improve scikit-learn compliance by removing attributes that are not set in the init function. Implemented new Explainer inheritance system to replace

Author: paulbkoch

docs/interpret/framework.ipynb

@@ -263,26 +263,6 @@
     "\n",
     "**Explainers** are glassbox or blackbox algorithms that will produce an **explanation**, an artifact that is ready for visualizations or further data processing.\n",
     "\n",
-    "<h2>Explainer</h2>\n",
-    "\n",
-    "An explainer will produce an explanation from its `.explain_*` method. These explanations normally provide an understanding of **global** model behavior or **local** individual predictions (`.explain_global` and `.explain_local` respectively).\n",
-    "\n",
-    "```{eval-rst}\n",
-    ".. autoclass:: interpret.api.base.ExplainerMixin\n",
-    "   :members:\n",
-    "   :inherited-members:\n",
-    "```\n",
-    "\n",
-    "<h2>Explanation</h2>\n",
-    "\n",
-    "An explanation is a self-contained object that help understands either its target model behavior, or a set of individual predictions. The explanation should provide access to visualizations through `.visualize`, and data processing the `.data` method. Both `.visualize` and `.data` should share the same function signature in terms of arguments.\n",
-    "\n",
-    "```{eval-rst}\n",
-    ".. autoclass:: interpret.api.base.ExplanationMixin\n",
-    "   :members:\n",
-    "   :inherited-members:\n",
-    "```\n",
-    "\n",
     "<h2>Show</h2>\n",
     "\n",
     "The `show` method is used as a universal function that provides visualizations for whatever explanation(s) is provided in its arguments. Implementation-wise it will provide some visualization platform (i.e. a dashboard or widget) and expose the explanation(s)' visualizations as given by the `.visualize` call.\n"

python/interpret-core/interpret-core.pyproj

@@ -138,7 +138,6 @@
     <Compile Include="tests\provider\__init__.py" />
     <Compile Include="tests\test_develop.py" />
     <Compile Include="tests\test_example_notebooks.py" />
-    <Compile Include="tests\test_explainers.py" />
     <Compile Include="tests\test_ext.py" />
     <Compile Include="tests\test_extension_utils.py" />
     <Compile Include="tests\test_mli_interop.py" />

python/interpret-core/interpret/api/base.py

@@ -8,30 +8,39 @@
 #      there
 
 
-# TODO v.3 PK Possibly rename explainer types to (blackbox, glassbox, greybox)
-class ExplainerMixin(abc.ABC):
-    """An object that computes explanations.
-        This is a contract required for InterpretML.
+class LocalExplainerMixin(metaclass=abc.ABCMeta):
+    """Mixin for explainers that provide local explanations."""
 
-    Attributes:
-        available_explanations: A list of strings subsetting the following
-            - "perf", "data", "local", "global".
-        explainer_type: A string that is one of the following
-            - "blackbox", "model", "specific", "data", "perf".
-    """
+    @abc.abstractmethod
+    def explain_local(self, X, y=None, name=None):
+        pass  # pragma: no cover
+
+
+class GlobalExplainerMixin(metaclass=abc.ABCMeta):
+    """Mixin for explainers that provide global explanations."""
 
-    @property
     @abc.abstractmethod
-    def available_explanations(self):
+    def explain_global(self, name=None):
         pass  # pragma: no cover
 
-    @property
+
+class DataExplainerMixin(metaclass=abc.ABCMeta):
+    """Mixin for explainers that provide data explanations."""
+
+    @abc.abstractmethod
+    def explain_data(self, X, y, name=None):
+        pass  # pragma: no cover
+
+
+class PerfExplainerMixin(metaclass=abc.ABCMeta):
+    """Mixin for explainers that provide performance explanations."""
+
     @abc.abstractmethod
-    def explainer_type(self):
+    def explain_perf(self, X, y, name=None):
         pass  # pragma: no cover
 
 
-class ExplanationMixin(abc.ABC):
+class ExplanationMixin(metaclass=abc.ABCMeta):
     """The result of calling explain_* from an Explainer. Responsible for providing data and/or visualization.
         This is a contract required for InterpretML.
 

