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Methods defined here:
- __init__(self, object=None, newdata=None, input_token_column=None, doc_id_columns=None, model_type='MULTINOMIAL', top_k=None, model_token_column=None, model_category_column=None, model_prob_column=None, newdata_partition_column=None, newdata_order_column=None, object_order_column=None)
- DESCRIPTION:
The NaiveBayesTextClassifierPredict function uses the model generated by the
NaiveBayesTextClassifier function to predict the outcomes for a test set
of data.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model data
or instance of NaiveBayesTextClassifier.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
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 input test
data.
newdata_partition_column:
Required Argument.
Specifies Partition By columns for newdata.
Values to this argument can be provided as a list, if multiple
columns are used for partition.
Types: str OR list of Strings (str)
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)
input_token_column:
Required Argument.
Specifies the name of the newdata column that contains the tokens.
Types: str
doc_id_columns:
Required Argument.
Specifies the names of the newdata columns that contain the
document identifier.
Types: str OR list of Strings (str)
model_type:
Optional Argument.
Specifies the model type of the text classifier.
Default Value: 'MULTINOMIAL'
Permitted Values: 'MULTINOMIAL', 'BERNOULLI'
Types: str
top_k:
Optional Argument.
Specifies the number of most likely prediction categories to output
with their log-likelihood values (for example, the top 10 most likely
prediction categories). The default is all prediction categories.
Types: int
model_token_column:
Optional Argument.
Specifies the name of the object column that contains the
tokens. The default value is the first column of object.
Types: str
model_category_column:
Optional Argument.
Specifies the name of the object column that contains the
prediction categories. The default value is the second column of
object.
Types: str
model_prob_column:
Optional Argument.
Specifies the name of the object column that contains the token
counts. The default value is the third column of object.
Types: str
RETURNS:
Instance of NaiveBayesTextClassifierPredict.
Output teradataml DataFrames can be accessed using attribute
references, such as
NaiveBayesTextClassifierPredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example
load_example_data("NaiveBayesTextClassifierPredict",["complaints_tokens_test","token_table"])
# Create teradataml DataFrame.
token_table = DataFrame("token_table")
complaints_tokens_test = DataFrame("complaints_tokens_test")
# Create a model which is output of NaiveBayesTextClassifier
nbt_out = NaiveBayesTextClassifier(data = token_table,
token_column = 'token',
doc_id_columns = 'doc_id',
doc_category_column = 'category',
model_type = "Bernoulli",
data_partition_column = 'category')
# Example 1 -
nbt_predict_out = NaiveBayesTextClassifierPredict(object = nbt_out,
newdata = complaints_tokens_test,
input_token_column = 'token',
doc_id_columns = 'doc_id',
model_type = "Bernoulli",
model_token_column = 'token',
model_category_column = 'category',
model_prob_column = 'prob',
newdata_partition_column = 'doc_id')
# Print the result DataFrame
print(nbt_predict_out.result)
- __repr__(self)
- Returns the string representation for a NaiveBayesTextClassifierPredict 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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