Teradata Package for Python Function Reference - 17.00 - GLMPredict - 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.analytics.sqle.GLMPredict = class GLMPredict(builtins.object)
     Methods defined here:
__init__(self, modeldata=None, newdata=None, terms=None, family=None, linkfunction='CANONICAL', newdata_order_column=None)
DESCRIPTION:
    The GLMPredict function uses the model generated by the function GLM
    to perform generalized linear model prediction on new input data.
 
 
PARAMETERS:
    modeldata:
        Required Argument.
        Specifies the teradataml DataFrame containing the model data
        or an instance of GLM.
 
    newdata:
        Required Argument.
        Specifies the teradataml DataFrame containing the input data.
 
    newdata_order_column:
        Optional Argument.
        Specifies Order By columns for newdata.
        Values to this argument can be provided as a list, if multiple
        columns are used for ordering.
        Types: str OR list of Strings (str)
 
    terms:
        Optional Argument.
        Specifies the names of newdata columns to copy to the output
        teradataml DataFrame.
        Types: str OR list of Strings (str)
 
    family:
        Optional Argument.
        Specifies the distribution exponential family. The default value is
        read from modeldata. If you specify this argument, you must give it
        the same value that you used for the Family argument of the function
        when you generated the model.
        Permitted Values: LOGISTIC, BINOMIAL, POISSON, GAUSSIAN, GAMMA, 
        INVERSE_GAUSSIAN, NEGATIVE_BINOMIAL
        Types: str
 
    linkfunction:
        Optional Argument.
        The canonical link functions (default link functions) and the link
        functions that are allowed for each exponential family.
        Note: Use the same value that you used for the Link argument of the
              function  when you generated the model.
        Default Value: "CANONICAL"
        Permitted Values: CANONICAL, IDENTITY, INVERSE, LOG,
        COMPLEMENTARY_LOG_LOG, SQUARE_ROOT, INVERSE_MU_SQUARED, LOGIT,
        PROBIT, CAUCHIT
        Types: str
 
RETURNS:
    Instance of GLMPredict.
    Output teradataml DataFrames can be accessed using attribute
    references, such as GLMPredictObj.<attribute_name>.
    Output teradataml DataFrame attribute name is:
        result
 
 
RAISES:
    TeradataMlException
 
 
EXAMPLES:
    # Load the data to run the example.
    load_example_data("GLMPredict", ["admissions_test","admissions_train","housing_test","housing_train"])
 
    # Create teradataml DataFrame objects.
    admissions_test = DataFrame.from_table("admissions_test")
    admissions_train = DataFrame.from_table("admissions_train")
    housing_test = DataFrame.from_table("housing_test")
    housing_train = DataFrame.from_table("housing_train")
 
    # Example 1 -
    # First train the data, i.e., create a GLM Model
    glm_out = GLM(formula = "admitted ~ stats + masters + gpa + programming",
                 family = "LOGISTIC",
                 linkfunction = "LOGIT",
                 data = admissions_train,
                 weights = "1",
                 threshold = 0.01,
                 maxit = 25,
                 step = False,
                 intercept = True
                 )
 
    # Run predict on the output of GLM
    glm_predict_out1 = GLMPredict(modeldata = glm_out,
                                  newdata = admissions_test,
                                  terms = ["id","masters","gpa","stats","programming","admitted"],
                                  family = "LOGISTIC",
                                  linkfunction = "LOGIT"
                                  )
 
    # Print the results.
    print(glm_predict_out1.result)
 
    # Example 2 -
    # First train the data, i.e., create a GLM Model
    glm_out_hs = GLM(formula = "price ~ recroom + lotsize + stories + garagepl + gashw + bedrooms + driveway + airco + homestyle + bathrms + fullbase + prefarea",
                      family = "GAUSSIAN",
                      linkfunction = "IDENTITY",
                      data = housing_train,
                      weights = "1",
                      threshold = 0.01,
                      maxit = 25,
                      step = False,
                      intercept = True
                      )
 
    # Run predict on the output of GLM by passing coefficients
    glm_predict_out2 = GLMPredict(modeldata = glm_out_hs.coefficients,
                                  newdata = housing_test,
                                  terms = ["sn", "price"],
                                  family = "GAUSSIAN",
                                  linkfunction = "CANONICAL"
                                  )
 
    # Print the results.
    print(glm_predict_out2)
__repr__(self)
Returns the string representation for a GLMPredict class instance.
get_build_time(self)
Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
get_prediction_type(self)
Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
get_target_column(self)
Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
show_query(self)
Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.