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Methods defined here:
- __init__(self, data=None, text_column=None, sentiment_column=None, language='en', model_file=None, data_sequence_column=None)
- DESCRIPTION:
The SentimentTrainer function trains a model. It takes training
documents and outputs a maximum entropy classification model, which
it installs on the ML Engine. For information about maximum entropy,
see http://en.wikipedia.org/wiki/Maximum entropy method.
PARAMETERS:
data:
Required Argument.
Specifies the name of the teradataml DataFrame that contains the
training data.
text_column:
Required Argument.
Specifies the name of the input teradataml DataFrame column that
contains the training data.
Types: str
sentiment_column:
Required Argument.
Specifies the name of the input teradataml DataFrame column that
contains the sentiment values, which are "POS" (positive), "NEG"
(negative), and "NEU" (neutral).
Types: str
language:
Optional Argument.
Specifies the language of the training data: "en" (English), "zh_CN"
(Simplified Chinese), "zh_TW" (Traditional Chinese).
Default Value: "en"
Permitted Values: en, zh_CN, zh_TW
Types: str
model_file:
Required Argument.
Specifies the name of the file to which the function outputs the
model.
Types: str
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "data". 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 SentimentTrainer.
Output teradataml DataFrames can be accessed using attribute
references, such as SentimentTrainerObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("sentimenttrainer", "sentiment_train")
# Create teradataml DataFrame objects.
# The sample dataset contains collection of user reviews for different products.
sentiment_train = DataFrame.from_table("sentiment_train")
# Example 1 - Build a model and output a maximum entropy classification
# model to a binary file
SentimentTrainer_out = SentimentTrainer(data = sentiment_train,
text_column = "review",
sentiment_column = "category",
model_file = "sentimentmodel1.bin"
)
# Print the results.
print(SentimentTrainer_out)
- __repr__(self)
- Returns the string representation for a SentimentTrainer class instance.
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