python/interpret-core/interpret/blackbox/_lime.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from ..api.base import ExplainerMixin
+from ..api.base import LocalExplainerMixin
 from ..api.templates import FeatureValueExplanation
 from ..utils._clean_simple import clean_dimensions, typify_classification
 from ..utils._clean_x import preclean_X
@@ -13,15 +13,12 @@
 
 
 # TODO: Make kwargs explicit.
-class LimeTabular(ExplainerMixin):
+class LimeTabular(LocalExplainerMixin):
     """Exposes LIME tabular explainer from lime package, in interpret API form.
     If using this please cite the original authors as can be found here:
     https://github.com/marcotcr/lime/blob/master/citation.bib
     """
 
-    available_explanations = ["local"]
-    explainer_type = "blackbox"
-
     def __init__(self, model, data, feature_names=None, feature_types=None, **kwargs):
         """Initializes class.
 

python/interpret-core/interpret/blackbox/_partialdependence.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from ..api.base import ExplainerMixin, ExplanationMixin
+from ..api.base import GlobalExplainerMixin, ExplanationMixin
 from ..utils._clean_x import preclean_X
 from ..utils._explanation import gen_global_selector, gen_name_from_class
 from ..utils._unify_data import unify_data
@@ -69,17 +69,14 @@ def _gen_pdp(
     }
 
 
-class PartialDependence(ExplainerMixin):
+class PartialDependence(GlobalExplainerMixin):
     """Partial dependence plots as defined in Friedman's paper on
     "Greedy function approximation: a gradient boosting machine".
 
     Friedman, Jerome H. "Greedy function approximation: a gradient boosting machine."
     Annals of statistics (2001): 1189-1232.
     """
 
-    available_explanations = ["global"]
-    explainer_type = "blackbox"
-
     def __init__(
         self,
         model,

python/interpret-core/interpret/blackbox/_permutationimportance.py

@@ -9,7 +9,7 @@
 #
 # from ..utils import gen_name_from_class, gen_local_selector
 #
-# from interpret.api.base import ExplainerMixin
+# from interpret.api.base import GlobalExplainerMixin
 # from interpret.api.templates import FeatureValueExplanation
 # from interpret.utils import unify_predict_fn, unify_data
 # from interpret.utils import gen_name_from_class, gen_global_selector
@@ -46,7 +46,7 @@
 #                          'recall_score'}
 #
 #
-# class PermutationImportance(ExplainerMixin):
+# class PermutationImportance(GlobalExplainerMixin):
 #     available_explanations = ["global"]
 #     explainer_type = "blackbox"
 #

python/interpret-core/interpret/blackbox/_sensitivity.py

@@ -1,11 +1,12 @@
 # Copyright (c) 2023 The InterpretML Contributors
 # Distributed under the MIT software license
 
-from abc import ABC, abstractmethod
+import abc
+
 
 import numpy as np
 
-from ..api.base import ExplainerMixin
+from ..api.base import GlobalExplainerMixin
 from ..api.templates import FeatureValueExplanation
 from ..utils._clean_x import preclean_X
 from ..utils._explanation import gen_global_selector, gen_name_from_class
@@ -14,8 +15,8 @@
 
 
 # TODO: move this to a more general location where other blackbox methods can access it
-class SamplerMixin(ABC):
-    @abstractmethod
+class SamplerMixin(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
     def sample(self, data, feature_names, feature_types):
         # if the blackbox or greybox underlying method accepts a sampling
         # function or abstract class they may want to pass us additional
@@ -41,17 +42,14 @@ def sample(self, data, feature_names, feature_types):
         )
 
 
-class MorrisSensitivity(ExplainerMixin):
+class MorrisSensitivity(GlobalExplainerMixin):
     """Method of Morris for analyzing blackbox systems.
     If using this please cite the package owners as can be found here: https://github.com/SALib/SALib
 
     Morris, Max D. "Factorial sampling plans for preliminary computational experiments."
     Technometrics 33.2 (1991): 161-174.
     """
 
