27.3.9.7 Regressor Class

• Regressor Constructor

• Regressor.train()

• Regressor.fit()

• Regressor.predict()

• Regressor.score()

• Regressor.explain()

• Regressor.getExplainer()

• Regressor.unload()

This class is similar to Classifier and Forecaster in that it represents an AutoML training model, but encapsulates the regression task as described in the MySQL HeatWave documentation (see Training a Model).

Regressor supports methods for loading, training, and unloading models, predicting labels, calculating probabilities, producing explainers, and related tasks; it also has three accessible instance properties, listed here:

• name (String): The model name.

• metadata (Object): Model metadata stored in the model catalog. See Model Metadata.

• trainOptions (Object): The training options specified in the constructor (shown following).

Regressor Constructor

To obtain an instance of Regressor, simply invoke its constructor, shown here:

Signature

• new ml.Regressor(
    String name[, 
    Object trainOptions]
  )

Arguments

• name (String): Unique identifier for this instance of Regressor.

• trainOptions (Object) (optional): Training options. These are the same as those used with sys.ML_TRAIN.

Return type

• An instance of Regressor.

Regressor was added in MySQL 9.2.0.

Regressor.train()

Trains and loads a new regressor, acting as a wrapper for sys.ML_TRAIN and sys.ML_MODEL_LOAD, specific to the AutoML regression task.

Signature

• Regressor.train(
    Table trainData, 
    String targetColumnName
  )

Arguments

• trainData (Table): A Table which contains a training dataset. The table must not exceed 10 GB in size, or contain more than 100 million rows or more than 1017 columns.

• targetColumnName (String): Name of the target column containing ground truth values; TEXT columns are not supported for this purpose.

Return type

• undefined.

Regressor.fit()

This is merely an alias for train(). In all respects except for their names, the two methods are identical. See Regressor.train(), for more information.

Regressor.predict()

This method predicts labels. predict() has two variants, listed here:

• Stores labels predicted from data found in the indicated table and stores them in an output table; a wrapper for sys.ML_PREDICT_TABLE.

• A wrapper for sys.ML_PREDICT_ROW; predicts a label for a single set of sample data and returns it to the caller.

Both versions of predict() are shown in this section.

Version 1

This version of predict() predicts labels, then saves them in an output table specified when invoking the method.

Signature

• Regressor.predict(
    Table testData, 
    Table outputTable[, 
    Object options]
  )

Arguments

• testData (Table): A table containing test data.

• outputTable (Table): A table for storing the predicted labels. The output's content and format are the same as for that produced by ML_PREDICT_TABLE.

• options (Object) (optional): Set of options in JSON format. See ML_PREDICT_TABLE, for more information.

Return type

• undefined.

Version 2

Predicts a label for a single sample of data, and returns it to the caller. See ML_PREDICT_ROW, for more information.

Signature

• String Regressor.predict(
    Object sample
  )

Arguments

• sample (Object): Sample data. This argument must contain members that were used for training; while extra members may be included, these are ignored for purposes of prediction.

Return type

• String. See ML_PREDICT_ROW.

Regressor.score()

Returns the score for the test data in the table and column indicated by the user, using a specified metric; a JavaScript wrapper for sys.ML_SCORE.

Signature

• score(
    Table testData,
    String targetColumnName, 
    String metric[, 
    Object options]
  )

Arguments

• testData (Table): Table containing test data to be scored; this table must contain the same columns as the training dataset.

• targetColumnName (String): The name of the target column containing ground truth values.

• metric (String): Name of the scoring metric to be employed. Optimization and Scoring Metrics, provides information about metrics compatible with the AutoML regression task.

• options (Object) (optional): A set of options, as keys and values, in JSON format. See the description of ML_SCORE for more information.

Return type

• Number.

Regressor.explain()

This method takes a Table containing a labeled, trained dataset and the name of a table column containing ground truth values, and returns the newly trained explainer; a wrapper for the MySQL HeatWave sys.ML_EXPLAIN routine.

Signature

• explain(
    Table data,
    String targetColumnName[,
    Object options]
  )

Arguments

• data (Table): Table containing trained data.

• targetColumnName (String): Name of column containing ground truth values.

• options (Object) (optional): Set of optional parameters, in JSON format.

Return type

• Adds a model explainer to the model catalog; does not return a value. See ML_EXPLAIN, for more information.

Regressor.getExplainer()

Returns an explainer for this Regressor.

Signature

• Object Regressor.getExplainer()

Arguments

• None.

Return type

• Object

Regressor.unload()

Unloads the model. This method is a wrapper for sys.ML_MODEL_UNLOAD.

Signature

• Regressor.unload()

Arguments

• None.

Return type

• undefined