`__. TF)graph_similarity) ds1ds2 prop_scores thresholdignore_spec_versionversioning_checks max_depth weight_dictsimilarity_results Z/home/james-whalen/.local/lib/python3.13/site-packages/stix2/equivalence/graph/__init__.pygraph_equivalencer s.d) +(3% c 0nSn[R"5n U(aU RU5 UUUUUS.U S'US::a [S5e[ [ UR /55[ UR /55U 5n [RSURUR5 U Hfup0n U SnU Sn[XXUX4U40U D6nX;a XU US.X~'OUX~S:aXU US.X~'X;a XU US.X'MQUXS:dM^XU US.X'Mh [S UR555n[U5nUS:aUU- nUUS 'UUS 'XrS '[RS UUU5 U$)aThis method returns a similarity score for two given graphs. Each DataStore can contain a connected or disconnected graph and the final result is weighted over the amount of objects we managed to compare. This approach builds on top of the object-based similarity process and each comparison can return a value between 0 and 100. Args: ds1: A DataStore object instance representing your graph ds2: A DataStore object instance representing your graph prop_scores: A dictionary that can hold individual property scores, weights, contributing score, matching score and sum of weights. ignore_spec_version: A boolean indicating whether to test object types that belong to different spec versions (STIX 2.0 and STIX 2.1 for example). If set to True this check will be skipped. versioning_checks: A boolean indicating whether to test multiple revisions of the same object (when present) to maximize similarity against a particular version. If set to True the algorithm will perform this step. max_depth: A positive integer indicating the maximum recursion depth the algorithm can reach when de-referencing objects and performing the object_similarity algorithm. weight_dict: A dictionary that can be used to override what checks are done to objects in the similarity process. Returns: float: A number between 0.0 and 100.0 as a measurement of similarity. Warning: Object types need to have property weights defined for the similarity process. Otherwise, those objects will not influence the final score. The WEIGHTS dictionary under `stix2.equivalence.graph` can give you an idea on how to add new entries and pass them via the `weight_dict` argument. Similarly, the values or methods can be fine tuned for a particular use case. Note: Default weight_dict: .. include:: ../../similarity_weights.rst Note: This implementation follows the Semantic Equivalence Committee Note. see `the Committee Note `__. r)rrr r r _internalz"'max_depth' must be greater than 0zCStarting graph similarity process between DataStores: '%s' and '%s'id)lhsrhs prop_scorevaluerc3*# UH oSv M g7f)rN).0xs r #graph_similarity..s>-=7-=smatching_score len_pairssummaryzBDONE LEN_PAIRS: %.2f MATCHING_SCORE: %.2f SIMILARITY_SCORE: %.2f)rcopyupdate ValueErrorrrqueryloggerdebugrrsumvalueslen)r r rrrrrresultssimilarity_scoreweightspairsobject1object2 iprop_score object1_id object2_idresultr%r&s rr r Fs^GllnG{# 3. GKA~=>> 2'2' E  LLVX[X^X^`c`f`fg! T] T] " k   !   $*4Wbms"tG  g)'2 2*4Wbms"tG   $*4Wbms"tG  g)'2 2*4Wbms"tG '"*>W^^-=>>NG I1})I5$2K !(K $  LLP  r) __doc__loggingobjectrrrr getLogger__name__r,rr r rrr@sNM   8 $8x%qlr