water

Class Model

java.lang.Object

water.Iced

water.Lockable<Model>

water.Model

All Implemented Interfaces:

java.lang.Cloneable, Freezable

Direct Known Subclasses:

DeepLearningModel, DTree.TreeModel, GapStatisticModel, GLMModel, KMeans2.KMeans2Model, NBModel, NeuralNet.NeuralNetModel, PCAModel, SpeeDRFModel

public abstract class Model
extends Lockable<Model>

A Model models reality (hopefully). A model can be used to 'score' a row, or a collection of rows on any compatible dataset - meaning the row has all the columns with the same names as used to build the mode.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Model.ModelCategory

Field Summary

Fields

Modifier and Type	Field and Description
Key	_dataKey Dataset key used to *build* the model, for models for which this makes sense, or null otherwise.
java.lang.String[][]	_domains Categorical/factor/enum mappings, per column.
protected float[]	_modelClassDist
java.lang.String[]	_names Columns used in the model and are used to match up with scoring data columns.
protected float[]	_priorClassDist
static DocGen.FieldDoc[]	DOC_FIELDS
long	training_duration_in_ms The duration in mS for model training.
long	training_start_time The start time in mS since the epoch for model training.

Fields inherited from class water.Lockable

_key, _lockers

Constructor Summary

Constructors

Constructor and Description
Model(Key selfKey, Key dataKey, Frame fr) Constructor from frame (without prior class dist): Strips out the Vecs to just the names needed to match columns later for future datasets.
Model(Key selfKey, Key dataKey, Frame fr, float[] priorClassDist) Full constructor from frame: Strips out the Vecs to just the names needed to match columns later for future datasets.
Model(Key selfKey, Key dataKey, java.lang.String[] names, java.lang.String[][] domains) Constructor without prior class distribution
Model(Key selfKey, Key dataKey, java.lang.String[] names, java.lang.String[][] domains, float[] priorClassDist) Full constructor
Model(Key selfKey, Model m) Simple shallow copy constructor to a new Key

Method Summary

Methods

Modifier and Type	Method and Description
Frame[]	adapt(Frame fr, boolean exact) Build an adapted Frame from the given Frame.
double	calcError(Frame ftest, Vec vactual, Frame fpreds, Frame hitratio_fpreds, java.lang.String label, boolean printMe, int max_conf_mat_size, ConfusionMatrix cm, AUC auc, HitRatio hr) Compute the model error for a given test data set For multi-class classification, this is the classification error based on assigning labels for the highest predicted per-class probability.
java.lang.String[]	classNames()
ConfusionMatrix	cm() For classifiers, confusion matrix on validation set.
Futures	delete_impl(Futures fs) Remove any Model internal Keys
java.lang.String	errStr()
Request2	get_params()
static int[][]	getDomainMapping(java.lang.String[] modelDom, java.lang.String[] colDom, boolean exact) Returns a mapping between values of model domains (modelDom) and given column domain.
static int[][]	getDomainMapping(java.lang.String colName, java.lang.String[] modelDom, java.lang.String[] colDom, boolean logNonExactMapping) Returns a mapping for given column according to given modelDom.
Model.ModelCategory	getModelCategory()
UniqueId	getUniqueId()
boolean	isClassifier()
Request2	job()
protected double	missingColumnsType() Type of missing columns during adaptation between train/test datasets Overload this method for models that have sparse data handling.
double	mse() Returns mse for validation set.
int	nclasses()
int	nfeatures() Returns number of input features
java.lang.String	responseName()
double	score(double[] data)
Frame	score(Frame fr) Bulk score for given fr frame.
Frame	score(Frame fr, boolean adapt) Bulk score the frame fr, producing a Frame result; the 1st Vec is the predicted class, the remaining Vecs are the probability distributions.
float[]	score(Frame fr, boolean exact, int row) Single row scoring, on a compatible Frame.
float[]	score(int[][][] map, double[] row, float[] preds) Single row scoring, on a compatible set of data, given an adaption vector
float[]	score(java.lang.String[] names, java.lang.String[][] domains, boolean exact, double[] row) Single row scoring, on a compatible set of data.
protected float[]	score0(Chunk[] chks, int row_in_chunk, double[] tmp, float[] preds) Bulk scoring API for one row.
protected abstract float[]	score0(double[] data, float[] preds) Subclasses implement the scoring logic.
void	setModelClassDistribution(float[] classdist)
void	start_training(long training_start_time)
void	start_training(Model previous)
void	stop_training()
void	testJavaScoring(Frame fr)
java.lang.String	toJava() Return a String which is a valid Java program representing a class that implements the Model.
SB	toJava(SB sb)
protected java.lang.String	toJavaDefaultMaxIters()
protected void	toJavaInit(javassist.CtClass ct)
protected SB	toJavaInit(SB sb, SB fileContextSB)
protected void	toJavaPredictBody(SB bodySb, SB classCtxSb, SB fileCtxSb)
protected SB	toJavaSuper(SB sb) Generate implementation for super class.
VarImp	varimp() Variable importance of individual input features measured by this model.

