BaseDatasetNormalizer (magpie API)

java.lang.Object
- magpie.data.utilities.normalizers.BaseDatasetNormalizer

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Commandable, Options, Printable

Direct Known Subclasses:

IndependentVariableNormalizer, InverseNormalizer, MultiNormalizer, RegularSolutionNormalizer
```
public abstract class BaseDatasetNormalizer
extends java.lang.Object
implements Options, Commandable, Printable, java.io.Serializable, java.lang.Cloneable
```
Base class for objects designed to scale attributes and or class variable to exist on the same range. By default, one of these objects will normalize the attributes of each entry but not the class variable.
Will only normalize class variable if a measure class is present. If it is present, this class will normalize both the measured and predicted class variable using the same function. Will throw an error if you try to normalize discrete classes.
Any implementation must provide the following operations:
Once you implement these functions, make sure to test them using the "test" command.
Implemented Commands:
normalize $<dataset> - Normalize attributes and/or class variable of a dataset
dataset: Dataset to be normalized
Must be trained first
restore $<dataset> - Restore attributes from normalized to original ranges
dataset: Dataset to be restored
train [attributes] [class] $<dataset> - Train normalizer on a dataset
attributes: Train to normalizing attributes
class: Train for normalizing class variable
dataset: Dataset to use for training
test $<dataset> - Test whether restoration actually maps dataset back onto original ranges.
dataset: Dataset used for testing
This operation is only used for debugging purposes.
See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

BaseDatasetNormalizer()

Constructors
Constructor and Description
`BaseDatasetNormalizer()`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`about()` Prints a simple status message about this object
`BaseDatasetNormalizer`	`clone()`
`boolean`	`isTrained()`
`void`	`normalize(Dataset Data)` Transform attributes from original to standardized range.
`protected abstract void`	`normalizeAttributes(Dataset Data)` Perform the actual normalization on the attributes
`protected abstract void`	`normalizeClassVariable(Dataset Data)` Perform the actual normalization on the class variable.
`java.lang.String`	`printCommand(java.util.List<java.lang.String> Command)` Handles more complicated printing commands.
`java.lang.String`	`printDescription(boolean htmlFormat)` Print full name of object, and a simple description of the options.
`void`	`restore(Dataset Data)` Restore attributes from scaled to original range.
`protected abstract void`	`restoreAttributes(Dataset Data)` Perform the actual restoration on the attributes
`protected abstract void`	`restoreClassVariable(Dataset Data)` Perform the actual restoration on the class variables (measured and predicted!)
`java.lang.Object`	`runCommand(java.util.List<java.lang.Object> Command)` Process some command described by a list of Objects.
`void`	`setToNormalizeAttributes(boolean input)` Set whether to normalize attributes
`void`	`setToNormalizeClass(boolean input)` Set whether to normalize class variable
`boolean`	`test(Dataset data)` Check whether the normalize/restore operations are reversible.
`void`	`train(Dataset Data)` Train the normalizer.
`protected abstract void`	`trainOnAttributes(Dataset Data)` Perform the actual training work for attributes.
`protected abstract void`	`trainOnMeasuredClass(Dataset Data)` Perform the actual training work for the class variable.
`boolean`	`willNormalizeAttributes()`
`boolean`	`willNormalizeClass()`

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface magpie.utility.interfaces.Options
printUsage, setOptions

- Constructor Detail
  - BaseDatasetNormalizer
```
public BaseDatasetNormalizer()
```
- Method Detail
  - clone
```
public BaseDatasetNormalizer clone()
```
    Overrides:
    
    clone in class java.lang.Object
  - isTrained
```
public boolean isTrained()
```
    Returns:
    
    Whether this normalizer has been trained
  - willNormalizeAttributes
```
public boolean willNormalizeAttributes()
```
    Returns:
    
    Whether attributes will be normalized
  - willNormalizeClass
```
public boolean willNormalizeClass()
```
    Returns:
    
    Whether class variable will be normalized
  - setToNormalizeAttributes
```
public void setToNormalizeAttributes(boolean input)
```
    Set whether to normalize attributes
    
    Parameters:
    
    input - Whether attributes should be normalized
  - setToNormalizeClass
```
public void setToNormalizeClass(boolean input)
```
    Set whether to normalize class variable
    
    Parameters:
    
    input - Whether class variable should be normalized.
  - train
```
public void train(Dataset Data)
```
    Train the normalizer. Calculates all parameters necessary to transform the data from the original range to a standardized range and back.
    
