Comments (23)
detrin
commented on June 10, 2024
1
Looking at https://github.com/ing-bank/probatus/blob/main/probatus/utils/shap_helpers.py#L200 I now understand what is the source of error on the side of probatus
Old version will return shap_values
[[ 0. 0. -0. ]
[-0.08174514 0. 0.72205473]
[ 0. 0.37990537 0.72205473]
[-0.08174514 0.37990537 0.72205473]]
The new version of shap will return
[[ 0. -0.37990537 -0.72205473]
[-0.08174514 -0.37990537 -0. ]
[ 0. 0. -0. ]
[-0.08174514 0. -0. ]]
Looking at those shap values, I don't see anything wrong with it. I will need to dig deeper on the side of shap.
@ReinierKoops If you want to have a look at my so far findings, or maybe you have similar experience with shap in past.
I would like to note that returning pd.DataFrame in calculate_shap_importance() is not a good pattern.
from probatus.
detrin
commented on June 10, 2024
1
@ReinierKoops No problem, I enjoyed this issue. The question now is whether you would like me to temp fix the tests or wait for the fix in shap or something else.
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ReinierKoops
commented on June 10, 2024
1
I think best way is to introduce a temp fix for now. We don’t know when shap will be fixed.
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ReinierKoops
commented on June 10, 2024
1
Perfect, thanks!
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detrin
commented on June 10, 2024
I am trying to solve this one for fun. Seems it won't be so simple. I am testing it with python3.11 and I am getting errors with tests
$ pytest -sv
======================================================================= FAILURES ========================================================================
_______________________________________________________ test_shap_automatic_num_feature_selection _______________________________________________________
def test_shap_automatic_num_feature_selection():
"""
Test automatic num feature selection methods
"""
X = pd.DataFrame(
{
"col_1": [1, 0, 1, 0, 1, 0, 1, 0],
"col_2": [0, 0, 0, 0, 0, 1, 1, 1],
"col_3": [1, 1, 1, 0, 0, 0, 0, 0],
}
)
y = pd.Series([0, 0, 0, 0, 1, 1, 1, 1])
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
shap_elimination = ShapRFECV(
clf,
random_state=1,
step=1,
cv=2,
scoring="roc_auc",
n_jobs=1,
)
_ = shap_elimination.fit_compute(X, y, approximate=True, check_additivity=False)
best_features = shap_elimination.get_reduced_features_set(num_features="best")
best_coherent_features = shap_elimination.get_reduced_features_set(
num_features="best_coherent",
)
best_parsimonious_features = shap_elimination.get_reduced_features_set(num_features="best_parsimonious")
> assert best_features == ["col_3"]
E AssertionError: assert array(['col_2'], dtype=object) == ['col_3']
E Full diff:
E - ['col_3']
E + array(['col_2'], dtype=object)
tests/feature_elimination/test_feature_elimination.py:328: AssertionError
__________________________________________________ test_shap_rfe_same_features_are_kept_after_each_run __________________________________________________
def test_shap_rfe_same_features_are_kept_after_each_run():
"""
Test a use case which appears to be flickering with Probatus 1.8.9 and lower.
Expected result: every run the same outcome.
Probatus <= 1.8.9: A different order every time.
"""
SEED = 1234
feature_names = [(f"f{num}") for num in range(1, 21)]
# Code from tutorial on probatus documentation
X, y = make_classification(
n_samples=100,
class_sep=0.05,
n_informative=6,
n_features=20,
random_state=SEED,
n_redundant=10,
n_clusters_per_class=1,
)
X = pd.DataFrame(X, columns=feature_names)
random_forest = RandomForestClassifier(
random_state=SEED,
n_estimators=70,
max_features="log2",
criterion="entropy",
class_weight="balanced",
)
shap_elimination = ShapRFECV(
clf=random_forest,
step=0.2,
cv=5,
scoring="f1_macro",
n_jobs=1,
random_state=SEED,
)
report = shap_elimination.fit_compute(X, y, check_additivity=True, seed=SEED)
# Return the set of features with the best validation accuracy
kept_features = list(report.iloc[[report["val_metric_mean"].idxmax() - 1]]["features_set"].to_list()[0])
# Results from the first run
> assert ["f6", "f10", "f12", "f14", "f15", "f17", "f18", "f20"] == kept_features
E AssertionError: assert ['f6', 'f10',...', 'f17', ...] == ['f2', 'f3', ...', 'f12', ...]
E At index 0 diff: 'f6' != 'f2'
E Right contains 5 more items, first extra item: 'f15'
E Full diff:
E [
E - 'f2',
E - 'f3',
E 'f6',...
