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suxinji
Employee
Employee

 

Overview

In this article, we assume that Incorta is running in an on-premise env and we can call Data Robot real-time API to get the prediction.
 

Solution

1. Read the Incorta data in Incorta Notebook.  

2. The Data Robot Prediction assumes that the data is available as a CSV format, we convert the data as a csv file via Pandas to_csv().

 

suxinji_0-1649872655423.png

3. We merge the prediction result with the original data.

The prediction result returned from DataRobot does not include the original data but only the prediction.  The order of records is the same as the order when we send the data for inference.

 

suxinji_1-1649872665122.png

 

Add property:

suxinji_2-1649872683050.png

 

Code sample:

"""
Usage:
    python datarobot-predict.py <input-file.csv>
 
This example uses the requests library which you can install with:
    pip install requests
We highly recommend that you update SSL certificates with:
    pip install -U urllib3[secure] certifi
"""
import sys
import json
import requests
 
API_URL = 'https://app2.datarobot.com/api/v2/deployments/{deployment_id}/predictions/'
API_KEY = 'NjFhNzNhNzllZjc0ZTljYWEZ6dzcvUHVWaEprejhja3VZOXdxaEU0Nm5wSVQ5bDRCeG5CanFjRnM9'
 
DEPLOYMENT_ID = '61ca66aebea608'
 
# Don't change this. It is enforced server-side too.
MAX_PREDICTION_FILE_SIZE_BYTES = 52428800  # 50 MB
 
 
class DataRobotPredictionError(Exception):
    """Raised if there are issues getting predictions from DataRobot"""
 
 
def make_datarobot_deployment_predictions(data, deployment_id):
    """
    Make predictions on data provided using DataRobot deployment_id provided.
    See docs for details:
         https://app2.datarobot.com/docs/predictions/api/dr-predapi.html
 
    Parameters
    ----------
    data : str
        If using CSV as input:
        Feature1,Feature2
        numeric_value,string
 
        Or if using JSON as input:
        [{"Feature1":numeric_value,"Feature2":"string"}]
 
    deployment_id : str
        The ID of the deployment to make predictions with.
 
    Returns
    -------
    Response schema:
        https://app2.datarobot.com/docs/predictions/api/dr-predapi.html#response-schema
 
    Raises
    ------
    DataRobotPredictionError if there are issues getting predictions from DataRobot
    """
    # Set HTTP headers. The charset should match the contents of the file.
    headers = {
        # As default, we expect CSV as input data.
        # Should you wish to supply JSON instead,
        # comment out the line below and use the line after that instead:
        'Content-Type': 'text/plain; charset=UTF-8',
        # 'Content-Type': 'application/json; charset=UTF-8',
 
        'Authorization': 'Bearer {}'.format(API_KEY),
    }
 
    url = API_URL.format(deployment_id=deployment_id)
 
    # Prediction Explanations:
    # See the documentation for more information:
    # https://app2.datarobot.com/docs/predictions/api/dr-predapi.html#request-pred-explanations
    # Should you wish to include Prediction Explanations or Prediction Warnings in the result,
    # Change the parameters below accordingly, and remove the comment from the params field below:
 
    params = {
        # If explanations are required, uncomment the line below
        # 'maxExplanations': 3,
        # 'thresholdHigh': 0.5,
        # 'thresholdLow': 0.15,
        # Uncomment this for Prediction Warnings, if enabled for your deployment.
        # 'predictionWarningEnabled': 'true',
    }
    # Make API request for predictions
    predictions_response = requests.post(
        url,
        data=data,
        headers=headers,
        # Prediction Explanations:
        # Uncomment this to include explanations in your prediction
        # params=params,
    )
    _raise_dataroboterror_for_status(predictions_response)
    # Return a Python dict following the schema in the documentation
    return predictions_response.json()
 
 
def _raise_dataroboterror_for_status(response):
    """Raise DataRobotPredictionError if the request fails along with the response returned"""
    try:
        response.raise_for_status()
    except requests.exceptions.HTTPError:
        err_msg = '{code} Error: {msg}'.format(
            code=response.status_code, msg=response.text)
        raise DataRobotPredictionError(err_msg)

df = read('CreditRisk.CreditRiskPredPrepared')
tenant_path = spark.conf.get("ml.incorta.tenant_path")
pdf = df.toPandas()
pdf.to_csv(tenant_path + '/data/CreditRiskPredPrepared.csv')
filename = tenant_path + '/data/CreditRiskPredPrepared.csv'
data = open(filename, 'rb').read()
predictions = make_datarobot_deployment_predictions(data, DEPLOYMENT_ID)
rows = predictions["data"]
import pandas as pd 
row_list = [(r['rowId'], r['prediction']) for r in rows]
# select columns from the prediction data frame
pdf2 = pd.DataFrame(row_list, columns = ['rowId', 'prediction'])
# concat prediction data frame and original data frame
result = pd.concat([pdf, pdf2], axis=1)
# create spark data frame
df_output=spark.createDataFrame(result)
incorta.show(df_output)
save(df_output)

3. Once the data is enriched with the prediction, they can be the source data in any dashboard insight like other tables.

 

Version history
Last update:
‎06-02-2022 12:16 PM
Updated by: