Package gov.nih.mipav.model.algorithms

Class StochasticForests.Forest

java.lang.Object

gov.nih.mipav.model.algorithms.StochasticForests.Forest

Direct Known Subclasses:

StochasticForests.ForestClassification, StochasticForests.ForestProbability, StochasticForests.ForestRegression, StochasticForests.ForestSurvival

Enclosing class:

StochasticForests

private abstract class StochasticForests.Forest
extends java.lang.Object

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
class	StochasticForests.Forest.computeTreePermutationImportanceInThread
class	StochasticForests.Forest.growTreesInThread
class	StochasticForests.Forest.predictInternalInThread
class	StochasticForests.Forest.predictTreesInThread
class	StochasticForests.Forest.showProgress

Field Summary

Fields

Modifier and Type	Field	Description
protected double	alpha
protected java.util.Vector<java.lang.Double>	case_weights
private java.util.concurrent.locks.Condition	condition_variable
protected StochasticForests.Data	data
protected int	dependent_varID
protected java.util.Vector<java.lang.Integer>	deterministic_varIDs
protected boolean	holdout
protected StochasticForests.ImportanceMode	importance_mode
protected boolean	keep_inbag
protected StochasticForests.MemoryMode	memory_mode
protected boolean	memory_saving_splitting
protected int	min_node_size
protected double	minprop
protected int	mtry
protected java.util.concurrent.locks.Lock	mutex
protected int	num_independent_variables
protected int	num_random_splits
protected int	num_samples
protected int	num_threads
protected int	num_trees
protected int	num_variables
protected java.lang.String	output_prefix
protected double	overall_prediction_error
protected boolean	predict_all
protected boolean	prediction_mode
protected StochasticForests.PredictionType	prediction_type
protected java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Double>>>	predictions
protected int	progress
protected java.util.Random	random
protected java.util.Vector<java.lang.Double>	sample_fraction
protected boolean	sample_with_replacement
protected long	seed
protected java.util.Vector<java.lang.Integer>	split_select_varIDs
protected java.util.Vector<java.util.Vector<java.lang.Double>>	split_select_weights
protected StochasticForests.SplitRule	splitrule
protected java.util.Vector<java.lang.Integer>	thread_ranges
protected java.util.Vector<StochasticForests.Tree>	trees
protected java.util.Vector<java.lang.Double>	variable_importance
protected boolean	verbose_out

Constructor Summary

Constructors

Constructor	Description
Forest()

Method Summary

Modifier and Type	Method	Description
protected abstract void	allocatePredictMemory()
void	computePermutationImportance()
void	computePredictionError()
protected abstract void	computePredictionErrorInternal()
void	dispose()
java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Integer>>>	getChildNodeIDs()
int	getDependentVarId()
java.util.Vector<java.util.Vector<java.lang.Integer>>	getInbagCounts()
java.util.Vector<java.lang.Boolean>	getIsOrderedVariable()
int	getMinNodeSize()
int	getMtry()
int	getNumIndependentVariables()
int	getNumTrees()
double	getOverallPredictionError()
java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Double>>>	getPredictions()
java.util.Vector<java.util.Vector<java.lang.Double>>	getSplitValues()
java.util.Vector<java.util.Vector<java.lang.Integer>>	getSplitVarIDs()
java.util.Vector<java.lang.Double>	getVariableImportance()
void	grow()
protected abstract void	growInternal()
void	init(java.lang.String dependent_variable_name, StochasticForests.MemoryMode memory_mode, StochasticForests.Data input_data, int mtry, java.lang.String output_prefix, int num_trees, long seed, int num_threads, StochasticForests.ImportanceMode importance_mode, int min_node_size, java.lang.String status_variable_name, boolean prediction_mode, boolean sample_with_replacement, java.util.Vector<java.lang.String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, boolean predict_all, java.util.Vector<java.lang.Double> sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)
void	initCpp(java.lang.String dependent_variable_name, StochasticForests.MemoryMode memory_mode, java.lang.String input_file, int mtry, java.lang.String output_prefix, int num_trees, boolean verbose_out, long seed, int num_threads, java.lang.String load_forest_filename, StochasticForests.ImportanceMode importance_mode, int min_node_size, java.lang.String split_select_weights_file, java.util.Vector<java.lang.String> always_split_variable_names, java.lang.String status_variable_name, boolean sample_with_replacement, java.util.Vector<java.lang.String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, java.lang.String case_weights_file, boolean predict_all, double sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)
abstract void	initInternal(java.lang.String status_variable_name)
void	initR(java.lang.String dependent_variable_name, StochasticForests.Data input_data, int mtry, int num_trees, boolean verbose_out, long seed, int num_threads, StochasticForests.ImportanceMode importance_mode, int min_node_size, java.util.Vector<java.util.Vector<java.lang.Double>> split_select_weights, java.util.Vector<java.lang.String> always_split_variable_names, java.lang.String status_variable_name, boolean prediction_mode, boolean sample_with_replacement, java.util.Vector<java.lang.String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, java.util.Vector<java.lang.Double> case_weights, boolean predict_all, boolean keep_inbag, java.util.Vector<java.lang.Double> sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)
void	loadFromFile(java.lang.String filename)
protected abstract void	loadFromFileInternal(java.io.BufferedReader br)
void	predict()
protected abstract void	predictInternal(int sample_idx)
void	run(boolean verbose)
void	saveToFile()
abstract void	saveToFileInternal(java.io.BufferedWriter bw)
void	setAlwaysSplitVariables(java.util.Vector<java.lang.String> always_split_variable_names)
void	setSplitWeightVector(java.util.Vector<java.util.Vector<java.lang.Double>> split_select_weights)
abstract void	writeConfusionFile()
void	writeImportanceFile()
void	writeOutput()
abstract void	writeOutputInternal()
abstract void	writePredictionFile()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