E
E ...Full output truncated (11 lines hidden), use '-vv' to show
tests/feature_elimination/test_feature_elimination.py:402: AssertionError
______________________________________________________________ test_shap_resemblance_class ______________________________________________________________
X1 = col_1 col_2 col_3
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
X2 = col_1 col_2 col_3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
def test_shap_resemblance_class(X1, X2):
"""
Test.
"""
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
rm = SHAPImportanceResemblance(clf, test_prc=0.5, n_jobs=1, random_state=42)
# Before fit it should raise an exception
with pytest.raises(NotFittedError) as _:
rm._check_if_fitted()
actual_report, train_score, test_score = rm.fit_compute(X1, X2, return_scores=True)
# After the fit this should not raise any error
rm._check_if_fitted()
assert train_score == 1
assert test_score == 1
# Check report shape
assert actual_report.shape == (3, 2)
# Check if it is sorted by importance
assert actual_report.iloc[0].name == "col_1"
# Check report values
assert actual_report.loc["col_1"]["mean_abs_shap_value"] > 0
> assert actual_report.loc["col_1"]["mean_shap_value"] >= 0
E assert -0.5 >= 0
tests/sample_similarity/test_resemblance_model.py:142: AssertionError
________________________________________________________ test_shap_resemblance_class_lin_models _________________________________________________________
X1 = col_1 col_2 col_3
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
X2 = col_1 col_2 col_3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
def test_shap_resemblance_class_lin_models(X1, X2):
"""
Test.
"""
# Test SHAP Resemblance Model for linear models.
clf = LogisticRegression()
rm = SHAPImportanceResemblance(clf, test_prc=0.5, n_jobs=1, random_state=42)
# Before fit it should raise an exception
with pytest.raises(NotFittedError) as _:
rm._check_if_fitted()
actual_report, train_score, test_score = rm.fit_compute(
X1, X2, return_scores=True, approximate=True, check_additivity=False
)
# After the fit this should not raise any error
rm._check_if_fitted()
assert train_score == 1
assert test_score == 1
# Check report shape
assert actual_report.shape == (3, 2)
# Check if it is sorted by importance
assert actual_report.iloc[0].name == "col_1"
# Check report values
assert actual_report.loc["col_1"]["mean_abs_shap_value"] > 0
> assert actual_report.loc["col_1"]["mean_shap_value"] > 0
E assert -0.4010580957517599 > 0
tests/sample_similarity/test_resemblance_model.py:184: AssertionError
=================================================================== warnings summary ====================================================================
tests/binning/test_binning.py::test_quantile_with_unique_values
Unable to calculate quantile bins for this feature, because possibly there is too many duplicate values.Approximated quantiles, as a result,the multiple boundaries have the same value. The number of bins has been lowered to [-1. -0.98970775 -0.83365883 -0.70486693 -0.46046316 -0.25796119
-0.03009015]. This can cause issue if you want to calculate the statistical test based on this binning. We suggest to retry with max number of bins of [-1. -0.98970775 -0.83365883 -0.70486693 -0.46046316 -0.25796119
-0.03009015] or apply different type of binning e.g. simple. If you run this functionality in AutoDist for multiple features, then you can decrease the bins only for that feature in a separate AutoDist run.
tests/docs/test_docstring.py::test_class_docstrings[QuantileBucketer]
Unable to calculate quantile bins for this feature, because possibly there is too many duplicate values.Approximated quantiles, as a result,the multiple boundaries have the same value. The number of bins has been lowered to [0. 0.33333333 1. ]. This can cause issue if you want to calculate the statistical test based on this binning. We suggest to retry with max number of bins of [0. 0.33333333 1. ] or apply different type of binning e.g. simple. If you run this functionality in AutoDist for multiple features, then you can decrease the bins only for that feature in a separate AutoDist run.
tests/docs/test_docstring.py: 14 warnings
tests/interpret/test_shap_dependence.py: 4 warnings
tests/metric_volatility/test_metric_volatility.py: 1 warning
tests/sample_similarity/test_resemblance_model.py: 2 warnings
Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
tests/docs/test_docstring.py: 5 warnings
tests/interpret/test_model_interpret.py: 2 warnings
tests/interpret/test_shap_dependence.py: 6 warnings
Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.
