water

Class SupervisedModel<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>

java.lang.Object

water.Iced

water.Keyed

water.Lockable<M>

water.Model<M,P,O>

water.SupervisedModel<M,P,O>

All Implemented Interfaces:

java.lang.Cloneable, Freezable

public abstract class SupervisedModel<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>
extends Model<M,P,O>

Supervised Model There is a response column used in training.

Nested Class Summary

Nested classes/interfaces inherited from class water.Model

Model.ModelCategory, Model.Output<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>, Model.Parameters<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>

Field Summary

Fields

Modifier and Type	Field and Description
protected float[]	_modelClassDist
protected float[]	_priorClassDist

Fields inherited from class water.Model

_output, _parms

Fields inherited from class water.Lockable

_lockers

Fields inherited from class water.Keyed

_key

Constructor Summary

Constructors

Constructor and Description
SupervisedModel(Key selfKey, Frame fr, P parms, O output, float[] priorClassDist)
SupervisedModel(Key selfKey, java.lang.String[] names, java.lang.String[][] domains, P parms, O output, float[] priorClassDist)

Method Summary

Methods

Modifier and Type	Method and Description
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.
protected float[]	score0(Chunk[] chks, int row_in_chunk, double[] tmp, float[] preds) Bulk scoring API for one row.
void	setModelClassDistribution(float[] classdist)

Methods inherited from class water.Model

adapt, getDomainMapping, getDomainMapping, schema, score, score, score, score, score, score, score0

Methods inherited from class water.Lockable

delete_and_lock, delete, delete, delete, errStr, read_lock, read_lock, unlock_all, unlock, update, write_lock

Methods inherited from class water.Keyed

remove_impl, remove, remove, remove, remove

Methods inherited from class water.Iced

clone, frozenType, read_impl, read, readJSON_impl, readJSON, write_impl, write, writeHTML_impl, writeHTML, writeJSON_impl, writeJSON

Methods inherited from class java.lang.Object

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

Field Detail

_priorClassDist

protected float[] _priorClassDist

_modelClassDist

protected float[] _modelClassDist

Constructor Detail

SupervisedModel

public SupervisedModel(Key selfKey,
               Frame fr,
               P parms,
               O output,
               float[] priorClassDist)

SupervisedModel

public SupervisedModel(Key selfKey,
               java.lang.String[] names,
               java.lang.String[][] domains,
               P parms,
               O output,
               float[] priorClassDist)

Method Detail

setModelClassDistribution

public void setModelClassDistribution(float[] classdist)

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 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.

Specified by:

score0 in class Model<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>