random

protected java.util.Random random

verbose_out

protected boolean verbose_out

num_trees

protected int num_trees

mtry

protected int mtry

min_node_size

protected int min_node_size

num_variables

protected int num_variables

num_independent_variables

protected int num_independent_variables

seed

protected long seed

dependent_varID

protected int dependent_varID

num_samples

protected int num_samples

prediction_mode

protected boolean prediction_mode

memory_mode

protected StochasticForests.MemoryMode memory_mode

sample_with_replacement

protected boolean sample_with_replacement

memory_saving_splitting

protected boolean memory_saving_splitting

splitrule

protected StochasticForests.SplitRule splitrule

predict_all

protected boolean predict_all

keep_inbag

protected boolean keep_inbag

sample_fraction

protected java.util.Vector<java.lang.Double> sample_fraction

holdout

protected boolean holdout

prediction_type

protected StochasticForests.PredictionType prediction_type

num_random_splits

protected int num_random_splits

alpha

protected double alpha

minprop

protected double minprop

num_threads

protected int num_threads

thread_ranges

protected java.util.Vector<java.lang.Integer> thread_ranges

trees

protected java.util.Vector<StochasticForests.Tree> trees

data

protected StochasticForests.Data data

predictions

protected java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Double>>> predictions

overall_prediction_error

protected double overall_prediction_error

deterministic_varIDs

protected java.util.Vector<java.lang.Integer> deterministic_varIDs

split_select_varIDs

protected java.util.Vector<java.lang.Integer> split_select_varIDs

split_select_weights

protected java.util.Vector<java.util.Vector<java.lang.Double>> split_select_weights

case_weights

protected java.util.Vector<java.lang.Double> case_weights

output_prefix

protected java.lang.String output_prefix

importance_mode

protected StochasticForests.ImportanceMode importance_mode

variable_importance

protected java.util.Vector<java.lang.Double> variable_importance

progress

protected int progress

mutex

protected final java.util.concurrent.locks.Lock mutex

condition_variable

private final java.util.concurrent.locks.Condition condition_variable

Constructor Detail

Forest

public Forest()

Method Detail

growInternal

protected abstract void growInternal()

allocatePredictMemory

protected abstract void allocatePredictMemory()

predictInternal

protected abstract void predictInternal(int sample_idx)

computePredictionErrorInternal

protected abstract void computePredictionErrorInternal()

loadFromFileInternal

protected abstract void loadFromFileInternal(java.io.BufferedReader br)

initInternal

public abstract void initInternal(java.lang.String status_variable_name)

writeOutputInternal

public abstract void writeOutputInternal()

writeConfusionFile

public abstract void writeConfusionFile()

writePredictionFile

public abstract void writePredictionFile()

saveToFileInternal

public abstract void saveToFileInternal(java.io.BufferedWriter bw)

getChildNodeIDs

public java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Integer>>> getChildNodeIDs()

getSplitVarIDs

public java.util.Vector<java.util.Vector<java.lang.Integer>> getSplitVarIDs()