tests/docs/test_docstring.py::test_class_docstrings[ShapModelInterpreter]
tests/interpret/test_model_interpret.py::test_shap_interpret
tests/interpret/test_model_interpret.py::test_shap_interpret
tests/interpret/test_model_interpret.py::test_shap_interpret_lin_models
tests/interpret/test_model_interpret.py::test_shap_interpret_lin_models
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class2
No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored
tests/docs/test_docstring.py::test_class_docstrings[ShapModelInterpreter]
tests/interpret/test_model_interpret.py::test_shap_interpret
tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class2
The figure layout has changed to tight
tests/docs/test_docstring.py::test_class_docstrings[TrainTestVolatility]
tests/docs/test_docstring.py::test_class_docstrings[SplitSeedVolatility]
tests/metric_volatility/test_metric_volatility.py::test_fit_train_test_sample_seed
tests/metric_volatility/test_metric_volatility.py::test_fit_compute_complex
This function is deprecated. Please call randint(0, 999999 + 1) instead
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_cols_to_keep
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_cols_to_keep
2 fits failed out of a total of 4.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
2 fits failed with the following error:
Traceback (most recent call last):
File "/Users/danielherman/Documents/projects/temp/probatus/env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py", line 732, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/Users/danielherman/Documents/projects/temp/probatus/env/lib/python3.11/site-packages/sklearn/base.py", line 1144, in wrapper
estimator._validate_params()
File "/Users/danielherman/Documents/projects/temp/probatus/env/lib/python3.11/site-packages/sklearn/base.py", line 637, in _validate_params
validate_parameter_constraints(
File "/Users/danielherman/Documents/projects/temp/probatus/env/lib/python3.11/site-packages/sklearn/utils/_param_validation.py", line 95, in validate_parameter_constraints
raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_split' parameter of DecisionTreeClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_cols_to_keep
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_cols_to_keep
One or more of the test scores are non-finite: [nan 1.]
tests/feature_elimination/test_feature_elimination.py::test_complex_dataset
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_early_stopping_lightGBM
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
tests/metric_volatility/test_metric_volatility.py::test_fit_compute_complex
The following variables in X contains missing values ['f2_missing']. Make sure to impute missing or apply a model that handles them automatically.
tests/feature_elimination/test_feature_elimination.py::test_complex_dataset
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_early_stopping_lightGBM
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
tests/metric_volatility/test_metric_volatility.py::test_fit_compute_complex
The following variables in X contains categorical variables: ['f1_categorical']. Make sure to use a model that handles them automatically or encode them into numerical variables.
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_early_stopping_CatBoost
numpy.ufunc size changed, may indicate binary incompatibility. Expected 216 from C header, got 232 from PyObject
tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_randomized_search_early_stopping_lightGBM
Early stopping will be used only during Shapley value estimation step, and not for hyperparameter optimization.
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
The following variables in X_train contains missing values ['f2_missing']. Make sure to impute missing or apply a model that handles them automatically.
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
The following variables in X_train contains categorical variables: ['f1_categorical']. Make sure to use a model that handles them automatically or encode them into numerical variables.
tests/interpret/test_model_interpret.py::test_shap_interpret_complex_data
The following variables in X_test contains categorical variables: ['f1_categorical']. Make sure to use a model that handles them automatically or encode them into numerical variables.
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_psi
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_default
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_default
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
tests/stat_tests/test_stat_tests.py::test_psi_returns_large
PSI: Some of the buckets have zero counts. In theory this situation would mean PSI=Inf due to division by 0. However, we artificially modified the count of samples in these bins to a small number. This may cause that the PSI value for this feature is over-estimated (larger). Decreasing the number of buckets may also help avoid buckets with zero counts.
tests/stat_tests/test_distribution_statistics.py: 10 warnings
tests/stat_tests/test_stat_tests.py: 1 warning
p-value capped: true value larger than 0.25
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
Input data for shapiro has range zero. The results may not be accurate.
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
PSI is not well-behaved when using more than 20 bins.
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
tests/stat_tests/test_distribution_statistics.py::test_distribution_statistics_autodist_base
invalid value encountered in log
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
Missing values in column 0 have been removed
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
Missing values in column 1 have been removed
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
Missing values in column 2 have been removed
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
Missing values in column 3 have been removed
tests/stat_tests/test_distribution_statistics.py::test_missing_values_in_autodist
Missing values in column 4 have been removed
tests/stat_tests/test_distribution_statistics.py::test_warnings_are_issued_for_missing
tests/stat_tests/test_distribution_statistics.py::test_warnings_are_issued_for_missing
tests/stat_tests/test_distribution_statistics.py::test_warnings_are_issued_for_missing
tests/stat_tests/test_distribution_statistics.py::test_warnings_are_issued_for_missing
Passing None has been deprecated.
