RegressionEvaluation (nd4j-api 1.0.0-M1.1 API)

java.lang.Object
- org.nd4j.evaluation.BaseEvaluation<RegressionEvaluation>
- - org.nd4j.evaluation.regression.RegressionEvaluation

All Implemented Interfaces:

Serializable, IEvaluation<RegressionEvaluation>
```
public class RegressionEvaluation
extends BaseEvaluation<RegressionEvaluation>
```
See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class RegressionEvaluation.Metric

Nested Classes
Modifier and Type	Class and Description
`static class`	`RegressionEvaluation.Metric`

Field Summary

Fields
Modifier and Type Field and Description

protected int axis

static int DEFAULT_PRECISION

Fields
Modifier and Type	Field and Description
`protected int`	`axis`
`static int`	`DEFAULT_PRECISION`

Constructor Summary

Constructors
Modifier	Constructor and Description
	`RegressionEvaluation()`
`protected`	`RegressionEvaluation(int axis, List<String> columnNames, long precision)`
	`RegressionEvaluation(List<String> columnNames)` Create a regression evaluation object with default precision for the stats() method
	`RegressionEvaluation(List<String> columnNames, long precision)` Create a regression evaluation object with specified precision for the stats() method
	`RegressionEvaluation(long nColumns)` Create a regression evaluation object with the specified number of columns, and default precision for the stats() method.
	`RegressionEvaluation(long nColumns, long precision)` Create a regression evaluation object with the specified number of columns, and specified precision for the stats() method.
	`RegressionEvaluation(String... columnNames)` Create a regression evaluation object with default precision for the stats() method

Method Summary

All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`double`	`averagecorrelationR2()` Deprecated. Use `averagePearsonCorrelation()` instead. For the R2 score use `averageRSquared()`.
`double`	`averageMeanAbsoluteError()` Average MAE across all columns
`double`	`averageMeanSquaredError()` Average MSE across all columns
`double`	`averagePearsonCorrelation()` Average Pearson Correlation Coefficient across all columns
`double`	`averagerelativeSquaredError()` Average RSE across all columns
`double`	`averagerootMeanSquaredError()` Average RMSE across all columns
`double`	`averageRSquared()` Average R2 across all columns
`double`	`correlationR2(int column)` Deprecated. Use `pearsonCorrelation(int)` instead. For the R2 score use `rSquared(int)`.
`void`	`eval(INDArray labels, INDArray predictions)`
`void`	`eval(INDArray labelsArr, INDArray predictionsArr, INDArray maskArr)`
`void`	`eval(INDArray labels, INDArray networkPredictions, INDArray maskArray, List<? extends Serializable> recordMetaData)`
`static RegressionEvaluation`	`fromJson(String json)`
`int`	`getAxis()` Get the axis - see `setAxis(int)` for details
`double`	`getValue(IMetric metric)` Get the value of a given metric for this evaluation.
`double`	`meanAbsoluteError(int column)`
`double`	`meanSquaredError(int column)`
`void`	`merge(RegressionEvaluation other)`
`RegressionEvaluation`	`newInstance()` Get a new instance of this evaluation, with the same configuration but no data.
`int`	`numColumns()`
`double`	`pearsonCorrelation(int column)` Pearson Correlation Coefficient for samples
`double`	`relativeSquaredError(int column)`
`void`	`reset()`
`double`	`rootMeanSquaredError(int column)`
`double`	`rSquared(int column)` Coefficient of Determination (R^2 Score)
`double`	`scoreForMetric(RegressionEvaluation.Metric metric)`
`void`	`setAxis(int axis)` Set the axis for evaluation - this is the dimension along which the probability (and label classes) are present. For DL4J, this can be left as the default setting (axis = 1). Axis should be set as follows: For 2D (OutputLayer), shape [minibatch, numClasses] - axis = 1 For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NCW format, shape [minibatch, numClasses, sequenceLength] - axis = 1 For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NWC format, shape [minibatch, sequenceLength, numClasses] - axis = 2 For 4D, CNN2D (DL4J CnnLossLayer), NCHW format, shape [minibatch, channels, height, width] - axis = 1 For 4D, CNN2D, NHWC format, shape [minibatch, height, width, channels] - axis = 3
`String`	`stats()`

