public abstract static class Model.Output extends Iced
Modifier and Type | Field and Description |
---|---|
java.lang.String[] |
_column_types |
Key<Frame> |
_cross_validation_fold_assignment_frame_id |
Key<Frame> |
_cross_validation_holdout_predictions_frame_id |
ModelMetrics |
_cross_validation_metrics
Cross-Validation metrics obtained during model training
|
TwoDimTable |
_cross_validation_metrics_summary
Summary of cross-validation metrics of all k-fold models
|
Key[] |
_cross_validation_models
List of Keys to cross-validation models (non-null iff _parms._nfolds > 1 or _parms._fold_column != null)
|
Key[] |
_cross_validation_predictions
List of Keys to cross-validation predictions (if requested)
|
TwoDimTable[] |
_cv_scoring_history |
double |
_defaultThreshold
Default threshold used to make decision about binomial predictions
-1 if is not set by user - than the default threshold is 0.5 if metrics are not set
(0, 1> custom default threshold or validation metric threshold or training metric threshold
|
double[] |
_distribution |
java.lang.String[][] |
_domains
Categorical/factor mappings, per column.
|
long |
_end_time |
protected boolean |
_hasFold |
protected boolean |
_hasOffset
The name of the response column (which is always the last column).
|
protected boolean |
_hasTreatment |
protected boolean |
_hasWeights |
protected boolean |
_isSupervised |
Job |
_job
Job info: final status (canceled, crashed), build time
|
TwoDimTable |
_model_summary
User-facing model summary - Display model type, complexity, size and other useful stats
|
double[] |
_modelClassDist |
java.lang.String[] |
_names
Columns used in the model and are used to match up with scoring data
columns.
|
double[] |
_orig_projection_array |
java.lang.String[][] |
_origDomains |
java.lang.String[] |
_origNames |
double[] |
_priorClassDist |
TwoDimTable[] |
_reproducibility_information_table
Reproducibility information describing the current cluster configuration, each node configuration
and checksums for each frame used on the input of the algorithm
|
long |
_run_time |
TwoDimTable |
_scoring_history
User-facing model scoring history - 2D table with modeling accuracy as a function of time/trees/epochs/iterations, etc.
|
long |
_start_time |
long |
_total_run_time |
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)
|
Modifier | Constructor and Description |
---|---|
|
Output() |
|
Output(boolean hasWeights,
boolean hasOffset,
boolean hasFold) |
|
Output(ModelBuilder b)
Any final prep-work just before model-building starts, but after the
user has clicked "go".
|
protected |
Output(ModelBuilder b,
Frame train) |
Modifier and Type | Method and Description |
---|---|
void |
changeModelMetricsKey(Key modelkey) |
protected long |
checksum_impl() |
java.lang.String[] |
classNames()
Names of levels for a categorical response column.
|
Key<ModelMetrics>[] |
clearModelMetrics(boolean keepModelTrainingMetrics) |
TwoDimTable |
createInputFramesInformationTable(ModelBuilder modelBuilder) |
double |
defaultThreshold() |
java.lang.String[] |
features()
Returns features used by the model
|
int |
foldIdx() |
java.lang.String |
foldName() |
int |
getInformationTableNumRows() |
hex.ModelCategory |
getModelCategory() |
Key<ModelMetrics>[] |
getModelMetrics() |
TwoDimTable |
getVariableImportances()
Retrieves variable importances
|
boolean |
hasFold() |
boolean |
hasOffset() |
boolean |
hasResponse() |
boolean |
hasTreatment() |
boolean |
hasWeights() |
Model.InteractionBuilder |
interactionBuilder() |
boolean |
isAutoencoder() |
boolean |
isBinomialClassifier()
Is this model a binomial classification model? (v.
|
boolean |
isClassifier()
Is this model a classification model? (v.
