retrieve_model() | Teradata Python Package - retrieve_model() - Teradata Package for Python

The retrieve_model() API allows a user to recreate a teradataml Analytic Function object from the information saved with the Model Catalog using save_model(). This analytic function object can then be used in the user’s workflow.

For example, if a user has built a user-base classification model, he or she can save the information related to this model and reuse it at every instance when there is need to score a new set of user data.

The required argument name, which is also the only argument, specifies the name of the model to retrieve.

Example Prerequisites

Follow the steps in save_model() to create a classification tree model that can be input to DecisionForestPredict and save the generated model.

Example

• View the saved models.

  >>> list_models()
                 ModelName  ModelAlgorithm ModelGeneratingEngine ModelGeneratingClient CreatedBy                 CreatedDate
  0  decision_forest_model  DecisionForest             ML Engine            teradataml     ALICE  2020-05-17 23:59:48.740000

• Retrieve the saved model.

  >>> # Retrieve the saved model
  >>> retrieved_rft_model = retrieve_model("decision_forest_model")

• Use the retrieved model in predict.

  >>> # Use the retrieved model in predict.
  >>> decision_forest_predict_out = DecisionForestPredict(object = retrieved_rft_model,
                                                          newdata = housing_test,
                                                          id_column = "sn",
                                                          detailed = False,
                                                          terms = ["homestyle"],
                                                          newdata_order_column=['sn', 'price'],
                                                          object_order_column=['worker_ip', 'task_index']
                                                         )

• Print the result.

  >>> # Print the results.
  >>> print(decision_forest_predict_out.result)
  ############ STDOUT Output ############
   
    homestyle   sn prediction  confidence_lower  confidence_upper
  0   Classic  260    Classic              0.90              0.90
  1   Classic   13    Classic              0.98              0.98
  2   Classic  142    Classic              0.86              0.86
  3  Eclectic   53   Eclectic              0.98              0.98
  4  Eclectic   38   Eclectic              0.90              0.90
  5   Classic  111    Classic              0.96              0.96
  6   Classic  251    Classic              0.76              0.76
  7  Eclectic  408   Eclectic              0.96              0.96
  8   Classic   16    Classic              0.90              0.90
  9   Classic  132    Classic              0.92              0.92