I am doing a shap tutorial, and attempting to get the shap values for each person in a dataset
from sklearn.model_selection import train_test_split
import xgboost
import shap
import numpy as np
import pandas as pd
import matplotlib.pylab as plX,y = shap.datasets.adult()
X_display,y_display = shap.datasets.adult(display=True)# create a train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
d_train = xgboost.DMatrix(X_train, label=y_train)
d_test = xgboost.DMatrix(X_test, label=y_test)
params = {"eta": 0.01,"objective": "binary:logistic","subsample": 0.5,"base_score": np.mean(y_train),"eval_metric": "logloss"
}
#model = xgboost.train(params, d_train, 5000, evals = [(d_test, "test")], verbose_eval=100, early_stopping_rounds=20)xg_clf = xgboost.XGBClassifier()
xg_clf.fit(X_train, y_train)
explainer = shap.TreeExplainer(xg_clf, X_train)
#shap_values = explainer(X)
shap_values = explainer.shap_values(X)
going through the Python3 interpreter, shap_values
is a massive array of 32,561 persons, each with a shap value for 12 features.
For example, the first individual has the following SHAP values:
>>> shap_values[0]
array([ 0.76437867, -0.11881508, 0.57451954, -0.41974955, -0.20982443,-0.38079952, -0.00986504, 0.32272505, -3.04392116, 0.00411322,-0.26587735, 0.02700199])
However, which value applies to which feature is a complete mystery to me.
the documentation says:
For models with a single output this returns a matrix of SHAP values(# samples x # features). Each row sums to the difference between the model output for thatsample and the expected value of the model output (which is stored in the expected_valueattribute of the explainer when it is constant). For models with vector outputs this returnsa list of such matrices, one for each output
When I go to explainer
which produced shap_values
I see that I can get feature names:
explainer.data_feature_names
['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country']
but I cannot see how to get feature names within shap_values at the Python interpreter, if they're even there:
>>> shap_values.
shap_values.all( shap_values.compress( shap_values.dump( shap_values.max( shap_values.ravel( shap_values.sort( shap_values.tostring(
shap_values.any( shap_values.conj( shap_values.dumps( shap_values.mean( shap_values.real shap_values.squeeze( shap_values.trace(
shap_values.argmax( shap_values.conjugate( shap_values.fill( shap_values.min( shap_values.repeat( shap_values.std( shap_values.transpose(
shap_values.argmin( shap_values.copy( shap_values.flags shap_values.nbytes shap_values.reshape( shap_values.strides shap_values.var(
shap_values.argpartition( shap_values.ctypes shap_values.flat shap_values.ndim shap_values.resize( shap_values.sum( shap_values.view(
shap_values.argsort( shap_values.cumprod( shap_values.flatten( shap_values.newbyteorder( shap_values.round( shap_values.swapaxes(
shap_values.astype( shap_values.cumsum( shap_values.getfield( shap_values.nonzero( shap_values.searchsorted( shap_values.T
shap_values.base shap_values.data shap_values.imag shap_values.partition( shap_values.setfield( shap_values.take(
shap_values.byteswap( shap_values.diagonal( shap_values.item( shap_values.prod( shap_values.setflags( shap_values.tobytes(
shap_values.choose( shap_values.dot( shap_values.itemset( shap_values.ptp( shap_values.shape shap_values.tofile(
shap_values.clip( shap_values.dtype shap_values.itemsize shap_values.put( shap_values.size shap_values.tolist(
My primary question: How can I figure out which feature in
['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country']
applies to which number in each row of shap_values
?
>>> shap_values[0]
array([ 0.76437867, -0.11881508, 0.57451954, -0.41974955, -0.20982443,-0.38079952, -0.00986504, 0.32272505, -3.04392116, 0.00411322,-0.26587735, 0.02700199])
I would assume that the features are in the same order, but I have no evidence for that.
My secondary question: how can I find the feature names in shap_values
?