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Methods defined here:
- __init__(self, model_file=None, newdata=None, text_column=None, accumulate=None, newdata_sequence_column=None, newdata_order_column=None)
- DESCRIPTION:
The TextClassifier function classifies input text, using a model
output by the function TextClassifierTrainer.
PARAMETERS:
model_file:
Required Argument.
Specifies the model installed in the database using the
TextClassifierTrainer function.
Types: str
newdata:
Required Argument.
Specifies the teradataml DataFrame that contains the text to be
classified.
newdata_order_column:
Required Argument.
Specifies Order By columns for newdata.
Values to this argument can be provided as list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
text_column:
Required Argument.
Specifies the column of the input teradataml DataFrame that
contains the text to be used for predicting classification.
Types: str
accumulate:
Optional Argument.
Specifies the names of the input 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)
RETURNS:
Instance of TextClassifier.
Output teradataml DataFrames can be accessed using attribute
references, such as TextClassifierObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("textclassifiertrainer", "texttrainer_input")
load_example_data("textclassifier", "textclassifier_input")
# Create teradataml DataFrame objects.
# The input table "texttrainer_input" contains text of the training
# documents and the category of the training documents.
texttrainer_input = DataFrame.from_table("texttrainer_input")
# The input table "textclassifier_input" contains the text to be
# classified.
textclassifier_input = DataFrame.from_table("textclassifier_input")
# Generate model file using TextClassifierTrainer function.
textclassifiertrainer_out = TextClassifierTrainer(data=texttrainer_input,
text_column='content',
category_column='category',
classifier_type='knn',
model_file='knn.bin',
nlp_parameters=['useStem:true','stopwordsFile:stopwords.txt'],
classifier_parameters='compress:0.9',
feature_selection='DF:[0.1:0.99]',
data_sequence_column='id'
)
# Example 1 - This example uses model_file "knn.bin" generated by
# TextClassifierTrainer function to classify the input text.
TextClassifier_out = TextClassifier(newdata = textclassifier_input,
model_file = "knn.bin",
text_column = "content",
accumulate = ["id","category"],
newdata_order_column = "id"
)
# Print the result teradataml DataFrame
print(TextClassifier_out)
- __repr__(self)
- Returns the string representation for a TextClassifier 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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