APIクイックスタート¶

endpoint: 'https://app.datarobot.com/api/v2'
token: 'NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G' 

import datarobot as dr 

import datarobot as dr
dr.Client(config_path = "<file-path-to-drconfig.yaml>") 

ConnectToDataRobot(configPath = "<file-path-to-drconfig.yaml>")) 

setx DATAROBOT_ENDPOINT "https://app.datarobot.com/api/v2"
setx DATAROBOT_API_TOKEN "your_api_token" 

export DATAROBOT_ENDPOINT="https://app.datarobot.com/api/v2"
export DATAROBOT_API_TOKEN="your_api_token" 

import datarobot as dr
dr.Project.list() 

curl --location -X GET "${DATAROBOT_API_ENDPOINT}/projects" --header "Authorization: Bearer ${DATAROBOT_API_TOKEN}" 

import datarobot as dr
dr.Client(endpoint='https://app.datarobot.com/api/v2', token='NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G') 

ConnectToDataRobot(endpoint =
"https://app.datarobot.com/api/v2",
token =
'NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G') 

GET https://app.datarobot.com/api/v2/ HTTP/1.1
Authorization: Bearer DnwzBUNTOtKBO6Sp1hoUByG4YgZwCCw4 

import datarobot as dr
dr.Client()

# Set to the location of your auto-mpg.csv and auto-mpg-test.csv data files
# Example: dataset_file_path = '/Users/myuser/Downloads/auto-mpg.csv'
training_dataset_file_path = ''
test_dataset_file_path = ''

# Load dataset
training_dataset = dr.Dataset.create_from_file(training_dataset_file_path)

# Create a new project based on dataset
project = dr.Project.create_from_dataset(training_dataset.id, project_name='Auto MPG DR-Client') 

# Set to the location of your auto-mpg.csv and auto-mpg-test.csv data files
# Example: dataset_file_path = '/Users/myuser/Downloads/auto-mpg.csv'
training_dataset_file_path <- ""
test_dataset_file_path <- ""
training_dataset <- utils::read.csv(training_dataset_file_path)
test_dataset <- utils::read.csv(test_dataset_file_path)
head(training_dataset)
project <- SetupProject(dataSource = training_dataset, projectName = "Auto MPG DR-Client", maxWait = 60 * 60) 

DATAROBOT_API_TOKEN=${DATAROBOT_API_TOKEN}
DATAROBOT_ENDPOINT=${DATAROBOT_ENDPOINT}
location=$(curl -Lsi \
  -X POST \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -F 'projectName="Auto MPG"' \
  -F "file=@${DATASET_FILE_PATH}" \
  "${DATAROBOT_ENDPOINT}"/projects/ | grep -i 'Location: .*$' | \
  cut -d " " -f2 | tr -d '\r')
echo "Uploaded dataset. Checking status of project at: ${location}"
while true; do
  project_id=$(curl -Ls \
    -X GET \
    -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${location}" \
    | grep -Eo 'id":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
  if [ "${project_id}" = "" ]
  then
    echo "Setting up project..."
    sleep 10
  else
    echo "Project setup complete."
    echo "Project ID: ${project_id}"
    break
  fi
done 

# Use training data to build models
from datarobot import AUTOPILOT_MODE

# Set the project's target and initiate Autopilot (runs in Quick mode unless a different mode is specified)
project.analyze_and_model(target='mpg', worker_count=-1, mode=AUTOPILOT_MODE.QUICK)

# Open the project's Leaderboard to monitor the progress in UI.
project.open_in_browser()

# Wait for the model creation to finish
project.wait_for_autopilot()

model = project.get_top_model() 

# Set the project target and initiate Autopilot
SetTarget(project,
  target = "mpg")
# Block execution until Autopilot is complete
WaitForAutopilot(project)
model <- GetRecommendedModel(project, type = RecommendedModelType$RecommendedForDeployment) 

response=$(curl -Lsi \
  -X PATCH \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "Content-Type: application/json" \
  --data '{"target": "mpg", "mode": "quick"}' \
  "${DATAROBOT_ENDPOINT}/projects/${project_id}/aim" | grep 'location: .*$' \
  | cut -d " " | tr -d '\r')
echo "AI training initiated. Checking status of training at: ${response}"
while true; do
  initial_project_status=$(curl -Ls \
  -X GET \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${response}" \
  | grep -Eo 'stage":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
  if [ "${initial_project_status}" = "" ]
  then
    echo "Setting up AI training..."
    sleep 10
  else
    echo "Training AI."
    echo "Grab a coffee or catch up on email."
    break
  fi
done

