pyspark.ml.regression.
GeneralizedLinearRegression
Generalized Linear Regression.
Fit a Generalized Linear Model 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.
“gaussian” -> “identity”, “log”, “inverse”
“binomial” -> “logit”, “probit”, “cloglog”
“poisson” -> “log”, “identity”, “sqrt”
“gamma” -> “inverse”, “identity”, “log”
“tweedie” -> power link function specified through “linkPower”. The default link power in the tweedie family is 1 - variancePower.
New in version 2.0.0.
Notes
For more information see Wikipedia page on GLM
Examples
>>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([ ... (1.0, Vectors.dense(0.0, 0.0)), ... (1.0, Vectors.dense(1.0, 2.0)), ... (2.0, Vectors.dense(0.0, 0.0)), ... (2.0, Vectors.dense(1.0, 1.0)),], ["label", "features"]) >>> glr = GeneralizedLinearRegression(family="gaussian", link="identity", linkPredictionCol="p") >>> glr.setRegParam(0.1) GeneralizedLinearRegression... >>> glr.getRegParam() 0.1 >>> glr.clear(glr.regParam) >>> glr.setMaxIter(10) GeneralizedLinearRegression... >>> glr.getMaxIter() 10 >>> glr.clear(glr.maxIter) >>> model = glr.fit(df) >>> model.setFeaturesCol("features") GeneralizedLinearRegressionModel... >>> model.getMaxIter() 25 >>> model.getAggregationDepth() 2 >>> transformed = model.transform(df) >>> abs(transformed.head().prediction - 1.5) < 0.001 True >>> abs(transformed.head().p - 1.5) < 0.001 True >>> model.coefficients DenseVector([1.5..., -1.0...]) >>> model.numFeatures 2 >>> abs(model.intercept - 1.5) < 0.001 True >>> glr_path = temp_path + "/glr" >>> glr.save(glr_path) >>> glr2 = GeneralizedLinearRegression.load(glr_path) >>> glr.getFamily() == glr2.getFamily() True >>> model_path = temp_path + "/glr_model" >>> model.save(model_path) >>> model2 = GeneralizedLinearRegressionModel.load(model_path) >>> model.intercept == model2.intercept True >>> model.coefficients[0] == model2.coefficients[0] True >>> model.transform(df).take(1) == model2.transform(df).take(1) True
Methods
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
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 < user-supplied values < extra.
fit(dataset[, params])
fit
Fits a model to the input dataset with optional parameters.
fitMultiple(dataset, paramMaps)
fitMultiple
Fits a model to the input dataset for each param map in paramMaps.
getAggregationDepth()
getAggregationDepth
Gets the value of aggregationDepth or its default value.
getFamily()
getFamily
Gets the value of family or its default value.
getFeaturesCol()
getFeaturesCol
Gets the value of featuresCol or its default value.
getFitIntercept()
getFitIntercept
Gets the value of fitIntercept or its default value.
getLabelCol()
getLabelCol
Gets the value of labelCol or its default value.
getLink()
getLink
Gets the value of link or its default value.
getLinkPower()
getLinkPower
Gets the value of linkPower or its default value.
getLinkPredictionCol()
getLinkPredictionCol
Gets the value of linkPredictionCol or its default value.
getMaxIter()
getMaxIter
Gets the value of maxIter or its default value.
getOffsetCol()
getOffsetCol
Gets the value of offsetCol or its default value.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)
getParam
Gets a param by its name.
getPredictionCol()
getPredictionCol
Gets the value of predictionCol or its default value.
getRegParam()
getRegParam
Gets the value of regParam or its default value.
getSolver()
getSolver
Gets the value of solver or its default value.
getTol()
getTol
Gets the value of tol or its default value.
getVariancePower()
getVariancePower
Gets the value of variancePower or its default value.
getWeightCol()
getWeightCol
Gets the value of weightCol or its default value.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setAggregationDepth(value)
setAggregationDepth
Sets the value of aggregationDepth.
aggregationDepth
setFamily(value)
setFamily
Sets the value of family.
family
setFeaturesCol(value)
setFeaturesCol
Sets the value of featuresCol.
featuresCol
setFitIntercept(value)
setFitIntercept
Sets the value of fitIntercept.
fitIntercept
setLabelCol(value)
setLabelCol
Sets the value of labelCol.
labelCol
setLink(value)
setLink
Sets the value of link.
link
setLinkPower(value)
setLinkPower
Sets the value of linkPower.
linkPower
setLinkPredictionCol(value)
setLinkPredictionCol
Sets the value of linkPredictionCol.
linkPredictionCol
setMaxIter(value)
setMaxIter
Sets the value of maxIter.
maxIter
setOffsetCol(value)
setOffsetCol
Sets the value of offsetCol.
offsetCol
setParams(self, \*[, labelCol, featuresCol, …])
setParams
Sets params for generalized linear regression.
setPredictionCol(value)
setPredictionCol
Sets the value of predictionCol.
predictionCol
setRegParam(value)
setRegParam
Sets the value of regParam.
regParam
setSolver(value)
setSolver
Sets the value of solver.
solver
setTol(value)
setTol
Sets the value of tol.
tol
setVariancePower(value)
setVariancePower
Sets the value of variancePower.
variancePower
setWeightCol(value)
setWeightCol
Sets the value of weightCol.
weightCol
write()
write
Returns an MLWriter instance for this ML instance.
Attributes
params
Returns all params ordered by name.
Methods Documentation
Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
extra param values
merged param map
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset.
an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
Transformer
fitted model(s)
New in version 2.3.0.
collections.abc.Sequence
A Sequence of param maps.
_FitMultipleIterator
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
New in version 2.2.0.
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 3.0.0.
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param