org.apache.spark.ml.regression
GeneralizedLinearRegression
Related Docs:
object GeneralizedLinearRegression
| package regression
org.apache.spark.ml.regression
An alias for getOrDefault().
An alias for getOrDefault().
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().
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately.
Default Params are copied from and to defaultParamMap, and explicitly set Params are
copied from and to paramMap.
Warning: This implicitly assumes that this Params instance and the target instance
share the same set of default Params.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
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().
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
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.
Param for the name of family which is a description of the error distribution to be used in the model.
Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".
Param for features column name.
Param for features column name.
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
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
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.
Param for label column name.
Param for label column name.
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.
Param for the index in the power link function.
Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.
Param for link prediction (linear predictor) column name.
Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
Param for offset column name.
Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.
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.
Param for prediction column name.
Param for prediction column name.
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).
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.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault.
Annotating this with varargs can cause compilation failures due to a Scala compiler bug.
See SPARK-9268.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
Sets the value of param family.
Sets the value of param family. Default is "gaussian".
Sets if we should fit the intercept.
Sets if we should fit the intercept. Default is true.
Sets the value of param link.
Sets the value of param link. Used only when family is not "tweedie".
Sets the value of param linkPower.
Sets the value of param linkPower. Used only when family is "tweedie".
Sets the link prediction (linear predictor) column name.
Sets the link prediction (linear predictor) column name.
Sets the maximum number of iterations (applicable for solver "irls").
Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.
Sets the value of param offsetCol.
Sets the value of param offsetCol. If this is not set or empty, we treat all instance offsets as 0.0. Default is not set, so all instances have offset 0.0.
Sets the regularization parameter for L2 regularization.
Sets the regularization parameter for L2 regularization. The regularization term is
$$ 0.5 * regParam * L2norm(coefficients)^2 $$Default is 0.0.
Sets the solver algorithm used for optimization.
Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.
Sets the convergence tolerance of iterations.
Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
Sets the value of param variancePower.
Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.
The solver algorithm for optimization.
The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters.
Developers can implement this instead of fit() to avoid dealing with schema validation
and copying parameters into the model.
Training dataset
Fitted model
:: DeveloperApi ::
:: DeveloperApi ::
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.
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
input schema
whether this is in fitting
SQL DataType for FeaturesType.
E.g., VectorUDT for vector features.
output schema
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
:: Experimental ::
Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.