See https://docs.pytest.org/en/latest/how-to/capture-warnings.html#additional-use-cases-of-warnings-in-tests for alternatives in common use cases.
tests/stat_tests/test_stat_tests.py::test_ad_returns_small
p-value floored: true value smaller than 0.001
tests/utils/test_utils_array_funcs.py::test_check_1d_array
tests/utils/test_utils_array_funcs.py::test_check_1d_array
Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
tests/utils/test_utils_array_funcs.py::test_preprocess_labels
The labels in y2 contain [False] unique values. The features in probatus support binary classification models, thus, the feature might not work correctly.
tests/utils/test_utils_array_funcs.py::test_preprocess_labels
The labels in y3 contain [0 1 2 3 4] unique values. The features in probatus support binary classification models, thus, the feature might not work correctly.
tests/utils/test_utils_array_funcs.py::test_preprocess_data
The following variables in X1 contains missing values ['2']. Make sure to impute missing or apply a model that handles them automatically.
tests/utils/test_utils_array_funcs.py::test_preprocess_data
The following variables in X1 contains categorical variables: ['1']. Make sure to use a model that handles them automatically or encode them into numerical variables.
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
================================================================ short test summary info ================================================================
FAILED tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection - AssertionError: assert array(['col_2'], dtype=object) == ['col_3']
FAILED tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_same_features_are_kept_after_each_run - AssertionError: assert ['f6', 'f10',...', 'f17', ...] == ['f2', 'f3', ...', 'f12', ...]
FAILED tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class - assert -0.5 >= 0
FAILED tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class_lin_models - assert -0.4010580957517599 > 0
===================================== 4 failed, 127 passed, 6 skipped, 1 xfailed, 114 warnings in 66.45s (0:01:06) ======================================
from probatus.
detrin
commented on June 10, 2024
With versions
shap==0.42.1
numpy==1.23.3
numba==0.57.0
so the trouble will be with new release of shape. I know there was some initiative about resurrecting the project. I will check later today what is the most likely issue in ShapRFECV
from probatus.
detrin
commented on June 10, 2024
Seems like the only import is
from shap import Explainer
from shap.explainers._tree import Tree
from shap.utils import sample
from sklearn.pipeline import Pipelineat https://github.com/ing-bank/probatus/blob/main/probatus/utils/shap_helpers.py#L25
from probatus.
detrin
commented on June 10, 2024
Okay so in test_shap_automatic_num_feature_selection when I am looking at the
X = pd.DataFrame(
{
"col_1": [1, 0, 1, 0, 1, 0, 1, 0],
"col_2": [0, 0, 0, 0, 0, 1, 1, 1],
"col_3": [1, 1, 1, 0, 0, 0, 0, 0],
}
)
y = pd.Series([0, 0, 0, 0, 1, 1, 1, 1])both "col_2" and "col_3" seems to be valid options, so this is more issue of designed test. It is a bit unfair that random seed in this version of shape is not preserved with results.
from probatus.
detrin
commented on June 10, 2024
The same is for test_shap_rfe_same_features_are_kept_after_each_run as the information value in columns is symmetrical. I will check it by editing the test.
from probatus.
ReinierKoops
commented on June 10, 2024
Amazing work, looking forward to review your pr
from probatus.
detrin
commented on June 10, 2024
Okay, so I started to dig into it right just a while ago and I found out that it is caused by packages in all extra. See with installing just the package at python3.11 I am getting right now obvious error
============================================================================================ short test summary info ============================================================================================
ERROR tests/feature_elimination/test_feature_elimination.py::test_get_feature_shap_values_per_fold_early_stopping_CatBoost - ModuleNotFoundError: No module named 'catboost'
======================================================================= 130 passed, 6 skipped, 1 xfailed, 106 warnings, 1 error in 53.97s =======================================================================
$ pip freeze
cloudpickle==2.2.1
contourpy==1.1.0
cycler==0.11.0
fonttools==4.42.1
joblib==1.3.2
kiwisolver==1.4.4
llvmlite==0.40.1
matplotlib==3.7.2
numba==0.57.1
numpy==1.24.4
packaging==23.1
pandas==2.0.3
Pillow==10.0.0
-e git+https://github.com/detrin/probatus.git@fd0cadd624f7aec32fc06a08336125a013d6e3e9#egg=probatus
pyparsing==3.0.9
python-dateutil==2.8.2
pytz==2023.3
scikit-learn==1.3.0
scipy==1.11.2
shap==0.42.1
six==1.16.0
slicer==0.0.7
threadpoolctl==3.2.0
tqdm==4.66.1
tzdata==2023.3
With installing python extra I am getting fail at
============================================================================================ short test summary info ============================================================================================
FAILED tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection - AssertionError: assert array(['col_2'], dtype=object) == ['col_3']
FAILED tests/feature_elimination/test_feature_elimination.py::test_shap_rfe_same_features_are_kept_after_each_run - AssertionError: assert ['f6', 'f10',...', 'f17', ...] == ['f2', 'f3', ...', 'f12', ...]
