hex

Class Model.Output

java.lang.Object

water.Iced

hex.Model.Output

All Implemented Interfaces:

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

Direct Known Subclasses:

ClusteringModel.ClusteringOutput, QuantileModel.QuantileOutput, SupervisedModel.SupervisedOutput

Enclosing class:

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

public abstract static class Model.Output
extends Iced

Model-specific output class. Each model sub-class contains an instance of one of these containing its "output": the pieces of the model needed for scoring. E.g. KMeansModel has a KMeansOutput extending Model.Output which contains the cluster centers. The output also includes the names, domains and other fields which are determined at training time.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String[][]	_domains Categorical/factor/enum mappings, per column.
Key[]	_model_metrics List of all the associated ModelMetrics objects, so we can delete them when we delete this model.
TwoDimTable	_model_summary User-facing model summary - Display model type, complexity, size and other useful stats
java.lang.String[]	_names Columns used in the model and are used to match up with scoring data columns.
TwoDimTable	_scoring_history User-facing model scoring history - 2D table with modeling accuracy as a function of time/trees/epochs/iterations, etc.
Job.JobState	_state Job state (CANCELLED, FAILED, DONE).
ModelMetrics	_training_metrics Training set metrics obtained during model training
ModelMetrics	_validation_metrics Validation set metrics obtained during model training (if a validation data set was specified)

Constructor Summary

Constructors

Constructor and Description
Model.Output(ModelBuilder b) Any final prep-work just before model-building starts, but after the user has clicked "go".

Method Summary

Methods

Modifier and Type	Method and Description
ModelMetrics	addModelMetrics(ModelMetrics mm)
java.lang.String[]	classNames() The names of the levels for an enum (categorical) response column.
hex.ModelCategory	getModelCategory()
boolean	isClassifier() Is this model a classification model? (v.
boolean	isSupervised()
int	nclasses()
int	nfeatures() Returns number of input features (OK for most unsupervised methods, need to override for supervised!)
java.lang.String	responseName() The name of the response column (which is always the last column).
java.lang.String	toString()

Methods inherited from class water.Iced

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

Methods inherited from class java.lang.Object

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

Field Detail

_names

public 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 (if any).

_domains

public java.lang.String[][] _domains

Categorical/factor/enum mappings, per column. Null for non-enum cols. Columns match the post-init cleanup columns. The last column holds the response col enums for SupervisedModels.

_model_metrics

public Key[] _model_metrics

List of all the associated ModelMetrics objects, so we can delete them when we delete this model.

_state

public Job.JobState _state

Job state (CANCELLED, FAILED, DONE). TODO: Really the whole Job (run-time, etc) but that has to wait until Job is split from ModelBuilder.

_training_metrics

public ModelMetrics _training_metrics

Training set metrics obtained during model training

_validation_metrics

public ModelMetrics _validation_metrics

Validation set metrics obtained during model training (if a validation data set was specified)

_model_summary

public TwoDimTable _model_summary

User-facing model summary - Display model type, complexity, size and other useful stats

_scoring_history

public TwoDimTable _scoring_history

User-facing model scoring history - 2D table with modeling accuracy as a function of time/trees/epochs/iterations, etc.

Constructor Detail

Model.Output

public Model.Output(ModelBuilder b)

Any final prep-work just before model-building starts, but after the user has clicked "go". E.g., converting a response column to an enum touches the entire column (can be expensive), makes a parallel vec (Key/Data leak management issues), and might throw IAE if there are too many classes.

Method Detail

nfeatures

public int nfeatures()

Returns number of input features (OK for most unsupervised methods, need to override for supervised!)

isSupervised

public boolean isSupervised()

responseName

public java.lang.String responseName()

The name of the response column (which is always the last column).

classNames

public java.lang.String[] classNames()

The names of the levels for an enum (categorical) response column.

isClassifier

public boolean isClassifier()

Is this model a classification model? (v. a regression or clustering model)

nclasses

public int nclasses()

getModelCategory

public hex.ModelCategory getModelCategory()

addModelMetrics

public ModelMetrics addModelMetrics(ModelMetrics mm)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object