public class FMClassificationModel extends ProbabilisticClassificationModel<Vector,FMClassificationModel> implements FMClassifierParams, MLWritable, HasTrainingSummary<FMClassificationTrainingSummary>
FMClassifier| Modifier and Type | Method and Description |
|---|---|
FMClassificationModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
FMClassificationSummary |
evaluate(Dataset<?> dataset)
Evaluates the model on a test dataset.
|
Matrix |
factors() |
IntParam |
factorSize()
Param for dimensionality of the factors (>= 0)
|
BooleanParam |
fitIntercept()
Param for whether to fit an intercept term.
|
BooleanParam |
fitLinear()
Param for whether to fit linear term (aka 1-way term)
|
DoubleParam |
initStd()
Param for standard deviation of initial coefficients
|
double |
intercept() |
Vector |
linear() |
static FMClassificationModel |
load(String path) |
IntParam |
maxIter()
Param for maximum number of iterations (>= 0).
|
DoubleParam |
miniBatchFraction()
Param for mini-batch fraction, must be in range (0, 1]
|
int |
numClasses()
Number of classes (values which the label can take).
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
Vector |
predictRaw(Vector features)
Raw prediction for each possible label.
|
static MLReader<FMClassificationModel> |
read() |
DoubleParam |
regParam()
Param for regularization parameter (>= 0).
|
LongParam |
seed()
Param for random seed.
|
Param<String> |
solver()
The solver algorithm for optimization.
|
DoubleParam |
stepSize()
Param for Step size to be used for each iteration of optimization (> 0).
|
FMClassificationTrainingSummary |
summary()
Gets summary of model on training set.
|
DoubleParam |
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).
|
String |
toString() |
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, predictProbability, probabilityCol, setProbabilityCol, setThresholds, thresholds, transform, transformSchemapredict, rawPredictionCol, setRawPredictionCol, transformImplfeaturesCol, labelCol, predictionCol, setFeaturesCol, setPredictionColtransform, transform, transformparamsvalidateAndTransformSchemaextractInstancesextractInstances, extractInstancesgetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwngetRawPredictionCol, rawPredictionColgetProbabilityCol, probabilityColgetThresholds, thresholdsgetFactorSize, getFitLinear, getInitStd, getMiniBatchFractiongetMaxItergetStepSizegetFitInterceptgetRegParamgetWeightColsavehasSummary, setSummary$init$, 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_, uninitializepublic static MLReader<FMClassificationModel> read()
public static FMClassificationModel load(String path)
public final IntParam factorSize()
FactorizationMachinesParamsfactorSize in interface FactorizationMachinesParamspublic final BooleanParam fitLinear()
FactorizationMachinesParamsfitLinear in interface FactorizationMachinesParamspublic final DoubleParam miniBatchFraction()
FactorizationMachinesParamsminiBatchFraction in interface FactorizationMachinesParamspublic final DoubleParam initStd()
FactorizationMachinesParamsinitStd in interface FactorizationMachinesParamspublic final Param<String> solver()
FactorizationMachinesParamssolver in interface HasSolversolver in interface FactorizationMachinesParamspublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final DoubleParam regParam()
HasRegParamregParam in interface HasRegParampublic final BooleanParam fitIntercept()
HasFitInterceptfitIntercept in interface HasFitInterceptpublic final LongParam seed()
HasSeedpublic final DoubleParam tol()
HasTolpublic DoubleParam stepSize()
HasStepSizestepSize in interface HasStepSizepublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic String uid()
Identifiableuid in interface Identifiablepublic double intercept()
public Vector linear()
public Matrix factors()
public int numClasses()
ClassificationModelnumClasses in class ClassificationModel<Vector,FMClassificationModel>public int numFeatures()
PredictionModelnumFeatures in class PredictionModel<Vector,FMClassificationModel>public FMClassificationTrainingSummary summary()
hasSummary is false.summary in interface HasTrainingSummary<FMClassificationTrainingSummary>public FMClassificationSummary evaluate(Dataset<?> dataset)
dataset - Test dataset to evaluate model on.public Vector predictRaw(Vector features)
ClassificationModeltransform() and output rawPredictionCol.
predictRaw in class ClassificationModel<Vector,FMClassificationModel>features - (undocumented)public FMClassificationModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<FMClassificationModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritablepublic String toString()
toString in interface IdentifiabletoString in class Object