Question

我想知道text2vec包是否可以用于多标签分类，例如skmultilearn.problem_transform中的python BinaryRelevance 我当前指的是在以下位置记录的管道： http://text2vec.org/vectorization.html

Answer 1

您可以使用text2vec创建document-term-matrix（dtm）。要创建dtm，可以使用http://text2vec.org/vectorization.html。准备好dtm矩阵后，可以将它们用于多标签分类。对于分类，xgboost模型是很好的模型之一，https://rpubs.com/mharris/multiclass_xgboost中对此进行了讨论。

public class BinaryToDoubleLinkedList {

    static class Node {
        int value;
        Node left;
        Node right;

        public Node(int value, Node left, Node right) {
            this.value = value;
            this.left = left;
            this.right = right;
        }
    }

    static class Pair {
        Node head;
        Node tail;

        public Pair(Node head, Node tail) {
            this.head = head;
            this.tail = tail;
        }
    }

    static Pair convertToDoubleLinkedList(Node root) {
        return convert(root);
    }

    static Pair convert(Node root) {
        if (root == null) return new Pair(null, null);

        Node head, last;

        Pair left = convert(root.left);
        if (left.tail != null) {
            left.tail.right = root;
            root.left = left.tail;
            head = left.head;
        } else {
            head = root;
        }

        Pair right = convert(root.right);
        if (right.head != null) {
            right.head.left = root;
            root.right = right.head;
            last = right.tail;
        } else {
            last = root;
        }

        return new Pair(head, last);
    }

    static void Print(Node root, boolean fromLeft) {
        System.out.println("---------");
        if (fromLeft) {
            while (root != null) {
                System.out.print(root.value + ",");
                root = root.right;
            }
        } else {
            while (root != null) {
                System.out.print(root.value + ",");
                root = root.left;
            }
        }

        System.out.println();
    }

    public static void main(String[] args) {
        test1();
        test2();
        test3();
    }

    // test 1: normal test case
    public static void test1() {
        Node root = new Node(10, null, null);
        root.left = new Node(12, null, null);
        root.right = new Node(15, null, null);

        root.left.left = new Node(25, null, null);
        root.left.right = new Node(30, null, null);
        root.right.left = new Node(36, null, null);

        Pair res = convertToDoubleLinkedList(root);
        Print(res.head, true);
        Print(res.tail, false);
    }

    // test 2: binary tree as linked list
    public static void test2() {
        Node root = new Node(1, null, null);
        root.left = new Node(2, null, null);
        root.left.left = new Node(3, null, null);
        root.left.left.left = new Node(4, null, null);

        Pair res = convertToDoubleLinkedList(root);
        Print(res.head, true);
        Print(res.tail, false);
    }

    // test 3: null and single
    public static void test3() {
        Node root = new Node(1, null, null);
        Pair res = convertToDoubleLinkedList(root);
        Print(res.head, true);
        Print(res.tail, false);

        res = convertToDoubleLinkedList(null);
        Print(res.head, true);
        Print(res.tail, false);
    }
}

希望能提供帮助。

如果需要进一步说明，请告诉我。

使用text2vec进行多标签分类

1 个答案: