Python svm.svm_problem函数代码示例

def do_one_cv_classify_predeffolds_valid(theinput):
	c = theinput[0]
	gamma = theinput[1]
	nf = theinput[2]
	output = theinput[3]
	input = theinput[4]
	output_valid = theinput[5]
	input_valid = theinput[6]
	useprob = theinput[7]
	fold_start = theinput[8]
	fold_start_valid = theinput[9]
	perfmetric = theinput[10]
	
	param = svm.svm_parameter('-c %g -g %g -b %d' % (c,gamma,int(useprob)))

	prob = svm.svm_problem(output, input)
	fold_start_p = (c_int *len(fold_start))()
	for i in xrange(len(fold_start)):
		fold_start_p[i] = fold_start[i]
		
	prob_valid = svm.svm_problem(output_valid, input_valid)
	fold_start_p_valid = (c_int *len(fold_start_valid))()
	for i in xrange(len(fold_start_valid)):
		fold_start_p_valid[i] = fold_start_valid[i]


	target = (c_double * prob_valid.l)()
	posclass = output[0]
	
#	print prob
	libsvm.svm_cross_validation_sepsets(prob, prob_valid,fold_start_p, fold_start_p_valid,param, nf, target)

	
	ys = prob.y[:prob_valid.l]
	db = array([[ys[i],target[i]] for i in range(prob_valid.l)])
#	print db
	del target
	del fold_start_p
	del fold_start_p_valid
	
	neg = len([x for x in ys if x != posclass])
#	print neg
	pos = prob_valid.l-neg
#	print pos
		
#	print fb,neg,pos,posclass,perfmetric
	
	[topacc,topphi,minfpfnratio,topf1,auc,optbias] = optimize_results(db,neg,pos,posclass,perfmetric)
		
	return topacc,topphi,minfpfnratio,topf1,auc,optbias

def build_problem(img_kind, subdir = "data/"):
	subdir = "data/"

	classes = []
	data = []

	the_ones = glob.glob(subdir + "f_" + img_kind + "*.jpg")
	all_of_them = glob.glob(subdir + "f_*_*.jpg")
	the_others = []

	for x in all_of_them:
		if the_ones.count(x) < 1:
			the_others.append(x)
	
	for x in the_ones:
		classes.append(1)
		data.append(get_image_features(cv.LoadImageM(x), True, img_kind))
	
	for x in the_others:
		classes.append(-1)
		data.append(get_image_features(cv.LoadImageM(x), True, img_kind))

	prob = svm.svm_problem(classes, data)

	return prob

def svm(y,K,**param_kw):
    """
    Solve the SVM problem. Return ``(alpha, b)``

    `y`
      labels
    `K`
      precopmuted kernel matrix

    Additional keyword arguments are passed on as svm parameters to
    the model.

    The wrapper is needed to precondition the precomputed matrix for
    use with libsvm, and to extract the model parameters and convert
    them into the canonical weight vector plus scalar offset. Normally
    libsvm hides these model paramters, preferring instead to provide
    a high-level model object that can be queried for results.

    """
    i = arange(1,len(K)+1).reshape((-1,1))
    X = hstack((i, K))
    y = asarray(y,dtype=double)
    X = asarray(X,dtype=double)
    prob = svm_problem(y,X)
    param = svm_parameter(kernel_type=PRECOMPUTED,**param_kw)
    model = svm_model(prob, param)
    return get_alpha_b(model)

    def train(self,labels,data):
        '''
        Train the classifier.
        
