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 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 get(self, fileid): """ ingest result tuples for topk responses to queries :param fileid: process id used to distinguish files :type fileid: string :return: query responses :rtype: dict mapping query_name -> CompQuery() Q queryID E search-expr R docID position expression score R docID position expression score ... Q queryID ... X """ if (self.runmode == "now"): reader = self.reader else: filename = "%s_r_%s.tsv" % (self.db, fileid) file_path = os.path.join(self.directory, filename) file = open(file_path, mode='r', encoding='utf-8', newline='') reader = csv.reader(file, delimiter='\t', lineterminator='\n', quoting=csv.QUOTE_NONE, escapechar="\\") print("Reading from math engine") doc_list = MathDocument(self.cntl) all_queries = {} for line in reader: if line: if line[0] == "Q": current_name = line[1] try: current_query = all_queries[current_name] except: current_query = CompQuery(current_name) all_queries[current_name] = current_query current_expr = None elif line[0] == "E": if current_name is None: print( "Invalid expression: Q tuple with query name expected first: " + str(line), flush=True) else: query_expression = line[1] current_expr = Query(current_name, query_expression) current_query.add_expr(current_expr) elif line[0] == "C": print("Constraint ignored: " + line) elif line[0] == "I": if current_name is None or current_expr is None: print( "Invalid information: Q tuple with query name and E tuple with expression expected first: " + str(line)) elif line[1] == "qt": current_expr.initRetrievalTime = float(line[2]) elif line[1] == "post": current_expr.postings = int(line[2]) elif line[1] == "expr": current_expr.matchedFormulae = int(line[2]) elif line[1] == "doc": current_expr.matchedDocs = int(line[2]) elif line[0] == "R": if current_name is None or current_expr is None: print( "Invalid result item: Q tuple with query name and E tuple with expression expected first: " + str(line)) else: doc_id = int(line[1]) doc_name = doc_list.find_doc_file(doc_id) if not doc_name: doc_name = "NotADoc" location = int(line[2]) expression = line[3] score = float(line[4]) current_expr.add_result(doc_id, doc_name, location, expression, score) elif line[0] == "X": break else: print("Ignoring invalid tuple: " + str(line)) print("Read " + str(len(all_queries)) + " queries") return all_queries
def find_substructures(expressions_data): sub_groups = [] query_expression, candidates_data = expressions_data if len(candidates_data) > 1: query = Query("query", query_expression) # create query tree .... rank = -1 scores = [-1.0, 0, 0] for data_idx, candidate_data in enumerate(candidates_data): candidate_exp = candidate_data[0] rank = int(candidate_data[1]) query.add_result(0, "", 0, candidate_exp, 0.0) result = query.results[candidate_exp] candidate_tree = result.tree try: scores, matched_q, matched_c, unified_c = similarity_v04(query.tree, candidate_tree, query.constraints) except: print("Error processing: ") print("Q: " + query_expression, flush=True) print("C: " + candidate_exp, flush=True) continue result.set_unified_elements(unified_c) result.set_matched_elements(matched_c) result.new_scores = scores query.sort_results() group = query.sorted_results[0] # for each sub group ... structures = [] current_structure = 0 for subgroup in group: # next substructure group in the overall rank... current_structure += 1 structure_elements = [] for sg_idx, expression in enumerate(subgroup): structure_elements.append(expression) structures.append(structure_elements) else: # just one expression in rank, no need to re-evaluate score ... candidate_data = candidates_data[0] candidate_exp = candidate_data[0] rank = int(candidate_data[1]) scores = [float(part) for part in candidate_data[2:5]] # the list of structures only contains one structure with the same structure structures = [[candidate_exp]] return (rank, scores, structures)
