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Merge pull request #496 from emmanvg/semantic-equivalence-part3 

Similarity/Equivalence Changes
pull/1/head
Chris Lenk 2021-03-10 14:08:45 -05:00 committed by GitHub
parent 173575205a c9e66def60
commit f155e3e571
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8 changed files with 364 additions and 429 deletions
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3
.gitignore vendored
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@ -55,8 +55,7 @@ coverage.xml
# Sphinx documentation
docs/_build/
.ipynb_checkpoints
graph_default_sem_eq_weights.rst
object_default_sem_eq_weights.rst
similarity_weights.rst
# PyBuilder
target/

10
docs/conf.py
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@ -7,7 +7,6 @@ import sys
from sphinx.ext.autodoc import ClassDocumenter
from stix2.base import _STIXBase
from stix2.equivalence.graph import GRAPH_WEIGHTS
from stix2.equivalence.object import WEIGHTS
from stix2.version import __version__
@ -66,16 +65,9 @@ object_default_sem_eq_weights = json.dumps(WEIGHTS, indent=4, default=lambda o:
object_default_sem_eq_weights = object_default_sem_eq_weights.replace('\n', '\n ')
object_default_sem_eq_weights = object_default_sem_eq_weights.replace(' "', ' ')
object_default_sem_eq_weights = object_default_sem_eq_weights.replace('"\n', '\n')
with open('object_default_sem_eq_weights.rst', 'w') as f:
with open('similarity_weights.rst', 'w') as f:
f.write(".. code-block:: python\n\n {}\n\n".format(object_default_sem_eq_weights))
graph_default_sem_eq_weights = json.dumps(GRAPH_WEIGHTS, indent=4, default=lambda o: o.__name__)
graph_default_sem_eq_weights = graph_default_sem_eq_weights.replace('\n', '\n ')
graph_default_sem_eq_weights = graph_default_sem_eq_weights.replace(' "', ' ')
graph_default_sem_eq_weights = graph_default_sem_eq_weights.replace('"\n', '\n')
with open('graph_default_sem_eq_weights.rst', 'w') as f:
f.write(".. code-block:: python\n\n {}\n\n".format(graph_default_sem_eq_weights))
def get_property_type(prop):
"""Convert property classname into pretty string name of property.

11
docs/guide/equivalence.ipynb
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@ -4607,20 +4607,11 @@
" ),\n",
"]\n",
"\n",
"\n",
"weights = {\n",
" \"_internal\": {\n",
" \"ignore_spec_version\": False,\n",
" \"versioning_checks\": False,\n",
" \"max_depth\": 1,\n",
" },\n",
"}\n",
"\n",
"memstore1 = MemoryStore(g1)\n",
"memstore2 = MemoryStore(g2)\n",
"prop_scores = {}\n",
"\n",
"similarity_result = env.graph_similarity(memstore1, memstore2, prop_scores, **weights)\n",
"similarity_result = env.graph_similarity(memstore1, memstore2, prop_scores)\n",
"equivalence_result = env.graph_equivalence(memstore1, memstore2, threshold=60)\n",
"\n",
"print(similarity_result)\n",

107
stix2/environment.py
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@ -189,7 +189,11 @@ class Environment(DataStoreMixin):
return None
@staticmethod
def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
def object_similarity(
obj1, obj2, prop_scores={}, ds1=None, ds2=None,
ignore_spec_version=False, versioning_checks=False,
max_depth=1, **weight_dict
):
"""This method returns a measure of how similar the two objects are.
Args:
@ -197,8 +201,19 @@ class Environment(DataStoreMixin):
obj2: A stix2 object instance
prop_scores: A dictionary that can hold individual property scores,
weights, contributing score, matching score and sum of weights.
weight_dict: A dictionary that can be used to override settings
in the similarity process
ds1 (optional): A DataStore object instance from which to pull related objects
ds2 (optional): A DataStore object instance from which to pull related objects
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.
@ -213,17 +228,24 @@ class Environment(DataStoreMixin):
Note:
Default weight_dict:
.. include:: ../object_default_sem_eq_weights.rst
.. include:: ../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
return object_similarity(obj1, obj2, prop_scores, **weight_dict)
return object_similarity(
obj1, obj2, prop_scores, ds1, ds2, ignore_spec_version,
versioning_checks, max_depth, **weight_dict
)
@staticmethod
def object_equivalence(obj1, obj2, prop_scores={}, threshold=70, **weight_dict):
def object_equivalence(
obj1, obj2, prop_scores={}, threshold=70, ds1=None, ds2=None,
ignore_spec_version=False, versioning_checks=False,
max_depth=1, **weight_dict
):
"""This method returns a true/false value if two objects are semantically equivalent.