    Parameters:
    
    Data - Training set
  - trainOnAttributes
```
protected abstract void trainOnAttributes(Dataset Data)
```
    Perform the actual training work for attributes. Must establish the parameters necessary to normalize attributes and return them to their original range.
    
    Parameters:
    
    Data - Training set
  - trainOnMeasuredClass
```
protected abstract void trainOnMeasuredClass(Dataset Data)
```
    Perform the actual training work for the class variable. Must establish the parameters necessary to normalize attributes and return them to their original range.
    NOTE: Use the measured class variable to train normalizer.
    
    Parameters:
    
    Data - Training set
  - normalize
```
public void normalize(Dataset Data)
```
    Transform attributes from original to standardized range.
    
    Parameters:
    
    Data - Dataset to be transformed
  - normalizeAttributes
```
protected abstract void normalizeAttributes(Dataset Data)
```
    Perform the actual normalization on the attributes
    
    Parameters:
    
    Data - Dataset to be transformed
  - normalizeClassVariable
```
protected abstract void normalizeClassVariable(Dataset Data)
```
    Perform the actual normalization on the class variable. Predicted and measured class variables must be transformed the same way
    
    Parameters:
    
    Data - Dataset to be transformed
  - restore
```
public void restore(Dataset Data)
```
    Restore attributes from scaled to original range.
    
    Parameters:
    
    Data - Dataset to be transformed
  - restoreAttributes
```
protected abstract void restoreAttributes(Dataset Data)
```
    Perform the actual restoration on the attributes
    
    Parameters:
    
    Data - Dataset to be transformed
  - restoreClassVariable
```
protected abstract void restoreClassVariable(Dataset Data)
```
    Perform the actual restoration on the class variables (measured and predicted!)
    
    Parameters:
    
    Data - Dataset to be transformed
  - test
```
public boolean test(Dataset data)
```
    Check whether the normalize/restore operations are reversible. Tests both the attribute and class variables.
    
    Parameters:
    
    data - Dataset to use for testings
    
    Returns:
    
    Whether the model passes
  - about
```
public java.lang.String about()
```
    Description copied from interface: Printable
    
    Prints a simple status message about this object
    
    Specified by:
    
    about in interface Printable
    
    Returns:
    
    Short status message
  - printDescription
```
public java.lang.String printDescription(boolean htmlFormat)
```
    Description copied from interface: Printable
    
    Print full name of object, and a simple description of the options. This description should print enough information to reproduce how this object was created, but not necessarily enough to run it (e.g., coefficients for the model).
    Example: For a model training a separate WekaRegression for intermetallics
    magpie.models.regression.SplitRegression
    Splitter: AllMetalsSplitter
    All Metals: magpie.models.regression.WekaRegression trees.REPTree
    Contains Nonmetal: magpie.regression.LASSORegreession -maxterms 2
    
    Specified by:
    
    printDescription in interface Printable
    
    Parameters:
    
    htmlFormat - Whether format for output to an HTML page (e.g., <div> to create indentation) or for printing to screen.
    
    Returns:
    
    String describing the model
    
    See Also:
    
    #printModel()
  - printCommand
```
public java.lang.String printCommand(java.util.List<java.lang.String> Command)
                              throws java.lang.Exception
```
    Description copied from interface: Printable
    
    Handles more complicated printing commands.
    
    Specified by:
    
    printCommand in interface Printable
    
    Parameters:
    
    Command - Command specifying what to print
    
    Returns:
    
    String formatted as requested
    
    Throws:
    
    java.lang.Exception - If command not understood
  - runCommand
```
public java.lang.Object runCommand(java.util.List<java.lang.Object> Command)
                            throws java.lang.Exception
```
    Description copied from interface: Commandable
    
    Process some command described by a list of Objects.
    
    Specified by:
    
    runCommand in interface Commandable
    
    Parameters:
    
    Command - Command as a list of objects
    
    Returns:
    
    Any output (null if no output generated by command)
    
    Throws:
    
    java.lang.Exception - If something goes wrong

Class BaseDatasetNormalizer

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface magpie.utility.interfaces.Options

Constructor Detail

BaseDatasetNormalizer

Method Detail

clone

isTrained

willNormalizeAttributes

willNormalizeClass

setToNormalizeAttributes

setToNormalizeClass

train

trainOnAttributes

trainOnMeasuredClass

normalize

normalizeAttributes

normalizeClassVariable

restore

restoreAttributes

restoreClassVariable

test

about

printDescription

printCommand

runCommand