project_status=$(curl -Lsi \
  -X GET \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  "${DATAROBOT_ENDPOINT}/projects/${project_id}/status" \
  | grep -Eo 'autopilotDone":\strue'
  )
if [ "${project_status}" = "" ]
then
  echo "Autopilot training in progress..."
  sleep 60
else
  echo "Autopilot training complete. Model ready to deploy."
  break
fi
done 

# Test predictions on new data
prediction_data = project.upload_dataset(test_dataset_file_path)  # Additional code to upload test data
predict_job = model.request_predictions(prediction_data.id)  # Directly add test data here instead of the ID of the uploaded dataset
predictions = predict_job.get_result_when_complete()
predictions.head() 

# Uploading the testing dataset
scoring <- UploadPredictionDataset(project, dataSource = test_dataset)

# Requesting prediction
predict_job_id <- RequestPredictions(project, modelId = model$modelId, datasetId = scoring$id)

# Grabbing predictions
predictions_prob <- GetPredictions(project, predictId = predict_job_id, type = "probability")
head(predictions_prob) 

# Test predictions on new data
# shellcheck disable=SC2089
prediction_location=$(curl -Lsi\
-X POST \
-H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
-F "file=@${TEST_DATASET_FILE_PATH}" \
"${DATAROBOT_ENDPOINT}/projects/${project_id}/predictionDatasets/fileUploads/"\
| grep -i 'location: .*$' | cut -d " " -f2 | tr -d '\r')
echo "Uploaded prediction dataset. Checking status of upload at: ${prediction_location}"
while true; do
  prediction_dataset_id=$(curl -Ls \
  -X GET \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${prediction_location}" \
  | grep -Eo 'id":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
  if [ "${prediction_dataset_id}" = "" ]
  then
  echo "Uploading predictions..."
  sleep 10
  else
  echo "Predictions upload complete."
  echo "Predictions dataset ID: ${prediction_dataset_id}"
  break
  fi
  done
  prediction_request_data="{\
      \"modelId\":\"${recommended_model_id}\",\
      \"datasetId\":\"${prediction_dataset_id}\"\
  }"
  predict_job=$(curl -Lsi \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  --data "${prediction_request_data}" \
  "${DATAROBOT_ENDPOINT}/projects/${project_id}/predictions/"\
  | grep -i 'location: .*$' | cut -d " " -f2 | tr -d '\r')
while true; do
  initial_job_response=$(curl -Ls \
    -X GET \
    -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${predict_job}" \
    | grep -Eo 'status":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
  if [ "${initial_job_status}" = "inprogress" ]
  then
    echo "Generating predictions..."
    sleep 10
  else
    echo "Predictions complete."
    break
  fi
done

curl -Ls \
  -X GET \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${predict_job}" 

# Deploy model
prediction_server = dr.PredictionServer.list()[0]
deployment = dr.Deployment.create_from_learning_model(
  model_id=model.id, label="MPG Prediction Server",
  description="Deployed with DataRobot client",
  default_prediction_server_id=prediction_server.id
)

# View deployment stats
service_stats = deployment.get_service_stats()
print(service_stats.metrics) 

predictionServer <- ListPredictionServers()[[1]]
deployment <- CreateDeployment(model,
                               label = "MPG Prediction Server",
                               description = "Deployed with DataRobot client",
                               defaultPredictionServerId = predictionServer) 

