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ML Exclusions

The ML Exclusions service collection provides operations for managing machine learning exclusions. Retrieve, create, delete, update, and query ML exclusions. Also supports aggregation, reporting, and actions on exclusions with ancestor fields.

LanguageLast Update
Pythonv1.6.5
PowerShellv2.2.9
Gov0.22.0
TypeScriptv0.6.0
Rustv0.7.1
Rubyv1.4.0

This service collection has code examples posted to the repository.



OperationDescription
createMLExclusionsV1
create_exclusions
Create the ML exclusions
deleteMLExclusionsV1
delete_exclusions
Delete the ML exclusions by id
exclusions.aggregates.v2
aggregate_exclusions
Get exclusion aggregates as specified via json in request body.
exclusions.create.v2
create_exclusions_v2
Create the exclusions, with ancestor fields.
exclusions.delete.v2
delete_exclusions_v2
Delete the exclusions by id, with ancestor fields.
exclusions.get-all.v2
get_all_exclusions
Get all exclusions.
exclusions.get-reports.v2
get_reports
Create a report of ML exclusions scoped by the given filters
exclusions.get.v2
get_exclusions_by_id
Get the exclusions by id, with ancestor fields.
exclusions.perform-action.v2
perform_actions
Actions used to manipulate the content of exclusions, with ancestor fields.
exclusions.sdmf-query.v1
exclusions_sdmf_query_v1
Executes an SDMF data frame query against exclusion entities
exclusions.search.v2
search_exclusions_v2
Search for exclusions, with ancestor fields.
exclusions.update.v2
update_exclusions_v2
Update the exclusions by id, with ancestor fields.
getMLExclusionsV1
get_exclusions
Get a set of ML Exclusions by specifying their IDs
queryMLExclusionsV1
query_exclusions
Search for ML exclusions.
updateMLExclusionsV1
update_exclusions
Update the ML exclusions

Create the ML exclusions

Method POST
Route /policy/entities/ml-exclusions/v1
Scope Machine Learning Exclusions: WRITE
PEP 8 create_exclusions
body body · dictionary
Full body payload as JSON formatted dictionary.
comment body · string
String comment describing why the exclusion is entered.
excluded_from body · array
Exclusion sources to apply.
groups body · array
Group IDs to exclude.
value body · string
Value to exclude.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.create_exclusions(comment="string",
excluded_from=id_list,
groups=id_list,
value="string")
print(response)
[
{
"applied_globally": false,
"created_by": "string",
"created_on": "string",
"excluded_from": [],
"groups": [],
"id": "string",
"last_modified": "string",
"modified_by": "string",
"regexp_value": "string",
"value": "string",
"value_hash": "string"
}
]


Delete the ML exclusions by id

Method DELETE
Route /policy/entities/ml-exclusions/v1
Scope Machine Learning Exclusions: WRITE
PEP 8 delete_exclusions
ids query · string or list of strings
The ids of the exclusions to delete
comment query · string
Explains why this exclusions was deleted
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.delete_exclusions(comment="string", ids=id_list)
print(response)
[
"string"
]


Get exclusion aggregates as specified via json in request body.

Method POST
Route /exclusions/aggregates/exclusions/GET/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 aggregate_exclusions
body body · dictionary
Full body payload as JSON formatted dictionary.
date_ranges body · array
Date range timeframe.
exclude body · string
Fields to exclude from results.
extended_bounds body · object
Extended bounds for histogram aggregations.
field body · string
Field to aggregate on.
filter body · string
Filter criteria in the form of an FQL query.
filters_spec body · object
Additional filter specifications.
from body · integer
Starting index of overall result set.
include body · string
Fields to include in results.
interval body · string
Time interval for date histogram aggregations.
max_doc_count body · integer
Maximum number of documents per bucket.
min_doc_count body · integer
Minimum number of documents per bucket.
missing body · string
Value to use for documents missing the field.
name body · string
Name of the aggregation.
percents body · array
Percentile values to calculate. List of floats.
q body · string
Full text search query.
ranges body · array
Range boundaries for range aggregations.
size body · integer
Maximum number of records to return.
sort body · string
The field to sort on.
sub_aggregates body · array
Nested aggregation definitions.
time_zone body · string
Time zone for date histogram aggregations.
type body · string
Type of aggregation to perform.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
date_ranges = [
{
"from": "string",
"to": "string"
}
]
extended_bounds = {
"max": "string",
"min": "string"
}
filters_spec = {
"filters": {
"additionalProp1": "string",
"additionalProp2": "string",
"additionalProp3": "string"
},
"other_bucket": True,
"other_bucket_key": "string"
}
ranges = [
{
"From": 0,
"To": 0
}
]
sub_aggregates = [
null
]
response = falcon.aggregate_exclusions(date_ranges=date_ranges,
exclude="string",
extended_bounds=extended_bounds,
field="string",
filter="string",
filters_spec=filters_spec,
from=integer,
include="string",
interval="string",
max_doc_count=integer,
min_doc_count=integer,
missing="string",
name="string",
percents=["string"],
q="string",
ranges=ranges,
size=integer,
sort="string",
sub_aggregates=sub_aggregates,
time_zone="string",
type="string")
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Create the exclusions, with ancestor fields.

