Merge pull request #51 from mmschlk/26-add-treeshap-iq-explainer

adds TreeExplainer with TreeSHAP-IQ

README.md

@@ -74,12 +74,14 @@ Explain your models with Shapley interaction values like the k-SII values:
 ```python
 # train a model
 from sklearn.ensemble import RandomForestRegressor
+
 model = RandomForestRegressor(n_estimators=50, random_state=42)
 model.fit(x_train, y_train)
 
 # explain with k-SII interaction scores
-from shapiq import InteractionExplainer
-explainer = InteractionExplainer(
+from shapiq import TabularExplainer
+
+explainer = TabularExplainer(
     model=model.predict,
     background_data=x_train,
     index="k-SII",
@@ -88,19 +90,19 @@ explainer = InteractionExplainer(
 interaction_values = explainer.explain(x_explain, budget=2000)
 print(interaction_values)
 
->>> InteractionValues(
->>>    index=k-SII, max_order=2, min_order=1, estimated=True, estimation_budget=2000,
->>>    values={
->>>        (0,): -91.0403,  # main effect for feature 0
->>>        (1,): 4.1264,    # main effect for feature 1
->>>        (2,): -0.4724,   # main effect for feature 2
->>>        ...
->>>        (0, 1): -0.8073, # 2-way interaction for feature 0 and 1
->>>        (0, 2): 2.469,   # 2-way interaction for feature 0 and 2
->>>        ...
->>>        (10, 11): 0.4057 # 2-way interaction for feature 10 and 11
->>>    }
->>> )
+>> > InteractionValues(
+     >> > index = k - SII, max_order = 2, min_order = 1, estimated = True, estimation_budget = 2000,
+>> > values = {
+              >> > (0,): -91.0403,  # main effect for feature 0
+>> > (1,): 4.1264,  # main effect for feature 1
+>> > (2,): -0.4724,  # main effect for feature 2
+>> > ...
+     >> > (0, 1): -0.8073,  # 2-way interaction for feature 0 and 1
+>> > (0, 2): 2.469,  # 2-way interaction for feature 0 and 2
+>> > ...
+     >> > (10, 11): 0.4057  # 2-way interaction for feature 10 and 11
+                    >> >}
+>> > )
 ```
 
 ## 📊 Visualize your Interactions

shapiq/__init__.py

@@ -15,19 +15,18 @@
 from .datasets import load_bike
 
 # explainer classes
-from .explainer import InteractionExplainer
+from .explainer import TabularExplainer, TreeExplainer
 
 # game classes
 from .games import DummyGame
+from .interaction_values import InteractionValues
 
 # plotting functions
 from .plot import network_plot, stacked_bar_plot
 
 # public utils functions
 from .utils import (  # sets.py  # tree.py
-    get_conditional_sample_weights,
     get_explicit_subsets,
-    get_parent_array,
     powerset,
     safe_isinstance,
     split_subsets_budget,
@@ -36,14 +35,17 @@
 __all__ = [
     # version
     "__version__",
+    # base
+    "InteractionValues",
     # approximators
     "ShapIQ",
     "PermutationSamplingSII",
     "PermutationSamplingSTI",
     "RegressionSII",
     "RegressionFSI",
     # explainers
-    "InteractionExplainer",
+    "TabularExplainer",
+    "TreeExplainer",
     # games
     "DummyGame",
     # plots
@@ -53,8 +55,6 @@
     "powerset",
     "get_explicit_subsets",
     "split_subsets_budget",
-    "get_conditional_sample_weights",
-    "get_parent_array",
     "safe_isinstance",
     # datasets
     "load_bike",

shapiq/approximator/_base.py

@@ -4,9 +4,11 @@
 from typing import Callable, Optional
 
 import numpy as np
-from approximator._config import AVAILABLE_INDICES
-from approximator._interaction_values import InteractionValues
-from approximator._utils import _generate_interaction_lookup
+from interaction_values import InteractionValues
+
+from shapiq.utils.sets import generate_interaction_lookup
+
+from ._config import AVAILABLE_INDICES
 