-    available_explanations = ["global"]
-    explainer_type = "blackbox"
-
     def __init__(
         self,
         model,

python/interpret-core/interpret/blackbox/_shap.py

@@ -2,21 +2,18 @@
 # Distributed under the MIT software license
 import numpy as np
 
-from ..api.base import ExplainerMixin
+from ..api.base import LocalExplainerMixin
 from ..utils._clean_x import preclean_X
 from ..utils._shap_common import shap_explain_local
 from ..utils._unify_data import unify_data
 from ..utils._unify_predict import determine_classes, unify_predict_fn
 
 
-class ShapKernel(ExplainerMixin):
+class ShapKernel(LocalExplainerMixin):
     """Exposes SHAP kernel explainer from shap package, in interpret API form.
     If using this please cite the original authors as can be found here: https://github.com/slundberg/shap
     """
 
-    available_explanations = ["local"]
-    explainer_type = "blackbox"
-
     def __init__(self, model, data, feature_names=None, feature_types=None, **kwargs):
         """Initializes class.
 

python/interpret-core/interpret/data/_response.py

@@ -3,19 +3,16 @@
 
 import numpy as np
 
-from ..api.base import ExplainerMixin, ExplanationMixin
+from ..api.base import DataExplainerMixin, ExplanationMixin
 from ..utils._clean_simple import clean_dimensions, typify_classification
 from ..utils._clean_x import preclean_X
 from ..utils._explanation import gen_global_selector, gen_name_from_class
 from ..utils._unify_data import unify_data
 
 
-class Marginal(ExplainerMixin):
+class Marginal(DataExplainerMixin):
     """Provides a marginal plot for provided data."""
 
-    available_explanations = ["data"]
-    explainer_type = "data"
-
     def __init__(
         self,
         feature_names=None,
@@ -282,12 +279,9 @@ def visualize(self, key=None):
         return go.Figure(data=data, layout=layout)
 
 
-class ClassHistogram(ExplainerMixin):
+class ClassHistogram(DataExplainerMixin):
     """Provides histogram visualizations for classification problems."""
 
-    available_explanations = ["data"]
-    explainer_type = "data"
-
     def __init__(self, feature_names=None, feature_types=None):
         """Initializes class.
 

python/interpret-core/interpret/ext/examples.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 The InterpretML Contributors
 # Distributed under the MIT software license
 
-from interpret.api.base import ExplainerMixin, ExplanationMixin
+from interpret.api.base import ExplanationMixin
 
 
 class ExampleExplanation(ExplanationMixin):
@@ -17,7 +17,7 @@ def visualize(self, key=None):
         return None
 
 
-class ExampleDataExplainer(ExplainerMixin):
+class ExampleDataExplainer:
     available_explanations = ["data"]
     explainer_type = "data"
 
@@ -28,7 +28,7 @@ def explain_data(self, X, y, name=None):
         return ExampleExplanation()
 
 
-class ExamplePerfExplainer(ExplainerMixin):
+class ExamplePerfExplainer:
     available_explanations = ["perf"]
     explainer_type = "perf"
 
@@ -39,7 +39,7 @@ def explain_perf(self, X, y, name=None):
         return ExampleExplanation()
 
 
-class ExampleGlassboxExplainer(ExplainerMixin):
+class ExampleGlassboxExplainer:
     available_explanations = ["local"]
     explainer_type = "model"
 
@@ -56,7 +56,7 @@ def explain_local(self, X, y=None, name=None):
         return ExampleExplanation()
 
 
-class ExampleGreyboxExplainer(ExplainerMixin):
+class ExampleGreyboxExplainer:
     available_explanations = ["local"]
     explainer_type = "specific"
 
@@ -67,7 +67,7 @@ def explain_local(self, X, y=None, name=None):
         return ExampleExplanation()
 
 
-class ExampleBlackboxExplainer(ExplainerMixin):
+class ExampleBlackboxExplainer:
     available_explanations = ["local"]
     explainer_type = "blackbox"