Method POST
Route /exclusions/entities/exclusions/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 create_exclusions_v2
body body · dictionary
Full body payload as JSON formatted dictionary.
exclusions body · array
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
exclusions = [
{
"comment": "string",
"excluded_from": [
"string"
],
"grandparent_value": "string",
"groups": [
"string"
],
"parent_value": "string",
"value": "string"
}
]
response = falcon.create_exclusions_v2(exclusions=exclusions)
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Delete the exclusions by id, with ancestor fields.

Method DELETE
Route /exclusions/entities/exclusions/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 delete_exclusions_v2
ids query · string or list of strings
The ids of the exclusions to delete
comment query · string
The comment why these exclusions were deleted
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.delete_exclusions_v2(ids=id_list, comment="string")
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Get all exclusions.

Method GET
Route /exclusions/entities/all-exclusions/v2
Scope Machine Learning Exclusions: READ
PEP 8 get_all_exclusions
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
response = falcon.get_all_exclusions()
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Create a report of ML exclusions scoped by the given filters

Method POST
Route /exclusions/entities/exclusions/reports/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 get_reports
body body · dictionary
Full body payload as JSON formatted dictionary.
report_format body · string
search body · object
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
search = {
"filter": "string",
"sort": "string"
}
response = falcon.get_reports(report_format="string", search=search)
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Get the exclusions by id, with ancestor fields.

Method GET
Route /exclusions/entities/exclusions/v2
Scope Machine Learning Exclusions: READ
PEP 8 get_exclusions_by_id
ids query · string or list of strings
The ids of the exclusions to retrieve
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.get_exclusions_by_id(ids=id_list)
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Actions used to manipulate the content of exclusions, with ancestor fields.

Method POST
Route /exclusions/entities/exclusion-actions/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 perform_actions
body body · dictionary
Full body payload as JSON formatted dictionary.
action_parameters body · array
Action-specific parameters.
available body · boolean
Flag indicating if the action is available.
description body · string
Description of the exclusion action.
group body · string
Group associated with the action.
label body · string
Display label for the action.
name body · string
Name of the action.
action_name query · string
The action to perform.
Available values (3)
add_itemremove_itemvalidate_filepath
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
action_parameters = [
{
"name": "string",
"value": "string"
}
]
response = falcon.perform_actions(action_name="string",
action_parameters=action_parameters,
available=boolean,
description="string",
group="string",
label="string",
name="string")
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Executes an SDMF data frame query against exclusion entities

Method POST
Route /exclusions/sdmf/query/v1
Scope Machine Learning Exclusions: WRITE
PEP 8 exclusions_sdmf_query_v1
body body · dictionary
Full body payload as JSON formatted dictionary.
control_info body · object
The control_info value.
id body · string
The id value.
nodes body · array
The nodes value.
res_id body · string
The res_id value.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
control_info = {
"deadline": "string",
"duration": "string",
"execution_context": {
"catalog_version": "string",
"execution_options": "string",
"extensions": "string",
"queried_cids": [
"string"
]
},
"execution_details": {
"driver_calls": "string"
},
"is_export_request": True,
"pagination_info": {
"limit": 0,
"offset": "string"
},
"partial_results": True,
"query_stats": {
"execution_stats": {
"visited_entities": 0,
"visited_relationships": 0
},
"total_hits": {
"relation": "string",
"total": 0
}
},
"store_headers": "string"
}
response = falcon.exclusions_sdmf_query_v1(control_info=control_info,
id="string",
nodes=["string"],
res_id="string")
print(response)
[
{}
]


Search for exclusions, with ancestor fields.

