pandita endpoint for risk

pandita-risk

import boto3
import datetime
import time
import tarfile
import boto3
import pandas as pd
import sagemaker
from sagemaker import sklearn, get_execution_role

sm_boto3 = boto3.client("sagemaker")
sess = sagemaker.Session()
region = sess.boto_session.region_name
bucket = sess.default_bucket() 

quality_map = {0: 0.9636, 1: 0.9589, 2: 0.9775}
cost_map = {0: 0.03049, 1: 0.015, 2: 0.01339}
print("validating model")
risk_type = 'RISK_VAL'
X = ['france-uber', cost_map, quality_map, [60, 30], risk_type]

# Instantiate a predictor object
predictor = sklearn.model.SKLearnPredictor(
    endpoint_name="sagemaker-scikit-learn-2023-01-28-05-31-02-930",
    sagemaker_session=sess)

# Invoke the endpoint
predictions = predictor.predict(X)

print(predictions)