public class SpeeDRFModel extends Model implements Job.Progress
| Modifier and Type | Class and Description |
|---|---|
static class |
SpeeDRFModel.SpeeDRFModel_DTree |
Model.ModelAutobufferSerializer, Model.ModelCategory| Modifier and Type | Field and Description |
|---|---|
double |
cv_error |
static DocGen.FieldDoc[] |
DOC_FIELDS |
Key[][] |
dtreeKeys |
SpeeDRFModel.SpeeDRFModel_DTree |
dtreeTreeModel |
static java.lang.String |
JSON_CM_CLASS_ERR |
static java.lang.String |
JSON_CM_CLASSES_ERRORS |
static java.lang.String |
JSON_CM_HEADER |
static java.lang.String |
JSON_CM_MATRIX |
static java.lang.String |
JSON_CM_ROWS |
static java.lang.String |
JSON_CM_ROWS_SKIPPED |
static java.lang.String |
JSON_CM_TREES |
static java.lang.String |
JSON_CM_TYPE |
static java.lang.String |
JSON_CONFUSION_KEY |
boolean |
useNonLocal |
AUCData |
validAUC |
VarImp |
varimp |
boolean |
verbose |
java.lang.String[] |
verbose_output |
protected long |
zeed |
_dataKey, _domains, _have_cv_results, _modelClassDist, _names, _priorClassDist, GEN_BENCHMARK_CODE, training_duration_in_ms, training_start_time, warnings| Modifier | Constructor and Description |
|---|---|
|
SpeeDRFModel(Key selfKey,
Key dataKey,
Frame fr,
java.lang.String[] domain,
SpeeDRF params,
float[] priorDist) |
protected |
SpeeDRFModel(SpeeDRFModel model,
double err,
ConfusionMatrix cm,
VarImp varimp,
AUCData auc) |
| Modifier and Type | Method and Description |
|---|---|
void |
buildCM(java.lang.StringBuilder sb) |
int |
classes() |
short |
classify(Chunk[] chks,
int row,
int[] modelDataMap,
int[] votes,
double[] classWt,
java.util.Random rand) |
short |
classify(int[] votes,
double[] classWt,
java.util.Random rand) |
float |
classify0(int tree_id,
Chunk[] chunks,
int row,
int[] modelDataMap,
short badrow,
boolean regression)
Classify a row according to one particular tree.
|
ConfusionMatrix |
cm()
For classifiers, confusion matrix on validation set.
|
int[] |
colMap(Frame df) |
static float[] |
computeVarImpSD(float[][] vote_diffs) |
Futures |
delete_impl(Futures fs)
Free all internal tree keys.
|
Counter |
depth() |
protected VarImp |
doVarImpCalc(Frame fr,
SpeeDRFModel model,
Vec resp) |
void |
find_leaves_depth()
Internal computation of depth and number of leaves.
|
void |
generateHTML(java.lang.String title,
java.lang.StringBuilder sb) |
protected void |
generateHTMLAUC(java.lang.StringBuilder sb) |
void |
generateHTMLTreeStats(java.lang.StringBuilder sb,
com.google.gson.JsonObject trees) |
protected void |
generateHTMLVarImp(java.lang.StringBuilder sb) |
SpeeDRF |
get_params() |
Request2 |
job() |
Counter |
leaves() |
static SpeeDRFModel |
make(SpeeDRFModel old,
Key tkey,
Key dtKey,
int nodeIdx,
java.lang.String tString,
int tree_id) |
protected static AUCData |
makeAUC(ConfusionMatrix[] cms,
float[] threshold,
java.lang.String[] cmDomain) |
float[] |
modeldist() |
java.lang.String |
name(int atree) |
float[] |
priordist() |
float |
progress() |
protected float[] |
score0(double[] data,
float[] preds)
Subclasses implement the scoring logic.
|
protected void |
setCrossValidationError(Job.ValidatedJob job,
double cv_error,
ConfusionMatrix cm,
AUCData auc,
HitRatio hr) |
int |
size() |
DTree.TreeModel |
transform2DTreeModel() |
byte[] |
tree(int tree_id)
Return the bits for a particular tree
|
int |
treeCount() |
VarImp |
varimp()
Variable importance of individual input features measured by this model.
