def __init__(self, name, expression, mathml=None, initRetrievalTime='undefined'):
self.name = name
self.mathml = mathml
self.results = {}
self.documents = {}
if mathml is not None:
# parse from mathml (additional information extracted)
self.tree = MathExtractor.convert_and_link_mathml(mathml)
self.expression = self.tree.tostring()
else:
# parse from SLT string (no mathml information available)
self.tree = SymbolTree.parse_from_slt(expression)
self.expression = expression
self.constraints = Query.create_default_constraints(self.tree)
self.sorted_results = None
self.sorted_result_index = None
self.sorted_abs_ranks = None
self.sorted_documents = None
self.sorted_document_index = None
self.elapsed_time = 0.0
# RZ: add tuple-based retrieval time and other measures.
self.initRetrievalTime = initRetrievalTime
self.postings = None
self.matchedFormulae = None
self.matchedDocs = None
# cache ...
self.html_queryblock = {}
def __init__(self, query, expression, original_ranking, original_score, mathml=None):
self.query = query
self.original_ranking = original_ranking
self.original_score = original_score
self.mathml = mathml
self.new_scores = [0.0]
if mathml is not None:
# parse from mathml (additional information extracted)
self.tree = MathExtractor.convert_and_link_mathml(mathml)
self.expression = self.tree.tostring()
out_file = open("probando.txt", 'w', encoding='utf-8')
out_file.write(self.tree.tostring())
out_file.close()
else:
# parse from SLT string (no mathml information available)
self.tree = SymbolTree.parse_from_slt(expression)
self.expression = expression
if self.tree.tostring() != expression:
print("Bad conversion for result for query " + query.name + ": " + expression + " -> " + self.tree.tostring())
exit(1)
self.locations = []
self.matched_elements = []
self.unified_elements = {}
self.wildcard_matches = {}
self.all_unified = []
self.times_rendered = 0
def create_default_constraints(query_tree, default_value="U"):
# duplicate structure ...
root = Query.duplicate_structure(query_tree.root, default_value)
# now create constraint nodes ....
Query.convert_to_constraint_tree(root)
# create and return symbol tree
return SymbolTree(root)
def eval_similarity(query_data):
# do actually evaluate similarity ....
query, start_idx, expressions = query_data
csv_reader = csv.reader(expressions,
delimiter='\t',
lineterminator='\n',
quoting=csv.QUOTE_NONE,
escapechar="\\")
end_idx = start_idx + len(expressions) - 1
#create query slt
query_name, query_expression = query
query_tree = SymbolTree.parse_from_slt(query_expression)
query_constraints = Query.create_default_constraints(query_tree)
results = []
for idx, parts in enumerate(csv_reader):
#for idx, expression_info in enumerate(expressions):
#parts = expression_info.strip().split("\t")
expression = parts[0]
doc_id = parts[1]
location = parts[2]
candidate_tree = SymbolTree.parse_from_slt(expression)
try:
data = SIM_FUNCTION(query_tree, candidate_tree, query_constraints)
scores = data[0]
except:
print("Error processing: ")
print(query_expression, flush=True)
print(expression, flush=True)
print("Doc: " + doc_id, flush=True)
print("Loc: " + location, flush=True)
continue
# the index is only returned because some expressions might be absent in case of errors
results.append((scores, start_idx + idx))
print("Processed: " + str(start_idx) + " to " + str(end_idx) + " finished",
flush=True)
return results
def set_constraints(self, slt_string):
# create the tree with the original text labels
tree_constraints = SymbolTree.parse_from_slt(slt_string)
# convert the text labels to constraints
Query.convert_to_constraint_tree(tree_constraints.root)
if not Query.equal_subtree_structure(self.tree.root, tree_constraints.root):
print("Warning: Invalid constraint tree specified for " + self.name)
else:
self.constraints = tree_constraints
def set_constraints(self, slt_string):
# create the tree with the original text labels
tree_constraints = SymbolTree.parse_from_slt(slt_string)
# convert the text labels to constraints
Query.convert_to_constraint_tree(tree_constraints.root)
if not Query.equal_subtree_structure(self.tree.root,
tree_constraints.root):
print("Warning: Invalid constraint tree specified for " +
self.name)
else:
self.constraints = tree_constraints
def eval_similarity(query_data):
# do actually evaluate similarity ....
query, start_idx, expressions = query_data
end_idx = start_idx + len(expressions) - 1
#create query slt
query_name, query_expression = query
query_tree = SymbolTree.parse_from_slt(query_expression)
query_constraints = Query.create_default_constraints(query_tree)
results = []
for idx, expression_info in enumerate(expressions):
parts = expression_info.strip().split("\t")
expression = parts[0]
doc_id = parts[1]
location = parts[2]
candidate_tree = SymbolTree.parse_from_slt(expression)
try:
scores, matched_q, matched_c, unified_c = similarity_v04(query_tree, candidate_tree, query_constraints)
except:
print("Error processing: ")
print(query_expression, flush=True)
print(expression, flush=True)
print("Doc: " + doc_id, flush=True)
print("Loc: " + location, flush=True)
continue
# the index is only returned because some expressions might be absent in case of errors
results.append((scores, start_idx + idx))
print("Processed: " + str(start_idx) + " to " + str(end_idx) + " finished", flush=True)
return results
def __init__(self,
name,
expression,
mathml=None,
initRetrievalTime='undefined',
max_results=0):
self.name = name
self.mathml = mathml
self.results = {}
self.documents = {}
if mathml is not None:
# parse from mathml (additional information extracted)
self.tree = MathExtractor.convert_and_link_mathml(mathml)
self.expression = self.tree.tostring()
else:
# parse from SLT string (no mathml information available)
self.tree = SymbolTree.parse_from_slt(expression)
self.expression = expression
self.constraints = Query.create_default_constraints(self.tree)
self.sorted_results = None
self.sorted_result_index = None
self.sorted_abs_ranks = None
self.sorted_documents = None
self.sorted_document_index = None
self.elapsed_time = 0.0
# RZ: add tuple-based retrieval time and other measures.
self.initRetrievalTime = initRetrievalTime
self.postings = None
self.matchedFormulae = None
self.matchedDocs = None
# Re-rank at most K results
self.max_results = max_results
# cache ...
self.html_queryblock = {}
def __init__(self, query, expression, original_ranking, original_score, mathml=None):
self.query = query
self.original_ranking = original_ranking
self.original_score = original_score
self.mathml = mathml
self.new_scores = [0.0]
if mathml is not None:
# parse from mathml (additional information extracted)
self.tree = MathExtractor.convert_and_link_mathml(mathml)
self.expression = self.tree.tostring()
else:
# parse from SLT string (no mathml information available)
self.tree = SymbolTree.parse_from_slt(expression)
self.expression = expression
# print(self.tree.tostring() == expression)
self.locations = []
self.matched_elements = []
self.unified_elements = []
self.times_rendered = 0