 __all__ = [
     "Approximator",
@@ -65,7 +67,7 @@ def __init__(
         self.max_order: int = max_order
         self.min_order: int = self.max_order if self.top_order else 1
         self.iteration_cost: int = 1  # default value, can be overwritten by subclasses
-        self._interaction_lookup = _generate_interaction_lookup(
+        self._interaction_lookup = generate_interaction_lookup(
             self.n, self.min_order, self.max_order
         )
         self._random_state: Optional[int] = random_state

shapiq/approximator/k_sii.py

@@ -3,12 +3,11 @@
 from typing import Optional, Union
 
 import numpy as np
-from approximator._base import Approximator
-from approximator._interaction_values import InteractionValues
-from approximator._utils import _generate_interaction_lookup
+from interaction_values import InteractionValues
 from scipy.special import bernoulli
 
-from shapiq.utils import powerset
+from shapiq.approximator._base import Approximator
+from shapiq.utils import generate_interaction_lookup, powerset
 
 
 class KShapleyMixin:
@@ -86,7 +85,7 @@ def transforms_sii_to_ksii(
         )
     elif n is not None and max_order is not None:
         if interaction_lookup is None:
-            interaction_lookup = _generate_interaction_lookup(n, 1, max_order)
+            interaction_lookup = generate_interaction_lookup(n, 1, max_order)
         return _calculate_ksii_from_sii(sii_values, n, max_order, interaction_lookup)
     else:
         raise ValueError(
@@ -120,9 +119,12 @@ def _calculate_ksii_from_sii(
     nsii_values = np.zeros_like(sii_values)
     # all subsets S with 1 <= |S| <= max_order
     for subset in powerset(set(range(n)), min_size=1, max_size=max_order):
-        interaction_index = interaction_lookup[subset]
         interaction_size = len(subset)
-        ksii_value = sii_values[interaction_index]
+        try:
+            interaction_index = interaction_lookup[subset]
+            ksii_value = sii_values[interaction_index]
+        except KeyError:
+            continue  # a zero value is not scaled # TODO: verify this
         # go over all subsets T of length |S| + 1, ..., n that contain S
         for T in powerset(set(range(n)), min_size=interaction_size + 1, max_size=max_order):
             if set(subset).issubset(T):

shapiq/approximator/permutation/sii.py

@@ -4,8 +4,8 @@
 
 import numpy as np
 from approximator._base import Approximator
-from approximator._interaction_values import InteractionValues
 from approximator.k_sii import KShapleyMixin
+from interaction_values import InteractionValues
 from utils import powerset
 
 

shapiq/approximator/permutation/sti.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 from approximator._base import Approximator
-from approximator._interaction_values import InteractionValues
+from interaction_values import InteractionValues
 from scipy.special import binom
 from utils import get_explicit_subsets, powerset
 

shapiq/approximator/regression/_base.py

@@ -4,8 +4,8 @@
 
 import numpy as np
 from approximator._base import Approximator
-from approximator._interaction_values import InteractionValues
 from approximator.sampling import ShapleySamplingMixin
+from interaction_values import InteractionValues
 from scipy.special import bernoulli, binom
 from utils import powerset
 

shapiq/approximator/shapiq/shapiq.py

@@ -5,9 +5,9 @@
 
 import numpy as np
 from approximator._base import Approximator
-from approximator._interaction_values import InteractionValues
 from approximator.k_sii import KShapleyMixin
 from approximator.sampling import ShapleySamplingMixin
+from interaction_values import InteractionValues
 from utils import powerset
 
 AVAILABLE_INDICES_SHAPIQ = {"SII", "STI", "FSI", "k-SII"}

shapiq/explainer/__init__.py

@@ -1,6 +1,7 @@
 """This module contains the explainer for the shapiq package."""
 
-from .interaction import InteractionExplainer
+
+from .tabular import TabularExplainer
 from .tree import TreeExplainer
 
-__all__ = ["InteractionExplainer", "TreeExplainer"]
+__all__ = ["TabularExplainer", "TreeExplainer"]