|
adapt, adapt, addWarning, calcError, classNames, errStr, getDomainMapping, getDomainMapping, getModelCategory, getModelSerializer, getUniqueId, isClassifier, isSupervised, missingColumnsType, mse, nclasses, nfeatures, printCrossValidationModelsHTML, responseName, score, score, score, score, score, score, score0, scoreCrossValidation, scoreImpl, setModelClassDistribution, start_training, start_training, stop_training, testJavaScoring, toJava, toJava, toJavaDefaultMaxIters, toJavaFillPreds0, toJavaInit, toJavaInit, toJavaPredictBody, toJavaSuper, toJavaUnifyPredsdelete_and_lock, delete, delete, delete, delete, is_unlocked, is_wlocked, read_lock, read_lock, unlock_all, unlock_lockable, unlock, update, write_lockclone, frozenType, init, newInstance, read, toDocField, write, writeJSON, writeJSONFieldspublic static DocGen.FieldDoc[] DOC_FIELDS
protected long zeed
@Request.API(help="AUC") public AUCData validAUC
@Request.API(help="Variable Importance") public VarImp varimp
@Request.API(help="CV Error") public double cv_error
@Request.API(help="Verbose Mode") public boolean verbose
@Request.API(help="Verbose Output") public java.lang.String[] verbose_output
@Request.API(help="Use non-local data") public boolean useNonLocal
@Request.API(help="Dtree keys") public Key[][] dtreeKeys
@Request.API(help="DTree Model") public SpeeDRFModel.SpeeDRFModel_DTree dtreeTreeModel
public static final java.lang.String JSON_CONFUSION_KEY
public static final java.lang.String JSON_CM_TYPE
public static final java.lang.String JSON_CM_HEADER
public static final java.lang.String JSON_CM_MATRIX
public static final java.lang.String JSON_CM_TREES
public static final java.lang.String JSON_CM_CLASS_ERR
public static final java.lang.String JSON_CM_ROWS
public static final java.lang.String JSON_CM_ROWS_SKIPPED
public static final java.lang.String JSON_CM_CLASSES_ERRORS
public SpeeDRFModel(Key selfKey, Key dataKey, Frame fr, java.lang.String[] domain, SpeeDRF params, float[] priorDist)
protected SpeeDRFModel(SpeeDRFModel model, double err, ConfusionMatrix cm, VarImp varimp, AUCData auc)
public final SpeeDRF get_params()
get_params in class Modelpublic final VarImp varimp()
Modelpublic float[] priordist()
public float[] modeldist()
public int treeCount()
public int size()
public int classes()
public ConfusionMatrix cm()
Modelpublic static SpeeDRFModel make(SpeeDRFModel old, Key tkey, Key dtKey, int nodeIdx, java.lang.String tString, int tree_id)
public java.lang.String name(int atree)
public byte[] tree(int tree_id)
public Futures delete_impl(Futures fs)
delete_impl in class Modelpublic float classify0(int tree_id,
Chunk[] chunks,
int row,
int[] modelDataMap,
short badrow,
boolean regression)
tree_id - the number of the tree to usechunks - the chunk we are usingrow - the row number in the chunkmodelDataMap - mapping from model/tree columns to data columnspublic short classify(Chunk[] chks, int row, int[] modelDataMap, int[] votes, double[] classWt, java.util.Random rand)
public short classify(int[] votes,
double[] classWt,
java.util.Random rand)
public void find_leaves_depth()
public Counter leaves()
public Counter depth()
public int[] colMap(Frame df)
protected float[] score0(double[] data,
float[] preds)
Modelpublic float progress()
progress in interface Job.Progresspublic void generateHTML(java.lang.String title,
java.lang.StringBuilder sb)
public DTree.TreeModel transform2DTreeModel()
public void generateHTMLTreeStats(java.lang.StringBuilder sb,
com.google.gson.JsonObject trees)
public void buildCM(java.lang.StringBuilder sb)
protected static AUCData makeAUC(ConfusionMatrix[] cms, float[] threshold, java.lang.String[] cmDomain)
protected void generateHTMLAUC(java.lang.StringBuilder sb)
protected void generateHTMLVarImp(java.lang.StringBuilder sb)
protected VarImp doVarImpCalc(Frame fr, SpeeDRFModel model, Vec resp)
public static float[] computeVarImpSD(float[][] vote_diffs)
protected void setCrossValidationError(Job.ValidatedJob job, double cv_error, ConfusionMatrix cm, AUCData auc, HitRatio hr)
setCrossValidationError in class Model