Movie Review Explanations

We will use an SKLearn classifier built on movie sentiment data which predicts positive or negative sentiment for review text.

The Kfserving resource provdes:

A pretrained sklearn model stored on a Google bucket
A Text Seldon Alibi Explainer. See the Alibi Docs for further details.



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!pygmentize moviesentiment.yaml



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!kubectl apply -f moviesentiment.yaml



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CLUSTER_IPS=!(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
CLUSTER_IP=CLUSTER_IPS[0]
print(CLUSTER_IP)



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SERVICE_HOSTNAMES=!(kubectl get inferenceservice moviesentiment -o jsonpath='{.status.url}' | cut -d "/" -f 3)
SERVICE_HOSTNAME=SERVICE_HOSTNAMES[0]
print(SERVICE_HOSTNAME)



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import sys
sys.path.append('../')
from alibi_helper import *



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from alibi.datasets import fetch_movie_sentiment
movies = fetch_movie_sentiment()



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idxNeg = 37
idxPos = 5227
for idx in [idxNeg,idxPos]:
    print(movies.data[idx])
    show_prediction(predict(movies.data[idx:idx+1],'moviesentiment',movies,SERVICE_HOSTNAME,CLUSTER_IP))

Get Explanation for Negative Prediction



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exp = explain(movies.data[idxNeg:idxNeg+1],"moviesentiment",SERVICE_HOSTNAME,CLUSTER_IP)



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show_anchors(exp['data']['anchor'])

Show precision. How likely predictions using the Anchor features would produce the same result.



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show_bar([exp['data']['precision']],[''],"Precision")
show_bar([exp['data']['coverage']],[''],"Coverage")



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show_feature_coverage(exp['data'])



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show_examples(exp['data'],0,movies)



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show_examples(exp['data'],0,movies,False)

Get Explanation for Positive Example



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exp = explain(movies.data[idxPos:idxPos+1],"moviesentiment",SERVICE_HOSTNAME,CLUSTER_IP)



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show_anchors(exp['data']['anchor'])

Show precision. How likely predictions using the Anchor features would produce the same result.



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show_bar([exp['data']['precision']],[''],"Precision")
show_bar([exp['data']['coverage']],[''],"Coverage")



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show_feature_coverage(exp['data'])



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show_examples(exp['data'],0,movies)



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show_examples(exp['data'],0,movies,False)

Teardown



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!kubectl delete -f moviesentiment.yaml



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