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Methods defined here:
- __init__(self, data=None, obs_column=None, predict_column=None, classes=None, beta=1.0, data_sequence_column=None, data_order_column=None)
- DESCRIPTION:
The FMeasure function calculates the accuracy of a test (usually the
output of a classifier).
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame containing the output of a classifier.
data_order_column:
Optional Argument.
Specifies Order By columns for data.
Values to this argument can be provided as a 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 teradataml DataFrame column that
contains the observed class.
Types: str
predict_column:
Required Argument.
Specifies the name of the input teradataml DataFrame column that
contains the predicted class.
Types: str
classes:
Optional Argument.
Specifies the class or classes to output in the result. The default
is all classes.
Types: str OR list of strs
beta:
Optional Argument.
Specifies the value of beta in the F-measure formula that the function
implements. The beta_value must be a positive float value.
Default Value: 1.0
Types: float
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 FMeasure.
Output teradataml DataFrames can be accessed using attribute
references, such as FMeasureObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("fmeasure", "computers_category")
# Create teradataml DataFrame object.
computers_category = DataFrame.from_table("computers_category")
# Example 1 - The input DataFrame is computers_category, running FMeasure to calculate
accuracy of all classes.
fmeasure_out1 = FMeasure(data=computers_category,
obs_column='expected_compcategory',
predict_column='predicted_compcategory',
beta=1.0,
data_sequence_column='compid'
)
# Print the output DataFrames.
print(fmeasure_out1.result)
# Example 2 - Running FMeasure to calculate accuracy of Specified Classes "special" and "hyper".
fmeasure_out2 = FMeasure(data=computers_category,
obs_column='expected_compcategory',
predict_column='predicted_compcategory',
classes=['special','hyper'],
beta=1.0,
data_sequence_column='compid'
)
# Print the output DataFrames.
print(fmeasure_out2.result)
- __repr__(self)
- Returns the string representation for a FMeasure 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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