FAILED tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class - assert -0.5 >= 0
FAILED tests/sample_similarity/test_resemblance_model.py::test_shap_resemblance_class_lin_models - assert -0.4010580957517599 > 0
FAILED tests/utils/test_utils_array_funcs.py::test_check_1d_array - ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
================================================================= 5 failed, 126 passed, 6 skipped, 1 xfailed, 112 warnings in 87.25s (0:01:27) ==================================================================
and the packages installed are
$ pip freeze
anyio==3.7.1
appnope==0.1.3
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-lru==2.0.4
attrs==23.1.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.12.2
black==23.7.0
bleach==6.0.0
bracex==2.3.post1
catboost==1.2
certifi==2023.7.22
cffi==1.15.1
cfgv==3.4.0
charset-normalizer==3.2.0
click==8.1.7
cloudpickle==2.2.1
codespell==2.2.5
colorama==0.4.6
comm==0.1.4
contourpy==1.1.0
coverage==7.3.0
csscompressor==0.9.5
cycler==0.11.0
debugpy==1.6.7.post1
decorator==5.1.1
defusedxml==0.7.1
distlib==0.3.7
executing==1.2.0
fastjsonschema==2.18.0
filelock==3.12.2
fonttools==4.42.1
fqdn==1.5.1
ghp-import==2.1.0
gitdb==4.0.10
GitPython==3.1.32
graphviz==0.20.1
griffe==0.34.0
htmlmin2==0.1.13
identify==2.5.26
idna==3.4
iniconfig==2.0.0
ipykernel==6.25.1
ipython==8.14.0
ipython-genutils==0.2.0
ipywidgets==8.1.0
isoduration==20.11.0
isort==5.12.0
jedi==0.19.0
Jinja2==3.1.2
joblib==1.3.2
jsmin==3.0.1
json5==0.9.14
jsonpointer==2.4
jsonschema==4.19.0
jsonschema-specifications==2023.7.1
jupyter==1.0.0
jupyter-console==6.6.3
jupyter-events==0.7.0
jupyter-lsp==2.2.0
jupyter_client==8.3.0
jupyter_core==5.3.1
jupyter_server==2.7.2
jupyter_server_terminals==0.4.4
jupyterlab==4.0.5
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.8
jupyterlab_server==2.24.0
kiwisolver==1.4.4
lightgbm==4.0.0
llvmlite==0.40.1
Markdown==3.4.4
MarkupSafe==2.1.3
matplotlib==3.7.2
matplotlib-inline==0.1.6
mergedeep==1.3.4
mistune==3.0.1
mkdocs==1.5.2
mkdocs-autorefs==0.5.0
mkdocs-awesome-pages-plugin==2.9.2
mkdocs-enumerate-headings-plugin==0.6.1
mkdocs-git-authors-plugin==0.7.2
mkdocs-git-revision-date-localized-plugin==1.2.0
mkdocs-markdownextradata-plugin==0.2.5
mkdocs-material==9.1.21
mkdocs-material-extensions==1.1.1
mkdocs-minify-plugin==0.7.1
mkdocs-print-site-plugin==2.3.5
mkdocs-table-reader-plugin==2.0.1
mkdocstrings==0.22.0
mkdocstrings-python==1.5.0
mknotebooks==0.8.0
mypy==1.5.1
mypy-extensions==1.0.0
natsort==8.4.0
nbclient==0.8.0
nbconvert==7.7.4
nbformat==5.9.2
nest-asyncio==1.5.7
nodeenv==1.8.0
notebook==7.0.2
notebook_shim==0.2.3
numba==0.57.1
numpy==1.24.4
overrides==7.4.0
packaging==23.1
pandas==2.0.3
pandocfilters==1.5.0
parso==0.8.3
pathspec==0.11.2
pexpect==4.8.0
pickleshare==0.7.5
Pillow==10.0.0
platformdirs==3.10.0
plotly==5.16.1
pluggy==1.2.0
pre-commit==3.3.3
-e git+https://github.com/detrin/probatus.git@fd0cadd624f7aec32fc06a08336125a013d6e3e9#egg=probatus
prometheus-client==0.17.1
prompt-toolkit==3.0.39
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
pycparser==2.21
pyflakes==3.1.0
Pygments==2.16.1
pymdown-extensions==10.1
pyparsing==3.0.9
pytest==7.4.0
pytest-cov==4.1.0
python-dateutil==2.8.2
python-json-logger==2.0.7
pytz==2023.3
PyYAML==6.0.1
pyyaml_env_tag==0.1
pyzmq==25.1.1
qtconsole==5.4.3
QtPy==2.3.1
referencing==0.30.2
regex==2023.8.8
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.9.2
ruff==0.0.285
scikit-learn==1.3.0
scipy==1.11.2
seaborn==0.12.2
Send2Trash==1.8.2
shap==0.42.1
six==1.16.0
slicer==0.0.7
