table_metric.py

# Copyright 2020 IBM
# Author: peter.zhong@au1.ibm.com
#
# This is free software; you can redistribute it and/or modify
# it under the terms of the Apache 2.0 License.
#
# This software is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# Apache 2.0 License for more details.

from rapidfuzz.distance import Levenshtein
from apted import APTED, Config
from apted.helpers import Tree
from lxml import etree, html
from collections import deque
from .parallel import parallel_process
from tqdm import tqdm


class TableTree(Tree):
    def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
        self.tag = tag
        self.colspan = colspan
        self.rowspan = rowspan
        self.content = content
        self.children = list(children)

    def bracket(self):
        """Show tree using brackets notation"""
        if self.tag == 'td':
            result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \
                     (self.tag, self.colspan, self.rowspan, self.content)
        else:
            result = '"tag": %s' % self.tag
        for child in self.children:
            result += child.bracket()
        return "{{{}}}".format(result)


class CustomConfig(Config):
    def rename(self, node1, node2):
        """Compares attributes of trees"""
        #print(node1.tag)
        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
            return 1.
        if node1.tag == 'td':
            if node1.content or node2.content:
                #print(node1.content, )
                return Levenshtein.normalized_distance(node1.content, node2.content)
        return 0.



class CustomConfig_del_short(Config):
    def rename(self, node1, node2):
        """Compares attributes of trees"""
        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
            return 1.
        if node1.tag == 'td':
            if node1.content or node2.content:
                #print('before')
                #print(node1.content, node2.content)
                #print('after')
                node1_content = node1.content
                node2_content = node2.content
                if len(node1_content) < 3:
                    node1_content = ['####']
                if len(node2_content) < 3:
                    node2_content = ['####']   
                return Levenshtein.normalized_distance(node1_content, node2_content)
        return 0.

class CustomConfig_del_block(Config):
    def rename(self, node1, node2):
        """Compares attributes of trees"""
        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
            return 1.
        if node1.tag == 'td':
            if node1.content or node2.content:
                
                node1_content = node1.content
                node2_content = node2.content
                while ' '  in node1_content:
                    print(node1_content.index(' '))
                    node1_content.pop(node1_content.index(' '))
                while ' ' in node2_content:
                    print(node2_content.index(' '))
                    node2_content.pop(node2_content.index(' '))
                return Levenshtein.normalized_distance(node1_content, node2_content)
        return 0.

class TEDS(object):
    ''' Tree Edit Distance basead Similarity
    '''

    def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None):
        assert isinstance(n_jobs, int) and (
            n_jobs >= 1), 'n_jobs must be an integer greather than 1'
        self.structure_only = structure_only
        self.n_jobs = n_jobs
        self.ignore_nodes = ignore_nodes
        self.__tokens__ = []

    def tokenize(self, node):
        ''' Tokenizes table cells
        '''
        self.__tokens__.append('<%s>' % node.tag)
        if node.text is not None:
            self.__tokens__ += list(node.text)
        for n in node.getchildren():
            self.tokenize(n)
        if node.tag != 'unk':
            self.__tokens__.append('</%s>' % node.tag)
        if node.tag != 'td' and node.tail is not None:
            self.__tokens__ += list(node.tail)

    def load_html_tree(self, node, parent=None):
        ''' Converts HTML tree to the format required by apted
        '''
        global __tokens__
        if node.tag == 'td':
            if self.structure_only:
                cell = []
            else:
                self.__tokens__ = []
                self.tokenize(node)
                cell = self.__tokens__[1:-1].copy()
            new_node = TableTree(node.tag,
                                 int(node.attrib.get('colspan', '1')),
                                 int(node.attrib.get('rowspan', '1')),
                                 cell, *deque())
        else:
            new_node = TableTree(node.tag, None, None, None, *deque())
        if parent is not None:
            parent.children.append(new_node)
        if node.tag != 'td':
            for n in node.getchildren():
                self.load_html_tree(n, new_node)
        if parent is None:
            return new_node

    def evaluate(self, pred, true):
        ''' Computes TEDS score between the prediction and the ground truth of a
            given sample
        '''
        if (not pred) or (not true):
            return 0.0
        parser = html.HTMLParser(remove_comments=True, encoding='utf-8')
        pred = html.fromstring(pred, parser=parser)
        true = html.fromstring(true, parser=parser)
        if pred.xpath('body/table') and true.xpath('body/table'):
            pred = pred.xpath('body/table')[0]
            true = true.xpath('body/table')[0]
            if self.ignore_nodes:
                etree.strip_tags(pred, *self.ignore_nodes)
                etree.strip_tags(true, *self.ignore_nodes)
            n_nodes_pred = len(pred.xpath(".//*"))
            n_nodes_true = len(true.xpath(".//*"))
            n_nodes = max(n_nodes_pred, n_nodes_true)
            tree_pred = self.load_html_tree(pred)
            tree_true = self.load_html_tree(true)
            distance = APTED(tree_pred, tree_true,
                             CustomConfig()).compute_edit_distance()
            return 1.0 - (float(distance) / n_nodes)
        else:
            return 0.0

    def batch_evaluate(self, pred_json, true_json):
        ''' Computes TEDS score between the prediction and the ground truth of
            a batch of samples
            @params pred_json: {'FILENAME': 'HTML CODE', ...}
            @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...}
            @output: {'FILENAME': 'TEDS SCORE', ...}
        '''
        samples = true_json.keys()
        if self.n_jobs == 1:
            scores = [self.evaluate(pred_json.get(
                filename, ''), true_json[filename]['html']) for filename in tqdm(samples)]
        else:
            inputs = [{'pred': pred_json.get(
                filename, ''), 'true': true_json[filename]['html']} for filename in samples]
            scores = parallel_process(
                inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
        scores = dict(zip(samples, scores))
        return scores

    def batch_evaluate_html(self, pred_htmls, true_htmls):
        ''' Computes TEDS score between the prediction and the ground truth of
            a batch of samples
        '''
        if self.n_jobs == 1:
            scores = [self.evaluate(pred_html, true_html) for (
                pred_html, true_html) in zip(pred_htmls, true_htmls)]
        else:
            inputs = [{"pred": pred_html, "true": true_html} for(
                pred_html, true_html) in zip(pred_htmls, true_htmls)]

            scores = parallel_process(
                inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
        return scores


if __name__ == '__main__':
    import json
    import pprint
    with open('sample_pred.json') as fp:
        pred_json = json.load(fp)
    with open('sample_gt.json') as fp:
        true_json = json.load(fp)
    teds = TEDS(n_jobs=4)
    scores = teds.batch_evaluate(pred_json, true_json)
    pp = pprint.PrettyPrinter()
    pp.pprint(scores)