wehub-resource-sync
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import numpy as np
|
|
|
|
from .._explainer import Explainer
|
|
|
|
|
|
class TreeGain(Explainer):
|
|
"""Simply returns the global gain/gini feature importances for tree models.
|
|
|
|
This is only for benchmark comparisons and is not meant to approximate SHAP values.
|
|
"""
|
|
|
|
def __init__(self, model):
|
|
if str(type(model)).endswith("sklearn.tree.tree.DecisionTreeRegressor'>"):
|
|
pass
|
|
elif str(type(model)).endswith("sklearn.tree.tree.DecisionTreeClassifier'>"):
|
|
pass
|
|
elif str(type(model)).endswith("sklearn.ensemble.forest.RandomForestRegressor'>"):
|
|
pass
|
|
elif str(type(model)).endswith("sklearn.ensemble.forest.RandomForestClassifier'>"):
|
|
pass
|
|
elif str(type(model)).endswith("xgboost.sklearn.XGBRegressor'>"):
|
|
pass
|
|
elif str(type(model)).endswith("xgboost.sklearn.XGBClassifier'>"):
|
|
pass
|
|
else:
|
|
raise NotImplementedError("The passed model is not yet supported by TreeGainExplainer: " + str(type(model)))
|
|
assert hasattr(model, "feature_importances_"), (
|
|
"The passed model does not have a feature_importances_ attribute!"
|
|
)
|
|
self.model = model
|
|
|
|
def attributions(self, X):
|
|
return np.tile(self.model.feature_importances_, (X.shape[0], 1))
|