Serializable, org.apache.spark.internal.Logging, RFormulaBase, Params, HasFeaturesCol, HasHandleInvalid, HasLabelCol, DefaultParamsWritable, Identifiable, MLWritable
Implements the transforms required for fitting a dataset against an R model formula. Currently we support a limited subset of the R operators, including '~', '.', ':', '+', '-', '*' and '^'. Also see the R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
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 - * factor crossing, includes the terms and interactions between them - ^ factor crossing to a specified degree
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. - y ~ a * b means model y ~ w0 + w1 * a + w2 * b + w3 * a * b where w0 is the intercept and w1, w2, w3 are coefficients - y ~ (a + b)^2 means model y ~ w0 + w1 * a + w2 * b + w3 * a * b where w0 is the intercept and 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.
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
Constructors
Creates a copy of this instance with the same UID and some extra params.
Param for features column name.
Fits a model to the input data.
Force to index label whether it is numeric or string type.
Param for how to handle invalid data (unseen or NULL values) in features and label column of string type.
Param for label column name.
Sets the formula to use for this transformer.
Param for how to order categories of a string FEATURE column used by StringIndexer.
Check transform validity and derive the output schema from the input schema.
An immutable unique ID for the object and its derivatives.
Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnpublic RFormula()
R formula parameter. The formula is provided in string form.
formula in interface RFormulaBase
Force to index label whether it is numeric or string type. Usually we index label only when it is string type. If the formula was used by classification algorithms, we can force to index label even it is numeric type by setting this param with true. Default: false.
forceIndexLabel in interface RFormulaBase
Param for how to handle invalid data (unseen or NULL values) in features and label column of string type. Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: "error"
handleInvalid in interface HasHandleInvalid
handleInvalid in interface RFormulaBase
Param for how to order categories of a string FEATURE column used by
StringIndexer
. The last category after ordering is dropped when encoding strings. Supported options: 'frequencyDesc', 'frequencyAsc', 'alphabetDesc', 'alphabetAsc'. The default value is 'frequencyDesc'. When the ordering is set to 'alphabetDesc',
RFormula
drops the same category as R when encoding strings.
The options are explained using an example 'b', 'a', 'b', 'a', 'c', 'b':
+-----------------+---------------------------------------+----------------------------------+
| Option | Category mapped to 0 by StringIndexer | Category dropped by RFormula |
+-----------------+---------------------------------------+----------------------------------+
| 'frequencyDesc' | most frequent category ('b') | least frequent category ('c') |
| 'frequencyAsc' | least frequent category ('c') | most frequent category ('b') |
| 'alphabetDesc' | last alphabetical category ('c') | first alphabetical category ('a')|
| 'alphabetAsc' | first alphabetical category ('a') | last alphabetical category ('c') |
+-----------------+---------------------------------------+----------------------------------+
Note that this ordering option is NOT used for the label column. When the label column is indexed, it uses the default descending frequency ordering in
StringIndexer
.
stringIndexerOrderType in interface RFormulaBase
Param for label column name.
labelCol in interface HasLabelCol
Param for features column name.
featuresCol in interface HasFeaturesCol
An immutable unique ID for the object and its derivatives.
uid in interface Identifiable
Sets the formula to use for this transformer. Must be called before use.
value - an R formula in string form (e.g. "y ~ x + z")
Fits a model to the input data.
fit in class Estimator<RFormulaModel>
dataset - (undocumented)
Check 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 PipelineStage
schema - (undocumented)
Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
copy in interface Params
copy in class Estimator<RFormulaModel>
extra - (undocumented)
toString in interface Identifiable
toString in class Object
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