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Methods defined here:
- __init__(self, object=None, obs_column=None, sentiment_column=None, object_sequence_column=None, object_order_column=None)
- DESCRIPTION:
The SentimentEvaluator function uses test data to evaluate the
precision and recall of the predictions output by the function
SentimentExtractor. The precision and recall are affected by the
model that SentimentExtractor uses; therefore, if you change the
model, you must rerun SentimentEvaluator on the new predictions.
PARAMETERS:
object:
Required Argument.
Specifies the input teradataml DataFrame containing a text
column with the input text.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
Values to this argument can be provided as list, if multiple columns
are used for ordering.
Types: str OR list of Strings (str)
obs_column:
Required Argument.
Specifies the name of the input column with the observed sentiment
(POS, NEG or NEU).
Types: str
sentiment_column:
Required Argument.
Specifies the name of the input column with the predicted sentiment
(POS, NEG or NEU).
Types: str
object_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "object". 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 SentimentEvaluator.
Output teradataml DataFrames can be accessed using attribute
references, such as SentimentEvaluatorObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("sentimenttrainer", "sentiment_train")
load_example_data("sentimentextractor", ["sentiment_extract_input", "sentiment_word"])
# Create teradataml DataFrame objects.
sentiment_train = DataFrame.from_table("sentiment_train")
sentiment_extract_input = DataFrame.from_table("sentiment_extract_input")
sentiment_word = DataFrame.from_table("sentiment_word")
# Example 1 -This example uses the dictionary model file 'default_sentiment_lexicon.txt'.
SentimentExtractorOut1 = SentimentExtractor(object = "dictionary",
newdata = sentiment_extract_input,
text_column = "review",
accumulate = ["category"]
)
SentimentEvaluatorOut1 = SentimentEvaluator(object=SentimentExtractorOut1.result,
obs_column='category',
sentiment_column='out_polarity'
)
# Print the results.
print(SentimentEvaluatorOut1)
# Example 2 - This example uses the classification model file
# 'default_sentiment_classification_model.bin'.
SentimentExtractorOut2 = SentimentExtractor(object = "classification:default_sentiment_classification_model.bin",
newdata = sentiment_extract_input,
text_column = "review",
accumulate = ["category"]
)
SentimentEvaluatorOut2 = SentimentEvaluator(object=SentimentExtractorOut2.result,
obs_column='category',
sentiment_column='out_polarity'
)
# Print the results.
print(SentimentEvaluatorOut2)
# Example 3 - This example uses classification model file output by
# the SentimentTrainer function.
SentimentTrainerOut = SentimentTrainer(data = sentiment_train,
text_column = "review",
sentiment_column = "category",
model_file = "sentimentmodel1.bin"
)
# Use the sentiment extractor function to extract sentiment of each input document.
SentimentExtractorOut3 = SentimentExtractor(object = "classification:sentimentmodel1.bin",
newdata = sentiment_extract_input,
text_column = "review",
accumulate = ["category"]
)
SentimentEvaluatorOut3 = SentimentEvaluator(object=SentimentExtractorOut3.result,
obs_column="category",
sentiment_column="out_polarity"
)
# Print the results.
print(SentimentEvaluatorOut3)
# Example 4 - This example uses a dictionary table ('sentiment_word')
# instead of model file.
SentimentExtractorOut4 = SentimentExtractor(newdata = sentiment_extract_input,
dict_data = sentiment_word,
text_column = "review",
accumulate = ["category"]
)
SentimentEvaluatorOut4 = SentimentEvaluator(object=SentimentExtractorOut4.result,
obs_column="category",
sentiment_column="out_polarity"
)
# Print the results.
print(SentimentEvaluatorOut4)
- __repr__(self)
- Returns the string representation for a SentimentEvaluator 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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