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Add validations for explainable model arguments in MimicExplainer #354

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Add validations for explainable model arguments in MimicExplainer #354

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40a7d31
Add validations for explainable model arguments in MimicExplainer
gaugup Dec 15, 2020
40e04df
Fix broken gates
gaugup Dec 15, 2020
cd75e88
Add more surrogate model params validation
gaugup Dec 15, 2020
5febdc4
Merge branch 'master' into gaugup/ValidateExplainableModelParams
gaugup Dec 15, 2020
a6c9c68
Merge branch 'master' into gaugup/ValidateExplainableModelParams
gaugup Jan 5, 2021
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8 changes: 8 additions & 0 deletions python/interpret_community/common/constants.py
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Expand Up @@ -159,6 +159,14 @@ class LightGBMParams(object):
"""Provide constants for LightGBM."""

CATEGORICAL_FEATURE = 'categorical_feature'
N_JOBS = 'n_jobs'
ALL = [CATEGORICAL_FEATURE, N_JOBS]


class LinearExplainableModelParams(object):
"""Provide constants for LinearExplainableModel."""
SPARSE_DATA = 'sparse_data'
ALL = [SPARSE_DATA]


class ShapValuesOutput(str, Enum):
Expand Down
56 changes: 48 additions & 8 deletions python/interpret_community/mimic/mimic_explainer.py
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Expand Up @@ -21,7 +21,7 @@
from ..common.blackbox_explainer import BlackBoxExplainer

from .model_distill import _model_distill
from .models import LGBMExplainableModel
from .models import LGBMExplainableModel, LinearExplainableModel
from ..explanation.explanation import _create_local_explanation, _create_global_explanation, \
_aggregate_global_from_local_explanation, _aggregate_streamed_local_explanations, \
_create_raw_feats_global_explanation, _create_raw_feats_local_explanation, \
Expand All @@ -30,7 +30,7 @@
from ..dataset.dataset_wrapper import DatasetWrapper
from ..common.constants import ExplainParams, ExplainType, ModelTask, \
ShapValuesOutput, MimicSerializationConstants, ExplainableModelType, \
LightGBMParams, Defaults, Extension, ResetIndex
LightGBMParams, Defaults, Extension, ResetIndex, LinearExplainableModelParams
import logging
import json

Expand Down Expand Up @@ -236,6 +236,8 @@ def __init__(self, model, initialization_examples, explainable_model, explainabl
"""
if transformations is not None and explain_subset is not None:
raise ValueError("explain_subset not supported with transformations")
self._validate_explainable_model_args(explainable_model=explainable_model,
explainable_model_args=explainable_model_args)
self.reset_index = reset_index
self._datamapper = None
if transformations is not None:
Expand All @@ -250,8 +252,7 @@ def __init__(self, model, initialization_examples, explainable_model, explainabl
wrapped_model, eval_ml_domain = _wrap_model(model, initialization_examples, model_task, is_function)
super(MimicExplainer, self).__init__(wrapped_model, is_function=is_function,
model_task=eval_ml_domain, **kwargs)
if explainable_model_args is None:
explainable_model_args = {}

if categorical_features is None:
categorical_features = []
self._logger.debug('Initializing MimicExplainer')
Expand Down Expand Up @@ -288,7 +289,6 @@ def __init__(self, model, initialization_examples, explainable_model, explainabl
# Index the categorical string columns for training data
self._column_indexer = initialization_examples.string_index(columns=categorical_features)
self._one_hot_encoder = None
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@imatiach-msft imatiach-msft Dec 31, 2020

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why was this removed?

             explainable_model_args[LightGBMParams.CATEGORICAL_FEATURE] = categorical_features

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@imatiach-msft imatiach-msft Dec 31, 2020

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oh I guess you moved it to line 347, I guess that's ok, my only slight concern is now we are doing the same checks in multiple places:

is_tree_model = explainable_model.explainable_model_type == ExplainableModelType.TREE_EXPLAINABLE_MODEL_TYPE
        if is_tree_model and self._supports_categoricals(explainable_model):

but it's not expensive so I think it's ok

explainable_model_args[LightGBMParams.CATEGORICAL_FEATURE] = categorical_features
else:
# One-hot-encode categoricals for models that don't support categoricals natively
self._column_indexer = initialization_examples.string_index(columns=categorical_features)
Expand All @@ -304,15 +304,55 @@ def __init__(self, model, initialization_examples, explainable_model, explainabl
if isinstance(training_data, DenseData):
training_data = training_data.data

