FeaturesType
- Type of features.
E.g., VectorUDT
for vector features.M
- Specialization of PredictionModel
. If you subclass this type, use this type
parameter to specify the concrete type for the corresponding model.public abstract class PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>> extends Model<M> implements PredictorParams
Constructor and Description |
---|
PredictionModel() |
Modifier and Type | Method and Description |
---|---|
Param<String> |
featuresCol()
Param for features column name.
|
Param<String> |
labelCol()
Param for label column name.
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
abstract double |
predict(FeaturesType features)
Predict label for the given features.
|
Param<String> |
predictionCol()
Param for prediction column name.
|
M |
setFeaturesCol(String value) |
M |
setPredictionCol(String value) |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol , calling predict , and storing
the predictions as a new column predictionCol . |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
transform, transform, transform
params
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
validateAndTransformSchema
getLabelCol
getFeaturesCol
getPredictionCol
clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString, uid
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public final Param<String> featuresCol()
HasFeaturesCol
featuresCol
in interface HasFeaturesCol
public final Param<String> labelCol()
HasLabelCol
labelCol
in interface HasLabelCol
public int numFeatures()
public abstract double predict(FeaturesType features)
transform()
and output predictionCol
.features
- (undocumented)public final Param<String> predictionCol()
HasPredictionCol
predictionCol
in interface HasPredictionCol
public M setFeaturesCol(String value)
public M setPredictionCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
featuresCol
, calling predict
, and storing
the predictions as a new column predictionCol
.
transform
in class Transformer
dataset
- input datasetpredictionCol
of type Double
public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema
and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate()
.
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema
in class PipelineStage
schema
- (undocumented)