Bayesian Optimization

Associated Constructors

Bayesian Optimization

Syntax: Bayesian Optimization( Y( columns ), X( columns ) )

Description: Recommends factor settings to optimize responses by augmenting the data table.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);

Columns

Iteration

Syntax: obj << Iteration( column )

Description: Specifies a batch label column. Batches are expected to be labeled 0, 1, 2, ..., where batch 0 indicates the original training data.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_itercol",
    Numeric,
    Ordinal,
    set values( V Concat( (Repeat( 0, N Rows( dt ) - 10 )), Repeat( 1, 10 ) ) )
);
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Iteration( _itercol )
);

Run Order

Syntax: obj << Run Order( column )

Description: Specifies a permutation column of row numbers that indicate the order of observations.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_runorder", Numeric, Ordinal, set values( 1 :: (N Rows( dt )) ) );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Run Order( _runorder )
);

X

Syntax: obj << X( column(s) )

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);

Y

Syntax: obj << Y( column(s) )

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);

Item Messages

Automatically Generate a Batch

Syntax: obj << Automatically Generate a Batch( state=0|1 )

Description: Indicates whether to run automatic candidate set generation and batch selection. Optionally, you can specify which method to use to select batches. This option is equivalent to specifying both the Generate Candidate Set option and Autoselect Batch option at the same time.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Automatically Generate a Batch( 1 )
);

Autoselect Batch

Syntax: obj << Autoselect Batch( state=0|1 )

Description: Selects a batch from the currently loaded candidate set. If no candidate set is loaded, a space-filling set of size 1000 times the number of input variables is generated. This option can also be used to turn off auto batch selection on launch.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Generate Candidate Set(
        Candidate Set Size( 10 ),
        Include Runs that Do Not Conform to Constraints( 0 )
    ),
    Autoselect Batch( Batch Size( 1 ), Minimum RSquare( 0.5 ) )
);

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Generate Candidate Set(
        Candidate Set Size( 10 ),
        Include Runs that Do Not Conform to Constraints( 0 )
    ),
    Autoselect Batch( 0 )
);

Example 3

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Automatically Generate a Batch( 0 )
);
obj << Autoselect Batch( Batch Size( 5 ), Augmentation Method( Space Filling Exploration ) );

Batch Size

Syntax: obj << Batch Size( number )

Description: Specifies the batch size to autoselect on launch.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Batch Size( 5 )
);

Candidate Set Size

Syntax: obj << Candidate Set Size( number )

Description: Specifies the desired candidate set size to generate.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Candidate Set Size( 10 )
);

Continuous Correlation Type

Syntax: obj << Continuous Correlation Type( "Gaussian"|"Matern 3/2"|"Matern 5/2"|"Exponential" )

Description: Specifies the desired kernel for continuous input variables.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Continuous Correlation Type( "Matern 5/2" )
);

Generate Candidate Set

Syntax: obj << Generate Candidate Set( Candidate Set Size( number ), <Include Runs that Do Not Conform to Constraints( state = 0|1 )> )

Description: Generates a candidate set. You can provide candidate set size and specify whether to allow points that violate the linear constraints in the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Generate Candidate Set(
        Candidate Set Size( 10 ),
        Include Runs that Do Not Conform to Constraints( 0 )
    )
);

Include Runs that Do Not Conform to Constraints

Syntax: obj << Include Runs that Do Not Conform to Constraints( state=0|1 )

Description: Specifies whether to include points that violate the linear constraints in the data table when generating or loading a candidate set.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Include Runs that Do Not Conform to Constraints( 0 )
);

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dtCand = New Table( "Tiretread Candidate Set",
    Add Rows( 15 ),
    New Column( "SILICA",
        Continuous,
        Set Values(
            [1.2, 1.60825, 0.79175, 0.995875, 1.812375, 1.404125, 0.587625, 0.6896875,
            1.5061875, 1.9144375, 1.0979375, 0.8938125, 1.7103125, 1.3020625, 0.4855625]
        )
    ),
    New Column( "SILANE",
        Continuous,
        Set Values(
            [50, 41.835, 58.165, 45.9175, 62.2475, 37.7525, 54.0825, 43.87625, 60.20625,
            35.71125, 52.04125, 39.79375, 56.12375, 47.95875, 64.28875]
        )
    ),
    New Column( "SULFUR",
        Continuous,
        Set Values(
            [2.3, 1.89175, 2.70825, 2.504125, 1.687625, 2.912375, 2.095875, 3.0144375,
            2.1979375, 2.6061875, 1.7896875, 1.9938125, 2.8103125, 1.5855625, 2.4020625]
        )
    )
);
dt << New Script( "Constraint", {:SILICA + :SULFUR <= 3} );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Include Runs that Do Not Conform to Constraints( 0 ),
    Load Candidate Set from Data Table( dtCand )
);

