Provider Class Reference

Implements the AutoML provider. More...

Inheritance diagram for Provider:

Collaboration diagram for Provider:

Public Member Functions
_.Library.String	pyval2str (pyval)
	Convert a python value to an SQL string.
Public Member Functions inherited from RegisteredObject
_.Library.Status	OnAddToSaveSet (_.Library.Integer depth, _.Library.Integer insert, _.Library.Integer callcount)
	This callback method is invoked when the current object is added to the SaveSet,. More...
_.Library.Status	OnConstructClone (_.Library.RegisteredObject object, _.Library.Boolean deep, _.Library.String cloned)
	This callback method is invoked by the <METHOD>ConstructClone</METHOD> method to. More...
_.Library.Status	OnNew ()
	This callback method is invoked by the <METHOD>New</METHOD> method to. More...
_.Library.Status	OnValidateObject ()
	This callback method is invoked by the <METHOD>ValidateObject</METHOD> method to. More...

Public Attributes
	initialized
	More...

Static Public Attributes
	PROVIDERNAME = None
	Provider name.
Static Public Attributes inherited from Provider
	DEFAULTPROVIDER = None
	Default provider name.
	PROVIDERNAME = None
	Provider name.
	SQLTYPE = None
	Type of SQL TRAIN will operate on Options are "resutset" or "query". More...
Static Public Attributes inherited from RegisteredObject
	CAPTION = None
	Optional name used by the Form Wizard for a class when generating forms. More...
	JAVATYPE = None
	The Java type to be used when exported.
	PROPERTYVALIDATION = None
	This parameter controls the default validation behavior for the object. More...

Private Member Functions
_.Library.Status	BeginTraining (_.ML.Model model, _.SQL.StatementResult data, _.ML.TrainingRun trainingrun, _.Library.String name, trainkey)
	Train an ML model. More...
_.Library.Status	DataFrameToTempFile (_.Library.Integer tfn, _.SYS.Python df, _.Library.List fieldnames, _.Library.List positions, _.Library.List types, _.Library.List isPredict)
	Update temp file #tfn using the data in DataFrame df Inputs: tfn: Temp file number df: a Python DataFrame fieldnames=$lb(field1, ...): A $List of strings that indicates names of fields in df that will be added to temp file #tfn positions=$lb(pos1, ...): A list of integers that indicates the corresponding positions of each df field in temp file #tfn types=$lb(type1, ...): A list of integers that indicates the corresponding ObjectScript type of each df field in temp file #tfn isPredict=$lb(predict1, ...): A list of integers that indicates if each df field is predict or probablity. More...
_.Library.Status	OnInit ()
	Initialize an ML provider.
_.Library.Status	PredictAll (_.ML.AutoML.TrainedModel trainedmodel, _.Library.Integer tfn, _.Library.List argspos, _.Library.List predpos, _.Library.List probpos, _.Library.String expr, _.Library.List mtorder, _.Library.List mtunary)
	Bulk Predict.
_.Library.Status	ResultSetToDataFrame (_.SQL.StatementResult data, _.Library.RegisteredObject info, _.Library.RegisteredObject df, _.Library.Integer count, _.Library.String predictingColumn)
	Convert an IRIS result set into a dataframe. More...
_.Library.Status	StartProfiler (_.Library.String options, _.SYS.Python profiler)
	Start the Python profiler.
_.Library.Status	StopProfiler (_.SYS.Python profiler, _.Library.String sortby, _.Library.String results)
	Stop the Python profiler.
_.Library.Status	TSDataFrameToTempFile (_.Library.Integer tfn, _.SYS.Python df, _.SYS.Python tsheaders, _.Library.String datetimecolumn, _.Library.List channelColumns, _.Library.List channelTypes, _.Library.List mtorder, _.Library.List mtunary)
	Update temp file #tfn using the data in DataFrame df acquired from TimeSeries predictions Inputs: tfn: Temp file number df: a Python DataFrame headers: IRIS table column names pcTypes: datetime column name.
_.Library.Status	TempFileToDataFrame (_.Library.List columns, _.Library.List types, _.Library.Integer tfn, _.Library.List argspos, _.SYS.Python df, _.Library.Integer count, _.Library.List mtorder, _.Library.List mtunary)
	Convert an IRIS temp file into Python Pandas DataFrame data.
_.Library.Status	WaitForTraining (trainkey, _.ML.TrainingRun trainingrun, _.ML.TrainedModel trainedmodel, _.Library.Integer timeoutMS)
	Check for training complete.

Static Private Member Functions
	GetDefaultSettings (_.Library.DynamicObject settings)
	Adds the default settings for AutoML to the settings dynamic object.
_.Library.Status	ResultSetMetaData (_.SQL.StatementResult data, _.Library.RegisteredObject info, _.Library.List columns, _.Library.List types)
	Determine the metadata for a result set.

Detailed Description

Implements the AutoML provider.

Member Function Documentation

BeginTraining()

_.Library.Status BeginTraining ( _.ML.Model  model, _.SQL.StatementResult  data, _.ML.TrainingRun  trainingrun, _.Library.String  name,   trainkey  )

private

Train an ML model.

name is no longer used. trainingrun.name is already defined

Reimplemented from Provider.

DataFrameToTempFile()

_.Library.Status DataFrameToTempFile ( _.Library.Integer  tfn, _.SYS.Python  df, _.Library.List  fieldnames, _.Library.List  positions, _.Library.List  types, _.Library.List  isPredict  )

private

Update temp file #tfn using the data in DataFrame df Inputs: tfn: Temp file number df: a Python DataFrame fieldnames=$lb(field1, ...): A $List of strings that indicates names of fields in df that will be added to temp file #tfn positions=$lb(pos1, ...): A list of integers that indicates the corresponding positions of each df field in temp file #tfn types=$lb(type1, ...): A list of integers that indicates the corresponding ObjectScript type of each df field in temp file #tfn isPredict=$lb(predict1, ...): A list of integers that indicates if each df field is predict or probablity.

If predict=1, this is predict, otherwise, probability

ResultSetToDataFrame()

_.Library.Status ResultSetToDataFrame ( _.SQL.StatementResult  data, _.Library.RegisteredObject  info, _.Library.RegisteredObject  df, _.Library.Integer  count, _.Library.String  predictingColumn  )

private

Convert an IRIS result set into a dataframe.

If the label column, predictingColumn, is defined,then rows with missing values in the label column will be excluded from the dataframe.

Member Data Documentation

initialized

initialized