public class RFormula extends Estimator<RFormulaModel> implements RFormulaBase, DefaultParamsWritable
The basic operators are:
- ~ separate target and terms
- + concat terms, "+ 0" means removing intercept
- - remove a term, "- 1" means removing intercept
- : interaction (multiplication for numeric values, or binarized categorical values)
- . all columns except target
Suppose a and b are double columns, we use the following simple examples
to illustrate the effect of RFormula:
- y ~ a + b means model y ~ w0 + w1 * a + w2 * b where w0 is the intercept and w1, w2
are coefficients.
- y ~ a + b + a:b - 1 means model y ~ w1 * a + w2 * b + w3 * a * b where w1, w2, w3
are coefficients.
RFormula produces a vector column of features and a double or string column of label.
Like when formulas are used in R for linear regression, string input columns will be one-hot
encoded, and numeric columns will be cast to doubles.
If the label column is of type string, it will be first transformed to double with
StringIndexer. If the label column does not exist in the DataFrame, the output label column
will be created from the specified response variable in the formula.
| Modifier and Type | Method and Description |
|---|---|
RFormula |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
RFormulaModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
static RFormula |
load(String path) |
static MLReader<T> |
read() |
RFormula |
setFeaturesCol(String value) |
RFormula |
setForceIndexLabel(boolean value) |
RFormula |
setFormula(String value)
Sets the formula to use for this transformer.
|
RFormula |
setHandleInvalid(String value) |
RFormula |
setLabelCol(String value) |
RFormula |
setStringIndexerOrderType(String value) |
String |
toString() |
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
forceIndexLabel, formula, getForceIndexLabel, getFormula, getStringIndexerOrderType, handleInvalid, hasLabelCol, stringIndexerOrderTypefeaturesCol, getFeaturesColgetLabelCol, labelColgetHandleInvalidclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnwritesaveinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static RFormula load(String path)
public static MLReader<T> read()
public String uid()
Identifiableuid in interface Identifiablepublic RFormula setFormula(String value)
value - an R formula in string form (e.g. "y ~ x + z")public RFormula setHandleInvalid(String value)
public RFormula setFeaturesCol(String value)
public RFormula setLabelCol(String value)
public RFormula setForceIndexLabel(boolean value)
public RFormula setStringIndexerOrderType(String value)
public RFormulaModel fit(Dataset<?> dataset)
Estimatorfit in class Estimator<RFormulaModel>dataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStageCheck transform validity and derive the output schema from the input schema.
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 PipelineStageschema - (undocumented)public RFormula copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Estimator<RFormulaModel>extra - (undocumented)public String toString()
toString in interface IdentifiabletoString in class Object