For users of KFServing v0.3.0 please follow the README and notebook for v0.3.0 branch.
This example uses a US income dataset
You can also try out the Jupyter notebook.
We can create a InferenceService with a trained sklearn predictor for this dataset and an associated model explainer. The black box explainer algorithm we will use is the Tabular version of Anchors from the Alibi open source library. More details on this algorithm and configuration settings that can be set can be found in the Seldon Alibi documentation.
The InferenceService is shown below:
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "income"
spec:
default:
predictor:
minReplicas: 1
sklearn:
storageUri: "gs://seldon-models/sklearn/income/model"
resources:
requests:
cpu: 0.1
limits:
cpu: 1
explainer:
minReplicas: 1
alibi:
type: AnchorTabular
storageUri: "gs://seldon-models/sklearn/income/explainer-py36-0.5.2"
resources:
requests:
cpu: 0.1
limits:
cpu: 1For KFS 0.4 the explainer storageUri is gs://seldon-models/sklearn/income/alibi/0.4.0
Create this InferenceService:
kubectl create -f income.yaml
Set up some environment variables for the model name and cluster entrypoint.
MODEL_NAME=income
INGRESS_GATEWAY=istio-ingressgateway
CLUSTER_IP=$(kubectl -n istio-system get service $INGRESS_GATEWAY -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
Test the predictor:
curl -H "Host: ${MODEL_NAME}.default.example.com" http://$CLUSTER_IP/v1/models/$MODEL_NAME:predict -d '{"instances":[[39, 7, 1, 1, 1, 1, 4, 1, 2174, 0, 40, 9]]}'
You should receive the response showing the prediction is for low salary:
{"predictions": [0]}
Now lets get an explanation for this:
curl -v -H "Host: ${MODEL_NAME}.default.example.com" http://$CLUSTER_IP/v1/models/$MODEL_NAME:explain -d '{"instances":[[39, 7, 1, 1, 1, 1, 4, 1, 2174, 0, 40, 9]]}'
The returned explanation will be like:
{
"names": [
"Marital Status = Never-Married",
"Workclass = State-gov"
],
"precision": 0.9724770642201835,
"coverage": 0.0147,
"raw": {
"feature": [
3,
1
],
"mean": [
0.9129746835443038,
0.9724770642201835
],
"precision": [
0.9129746835443038,
0.9724770642201835
],
"coverage": [
0.3327,
0.0147
],
"examples": [
{
"covered": [
[
30,
"Self-emp-not-inc",
"Bachelors",
"Never-Married",
"Sales",
"Unmarried",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
69,
"Private",
"Dropout",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
9386,
"Capital Loss <= 0.00",
60,
"United-States"
],
[
44,
"Local-gov",
"Bachelors",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
52,
"United-States"
],
[
59,
"Private",
"High School grad",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
50,
"United-States"
],
[
55,
"Private",
"Bachelors",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
15024,
"Capital Loss <= 0.00",
55,
"United-States"
],
[
32,
"?",
"Bachelors",
"Never-Married",
"?",
"Unmarried",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
32,
"United-States"
],
[
47,
"Private",
"Dropout",
"Never-Married",
"Blue-Collar",
"Unmarried",
"Black",
"Female",
6849,
"Capital Loss <= 0.00",
40,
"United-States"
],
[
35,
"Private",
"Associates",
"Never-Married",
"Service",
"Not-in-family",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
65,
"United-States"
],
[
32,
"Private",
"High School grad",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
48,
"Private",
"Masters",
"Never-Married",
"White-Collar",
"Husband",
"Black",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
45,
"United-States"
]
],
"covered_true": [
[
32,
"Private",
"High School grad",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
44,
"Local-gov",
"Bachelors",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
52,
"United-States"
],
[
36,
"Private",
"High School grad",
"Never-Married",
"Blue-Collar",
"Unmarried",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
30,
"United-States"
],
[
56,
"Private",
"High School grad",
"Never-Married",
"Blue-Collar",
"Unmarried",
"Black",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
49,
"Local-gov",
"High School grad",
"Never-Married",
"Service",
"Unmarried",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
30,
"United-States"
],
[
20,
"?",
"High School grad",
"Never-Married",
"?",
"Own-child",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
10,
"United-States"
],
[
22,
"?",
"High School grad",
"Never-Married",
"?",
"Own-child",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
"Hours per week > 45.00",
"United-States"
],
[
29,
"Private",
"High School grad",
"Never-Married",
"Service",
"Own-child",
"Asian-Pac-Islander",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
25,
"SE-Asia"
],
[
45,
"Local-gov",
"Masters",
"Never-Married",
"Professional",
"Unmarried",
"White",
"Female",
1506,
"Capital Loss <= 0.00",
45,
"United-States"
],
[
27,
"Private",
"High School grad",
"Never-Married",
"Blue-Collar",
"Not-in-family",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
50,
"United-States"
]
],
"covered_false": [
[
29,
"Private",
"Bachelors",
"Never-Married",
"Service",
"Husband",
"White",
"Male",
7298,
"Capital Loss <= 0.00",
42,
"United-States"
],
[
56,
"Private",
"Associates",
"Never-Married",
"Sales",
"Husband",
"White",
"Male",
15024,
"Capital Loss <= 0.00",
40,
"United-States"
],
[
47,
"Private",
"Masters",
"Never-Married",
"Sales",
"Not-in-family",
"White",
"Male",
27828,
