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Methods defined here:
- __init__(self, formula=None, modeldata=None, newdata=None, id_col=None, output_prob=False, responses=None, terms=None, newdata_sequence_column=None, modeldata_sequence_column=None, newdata_order_column=None, modeldata_order_column=None)
- DESCRIPTION:
The NaiveBayesPredict function uses the model output by the
NaiveBayes function to predict the outcomes for a test set
of data.
Note: This function is available only when teradataml is connected to
Vantage 1.1 or later versions.
PARAMETERS:
formula:
Optional Argument.
Required when the argument "modeldata" is teradataml DataFrame.
Specifies a string consisting of "formula" which was used to fit in model data.
Only basic formula of the "col1 ~ col2 + col3 +..." form is supported and
all variables must be from the same virtual DataFrame object. The
response should be column of type real, numeric, integer or boolean.
Types: str
modeldata:
Required Argument.
Specifies the teradataml DataFrame containing the model data.
This argument can accept teradataml DataFrame or
instance of NaiveBayes class.
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 that defines the input test 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)
id_col:
Required Argument.
Specifies the name of the column that contains the ID that uniquely
identifies the test input data.
Types: str
output_prob:
Optional Argument.
Specifies whether to output probabilities.
Default Value: False
Types: bool
responses:
Optional Argument.
Specifies a list of responses to output.
Note: This argument is required when connected to Vantage prior to Vantage 1.1.1.
Types: str OR list of Strings (str)
terms:
Optional Argument.
Specifies the names of input teradataml DataFrame columns to copy to
the output teradataml DataFrame.
Types: str OR list of Strings (str)
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 NaiveBayesPredict.
Output teradataml DataFrames can be accessed using attribute
references, such as NaiveBayesPredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example
load_example_data("NaiveBayesPredict",["nb_iris_input_test","nb_iris_input_train"])
# Create teradataml DataFrame objects.
nb_iris_input_train = DataFrame.from_table("nb_iris_input_train")
nb_iris_input_test = DataFrame.from_table("nb_iris_input_test")
# Example 1 -
# We will try to predict the 'species' for the flowers represented
# by the data points in the train data (nb_iris_input_train).
naivebayes_train = NaiveBayes(formula="species ~ petal_length + sepal_width + petal_width + sepal_length",
data=nb_iris_input_train)
# Use the generated model to predict the 'species' on the test data
# nb_iris_input_test by using naivebayes_train which is already
# in the sparse format.
naivebayes_predict_result = NaiveBayesPredict(newdata=nb_iris_input_test,
modeldata=naivebayes_train,
newdata_sequence_column=['sepal_width','petal_width'],
id_col='id',
responses=['virginica','setosa','versicolor'],
output_prob=False
)
# Print the result DataFrame
print(naivebayes_predict_result)
- __repr__(self)
- Returns the string representation for a NaiveBayesPredict 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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