def main(): if len(sys.argv) < 5: print("Usage") print( "\tpython3 rerank_results.py control input_results metric output_results" ) print("") print("Where:") print("\tcontrol:\tPath to tangent control file") print("\tinput_results:\tPath to file with results to re-rank") print("\tmetric:\t\tSimilarity metric to use [0-4]") print( "\toutput_results:\tPath to file where re-ranked results will be stored" ) print("") print("Optional:") print("\t-w\twindow\t\t: Window for pair generation") print("\t-h\thtml_prefix\t: Prefix for HTML output (requires dot)") print("\t-c\tcondition\t: Current test condition") print("\t-s\tstats\t\t: File to store stats") print("\t-t\ttimes\t\t: File to accumulate time stats") print("\t-k\tmax_results\t: K number of results to rerank as maximum") return control_filename = sys.argv[1] input_filename = sys.argv[2] try: metric = int(sys.argv[3]) if metric < -1 or metric > 11: print("Invalid similarity metric function") return except: print("Invalid similarity metric function") return output_filename = sys.argv[4] optional_params = optional_parameters(sys.argv[5:]) #load control file control = Control(control_filename) # control file name (after indexing) math_doc = MathDocument(control) if "w" in optional_params: try: window = int(optional_params["w"]) if window <= 0: print("Invalid window") return except: print("Invalid value for window") return else: window = int(control.read("window")) if "h" in optional_params: html_prefix = optional_params["h"] if not os.path.isdir(html_prefix): os.makedirs(html_prefix) else: html_prefix = None if "c" in optional_params: condition = optional_params["c"] print("testing condition: " + condition) else: condition = "undefined" if "s" in optional_params: stats_file = optional_params["s"] else: stats_file = None if "k" in optional_params: try: max_k = int(optional_params["k"]) except: print("Invalid max_results parameter") return else: max_k = 0 if "t" in optional_params: times_file = optional_params["t"] else: times_file = None in_file = open(input_filename, 'r', newline='', encoding='utf-8') reader = csv.reader(in_file, delimiter='\t', lineterminator='\n', quoting=csv.QUOTE_NONE, escapechar="\\") lines = [row for row in reader] in_file.close() mathml_cache_file = control_filename + ".retrieval_2.cache" if not os.path.exists(mathml_cache_file): mathml_cache = MathMLCache(control_filename) else: cache_file = open(mathml_cache_file, "rb") mathml_cache = pickle.load(cache_file) cache_file.close() current_query = None current_name = None current_tuple_retrieval_time = 'undefined' all_queries = [] #read all results to re-rank for idx, line in enumerate(lines): #parts = line.strip().split("\t") parts = line if len(parts) == 2: if parts[0][0] == "Q": current_name = parts[1] current_query = None elif parts[0][0] == "E": if current_name is None: print("invalid expression at " + str(idx) + ": query name expected first") else: query_expression = parts[1] #query_offset = len(all_queries) query_offset = int(current_name.split("-")[-1]) - 1 if html_prefix != None: mathml = mathml_cache.get(-1, query_offset, query_expression, True) # create empty directories for this query ... if not os.path.isdir(html_prefix + "/" + current_name): os.makedirs(html_prefix + "/" + current_name) if not os.path.isdir(html_prefix + "/" + current_name + "/images"): os.makedirs(html_prefix + "/" + current_name + "/images") else: mathml = None current_query = Query(current_name, query_expression, mathml, current_tuple_retrieval_time, max_k) current_name = None all_queries.append(current_query) print("Query: " + current_query.name + ": " + current_query.expression) #print(mathml) #current_query.tree.save_as_dot("expre_" + str(idx) + ".gv") elif parts[0][0] == "C": if current_query is None: print("invalid constraint at " + str(idx) + ": query expression expected first") else: # create a constraint tree current_query.set_constraints(parts[1]) # RZ: Record tuple-based retrieval time and other metrics. if len(parts) == 3 and parts[0][0] == "I" and current_query != None: if parts[1] == "qt": current_query.initRetrievalTime = float(parts[2]) elif parts[1] == "post": current_query.postings = int(parts[2]) elif parts[1] == "expr": current_query.matchedFormulae = int(parts[2]) elif parts[1] == "doc": current_query.matchedDocs = int(parts[2]) if len(parts) == 5: if parts[0][0] == "R": doc_id = int(parts[1]) location = int(parts[2]) doc_name = math_doc.find_doc_file(doc_id) expression = parts[3] score = float(parts[4]) if html_prefix != None: mathml = mathml_cache.get(doc_id, location, expression) else: mathml = None if current_query is None: print("Error: result listed before a query, line " + str(idx)) else: current_query.add_result(doc_id, doc_name, location, expression, score, mathml) cache_file = open(mathml_cache_file, "wb") pickle.dump(mathml_cache, cache_file, pickle.HIGHEST_PROTOCOL) cache_file.close() # now, re-rank... print("Results loaded, reranking ...") # compute similarity first... start_time = time.time() for q_idx, query in enumerate(all_queries): #print("Evaluating: " + query.name + " - " + query.expression) query_start_time = time.time() * 1000 # RZ: ms for res_idx, exp_result in enumerate(query.results): result = query.results[exp_result] #print("Candidate: " + result.expression) scores = [0.0] if metric == -1: # bypass mode, generate HTML for original core ranking scores = [result.original_score] matched_c = {} elif metric == 0: # same as original based on f-measure of matched pairs.. pairs_query = query.tree.root.get_pairs("", window) pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c = similarity_v00( pairs_query, pairs_candidate) elif metric == 1: # based on testing of alignments.... scores, matched_q, matched_c = similarity_v01( query.tree, result.tree) elif metric == 2: # Same as 0 but limiting to matching total symbols first... pairs_query = query.tree.root.get_pairs("", window) pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c = similarity_v02( pairs_query, pairs_candidate) elif metric == 3: # modified version of 2 which performs unification.... pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c, unified_c = similarity_v03( pairs_query, pairs_candidate) result.set_unified_elements(unified_c) elif metric == 4: # modified version of 1 which performs unification ... sim_res = similarity_v04(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 5: # modified version of 4 which allows multiple sub matches sim_res = similarity_v05(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) elif metric == 6: # modified version of 4 which allows subtree matches for wildcards (partial support)... sim_res = similarity_v06(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 7: # modified version of 4 which allows subtree matches for wildcards (partial support)... sim_res = similarity_v07(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 8: # modified version of 4 which allows subtree matches for wildcards (partial support)... sim_res = similarity_v08(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 9: # modified version of 4 which allows subtree matches for wildcards (partial support)... sim_res = similarity_v09(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 10: # modified version of 4 which allows subtree matches for wildcards (partial support)... sim_res = similarity_v10(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) elif metric == 11: # matching of metric 06 with scores from metric 04 (MSS) sim_res = similarity_v11(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res result.set_unified_elements(unified_c) result.set_wildcard_matches(wildcard_c) result.set_all_unified(unified) result.set_matched_elements(matched_c) result.new_scores = scores query_end_time = time.time() * 1000 # RZ: ms # re-rank based on new score(s) query.sort_results() query.sort_documents() query.elapsed_time = query_end_time - query_start_time end_time = time.time() elapsed = end_time - start_time print("Elapsed Time Ranking: " + str(elapsed) + "s") #now, store the re-ranked results... out_file = open(output_filename, 'w', newline='', encoding='utf-8') csv_writer = csv.writer(out_file, delimiter='\t', lineterminator='\n', quoting=csv.QUOTE_NONE, escapechar="\\") for query in all_queries: csv_writer.writerow([]) query.output_query(csv_writer) query.output_sorted_results(csv_writer) if html_prefix is not None: print("Saving " + query.name + " to HTML file.....") query.save_html(html_prefix + "/" + query.name) out_file.close() #if stats file is requested ... if stats_file is not None: out_file = open(stats_file, "w") out_file.write(Query.stats_header("\t")) for query in all_queries: query.output_stats(out_file, "\t", condition) out_file.close() # if times file is requested ... if times_file is not None: sorted_queries = sorted([(query.name.strip(), query) for query in all_queries]) if os.path.exists(times_file): out_file = open(times_file, "a") else: out_file = open(times_file, "w") header = "condition," + ",".join( [name for (name, query) in sorted_queries]) out_file.write(header + "\n") line = condition for name, query in sorted_queries: line += "," + str(query.elapsed_time) out_file.write(line + "\n") out_file.close() print("Finished successfully")