"Capital Loss <= 0.00",
60,
"United-States"
],
[
40,
"Private",
"Associates",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
7688,
"Capital Loss <= 0.00",
44,
"United-States"
],
[
55,
"Self-emp-not-inc",
"High School grad",
"Never-Married",
"White-Collar",
"Not-in-family",
"White",
"Male",
34095,
"Capital Loss <= 0.00",
60,
"United-States"
],
[
53,
"Private",
"Masters",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
48,
"United-States"
],
[
47,
"Federal-gov",
"Doctorate",
"Never-Married",
"Professional",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
53,
"Private",
"High School grad",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
1977,
40,
"United-States"
],
[
46,
"Private",
"Bachelors",
"Never-Married",
"Sales",
"Not-in-family",
"White",
"Male",
8614,
"Capital Loss <= 0.00",
40,
"United-States"
],
[
44,
"Local-gov",
"Prof-School",
"Never-Married",
"Professional",
"Not-in-family",
"White",
"Male",
10520,
"Capital Loss <= 0.00",
40,
"United-States"
]
],
"uncovered_true": [],
"uncovered_false": []
},
{
"covered": [
[
41,
"State-gov",
"High School grad",
"Never-Married",
"White-Collar",
"Not-in-family",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
64,
"State-gov",
"High School grad",
"Never-Married",
"Blue-Collar",
"Not-in-family",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
33,
"State-gov",
"High School grad",
"Never-Married",
"Service",
"Unmarried",
"Black",
"Female",
1831,
"Capital Loss <= 0.00",
40,
"United-States"
],
[
35,
"State-gov",
"High School grad",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
60,
"United-States"
],
[
25,
"State-gov",
"Dropout",
"Never-Married",
"Blue-Collar",
"Own-child",
"Black",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
40,
"State-gov",
"Associates",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
19,
"State-gov",
"High School grad",
"Never-Married",
"Blue-Collar",
"Other-relative",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
20,
"United-States"
],
[
44,
"State-gov",
"Dropout",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
88,
"United-States"
],
[
80,
"State-gov",
"Associates",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
24,
"United-States"
],
[
21,
"State-gov",
"High School grad",
"Never-Married",
"Professional",
"Own-child",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
20,
"United-States"
]
],
"covered_true": [
[
22,
"State-gov",
"High School grad",
"Never-Married",
"Service",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
25,
"United-States"
],
[
49,
"State-gov",
"High School grad",
"Never-Married",
"Service",
"Not-in-family",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
22,
"State-gov",
"Bachelors",
"Never-Married",
"?",
"Not-in-family",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
25,
"United-States"
],
[
31,
"State-gov",
"Bachelors",
"Never-Married",
"Professional",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
50,
"United-States"
],
[
18,
"State-gov",
"Dropout",
"Never-Married",
"Blue-Collar",
"Not-in-family",
"Black",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
56,
"State-gov",
"High School grad",
"Never-Married",
"Blue-Collar",
"Unmarried",
"Black",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
26,
"State-gov",
"Dropout",
"Never-Married",
"Service",
"Unmarried",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
38,
"State-gov",
"Bachelors",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
52,
"State-gov",
"High School grad",
"Never-Married",
"Blue-Collar",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
70,
"United-States"
],
[
25,
"State-gov",
"Associates",
"Never-Married",
"Professional",
"Wife",
"White",
"Female",
"Capital Gain <= 0.00",
1887,
40,
"United-States"
]
],
"covered_false": [
[
46,
"State-gov",
"Prof-School",
"Never-Married",
"Professional",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
45,
"United-States"
],
[
42,
"State-gov",
"Bachelors",
"Never-Married",
"White-Collar",
"Husband",
"White",
"Male",
15024,
"Capital Loss <= 0.00",
50,
"United-States"
],
[
46,
"State-gov",
"Prof-School",
"Never-Married",
"Professional",
"Husband",
"White",
"Male",
15024,
"Capital Loss <= 0.00",
40,
"United-States"
],
[
54,
"State-gov",
"Doctorate",
"Never-Married",
"White-Collar",
"Not-in-family",
"White",
"Female",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
40,
"United-States"
],
[
42,
"State-gov",
"Masters",
"Never-Married",
"White-Collar",
"Not-in-family",
"White",
"Female",
14084,
"Capital Loss <= 0.00",
60,
"United-States"
],
[
37,
"State-gov",
"Masters",
"Never-Married",
"Professional",
"Husband",
"White",
"Male",
"Capital Gain <= 0.00",
"Capital Loss <= 0.00",
45,
"United-States"
]
],
"uncovered_true": [],
"uncovered_false": []
}
],
"all_precision": 0,
"num_preds": 1000101,
"names": [
"Marital Status = Never-Married",
"Workclass = State-gov"
],
"instance": [
[
39
],
[
7
],
[
"28.00 < Age <= 37.00"
],
[
"28.00 < Age <= 37.00"
],
[
"28.00 < Age <= 37.00"
],
[
"28.00 < Age <= 37.00"
],
[
4
],
[
"28.00 < Age <= 37.00"
],
[
2174
],
[
"Age <= 28.00"
],
[
40
],
[
9
]
],
"prediction": 0
}
}
If you wish to test locally first install the requirements:
pip install -r requirements.txt
Now train the model:
make train
You can then store the model.joblib for the model and explainer.dill for the explainer in a bucket accessible from your Kubernetes cluster.