Internally, it calls the object_similarity function and compares it against the given
threshold value.
@ -236,8 +258,19 @@ class Environment(DataStoreMixin):
threshold: A numerical value between 0 and 100 to determine the minimum
score to result in successfully calling both objects equivalent. This
value can be tuned.
weight_dict: A dictionary that can be used to override settings
in the similarity process
ds1 (optional): A DataStore object instance from which to pull related objects
ds2 (optional): A DataStore object instance from which to pull related objects
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:
bool: True if the result of the object similarity is greater than or equal to
@ -253,17 +286,23 @@ class Environment(DataStoreMixin):
Note:
Default weight_dict:
.. include:: ../object_default_sem_eq_weights.rst
.. include:: ../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
return object_equivalence(obj1, obj2, prop_scores, threshold, **weight_dict)
return object_equivalence(
obj1, obj2, prop_scores, threshold, ds1, ds2,
ignore_spec_version, versioning_checks, max_depth, **weight_dict
)
@staticmethod
def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
def graph_similarity(
ds1, ds2, prop_scores={}, ignore_spec_version=False,
versioning_checks=False, max_depth=1, **weight_dict
):
"""This 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.
@ -275,8 +314,17 @@ class Environment(DataStoreMixin):
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.
weight_dict: A dictionary that can be used to override settings
in the similarity process
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.
@ -291,17 +339,24 @@ class Environment(DataStoreMixin):
Note:
Default weight_dict:
.. include:: ../graph_default_sem_eq_weights.rst
.. include:: ../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
return graph_similarity(ds1, ds2, prop_scores, **weight_dict)
return graph_similarity(
ds1, ds2, prop_scores, ignore_spec_version,
versioning_checks, max_depth, **weight_dict
)
@staticmethod
def graph_equivalence(ds1, ds2, prop_scores={}, threshold=70, **weight_dict):
def graph_equivalence(
ds1, ds2, prop_scores={}, threshold=70,
ignore_spec_version=False, versioning_checks=False,
max_depth=1, **weight_dict
):
"""This method returns a true/false value if two graphs are semantically equivalent.
Internally, it calls the graph_similarity function and compares it against the given
threshold value.
@ -314,8 +369,17 @@ class Environment(DataStoreMixin):
threshold: A numerical value between 0 and 100 to determine the minimum
score to result in successfully calling both graphs equivalent. This
value can be tuned.
weight_dict: A dictionary that can be used to override settings
in the similarity process
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:
bool: True if the result of the graph similarity is greater than or equal to
@ -331,11 +395,14 @@ class Environment(DataStoreMixin):
Note:
Default weight_dict:
.. include:: ../graph_default_sem_eq_weights.rst
.. include:: ../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
return graph_equivalence(ds1, ds2, prop_scores, threshold, **weight_dict)
return graph_equivalence(
ds1, ds2, prop_scores, threshold, ignore_spec_version,
versioning_checks, max_depth, **weight_dict
)

110
stix2/equivalence/graph/__init__.py
View File

@ -2,15 +2,17 @@
import logging
from ..object import (
WEIGHTS, _bucket_per_type, _object_pairs, exact_match,
list_reference_check, object_similarity, partial_string_based,
partial_timestamp_based, reference_check,
WEIGHTS, _bucket_per_type, _object_pairs, object_similarity,
)
logger = logging.getLogger(__name__)
def graph_equivalence(ds1, ds2, prop_scores={}, threshold=70, **weight_dict):
def graph_equivalence(
ds1, ds2, prop_scores={}, threshold=70,
ignore_spec_version=False, versioning_checks=False,
max_depth=1, **weight_dict
):
"""This method returns a true/false value if two graphs are semantically equivalent.
Internally, it calls the graph_similarity function and compares it against the given
threshold value.
@ -23,8 +25,17 @@ def graph_equivalence(ds1, ds2, prop_scores={}, threshold=70, **weight_dict):
threshold: A numerical value between 0 and 100 to determine the minimum
score to result in successfully calling both graphs equivalent. This
value can be tuned.
weight_dict: A dictionary that can be used to override settings
in the similarity process
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:
bool: True if the result of the graph similarity is greater than or equal to
@ -40,20 +51,26 @@ def graph_equivalence(ds1, ds2, prop_scores={}, threshold=70, **weight_dict):
Note:
Default weight_dict:
.. include:: ../../graph_default_sem_eq_weights.rst
.. include:: ../../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
similarity_result = graph_similarity(ds1, ds2, prop_scores, **weight_dict)
similarity_result = graph_similarity(
ds1, ds2, prop_scores, ignore_spec_version,
versioning_checks, max_depth, **weight_dict
)
if similarity_result >= threshold:
return True
return False
def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
def graph_similarity(
ds1, ds2, prop_scores={}, ignore_spec_version=False,
versioning_checks=False, max_depth=1, **weight_dict
):
"""This 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.
