Predictions¶

Predictions generation is an asynchronous process. This means that when starting predictions with 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 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 Project.get_datasets. When uploading the dataset you can provide the path to a local file, a file object, raw file content, a pandas.DataFrame object, or the url to a publicly available dataset.

To start predicting on new data using a finished model use Model.request_predictions(). 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,
)

# As of v3.0, in addition to passing a ``dataset_id``, you can pass in a ``dataset``, ``file``, ``file_path`` or
# ``dataframe`` to `Model.request_predictions`.

predict_job = model.request_predictions(file_path='./data_to_predict.csv')

# Alternative version uploading the dataset from a local path and passing it by its id
dataset_from_path = project.upload_dataset('./data_to_predict.csv')
predict_job = model.request_predictions(dataset_id=dataset_from_path.id)

# Alternative version: upload the dataset as a file object and pass it by using its dataset id
with open('./data_to_predict.csv') as data_to_predict:
    dataset_from_file = project.upload_dataset(data_to_predict)
predict_job = model.request_predictions(dataset_id=dataset_from_file.id)  # OR predict_job = model.request_predictions(dataset_id=dataset_from_file.id)

Listing Predictions¶

You can use the Predictions.list() method to return a list of predictions generated on a project.

import datarobot as dr
predictions = dr.Predictions.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 PendingJobFinished exception.

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 PredictJob.get_predictions to get newly generated predictions.

If predictions have not yet been finished, it will raise a JobNotFinished exception.

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 PredictJob.get_result_when_complete function. It will poll the status of the predictions generation process until it has finished, and then will return predictions.

dataset = project.get_datasets()[0]
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 Predictions interface:

Get all prediction rows as a pandas.DataFrame object:

import datarobot as dr

preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411")
df = preds.get_all_as_dataframe()
df_with_serializer = preds.get_all_as_dataframe(serializer='csv')

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')

preds.download_to_csv('predictions_with_serializer.csv', serializer='csv')

Training predictions¶

The training predictions interface allows computing and retrieving out-of-sample predictions for a model using the original project dataset. The predictions can be computed for all the rows, or restricted to validation or holdout data. As the predictions generated will be out-of-sample, they can be expected to have different results than if the project dataset were reuploaded as a prediction dataset.

Quick reference¶

Training predictions generation is an asynchronous process. This means that when starting predictions with datarobot.models.Model.request_training_predictions() you will receive back a datarobot.models.TrainingPredictionsJob for tracking the process responsible for fulfilling your request. Actual predictions may be obtained with the help of a datarobot.models.training_predictions.TrainingPredictions object returned as the result of the training predictions job. There are three ways to retrieve them:

Iterate prediction rows one by one as named tuples:

import datarobot as dr

# Calculate new training predictions on all dataset
training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL)
training_predictions = training_predictions_job.get_result_when_complete()

# Fetch rows from API and print them
for prediction in training_predictions.iterate_rows(batch_size=250):
    print(prediction.row_id, prediction.prediction)

Get all prediction rows as a pandas.DataFrame object:

import datarobot from dr

# Calculate new training predictions on holdout partition of dataset
training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.HOLDOUT)
training_predictions = training_predictions_job.get_result_when_complete()

# Fetch training predictions as data frame
dataframe = training_predictions.get_all_as_dataframe()

Download all prediction rows to a file as a CSV document:

import datarobot from dr

# Calculate new training predictions on all dataset
training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL)
training_predictions = training_predictions_job.get_result_when_complete()

# Fetch training predictions and save them to file
training_predictions.download_to_csv('my-training-predictions.csv')