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cti-python-stix2
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add methods to environment.py

pull/1/head
Emmanuelle Vargas-Gonzalez 2021-02-16 00:58:33 -05:00
parent 02b076b3bb
commit 690a515f00
1 changed files with 92 additions and 12 deletions
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104
stix2/environment.py
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@ -2,12 +2,12 @@
import copy
from .datastore import CompositeDataSource, DataStoreMixin
from .equivalence.graph import graph_similarity
from .equivalence.graph import graph_equivalence, graph_similarity
from .equivalence.object import ( # noqa: F401
WEIGHTS, check_property_present, custom_pattern_based, exact_match,
list_reference_check, partial_external_reference_based, partial_list_based,
partial_location_distance, partial_string_based, partial_timestamp_based,
reference_check, object_similarity,
reference_check, object_equivalence, object_similarity,
)
from .parsing import parse as _parse
@ -198,8 +198,8 @@ class Environment(DataStoreMixin):
@staticmethod
def object_similarity(obj1, obj2, prop_scores={}, **weight_dict):
"""This method verifies if two objects of the same type are
semantically equivalent.
"""This method returns a measure of similarity depending on how
similar the two objects are.
Args:
obj1: A stix2 object instance
@ -210,10 +210,50 @@ class Environment(DataStoreMixin):
in the semantic equivalence process
Returns:
float: A number between 0.0 and 100.0 as a measurement of equivalence.
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 equivalence process.
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.object` 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:: ../../object_default_sem_eq_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)
@staticmethod
def object_equivalence(obj1, obj2, prop_scores={}, threshold=70, **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.
Args:
obj1: A stix2 object instance
obj2: A stix2 object instance
prop_scores: A dictionary that can hold individual property scores,
weights, contributing score, matching score and sum of weights.
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 semantic equivalence process
Returns:
bool: True if the result of the object similarity is greater than or equal to
the threshold value. False otherwise.
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.object` can give you an idea on how to add
new entries and pass them via the `weight_dict` argument. Similarly, the values
@ -229,14 +269,14 @@ class Environment(DataStoreMixin):
see `the Committee Note <link here>`__.
"""
return object_similarity(obj1, obj2, prop_scores, **weight_dict)
return object_equivalence(obj1, obj2, prop_scores, threshold, **weight_dict)
@staticmethod
def graph_similarity(ds1, ds2, prop_scores={}, **weight_dict):
"""This method verifies if two graphs are semantically equivalent.
"""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.
This approach builds on top of the object-based semantic equivalence process
This approach builds on top of the object-based similarity process
and each comparison can return a value between 0 and 100.
Args:
@ -245,13 +285,13 @@ class Environment(DataStoreMixin):
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 semantic equivalence process
in the similarity process
Returns:
float: A number between 0.0 and 100.0 as a measurement of equivalence.
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 equivalence process.
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
@ -268,3 +308,43 @@ class Environment(DataStoreMixin):
"""
return graph_similarity(ds1, ds2, prop_scores, **weight_dict)
@staticmethod
def graph_equivalence(ds1, ds2, prop_scores={}, threshold=70, **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.
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.
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
Returns:
bool: True if the result of the graph similarity is greater than or equal to
the threshold value. False otherwise.
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:: ../graph_default_sem_eq_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)