The MLPCRunDF class defines a new wrapper function that uses the Aster Spark API and implements the run/test phase of the Spark MLlib MultilayerPerceptronClassifier (MLP), both by itself and in a pipeline with Principal Component Analysis (PCA). The function uses a model that is typically generated by the MLPCTrainDF function.
Run Method Signature
run(inputDF: DataFrame, functParams: String): DataFrame
Parameters
String representing the parameters specific to the function you are implementing. The string has this syntax:
'--option_value_pair [,...]'
option_value_pair is one of the following:
-
labelCol label_column
[Optional] Specifies the name of the column that contains labels.
-
modelLocation model_location
Required. Specifies the HDFS path to the location where the function is to save the model.
-
ignoreCols column[,...]
[Optional] Specifies the names of input columns to copy to the output table.
Returns
The labels in label_column (if specified), the predicted values, and the input columns.
Version
Spark 1.5 and later.