public class FrameMetadata
extends water.Iced
| Modifier and Type | Field and Description |
|---|---|
int[] |
_binaryCols |
int[] |
_catFeats |
ColMeta[] |
_cols |
int[] |
_dblCols |
water.fvec.Frame |
_fr |
int[] |
_intCols |
int[] |
_intNotBinaryCols |
int[] |
_numFeats |
int |
_response |
water.fvec.Vec[] |
_trainTestWeight |
static java.lang.String[] |
METAVALUES |
static double |
SQLNAN |
| Constructor and Description |
|---|
FrameMetadata(EventLog eventLog,
water.fvec.Frame fr,
int response,
int[] predictors,
java.lang.String datasetName,
boolean isClassification) |
FrameMetadata(EventLog eventLog,
water.fvec.Frame fr,
int response,
java.lang.String datasetName) |
FrameMetadata(EventLog eventLog,
water.fvec.Frame fr,
int response,
java.lang.String[] predictors,
java.lang.String datasetName,
boolean isClassification) |
FrameMetadata(EventLog eventLog,
water.fvec.Frame fr,
int response,
java.lang.String datasetName,
boolean isClassification) |
| Modifier and Type | Method and Description |
|---|---|
FrameMetadata |
computeFrameMetaPass1() |
void |
delete() |
int[] |
diffCols(int[] filterThese)
Get the non-ignored columns that are not in the filter; do not include the response.
|
void |
fillSimpleMeta(java.util.HashMap<java.lang.String,java.lang.Object> fm) |
java.lang.String[] |
ignoredCols() |
java.lang.String[] |
includedCols() |
static java.lang.String[] |
intAtoStringA(int[] select,
java.lang.String[] names) |
boolean |
isAnyCategorical()
checks if there are any categorical features in the frame
|
boolean |
isAnyNumeric()
checks if there are any numeric features in the frame
|
boolean |
isClassification() |
static java.util.HashMap<java.lang.String,java.lang.Object> |
makeEmptyFrameMeta() |
long |
na_FeatureCount() |
long |
nClass() |
int |
numberOfCategoricalFeatures() |
int |
numberOfNumericFeatures()
If predictors were passed, then any values computed/cached are based on those
predictors
|
ColMeta |
response() |
long |
rowsWithNa() |
boolean |
stratify() |
water.fvec.Vec[] |
weights() |
public final water.fvec.Frame _fr
public int[] _catFeats
public int[] _numFeats
public int[] _intCols
public int[] _dblCols
public int[] _binaryCols
public int[] _intNotBinaryCols
public int _response
public ColMeta[] _cols
public water.fvec.Vec[] _trainTestWeight
public static final double SQLNAN
public static final java.lang.String[] METAVALUES
public FrameMetadata(EventLog eventLog, water.fvec.Frame fr, int response, java.lang.String datasetName)
public FrameMetadata(EventLog eventLog, water.fvec.Frame fr, int response, java.lang.String datasetName, boolean isClassification)
public FrameMetadata(EventLog eventLog, water.fvec.Frame fr, int response, int[] predictors, java.lang.String datasetName, boolean isClassification)
public FrameMetadata(EventLog eventLog, water.fvec.Frame fr, int response, java.lang.String[] predictors, java.lang.String datasetName, boolean isClassification)
public void delete()
public static java.util.HashMap<java.lang.String,java.lang.Object> makeEmptyFrameMeta()
public void fillSimpleMeta(java.util.HashMap<java.lang.String,java.lang.Object> fm)
public int[] diffCols(int[] filterThese)
filterThese - remove these columnspublic long na_FeatureCount()
public long rowsWithNa()
public long nClass()
public boolean isAnyNumeric()
public boolean isAnyCategorical()
public int numberOfNumericFeatures()
public int numberOfCategoricalFeatures()
public boolean isClassification()
public java.lang.String[] ignoredCols()
public java.lang.String[] includedCols()
public ColMeta response()
public boolean stratify()
public water.fvec.Vec[] weights()
public FrameMetadata computeFrameMetaPass1()
public static java.lang.String[] intAtoStringA(int[] select,
java.lang.String[] names)