class MiningDataRecord(object):
"""
mining the data record from a region.
basic assumption:
the subtree of data records also similar. so if not any adjacent pair of them are
similar (less than threshold), data region itself is a data record,
otherwise children are individual data record.
"""
def __init__(self, threshold):
self.stm = SimpleTreeMatch()
self.threshold = threshold
def find_records(self, region):
if region.k == 1:
records = []
# if all the individual node of children node of Generalized node are similar
for i in range(region.start, region.start + region.covered):
for child1, child2 in pairwise(region.parent[i], 1, 0):
sim = self.stm.normalized_match_score(child1, child2)
if sim < self.threshold:
return self.slice_region(region)
else:
# each child of generalized node is a data record
for gn in region.iter(1):
records.extend([Record(c) for c in gn])
return records
else:
# if almost all the individual node in Generalized Node are similar
children = [region.parent[region.start + i] for i in range(region.covered)]
sizes = Counter([tree_size(child) for child in children])
most_common_size, _= sizes.most_common(1)[0]
most_typical_child = [child for child in children if tree_size(child) == most_common_size][0]
similarities = dict([child, self.stm.normalized_match_score([most_typical_child], [child])] for child in children)
if self.almost_similar(list(similarities.values()), self.threshold):
return [Record(child) for child in children if similarities[child] >= self.threshold]
else:
return self.slice_region(region)
def slice_region(self, region):
"""
slice every generalized node of region to a data record
"""
records = []
for gn in region.iter(region.k):
elements = [element for element in gn]
records.append(Record(*elements))
return records
def almost_similar(self, similarities, threshold):
sims = [1 for sim in similarities if sim >= threshold]
return len(sims) / len(similarities) > 0.8
class MiningDataRecord(object):
"""
mining the data record from a region.
basic assumption:
the subtree of data records also similar. so if not any adjacent pair of them are
similar (less than threshold), data region itself is a data record,
otherwise children are individual data record.
"""
def __init__(self, threshold=0.3):
self.stm = SimpleTreeMatch()
self.threshold = threshold
def find_records(self, region):
records = []
if region.k == 1:
for i in xrange(region.start, region.start + region.covered):
for child1, child2 in pairwise(region.parent, 1, region.start):
similarity = self.stm.normalized_match_score(child1, child2)
if similarity < self.threshold:
return self.slice_region(region)
else:
# each child of generalized node is a data record
for gn in region.iter(1):
records.extend([Record(c) for c in gn])
return self.slice_region(region)
def slice_region(self, region):
"""
slice every generalized node of region to a data record
"""
records = []
for gn in region.iter(region.k):
elements = [element for element in gn]
records.append(Record(*elements))
return records
def __init__(self, root, max_generalized_nodes=3, threshold=0.3):
self.root = root
self.max_generalized_nodes = max_generalized_nodes
self.threshold = threshold
self.stm = SimpleTreeMatch()
class MiningDataRegion(object):
def __init__(self, root, max_generalized_nodes=3, threshold=0.3):
self.root = root
self.max_generalized_nodes = max_generalized_nodes
self.threshold = threshold
self.stm = SimpleTreeMatch()
def find_regions(self, root):
data_regions = []
if tree_depth(root) >= 2:
scores = self.compare_generalized_nodes(root, self.max_generalized_nodes)
data_regions.extend(self.identify_regions(0, root, self.max_generalized_nodes, self.threshold, scores))
covered = set()
for data_region in data_regions:
for i in xrange(data_region.start, data_region.covered):
covered.add(data_region.parent[i])
for child in root:
if child not in covered:
data_regions.extend(self.find_regions(child))
return data_regions
def identify_regions(self, start, root, max_generalized_nodes, threshold, scores):
cur_region = Region(parent=root, start=0, k=0, covered=0)
max_region = Region(parent=root, start=0, k=0, covered=0)
data_regions = []
for k in xrange(1, max_generalized_nodes + 1):
for i in xrange(start, k + start):
flag = True
for j in xrange(i, len(root) - k, k):
pair = GeneralizedNode(root[j], k), GeneralizedNode(root[j + k], k)
score = scores.get(pair)
if score >= threshold:
if flag:
cur_region.k = k
cur_region.start = j
cur_region.covered = 2 * k
flag = False
else:
cur_region.covered += k
elif not flag: # doesn't match but previous match
break
if max_region.covered <= cur_region.covered and (
max_region.start == 0 or cur_region.start <= max_region.start):
max_region.k = cur_region.k
max_region.start = cur_region.start
max_region.covered = cur_region.covered
if max_region.covered:
data_regions.append(max_region)
if max_region.start + max_region.covered < len(max_region.parent):
data_regions.extend(self.identify_regions(max_region.start + max_region.covered, root,
max_generalized_nodes, threshold, scores))
return data_regions
def compare_generalized_nodes(self, parent, k):
"""
compare the adjacent children generalized nodes similarity of a given element
Arguments:
`parent`: the lxml element to compare children of.
`k`: the maximum length of generalized node.
"""
scores = {}
for a, b in pairwise(parent, k):
score = self.stm.normalized_match_score(a, b)
gn1 = GeneralizedNode(a[0], len(a))
gn2 = GeneralizedNode(b[0], len(b))
scores.setdefault((gn1, gn2), score)
return scores
def __init__(self, threshold=0.3):
self.stm = SimpleTreeMatch()
self.threshold = threshold