explainable_model_args[ExplainParams.CLASSIFICATION] = self.predict_proba_flag
if self._supports_shap_values_output(explainable_model):
explainable_model_args[ExplainParams.SHAP_VALUES_OUTPUT] = shap_values_output
explainable_model_args = self._supplement_explainable_model_args(
explainable_model=explainable_model,
explainable_model_args=explainable_model_args,
categorical_features=categorical_features,
shap_values_output=shap_values_output)
self.surrogate_model = _model_distill(self.function, explainable_model, training_data,
original_training_data, explainable_model_args)
self._method = self.surrogate_model._method
self._original_eval_examples = None
self._allow_all_transformations = allow_all_transformations

def _validate_explainable_model_args(self, explainable_model, explainable_model_args):
if explainable_model_args is None:
return

if explainable_model == LGBMExplainableModel:
for linear_param in LinearExplainableModelParams.ALL:
if linear_param in explainable_model_args:
raise Exception(linear_param +
" found in params for LightGBM explainable model")

if explainable_model == LinearExplainableModel:
for lightgbm_param in LightGBMParams.ALL:
if lightgbm_param in explainable_model_args:
raise Exception(lightgbm_param +
" found in params for Linear explainable model")

all_supported_explainable_model_args = [LightGBMParams.ALL, LinearExplainableModelParams.ALL]
for explainable_model_arg in explainable_model_args:
if explainable_model_arg not in all_supported_explainable_model_args:
raise Exception(
"Found unsupported explainable model argument " + explainable_model_arg)

def _supplement_explainable_model_args(self, explainable_model, explainable_model_args,
categorical_features, shap_values_output):
if explainable_model_args is None:
explainable_model_args = {}

if explainable_model.explainable_model_type == ExplainableModelType.TREE_EXPLAINABLE_MODEL_TYPE and \
self._supports_categoricals(explainable_model):
explainable_model_args[LightGBMParams.CATEGORICAL_FEATURE] = categorical_features

explainable_model_args[ExplainParams.CLASSIFICATION] = self.predict_proba_flag

if self._supports_shap_values_output(explainable_model):
explainable_model_args[ExplainParams.SHAP_VALUES_OUTPUT] = shap_values_output

return explainable_model_args

def _supports_categoricals(self, explainable_model):
return issubclass(explainable_model, LGBMExplainableModel)

Expand Down
26 changes: 25 additions & 1 deletion test/test_mimic_explainer.py
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Expand Up @@ -18,7 +18,8 @@
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sys import platform
from interpret_community.common.constants import ShapValuesOutput, ModelTask
from interpret_community.common.constants import ShapValuesOutput, ModelTask, \
LinearExplainableModelParams, LightGBMParams
from interpret_community.mimic.models.lightgbm_model import LGBMExplainableModel
from interpret_community.mimic.models.linear_model import LinearExplainableModel
from common_utils import create_sklearn_svm_classifier, create_sklearn_linear_regressor, \
Expand Down Expand Up @@ -521,6 +522,29 @@ def test_dense_wide_data(self, mimic_explainer):
global_explanation = explainer.explain_global(df_X)
assert global_explanation.method == LIGHTGBM_METHOD

@pytest.mark.parametrize("error_config",
[(LGBMExplainableModel, {LinearExplainableModelParams.SPARSE_DATA: True}),
(LinearExplainableModel, {LightGBMParams.N_JOBS: -1}),
(LinearExplainableModel, {LightGBMParams.CATEGORICAL_FEATURE: []}),
(LGBMExplainableModel, {"unsupported": True}),
(LinearExplainableModel, {"unsupported": True})])
def test_validate_explainable_model_args(self, error_config, mimic_explainer):
num_features = 100
num_rows = 1000
test_size = 0.2
X, y = make_regression(n_samples=num_rows, n_features=num_features)
x_train, x_test, y_train, _ = train_test_split(X, y, test_size=test_size, random_state=42)

model = LinearRegression(normalize=True)
model.fit(x_train, y_train)

explainable_model = error_config[0]
explainable_model_args = error_config[1]
with pytest.raises(Exception):
mimic_explainer(model, x_train, explainable_model,
explainable_model_args=explainable_model_args,
augment_data=False)

@property
def iris_overall_expected_features(self):
return [['petal length', 'petal width', 'sepal width', 'sepal length'],
Expand Down