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Methods defined here:
- __init__(self, modeldata=None, newdata=None, accumulate=None, output_prob=False, output_responses=None, newdata_sequence_column=None, modeldata_sequence_column=None, newdata_order_column=None, modeldata_order_column=None)
- DESCRIPTION:
The GLML1L2Predict function uses the model output by the GLML1L2
function to perform generalized linear model prediction on new input
data.
PARAMETERS:
modeldata:
Required Argument.
Specifies the teradataml DataFrame which contains the model
data generated by the GLML1L2 function or instance of GLML1L2.
modeldata_order_column:
Optional Argument.
Specifies Order By columns for modeldata.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
newdata:
Required Argument.
Specifies the teradataml DataFrame containing the test data set.
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)
accumulate:
Optional Argument.
Specifies the names of input teradataml DataFrame columns to copy to
the output.
Types: str OR list of Strings (str)
output_prob:
Optional Argument.
Specifies whether to output probabilities or not.
Note: "output_prob" argument support is only available
when teradataml is connected to Vantage 1.1 or later.
Default Value: False
Types: bool
output_responses:
Optional Argument.
Specifies responses in the input teradataml DataFrame.
output_prob must be set to True in order to use this argument.
Note: "output_responses" argument support is only available
when teradataml is connected to Vantage 1.1.1 or later.
Permitted Values: 0, 1
Types: str OR list of strs
newdata_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "newdata". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
modeldata_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "modeldata". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of GLML1L2Predict.
Output teradataml DataFrames can be accessed using attribute
references, such as GLML1L2PredictObj.<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 -
# Generate a model based on train data "admissions_train" & Ridge Regression, Family ('BINOMIAL').
glml1l2_out = GLML1L2(data=admissions_train,
formula = "admitted ~ masters + gpa + stats + programming",
alpha=0.0,
lambda1=0.02,
family='Binomial',
randomization=True
)
# Use the generated model to predict the 'admissions' on the test data
# admissions_test by using generated model by GLM.
glml1l2_predict_out1 = GLML1L2Predict(modeldata = glml1l2_out,
newdata = admissions_test,
accumulate = "id",
output_prob=True
)
# Print the result DataFrame.
print(glml1l2_predict_out1.result)
# Example 2 - Generate a model based on train data "housing_train" & LASSO, Family ('GAUSSIAN').
glml1l2_out_hs = GLML1L2(data=housing_train ,
formula = "price ~ lotsize + bedrooms + bathrms + stories + garagepl + driveway + recroom + fullbase + gashw + airco + prefarea + homestyle",
alpha=1.0,
lambda1=0.02,
family='Gaussian'
)
# Use the generated model to predict the 'price' on the test data
# housing_test by using generated model by GLM.
glml1l2_predict_out2 = GLML1L2Predict(modeldata = glml1l2_out_hs.output,
newdata = housing_test,
accumulate = "sn"
)
# Print the result.
print(glml1l2_predict_out2)
- __repr__(self)
- Returns the string representation for a GLML1L2Predict 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.
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