public class CoxPHModel extends hex.Model<CoxPHModel,CoxPHModel.CoxPHParameters,CoxPHModel.CoxPHOutput>
Modifier and Type | Class and Description |
---|---|
static class |
CoxPHModel.CoxPHOutput |
static class |
CoxPHModel.CoxPHParameters |
Constructor and Description |
---|
CoxPHModel(water.Key destKey,
CoxPHModel.CoxPHParameters parms,
CoxPHModel.CoxPHOutput output) |
Modifier and Type | Method and Description |
---|---|
CoxPHModel.CoxPHParameters |
get_params() |
hex.ModelMetrics.MetricBuilder |
makeMetricBuilder(java.lang.String[] domain) |
water.api.ModelSchema |
schema() |
double[] |
score0(double[] data,
double[] preds)
Predict from raw double values representing the data
|
java.lang.String |
toString() |
java.lang.String |
toStringAll() |
adaptTestForTrain, adaptTestForTrain, addMetrics, addWarning, checksum_impl, cleanup_adapt, defaultThreshold, getPublishedKeys, isSupervised, remove_impl, score, score, score, score0, score0, score0, scoreImpl, testJavaScoring, toJava, toJava, toJavaCheckTooBig, toJavaInit, toJavaNCLASSES, toJavaPredictBody, toJavaPROB, toJavaSuper
delete_and_lock, delete, delete, delete, read_lock, read_lock, unlock_all, unlock, update, write_lock
checksum, getBinarySerializer, remove, remove, remove, remove
public CoxPHModel(water.Key destKey, CoxPHModel.CoxPHParameters parms, CoxPHModel.CoxPHOutput output)
public hex.ModelMetrics.MetricBuilder makeMetricBuilder(java.lang.String[] domain)
makeMetricBuilder
in class hex.Model<CoxPHModel,CoxPHModel.CoxPHParameters,CoxPHModel.CoxPHOutput>
public water.api.ModelSchema schema()
public final CoxPHModel.CoxPHParameters get_params()
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String toStringAll()
public double[] score0(double[] data, double[] preds)
score0
in class hex.Model<CoxPHModel,CoxPHModel.CoxPHParameters,CoxPHModel.CoxPHOutput>
data
- raw array containing categorical values (horizontalized to 1,0,0,1,0,0 etc.) and numerical values (0.35,1.24,5.3234,etc), both can contain NaNspreds
- predicted label and per-class probabilities (for classification), predicted target (regression), can contain NaNs