-------------------------------------
--- Python interface of LIBLINEAR ---
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Table of Contents
=================
- Introduction
- Installation
- Quick Start
- 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
============
On Unix systems, type
> make
The interface needs only LIBLINEAR shared library, which is generated by
the above command. We assume that the shared library is on the LIBLINEAR
main directory or in the system path.
For windows, the shared library liblinear.dll is ready in the directory
`..\windows'. You can also copy it to the system directory (e.g.,
`C:\WINDOWS\system32\' for Windows XP). To regenerate the shared library,
please follow the instruction of building windows binaries in LIBLINEAR README.
Quick Start
===========
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.
>>> from 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('-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 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)
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 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 [,issparse=True]])
Generate a feature vector from a Python list/tuple or a dictionary:
>>> xi, max_idx = gen_feature_nodearray({1:1, 3:1, 5:-2})
xi: the returned feature_nodearray (a ctypes structure)
max_idx: the maximal feature index of xi
issparse: if issparse == True, zero feature values are removed. The default
value is True for the sparsity.
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 of l labels (type must be int/double).
x: a Python list/tuple of l data instances. Each element of x must be
an instance of list/tuple/dictionary type.
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()
Utility Functions
=================
To use utility functions, type
>>> from 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 of l training labels (type must be int/double).
x: a list/tuple of l training instances. The feature vector of
each training instance is an instance of list/tuple or dictionary.
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.
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')
- 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 of l true labels (type must be int/double). It is used
for calculating the accuracy. Use [] if true labels are
unavailable.
x: a list/tuple of l predicting instances. The feature vector of
each predicting instance is an instance of list/tuple or dictionary.
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 predicted
values (pv):
>>> (ACC, MSE, SCC) = evaluations(ty, pv)
ty: a list of true values.
pv: a list of predict values.
ACC: accuracy.
MSE: mean squared error.
SCC: squared correlation coefficient.
Additional Information
======================
This interface was 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
