[UFC] Add bandit to SDK

eppo_client/__init__.py

@@ -6,7 +6,7 @@
 )
 from eppo_client.configuration_store import ConfigurationStore
 from eppo_client.http_client import HttpClient, SdkParams
-from eppo_client.models import Flag
+from eppo_client.models import BanditData, Flag
 from eppo_client.read_write_lock import ReadWriteLock
 from eppo_client.version import __version__
 
@@ -30,9 +30,12 @@ def init(config: Config) -> EppoClient:
         apiKey=config.api_key, sdkName="python", sdkVersion=__version__
     )
     http_client = HttpClient(base_url=config.base_url, sdk_params=sdk_params)
-    config_store: ConfigurationStore[Flag] = ConfigurationStore()
+    flag_config_store: ConfigurationStore[Flag] = ConfigurationStore()
+    bandit_config_store: ConfigurationStore[BanditData] = ConfigurationStore()
     config_requestor = ExperimentConfigurationRequestor(
-        http_client=http_client, config_store=config_store
+        http_client=http_client,
+        flag_config_store=flag_config_store,
+        bandit_config_store=bandit_config_store,
     )
     assignment_logger = config.assignment_logger
     is_graceful_mode = config.is_graceful_mode

eppo_client/assignment_logger.py

@@ -8,3 +8,6 @@ class AssignmentLogger(BaseModel):
 
     def log_assignment(self, assignment_event: Dict):
         pass
+
+    def log_bandit_action(self, bandit_event: Dict):
+        pass