def read_math_results(cls, input_filename, doc_list): """ :param input_filename: output of core engine or reranked math results :type input_filename: string :return: query responses :rtype: dict mapping query_name -> CompQuery() """ in_file = open(input_filename, 'r', encoding="utf-8") print("Opened " + input_filename, flush=True) lines = in_file.readlines() in_file.close() print("Reading " + str(len(lines)) + " lines of input", flush=True) current_name = None all_queries = {} for idx, line in enumerate(lines): ## print(str(idx) + line, flush=True) parts = line.strip().split("\t") if len(parts[0]) == 0: # do nothing nothing = None elif len(parts) == 2: if parts[0][0] == "Q": current_name = parts[1] try: current_query = all_queries[current_name] except: current_query = CompQuery(current_name) all_queries[current_name] = current_query current_expr = None elif parts[0][0] == "E": if current_name is None: print("Invalid expression at " + str(idx) + ": Q tuple with query name expected first", flush=True) else: query_expression = parts[1] current_expr = Query(current_name, query_expression) current_query.add_expr(current_expr) elif parts[0][0] == "C": print("Constraint at " + str(idx) + " ignored: " + line) elif len(parts) == 3 and parts[0][0] == "I": if current_name is None or current_expr is None: print( "Invalid information at " + str(idx) + ": Q tuple with query name and E tuple with expression expected first" ) elif parts[1] == "qt": current_expr.initRetrievalTime = float(parts[2]) elif parts[1] == "post": current_expr.postings = int(parts[2]) elif parts[1] == "expr": current_expr.matchedFormulae = int(parts[2]) elif parts[1] == "doc": current_expr.matchedDocs = int(parts[2]) elif len(parts) == 5 and parts[0][0] == "R": if current_name is None or current_expr is None: print( "Invalid result item at " + str(idx) + ": Q tuple with query name and E tuple with expression expected first" ) else: doc_id = int(parts[1]) doc_name = doc_list.find_doc_file(doc_id) if not doc_name: doc_name = "NotADoc" location = int(parts[2]) expression = parts[3] score = float(parts[4]) current_expr.add_result(doc_id, doc_name, location, expression, score) else: print("Ignoring invalid tuple at " + str(idx) + ": " + line) print("Read " + str(len(all_queries)) + " queries", flush=True) return all_queries
def find_substructures(expressions_data): sub_groups = [] query_expression, candidates_data = expressions_data if len(candidates_data) > 1: query = Query("query", query_expression) # create query tree .... rank = -1 scores = None prev_scores = None for data_idx, candidate_data in enumerate(candidates_data): candidate_exp = candidate_data[0] rank = int(candidate_data[1]) query.add_result(0, "", 0, candidate_exp, 0.0) result = query.results[candidate_exp] try: sim_res = SIM_FUNCTION(query.tree, result.tree, query.constraints) scores, matched_q, matched_c, unified_c, wildcard_c, unified = sim_res except: print("Error processing: ") print("Q: " + query_expression, flush=True) print("C: " + candidate_exp, flush=True) continue result.set_unified_elements(unified_c) result.set_matched_elements(matched_c) result.set_wildcard_matches(wildcard_c) result.new_scores = scores if prev_scores is None: prev_scores = scores else: if prev_scores != scores: print("Error: Scores changed!") print(prev_scores) print(scores) prev_scores = scores query.sort_results() if len(query.sorted_results) > 1: print("Error: Did not expect More than 1 group") print("-> " + str(len(query.sorted_results))) group = query.sorted_results[0] # for each sub group ... structures = [] current_structure = 0 for subgroup in group: # next substructure group in the overall rank... current_structure += 1 structure_elements = [] for sg_idx, expression in enumerate(subgroup): structure_elements.append(expression) structures.append(structure_elements) else: # just one expression in rank, no need to re-evaluate score ... candidate_data = candidates_data[0] candidate_exp = candidate_data[0] rank = int(candidate_data[1]) scores = [float(part) for part in candidate_data[2:(2 + N_SCORES)]] # the list of structures only contains one structure with the same structure structures = [[candidate_exp]] return (rank, scores, structures)