Method GET
Route /exclusions/queries/exclusions/v2
Scope Machine Learning Exclusions: READ
PEP 8 search_exclusions_v2
filter query · string
The filter expression that should be used to limit the results. FQL syntax.
offset query · integer
The offset to start retrieving records from
limit query · integer
The maximum records to return. [1-500]
sort query · string
The sort expression that should be used to sort the results. Available sort fields:
Available values (9)
parent_valuevaluegrandparent_value
applied_globallycreated_oncreated_by
last_modifiedmodified_byis_descendant_process
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
response = falcon.search_exclusions_v2(filter="string",
offset=integer,
limit=integer,
sort="string")
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Update the exclusions by id, with ancestor fields.

Method PATCH
Route /exclusions/entities/exclusions/v2
Scope Machine Learning Exclusions: WRITE
PEP 8 update_exclusions_v2
body body · dictionary
Full body payload as JSON formatted dictionary.
comment body · string
Comment describing why the exclusion is updated.
excluded_from body · array
Exclusion sources.
grandparent_value body · string
Grandparent process value for the exclusion.
groups body · array
Group IDs to associate with the exclusion.
id body · string
Identifier of the exclusion to update.
parent_value body · string
Parent process value for the exclusion.
value body · string
Value to exclude.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.update_exclusions_v2(comment="string",
excluded_from=id_list,
grandparent_value="string",
groups=id_list,
id="string",
parent_value="string",
value="string")
print(response)
{
"errors": [
{
"code": 0,
"id": "string",
"message": "string"
}
],
"meta": {
"pagination": {
"limit": 0,
"offset": 0,
"total": 0
},
"powered_by": "string",
"query_time": 0.0,
"trace_id": "string",
"writes": {
"resources_affected": 0
}
}
}


Get a set of ML Exclusions by specifying their IDs

Method GET
Route /policy/entities/ml-exclusions/v1
Scope Machine Learning Exclusions: READ
PEP 8 get_exclusions
ids query · string or list of strings
The ids of the exclusions to retrieve
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.get_exclusions(ids=id_list)
print(response)
[
{
"applied_globally": false,
"created_by": "string",
"created_on": "string",
"excluded_from": [],
"groups": [],
"id": "string",
"last_modified": "string",
"modified_by": "string",
"regexp_value": "string",
"value": "string",
"value_hash": "string"
}
]


Search for ML exclusions.

Method GET
Route /policy/queries/ml-exclusions/v1
Scope Machine Learning Exclusions: READ
PEP 8 query_exclusions
filter query · string
The filter expression that should be used to limit the results. FQL syntax. Available filters:
Available values (6)
applied_globallycreated_bycreated_on
last_modifiedmodified_byvalue
offset query · integer
The offset to start retrieving records from
limit query · integer
The maximum records to return. [1-500]
sort query · string
The property to sort by. FQL syntax. (e.g. last_behavior|asc) Available sort fields:
Available values (12)
applied_globally.ascapplied_globally.desccreated_by.asc
created_by.desccreated_on.asccreated_on.desc
last_modified.asclast_modified.descmodified_by.asc
modified_by.descvalue.ascvalue.desc
parameters query · dictionary
Full query string parameters payload in JSON format. Not required when using other keywords.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
response = falcon.query_exclusions(filter="string",
limit=integer,
offset=integer,
sort="string")
print(response)
[
"string"
]


Update the ML exclusions

Method PATCH
Route /policy/entities/ml-exclusions/v1
Scope Machine Learning Exclusions: WRITE
PEP 8 update_exclusions
body body · dictionary
Full body payload as JSON formatted dictionary.
comment body · string
String comment describing why the exclusion is entered.
groups body · array
Group IDs to exclude.
id body · string
Exclusion ID to update.
is_descendant_process body · boolean
Flag indicating if this is a descendant process.
value body · string
Value to exclude.
from falconpy import MLExclusions
falcon = MLExclusions(client_id=CLIENT_ID,
client_secret=CLIENT_SECRET
)
id_list = 'ID1,ID2,ID3' # Can also pass a list here: ['ID1', 'ID2', 'ID3']
response = falcon.update_exclusions(comment="string",
groups=id_list,
id="string",
is_descendant_process=boolean,
value="string")
print(response)
[
{
"applied_globally": false,
"created_by": "string",
"created_on": "string",
"excluded_from": [],
"groups": [],
"id": "string",
"last_modified": "string",
"modified_by": "string",
"regexp_value": "string",
"value": "string",
"value_hash": "string"
}
]