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- NaiveBayesTextClassifierPredict(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, output_prob=False, responses=None, accumulate=None, **generic_arguments)
- DESCRIPTION:
The NaiveBayesTextClassifierPredict() function uses the model generated by the
NaiveBayesTextClassifierTrainer() function to predict the outcomes for a test set
of data.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame which contains the model
data generated by the NaiveBayesTextClassifierTrainer() function or
instance of NaiveBayesTextClassifierTrainer.
Types: teradataml DataFrame or NaiveBayesTextClassifierTrainer
newdata:
Required Argument.
Specifies the teradataml DataFrame containing the input test
data.
Types: teradataml DataFrame
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.
Permitted Values: 'MULTINOMIAL', 'BERNOULLI'
Default Value: 'MULTINOMIAL'
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
output_prob:
Optional Argument.
Specifies whether to output probabilities.
Default Value: False
Types: bool
responses:
Optional Argument.
Specifies a list of Responses to output.
Types: str OR list of strs
accumulate:
Optional Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to copy to the
output. By default, the function copies no input teradataml
DataFrame columns to the output.
Types: str OR list of Strings (str)
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in a table or not.
When set to True, results are persisted in a table; otherwise, results
are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not.
When set to True, results are stored in a volatile table, otherwise not.
Default Value: False
Types: boolean
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying
SQL Engine function supports, else an exception is raised.
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:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Load the example data.
load_example_data("NaiveBayesTextClassifierPredict",["complaints_tokens_test","token_table"])
# Create teradataml DataFrame objects.
token_table = DataFrame("token_table")
complaints_tokens_test = DataFrame("complaints_tokens_test")
# Check the list of available analytic functions.
display_analytic_functions()
# Create a model which is output of NaiveBayesTextClassifierTrainer.
nbt_out = NaiveBayesTextClassifierTrainer(data = token_table,
token_column = 'token',
doc_id_column = 'doc_id',
doc_category_column = 'category',
model_type = "Bernoulli",
data_partition_column = 'category')
# Example: Run NaiveBayesTextClassifierPredict() on model generated by
# NaiveBayesTextClassifierTrainer() where model_type is "Bernoulli".
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)
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