@ -65,8 +82,17 @@ def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
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.
weight_dict: A dictionary that can be used to override settings
in the similarity process
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.
@ -81,7 +107,7 @@ def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
Note:
Default weight_dict:
.. include:: ../../graph_default_sem_eq_weights.rst
.. include:: ../../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
@ -90,13 +116,21 @@ def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
"""
results = {}
similarity_score = 0
weights = GRAPH_WEIGHTS.copy()
weights = WEIGHTS.copy()
if weight_dict:
weights.update(weight_dict)
if weights["_internal"]["max_depth"] <= 0:
raise ValueError("weight_dict['_internal']['max_depth'] must be greater than 0")
weights["_internal"] = {
"ignore_spec_version": ignore_spec_version,
"versioning_checks": versioning_checks,
"ds1": ds1,
"ds2": ds2,
"max_depth": max_depth,
}
if max_depth <= 0:
raise ValueError("'max_depth' must be greater than 0")
pairs = _object_pairs(
_bucket_per_type(ds1.query([])),
@ -104,16 +138,17 @@ def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
weights,
)
weights["_internal"]["ds1"] = ds1
weights["_internal"]["ds2"] = ds2
logger.debug("Starting graph similarity process between DataStores: '%s' and '%s'", ds1.id, ds2.id)
for object1, object2 in pairs:
iprop_score = {}
object1_id = object1["id"]
object2_id = object2["id"]
result = object_similarity(object1, object2, iprop_score, **weights)
result = object_similarity(
object1, object2, iprop_score, ds1, ds2,
ignore_spec_version, versioning_checks,
max_depth, **weights
)
if object1_id not in results:
results[object1_id] = {"lhs": object1_id, "rhs": object2_id, "prop_score": iprop_score, "value": result}
@ -141,40 +176,3 @@ def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
similarity_score,
)
return similarity_score
# default weights used for the graph similarity process
GRAPH_WEIGHTS = WEIGHTS.copy()
GRAPH_WEIGHTS.update({
"grouping": {
"name": (20, partial_string_based),
"context": (20, partial_string_based),
"object_refs": (60, list_reference_check),
},
"relationship": {
"relationship_type": (20, exact_match),
"source_ref": (40, reference_check),
"target_ref": (40, reference_check),
},
"report": {
"name": (30, partial_string_based),
"published": (10, partial_timestamp_based),
"object_refs": (60, list_reference_check),
"tdelta": 1, # One day interval
},
"sighting": {
"first_seen": (5, partial_timestamp_based),
"last_seen": (5, partial_timestamp_based),
"sighting_of_ref": (40, reference_check),
"observed_data_refs": (20, list_reference_check),
"where_sighted_refs": (20, list_reference_check),
"summary": (10, exact_match),
},
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"ds1": None,
"ds2": None,
"max_depth": 1,
},
}) # :autodoc-skip:

235
stix2/equivalence/object/__init__.py
View File

@ -4,14 +4,18 @@ import itertools
import logging
import time
from ...datastore import Filter
from ...datastore import DataSource, DataStoreMixin, Filter
from ...utils import STIXdatetime, parse_into_datetime
from ..pattern import equivalent_patterns
logger = logging.getLogger(__name__)
def object_equivalence(obj1, obj2, prop_scores={}, threshold=70, **weight_dict):
def object_equivalence(
obj1, obj2, prop_scores={}, threshold=70, ds1=None,
ds2=None, ignore_spec_version=False,
versioning_checks=False, max_depth=1, **weight_dict
):
"""This method returns a true/false value if two objects are semantically equivalent.
Internally, it calls the object_similarity function and compares it against the given
threshold value.
@ -24,8 +28,19 @@ def object_equivalence(obj1, obj2, prop_scores={}, threshold=70, **weight_dict):
threshold: A numerical value between 0 and 100 to determine the minimum
score to result in successfully calling both objects equivalent. This
value can be tuned.
weight_dict: A dictionary that can be used to override settings
in the similarity process
ds1 (optional): A DataStore object instance from which to pull related objects
ds2 (optional): A DataStore object instance from which to pull related objects
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:
bool: True if the result of the object similarity is greater than or equal to
@ -41,20 +56,27 @@ def object_equivalence(obj1, obj2, prop_scores={}, threshold=70, **weight_dict):
Note:
Default weight_dict:
.. include:: ../../object_default_sem_eq_weights.rst
.. include:: ../../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
see `the Committee Note <link here>`__.
