Python SVMLight.get_num_support_vectors方法代码示例

# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_num_support_vectors [as 别名]
    def _inner_train(self, train_data, param):
        """
        perform inner training by processing the tree
        """

        data_keys = []
        # top-down processing of taxonomy


        classifiers = []
        classifier_at_node = {}

        root = param.taxonomy.data

        grey_nodes = [root]
        
        while len(grey_nodes)>0:
           
            node = grey_nodes.pop(0) # pop first item
            
            # enqueue children
            if node.children != None:
                grey_nodes.extend(node.children)
    

    
            #####################################################
            #     init data structures
            #####################################################

            # get data below current node
            data = [train_data[key] for key in node.get_data_keys()]
            
            data_keys.append(node.get_data_keys())
    
            print "data at current level"
            for instance_set in data:        
                print instance_set[0].dataset
            
            
            # initialize containers
            examples = []
            labels = []       
    

            # concatenate data
            for instance_set in data:
      
                print "train split_set:", instance_set[0].dataset.organism
                
                for inst in instance_set:
                    examples.append(inst.example)
                    labels.append(inst.label)
    

            # create shogun data objects
            k = shogun_factory.create_kernel(examples, param)
            lab = shogun_factory.create_labels(labels)


            #####################################################
            #    train weak learners    
            #####################################################
            
            cost = param.cost
            
            # set up svm
            svm = SVMLight(cost, k, lab)
                        
            if param.flags["normalize_cost"]:
                # set class-specific Cs
                norm_c_pos = param.cost / float(len([l for l in labels if l==1]))
                norm_c_neg = param.cost / float(len([l for l in labels if l==-1]))
                svm.set_C(norm_c_neg, norm_c_pos)
            
            
            print "using cost: negative class=%f, positive class=%f" % (norm_c_neg, norm_c_pos) 
            
            # enable output
            svm.io.enable_progress()
            svm.io.set_loglevel(shogun.Classifier.MSG_INFO)
            
            # train
            svm.train()
            
            # append svm object
            classifiers.append(svm)
            classifier_at_node[node.name] = svm                            
            
            # save some information
            self.additional_information[node.name + " svm obj"] = svm.get_objective()
            self.additional_information[node.name + " svm num sv"] = svm.get_num_support_vectors()
            self.additional_information[node.name + " runtime"] = svm.get_runtime()


        return (classifiers, classifier_at_node)

# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_num_support_vectors [as 别名]
    def _train(self, train_data, param):
        """
        training procedure using training examples and labels
        
        @param train_data: Data relevant to SVM training
        @type train_data: dict<str, list<instances> >
        @param param: Parameters for the training procedure
        @type param: ParameterSvm
        """


        assert(param.base_similarity >= 1)
        
        # merge data sets
        data = PreparedMultitaskData(train_data, shuffle=False)
        
        
        # create shogun data objects
        base_wdk = shogun_factory.create_kernel(data.examples, param)
        lab = shogun_factory.create_labels(data.labels)

        # set normalizer
        normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
        
        # load data
        #f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_pearson.txt")
        f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/All_PseudoSeq_Hamming.txt")
        #f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_euklid.txt")
        #f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_RAxML.txt")
        
        num_lines = int(f.readline().strip())
        task_distances = numpy.zeros((num_lines, num_lines))
        name_to_id = {}
        for (i, line) in enumerate(f):
            tokens = line.strip().split("\t")
            name = str(tokens[0])
            name_to_id[name] = i
            entry = numpy.array([v for (j,v) in enumerate(tokens) if j!=0])
            assert len(entry)==num_lines, "len_entry %i, num_lines %i" % (len(entry), num_lines)
            task_distances[i,:] = entry
            
        
        # cut relevant submatrix
        active_ids = [name_to_id[name] for name in data.get_task_names()] 
        tmp_distances = task_distances[active_ids, :]
        tmp_distances = tmp_distances[:, active_ids]
        print "distances ", tmp_distances.shape

        
        # normalize distances
        task_distances = task_distances / numpy.max(tmp_distances)
        
        
        similarities = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
                                
        
        # convert distance to similarity
        for task_name_lhs in data.get_task_names():
            for task_name_rhs in data.get_task_names():
                
