Please, make sure:
- Python version is smaller than 3.11 (some dependencies like numba currently are not implemented for >= 3.11);
- Your CPU is little-endian (only for this format bitset packing is currently implemented and tested. It is required for Data-based SHAP explanation).
Usage examples are located in notebooks/, but a brief introduction is provided below.
Currently two base models are available:
Note that by default these models assume that target variable is scaled to small range (like [-1, 1]).
hrbm.wrappers.scaler.ScalerWrapper can be used to wrap a model and automatically transform target values, and rescale them back after prediction.
Example:
from hrbm.wrappers.scaler import ScalerWrapper
from hrbm.boosting.corner import CornerBoosting
model = ScalerWrapper(
CornerBoosting(
learning_rate=1.0,
need_rsm=True,
rsm_size=5,
val_size=0.5,
hold_validation_set=False,
enable_validation=False,
reg_type='value',
l1_reg=0.0,
l2_reg='auto',
l1_reg_min=0.0,
l2_reg_min=0.01,
n_estimators=10000,
reg_beta=0.01,
random_state=12345,
)
)
model.fit(X_train, y_train)
Two explainers are available:
hrbm.shap.model_based.ModelBasedEnsembleSHAP– Linear-time model-based data-agnostic;hrbm.shap.data_based.DataBasedEnsembleSHAP– Fast, but, strictly speaking, exponential (number of features) implementation that is equivalent to model-agnostic SHAP and much more effective.
from hrbm.shap.model_based import ModelBasedEnsembleSHAP
from hrbm.shap.data_based import DataBasedEnsembleSHAP
model_explainer = ModelBasedEnsembleSHAP(model.model)
model_values, model_bias = model_explainer(explain_points)
data_explainer = DataBasedEnsembleSHAP(model.model, X_train)
data_values, data_bias = data_explainer(explain_points)
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