"""
similarity_result = object_similarity(obj1, obj2, prop_scores, **weight_dict)
similarity_result = object_similarity(
obj1, obj2, prop_scores, ds1, ds2, ignore_spec_version,
versioning_checks, max_depth, **weight_dict
)
if similarity_result >= threshold:
return True
return False
def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
def object_similarity(
obj1, obj2, prop_scores={}, ds1=None, ds2=None,
ignore_spec_version=False, versioning_checks=False,
max_depth=1, **weight_dict
):
"""This method returns a measure of similarity depending on how
similar the two objects are.
@ -63,8 +85,19 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
obj2: A stix2 object instance
prop_scores: A dictionary that can hold individual property scores,
weights, contributing score, matching score and sum of weights.
weight_dict: A dictionary that can be used to override settings
in the similarity process
ds1 (optional): A DataStore object instance from which to pull related objects
ds2 (optional): A DataStore object instance from which to pull related objects
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.
@ -79,7 +112,7 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
Note:
Default weight_dict:
.. include:: ../../object_default_sem_eq_weights.rst
.. include:: ../../similarity_weights.rst
Note:
This implementation follows the Semantic Equivalence Committee Note.
@ -91,8 +124,15 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
if weight_dict:
weights.update(weight_dict)
weights["_internal"] = {
"ignore_spec_version": ignore_spec_version,
"versioning_checks": versioning_checks,
"ds1": ds1,
"ds2": ds2,
"max_depth": max_depth,
}
type1, type2 = obj1["type"], obj2["type"]
ignore_spec_version = weights["_internal"]["ignore_spec_version"]
if type1 != type2:
raise ValueError('The objects to compare must be of the same type!')
@ -117,6 +157,7 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
if check_property_present(prop, obj1, obj2):
w = weights[type1][prop][0]
comp_funct = weights[type1][prop][1]
prop_scores[prop] = {}
if comp_funct == partial_timestamp_based:
contributing_score = w * comp_funct(obj1[prop], obj2[prop], weights[type1]["tdelta"])
@ -124,11 +165,18 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
threshold = weights[type1]["threshold"]
contributing_score = w * comp_funct(obj1["latitude"], obj1["longitude"], obj2["latitude"], obj2["longitude"], threshold)
elif comp_funct == reference_check or comp_funct == list_reference_check:
max_depth = weights["_internal"]["max_depth"]
if max_depth > 0:
weights["_internal"]["max_depth"] = max_depth - 1
ds1, ds2 = weights["_internal"]["ds1"], weights["_internal"]["ds2"]
contributing_score = w * comp_funct(obj1[prop], obj2[prop], ds1, ds2, **weights)
if _datastore_check(ds1, ds2):
contributing_score = w * comp_funct(obj1[prop], obj2[prop], ds1, ds2, **weights)
elif comp_funct == reference_check:
comp_funct = exact_match
contributing_score = w * comp_funct(obj1[prop], obj2[prop])
elif comp_funct == list_reference_check:
comp_funct = partial_list_based
contributing_score = w * comp_funct(obj1[prop], obj2[prop])
prop_scores[prop]["check_type"] = comp_funct.__name__
else:
continue # prevent excessive recursion
weights["_internal"]["max_depth"] = max_depth
@ -138,10 +186,8 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
sum_weights += w
matching_score += contributing_score
prop_scores[prop] = {
"weight": w,
"contributing_score": contributing_score,
}
prop_scores[prop]["weight"] = w
prop_scores[prop]["contributing_score"] = contributing_score
logger.debug("'%s' check -- weight: %s, contributing score: %s", prop, w, contributing_score)
prop_scores["matching_score"] = matching_score
@ -165,7 +211,7 @@ def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
def check_property_present(prop, obj1, obj2):
"""Helper method checks if a property is present on both objects."""
if prop == "longitude_latitude":
if all(x in obj1 and x in obj2 for x in ['latitude', 'longitude']):
if all(x in obj1 and x in obj2 for x in ('latitude', 'longitude')):
return True
elif prop in obj1 and prop in obj2:
return True
@ -196,7 +242,9 @@ def partial_timestamp_based(t1, t2, tdelta):
def partial_list_based(l1, l2):
"""Performs a partial list matching via finding the intersection between common values.
"""Performs a partial list matching via finding the intersection between
common values. Repeated values are counted only once. This method can be
used for *_refs equality checks when de-reference is not possible.
