Merge pull request #60 from mmschlk/development

Development

examples/agrawal_accuracy_loss.py

@@ -14,7 +14,7 @@
 
 if __name__ == "__main__":
 
-    # Get Data ---------------------------------------------------------------------------------------------------------
+    # Get Data -------------------------------------------------------------------------------------
     stream = Agrawal(classification_function=1, seed=42)
     feature_names = list([x_0 for x_0, _ in stream.take(1)][0].keys())
 
@@ -24,7 +24,7 @@
     model = AdaptiveRandomForestClassifier(n_models=15, max_depth=10, leaf_prediction='mc')
     model_function = RiverWrapper(model.predict_one)
 
-    # Get imputer and explainers ---------------------------------------------------------------------------------------
+    # Get imputer and explainers -------------------------------------------------------------------
     storage = GeometricReservoirStorage(
         size=200,
         store_targets=False

examples/agrawal_cross_entropy_loss.py

@@ -14,7 +14,7 @@
 
 if __name__ == "__main__":
 
-    # Get Data ---------------------------------------------------------------------------------------------------------
+    # Get Data -------------------------------------------------------------------------------------
     stream = Agrawal(classification_function=1, seed=42)
     feature_names = list([x_0 for x_0, _ in stream.take(1)][0].keys())
 
@@ -24,7 +24,7 @@
     model = AdaptiveRandomForestClassifier(n_models=15, max_depth=10, leaf_prediction='mc')
     model_function = RiverWrapper(model.predict_proba_one)
 
-    # Get imputer and explainers ---------------------------------------------------------------------------------------
+    # Get imputer and explainers -------------------------------------------------------------------
     storage = GeometricReservoirStorage(
         size=200,
         store_targets=False

ixai/__version__.py

@@ -1 +1 @@
-__version__ = "0.1.1"
+__version__ = "0.1.2"

ixai/explainer/base.py

@@ -1,15 +1,12 @@
 """
 This module gathers base Explanation Methods
 """
-
-# Authors: Maximilian Muschalik <[email protected]>
-#          Fabian Fumagalli <[email protected]>
-#          Rohit Jagtani
-
 import copy
 import math
 import abc
-import typing
+from typing import Union, Sequence, Dict, List, Callable, Any, Optional
+
+from river.metrics.base import Metric
 
 from ixai.imputer import BaseImputer, MarginalImputer
 from ixai.storage import GeometricReservoirStorage, UniformReservoirStorage
@@ -35,8 +32,8 @@ class BaseIncrementalExplainer(metaclass=abc.ABCMeta):
     @abc.abstractmethod
     def __init__(
             self,
-            model_function: typing.Callable,
-            feature_names: list
+            model_function: Callable[[Any], Any],
+            feature_names: Sequence[Union[str, int, float]]
     ):
         """
         Args:
@@ -62,13 +59,13 @@ class BaseIncrementalFeatureImportance(BaseIncrementalExplainer):
     @abc.abstractmethod
     def __init__(
             self,
-            model_function,
-            loss_function,
-            feature_names: list,
-            storage: typing.Optional[BaseStorage] = None,
-            imputer: typing.Optional[BaseImputer] = None,
+            model_function: Callable[[Any], Any],
+            loss_function: Union[Metric, Callable[[Any, Dict], float]],
+            feature_names: Sequence[Union[str, int, float]],
+            storage: Optional[BaseStorage] = None,
+            imputer: Optional[BaseImputer] = None,
             dynamic_setting: bool = False,
-            smoothing_alpha: typing.Optional[float] = None
+            smoothing_alpha: Optional[float] = None
     ):
         super().__init__(model_function, feature_names)
         self._loss_function = validate_loss_function(loss_function)
@@ -157,12 +154,16 @@ def _normalize_importance_values(importance_values: dict, mode: str = 'sum') ->
         except ZeroDivisionError:
             return {feature: 0.0 for feature, importance_value in importance_values.items()}
 
-    def update_storage(self, x_i: dict, y_i: typing.Optional[typing.Any] = None):
-        """Manually updates the data storage with the given observation."""
+    def update_storage(self, x_i: dict, y_i: Optional[Any] = None):
+        """Manually updates the data storage with the given observation.
+        Args:
+            x_i (dict): The input features of the current observation.
+            y_i (Any, optional): Target label of the current observation. Defaults to `None`
+        """
         self._storage.update(x=x_i, y=y_i)
 
 
-def _get_mean_model_output(model_outputs: typing.List[dict]) -> dict:
+def _get_mean_model_output(model_outputs: List[dict]) -> dict:
     """Calculates the mean values of a list of dict model outputs.
 
     Args:

ixai/explainer/pfi.py

@@ -1,22 +1,17 @@
 """
 This module gathers PFI Explanation Methods
 """
+from typing import Optional, Union, Callable, Any
 
-# Authors: Maximilian Muschalik <[email protected]>
-#          Fabian Fumagalli <[email protected]>
-#          Rohit Jagtani
-
-from typing import Optional, Union, Callable, List, Any
-from .base import BaseIncrementalFeatureImportance
 import numpy as np
 from river.metrics.base import Metric
-from ..imputer import BaseImputer
-from ..storage.base import BaseStorage
+
+from ixai.imputer import BaseImputer
+from ixai.storage.base import BaseStorage
+from .base import BaseIncrementalFeatureImportance
 
 
-__all__ = [
-    "IncrementalPFI",
-]
+__all__ = ["IncrementalPFI"]
 
 
 class IncrementalPFI(BaseIncrementalFeatureImportance):
@@ -35,7 +30,7 @@ def __init__(
             feature_names: list,
             storage: Optional[BaseStorage] = None,
             imputer: Optional[BaseImputer] = None,
-            n_inner_samples: int = 5,
+            n_inner_samples: int = 1,
             smoothing_alpha: float = 0.001,
             dynamic_setting: bool = True
     ):
@@ -66,7 +61,7 @@ def __init__(
                 adaptive explanation) or a static modelling setting `False` (all observations contribute equally to the
                 final importance) is assumed. Defaults to `True`.
         """
-        super(IncrementalPFI, self).__init__(
+        super().__init__(
             model_function=model_function,
             loss_function=loss_function,
             feature_names=feature_names,