Teradata Package for Python Function Reference - 17.00 - retrieve_model - Teradata Package for Python

Teradata® Package for Python Function Reference

Product
Teradata Package for Python
Release Number
17.00
Release Date
April 2021
Content Type
Programming Reference
Publication ID
B700-4008-070K
Language
English (United States)
teradataml.catalog.model_cataloging.retrieve_model = retrieve_model(name)
DESCRIPTION:
    Function to retrieve a saved model accessible to the current user.
 
PARAMETERS:
    name:
        Required Argument.
        Specifies the name of the model to retrieve.
        Types: str
 
RETURNS:
    The Analytic function object corresponding to the model retrieved.
 
RAISES:
    TeradataMlException, TypeError, ValueError
 
EXAMPLES:
    # Load the data to run the example
    load_example_data("decisionforestpredict", ["housing_train","housing_test"])
 
    # Create teradataml DataFrame objects.
    housing_train = DataFrame.from_table("housing_train")
    housing_test = DataFrame.from_table("housing_test")
 
    # This example uses home sales data to create a
    # classification tree that predicts home style, which can be input
    # to the DecisionForestPredict.
    formula = "homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl"
    rft_model = DecisionForest(data=housing_train,
                               formula = formula,
                               tree_type="classification",
                               ntree=50,
                               tree_size=100,
                               nodesize=1,
                               variance=0.0,
                               max_depth=12,
                               maxnum_categorical=20,
                               mtry=3,
                               mtry_seed=100,
                               seed=100
                               )
 
    # Let's save this generated model.
    save_model(model=rft_model, name="decision_forest_model", description="Decision Forest test")
 
    # Retrieve the saved model.
    retrieved_rft_model = retrieve_model("decision_forest_model")
 
    # 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 results.
    decision_forest_predict_out.result