Minimum RSquare

Syntax: obj << Minimum RSquare( number )

Description: Specifies the minimum required R-square metric for the batch autoselection algorithm. In the Bayesian Optimization platform launch window, this option is referred to as Model Based Augmentation RSquare Threshold.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Minimum RSquare( 0.25 )
);

Nominal Correlation Type

Syntax: obj << Nominal Correlation Type( "Equal Correlations"|"Unequal Correlations" )

Description: Specifies the desired kernel for nominal input variables.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Nominal Correlation Type( "Equal Correlations" )
);

Ordinal Correlation Type

Syntax: obj << Ordinal Correlation Type( "Equal Correlations"|"Unequal Correlations"|"Latent Variable" )

Description: Specifies the desired kernel for ordinal input variables.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Ordinal Correlation Type( "Equal Correlations" )
);

Save Prediction Formula

Syntax: obj << Save Prediction Formula

Description: Saves the prediction formula to a new column in the data table.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Prediction Formula;

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Prediction Formula( Elong );

Set Tab

Syntax: obj << Set Tab( number )

Description: Specifies the current tab. The argument interprets 0 as the Model Summary tab, 1 as Batch Selection, and so on, in the order in which the tabs appear in the report window.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << set tab( 1 );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << set tab( "ABRASION" );

Shared Item Messages

Action

Syntax: obj << Action

Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Apply Preset

Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )

Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.

JMP Version Added: 18

Anonymous preset

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
dt2 = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj2 = dt2 << Oneway( Y( :LogHist0 ), X( :drug ) );
Wait( 1 );
obj2 << Apply Preset( preset );

Search by name

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "Compare Distributions" );

Search within folder(s)

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "t-Tests", Folder( "Compare Means" ) );

Automatic Recalc

Syntax: obj << Automatic Recalc( state=0|1 )

Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Column Switcher

Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)

Description: Adds a control panel for changing the platform's variables

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);

Copy Script

Syntax: obj << Copy Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Copy Script;

Data Table Window

Syntax: obj << Data Table Window

Description: Move the data table window for this analysis to the front.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Data Table Window;

Get By Levels

Syntax: obj << Get By Levels

Description: Returns an associative array mapping the by group columns to their values.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;

Get Container

Syntax: obj << Get Container

Description: Returns a reference to the container box that holds the content for the object.

General

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );

Platform with Filter

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
gb = Graph Builder(
    Show Control Panel( 0 ),
    Variables( X( :height ), Y( :weight ) ),
    Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
    Local Data Filter(
        Add Filter(
            columns( :age, :sex, :height ),
            Where( :age == {12, 13, 14} ),
            Where( :sex == "F" ),
            Where( :height >= 55 ),
            Display( :age, N Items( 6 ) )
        )
    )
);
New Window( "platform boxes",
    H List Box(
        Outline Box( "Report(platform)", Report( gb ) << Get Picture ),
        Outline Box( "platform << Get Container", (gb << Get Container) << Get Picture )
    )
);

Get Data Table

Syntax: obj << Get Data Table

Description: Returns a reference to the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Script

Syntax: obj << Get Script

Description: Creates a script (JSL) to produce this analysis and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
t = obj << Get Script;
Show( t );

Get Script With Data Table

Syntax: obj << Get Script With Data Table

Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
t = obj << Get Timing;
Show( t );

Get Web Support

Syntax: obj << Get Web Support

Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
s = obj << Get Web Support();
Show( s );

Get Where Expr

Syntax: obj << Get Where Expr

Description: Returns the Where expression for the data subset, if the platform was launched with By() or Where(). Otherwise, returns Empty()

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv2 = dt << Bivariate( X( :height ), Y( :weight ), Where( :age < 14 & :height > 60 ) );
Show( biv[1] << Get Where Expr, biv2 << Get Where Expr );

Ignore Platform Preferences

Syntax: Ignore Platform Preferences( state=0|1 )

Description: Ignores the current settings of the platform's preferences. The message is ignored when sent to the platform after creation.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Ignore Platform Preferences( 1 ),
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Local Data Filter

