hex

Class SupervisedModel<M extends Model<M,P,O>,P extends SupervisedModel.SupervisedParameters,O extends SupervisedModel.SupervisedOutput>

java.lang.Object

water.Iced

water.Keyed

water.Lockable<M>

hex.Model<M,P,O>

hex.SupervisedModel<M,P,O>

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, Freezable

public abstract class SupervisedModel<M extends Model<M,P,O>,P extends SupervisedModel.SupervisedParameters,O extends SupervisedModel.SupervisedOutput>
extends Model<M,P,O>

Supervised Model There is a response column used in training.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SupervisedModel.SupervisedOutput Output from all Supervised Models, includes class distribtion
static class	SupervisedModel.SupervisedParameters Supervised Model Parameters includes a response column, and whether or not rebalancing classes is desirable.

Nested classes/interfaces inherited from class hex.Model

Model.ModelCategory, Model.Output, Model.Parameters

Field Summary

Fields inherited from class hex.Model

_output, _parms, _warnings, training_duration_in_ms, training_start_time

Fields inherited from class water.Lockable

_lockers

Fields inherited from class water.Keyed

_key

Constructor Summary

Constructors

Constructor and Description
SupervisedModel(Key selfKey, P parms, O output)

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.
boolean	isSupervised() Needs to be set correctly otherwise eg scoring does not work.
protected float[]	score0(Chunk[] chks, int row_in_chunk, double[] tmp, float[] preds) Bulk scoring API for one row.

Methods inherited from class hex.Model

adapt, adapt, adapt, addWarning, checksum, computeModelMetrics, getDomainMapping, getDomainMapping, missingColumnsType, remove_impl, schema, score, score, score, score, score, score, score0, start_training, start_training, stop_training

Methods inherited from class water.Lockable

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

Methods inherited from class water.Keyed

remove, remove, remove, remove

Methods inherited from class water.Iced

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

Methods inherited from class java.lang.Object

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

Constructor Detail

SupervisedModel

public SupervisedModel(Key selfKey,
               P parms,
               O output)

Method Detail

isSupervised

public boolean isSupervised()

Description copied from class: Model

Needs to be set correctly otherwise eg scoring does not work.

Overrides:

isSupervised in class Model<M extends Model<M,P,O>,P extends SupervisedModel.SupervisedParameters,O extends SupervisedModel.SupervisedOutput>

Returns:

true if there was a response column used during training.

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.

Overrides:

score0 in class Model<M extends Model<M,P,O>,P extends SupervisedModel.SupervisedParameters,O extends SupervisedModel.SupervisedOutput>