docs(abtests): Added documentation for featureFilters configuration i… (generated)

algolia/api-clients-automation#5688

Co-authored-by: algolia-bot <[email protected]>
Co-authored-by: Samy Kettani <[email protected]>

Author: algolia-bot

algoliasearch/abtesting/models/__init__.py

@@ -23,6 +23,7 @@
 from .estimate_ab_test_request import EstimateABTestRequest
 from .estimate_ab_test_response import EstimateABTestResponse
 from .estimate_configuration import EstimateConfiguration
+from .feature_filters import FeatureFilters
 from .filter_effects import FilterEffects
 from .list_ab_tests_response import ListABTestsResponse
 from .minimum_detectable_effect import MinimumDetectableEffect
@@ -47,6 +48,7 @@
     "EstimateABTestRequest",
     "EstimateABTestResponse",
     "EstimateConfiguration",
+    "FeatureFilters",
     "FilterEffects",
     "ListABTestsResponse",
     "MinimumDetectableEffect",

algoliasearch/abtesting/models/ab_test_configuration.py

@@ -19,12 +19,14 @@
 
 
 from algoliasearch.abtesting.models.empty_search import EmptySearch
+from algoliasearch.abtesting.models.feature_filters import FeatureFilters
 from algoliasearch.abtesting.models.minimum_detectable_effect import (
     MinimumDetectableEffect,
 )
 from algoliasearch.abtesting.models.outliers import Outliers
 
 _ALIASES = {
+    "feature_filters": "featureFilters",
     "outliers": "outliers",
     "empty_search": "emptySearch",
     "minimum_detectable_effect": "minimumDetectableEffect",
@@ -40,6 +42,7 @@ class ABTestConfiguration(BaseModel):
     A/B test configuration.
     """
 
+    feature_filters: Optional[FeatureFilters] = None
     outliers: Optional[Outliers] = None
     empty_search: Optional[EmptySearch] = None
     minimum_detectable_effect: Optional[MinimumDetectableEffect] = None
@@ -79,6 +82,11 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
         if not isinstance(obj, dict):
             return cls.model_validate(obj)
 
+        obj["featureFilters"] = (
+            FeatureFilters.from_dict(obj["featureFilters"])
+            if obj.get("featureFilters") is not None
+            else None
+        )
         obj["outliers"] = (
             Outliers.from_dict(obj["outliers"])
             if obj.get("outliers") is not None

algoliasearch/abtesting/models/estimate_configuration.py

@@ -19,12 +19,14 @@
 
 
 from algoliasearch.abtesting.models.empty_search import EmptySearch
+from algoliasearch.abtesting.models.feature_filters import FeatureFilters
 from algoliasearch.abtesting.models.minimum_detectable_effect import (
     MinimumDetectableEffect,
 )
 from algoliasearch.abtesting.models.outliers import Outliers
 
 _ALIASES = {
+    "feature_filters": "featureFilters",
     "outliers": "outliers",
     "empty_search": "emptySearch",
     "minimum_detectable_effect": "minimumDetectableEffect",
@@ -40,6 +42,7 @@ class EstimateConfiguration(BaseModel):
     A/B test configuration for estimating the sample size and duration using minimum detectable effect.
     """
 
+    feature_filters: Optional[FeatureFilters] = None
     outliers: Optional[Outliers] = None
     empty_search: Optional[EmptySearch] = None
     minimum_detectable_effect: MinimumDetectableEffect
@@ -79,6 +82,11 @@ def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
         if not isinstance(obj, dict):
             return cls.model_validate(obj)
 
+        obj["featureFilters"] = (
+            FeatureFilters.from_dict(obj["featureFilters"])
+            if obj.get("featureFilters") is not None
+            else None
+        )
         obj["outliers"] = (
             Outliers.from_dict(obj["outliers"])
             if obj.get("outliers") is not None

algoliasearch/abtesting/models/feature_filters.py

@@ -0,0 +1,79 @@
+# coding: utf-8
+
+"""
+Code generated by OpenAPI Generator (https://openapi-generator.tech), manual changes will be lost - read more on https://github.com/algolia/api-clients-automation. DO NOT EDIT.
+"""
+
+from __future__ import annotations
+
+from json import loads
+from sys import version_info
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, ConfigDict
+
+if version_info >= (3, 11):
+    from typing import Self
+else:
+    from typing_extensions import Self
+
+
+_ALIASES = {
+    "dynamic_re_ranking": "dynamicReRanking",
+    "ai_perso": "aiPerso",
+    "multi_signal_ranking": "multiSignalRanking",
+}
+
+
+def _alias_generator(name: str) -> str:
+    return _ALIASES.get(name, name)
+
+
+class FeatureFilters(BaseModel):
+    """
+    Configuration of feature-based filters applied to the A/B test population.
+    """
+
+    dynamic_re_ranking: Optional[bool] = None
+    """ Whether to apply Dynamic Re-Ranking feature filters. """
+    ai_perso: Optional[bool] = None
+    """ Whether to apply AI Personalization feature filters. """
+    multi_signal_ranking: Optional[bool] = None
+    """ Whether to apply Multi-Signal Re-Ranking feature filters. """
+
+    model_config = ConfigDict(
+        strict=False,
+        use_enum_values=True,
+        populate_by_name=True,
+        validate_assignment=True,
+        protected_namespaces=(),
+        alias_generator=_alias_generator,
+        extra="allow",
+    )
+
+    def to_json(self) -> str:
+        return self.model_dump_json(by_alias=True, exclude_unset=True)
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Optional[Self]:
+        """Create an instance of FeatureFilters from a JSON string"""
+        return cls.from_dict(loads(json_str))
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return the dictionary representation of the model using alias."""
+        return self.model_dump(
+            by_alias=True,
+            exclude_none=True,
+            exclude_unset=True,
+        )
+
+    @classmethod
+    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
+        """Create an instance of FeatureFilters from a dict"""
+        if obj is None:
+            return None
+
+        if not isinstance(obj, dict):
+            return cls.model_validate(obj)
+
+        return cls.model_validate(obj)