How to get feature names of shap_values from TreeExplainer?
Question:
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 pl
X,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 that
sample and the expected value of the model output (which is stored in the expected_value
attribute of the explainer when it is constant). For models with vector outputs this returns
a 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?
Answers:
The features are indeed in the same order, as you assume; see how to extract the most important feature names? and how to get feature names from explainer issues in Github.
To find the feature name, you simply need to access the element with the same index of the array with the names
For example:
shap_values = np.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])
features_names = ['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation',
'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss',
'Hours per week', 'Country']
features_names[shap_values.argmin()] # the index 8 -> Capital Gain
features_names[shap_values.argmax()] # the index 0 -> Age
If you find this answer useful, upvote @lucas’s answer and GitHub user ba1mn’s post. I’m just adding it here in case the link breaks.
The following function will return the feature names along with their corresponding importance in a DataFrame.
def global_shap_importance(model, X):
""" Return a dataframe containing the features sorted by Shap importance
Parameters
----------
model : The tree-based model
X : pd.Dataframe
training set/test set/the whole dataset ... (without the label)
Returns
-------
pd.Dataframe
A dataframe containing the features sorted by Shap importance
"""
explainer = shap.Explainer(model)
shap_values = explainer(X)
cohorts = {"": shap_values}
cohort_labels = list(cohorts.keys())
cohort_exps = list(cohorts.values())
for i in range(len(cohort_exps)):
if len(cohort_exps[i].shape) == 2:
cohort_exps[i] = cohort_exps[i].abs.mean(0)
features = cohort_exps[0].data
feature_names = cohort_exps[0].feature_names
values = np.array([cohort_exps[i].values for i in range(len(cohort_exps))])
feature_importance = pd.DataFrame(
list(zip(feature_names, sum(values))), columns=['features', 'importance'])
feature_importance.sort_values(
by=['importance'], ascending=False, inplace=True)
return feature_importance
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 pl
X,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 that
sample and the expected value of the model output (which is stored in the expected_value
attribute of the explainer when it is constant). For models with vector outputs this returns
a 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?
The features are indeed in the same order, as you assume; see how to extract the most important feature names? and how to get feature names from explainer issues in Github.
To find the feature name, you simply need to access the element with the same index of the array with the names
For example:
shap_values = np.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])
features_names = ['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation',
'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss',
'Hours per week', 'Country']
features_names[shap_values.argmin()] # the index 8 -> Capital Gain
features_names[shap_values.argmax()] # the index 0 -> Age
If you find this answer useful, upvote @lucas’s answer and GitHub user ba1mn’s post. I’m just adding it here in case the link breaks.
The following function will return the feature names along with their corresponding importance in a DataFrame.
def global_shap_importance(model, X):
""" Return a dataframe containing the features sorted by Shap importance
Parameters
----------
model : The tree-based model
X : pd.Dataframe
training set/test set/the whole dataset ... (without the label)
Returns
-------
pd.Dataframe
A dataframe containing the features sorted by Shap importance
"""
explainer = shap.Explainer(model)
shap_values = explainer(X)
cohorts = {"": shap_values}
cohort_labels = list(cohorts.keys())
cohort_exps = list(cohorts.values())
for i in range(len(cohort_exps)):
if len(cohort_exps[i].shape) == 2:
cohort_exps[i] = cohort_exps[i].abs.mean(0)
features = cohort_exps[0].data
feature_names = cohort_exps[0].feature_names
values = np.array([cohort_exps[i].values for i in range(len(cohort_exps))])
feature_importance = pd.DataFrame(
list(zip(feature_names, sum(values))), columns=['features', 'importance'])
feature_importance.sort_values(
by=['importance'], ascending=False, inplace=True)
return feature_importance