Methods inherited from class water.Lockable

delete_and_lock, delete, delete, delete, delete, is_unlocked, is_wlocked, read_lock, read_lock, unlock_all, unlock, update, write_lock

Methods inherited from class water.Iced

clone, frozenType, init, newInstance, read, toDocField, write, writeJSON, writeJSONFields

Methods inherited from class java.lang.Object

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

Field Detail

DOC_FIELDS

public static DocGen.FieldDoc[] DOC_FIELDS

_dataKey

@Request.API(help="Datakey used to *build* the model")
public final Key _dataKey

Dataset key used to *build* the model, for models for which this makes sense, or null otherwise. Not all models are built from a dataset (eg artificial models), or are built from a single dataset (various ensemble models), so this key has no *mathematical* significance in the model but is handy during common model-building and for the historical record.

_names

@Request.API(help="Column names used to build the model")
public final java.lang.String[] _names

Columns used in the model and are used to match up with scoring data columns. The last name is the response column name.

_domains

@Request.API(help="Column names used to build the model")
public final java.lang.String[][] _domains

Categorical/factor/enum mappings, per column. Null for non-enum cols. The last column holds the response col enums.

_priorClassDist

@Request.API(help="Relative class distribution factors in original data")
protected final float[] _priorClassDist

_modelClassDist

@Request.API(help="Relative class distribution factors used for model building")
protected float[] _modelClassDist

training_start_time

public long training_start_time

The start time in mS since the epoch for model training.

training_duration_in_ms

public long training_duration_in_ms

The duration in mS for model training.

Constructor Detail

Model

public Model(Key selfKey,
     Key dataKey,
     Frame fr,
     float[] priorClassDist)

Full constructor from frame: Strips out the Vecs to just the names needed to match columns later for future datasets.

Model

public Model(Key selfKey,
     Key dataKey,
     Frame fr)

Constructor from frame (without prior class dist): Strips out the Vecs to just the names needed to match columns later for future datasets.

Model

public Model(Key selfKey,
     Key dataKey,
     java.lang.String[] names,
     java.lang.String[][] domains)

Constructor without prior class distribution

Model

public Model(Key selfKey,
     Key dataKey,
     java.lang.String[] names,
     java.lang.String[][] domains,
     float[] priorClassDist)

Full constructor

Model

public Model(Key selfKey,
     Model m)

Simple shallow copy constructor to a new Key

Method Detail

setModelClassDistribution

public void setModelClassDistribution(float[] classdist)

get_params

public Request2 get_params()

job

public Request2 job()

getModelCategory

public Model.ModelCategory getModelCategory()

delete_impl

public Futures delete_impl(Futures fs)

Remove any Model internal Keys

Specified by:

delete_impl in class Lockable<Model>

errStr

public java.lang.String errStr()

Specified by:

errStr in class Lockable<Model>

getUniqueId

public UniqueId getUniqueId()

start_training

public void start_training(long training_start_time)

start_training

public void start_training(Model previous)

stop_training

public void stop_training()

responseName

public java.lang.String responseName()

classNames

public java.lang.String[] classNames()

isClassifier

public boolean isClassifier()

nclasses

public int nclasses()

nfeatures

public int nfeatures()

Returns number of input features

cm

public ConfusionMatrix cm()

For classifiers, confusion matrix on validation set.

mse

public double mse()

Returns mse for validation set.

varimp

public VarImp varimp()

Variable importance of individual input features measured by this model.

score

public final Frame score(Frame fr)

Bulk score for given fr frame. The frame is always adapted to this model.

Parameters:

fr - frame to be scored

Returns:

frame holding predicted values

See Also:

score(Frame, boolean)

score

public final Frame score(Frame fr,
          boolean adapt)

Bulk score the frame fr, producing a Frame result; the 1st Vec is the predicted class, the remaining Vecs are the probability distributions. For Regression (single-class) models, the 1st and only Vec is the prediction value. The flat adapt

Parameters:

fr - frame which should be scored

adapt - a flag enforcing an adaptation of fr to this model. If flag is false scoring code expect that fr is already adapted.

Returns:

a new frame containing a predicted values. For classification it contains a column with prediction and distribution for all response classes. For regression it contains only one column with predicted values.

score

public final float[] score(Frame fr,
            boolean exact,
            int row)

Single row scoring, on a compatible Frame.

score

public final float[] score(java.lang.String[] names,
            java.lang.String[][] domains,
            boolean exact,
            double[] row)

Single row scoring, on a compatible set of data. Fairly expensive to adapt.

score

public final float[] score(int[][][] map,
            double[] row,
            float[] preds)

Single row scoring, on a compatible set of data, given an adaption vector

missingColumnsType

protected double missingColumnsType()

Type of missing columns during adaptation between train/test datasets Overload this method for models that have sparse data handling. Otherwise, NaN is used.