                
                # convert similarity with simple transformation
                similarity = param.base_similarity - task_distances[name_to_id[task_name_lhs], name_to_id[task_name_rhs]]
                normalizer.set_task_similarity(data.name_to_id(task_name_lhs), data.name_to_id(task_name_rhs), similarity)
                
                # save for later
                similarities[data.name_to_id(task_name_lhs),data.name_to_id(task_name_rhs)] = similarity
                
                
        # set normalizer                
        base_wdk.set_normalizer(normalizer)
        base_wdk.init_normalizer()
        

        # set up svm
        svm = SVMLight(param.cost, base_wdk, lab)
        svm.set_linadd_enabled(False)
        svm.set_batch_computation_enabled(False)
        
        
        # normalize cost
        norm_c_pos = param.cost / float(len([l for l in data.labels if l==1]))
        norm_c_neg = param.cost / float(len([l for l in data.labels if l==-1]))
        
        svm.set_C(norm_c_neg, norm_c_pos)
        
        
        # start training
        svm.train()


        # save additional information
        self.additional_information["svm objective"] = svm.get_objective()
        self.additional_information["num sv"] = svm.get_num_support_vectors()
        #self.additional_information["distances"] = distances
        self.additional_information["similarities"] = similarities


        # wrap up predictors
        svms = {}
#.........这里部分代码省略.........

# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_num_support_vectors [as 别名]
    def _train(self, train_data, param):
        """
        training procedure using training examples and labels
        
        @param train_data: Data relevant to SVM training
        @type train_data: dict<str, list<instances> >
        @param param: Parameters for the training procedure
        @type param: ParameterSvm
        """
        
          
        # merge data sets
        data = PreparedMultitaskData(train_data, shuffle=False)
        
        
        # create shogun data objects
        base_wdk = shogun_factory.create_kernel(data.examples, param)
        lab = shogun_factory.create_labels(data.labels)

        # set normalizer
        normalizer = MultitaskKernelNormalizer(data.task_vector_nums)

        ########################################################
        print "creating a kernel for each node:"
        ########################################################

        
        # init seq handler 
        task_kernel = SequencesHandlerRbf(1, param.base_similarity, data.get_task_names(), param.flags["wdk_rbf_on"])
        similarities = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
        
        # convert distance to similarity
        for task_name_lhs in data.get_task_names():
            for task_name_rhs in data.get_task_names():
                
                
                 
                
                # convert similarity with simple transformation
                similarity = task_kernel.get_similarity(task_name_lhs, task_name_rhs)
                
                print similarity
                
                print "similarity (%s,%s)=%f" % (task_name_lhs, task_name_rhs, similarity)
                
                normalizer.set_task_similarity(data.name_to_id(task_name_lhs), data.name_to_id(task_name_rhs), similarity)
                
                # save for later
                similarities[data.name_to_id(task_name_lhs),data.name_to_id(task_name_rhs)] = similarity
                
                
        # set normalizer                
        base_wdk.set_normalizer(normalizer)
        base_wdk.init_normalizer()
        

        # set up svm
        svm = SVMLight(param.cost, base_wdk, lab)
        svm.set_linadd_enabled(False)
        svm.set_batch_computation_enabled(False)
        
        
        # normalize cost
        norm_c_pos = param.cost / float(len([l for l in data.labels if l==1]))
        norm_c_neg = param.cost / float(len([l for l in data.labels if l==-1]))
        
        svm.set_C(norm_c_neg, norm_c_pos)
        
        
        # start training
        svm.train()


        # save additional information
        self.additional_information["svm objective"] = svm.get_objective()
        self.additional_information["num sv"] = svm.get_num_support_vectors()
        #self.additional_information["distances"] = distances
        self.additional_information["similarities"] = similarities


        # wrap up predictors
        svms = {}
        
        # use a reference to the same svm several times
        for task_name in data.get_task_names():
            
            task_num = data.name_to_id(task_name)
            
            # save svm and task_num
            svms[task_name] = (task_num, param, svm)

        return svms