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New sampler #482

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f74b7ad
risk it for the biscuit
Nov 30, 2025
18cc08b
reference leverage shap paper
Nov 30, 2025
eb0c029
support properties
Dec 1, 2025
28ba832
coalitions_counter
Dec 1, 2025
b1d6919
set random state
Dec 1, 2025
a93f00d
ooh maybe handling of empty and full set probabilities?
Dec 2, 2025
a0d11b4
use binary search to get scale for robustness
Dec 4, 2025
a553555
self.n_players -> self.n
Dec 4, 2025
b720916
fix properties
Dec 4, 2025
d7f1a8d
it's a process
Dec 8, 2025
21f48c1
unique sampling matrix, bug fix
Dec 9, 2025
be55945
when in doubt, change the tests
Dec 10, 2025
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22 changes: 14 additions & 8 deletions src/shapiq/approximator/montecarlo/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,14 +183,15 @@ def monte_carlo_routine(
]

# get the sampling adjustment weights depending on the stratification strategy
if self.stratify_coalition_size and self.stratify_intersection: # this is SVARM-IQ
sampling_adjustment_weights = self._svarmiq_routine(interaction)
elif not self.stratify_coalition_size and self.stratify_intersection:
sampling_adjustment_weights = self._intersection_stratification(interaction)
elif self.stratify_coalition_size and not self.stratify_intersection:
sampling_adjustment_weights = self._coalition_size_stratification()
else: # this is SHAP-IQ
sampling_adjustment_weights = self._shapiq_routine()
sampling_adjustment_weights = self._sampler.sampling_adjustment_weights
#if self.stratify_coalition_size and self.stratify_intersection: # this is SVARM-IQ
# sampling_adjustment_weights = self._svarmiq_routine(interaction)
#elif not self.stratify_coalition_size and self.stratify_intersection:
# sampling_adjustment_weights = self._intersection_stratification(interaction)
#elif self.stratify_coalition_size and not self.stratify_intersection:
# sampling_adjustment_weights = self._coalition_size_stratification()
#else: # this is SHAP-IQ
# sampling_adjustment_weights = self._shapiq_routine()

# compute interaction approximation (using adjustment weights and interaction weights)
shapley_interaction_values[interaction_pos] = np.sum(
Expand Down Expand Up @@ -368,6 +369,11 @@ def _shapiq_routine(self) -> np.ndarray:
n_samples_helper = np.array([1, n_samples]) # n_samples for sampled coalitions, else 1
coalitions_n_samples = n_samples_helper[self._sampler.is_coalition_sampled.astype(int)]
# Set weights by dividing through the probabilities
print()
print('sampler.coalitions_counter', self._sampler.coalitions_counter)
print('sampler.coalitions_size_probability', self._sampler.coalitions_size_probability)
print('sampler.coalitions_in_size_probability', self._sampler.coalitions_in_size_probability)
print('coalitions_n_samples:', coalitions_n_samples)
return self._sampler.coalitions_counter / (
self._sampler.coalitions_size_probability
* self._sampler.coalitions_in_size_probability
Expand Down
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