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Methods defined here:
- __init__(self, object=None, newdata=None, sample_id_column=None, attribute_column=None, value_column=None, accumulate_label=None, output_class_num=1, newdata_partition_column=None, newdata_order_column=None, object_order_column=None)
- DESCRIPTION:
The SVMSparsePredict function takes the model generated by the
SVMSparse trainer function and a set of test samples (in sparse
format) and outputs a prediction for each sample.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model
data generated by SVMSparse or instance of SVMSparse.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
newdata:
Required Argument.
Specifies the teradataml DataFrame containing the input test data.
newdata_partition_column:
Required Argument.
Specifies Partition By columns for newdata.
Values to this argument can be provided as a list, if multiple
columns are used for partition.
Types: str OR list of Strings (str)
newdata_order_column:
Optional Argument.
Specifies Order By columns for newdata.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
sample_id_column:
Required Argument.
Specifies the name of the newdata column that contains the
identifiers of the test samples. The newdata table must be
partitioned by this column.
Types: str
attribute_column:
Required Argument.
Specifies the name of the newdata column that contains the
attributes of the test samples.
Types: str
value_column:
Optional Argument.
Specifies the name of the newdata column that contains the
attribute values. By default, each attribute has the value 1.
Types: str
accumulate_label:
Optional Argument.
Specifies the names of the newdata columns to copy to the
output teradataml DataFrame.
Types: str OR list of Strings (str)
output_class_num:
Optional Argument.
Valid only for multiple-class models. Specifies the number of class
labels to appear in the output teradataml DataFrame, with its corresponding
prediction confidence.
Default Value: 1
Types: int
RETURNS:
Instance of SVMSparsePredict.
Output teradataml DataFrames can be accessed using attribute
references, such as SVMSparsePredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("SVMSparsePredict",["svm_iris_input_train","svm_iris_input_test"])
# Create teradataml DataFrame
svm_iris_input_train = DataFrame.from_table("svm_iris_input_train")
svm_iris_input_test = DataFrame.from_table("svm_iris_input_test")
# Create SparseSVMTrainer object
svm_train = SVMSparse(data=svm_iris_input_train,
sample_id_column='id',
attribute_column='attribute',
label_column='species',
value_column='value1',
max_step=150,
seed=0,
)
# Example 1
# Instance of SVMTrainer is passed as input to object argument
svm_sparse_predict_result1 = SVMSparsePredict(newdata=svm_iris_input_test,
newdata_partition_column=['id'],
object=svm_train,
attribute_column='attribute',
sample_id_column='id',
value_column='value1',
accumulate_label='species'
)
# Print the result DataFrame
print(svm_sparse_predict_result1.result)
# Example 2
# teradataml DataFrame containing the model data generated by SVMSparse is passed as input to object argument
svm_sparse_predict_result2 = SVMSparsePredict(newdata=svm_iris_input_test,
newdata_partition_column=['id'],
object=svm_train.model_table,
attribute_column='attribute',
sample_id_column='id',
value_column='value1',
accumulate_label='species'
)
# Print the result DataFrame
print(svm_sparse_predict_result2.result)
- __repr__(self)
- Returns the string representation for a SVMSparsePredict 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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