def pivot_by_docs(self, how): # process all query results """ how = "core" => use core value ranks directly "MSS" => use reranking scores """ self.by_document = {} ## intID = True # CHANGED TO MATCH ON DOC NAME ALWAYS if self.tquery: for doc_id in self.tquery.results.keys(): ## try: ## intID = (int(doc_id) == doc_id) # True if doc_id is an integer ## except: ## intID = False # otherwise need to match on filename (docname, score, positions) = self.tquery.results[doc_id] # add document if first time seen # join on docname, not doc_id try: doc = self.by_document[docname] except: doc = CompQueryResult(doc_id, docname) self.by_document[docname] = doc # add score of keyword match to current document doc.set_tscore(score) doc.set_tpos(positions) if self.mqueries: for qexprnum, query in enumerate(self.mqueries): # keep scores for all existing formulas over all documents for result in query.results.values(): # N.B. only one Result structure per matched formula expression #print("Candidate: " + result.tree.tostring(), flush=True) if how == "MSS": # compute the MSS score if requested sim_res = similarity_v06( query.tree, result.tree, Query.create_default_constraints(query.tree)) result.new_scores = sim_res[ 0] # scores returned as first component of result -- other components are node sets elif how == "v09": sim_res = similarity_v09( query.tree, result.tree, Query.create_default_constraints(query.tree)) result.new_scores = sim_res[0] # only use scores elif how == "v10": sim_res = similarity_v10( query.tree, result.tree, Query.create_default_constraints(query.tree)) result.new_scores = sim_res[0] # only use scores elif how == "v11": sim_res = similarity_v11( query.tree, result.tree, Query.create_default_constraints(query.tree)) result.new_scores = sim_res[0] # only use scores else: result.new_scores = [ result.original_score ] # otherwise, just use original score for doc_id, offset in result.locations: title = query.documents[doc_id] #title = title.rpartition('\\')[2] # just last part title = os.path.basename(title) # just last part (KMD) ## if not intID: # join on title instead of doc_id joiner = title ## else: ## joiner = doc_id # add document if first time seen try: doc = self.by_document[joiner] doc.doc_id = doc_id # prefer using math ids (to match positions later) except: doc = CompQueryResult(doc_id, title) self.by_document[joiner] = doc # add current result to current document doc.add_mscore(qexprnum, result)
def main(): if len(sys.argv) < 5: print("Usage") print("\tpython3 rerank_results.py control input_results metric output_results") print("") print("Where:") print("\tcontrol:\tPath to tangent control file") print("\tinput_results:\tPath to file with results to re-rank") print("\tmetric:\t\tSimilarity metric to use [0-4]") print("\toutput_results:\tPath to file where re-ranked results will be stored") print("") print("Optional:") print("\t-w\twindow\t\t: Window for pair generation") print("\t-h\thtml_prefix\t: Prefix for HTML output (requires dot)") print("\t-c\tcondition\t: Current test condition") print("\t-s\tstats\t\t: File to store stats") print("\t-t\ttimes\t\t: File to accumulate time stats") return control_filename = sys.argv[1] input_filename = sys.argv[2] try: metric = int(sys.argv[3]) if metric < 0 or metric > 5: print("Invalid similarity metric function") return except: print("Invalid similarity metric function") return output_filename = sys.argv[4] optional_params = optional_parameters(sys.argv[5:]) #load control file control = Control(control_filename) # control file name (after indexing) math_doc = MathDocument(control) if "w" in optional_params: try: window = int(optional_params["w"]) if window <= 0: print("Invalid window") return except: print("Invalid value for window") return else: window = int(control.read("window")) if "h" in optional_params: html_prefix = optional_params["h"] if not os.path.isdir(html_prefix): os.makedirs(html_prefix) if not os.path.isdir(html_prefix + "/images"): os.makedirs(html_prefix + "/images") else: html_prefix = None if "c" in optional_params: condition = optional_params["c"] print("testing condition: " + condition) else: condition = "undefined" if "s" in optional_params: stats_file = optional_params["s"] else: stats_file = None if "t" in optional_params: times_file = optional_params["t"] else: times_file = None in_file = open(input_filename, 'r', encoding="utf-8") lines = in_file.readlines() in_file.close() mathml_cache_file = control_filename + ".retrieval_2.cache" if not os.path.exists(mathml_cache_file): mathml_cache = MathMLCache(control_filename) else: cache_file = open(mathml_cache_file, "rb") mathml_cache = pickle.load(cache_file) cache_file.close() current_query = None current_name = None current_tuple_retrieval_time = 'undefined' all_queries = [] #read all results to re-rank for idx, line in enumerate(lines): parts = line.strip().split("\t") if len(parts) == 2: if parts[0][0] == "Q": current_name = parts[1] current_query = None elif parts[0][0] == "E": if current_name is None: print("invalid expression at " + str(idx) + ": query name expected first") else: query_expression = parts[1] if html_prefix != None: mathml = mathml_cache.get(-1, len(all_queries), query_expression) else: mathml = None current_query = Query(current_name, query_expression, mathml, current_tuple_retrieval_time) current_name = None all_queries.append(current_query) print("Query: " + current_query.name + ": " + current_query.expression, flush=True) #print(mathml) #current_query.tree.save_as_dot("expre_" + str(idx) + ".gv") elif parts[0][0] == "C": if current_query is None: print("invalid constraint at " + str(idx) + ": query expression expected first") else: # create a constraint tree current_query.set_constraints(parts[1]) # RZ: Record tuple-based retrieval time and other metrics. if len(parts) == 3 and parts[0][0] == "I" and current_query != None: if parts[1] == "qt": current_query.initRetrievalTime = float( parts[2] ) elif parts[1] == "post": current_query.postings = int( parts[2] ) elif parts[1] == "expr": current_query.matchedFormulae = int( parts[2] ) elif parts[1] == "doc": current_query.matchedDocs = int( parts[2] ) if len(parts) == 5: if parts[0][0] == "R": doc_id = int(parts[1]) location = int(parts[2]) doc_name = math_doc.find_doc_file(doc_id) expression = parts[3] score = float(parts[4]) if html_prefix != None: mathml = mathml_cache.get(doc_id, location, expression) else: mathml = None if current_query is None: print("Error: result listed before a query, line " + str(idx)) else: current_query.add_result(doc_id, doc_name, location, expression, score, mathml) cache_file = open(mathml_cache_file, "wb") pickle.dump(mathml_cache, cache_file, pickle.HIGHEST_PROTOCOL) cache_file.close() # now, re-rank... # compute similarity first... start_time = time.time() for q_idx, query in enumerate(all_queries): pairs_query = query.tree.root.get_pairs("", window) #print("Evaluating: " + query.expression) query_start_time = time.time() * 1000 # RZ: ms for res_idx, exp_result in enumerate(query.results): result = query.results[exp_result] #print("Candidate: " + result.expression) scores = [0.0] if metric == 0: # same as original based on f-measure of matched pairs... pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c = similarity_v00(pairs_query, pairs_candidate) elif metric == 1: # based on testing of alignments.... scores, matched_q, matched_c = similarity_v01(query.tree, result.tree) elif metric == 2: # Same as 0 but limiting to matching total symbols first... pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c = similarity_v02(pairs_query, pairs_candidate) elif metric == 3: # modified version of 2 which performs unification.... pairs_candidate = result.tree.root.get_pairs("", window) scores, matched_q, matched_c, unified_c = similarity_v03(pairs_query, pairs_candidate) result.set_unified_elements(unified_c) elif metric == 4: # modified version of 1 which performs unification ... scores, matched_q, matched_c, unified_c = similarity_v04(query.tree, result.tree, query.constraints) result.set_unified_elements(unified_c) elif metric == 5: # modified version of 4 which allows multiple sub matches scores, matched_q, matched_c, unified_c = similarity_v05(query.tree, result.tree, query.constraints) result.set_unified_elements(unified_c) result.set_matched_elements(matched_c) result.new_scores = scores query_end_time = time.time() * 1000 # RZ: ms # re-rank based on new score(s) query.sort_results() query.sort_documents() query.elapsed_time = query_end_time - query_start_time end_time = time.time() elapsed = end_time - start_time print("Elapsed Time Ranking: " + str(elapsed) + "s") #now, store the re-ranked results... out_file = open(output_filename, "w") for query in all_queries: out_file.write("\n") query.output_query(out_file) query.output_sorted_results(out_file) if html_prefix is not None: print("Saving " + query.name + " to HTML file.....") query.save_html(html_prefix) out_file.close() #if stats file is requested ... if stats_file is not None: out_file = open(stats_file, "w") out_file.write(Query.stats_header("\t")) for query in all_queries: query.output_stats(out_file,"\t", condition) out_file.close() # if times file is requested ... if times_file is not None: sorted_queries = sorted([(query.name.strip(), query) for query in all_queries]) if os.path.exists(times_file): out_file = open(times_file, "a") else: out_file = open(times_file, "w") header = "condition," + ",".join([name for (name, query) in sorted_queries]) out_file.write(header + "\n") line = condition for name, query in sorted_queries: line += "," + str(query.elapsed_time) out_file.write(line + "\n") out_file.close() print("Finished successfully")