Syntax: obj << Local Data Filter

Description: To filter data to specific groups or ranges, but local to this platform

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);

New Preset

Syntax: obj = New Preset()

Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();

Paste Local Data Filter

Syntax: obj << Paste Local Data Filter

Description: Apply the local data filter from the clipboard to the current report.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
filter = dist << Local Data Filter(
    Add Filter( columns( :Region ), Where( :Region == "MW" ) )
);
filter << Copy Local Data Filter;
dist2 = Distribution( Continuous Distribution( Column( :Lead ) ) );
Wait( 1 );
dist2 << Paste Local Data Filter;

Redo Analysis

Syntax: obj << Redo Analysis

Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Redo Analysis;

Relaunch Analysis

Syntax: obj << Relaunch Analysis

Description: Opens the platform launch window and recalls the settings that were used to create the report.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Relaunch Analysis;

Remove Column Switcher

Syntax: obj << Remove Column Switcher

Description: Removes the most recent Column Switcher that has been added to the platform.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;

Remove Local Data Filter

Syntax: obj << Remove Local Data Filter

Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);
Wait( 2 );
dist << remove local data filter;

Report

Syntax: obj << Report; Report( obj )

Description: Returns a reference to the report object.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Report View

Syntax: obj << Report View( "Full"|"Summary" )

Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Report View( "Summary" );

Save Script for All Objects

Syntax: obj << Save Script for All Objects

Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Script for All Objects;

Save Script for All Objects To Data Table

Syntax: obj << Save Script for All Objects To Data Table( <name> )

Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    Set Values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    By( :_bycol ),
    Group Options( Return Group( 1 ) )
);
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    Set Values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    By( :_bycol ),
    Group Options( Return Group( 1 ) )
);
obj[1] << Save Script for All Objects To Data Table( "My Script" );

Save Script to Data Table

Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );

Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );

Save Script to Journal

Syntax: obj << Save Script to Journal

Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Script to Journal;

Save Script to Report

Syntax: obj << Save Script to Report

Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Script to Report;

Save Script to Script Window

Syntax: obj << Save Script to Script Window

Description: Create a JSL script to produce this analysis, and append it to the current Script text window.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Save Script to Script Window;

SendToByGroup

Syntax: SendToByGroup( {":Column == level"}, command );

Description: Sends platform commands or display customization commands to each level of a by-group.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    By( :Sex ),
    SendToByGroup(
        {:sex == "F"},
        Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) )
    ),
    SendToByGroup( {:sex == "M"}, Continuous Distribution( Column( :weight ) ) )
);

SendToEmbeddedScriptable

Syntax: SendToEmbeddedScriptable( Dispatch( "Outline name", "Element name", command );

Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.

dt = Open( "$SAMPLE_DATA/Reliability/Fan.jmp" );
dt << Life Distribution(
    Y( :Time ),
    Censor( :Censor ),
    Censor Code( 1 ),
    <<Fit Weibull,
    SendToEmbeddedScriptable(
        Dispatch(
            {"Statistics", "Parametric Estimate - Weibull", "Profilers", "Density Profiler"},
            {1, Confidence Intervals( 0 ), Term Value( Time( 6000, Lock( 0 ), Show( 1 ) ) )}
        )
    )
);

SendToReport

Syntax: SendToReport( Dispatch( "Outline name", "Element name", Element type, command );

Description: Send To Report is used in tandem with the Dispatch command to customize the appearance of a report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :age ) ),
    Continuous Distribution( Column( :weight ) ),
    SendToReport( Dispatch( "age", "Distrib Nom Hist", FrameBox, {Frame Size( 178, 318 )} ) )
);

Sync to Data Table Changes

Syntax: obj << Sync to Data Table Changes

Description: Sync with the exclude and data changes that have been made.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
Wait( 1 );
dt << Delete Rows( dt << Get Rows Where( :Region == "W" ) );
dist << Sync To Data Table Changes;

Title

Syntax: obj << Title( "new title" )

Description: Sets the title of the platform.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Title( "My Platform" );

Top Report

Syntax: obj << Top Report

Description: Returns a reference to the root node in the report.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

View Web XML

Syntax: obj << View Web XML

Description: Returns the XML code that is used to create the interactive HTML report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;

Bayesian Optimization Batch Customizer > Candidate Set View

Item Messages

Export Candidate Set to Data Table

Syntax: obj << Export Candidate Set to Data Table

Description: Exports the currently loaded candidate set to a new data table. You can specify desired column groups as arguments. This option exports factor settings by default if no column groups provided. If no arguments are specified, a window appears where you can specify options.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Export Candidate Set to Data Table( Go );