        @param labels: A list of class labels.
        @param data: A 2D array or list of feature vectors.  One feature vector per row.
        '''
        
        # Check the types and convert to np arrays
        if isinstance(data,list) or isinstance(data,tuple):
            data = np.array(data,dtype=np.double)
            

        labels = np.array(labels,dtype=np.double)
            
        # Preprocess the data    
        labels,data = self._preprocessor.train(labels,data)
        labels,data = self._label_scale.train(labels,data)
        
        
        # Create the svm parameter data and problem description
        param = svm.svm_parameter(svm_type=svm.EPSILON_SVR,kernel_type = svm.RBF, p = self._epsilon, gamma=self._gamma)
        prob = svm.svm_problem(labels.tolist(),data.tolist())
        
        # train the svm
        self._model = svm.svm_model(prob, param)

def do_one_cv_classify_predeffolds_multi(theinput):
	c = theinput[0]
	gamma = theinput[1]
	nf = theinput[2]
	output = theinput[3]
	input = theinput[4]
	useprob = theinput[5]
	fold_start = theinput[6]
			
		
		
	param = svm.svm_parameter('-c %g -g %g -b %d' % (c,gamma,int(useprob)))
	
	prob = svm.svm_problem(output, input)
	target = (c_double * prob.l)()
	posclass = output[0]
	fold_start_p = (c_int *len(fold_start))()
	for i in xrange(len(fold_start)):
		fold_start_p[i] = fold_start[i]
	libsvm.svm_cross_validation_labeltargets(prob, fold_start_p,param, nf, target)

	acc = len([i for i in xrange(len(output)) if output[i] == target[i]])*1.0/prob.l
	del target
	del fold_start_p
	return acc

def do_one_cv_classify(theinput):
	c = theinput[0]
	gamma = theinput[1]
	nf = theinput[2]
	output = theinput[3]
	input = theinput[4]
	useprob = theinput[5]	
	perfmetric = theinput[6]

	param = svm.svm_parameter('-c %g -g %g -b %d' % (c,gamma,int(useprob)))

	prob = svm.svm_problem(output, input)
	target = (c_double * prob.l)()
	
	posclass = output[0]
	fold_start = (c_int *1)();
	fold_start[0] = -1;
	libsvm.svm_cross_validation(prob, fold_start, param, nf, target)
	ys = prob.y[:prob.l]
	db = array([[ys[i],target[i]] for i in range(prob.l)])
	
	del target
	
	neg = len([x for x in ys if x != posclass])
	pos = prob.l-neg
	
	
	
	[topacc,topphi,minfpfnratio,topf1,auc,optbias] = optimize_results(db,neg,pos,posval,perfmetric)
		
	return topacc,topphi,minfpfnratio,topf1,auc,optbias

  def generate_model(self, variant_name, models_folder):
    training_file = variant_name + ".t"
    if self.feature_scaling:
      self.scale_features(variant_name, models_folder)
      training_file += ".scale"
    (y, x) = svm_read_problem(training_file)
    self.m_prob = svm.svm_problem(y, x, self.m_params.kernel_type == PRECOMPUTED)

    libsvm_path = os.environ['LIBSVM_PATH']
    scaled_filename = os.path.abspath(training_file)
    cp = "python grid.py " + scaled_filename
    curdir = os.getcwd()
    os.chdir(libsvm_path + "/tools/")
    result = call_process(cp)
    os.chdir(curdir)
    C,g,rate = [float(l) for l in result.split("\n")[-2].split(" ")]

    print "C: %.8f, gamma: %.8f\n" % (C,g)

    self.m_params.C = C
    self.m_params.gamma = g

    print "\n-----------------------------"
    model = svm.svm_train(self.m_prob, self.m_params)
    print "-----------------------------\n"

    svm_save_model(models_folder + variant_name + ".model", model)

 def __init__(self, data_dictionary, model_target, kernel=LINEAR, cv_segments=10, **args):
     #Create an SVM model object
 
     #Check to see if a threshold has been specified in the function's arguments
     try: self.threshold = args['threshold']
     except KeyError: self.threshold=2.3711   # if there is no 'threshold' key, then use the default (2.3711)
     
     #Store some object data
     model_dict = deepcopy(data_dictionary)
     self.model_target = model_target
     self.folds = cv_segments
            
     #Label the exceedances in the training set.
     model_dict[model_target] = self.Assign_Labels(model_dict[model_target])
     
