Strategy (Spark 2.0.1 JavaDoc)

Object
- org.apache.spark.mllib.tree.configuration.Strategy

All Implemented Interfaces:

java.io.Serializable
```
public class Strategy
extends Object
implements scala.Serializable
```
Stores all the configuration options for tree construction param: algo Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression param: impurity Criterion used for information gain calculation. Supported for Classification: Gini, Entropy. Supported for Regression: Variance. param: maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). param: numClasses Number of classes for classification. (Ignored for regression.) Default value is 2 (binary classification). param: maxBins Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. param: quantileCalculationStrategy Algorithm for calculating quantiles. Supported: org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort param: categoricalFeaturesInfo A map storing information about the categorical variables and the number of discrete values they take. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}. param: minInstancesPerNode Minimum number of instances each child must have after split. Default value is 1. If a split cause left or right child to have less than minInstancesPerNode, this split will not be considered as a valid split. param: minInfoGain Minimum information gain a split must get. Default value is 0.0. If a split has less information gain than minInfoGain, this split will not be considered as a valid split. param: maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. Default value is 256 MB. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. param: subsamplingRate Fraction of the training data used for learning decision tree. param: useNodeIdCache If this is true, instead of passing trees to executors, the algorithm will maintain a separate RDD of node Id cache for each row. param: checkpointInterval How often to checkpoint when the node Id cache gets updated. E.g. 10 means that the cache will get checkpointed every 10 updates. If the checkpoint directory is not set in SparkContext, this setting is ignored.

See Also:
Serialized Form

Constructor Summary

Constructors
Constructor and Description
`Strategy(scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, int minInstancesPerNode, double minInfoGain, int maxMemoryInMB, double subsamplingRate, boolean useNodeIdCache, int checkpointInterval)`
`Strategy(scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, java.util.Map<Integer,Integer> categoricalFeaturesInfo)` Java-friendly constructor for `Strategy`

Method Summary

Methods
Modifier and Type	Method and Description
`scala.Enumeration.Value`	`algo()`
`scala.collection.immutable.Map<Object,Object>`	`categoricalFeaturesInfo()`
`int`	`checkpointInterval()`
`Strategy`	`copy()` Returns a shallow copy of this instance.
`static Strategy`	`defaultStrategy(scala.Enumeration.Value algo)` Construct a default set of parameters for `DecisionTree`
`static Strategy`	`defaultStrategy(String algo)` Construct a default set of parameters for `DecisionTree`
`scala.Enumeration.Value`	`getAlgo()`
`scala.collection.immutable.Map<Object,Object>`	`getCategoricalFeaturesInfo()`
`int`	`getCheckpointInterval()`
`Impurity`	`getImpurity()`
`int`	`getMaxBins()`
`int`	`getMaxDepth()`
`int`	`getMaxMemoryInMB()`
`double`	`getMinInfoGain()`
`int`	`getMinInstancesPerNode()`
`int`	`getNumClasses()`
`scala.Enumeration.Value`	`getQuantileCalculationStrategy()`
`double`	`getSubsamplingRate()`
`boolean`	`getUseNodeIdCache()`
`Impurity`	`impurity()`
`boolean`	`isMulticlassClassification()`
`boolean`	`isMulticlassWithCategoricalFeatures()`
`int`	`maxBins()`
`int`	`maxDepth()`
`int`	`maxMemoryInMB()`
`double`	`minInfoGain()`
`int`	`minInstancesPerNode()`
`int`	`numClasses()`
`scala.Enumeration.Value`	`quantileCalculationStrategy()`
`void`	`setAlgo(String algo)` Sets Algorithm using a String.
`void`	`setCategoricalFeaturesInfo(java.util.Map<Integer,Integer> categoricalFeaturesInfo)` Sets categoricalFeaturesInfo using a Java Map.
`void`	`setCheckpointInterval(int x$1)`
`void`	`setImpurity(Impurity x$1)`
`void`	`setMaxBins(int x$1)`
`void`	`setMaxDepth(int x$1)`
`void`	`setMaxMemoryInMB(int x$1)`
`void`	`setMinInfoGain(double x$1)`
`void`	`setMinInstancesPerNode(int x$1)`
`void`	`setNumClasses(int x$1)`
`void`	`setQuantileCalculationStrategy(scala.Enumeration.Value x$1)`
`void`	`setSubsamplingRate(double x$1)`
`void`	`setUseNodeIdCache(boolean x$1)`
`double`	`subsamplingRate()`
`boolean`	`useNodeIdCache()`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Strategy

public Strategy(scala.Enumeration.Value algo,
        Impurity impurity,
        int maxDepth,
        int numClasses,
        int maxBins,
        scala.Enumeration.Value quantileCalculationStrategy,
        scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
        int minInstancesPerNode,
        double minInfoGain,
        int maxMemoryInMB,
        double subsamplingRate,
        boolean useNodeIdCache,
        int checkpointInterval)

Strategy

public Strategy(scala.Enumeration.Value algo,
        Impurity impurity,
        int maxDepth,
        int numClasses,
        int maxBins,
        java.util.Map<Integer,Integer> categoricalFeaturesInfo)

