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- NaiveBayesTextClassifierTrainer(data=None, doc_category_column=None, token_column=None, doc_id_column=None, model_type='MULTINOMIAL', **generic_arguments)
- DESCRIPTION:
The NaiveBayesTextClassifierTrainer() function calculates the conditional probabilities for
token-category pairs, the prior probabilities, and the missing token probabilities for
all categories. The trainer function trains the model with the probability values, and
the predict function uses the values to classify documents into categories.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
doc_category_column:
Required Argument.
Specifies the name of the input data column that contains the
document category.
Types: str
token_column:
Required Argument.
Specifies the name of the input data column that contains the tokens.
Types: str
doc_id_column:
Optional Argument.
Specifies the name of the input data column that contains the
document identifier.
Types: str
model_type:
Optional Argument.
Specifies the model type of the text classifier.
Default Value: "MULTINOMIAL"
Permitted Values: MULTINOMIAL, BERNOULLI
Types: 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: bool
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: bool
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 NaiveBayesTextClassifierTrainer.
Output teradataml DataFrames can be accessed using attribute
references, such as
NaiveBayesTextClassifierTrainerObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. result
2. model_data
RAISES:
TeradataMlException, TypeError, ValueError
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("textparser", ["complaints", "stop_words"])
# Create teradataml DataFrame objects.
complaints = DataFrame.from_table("complaints")
stop_words = DataFrame.from_table("stop_words")
# Check the list of available analytic functions.
display_analytic_functions()
# Tokenize the "text_column" and accumulate result by "doc_id" and "category".
complaints_tokenized = TextParser(data=complaints,
text_column="text_data",
object=stop_words,
remove_stopwords=True,
accumulate=["doc_id", "category"])
# Example 1 : Calculate the conditional probabilities for token-category pairs.
NaiveBayesTextClassifierTrainer_out = NaiveBayesTextClassifierTrainer(data=complaints_tokenized.result,
token_column="token",
doc_category_column="category")
# Print the result DataFrames.
print(NaiveBayesTextClassifierTrainer_out.result)
print(NaiveBayesTextClassifierTrainer_out.model_data)
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