eppo_client/bandit.py

@@ -0,0 +1,282 @@
+from dataclasses import dataclass
+import logging
+from typing import Dict, List, Optional, Tuple
+
+from eppo_client.models import (
+    BanditCategoricalAttributeCoefficient,
+    BanditCoefficients,
+    BanditModelData,
+    BanditNumericAttributeCoefficient,
+)
+from eppo_client.sharders import Sharder
+
+
+logger = logging.getLogger(__name__)
+
+
+class BanditEvaluationError(Exception):
+    pass
+
+
+@dataclass
+class Attributes:
+    numeric_attributes: Dict[str, float]
+    categorical_attributes: Dict[str, str]
+
+
+@dataclass
+class ActionContext:
+    action_key: str
+    attributes: Attributes
+
+    @classmethod
+    def create(
+        cls,
+        action_key: str,
+        numeric_attributes: Dict[str, float],
+        categorical_attributes: Dict[str, str],
+    ):
+        """
+        Create an instance of ActionContext.
+
+        Args:
+            action_key (str): The key representing the action.
+            numeric_attributes (Dict[str, float]): A dictionary of numeric attributes.
+            categorical_attributes (Dict[str, str]): A dictionary of categorical attributes.
+
+        Returns:
+            ActionContext: An instance of ActionContext with the provided action key and attributes.
+        """
+        return cls(
+            action_key,
+            Attributes(
+                numeric_attributes=numeric_attributes,
+                categorical_attributes=categorical_attributes,
+            ),
+        )
+
+    @property
+    def numeric_attributes(self):
+        return self.attributes.numeric_attributes
+
+    @property
+    def categorical_attributes(self):
+        return self.attributes.categorical_attributes
+
+
+@dataclass
+class BanditEvaluation:
+    flag_key: str
+    subject_key: str
+    subject_attributes: Attributes
+    action_key: Optional[str]
+    action_attributes: Optional[Attributes]
+    action_score: float
+    action_weight: float
+    gamma: float
+
+
+@dataclass
+class BanditResult:
+    variation: str
+    action: Optional[str]
+
+    def to_string(self) -> str:
+        return coalesce(self.action, self.variation)
+
+
+def null_evaluation(
+    flag_key: str, subject_key: str, subject_attributes: Attributes, gamma: float
+):
+    return BanditEvaluation(
+        flag_key,
+        subject_key,
+        subject_attributes,
+        None,
+        None,
+        0.0,
+        0.0,
+        gamma,
+    )
+
+
+@dataclass
+class BanditEvaluator:
+    sharder: Sharder
+    total_shards: int = 10_000
+
+    def evaluate_bandit(
+        self,
+        flag_key: str,
+        subject_key: str,
+        subject_attributes: Attributes,
+        actions_with_contexts: List[ActionContext],
+        bandit_model: BanditModelData,
+    ) -> BanditEvaluation:
+        # handle the edge case that there are no actions
+        if not actions_with_contexts:
+            return null_evaluation(
+                flag_key, subject_key, subject_attributes, bandit_model.gamma
+            )
+
+        action_scores = self.score_actions(
+            subject_attributes, actions_with_contexts, bandit_model
+        )
+
+        action_weights = self.weigh_actions(
+            action_scores,
+            bandit_model.gamma,
+            bandit_model.action_probability_floor,
+        )
+
+        selected_idx, selected_action = self.select_action(
+            flag_key, subject_key, action_weights
+        )
+        return BanditEvaluation(
+            flag_key,
+            subject_key,
+            subject_attributes,
+            selected_action,
+            actions_with_contexts[selected_idx].attributes,
+            action_scores[selected_idx][1],
+            action_weights[selected_idx][1],
+            bandit_model.gamma,
+        )
+
+    def score_actions(
+        self,
+        subject_attributes: Attributes,
+        actions_with_contexts: List[ActionContext],
+        bandit_model: BanditModelData,
+    ) -> List[Tuple[str, float]]:
+        return [
+            (
+                action_context.action_key,
+                (
+                    score_action(
+                        subject_attributes,
+                        action_context.attributes,
+                        bandit_model.coefficients[action_context.action_key],
+                    )
+                    if action_context.action_key in bandit_model.coefficients
+                    else bandit_model.default_action_score
+                ),
+            )
+            for action_context in actions_with_contexts
+        ]
+
+    def weigh_actions(
+        self, action_scores, gamma, probability_floor
+    ) -> List[Tuple[str, float]]:
+        number_of_actions = len(action_scores)
+        best_action, best_score = max(action_scores, key=lambda t: t[1])
+
+        # adjust probability floor for number of actions to control the sum
+        min_probability = probability_floor / number_of_actions
+
+        # weight all but the best action
+        weights = [
+            (
+                action_key,
+                max(
+                    min_probability,
+                    1.0 / (number_of_actions + gamma * (best_score - score)),
+                ),
+            )
+            for action_key, score in action_scores
+            if action_key != best_action
+        ]
+
+        # remaining weight goes to best action
+        remaining_weight = max(0.0, 1.0 - sum(weight for _, weight in weights))
+        weights.append((best_action, remaining_weight))
+        return weights
+
+    def select_action(self, flag_key, subject_key, action_weights) -> Tuple[int, str]:
+        # deterministic ordering
+        sorted_action_weights = sorted(
+            action_weights,
+            key=lambda t: (
+                self.sharder.get_shard(
+                    f"{flag_key}-{subject_key}-{t[0]}", self.total_shards
+                ),
+                t[0],  # tie-break using action name
+            ),
+        )
+
+        # select action based on weights
+        shard = self.sharder.get_shard(f"{flag_key}-{subject_key}", self.total_shards)
+        cumulative_weight = 0.0
+        shard_value = shard / self.total_shards
+
+        for idx, (action_key, weight) in enumerate(sorted_action_weights):
+            cumulative_weight += weight
+            if cumulative_weight > shard_value:
+                return idx, action_key
+
+        # If no action is selected, return the last action (fallback)
+        raise BanditEvaluationError(
+            f"[Eppo SDK] No action selected for {flag_key} {subject_key}"
+        )
+
+
+def score_action(
+    subject_attributes: Attributes,
+    action_attributes: Attributes,
+    coefficients: BanditCoefficients,
+) -> float:
+    score = coefficients.intercept
+    score += score_numeric_attributes(
+        coefficients.subject_numeric_coefficients,
+        subject_attributes.numeric_attributes,
+    )
+    score += score_categorical_attributes(
+        coefficients.subject_categorical_coefficients,
+        subject_attributes.categorical_attributes,
+    )
+    score += score_numeric_attributes(
+        coefficients.action_numeric_coefficients,
+        action_attributes.numeric_attributes,
+    )
+    score += score_categorical_attributes(
+        coefficients.action_categorical_coefficients,
+        action_attributes.categorical_attributes,
+    )
+    return score
+
+
+def coalesce(value, default=0):
+    return value if value is not None else default
+
+
+def score_numeric_attributes(
+    coefficients: List[BanditNumericAttributeCoefficient],
+    attributes: Dict[str, float],
+) -> float:
+    score = 0.0
+    for coefficient in coefficients:
+        if (
+            coefficient.attribute_key in attributes
+            and attributes[coefficient.attribute_key] is not None
+        ):
+            score += coefficient.coefficient * attributes[coefficient.attribute_key]
+        else:
+            score += coefficient.missing_value_coefficient
+
+    return score
+
+
+def score_categorical_attributes(
+    coefficients: List[BanditCategoricalAttributeCoefficient],
+    attributes: Dict[str, str],
+) -> float:
+    score = 0.0
+    for coefficient in coefficients:
+        if coefficient.attribute_key in attributes:
+            score += coefficient.value_coefficients.get(
+                attributes[coefficient.attribute_key],
+                coefficient.missing_value_coefficient,
+            )
+        else:
+            score += coefficient.missing_value_coefficient
+    return score