smmap==5.0.0
sniffio==1.3.0
soupsieve==2.4.1
stack-data==0.6.2
tabulate==0.9.0
tenacity==8.2.3
terminado==0.17.1
threadpoolctl==3.2.0
tinycss2==1.2.1
tornado==6.3.3
tqdm==4.66.1
traitlets==5.9.0
typing_extensions==4.7.1
tzdata==2023.3
uri-template==1.3.0
urllib3==2.0.4
virtualenv==20.24.3
watchdog==3.0.0
wcmatch==8.4.1
wcwidth==0.2.6
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.1
widgetsnbextension==4.0.8
xgboost==1.7.6
Seems that none of the extra packages are pulling back any normal dependency.
I had an idea that it might be caused by pytest itself in some way. Comparing pytest with installation without extra
~/Documents/projects/temp/probatus_fork main !1 ?2 ❯ pytest -sv Py probatus_fork
============================================================================================== test session starts ==============================================================================================
platform darwin -- Python 3.10.6, pytest-7.1.3, pluggy-1.0.0 -- /Users/danielherman/.pyenv/versions/3.10.6/bin/python3.10
cachedir: .pytest_cache
rootdir: /Users/danielherman/Documents/projects/temp/probatus_fork
plugins: anyio-3.6.2
and of course it just spits out the error with catboots. Perhaps the pytest is accessing system installed packages. So, I have created following test
import subprocess
def test_pip_feeeze():
try:
result = subprocess.run(['pip', 'freeze'], capture_output=True, text=True, check=True)
print("Pip freeze output:")
print(result.stdout)
except subprocess.CalledProcessError as e:
print("An error occurred:", e)and of course it prints out my system packages, so this error might lead to finding what packages caused fail errors.
Here are the differences
cloudpickle==2.2.0
contourpy==1.0.5
cycler==0.11.0
fonttools==4.37.4
joblib==1.2.0
kiwisolver==1.4.4
llvmlite==0.39.1
matplotlib==3.5.3
numba==0.56.4
numpy==1.21.6
packaging==21.3
pandas==1.3.5
Pillow==9.3.0
grep: git+https://github.com/detrin/probatus.git@fd0cadd624f7aec32fc06a08336125a013d6e3e9#egg=: No such file or directory
pyparsing==3.0.9
python-dateutil==2.8.1
pytz==2019.3
scikit-learn==1.1.2
scipy==1.8.1
shap==0.41.0
six==1.16.0
slicer==0.0.7
threadpoolctl==3.1.0
tqdm==4.64.1
from probatus.
detrin
commented on June 10, 2024
I am not sure how much time I will have today for this issue. Apparently, it is not caused by scikit-lean. I have tried 1.2.0 and I am still getting fail on those errors.
from probatus.
detrin
commented on June 10, 2024
It won't be numba, version 0.57.0 gives still error in pytest. When switching back to shap==0.41.0 it gives me a lot of jit errors even tho I have numba==0.57.1
from probatus.
detrin
commented on June 10, 2024
Okay, so all tests are passing with
numpy==1.23.5
shap==0.41.0
numba==0.57.1
the rest can be updated. You know that shap had fixed dependency of numpy<=1.23.5, so I am pretty confident it was caused solely by shap release.
from probatus.
detrin
commented on June 10, 2024
Looking at changes in Explainer class that is used in this package https://github.com/shap/shap/blame/bbee3787139954ea355854001a977132ec8123b0/shap/explainers/_explainer.py#L17 I don't see anything relevant.
from probatus.
detrin
commented on June 10, 2024
Inspecting shap codebase I don't see any relevant change in logic of _kernel.py, _linear.py or _tree.py between tag v0.42.1 and v0.41.0. I will look whether there are any changes in output of shap_calc()
from probatus.
detrin
commented on June 10, 2024
I can confirm now that the logic on the side of shap package changes and that is the reason why the tests are failing!
