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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.
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