wehub-resource-sync
166 lines
4.2 KiB
Python
166 lines
4.2 KiB
Python
import numpy as np
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import sklearn
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from .. import (
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DeepExplainer,
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GradientExplainer,
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KernelExplainer,
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LinearExplainer,
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SamplingExplainer,
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TreeExplainer,
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kmeans,
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)
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from ..explainers import other
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from .models import KerasWrap
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def linear_shap_corr(model, data):
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"""Linear SHAP (corr 1000)"""
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return LinearExplainer(model, data, feature_perturbation="correlation_dependent", nsamples=1000).shap_values
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def linear_shap_ind(model, data):
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"""Linear SHAP (ind)"""
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return LinearExplainer(model, data, feature_perturbation="interventional").shap_values
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def coef(model, data):
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"""Coefficients"""
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return other.CoefficentExplainer(model).attributions
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def random(model, data):
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"""Random
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color = #777777
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linestyle = solid
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"""
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return other.RandomExplainer().attributions
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def kernel_shap_1000_meanref(model, data):
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"""Kernel SHAP 1000 mean ref.
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color = red_blue_circle(0.5)
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linestyle = solid
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"""
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return lambda X: KernelExplainer(model.predict, kmeans(data, 1)).shap_values(X, nsamples=1000, l1_reg=0)
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def sampling_shap_1000(model, data):
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"""IME 1000
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color = red_blue_circle(0.5)
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linestyle = dashed
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"""
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return lambda X: SamplingExplainer(model.predict, data).shap_values(X, nsamples=1000)
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def tree_shap_tree_path_dependent(model, data):
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"""TreeExplainer
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color = red_blue_circle(0)
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linestyle = solid
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"""
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return TreeExplainer(model, feature_perturbation="tree_path_dependent").shap_values
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def tree_shap_independent_200(model, data):
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"""TreeExplainer (independent)
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color = red_blue_circle(0)
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linestyle = dashed
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"""
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data_subsample = sklearn.utils.resample(data, replace=False, n_samples=min(200, data.shape[0]), random_state=0)
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return TreeExplainer(model, data_subsample, feature_perturbation="interventional").shap_values
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def mean_abs_tree_shap(model, data):
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"""mean(|TreeExplainer|)
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color = red_blue_circle(0.25)
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linestyle = solid
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"""
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def f(X):
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v = TreeExplainer(model).shap_values(X)
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if isinstance(v, list):
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return [np.tile(np.abs(sv).mean(0), (X.shape[0], 1)) for sv in v]
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else:
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return np.tile(np.abs(v).mean(0), (X.shape[0], 1))
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return f
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def saabas(model, data):
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"""Saabas
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color = red_blue_circle(0)
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linestyle = dotted
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"""
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return lambda X: TreeExplainer(model).shap_values(X, approximate=True)
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def tree_gain(model, data):
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"""Gain/Gini Importance
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color = red_blue_circle(0.25)
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linestyle = dotted
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"""
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return other.TreeGainExplainer(model).attributions
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def lime_tabular_regression_1000(model, data):
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"""LIME Tabular 1000
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color = red_blue_circle(0.75)
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"""
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return lambda X: other.LimeTabularExplainer(model.predict, data, mode="regression").attributions(X, nsamples=1000)
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def lime_tabular_classification_1000(model, data):
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"""LIME Tabular 1000
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color = red_blue_circle(0.75)
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"""
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return lambda X: other.LimeTabularExplainer(model.predict_proba, data, mode="classification").attributions(
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X, nsamples=1000
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)[1]
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def maple(model, data):
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"""MAPLE
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color = red_blue_circle(0.6)
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"""
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return lambda X: other.MapleExplainer(model.predict, data).attributions(X, multiply_by_input=False)
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def tree_maple(model, data):
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"""Tree MAPLE
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color = red_blue_circle(0.6)
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linestyle = dashed
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"""
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return lambda X: other.TreeMapleExplainer(model, data).attributions(X, multiply_by_input=False)
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def deep_shap(model, data):
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"""Deep SHAP (DeepLIFT)"""
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if isinstance(model, KerasWrap):
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model = model.model
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explainer = DeepExplainer(model, kmeans(data, 1).data)
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def f(X):
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phi = explainer.shap_values(X)
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if isinstance(phi, list) and len(phi) == 1:
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return phi[0]
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else:
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return phi
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return f
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def expected_gradients(model, data):
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"""Expected Gradients"""
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if isinstance(model, KerasWrap):
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model = model.model
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explainer = GradientExplainer(model, data)
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def f(X):
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phi = explainer.shap_values(X)
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if isinstance(phi, list) and len(phi) == 1:
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return phi[0]
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else:
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return phi
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return f
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