prob = class1_prob.copy()
    >>> class2_prob.y = (ctypes.c_double * class1_prob.l)(*y2)

    Note that although the copied problem is a new instance, attributes such as
    y (POINTER(c_double)), x (POINTER(POINTER(feature_node))), and x_space (list/np.ndarray)
    are copied by reference. That is, class1_prob and class2_prob share the same
    y, x and x_space after the copy.

- 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 or object.
    load_model()           : load a LIBLINEAR model.
    save_model()           : save model to a file.
    evaluations()          : evaluate prediction results.
    csr_find_scale_param() : find scaling parameter for data in csr format.
    csr_scale()            : apply data scaling to data in csr format.

- 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

    >>> pred_labels, pred_metrics, pred_values = 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.

    pred_labels: a list of predicted labels

    pred_metrics: a tuple of metrics including accuracy (for classification), mean
           squared error, and squared correlation coefficient (for
           regression).

    pred_values: 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')
    >>> pred_labels, pred_metrics, pred_values = predict(y, x, m)

- Function: svm_read_problem

    Read the data from a LIBSVM-format file or object.

    # Read the data from a file path
    >>> y, x = svm_read_problem('data.txt')

    # Read the data from an object created from an url
    >>> import urllib.request
    >>> import bz2
    >>> url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/leu.bz2'
    >>> with urllib.request.urlopen(url) as r:
    >>>     with bz2.open(r, 'rt') as f:
    >>>         y, x = svm_read_problem(f)

    Note that we must convert the file object to text mode before
    passing it to svm_read_problem() as we do not support binary
    mode.

- Functions: load_model/save_model

    See the usage by examples:

    >>> 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 <cjlin@csie.ntu.edu.tw>,
or check the FAQ page:

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