recommended_model_id=$(curl -s \
-X GET \
-H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
"${DATAROBOT_ENDPOINT}/projects/${project_id}/recommendedModels"\
"/recommendedModel/" \
| grep -Eo 'modelId":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
server_data=$(curl -s -X GET \
-H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
"${DATAROBOT_ENDPOINT}/predictionServers/")
default_server_id=$(echo $server_data \
| grep -Eo 'id":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
server_url=$(echo $server_data | grep -Eo 'url":\s".*?"' \
| cut -d '"' -f3 | tr -d '\r')
server_key=$(echo $server_data | grep -Eo 'datarobot-key":\s".*?"' \
| cut -d '"' -f3 | tr -d '\r')
request_data="{\
    \"defaultPredictionServerId\":\"${default_server_id}\",\
    \"modelId\":\"${recommended_model_id}\",\
    \"description\":\"Deployed with cURL\",\
    \"label\":\"MPG Prediction Server\"\
}"
deployment_response=$(curl -Lsi -X POST \
-H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
-H "Content-Type: application/json" \
--data "${request_data}" \
"${DATAROBOT_ENDPOINT}/deployments/fromLearningModel/")
deploy_response_code_202=$(echo $deployment_response | grep -Eo 'HTTP/2 202')
if [ "${deploy_response_code_202}" = "" ]
then
  deployment_id=$(echo "$deployment_response" | grep -Eo 'id":\s"\w+' \
  | cut -d '"' -f3 | tr -d '\r')
  echo "Prediction server ready."
else
  deployment_status=$(echo "$deployment_response" | grep -Eo 'location: .*$' \
  | cut -d " " | tr -d '\r')
  while true; do
    deployment_ready=$(curl -Ls \
      -X GET \
      -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${deployment_status}" \
      | grep -Eo 'id":\s"\w+' | cut -d '"' -f3 | tr -d '\r')
    if [ "${deployment_ready}" = "" ]
    then
      echo "Waiting for deployment..."
      sleep 10
    else
      deployment_id=$deployment_ready
      echo "Prediction server ready."
      break
    fi
  done
fi 

import requests
from pprint import pprint
import json
import os
# JSON records for example autos for which to predict mpg
autos = [
    {
        "cylinders": 4,
        "displacement": 119.0,
        "horsepower": 82.00,
        "weight": 2720.0,
        "acceleration": 19.4,
        "model year": 82,
        "origin": 1,
    },
    {
        "cylinders": 8,
        "displacement": 120.0,
        "horsepower": 79.00,
        "weight": 2625.0,
        "acceleration": 18.6,
        "model year": 82,
        "origin": 1,
    },
]

# Use the following code to fetch DATAROBOT_API_TOKEN
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

# Create REST request for prediction API
prediction_server = deployment.default_prediction_server
prediction_headers = {
    "Authorization": "Bearer {}".format(config.get("DATAROBOT_API_TOKEN", None)),
    "Content-Type": "application/json",
    "datarobot-key": prediction_servers[1].datarobot_key
}

predictions = requests.post(
    f"{prediction_server['url']}/predApi/v1.0/deployments"
    f"/{deployment.id}/predictions",
    headers=prediction_headers,
    data=json.dumps(autos),
)
pprint(predictions.json()) 

# Prepare to connect to the prediction server
URL <- paste0(deployment$defaultPredictionServer$url,
        "/predApi/v1.0/deployments/",
        deployment$id,
        "/predictions")
  USERNAME <- "deployment$owners$preview$email" # This should be your DR email account
  API_TOKEN <- Sys.getenv("DATAROBOT_API_TOKEN") # This is configured implicitly when you first run `library(datarobot)`

  # Invoke Predictions API with the test_dataset
  response <- httr::POST(URL,
                   body = jsonlite::toJSON(test_dataset),
                   httr::add_headers("datarobot-key" = deployment$defaultPredictionServer$dataRobotKey),
                   httr::content_type_json(),
                   authenticate(USERNAME, API_TOKEN, "basic"))

  # Parse the results from the prediction server
  predictionResults <- fromJSON(httr::content(response, as = "text"),
                   simplifyDataFrame = TRUE,
                   flatten = TRUE)$data
  print(predictionResults) 

autos='[{
  "cylinders": 4,
  "displacement": 119.0,
  "horsepower": 82.00,
  "weight": 2720.0,
  "acceleration": 19.4,
  "model year": 82,
  "origin":1
},{
  "cylinders": 8,
  "displacement": 120.0,
  "horsepower": 79.00,
  "weight": 2625.0,
  "acceleration": 18.6,
  "model year": 82,
  "origin":1
}]'
curl -X POST \
  -H 'Content-Type: application/json' \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "datarobot-key: ${server_key}" \
  --data "${autos}" \
  "${server_url}/predApi/v1.0/deployments/${deployment_id}/predictions"