Methods inherited from class org.nd4j.evaluation.BaseEvaluation
attempFromLegacyFromJson, eval, evalTimeSeries, evalTimeSeries, fromJson, fromYaml, reshapeAndExtractNotMasked, toJson, toString, toYaml

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

- Field Detail
  - DEFAULT_PRECISION
```
public static final int DEFAULT_PRECISION
```
    See Also:
    
    Constant Field Values
  - axis
```
protected int axis
```
- Constructor Detail
  - RegressionEvaluation
```
protected RegressionEvaluation(int axis,
                               List<String> columnNames,
                               long precision)
```
  - RegressionEvaluation
```
public RegressionEvaluation()
```
  - RegressionEvaluation
```
public RegressionEvaluation(long nColumns)
```
    Create a regression evaluation object with the specified number of columns, and default precision for the stats() method.
    
    Parameters:
    
    nColumns - Number of columns
  - RegressionEvaluation
```
public RegressionEvaluation(long nColumns,
                            long precision)
```
    Create a regression evaluation object with the specified number of columns, and specified precision for the stats() method.
    
    Parameters:
    
    nColumns - Number of columns
  - RegressionEvaluation
```
public RegressionEvaluation(String... columnNames)
```
    Create a regression evaluation object with default precision for the stats() method
    
    Parameters:
    
    columnNames - Names of the columns
  - RegressionEvaluation
```
public RegressionEvaluation(List<String> columnNames)
```
    Create a regression evaluation object with default precision for the stats() method
    
    Parameters:
    
    columnNames - Names of the columns
  - RegressionEvaluation
```
public RegressionEvaluation(List<String> columnNames,
                            long precision)
```
    Create a regression evaluation object with specified precision for the stats() method
    
    Parameters:
    
    columnNames - Names of the columns
- Method Detail
  - setAxis
```
public void setAxis(int axis)
```
    Set the axis for evaluation - this is the dimension along which the probability (and label classes) are present.
    For DL4J, this can be left as the default setting (axis = 1).
    Axis should be set as follows:
    For 2D (OutputLayer), shape [minibatch, numClasses] - axis = 1
    For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NCW format, shape [minibatch, numClasses, sequenceLength] - axis = 1
    For 3D, RNNs/CNN1D (DL4J RnnOutputLayer), NWC format, shape [minibatch, sequenceLength, numClasses] - axis = 2
    For 4D, CNN2D (DL4J CnnLossLayer), NCHW format, shape [minibatch, channels, height, width] - axis = 1
    For 4D, CNN2D, NHWC format, shape [minibatch, height, width, channels] - axis = 3
    
    Parameters:
    
    axis - Axis to use for evaluation
  - getAxis
```
public int getAxis()
```
    Get the axis - see setAxis(int) for details
  - reset
```
public void reset()
```
  - eval
```
public void eval(INDArray labels,
                 INDArray predictions)
```
    Specified by:
    
    eval in interface IEvaluation<RegressionEvaluation>
    
    Overrides:
    
    eval in class BaseEvaluation<RegressionEvaluation>
  - eval
```
public void eval(INDArray labels,
                 INDArray networkPredictions,
                 INDArray maskArray,
                 List<? extends Serializable> recordMetaData)
```
  - eval
```
public void eval(INDArray labelsArr,
                 INDArray predictionsArr,
                 INDArray maskArr)
```
    Specified by:
    
    eval in interface IEvaluation<RegressionEvaluation>
    
    Overrides:
    
    eval in class BaseEvaluation<RegressionEvaluation>
  - merge
```
public void merge(RegressionEvaluation other)
```
  - stats
```
public String stats()
```
    Returns:
  - numColumns
```
public int numColumns()
```
  - meanSquaredError
```
public double meanSquaredError(int column)
```
  - meanAbsoluteError
```
public double meanAbsoluteError(int column)
```
  - rootMeanSquaredError
```
public double rootMeanSquaredError(int column)
```
  - correlationR2
```
@Deprecated
public double correlationR2(int column)
```
    Deprecated. Use pearsonCorrelation(int) instead. For the R2 score use rSquared(int).
    
