文件列表:

python (0, 2013-01-28)
python\Makefile (32, 2011-05-10)
python\svm.py (9112, 2012-10-02)
python\svmutil.py (8465, 2012-07-20)

---------------------------------- --- Python interface of LIBSVM --- ---------------------------------- 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 LIBSVM, a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). The interface is very easy to use as the usage is the same as that of LIBSVM. The interface is developed with the built-in Python library "ctypes." Installation ============ On Unix systems, type > make The interface needs only LIBSVM shared library, which is generated by the above command. We assume that the shared library is on the LIBSVM main directory or in the system path. For windows, the shared library libsvm.dll for 32-bit python 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 LIBSVM README. Quick Start =========== There are two levels of usage. The high-level one uses utility functions in svmutil.py and the usage is the same as the LIBSVM MATLAB interface. >>> from svmutil import * # Read data in LIBSVM format >>> y, x = svm_read_problem('../heart_scale') >>> m = svm_train(y[:200], x[:200], '-c 4') >>> p_label, p_acc, p_val = svm_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 = svm_problem(y, x) >>> param = svm_parameter('-t 0 -c 4 -b 1') >>> m = svm_train(prob, param) # Precomputed kernel data (-t 4) # Dense data >>> y, x = [1,-1], [[1, 2, -2], [2, -2, 2]] # Sparse data >>> y, x = [1,-1], [{0:1, 1:2, 2:-2}, {0:2, 1:-2, 2:2}] # isKernel=True must be set for precomputer kernel >>> prob = svm_problem(y, x, isKernel=True) >>> param = svm_parameter('-t 4 -c 4 -b 1') >>> m = svm_train(prob, param) # For the format of precomputed kernel, please read LIBSVM README. # Other utility functions >>> svm_save_model('heart_scale.model', m) >>> m = svm_load_model('heart_scale.model') >>> p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1') >>> ACC, MSE, SCC = evaluations(y, p_label) # Getting online help >>> help(svm_train) The low-level use directly calls C interfaces imported by svm.py. Note that all arguments and return values are in ctypes format. You need to handle them carefully. >>> from svm import * >>> prob = svm_problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}]) >>> param = svm_parameter('-c 4') >>> m = libsvm.svm_train(prob, param) # m is a ctype pointer to an svm_model # Convert a Python-format instance to svm_nodearray, a ctypes structure >>> x0, max_idx = gen_svm_nodearray({1:1, 3:1}) >>> label = libsvm.svm_predict(m, x0) Design Description ================== There are two files svm.py and svmutil.py, which respectively correspond to low-level and high-level use of the interface. In svm.py, we adopt the Python built-in library "ctypes," so that Python can directly access C structures and interface functions defined in svm.h. While advanced users can use structures/functions in svm.py, to avoid handling ctypes structures, in svmutil.py we provide some easy-to-use functions. The usage is similar to LIBSVM MATLAB interface. Data Structures =============== Four data structures derived from svm.h are svm_node, svm_problem, svm_parameter, and svm_model. They all contain fields with the same names in svm.h. Access these fields carefully because you directly use a C structure instead of a Python object. For svm_model, accessing the field directly is not recommanded. Programmers should use the interface functions or methods of svm_model class in Python to get the values. The following description introduces additional fields and methods. Before using the data structures, execute the following command to load the LIBSVM shared library: >>> from svm import * - class svm_node: Construct an svm_node. >>> node = svm_node(idx, val) idx: an integer indicates the feature index. val: a float indicates the feature value. - Function: gen_svm_nodearray(xi [,feature_max=None [,isKernel=False]]) Generate a feature vector from a Python list/tuple or a dictionary: >>> xi, max_idx = gen_svm_nodearray({1:1, 3:1, 5:-2}) xi: the returned svm_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. isKernel: if isKernel == True, the list index starts from 0 for precomputed kernel. Otherwise, the list index starts from 1. The default value is False. - class svm_problem: Construct an svm_problem instance >>> prob = svm_problem(y, x) 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. Note that if your x contains sparse data (i.e., dictionary), the internal ctypes data format is still sparse. For pre-computed kernel, the isKernel flag should be set to True: >>> prob = svm_problem(y, x, isKernel=True) Please read LIBSVM README for more details of pre-computed kernel. - class svm_parameter: Construct an svm_parameter instance >>> param = svm_parameter('training_options') If 'training_options' is empty, LIBSVM default values are applied. Set param to LIBSVM default values. >>> param.set_to_default_values() Parse a string of options. >>> param.parse_options('training_options') Show values of parameters. >>> param.show() - class svm_model: There are two ways to obtain an instance of svm_model: >>> model = svm_train(y, x) >>> model = svm_load_model('model_file_name') Note that the returned structure of interface functions libsvm.svm_train and libsvm.svm_load_model is a ctypes pointer of svm_model, which is different from the svm_model object returned by svm_train and svm_load_model in svmutil.py. We provide a function toPyModel for the conversion: >>> model_ptr = libsvm.svm_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 LIBSVM models are wrapped as members of the class svm_model: >>> svm_type = model.get_svm_type() >>> nr_class = model.get_nr_class() >>> svr_probability = model.get_svr_probability() >>> class_labels = model.get_labels() >>> sv_indices = model.get_sv_indices() >>> nr_sv = model.get_nr_sv() >>> is_prob_model = model.is_probability_model() >>> support_vector_coefficients = model.get_sv_coef() >>> support_vectors = model.get_SV() Utility Functions ================= To use utility functions, type >>> from svmutil import * The above command loads svm_train() : train an SVM model svm_predict() : predict testing data svm_read_problem() : read the data from a LIBSVM-format file. svm_load_model() : load a LIBSVM model. svm_save_model() : save model to a file. evaluations() : evaluate prediction results. - Function: svm_train There are three ways to call svm_train() >>> model = svm_train(y, x [, 'training_options']) >>> model = svm_train(prob [, 'training_options']) >>> model = svm_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 LIBSVM command mode. prob: an svm_problem instance generated by calling svm_problem(y, x). For pre-computed kernel, you should use svm_problem(y, x, isKernel=True) param: an svm_parameter instance generated by calling svm_parameter('training_options') model: the returned svm_model instance. See svm.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 = svm_problem(y, x) >>> param = svm_parameter('-s 3 -c 5 -h 0') >>> m = svm_train(y, x, '-c 5') >>> m = svm_train(prob, '-t 2 -c 5') >>> m = svm_train(prob, param) >>> CV_ACC = svm_train(y, x, '-v 3') - Function: svm_predict To predict testing data with a model, use >>> p_labs, p_acc, p_vals = svm_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 [0]*len(x) 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 LIBSVM. model: an svm_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 in training data, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a special case. Decision value [+1] is returned for each testing instance, instead of an empty list. For probabilities, each element contains k values indicating the probability that the testing instance is in each class. Note that the order of classes is the same as the 'model.label' field in the model structure. Example: >>> m = svm_train(y, x, '-c 5') >>> p_labels, p_acc, p_vals = svm_predict(y, x, m) - Functions: svm_read_problem/svm_load_model/svm_save_model See the usage by examples: >>> y, x = svm_read_problem('data.txt') >>> m = svm_load_model('model_file') >>> svm_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 LIBSVM as follows Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm For any question, please contact Chih-Jen Lin , or check the FAQ page: http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html

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