Python Tree.label方法代码示例

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
    def _construct_node_from_actions(self,
                                     current_node: Tree,
                                     remaining_actions: List[List[str]],
                                     add_var_function: bool) -> List[List[str]]:
        """
        Given a current node in the logical form tree, and a list of actions in an action sequence,
        this method fills in the children of the current node from the action sequence, then
        returns whatever actions are left.

        For example, we could get a node with type ``c``, and an action sequence that begins with
        ``c -> [<r,c>, r]``.  This method will add two children to the input node, consuming
        actions from the action sequence for nodes of type ``<r,c>`` (and all of its children,
        recursively) and ``r`` (and all of its children, recursively).  This method assumes that
        action sequences are produced `depth-first`, so all actions for the subtree under ``<r,c>``
        appear before actions for the subtree under ``r``.  If there are any actions in the action
        sequence after the ``<r,c>`` and ``r`` subtrees have terminated in leaf nodes, they will be
        returned.
        """
        if not remaining_actions:
            logger.error("No actions left to construct current node: %s", current_node)
            raise ParsingError("Incomplete action sequence")
        left_side, right_side = remaining_actions.pop(0)
        if left_side != current_node.label():
            logger.error("Current node: %s", current_node)
            logger.error("Next action: %s -> %s", left_side, right_side)
            logger.error("Remaining actions were: %s", remaining_actions)
            raise ParsingError("Current node does not match next action")
        if right_side[0] == '[':
            # This is a non-terminal expansion, with more than one child node.
            for child_type in right_side[1:-1].split(', '):
                if child_type.startswith("'lambda"):
                    # We need to special-case the handling of lambda here, because it's handled a
                    # bit weirdly in the action sequence.  This is stripping off the single quotes
                    # around something like `'lambda x'`.
                    child_type = child_type[1:-1]
                child_node = Tree(child_type, [])
                current_node.append(child_node)  # you add a child to an nltk.Tree with `append`
                if not self.is_terminal(child_type):
                    remaining_actions = self._construct_node_from_actions(child_node,
                                                                          remaining_actions,
                                                                          add_var_function)
        elif self.is_terminal(right_side):
            # The current node is a pre-terminal; we'll add a single terminal child.  We need to
            # check first for whether we need to add a (var _) around the terminal node, though.
            if add_var_function and right_side in self._lambda_variables:
                right_side = f"(var {right_side})"
            if add_var_function and right_side == 'var':
                raise ParsingError('add_var_function was true, but action sequence already had var')
            current_node.append(Tree(right_side, []))  # you add a child to an nltk.Tree with `append`
        else:
            # The only way this can happen is if you have a unary non-terminal production rule.
            # That is almost certainly not what you want with this kind of grammar, so we'll crash.
            # If you really do want this, open a PR with a valid use case.
            raise ParsingError(f"Found a unary production rule: {left_side} -> {right_side}. "
                               "Are you sure you want a unary production rule in your grammar?")
        return remaining_actions

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
    def _build_hierplane_tree(self, tree: Tree, index: int, is_root: bool) -> JsonDict:
        """
        Recursively builds a JSON dictionary from an NLTK ``Tree`` suitable for
        rendering trees using the `Hierplane library<https://allenai.github.io/hierplane/>`.

        Parameters
        ----------
        tree : ``Tree``, required.
            The tree to convert into Hierplane JSON.
        index : int, required.
            The character index into the tree, used for creating spans.
        is_root : bool
            An indicator which allows us to add the outer Hierplane JSON which
            is required for rendering.

