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Replace grid_resolution np.inf value with large number #940

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Merged
ascillitoe merged 6 commits into from
Jul 7, 2023
Merged

Replace grid_resolution np.inf value with large number #940

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bb00413
Replace grid_resolution np.inf value with large number
mauicv Jul 4, 2023
329132d
Pass categorical_names to sklearn partial_dependency
mauicv Jul 6, 2023
ddf3594
Make test beahvour depend on sklearn version
mauicv Jul 6, 2023
a42f01e
Fix linting error
mauicv Jul 6, 2023
e88d4c8
Bump shap version
mauicv Jul 7, 2023
65f7c74
Merge branch 'master' into bugfix/pd-grid-res-fix
mauicv Jul 7, 2023
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15 changes: 13 additions & 2 deletions alibi/explainers/tests/test_partial_dependence.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import sys
from copy import deepcopy
from typing import Dict, List, Optional, Tuple, Union
from packaging import version

import matplotlib.pyplot as plt
import numpy as np
Expand All @@ -17,6 +18,7 @@
from sklearn.multioutput import MultiOutputClassifier
from sklearn.pipeline import Pipeline
from sklearn.utils import Bunch, shuffle
import sklearn

from alibi.api.defaults import DEFAULT_DATA_PD, DEFAULT_META_PD
from alibi.api.interfaces import Explanation
Expand Down Expand Up @@ -387,19 +389,28 @@ def test_sklearn_numerical(rf_classifier, iris_data, features, params):
@pytest.mark.parametrize('params', [
{
'percentiles': (0, 1),
'grid_resolution': np.inf,
'method': 'brute',
'kind': 'average'
}
])
def test_sklearn_categorical(rf_classifier, adult_data, features, params):
""" Checks `alibi` pd black-box implementation against the `sklearn` implementation for categorical features."""

rf, preprocessor = rf_classifier
rf_pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('predictor', rf)])

# subsample data for faster computation
X_train = adult_data['X_train'][:100]

# Behaviour depends on sklearn version, See https://github.com/SeldonIO/alibi/pull/940#issuecomment-1623783025
sklearn_version = version.parse(sklearn.__version__)
if sklearn_version >= version.parse('1.3.0'):
categorical_names = adult_data['metadata']['category_map']
categorical_names = list(categorical_names.keys())
params.update(categorical_features=categorical_names)
else:
params.update(grid_resolution=np.inf)

# compute `sklearn` explanation
exp_sklearn = partial_dependence(X=X_train,
estimator=rf_pipeline,
Expand Down Expand Up @@ -432,7 +443,7 @@ def test_sklearn_categorical(rf_classifier, adult_data, features, params):
@pytest.mark.parametrize('params', [
{
'percentiles': (0, 1),
'grid_resolution': np.inf,
'grid_resolution': 30,
'method': 'recursion',
'kind': 'average'
}
Expand Down