Predictions generation is an asynchronous process. This means that when starting
Model.request_predictions you will receive back a PredictJob for tracking
the process responsible for fulfilling your request.
With this object you can get info about the predictions generation process before it has finished and be rerouted to the predictions themselves when the process is finished. For this you should use the PredictJob (API reference) class.
Starting predictions generation¶
Before actually requesting predictions, you should upload the dataset you wish to predict via
Project.upload_dataset. Previously uploaded datasets can be seen under
When uploading the dataset you can provide the path to a local file, a file object, raw file content,
pandas.DataFrame object, or the url to a publicly available dataset.
To start predicting on new data using a finished model use
It will create a new predictions generation process and return a PredictJob object tracking this process.
With it, you can monitor an existing PredictJob and retrieve generated predictions when the corresponding
PredictJob is finished.
import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' project = dr.Project.get(project_id) model = dr.Model.get(project=project_id, model_id=model_id) # Using path to local file to generate predictions dataset_from_path = project.upload_dataset('./data_to_predict.csv') # Using file object to generate predictions with open('./data_to_predict.csv') as data_to_predict: dataset_from_file = project.upload_dataset(data_to_predict) predict_job_1 = model.request_predictions(dataset_from_path.id) predict_job_2 = model.request_predictions(dataset_from_file.id)
You can use the
Predictions.list() method to return a list of predictions generated on a project.
import datarobot as dr predictions = dr.Prediction.list('58591727100d2b57196701b3') print(predictions) >>>[Predictions(prediction_id='5b6b163eca36c0108fc5d411', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c05744846630', dataset_id='5b6b1632ca36c03b5875e6a0'), Predictions(prediction_id='5b6b2315ca36c0108fc5d41b', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c0574484662e', dataset_id='5b6b1632ca36c03b5875e6a0'), Predictions(prediction_id='5b6b23b7ca36c0108fc5d422', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c0574484662e', dataset_id='55b6b1632ca36c03b5875e6a0') ]
You can pass following parameters to filter the result:
model_id– str, used to filter returned predictions by model_id.
dataset_id– str, used to filter returned predictions by dataset_id.
Get an existing PredictJob¶
To retrieve an existing PredictJob use the
PredictJob.get method. This will give you
a PredictJob matching the latest status of the job if it has not completed.
If predictions have finished building,
PredictJob.get will raise a
import time import datarobot as dr predict_job = dr.PredictJob.get(project_id=project_id, predict_job_id=predict_job_id) predict_job.status >>> 'queue' # wait for generation of predictions (in a very inefficient way) time.sleep(10 * 60) predict_job = dr.PredictJob.get(project_id=project_id, predict_job_id=predict_job_id) >>> dr.errors.PendingJobFinished # now the predictions are finished predictions = dr.PredictJob.get_predictions(project_id=project.id, predict_job_id=predict_job_id)
Get generated predictions¶
After predictions are generated, you can use
to get newly generated predictions.
If predictions have not yet been finished, it will raise a
import datarobot as dr predictions = dr.PredictJob.get_predictions(project_id=project.id, predict_job_id=predict_job_id)
Wait for and Retrieve results¶
If you just want to get generated predictions from a PredictJob, you
can use the
It will poll the status of predictions generation process until it has finished, and
then will return predictions.
dataset = project.get_datasets() predict_job = model.request_predictions(dataset.id) predictions = predict_job.get_result_when_complete()
Get previously generated predictions¶
If you don’t have a
Model.predict_job on hand, there are two more ways to retrieve predictions from the
- Get all prediction rows as a
import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") df = preds.get_all_as_dataframe()
- Download all prediction rows to a file as a CSV document:
import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") preds.download_to_csv('predictions.csv)