     #Extract the training labels and training set
     self.training_labels = model_dict.pop(model_target)
     self.training_set = np.transpose(model_dict.values())
     self.headers = model_dict.keys()
             
     #Scale the covariates to [-1,1]
     self.Scale_Covariates()
     
     #Generate an SVM model.
     self.svm_problem = svm.svm_problem(self.training_labels, self.training_set)
     self.svm_params = {'kernel_type' : kernel, 'weight_label' : [0,1], 'weight' : [10,1]}
     self.model=svm.svm_model(self.svm_problem, svm.svm_parameter(**self.svm_params))
     
     #Use cross-validation to find the best number of components in the model.
     self.Select_Linear_Model(-5, 10)
     
     #Rebuild the model, calculating the probabilities of class membership
     self.svm_params['probability']=1
     self.model=svm.svm_model(self.svm_problem, svm.svm_parameter(**self.svm_params))

    def train(self, c, g, probability=True, compensation=True,
              path=None, filename=None, save=True):
        if filename is None:
            filename = os.path.splitext(self.getOption('strArffFileName'))[0]
            filename += '.model'
        if path is None:
            path = self.dctEnvPaths['data']
        param = svm.svm_parameter(kernel_type=svm.RBF,
                                  C=c, gamma=g,
                                  probability=1 if probability else 0)

        labels, samples = self.getData(normalize=True)

        # because we train the SVM with dict we need to redefine the zero-insert
        self.hasZeroInsert = False
        if not self.oClassifier is None:
            self.oClassifier.setOption('hasZeroInsert', True)

        if compensation:
            weight, weight_label = self._calculateCompensation(labels)
            param.weight = weight
            param.weight_label = weight_label
            param.nr_weight = len(weight)

        problem = svm.svm_problem(labels, samples)
        model = svm.svm_model(problem, param)
        if save:
            model.save(os.path.join(path, filename))
        return problem, model

def iqr_model_train(matrix_kernel_train, labels_train, idx2clipid,
                    svm_para = '-w1 50 -t 4 -b 1 -c 1'):
    """
    Light-weighted SVM learning module for online IQR

    @param matrix_kernel_train: n-by-n square numpy array with kernel values
        between training data
    @param labels_train: row-wise labels of training data (1 or True indicates
        positive, 0 or False otherwise
    @param idx2clipid: idx2clipid(row_idx) returns the clipid for the 0-base row
        in matrix
    @param svm_para: (optional) SVM learning parameter

    @rtype: dictionary with 'clipids_SV': list of clipids for support vectors
    @return: output as a dictionary with 'clipids_SV'

    """
    log = logging.getLogger('iqr_model_train')

    # set training inputs
    matrix_kernel_train = np.vstack((np.arange(1, len(matrix_kernel_train)+1),
                                     matrix_kernel_train)).T
    log.debug("Done matrix_kernel_train")

    problem = svm.svm_problem(labels_train.tolist(), matrix_kernel_train.tolist(), isKernel=True)
    log.debug("Done problem")
    svm_param = svm.svm_parameter(svm_para)
    log.debug("Done svm_param")

    # train model
    model = svmutil.svm_train(problem, svm_param)
    log.debug("Done train model")

    # release memory
    del problem
    del svm_param
    log.debug("Done release memory")

    # check learning failure
    if model.l == 0:
        raise Exception('svm model learning failure')
    log.debug("Done checking learning failure (no failure)")

    n_SVs = model.l
    clipids_SVs = []
    idxs_train_SVs = svmtools.get_SV_idxs_nonlinear_svm(model)
    for i in range(n_SVs):
        _idx_1base = idxs_train_SVs[i]
        _idx_0base = _idx_1base - 1
        clipids_SVs.append(idx2clipid[_idx_0base])
        model.SV[i][0].value = i+1 # within SVM model, index needs to be 1-base
    log.debug("Done collecting support vector IDs")

