public static class InfogramModel.InfogramModelOutput
extends hex.Model.Output
| Modifier and Type | Field and Description |
|---|---|
double[] |
_admissible |
double[] |
_admissible_cmi |
double[] |
_admissible_cmi_raw |
java.lang.String[] |
_admissible_features |
java.lang.String[] |
_admissible_features_valid |
java.lang.String[] |
_admissible_features_xval |
double[] |
_admissible_index |
double[] |
_admissible_index_valid |
static int |
_ADMISSIBLE_PREDICTOR_INDEX |
double[] |
_admissible_relevance |
water.Key<water.fvec.Frame> |
_admissible_score_key |
water.Key<water.fvec.Frame> |
_admissible_score_key_valid |
water.Key<water.fvec.Frame> |
_admissible_score_key_xval |
double[] |
_admissible_valid |
java.lang.String[] |
_all_predictor_names |
java.lang.String[] |
_all_predictor_names_valid |
double[] |
_cmi |
static int |
_CMI_INDEX |
double[] |
_cmi_raw |
static int |
_CMI_RAW_INDEX |
double[] |
_cmi_raw_valid |
double[] |
_cmi_valid |
static int |
_COLUMN_INDEX |
hex.genmodel.utils.DistributionFamily |
_distribution |
double[] |
_relevance |
static int |
_RELEVANCE_INDEX |
double[] |
_relevance_valid |
java.lang.String[] |
_topKFeatures |
long |
_validNonZeroNumRows |
_column_types, _cross_validation_fold_assignment_frame_id, _cross_validation_holdout_predictions_frame_id, _cross_validation_metrics, _cross_validation_metrics_summary, _cross_validation_models, _cross_validation_predictions, _cv_scoring_history, _defaultThreshold, _domains, _end_time, _hasFold, _hasOffset, _hasTreatment, _hasWeights, _isSupervised, _job, _model_summary, _modelClassDist, _names, _orig_projection_array, _origDomains, _origNames, _priorClassDist, _reproducibility_information_table, _run_time, _scoring_history, _start_time, _total_run_time, _training_metrics, _validation_metrics| Constructor and Description |
|---|
InfogramModelOutput(Infogram b) |
| Modifier and Type | Method and Description |
|---|---|
void |
extractAdmissibleFeatures(water.fvec.Frame relCMIFrame,
boolean validFrame,
boolean cvFrame)
Generate arrays containing only admissible features which are predictors with both cmi >= cmi_threshold and
relevance >= relevance_threshold
|
hex.ModelCategory |
getModelCategory() |
void |
setDistribution(hex.genmodel.utils.DistributionFamily distribution) |
static void |
sortCMIRel(int[] indices,
double[] relevance,
double[] cmiRawA,
double[] cmi,
java.lang.String[] allPredictorNames,
double[] admissibleIndex,
double[] admissibleA)
This method will sort _relvance, _cmi_raw, _cmi_normalize, _all_predictor_names such that features that
are closest to upper right corner of infogram comes first with the order specified in the index
|
changeModelMetricsKey, checksum_impl, classNames, clearModelMetrics, createInputFramesInformationTable, defaultThreshold, features, foldIdx, foldName, getInformationTableNumRows, getModelMetrics, getVariableImportances, hasFold, hasOffset, hasResponse, hasTreatment, hasWeights, interactionBuilder, isAutoencoder, isBinomialClassifier, isClassifier, isMultinomialClassifier, isSupervised, lastSpecialColumnIdx, nclasses, nfeatures, offsetIdx, offsetName, printTwoDimTables, resetThreshold, responseIdx, responseName, setNames, setNames, startClock, stopClock, toString, treatmentIdx, weightsIdx, weightsNamepublic static final int _COLUMN_INDEX
public static final int _ADMISSIBLE_PREDICTOR_INDEX
public static final int _RELEVANCE_INDEX
public static final int _CMI_INDEX
public static final int _CMI_RAW_INDEX
public double[] _admissible_cmi
public double[] _admissible_cmi_raw
public double[] _admissible_relevance
public java.lang.String[] _admissible_features
public java.lang.String[] _admissible_features_valid
public java.lang.String[] _admissible_features_xval
public double[] _admissible_index
public double[] _admissible_index_valid
public double[] _admissible
public double[] _admissible_valid
public hex.genmodel.utils.DistributionFamily _distribution
public double[] _cmi_raw
public double[] _cmi_raw_valid
public double[] _cmi
public double[] _cmi_valid
public java.lang.String[] _all_predictor_names
public java.lang.String[] _all_predictor_names_valid
public double[] _relevance
public double[] _relevance_valid
public water.Key<water.fvec.Frame> _admissible_score_key
public water.Key<water.fvec.Frame> _admissible_score_key_valid
public water.Key<water.fvec.Frame> _admissible_score_key_xval
public java.lang.String[] _topKFeatures
public long _validNonZeroNumRows
public InfogramModelOutput(Infogram b)
public hex.ModelCategory getModelCategory()
getModelCategory in class hex.Model.Outputpublic void setDistribution(hex.genmodel.utils.DistributionFamily distribution)
public void extractAdmissibleFeatures(water.fvec.Frame relCMIFrame,
boolean validFrame,
boolean cvFrame)
relCMIFrame - H2O Frame containing relevance, cmi, ... infovalidFrame - true if validation dataset existscvFrame - true if cross-validation is enabledpublic static void sortCMIRel(int[] indices,
double[] relevance,
double[] cmiRawA,
double[] cmi,
java.lang.String[] allPredictorNames,
double[] admissibleIndex,
double[] admissibleA)