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import sys
import json
import requests
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://app.datarobot.com/docs-jp/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://app.datarobot.com/docs-jp/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('NjQwMDVmNGI0ZDQzZDFhYzI2YThmZDJiOnVZejljTXFNTXNoUnlKMStoUFhXSFdYMEZRck9lY3dobnEvRFZ1aVBHbVE9'),
'DataRobot-Key': '84f96e49-d400-ec9c-92fc-30fc6e9329d1',
}
API_URL = 'https://jppdemo.orm.datarobot.com/predApi/v1.0/deployments/{deployment_id}/predictions'
url = API_URL.format(deployment_id=deployment_id)
# Prediction Explanations:
# See the documentation for more information:
# https://app.datarobot.com/docs-jp/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,
# If text explanations are required, uncomment the line below.
# 'maxNgramExplanations': 'all',
# 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)
def main(filename, deployment_id):
"""
Return an exit code on script completion or error. Codes > 0 are errors to the shell.
Also useful as a usage demonstration of
`make_datarobot_deployment_predictions(data, deployment_id)`
"""
MAX_PREDICTION_FILE_SIZE_BYTES = 52428800 # 50 MB
if not filename:
print(
'Input file is required argument. '
'Usage: python datarobot-predict.py <input-file.csv>')
return 1
data = open(filename, 'rb').read()
data_size = sys.getsizeof(data)
if data_size >= MAX_PREDICTION_FILE_SIZE_BYTES:
print((
'Input file is too large: {} bytes. '
'Max allowed size is: {} bytes.'
).format(data_size, MAX_PREDICTION_FILE_SIZE_BYTES))
return 1
try:
predictions = make_datarobot_deployment_predictions(data, deployment_id)
except DataRobotPredictionError as exc:
print(exc)
return 1
return predictions
# def prediction_formatting_to_dataframe(prediction_json):
# prediction_df = pd.jason_normalize(prediction_json)[['rowId'
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