    Legacy method for the correlation score.
    
    Parameters:
    
    column - Column to evaluate
    
    Returns:
    
    Pearson Correlation for the given column
    
    See Also:
    
    #pearsonCorrelation(int)}
  - pearsonCorrelation
```
public double pearsonCorrelation(int column)
```
    Pearson Correlation Coefficient for samples
    
    Parameters:
    
    column - Column to evaluate
    
    Returns:
    
    Pearson Correlation Coefficient for column with index column
    
    See Also:
    
    Wikipedia
  - rSquared
```
public double rSquared(int column)
```
    Coefficient of Determination (R^2 Score)
    
    Parameters:
    
    column - Column to evaluate
    
    Returns:
    
    R^2 score for column with index column
    
    See Also:
    
    Wikipedia
  - relativeSquaredError
```
public double relativeSquaredError(int column)
```
  - averageMeanSquaredError
```
public double averageMeanSquaredError()
```
    Average MSE across all columns
    
    Returns:
  - averageMeanAbsoluteError
```
public double averageMeanAbsoluteError()
```
    Average MAE across all columns
    
    Returns:
  - averagerootMeanSquaredError
```
public double averagerootMeanSquaredError()
```
    Average RMSE across all columns
    
    Returns:
  - averagerelativeSquaredError
```
public double averagerelativeSquaredError()
```
    Average RSE across all columns
    
    Returns:
  - averagecorrelationR2
```
@Deprecated
public double averagecorrelationR2()
```
    Deprecated. Use averagePearsonCorrelation() instead. For the R2 score use averageRSquared().
    
    Legacy method for the correlation average across all columns.
    
    Returns:
    
    Pearson Correlation averaged over all columns
    
    See Also:
    
    #averagePearsonCorrelation()}
  - averagePearsonCorrelation
```
public double averagePearsonCorrelation()
```
    Average Pearson Correlation Coefficient across all columns
    
    Returns:
    
    Pearson Correlation Coefficient across all columns
  - averageRSquared
```
public double averageRSquared()
```
    Average R2 across all columns
    
    Returns:
    
    R2 score accross all columns
  - getValue
```
public double getValue(IMetric metric)
```
    Description copied from interface: IEvaluation
    
    Get the value of a given metric for this evaluation.
  - scoreForMetric
```
public double scoreForMetric(RegressionEvaluation.Metric metric)
```
  - fromJson
```
public static RegressionEvaluation fromJson(String json)
```
  - newInstance
```
public RegressionEvaluation newInstance()
```
    Description copied from interface: IEvaluation
    
    Get a new instance of this evaluation, with the same configuration but no data.

Class RegressionEvaluation

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.nd4j.evaluation.BaseEvaluation

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_PRECISION

axis

Constructor Detail

RegressionEvaluation

RegressionEvaluation

RegressionEvaluation

RegressionEvaluation

RegressionEvaluation

RegressionEvaluation

RegressionEvaluation

Method Detail

setAxis

getAxis

reset

eval

eval

eval

merge

stats

numColumns

meanSquaredError

meanAbsoluteError

rootMeanSquaredError

correlationR2

pearsonCorrelation

rSquared

relativeSquaredError

averageMeanSquaredError

averageMeanAbsoluteError

averagerootMeanSquaredError

averagerelativeSquaredError

averagecorrelationR2

averagePearsonCorrelation

averageRSquared

getValue

scoreForMetric

fromJson

newInstance