Args:
l1: A list of values.
@ -213,7 +261,8 @@ def partial_list_based(l1, l2):
def exact_match(val1, val2):
"""Performs an exact value match based on two values
"""Performs an exact value match based on two values. This method can be
used for *_ref equality check when de-reference is not possible.
Args:
val1: A value suitable for an equality test.
@ -261,12 +310,12 @@ def custom_pattern_based(pattern1, pattern2):
return equivalent_patterns(pattern1, pattern2)
def partial_external_reference_based(refs1, refs2):
def partial_external_reference_based(ext_refs1, ext_refs2):
"""Performs a matching on External References.
Args:
refs1: A list of external references.
refs2: A list of external references.
ext_refs1: A list of external references.
ext_refs2: A list of external references.
Returns:
float: Number between 0.0 and 1.0 depending on matches.
@ -275,51 +324,47 @@ def partial_external_reference_based(refs1, refs2):
allowed = {"veris", "cve", "capec", "mitre-attack"}
matches = 0
if len(refs1) >= len(refs2):
l1 = refs1
l2 = refs2
else:
l1 = refs2
l2 = refs1
ref_pairs = itertools.chain(
itertools.product(ext_refs1, ext_refs2),
)
for ext_ref1 in l1:
for ext_ref2 in l2:
sn_match = False
ei_match = False
url_match = False
source_name = None
for ext_ref1, ext_ref2 in ref_pairs:
sn_match = False
ei_match = False
url_match = False
source_name = None
if check_property_present("source_name", ext_ref1, ext_ref2):
if ext_ref1["source_name"] == ext_ref2["source_name"]:
source_name = ext_ref1["source_name"]
sn_match = True
if check_property_present("external_id", ext_ref1, ext_ref2):
if ext_ref1["external_id"] == ext_ref2["external_id"]:
ei_match = True
if check_property_present("url", ext_ref1, ext_ref2):
if ext_ref1["url"] == ext_ref2["url"]:
url_match = True
if check_property_present("source_name", ext_ref1, ext_ref2):
if ext_ref1["source_name"] == ext_ref2["source_name"]:
source_name = ext_ref1["source_name"]
sn_match = True
if check_property_present("external_id", ext_ref1, ext_ref2):
if ext_ref1["external_id"] == ext_ref2["external_id"]:
ei_match = True
if check_property_present("url", ext_ref1, ext_ref2):
if ext_ref1["url"] == ext_ref2["url"]:
url_match = True
# Special case: if source_name is a STIX defined name and either
# external_id or url match then its a perfect match and other entries
# can be ignored.
if sn_match and (ei_match or url_match) and source_name in allowed:
result = 1.0
logger.debug(
"--\t\tpartial_external_reference_based '%s' '%s'\tresult: '%s'",
refs1, refs2, result,
)
return result
# Special case: if source_name is a STIX defined name and either
# external_id or url match then its a perfect match and other entries
# can be ignored.
if sn_match and (ei_match or url_match) and source_name in allowed:
result = 1.0
logger.debug(
"--\t\tpartial_external_reference_based '%s' '%s'\tresult: '%s'",
ext_refs1, ext_refs2, result,
)
return result
# Regular check. If the source_name (not STIX-defined) or external_id or
# url matches then we consider the entry a match.
if (sn_match or ei_match or url_match) and source_name not in allowed:
matches += 1
# Regular check. If the source_name (not STIX-defined) or external_id or
# url matches then we consider the entry a match.
if (sn_match or ei_match or url_match) and source_name not in allowed:
matches += 1
result = matches / max(len(refs1), len(refs2))
result = matches / max(len(ext_refs1), len(ext_refs2))
logger.debug(
"--\t\tpartial_external_reference_based '%s' '%s'\tresult: '%s'",
refs1, refs2, result,
ext_refs1, ext_refs2, result,
)
return result
@ -352,17 +397,23 @@ def _versioned_checks(ref1, ref2, ds1, ds2, **weights):
"""Checks multiple object versions if present in graph.