|
boolean |
isMultinomialClassifier()
Is this model a multinomial classification model (supervised and nclasses() > 2
|
boolean |
isSupervised() |
protected int |
lastSpecialColumnIdx() |
int |
nclasses()
Number of classes in the response column if it is categorical and the model is supervised.
|
int |
nfeatures()
Returns number of input features (OK for most supervised methods, need to override for unsupervised!)
|
int |
offsetIdx() |
java.lang.String |
offsetName() |
void |
printTwoDimTables(java.lang.StringBuilder sb,
java.lang.Object o) |
void |
resetThreshold(double value) |
int |
responseIdx() |
java.lang.String |
responseName() |
void |
setNames(java.lang.String[] names)
Deprecated.
as of March 6, 2019, replaced by (@link #setNames(String[] names, String[] columnTypes))
|
void |
setNames(java.lang.String[] names,
java.lang.String[] columntypes) |
protected void |
startClock() |
protected void |
stopClock() |
java.lang.String |
toString() |
int |
treatmentIdx() |
int |
weightsIdx() |
java.lang.String |
weightsName() |
asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonBytes, toJsonString, write, writeExternal, writeJSON
public java.lang.String[] _names
public java.lang.String[] _column_types
public java.lang.String[] _origNames
public java.lang.String[][] _domains
public java.lang.String[][] _origDomains
public double[] _orig_projection_array
public Key[] _cross_validation_models
public Key[] _cross_validation_predictions
public Key<Frame> _cross_validation_holdout_predictions_frame_id
public Key<Frame> _cross_validation_fold_assignment_frame_id
public long _start_time
public long _end_time
public long _run_time
public long _total_run_time
public Job _job
public ModelMetrics _training_metrics
public ModelMetrics _validation_metrics
public ModelMetrics _cross_validation_metrics
public TwoDimTable _cross_validation_metrics_summary
public TwoDimTable _model_summary
public TwoDimTable[] _reproducibility_information_table
public TwoDimTable _scoring_history
public TwoDimTable[] _cv_scoring_history
public double[] _distribution
public double[] _modelClassDist
public double[] _priorClassDist
protected boolean _isSupervised
public double _defaultThreshold
protected boolean _hasOffset
protected boolean _hasWeights
protected boolean _hasFold
protected boolean _hasTreatment
public Output()
public Output(boolean hasWeights, boolean hasOffset, boolean hasFold)
public Output(ModelBuilder b)
protected Output(ModelBuilder b, Frame train)
@Deprecated public void setNames(java.lang.String[] names)
public void setNames(java.lang.String[] names, java.lang.String[] columntypes)
protected void startClock()
protected void stopClock()
public int nfeatures()
public java.lang.String[] features()
public boolean isSupervised()
public boolean hasOffset()
public boolean hasWeights()
public boolean hasFold()
public boolean hasTreatment()
public boolean hasResponse()
public java.lang.String responseName()
public java.lang.String weightsName()
public java.lang.String offsetName()
public java.lang.String foldName()
public Model.InteractionBuilder interactionBuilder()
protected int lastSpecialColumnIdx()
public int weightsIdx()
public int offsetIdx()
public int foldIdx()
public int responseIdx()
public int treatmentIdx()
public java.lang.String[] classNames()
public boolean isClassifier()
public boolean isBinomialClassifier()
public boolean isMultinomialClassifier()
public int nclasses()
public hex.ModelCategory getModelCategory()
public boolean isAutoencoder()
public TwoDimTable getVariableImportances()
public Key<ModelMetrics>[] clearModelMetrics(boolean keepModelTrainingMetrics)
public Key<ModelMetrics>[] getModelMetrics()
public void changeModelMetricsKey(Key modelkey)
protected long checksum_impl()
public double defaultThreshold()
public void resetThreshold(double value)
public void printTwoDimTables(java.lang.StringBuilder sb, java.lang.Object o)
public java.lang.String toString()
toString
in class java.lang.Object
public TwoDimTable createInputFramesInformationTable(ModelBuilder modelBuilder)
public int getInformationTableNumRows()