    #svmutil.svm_save_model(filepath_model, model)

    output = dict()
    output['model'] = model
    output['clipids_SVs'] = clipids_SVs

    return output

def trainSVM(kernel, labels):
    #need to add an id number as the first column of the list
    svmKernel = column_stack((arange(1, len(kernel.tolist()) + 1), kernel))
    prob = svm_problem(labels.tolist(), svmKernel.tolist(), isKernel=True)
    param = svm_parameter('-t 4')   

    model = svm_train(prob, param)
    return model

 def train(self, session, doc):
     # doc here is [[class,...], [{vector},...]]
     (labels, vectors) = doc.get_raw(session)
     problem = svm.svm_problem(labels, vectors)
     self.model = svm.svm_model(problem, self.param)
     modelPath = self.get_path(session, 'modelPath')
     self.model.save(str(modelPath))
     self.predicting = 1

 def train(self, dataset):
     """
     Trains the svm classifier. Converts words to real numbers for training
     as SVM expects only numbers.
     """
     super(SvmLearner, self).train(dataset)
     prob  = svm.svm_problem(self.results, self.observations)
     param = svm.svm_parameter(kernel_type=svm.LINEAR, C=10, probability=1)
     self.model = svm.svm_model(prob, param)

    def train(self,trainset):
        """
        Trains the SVM.
        """

        self.n_classes = len(trainset.metadata['targets'])

        # Set LIBSVM parameters
        kernel_types = {'linear':libsvm.LINEAR,'polynomial':libsvm.POLY,
                        'rbf':libsvm.RBF,'sigmoid':libsvm.SIGMOID}
        if self.kernel not in kernel_types:
            raise ValueError('Invalid kernel: '+self.kernel+'. Should be either \'linear\', \'polynomial\', \'rbf\' or \'sigmoid\'')

        if self.label_weights != None:
            class_to_id = trainset.metadata['class_to_id']
            nr_weight = self.n_classes
            weight_label = range(self.n_classes)
            weight = [1]*self.n_classes
            for k,v in self.label_weights.iteritems():
                weight[class_to_id[k]] = v
        else:
            nr_weight = 0
            weight_label = []
            weight = []

        libsvm_params = libsvm.svm_parameter(svm_type = libsvm.C_SVC,
                                             kernel_type = kernel_types[self.kernel],
                                             degree=self.degree,
                                             gamma=self.gamma,
                                             coef0=self.coef0,
                                             C=self.C,
                                             probability=int(self.output_probabilities),
                                             cache_size=self.cache_size,
                                             eps=self.tolerance,
                                             shrinking=int(self.shrinking),
                                             nr_weight = nr_weight,
                                             weight_label = weight_label,
                                             weight = weight)
        

        # Put training set in the appropriate format:
        #  if is sparse (i.e. a pair), inputs are converted to dictionaries
        #  if not, inputs are assumed to be sequences and are kept intact
        libsvm_inputs = []
        libsvm_targets = []
        for input,target in trainset:
            if type(input) == tuple:
                libsvm_inputs += [dict(zip(input[1],input[0]))]
            else:
                libsvm_inputs += [input]
            libsvm_targets += [float(target)] # LIBSVM requires double-valued targets

        libsvm_problem = libsvm.svm_problem(libsvm_targets,libsvm_inputs)

        # Train SVM
        self.svm = libsvm.svm_model(libsvm_problem,libsvm_params)

def leave_one_out(y, x, param, n='DUMMY'):
    results = []
    for i, test in enumerate(zip(y, x)):
        training_y = y[:i] + y[i+1:]
        training_x = x[:i] + x[i+1:]
        problem = svm.svm_problem(training_y, training_x)
        model = svmutil.svm_train(problem, param, '-q')
        result = svmutil.svm_predict(y[i:i+1], x[i:i+1], model, '-b 1')
        results.append(result + (test[0], make_d.decode(x[i], make_d.decode_dic)))
    return results