Maximizes for the similarity score of a particular version."""
results = {}
objects1 = ds1.query([Filter("id", "=", ref1)])
objects2 = ds2.query([Filter("id", "=", ref2)])
pairs = _object_pairs(
_bucket_per_type(objects1),
_bucket_per_type(objects2),
_bucket_per_type(ds1.query([Filter("id", "=", ref1)])),
_bucket_per_type(ds2.query([Filter("id", "=", ref2)])),
weights,
)
ignore_spec_version = weights["_internal"]["ignore_spec_version"]
versioning_checks = weights["_internal"]["versioning_checks"]
max_depth = weights["_internal"]["max_depth"]
for object1, object2 in pairs:
result = object_similarity(object1, object2, **weights)
result = object_similarity(
object1, object2, ds1=ds1, ds2=ds2,
ignore_spec_version=ignore_spec_version,
versioning_checks=versioning_checks,
max_depth=max_depth, **weights,
)
if ref1 not in results:
results[ref1] = {"matched": ref2, "value": result}
elif result > results[ref1]["value"]:
@ -383,12 +434,20 @@ def reference_check(ref1, ref2, ds1, ds2, **weights):
result = 0.0
if type1 == type2 and type1 in weights:
if weights["_internal"]["versioning_checks"]:
ignore_spec_version = weights["_internal"]["ignore_spec_version"]
versioning_checks = weights["_internal"]["versioning_checks"]
max_depth = weights["_internal"]["max_depth"]
if versioning_checks:
result = _versioned_checks(ref1, ref2, ds1, ds2, **weights) / 100.0
else:
o1, o2 = ds1.get(ref1), ds2.get(ref2)
if o1 and o2:
result = object_similarity(o1, o2, **weights) / 100.0
result = object_similarity(
o1, o2, ds1=ds1, ds2=ds2,
ignore_spec_version=ignore_spec_version,
versioning_checks=versioning_checks,
max_depth=max_depth, **weights,
) / 100.0
logger.debug(
"--\t\treference_check '%s' '%s'\tresult: '%s'",
@ -439,6 +498,15 @@ def list_reference_check(refs1, refs2, ds1, ds2, **weights):
return result
def _datastore_check(ds1, ds2):
if (
issubclass(ds1.__class__, (DataStoreMixin, DataSource)) or
issubclass(ds2.__class__, (DataStoreMixin, DataSource))
):
return True
return False
def _bucket_per_type(graph, mode="type"):
"""Given a list of objects or references, bucket them by type.
Depending on the list type: extract from 'type' property or using
@ -480,11 +548,20 @@ WEIGHTS = {
"name": (60, partial_string_based),
"external_references": (40, partial_external_reference_based),
},
"grouping": {
"name": (20, partial_string_based),
"context": (20, partial_string_based),
"object_refs": (60, list_reference_check),
},
"identity": {
"name": (60, partial_string_based),
"identity_class": (20, exact_match),
"sectors": (20, partial_list_based),
},
"incident": {
"name": (60, partial_string_based),
"external_references": (40, partial_external_reference_based),
},
"indicator": {
"indicator_types": (15, partial_list_based),
"pattern": (80, custom_pattern_based),
@ -511,6 +588,25 @@ WEIGHTS = {
"definition": (60, exact_match),
"definition_type": (20, exact_match),
},
"relationship": {
"relationship_type": (20, exact_match),
"source_ref": (40, reference_check),
"target_ref": (40, reference_check),
},
"report": {
"name": (30, partial_string_based),
"published": (10, partial_timestamp_based),
"object_refs": (60, list_reference_check),
"tdelta": 1, # One day interval
},
"sighting": {
"first_seen": (5, partial_timestamp_based),
"last_seen": (5, partial_timestamp_based),
"sighting_of_ref": (40, reference_check),
"observed_data_refs": (20, list_reference_check),
"where_sighted_refs": (20, list_reference_check),
"summary": (10, exact_match),
},
"threat-actor": {
"name": (60, partial_string_based),
"threat_actor_types": (20, partial_list_based),
@ -524,7 +620,4 @@ WEIGHTS = {
"name": (30, partial_string_based),
"external_references": (70, partial_external_reference_based),
},
"_internal": {
"ignore_spec_version": False,
},
} # :autodoc-skip:

143
stix2/test/v20/test_environment.py
View File

@ -424,7 +424,7 @@ def test_related_to_by_target(ds):
def test_versioned_checks(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": True,
@ -437,7 +437,7 @@ def test_versioned_checks(ds, ds2):
def test_semantic_check_with_versioning(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": False,
@ -467,13 +467,11 @@ def test_semantic_check_with_versioning(ds, ds2):
def test_list_semantic_check(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"ds1": ds,
"ds2": ds2,
"max_depth": 1,
},
})
@ -504,39 +502,18 @@ def test_list_semantic_check(ds, ds2):
def test_graph_similarity_raises_value_error(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": -1,
},
}
with pytest.raises(ValueError):
prop_scores1 = {}
stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
stix2.Environment().graph_similarity(ds, ds2, prop_scores1, max_depth=-1)
def test_graph_similarity_with_filesystem_source(ds, fs):
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(fs, ds, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(fs, ds, prop_scores1, ignore_spec_version=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds, fs, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(ds, fs, prop_scores2, ignore_spec_version=True)