By adding following after line https://github.com/ing-bank/probatus/blob/main/probatus/feature_elimination/feature_elimination.py#L398
shap_values = shap_calc(clf, X_val, verbose=self.verbose, **shap_kwargs)
print(shap_values.shape)
print(shap_values)With shap==0.41.0
tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection (4, 3)
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 1. 0.]]
(4, 3)
[[0. 0. 0.]
[0. 0. 1.]
[0. 0. 1.]
[0. 0. 1.]]
(4, 2)
[[0. 0.]
[0. 0.]
[0. 0.]
[1. 0.]]
(4, 2)
[[0. 0.]
[0. 1.]
[0. 1.]
[0. 1.]]
(4, 1)
[[0. ]
[0. ]
[0.66666667]
[0.66666667]]
(4, 1)
[[0.]
[1.]
[1.]
[1.]]
With shap==0.42.1
tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection (4, 3)
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 1. 0.]]
(4, 3)
[[ 0. 0. -1.]
[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
(4, 2)
[[0. 0.]
[0. 0.]
[0. 0.]
[1. 0.]]
(4, 2)
[[ 0. -1.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]]
(4, 1)
[[0.]
[0.]
[0.]
[1.]]
(4, 1)
[[-0.66666667]
[-0.66666667]
[ 0. ]
[ 0. ]]
from probatus.
detrin
commented on June 10, 2024
To further prove the point I used
print("================")
print("shap_values")
print(shap_values.shape)
print(shap_values)
print("X_val")
print(X_val.shape)
print(X_val)
print("clf LogReg weights")
print(clf.coef_, clf.intercept_)and changed in test_shap_automatic_num_feature_selection()
clf = DecisionTreeClassifier(max_depth=1, random_state=1)into
clf = LogisticRegression(C=1, solver="liblinear")We have with shap==0.41.0
tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection ================
shap_values
(4, 3)
[[-0. 0. -0. ]
[ 0.01426327 0. -0. ]
[-0. 0. 0.37412245]
[ 0.01426327 0.71643134 0.37412245]]
X_val
(4, 3)
col_1 col_2 col_3
0 1 0 1
1 0 0 1
4 1 0 0
5 0 1 0
clf LogReg weights
[[-0.01426327 0.71643134 -0.37412245]] [-0.12618387]
================
shap_values
(4, 3)
[[ 0. 0. -0. ]
[-0.08174514 0. 0.72205473]
[ 0. 0.37990537 0.72205473]
[-0.08174514 0.37990537 0.72205473]]
X_val
(4, 3)
col_1 col_2 col_3
2 1 0 1
3 0 0 0
6 1 1 0
7 0 1 0
clf LogReg weights
[[ 0.08174514 0.37990537 -0.72205473]] [0.11004584]
================
shap_values
(4, 2)
[[ 0. -0. ]
[ 0. -0. ]
[ 0. 0.37629223]
[ 0.71507298 0.37629223]]
X_val
(4, 2)
col_2 col_3
0 0 1
1 0 1
4 0 0
5 1 0
clf LogReg weights
[[ 0.71507298 -0.37629223]] [-0.12901078]
================
shap_values
(4, 2)
[[ 0. -0. ]
[ 0. 0.71507298]
[ 0.37629223 0.71507298]
[ 0.37629223 0.71507298]]
X_val
(4, 2)
col_2 col_3
2 0 1
3 0 0
6 1 0
7 1 0
clf LogReg weights
[[ 0.37629223 -0.71507298]] [0.12901078]
================
shap_values
(4, 1)
[[-0. ]
[-0. ]
[ 0.41094646]
[ 0.41094646]]
X_val
(4, 1)
col_3
0 1
1 1
4 0
5 0
clf LogReg weights
[[-0.41094646]] [0.05089244]
================
shap_values
(4, 1)
[[-0. ]
[ 0.73101554]
[ 0.73101554]
[ 0.73101554]]
X_val
(4, 1)
col_3
2 1
3 0
6 0
7 0
clf LogReg weights
[[-0.73101554]] [0.17948298]
With shap==0.42.1
tests/feature_elimination/test_feature_elimination.py::test_shap_automatic_num_feature_selection ================
shap_values
(4, 3)
[[-0. 0. -0.37412245]
[ 0.01426327 0. -0.37412245]
[-0. 0. -0. ]
[ 0.01426327 0.71643134 -0. ]]
X_val
(4, 3)
col_1 col_2 col_3
0 1 0 1
1 0 0 1
4 1 0 0
5 0 1 0
clf LogReg weights
[[-0.01426327 0.71643134 -0.37412245]] [-0.12618387]
================
shap_values
(4, 3)
[[ 0. -0.37990537 -0.72205473]
[-0.08174514 -0.37990537 -0. ]
[ 0. 0. -0. ]
[-0.08174514 0. -0. ]]
X_val
(4, 3)
col_1 col_2 col_3
2 1 0 1
3 0 0 0
6 1 1 0
7 0 1 0
clf LogReg weights
[[ 0.08174514 0.37990537 -0.72205473]] [0.11004584]
