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# 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.
import distance
from apted import APTED, Config
from apted.helpers import Tree
from lxml import etree, html
from collections import deque
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0):
"""
A parallel version of the map function with a progress bar.
Args:
array (array-like): An array to iterate over.
function (function): A python function to apply to the elements of array
n_jobs (int, default=16): The number of cores to use
use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of
keyword arguments to function
front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job.
Useful for catching bugs
Returns:
[function(array[0]), function(array[1]), ...]
"""
# We run the first few iterations serially to catch bugs
if front_num > 0:
front = [function(**a) if use_kwargs else function(a) for a in array[:front_num]]
else:
front = []
# If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging.
if n_jobs == 1:
return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])]
# Assemble the workers
with ProcessPoolExecutor(max_workers=n_jobs) as pool:
# Pass the elements of array into function
if use_kwargs:
futures = [pool.submit(function, **a) for a in array[front_num:]]
else:
futures = [pool.submit(function, a) for a in array[front_num:]]
kwargs = {
'total': len(futures),
'unit': 'it',
'unit_scale': True,
'leave': True
}
# Print out the progress as tasks complete
for f in tqdm(as_completed(futures), **kwargs):
pass
out = []
# Get the results from the futures.
for i, future in tqdm(enumerate(futures)):
try:
out.append(future.result())
except Exception as e:
out.append(e)
return front + out
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):
@staticmethod
def maximum(*sequences):
"""Get maximum possible value
"""
return max(map(len, sequences))
def normalized_distance(self, *sequences):
"""Get distance from 0 to 1
"""
return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
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:
return self.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
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)
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