assert round(env1) == 25
assert round(prop_scores1["matching_score"]) == 451
@ -552,41 +529,20 @@ def test_graph_similarity_with_filesystem_source(ds, fs):
def test_graph_similarity_with_duplicate_graph(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores = {}
env = stix2.Environment().graph_similarity(ds, ds, prop_scores, **weights)
env = stix2.Environment().graph_similarity(ds, ds, prop_scores)
assert round(env) == 100
assert round(prop_scores["matching_score"]) == 800
assert round(prop_scores["len_pairs"]) == 8
def test_graph_similarity_with_versioning_check_on(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, versioning_checks=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, versioning_checks=True)
assert round(env1) == 88
assert round(prop_scores1["matching_score"]) == 789
@ -602,26 +558,12 @@ def test_graph_similarity_with_versioning_check_on(ds2, ds):
def test_graph_similarity_with_versioning_check_off(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2)
assert round(env1) == 88
assert round(prop_scores1["matching_score"]) == 789
@ -637,26 +579,12 @@ def test_graph_similarity_with_versioning_check_off(ds2, ds):
def test_graph_equivalence_with_filesystem_source(ds, fs):
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(fs, ds, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(fs, ds, prop_scores1, ignore_spec_version=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds, fs, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds, fs, prop_scores2, ignore_spec_version=True)
assert env1 is False
assert round(prop_scores1["matching_score"]) == 451
@ -672,41 +600,20 @@ def test_graph_equivalence_with_filesystem_source(ds, fs):
def test_graph_equivalence_with_duplicate_graph(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores = {}
env = stix2.Environment().graph_equivalence(ds, ds, prop_scores, **weights)
env = stix2.Environment().graph_equivalence(ds, ds, prop_scores)
assert env is True
assert round(prop_scores["matching_score"]) == 800
assert round(prop_scores["len_pairs"]) == 8
def test_graph_equivalence_with_versioning_check_on(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, versioning_checks=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, versioning_checks=True)
assert env1 is True
assert round(prop_scores1["matching_score"]) == 789
@ -722,26 +629,12 @@ def test_graph_equivalence_with_versioning_check_on(ds2, ds):
def test_graph_equivalence_with_versioning_check_off(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2)
assert env1 is True
assert round(prop_scores1["matching_score"]) == 789

174
stix2/test/v21/test_environment.py
View File

@ -760,16 +760,13 @@ def test_object_similarity_different_spec_version():
"valid_from": (5, stix2.equivalence.object.partial_timestamp_based),
"tdelta": 1, # One day interval
},
"_internal": {
"ignore_spec_version": True, # Disables spec_version check.
},
}
ind1 = stix2.v21.Indicator(id=INDICATOR_ID, **INDICATOR_KWARGS)
ind2 = stix2.v20.Indicator(id=INDICATOR_ID, **IND_KWARGS)
env = stix2.Environment().object_similarity(ind1, ind2, **weights)
env = stix2.Environment().object_similarity(ind1, ind2, ignore_spec_version=True, **weights)
assert round(env) == 0
env = stix2.Environment().object_similarity(ind2, ind1, **weights)
env = stix2.Environment().object_similarity(ind2, ind1, ignore_spec_version=True, **weights)
assert round(env) == 0
@ -858,10 +855,12 @@ def test_object_similarity_exact_match():
assert stix2.equivalence.object.exact_match(t1, t2) == 0.0
def test_non_existent_config_for_object():
def test_no_datastore_fallsback_list_based_check_for_refs_check():
r1 = stix2.v21.Report(id=REPORT_ID, **REPORT_KWARGS)
r2 = stix2.v21.Report(id=REPORT_ID, **REPORT_KWARGS)
assert stix2.Environment().object_similarity(r1, r2) == 0.0
prop_scores = {}
assert stix2.Environment().object_similarity(r1, r2, prop_scores) == 100.0
assert prop_scores["object_refs"]["check_type"] == "partial_list_based"
def custom_semantic_equivalence_method(obj1, obj2, **weights):
@ -937,7 +936,8 @@ def test_object_similarity_prop_scores_method_provided():
def test_versioned_checks(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
# Testing internal method
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": True,
@ -950,7 +950,7 @@ def test_versioned_checks(ds, ds2):
def test_semantic_check_with_versioning(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": False,
@ -981,7 +981,7 @@ def test_semantic_check_with_versioning(ds, ds2):
def test_list_semantic_check(ds, ds2):
weights = stix2.equivalence.graph.GRAPH_WEIGHTS.copy()
weights = stix2.equivalence.graph.WEIGHTS.copy()
weights.update({
"_internal": {
"ignore_spec_version": False,
@ -1027,39 +1027,28 @@ def test_list_semantic_check(ds, ds2):