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Export Candidate Set to Data Table();

Example 3

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Export Candidate Set to Data Table(
    Order Added, Factor Settings, Bayesian Desirability, Bayesian Desirability Std Dev,
    Multimodel Prediction Std Dev, MaxPro Space Filling Criterion,
    Bayesian Desirability Expected Improvement, Bayesian Desirability Upper Confidence Bound,
    Training Response Predictions, Augmented Response Prediction Std Dev,
    Augmented Response Prediction Confidence Intervals
);

Select Runs

Syntax: obj << Select Runs( Row Index( [ numbers ] ), <Order Added( [ numbers ]>, <Reason Added( { text } )>, <Replace( 0|1 )> )

Description: Selects rows from the candidate set table to add to the current batch.

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Select Runs(
        Row Index( [3 5] ),
        Order Added( [1 2] ),
        Reason Added( {"Custom Reason", "Custom Reason"} ),
        Replace( 1 )
    )
);

Example 2

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    autoselect batch( 0 )
);
obj << Select Runs(
    Row Index( [3 5] ),
    Order Added( [1 2] ),
    Reason Added( {"Custom Reason", "Custom Reason"} ),
    Replace( 0 )
);

Show Table Columns

Syntax: obj << Show Table Columns( <"Column Group Name">,... )

Description: Specifies which groups of columns are visible in the candidate set table.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Show Table Columns( Order Added, Factor Settings, Bayesian Desirability )
);

Bayesian Optimization Batch Customizer

Item Messages

Add Current Profiler Settings to Batch

Syntax: obj << Add Current Profiler Settings to Batch

Description: Adds the current profiler settings to the candidate set and selects it for inclusion as a run in the next augmentation batch.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Add Current Profiler Settings to Batch;

Augmented Acquisition Functions Profiler

Syntax: obj << Augmented Acquisition Functions Profiler( state=0|1 )

Description: Shows or hides a profiler that enables you to explore how each acquisition function changes with respect to changes in each factor value. Functions are conditional on the assumption that the points in the current batch will be sampled. This profiler reflects changes to factor levels and desirability functions made in the Augmented Prediction Profiler.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Augmented Acquisition Functions Profiler( 0 );

Augmented Prediction Profiler

Syntax: obj << Augmented Prediction Profiler( state=0|1 )

Description: Shows or hides a profiler that enables you to explore how each column changes with respect to changes in each factor value across models. Predictions are conditional on the assumption that the points in the current batch will be sampled.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Augmented Prediction Profiler( 0 );

Deselect All

Syntax: obj << Deselect All

Description: Deselect all points in current batch.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Deselect All;

Load Candidate Set from Data Table

Syntax: obj << Load Candidate Set from Data Table

Example 1

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Load Candidate Set from Data Table()
);

Example 2

dt = Open( "$SAMPLE_DATA/Design Experiment/Borehole Latin Hypercube.jmp" );
:log y << Set Property( "Response Limits", {Goal( maximize ), Importance( 1 )} );
obj = dt << Bayesian Optimization(
    Y( :log y ),
    X( :log10 Rw, :log10 R, :Tu, :Tl, :Hu, :Hl, :L, :Kw )
);
dt_candidate = Open( "$SAMPLE_DATA/Design Experiment/Borehole Uniform.jmp" );
obj << Load Candidate Set from Data Table( dt_candidate );

Make Table

Syntax: obj << Make Table

Description: Export currently selected batch points to data table based on current settings.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Make Table;

Make Table Options

Syntax: obj << Make Table Options( <Location( state = 0|1 )>, <Randomize Runs( state = 0|1 )>, < "Include Option Name"( state = 0|1 ) > , ... )

Description: Enables you to select option settings that are used when exporting the selected batch to a data table. Note the "Include Option Name" syntax input refers to any of the options under the Include Options menu.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Make Table Options(
        Location( 1 ),
        Randomize Runs( 0 ),
        Save desirability function values to columns( 1 ),
        Save startup script for next batch selection to data table( 1 ),
        Include observed desirabilities( 1 ),
        Include original candidate set row indices( 1 ),
        Include reason added column( 1 ),
        Include predicted response values( 1 ),
        Include prediction standard deviations( 1 ),
        Include Bayesian desirability expected improvement column( 1 )
    )
);

Maximize Bayesian Desirability

Syntax: obj << Maximize Bayesian Desirability

Description: Finds the factor settings that maximize the posterior mean of the desirability distribution.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Bayesian Desirability;

Maximize Bayesian Desirability Std Dev

Syntax: obj << Maximize Bayesian Desirability Std Dev

Description: Finds the factor settings that maximize the posterior deviation of the desirability distribution.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Bayesian Desirability Std Dev;

Maximize Expected Improvement

Syntax: obj << Maximize Expected Improvement

Description: Finds the factor settings with the largest expected improvement as measured by Bayesian desirability.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Expected Improvement;

Maximize MaxPro Criterion

Syntax: obj << Maximize MaxPro Criterion

Description: Finds the most space filling factor settings using the MaxPro criterion.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize MaxPro Criterion;

Maximize Multimodel Std Dev

Syntax: obj << Maximize Multimodel Std Dev

Description: Finds the factor settings that maximize the multiple response prediction standard deviation.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Multimodel Std Dev;

Maximize Upper Confidence Bound

Syntax: obj << Maximize Upper Confidence Bound

Description: Finds the factor settings for the Bayesian desirability prediction with the highest upper confidence bound. This is often called the UCB criterion.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Upper Confidence Bound;

Restore Best Training Point

Syntax: obj << Restore Best Training Point

Description: Returns factor settings to those of the training row with the highest observed desirability.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR )
);
obj << Maximize Bayesian Desirability;
obj << Add Current Profiler Settings to Batch;
obj << Restore Best Training Point;

Bayesian Optimization Model Summary

Item Messages

All Responses Profiler

Syntax: obj << All Responses Profiler( state=0|1 )

Description: Explores how each column changes with respect to changes in each factor value across models.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    All Responses Profiler( 1 )
);

Gaussian Process Model

Item Messages

Intercept

Syntax: obj << Intercept( number )

Description: Specifies the value to use as an intercept parameter for fitting a Gaussian Process model. If all theta, nugget, residual, and intercept values are provided, the values are treated as fixed. If a partial set of values is provided, the given values are treated as starting values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab(
        Y( :ABRASION ),
        Theta Values( {0.5, 0.5, 0.5} ),
        Nugget( 0.05 ),
        Residual( 500 ),
        Intercept( 100 )
    )
);

Nugget

Syntax: obj << Nugget( number )

Description: Specifies the value to use as a nugget for fitting a Gaussian Process model. If all theta, nugget, residual, and intercept values are provided, the values are treated as fixed. If a partial set of values is provided, the given values are treated as starting values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab(
        Y( :ABRASION ),
        Theta Values( {0.5, 0.5, 0.5} ),
        Nugget( 0.05 ),
        Residual( 500 ),
        Intercept( 100 )
    )
);

Profiler

Syntax: obj << Profiler( state=0|1 )

Description: Explores how each column changes with respect to changes in each factor value across models.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
obj = dt << Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab( Y( :MODULUS ), Profiler( 0 ) )
);

Residual

Syntax: obj << Residual( number )

Description: Specifies the value to use as a residual parameter for fitting a Gaussian Process model. If all theta, nugget, residual, and intercept values are provided, the values are treated as fixed. If a partial set of values is provided, the given values are treated as starting values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab(
        Y( :ABRASION ),
        Theta Values( {0.5, 0.5, 0.5} ),
        Nugget( 0.05 ),
        Residual( 500 ),
        Intercept( 100 )
    )
);

Starting Values

Syntax: obj << Starting Values( number )

Description: Specifies the starting values to use for fitting a Gaussian Process model.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab(
        Y( :ABRASION ),
        Starting Values(
            Theta Values( {0.5, 0.5, 0.5} ),
            Nugget( 0.05 ),
            Residual( 500 ),
            Intercept( 100 )
        )
    )
);

Theta Values

Syntax: obj << Theta Values( number )

Description: Specifies the values to use as theta parameters for fitting a Gaussian Process model. If all theta, nugget, residual, and intercept values are provided, the values are treated as fixed. If a partial set of values is provided, the given values are treated as starting values.

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );
Bayesian Optimization(
    Y( :ABRASION, :MODULUS, :ELONG, :HARDNESS ),
    X( :SILICA, :SILANE, :SULFUR ),
    Response Model Tab(
        Y( :ABRASION ),
        Theta Values( {0.5, 0.5, 0.5} ),
        Nugget( 0.05 ),
        Residual( 500 ),
        Intercept( 100 )
    )
);