Returns:

real-valued number (can be NaN)

adapt

public Frame[] adapt(Frame fr,
            boolean exact)

Build an adapted Frame from the given Frame. Useful for efficient bulk scoring of a new dataset to an existing model. Same adaption as above, but expressed as a Frame instead of as an int[][]. The returned Frame does not have a response column. It returns a two element array containing an adapted frame and a frame which contains only vectors which where adapted (the purpose of the second frame is to delete all adapted vectors with deletion of the frame).

getDomainMapping

public static int[][] getDomainMapping(java.lang.String[] modelDom,
                       java.lang.String[] colDom,
                       boolean exact)

Returns a mapping between values of model domains (modelDom) and given column domain.

See Also:

getDomainMapping(String, String[], String[], boolean)

getDomainMapping

public static int[][] getDomainMapping(java.lang.String colName,
                       java.lang.String[] modelDom,
                       java.lang.String[] colDom,
                       boolean logNonExactMapping)

Returns a mapping for given column according to given modelDom. In this case, modelDom is

Parameters:

colName - name of column which is mapped, can be null.

modelDom -

logNonExactMapping -

Returns:

score0

protected float[] score0(Chunk[] chks,
             int row_in_chunk,
             double[] tmp,
             float[] preds)

Bulk scoring API for one row. Chunks are all compatible with the model, and expect the last Chunks are for the final distribution and prediction. Default method is to just load the data into the tmp array, then call subclass scoring logic.

calcError

public double calcError(Frame ftest,
               Vec vactual,
               Frame fpreds,
               Frame hitratio_fpreds,
               java.lang.String label,
               boolean printMe,
               int max_conf_mat_size,
               ConfusionMatrix cm,
               AUC auc,
               HitRatio hr)

Compute the model error for a given test data set For multi-class classification, this is the classification error based on assigning labels for the highest predicted per-class probability. For binary classification, this is the classification error based on assigning labels using the optimal threshold for maximizing the F1 score. For regression, this is the mean squared error (MSE).

Parameters:

ftest - Frame containing test data

vactual - The response column Vec

fpreds - Frame containing ADAPTED (domain labels from train+test data) predicted data (classification: label + per-class probabilities, regression: target)

hitratio_fpreds - Frame containing predicted data (domain labels from test data) (classification: label + per-class probabilities, regression: target)

label - Name for the scored data set to be printed

printMe - Whether to print the scoring results to Log.info

max_conf_mat_size - Largest size of Confusion Matrix (#classes) for it to be printed to Log.info

cm - Confusion Matrix object to populate for multi-class classification (also used for regression)

auc - AUC object to populate for binary classification

hr - HitRatio object to populate for classification

Returns:

model error, see description above

score0

protected abstract float[] score0(double[] data,
             float[] preds)

Subclasses implement the scoring logic. The data is pre-loaded into a re-used temp array, in the order the model expects. The predictions are loaded into the re-used temp array, which is also returned.

score

public double score(double[] data)

toJava

public java.lang.String toJava()

Return a String which is a valid Java program representing a class that implements the Model. The Java is of the form:

    class UUIDxxxxModel {
      public static final String NAMES[] = { ....column names... }
      public static final String DOMAINS[][] = { ....domain names... }
      // Pass in data in a double[], pre-aligned to the Model's requirements.
      // Jam predictions into the preds[] array; preds[0] is reserved for the
      // main prediction (class for classifiers or value for regression),
      // and remaining columns hold a probability distribution for classifiers.
      float[] predict( double data[], float preds[] );
      double[] map( HashMap < String,Double > row, double data[] );
      // Does the mapping lookup for every row, no allocation
      float[] predict( HashMap < String,Double > row, double data[], float preds[] );
      // Allocates a double[] for every row
      float[] predict( HashMap < String,Double > row, float preds[] );
      // Allocates a double[] and a float[] for every row
      float[] predict( HashMap < String,Double > row );
    }
  

toJava

public SB toJava(SB sb)

toJavaSuper

protected SB toJavaSuper(SB sb)

Generate implementation for super class.

toJavaInit

protected SB toJavaInit(SB sb,
            SB fileContextSB)

toJavaInit

protected void toJavaInit(javassist.CtClass ct)

toJavaPredictBody

protected void toJavaPredictBody(SB bodySb,
                     SB classCtxSb,
                     SB fileCtxSb)

toJavaDefaultMaxIters

protected java.lang.String toJavaDefaultMaxIters()

testJavaScoring

public void testJavaScoring(Frame fr)