getSplitValues

public java.util.Vector<java.util.Vector<java.lang.Double>> getSplitValues()

getVariableImportance

public java.util.Vector<java.lang.Double> getVariableImportance()

getOverallPredictionError

public double getOverallPredictionError()

getPredictions

public java.util.Vector<java.util.Vector<java.util.Vector<java.lang.Double>>> getPredictions()

getDependentVarId

public int getDependentVarId()

getNumTrees

public int getNumTrees()

getMtry

public int getMtry()

getMinNodeSize

public int getMinNodeSize()

getNumIndependentVariables

public int getNumIndependentVariables()

getIsOrderedVariable

public java.util.Vector<java.lang.Boolean> getIsOrderedVariable()

getInbagCounts

public java.util.Vector<java.util.Vector<java.lang.Integer>> getInbagCounts()

dispose

public void dispose()

initCpp

public void initCpp(java.lang.String dependent_variable_name,
                    StochasticForests.MemoryMode memory_mode,
                    java.lang.String input_file,
                    int mtry,
                    java.lang.String output_prefix,
                    int num_trees,
                    boolean verbose_out,
                    long seed,
                    int num_threads,
                    java.lang.String load_forest_filename,
                    StochasticForests.ImportanceMode importance_mode,
                    int min_node_size,
                    java.lang.String split_select_weights_file,
                    java.util.Vector<java.lang.String> always_split_variable_names,
                    java.lang.String status_variable_name,
                    boolean sample_with_replacement,
                    java.util.Vector<java.lang.String> unordered_variable_names,
                    boolean memory_saving_splitting,
                    StochasticForests.SplitRule splitrule,
                    java.lang.String case_weights_file,
                    boolean predict_all,
                    double sample_fraction,
                    double alpha,
                    double minprop,
                    boolean holdout,
                    StochasticForests.PredictionType prediction_type,
                    int num_random_splits)

initR

public void initR(java.lang.String dependent_variable_name,
                  StochasticForests.Data input_data,
                  int mtry,
                  int num_trees,
                  boolean verbose_out,
                  long seed,
                  int num_threads,
                  StochasticForests.ImportanceMode importance_mode,
                  int min_node_size,
                  java.util.Vector<java.util.Vector<java.lang.Double>> split_select_weights,
                  java.util.Vector<java.lang.String> always_split_variable_names,
                  java.lang.String status_variable_name,
                  boolean prediction_mode,
                  boolean sample_with_replacement,
                  java.util.Vector<java.lang.String> unordered_variable_names,
                  boolean memory_saving_splitting,
                  StochasticForests.SplitRule splitrule,
                  java.util.Vector<java.lang.Double> case_weights,
                  boolean predict_all,
                  boolean keep_inbag,
                  java.util.Vector<java.lang.Double> sample_fraction,
                  double alpha,
                  double minprop,
                  boolean holdout,
                  StochasticForests.PredictionType prediction_type,
                  int num_random_splits)

init

public void init(java.lang.String dependent_variable_name,
                 StochasticForests.MemoryMode memory_mode,
                 StochasticForests.Data input_data,
                 int mtry,
                 java.lang.String output_prefix,
                 int num_trees,
                 long seed,
                 int num_threads,
                 StochasticForests.ImportanceMode importance_mode,
                 int min_node_size,
                 java.lang.String status_variable_name,
                 boolean prediction_mode,
                 boolean sample_with_replacement,
                 java.util.Vector<java.lang.String> unordered_variable_names,
                 boolean memory_saving_splitting,
                 StochasticForests.SplitRule splitrule,
                 boolean predict_all,
                 java.util.Vector<java.lang.Double> sample_fraction,
                 double alpha,
                 double minprop,
                 boolean holdout,
                 StochasticForests.PredictionType prediction_type,
                 int num_random_splits)

run

public void run(boolean verbose)

writeOutput

public void writeOutput()

writeImportanceFile

public void writeImportanceFile()

saveToFile

public void saveToFile()

grow

public void grow()

predict

public void predict()

computePredictionError

public void computePredictionError()

computePermutationImportance

public void computePermutationImportance()

loadFromFile

public void loadFromFile(java.lang.String filename)

setSplitWeightVector

public void setSplitWeightVector(java.util.Vector<java.util.Vector<java.lang.Double>> split_select_weights)

setAlwaysSplitVariables

public void setAlwaysSplitVariables(java.util.Vector<java.lang.String> always_split_variable_names)