================
shap_values
(4, 2)
[[ 0. -0.37629223]
[ 0. -0.37629223]
[ 0. -0. ]
[ 0.71507298 -0. ]]
X_val
(4, 2)
col_2 col_3
0 0 1
1 0 1
4 0 0
5 1 0
clf LogReg weights
[[ 0.71507298 -0.37629223]] [-0.12901078]
================
shap_values
(4, 2)
[[-0.37629223 -0.71507298]
[-0.37629223 -0. ]
[ 0. -0. ]
[ 0. -0. ]]
X_val
(4, 2)
col_2 col_3
2 0 1
3 0 0
6 1 0
7 1 0
clf LogReg weights
[[ 0.37629223 -0.71507298]] [0.12901078]
================
shap_values
(4, 1)
[[0. ]
[0. ]
[0. ]
[0.73101554]]
X_val
(4, 1)
col_2
0 0
1 0
4 0
5 1
clf LogReg weights
[[0.73101554]] [-0.17948298]
================
shap_values
(4, 1)
[[-0.41094646]
[-0.41094646]
[ 0. ]
[ 0. ]]
X_val
(4, 1)
col_2
2 0
3 0
6 1
7 1
clf LogReg weights
[[0.41094646]] [-0.05089244]
I am now confident that the change is in tree kernel in shap pkg
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ReinierKoops
commented on June 10, 2024
@detrin Ive been reading along with your findings and confirm your thorough approach. Thank you. I’m a bit puzzled now by the versioning of shap making me question it’s backward compatibility… other than that now I’m also thinking along what the next step would be in this case.
from probatus.
detrin
commented on June 10, 2024
I think the best for the sale of this package would be find the cause on the side of shap package.
- If we find that it is just some trivial change and the logic behind shap values didn't changed. We can just adjust the
shap_calc()so that it spits out the same values as before. - In the case that the change is bigger in the sense that the logic behind shap values changes. The logic behind shap helper function will need to change accordingly so that the selection gives roughly same features back.
from probatus.
detrin
commented on June 10, 2024
After additional 40 min of debugging and digging in shap and probatus I found the error. The issue is not preserved random_state in shap.utils.sample. I will submit issue to shap repository.
My suggestion for fix would be to correct failing tests as now we can be sure that the logic of shap didn't change and this change in calculated masker should not affect the quality of a result on probatus side. @ReinierKoops
from probatus.
ReinierKoops
commented on June 10, 2024
Amazing find, thank you @detrin
from probatus.
detrin
commented on June 10, 2024
Awesome, you can expect from me PR in following days, probably today.
from probatus.
Related Issues (20)
- Unit tests should only contain assertions that make sense in the context of the functionality. HOT 2
- Mkdocs fails HOT 2
- Antivirus blacklisted and blocked Probatus website HOT 7
- Option early_stopping_rounds missing for LightGBM in ShapRFECV HOT 11
- Patch release v2.1.1 HOT 2
- Spark Support of ShapRFECV HOT 3
- python3.12 support HOT 2
- Support for shap==0.43.0 HOT 6
- AttributeError: module 'numpy' has no attribute 'bool'. HOT 2
- Random state not set consistently. HOT 1
- Add explicit support for regressors next to classifiers HOT 1
- Introduce dependabot for help with dependency updates
- Investigate if parts of the codebase can leverage other libraries code HOT 2
- Update all notebooks according to latest code. HOT 1
- Probatus v3.0.0+ missing features & issues.
- Add a notebook which shows the use of Probatus with pySpark
- Add seed to explainer + remove np.random.state() HOT 1
- Create a new tag HOT 3
- eval_metric in EarlyStoppingShapRFECV not used for LGBMClassifier
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