RFormula
Related Docs:
object RFormula
| package feature
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
Param for features column name.
Param for features column name.
Force to index label whether it is numeric or string type.
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.
R formula parameter.
R formula parameter. The formula is provided in string form.
Param for how to handle invalid data (unseen or NULL values) in features and label column of string type.
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"
Param for label column name.
Param for label column name.
Param for how to order categories of a string FEATURE column used by StringIndexer.
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.
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Creates a copy of this instance with the same UID and some extra 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().
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance. See explainParam().
extractParamMap with no extra values.
extractParamMap with no extra values.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Fits a model to the input data.
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
input dataset
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
fitted models, matching the input parameter maps
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
input dataset
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
fitted model
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
input dataset
the first param pair, overrides embedded params
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
fitted model
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
Gets a param by its name.
Gets a param by its name.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
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.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Sets the formula to use for this transformer.
Sets the formula to use for this transformer. Must be called before use.
an R formula in string form (e.g. "y ~ x + z")
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 targetSuppose
aandbare double columns, we use the following simple examples to illustrate the effect ofRFormula:y ~ a + bmeans modely ~ w0 + w1 * a + w2 * bwherew0is the intercept andw1, w2are coefficients.y ~ a + b + a:b - 1means modely ~ w1 * a + w2 * b + w3 * a * bwherew1, w2, w3are 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.