Java-friendly constructor for Strategy

Parameters:: algo - (undocumented); impurity - (undocumented); maxDepth - (undocumented); numClasses - (undocumented); maxBins - (undocumented); categoricalFeaturesInfo - (undocumented)

Method Detail

defaultStrategy
```
public static Strategy defaultStrategy(String algo)
```
Construct a default set of parameters for DecisionTree

Parameters:
algo - "Classification" or "Regression"

Returns:
(undocumented)

defaultStrategy
```
public static Strategy defaultStrategy(scala.Enumeration.Value algo)
```
Construct a default set of parameters for DecisionTree

Parameters:
algo - Algo.Classification or Algo.Regression

Returns:
(undocumented)

algo
```
public scala.Enumeration.Value algo()
```

impurity
```
public Impurity impurity()
```

setImpurity

public void setImpurity(Impurity x$1)

maxDepth
```
public int maxDepth()
```

setMaxDepth
```
public void setMaxDepth(int x$1)
```

numClasses
```
public int numClasses()
```

setNumClasses
```
public void setNumClasses(int x$1)
```

maxBins
```
public int maxBins()
```

setMaxBins
```
public void setMaxBins(int x$1)
```

quantileCalculationStrategy

public scala.Enumeration.Value quantileCalculationStrategy()

setQuantileCalculationStrategy

public void setQuantileCalculationStrategy(scala.Enumeration.Value x$1)

categoricalFeaturesInfo

public scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo()

minInstancesPerNode
```
public int minInstancesPerNode()
```

setMinInstancesPerNode

public void setMinInstancesPerNode(int x$1)

minInfoGain
```
public double minInfoGain()
```

setMinInfoGain

public void setMinInfoGain(double x$1)

maxMemoryInMB
```
public int maxMemoryInMB()
```

setMaxMemoryInMB

public void setMaxMemoryInMB(int x$1)

subsamplingRate
```
public double subsamplingRate()
```

setSubsamplingRate

public void setSubsamplingRate(double x$1)

useNodeIdCache
```
public boolean useNodeIdCache()
```

setUseNodeIdCache

public void setUseNodeIdCache(boolean x$1)

checkpointInterval
```
public int checkpointInterval()
```

setCheckpointInterval

public void setCheckpointInterval(int x$1)

isMulticlassClassification
```
public boolean isMulticlassClassification()
```
Returns:
(undocumented)

isMulticlassWithCategoricalFeatures
```
public boolean isMulticlassWithCategoricalFeatures()
```
Returns:
(undocumented)

setAlgo
```
public void setAlgo(String algo)
```
Sets Algorithm using a String.

Parameters:
algo - (undocumented)

setCategoricalFeaturesInfo
```
public void setCategoricalFeaturesInfo(java.util.Map<Integer,Integer> categoricalFeaturesInfo)
```
Sets categoricalFeaturesInfo using a Java Map.

Parameters:
categoricalFeaturesInfo - (undocumented)

copy
```
public Strategy copy()
```
Returns a shallow copy of this instance.

Returns:
(undocumented)

getAlgo

public scala.Enumeration.Value getAlgo()

getImpurity
```
public Impurity getImpurity()
```

getMaxDepth
```
public int getMaxDepth()
```

getNumClasses
```
public int getNumClasses()
```

getMaxBins
```
public int getMaxBins()
```

getQuantileCalculationStrategy

public scala.Enumeration.Value getQuantileCalculationStrategy()

getCategoricalFeaturesInfo

public scala.collection.immutable.Map<Object,Object> getCategoricalFeaturesInfo()

getMinInstancesPerNode
```
public int getMinInstancesPerNode()
```

getMinInfoGain
```
public double getMinInfoGain()
```

getMaxMemoryInMB
```
public int getMaxMemoryInMB()
```

getSubsamplingRate
```
public double getSubsamplingRate()
```

getUseNodeIdCache
```
public boolean getUseNodeIdCache()
```

getCheckpointInterval
```
public int getCheckpointInterval()
```

Class Strategy

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

Strategy

Strategy

Method Detail

defaultStrategy

defaultStrategy

algo

impurity

setImpurity

maxDepth

setMaxDepth

numClasses

setNumClasses

maxBins

setMaxBins

quantileCalculationStrategy

setQuantileCalculationStrategy

categoricalFeaturesInfo

minInstancesPerNode

setMinInstancesPerNode

minInfoGain

setMinInfoGain

maxMemoryInMB

setMaxMemoryInMB

subsamplingRate

setSubsamplingRate

useNodeIdCache

setUseNodeIdCache

checkpointInterval

setCheckpointInterval

isMulticlassClassification

isMulticlassWithCategoricalFeatures

setAlgo

setCategoricalFeaturesInfo

copy

getAlgo

getImpurity

getMaxDepth

getNumClasses

getMaxBins

getQuantileCalculationStrategy

getCategoricalFeaturesInfo

getMinInstancesPerNode

getMinInfoGain

getMaxMemoryInMB

getSubsamplingRate

getUseNodeIdCache

getCheckpointInterval