def test_graph_similarity_raises_value_error(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": -1,
},
}
with pytest.raises(ValueError):
prop_scores1 = {}
stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
stix2.Environment().graph_similarity(ds, ds2, prop_scores1, max_depth=-1)
def test_graph_similarity_with_filesystem_source(ds, fs):
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(fs, ds, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(
fs, ds, prop_scores1,
ignore_spec_version=True,
versioning_checks=False,
max_depth=1,
)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds, fs, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(
ds, fs, prop_scores2,
ignore_spec_version=True,
versioning_checks=False,
max_depth=1,
)
assert round(env1) == 23
assert round(prop_scores1["matching_score"]) == 411
@ -1154,14 +1143,11 @@ def test_depth_limiting():
"some2_ref": (33, stix2.equivalence.object.reference_check),
"name": (34, stix2.equivalence.object.partial_string_based),
},
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.equivalence.graph.graph_similarity(mem_store1, mem_store2, prop_scores1, **custom_weights)
env1 = stix2.equivalence.graph.graph_similarity(
mem_store1, mem_store2, prop_scores1, **custom_weights
)
assert round(env1) == 38
assert round(prop_scores1["matching_score"]) == 300
@ -1185,44 +1171,23 @@ def test_depth_limiting():
def test_graph_similarity_with_duplicate_graph(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores = {}
env = stix2.Environment().graph_similarity(ds, ds, prop_scores, **weights)
env = stix2.Environment().graph_similarity(ds, ds, prop_scores)
assert round(env) == 100
assert round(prop_scores["matching_score"]) == 800
assert round(prop_scores["len_pairs"]) == 8
def test_graph_similarity_with_versioning_check_on(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, versioning_checks=True)
assert round(env1) == 88
assert round(prop_scores1["matching_score"]) == 789
assert round(prop_scores1["len_pairs"]) == 9
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, versioning_checks=True)
assert round(env2) == 88
assert round(prop_scores2["matching_score"]) == 789
assert round(prop_scores2["len_pairs"]) == 9
@ -1233,29 +1198,15 @@ def test_graph_similarity_with_versioning_check_on(ds2, ds):
def test_graph_similarity_with_versioning_check_off(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_similarity(ds, ds2, prop_scores1)
assert round(env1) == 88
assert round(prop_scores1["matching_score"]) == 789
assert round(prop_scores1["len_pairs"]) == 9
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_similarity(ds2, ds, prop_scores2)
assert round(env2) == 88
assert round(prop_scores2["matching_score"]) == 789
assert round(prop_scores2["len_pairs"]) == 9
@ -1266,26 +1217,12 @@ def test_graph_similarity_with_versioning_check_off(ds2, ds):
def test_graph_equivalence_with_filesystem_source(ds, fs):
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(fs, ds, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(fs, ds, prop_scores1, ignore_spec_version=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": True,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds, fs, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds, fs, prop_scores2, ignore_spec_version=True)
assert env1 is False
assert round(prop_scores1["matching_score"]) == 411
@ -1301,41 +1238,20 @@ def test_graph_equivalence_with_filesystem_source(ds, fs):
def test_graph_equivalence_with_duplicate_graph(ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores = {}
env = stix2.Environment().graph_equivalence(ds, ds, prop_scores, **weights)
env = stix2.Environment().graph_equivalence(ds, ds, prop_scores)
assert env is True
assert round(prop_scores["matching_score"]) == 800
assert round(prop_scores["len_pairs"]) == 8
def test_graph_equivalence_with_versioning_check_on(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, versioning_checks=True)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": True,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, versioning_checks=True)
assert env1 is True
assert round(prop_scores1["matching_score"]) == 789
@ -1351,26 +1267,12 @@ def test_graph_equivalence_with_versioning_check_on(ds2, ds):
def test_graph_equivalence_with_versioning_check_off(ds2, ds):
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores1 = {}
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1, **weights)
env1 = stix2.Environment().graph_equivalence(ds, ds2, prop_scores1)
# Switching parameters
weights = {
"_internal": {
"ignore_spec_version": False,
"versioning_checks": False,
"max_depth": 1,
},
}
prop_scores2 = {}
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2, **weights)
env2 = stix2.Environment().graph_equivalence(ds2, ds, prop_scores2)
assert env1 is True
assert round(prop_scores1["matching_score"]) == 789