version 1.0.1 of shapiq

CHANGELOG.md

@@ -2,8 +2,8 @@
 
 ### v1.0.1 (2024-06-05)
 
-- add `max_order=1` to `TabularExplainer`
--
+- add `max_order=1` to `TabularExplainer` and `TreeExplainer`
+- fix `TreeExplainer.explain_X(..., njobs=2, random_state=0)`
 
 ### v1.0.0 (2024-06-04)
 

shapiq/__init__.py

@@ -2,7 +2,7 @@
 the well established Shapley value and its generalization to interaction.
 """
 
-__version__ = "1.0.0.9000"
+__version__ = "1.0.1"
 
 # approximator classes
 from .approximator import (

shapiq/explainer/_base.py

@@ -73,9 +73,12 @@ def explain_X(
  """
  assert len(X.shape) == 2
  if random_state is not None:
- self._imputer._rng = np.random.default_rng(random_state)
- self._approximator._rng = np.random.default_rng(random_state)
- self._approximator._sampler._rng = np.random.default_rng(random_state)
+ if hasattr(self, "_imputer"):
+ self._imputer._rng = np.random.default_rng(random_state)
+ if hasattr(self, "_approximator"):
+ self._approximator._rng = np.random.default_rng(random_state)
+ if hasattr(self._approximator, "_sampler"):
+ self._approximator._sampler._rng = np.random.default_rng(random_state)
  if n_jobs:
  import joblib
 

shapiq/explainer/tree/explainer.py

@@ -32,7 +32,7 @@ class TreeExplainer(Explainer):
  interaction values up to that order. Defaults to ``2``.
  min_order: The minimum interaction order to be computed. Defaults to ``1``.
  index: The type of interaction to be computed. It can be one of
- ``["k-SII", "SII", "STII", "FSII", "BII"]``. All indices apart from ``"BII"`` will
+ ``["k-SII", "SII", "STII", "FSII", "BII", "SV"]``. All indices apart from ``"BII"`` will
  reduce to the ``"SV"`` (Shapley value) for order 1. Defaults to ``"k-SII"``.
  class_label: The class label of the model to explain.
  """
@@ -52,6 +52,9 @@ def __init__(
  if index == "SV" and max_order > 1:
  warnings.warn("For index='SV' the max_order is set to 1.")
  max_order = 1
+ elif max_order == 1 and index != "SV":
+ warnings.warn("For max_order=1 the index is set to 'SV'.")
+ index = "SV"
 
  # validate and parse model
  validated_model = validate_tree_model(model, class_label=class_label)