        Returns
        -------
        A JSON dictionary render-able by Hierplane for the given tree.
        """
        children = []
        for child in tree:
            if isinstance(child, Tree):
                # If the child is a tree, it has children,
                # as NLTK leaves are just strings.
                children.append(self._build_hierplane_tree(child, index, is_root=False))
            else:
                # We're at a leaf, so add the length of
                # the word to the character index.
                index += len(child)

        label = tree.label()
        span = " ".join(tree.leaves())
        hierplane_node = {
                "word": span,
                "nodeType": label,
                "attributes": [label],
                "link": label
        }
        if children:
            hierplane_node["children"] = children
        # TODO(Mark): Figure out how to span highlighting to the leaves.
        if is_root:
            hierplane_node = {
                    "linkNameToLabel": LINK_TO_LABEL,
                    "nodeTypeToStyle": NODE_TYPE_TO_STYLE,
                    "text": span,
                    "root": hierplane_node
            }
        return hierplane_node

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
def nltk_tree_to_logical_form(tree: Tree) -> str:
    """
    Given an ``nltk.Tree`` representing the syntax tree that generates a logical form, this method
    produces the actual (lisp-like) logical form, with all of the non-terminal symbols converted
    into the correct number of parentheses.
    """
    # nltk.Tree actually inherits from `list`, so you use `len()` to get the number of children.
    # We're going to be explicit about checking length, instead of using `if tree:`, just to avoid
    # any funny business nltk might have done (e.g., it's really odd if `if tree:` evaluates to
    # `False` if there's a single leaf node with no children).
    if len(tree) == 0:  # pylint: disable=len-as-condition
        return tree.label()
    if len(tree) == 1:
        return tree[0].label()
    return '(' + ' '.join(nltk_tree_to_logical_form(child) for child in tree) + ')'

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
    def _construct_node_from_actions(self,
                                     current_node: Tree,
                                     remaining_actions: List[List[str]]) -> List[List[str]]:
        """
        Given a current node in the logical form tree, and a list of actions in an action sequence,
        this method fills in the children of the current node from the action sequence, then
        returns whatever actions are left.

        For example, we could get a node with type ``c``, and an action sequence that begins with
        ``c -> [<r,c>, r]``.  This method will add two children to the input node, consuming
        actions from the action sequence for nodes of type ``<r,c>`` (and all of its children,
        recursively) and ``r`` (and all of its children, recursively).  This method assumes that
        action sequences are produced `depth-first`, so all actions for the subtree under ``<r,c>``
        appear before actions for the subtree under ``r``.  If there are any actions in the action
        sequence after the ``<r,c>`` and ``r`` subtrees have terminated in leaf nodes, they will be
        returned.
        """
        if not remaining_actions:
            logger.error("No actions left to construct current node: %s", current_node)
            raise ParsingError("Incomplete action sequence")
        left_side, right_side = remaining_actions.pop(0)
        if left_side != current_node.label():
            logger.error("Current node: %s", current_node)
            logger.error("Next action: %s -> %s", left_side, right_side)
            logger.error("Remaining actions were: %s", remaining_actions)
            raise ParsingError("Current node does not match next action")
        if right_side[0] == '[':
            # This is a non-terminal expansion, with more than one child node.
            for child_type in right_side[1:-1].split(', '):
                child_node = Tree(child_type, [])
                current_node.append(child_node)  # you add a child to an nltk.Tree with `append`
                # For now, we assume that all children in a list like this are non-terminals, so we
                # recurse on them.  I'm pretty sure that will always be true for the way our
                # grammar induction works.  We can revisit this later if we need to.
                remaining_actions = self._construct_node_from_actions(child_node, remaining_actions)
        else:
            # The current node is a pre-terminal; we'll add a single terminal child.  By
            # construction, the right-hand side of our production rules are only ever terminal
            # productions or lists of non-terminals.
            current_node.append(Tree(right_side, []))  # you add a child to an nltk.Tree with `append`
        return remaining_actions

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
 def convert(tree: Tree) -> Node:
     root = StanfordNode(tree.label())
     for child in tree: root.append(child if isinstance(child, str) else StanfordParser.convert(child))
     return root

# 需要导入模块: from nltk import Tree [as 别名]
# 或者: from nltk.Tree import label [as 别名]
def FindNode(lab:str, tree:Tree) -> Tree:
    if tree.label()[TYPE] == lab:
        return tree
    for t in tree:
        if isinstance(t, Tree):
            return FindNode(lab, t)