GeneralizedLinearRegressionModel¶
-
class
pyspark.ml.regression.
GeneralizedLinearRegressionModel
(java_model=None)[source]¶ Model fitted by
GeneralizedLinearRegression
.New in version 2.0.0.
Methods
clear
(param)Clears a param from the param map if it has been explicitly set.
copy
([extra])Creates a copy of this instance with the same uid and some extra params.
evaluate
(dataset)Evaluates the model on a test dataset.
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap
([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 < user-supplied values < extra.
Gets the value of aggregationDepth or its default value.
Gets the value of family or its default value.
Gets the value of featuresCol or its default value.
Gets the value of fitIntercept or its default value.
Gets the value of labelCol or its default value.
getLink
()Gets the value of link or its default value.
Gets the value of linkPower or its default value.
Gets the value of linkPredictionCol or its default value.
Gets the value of maxIter or its default value.
Gets the value of offsetCol or its default value.
getOrDefault
(param)Gets the value of a param in the user-supplied param map or its default value.
getParam
(paramName)Gets a param by its name.
Gets the value of predictionCol or its default value.
Gets the value of regParam or its default value.
Gets the value of solver or its default value.
getTol
()Gets the value of tol or its default value.
Gets the value of variancePower or its default value.
Gets the value of weightCol or its default value.
hasDefault
(param)Checks whether a param has a default value.
hasParam
(paramName)Tests whether this instance contains a param with a given (string) name.
isDefined
(param)Checks whether a param is explicitly set by user or has a default value.
isSet
(param)Checks whether a param is explicitly set by user.
load
(path)Reads an ML instance from the input path, a shortcut of read().load(path).
predict
(value)Predict label for the given features.
read
()Returns an MLReader instance for this class.
save
(path)Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set
(param, value)Sets a parameter in the embedded param map.
setFeaturesCol
(value)Sets the value of
featuresCol
.setLinkPredictionCol
(value)Sets the value of
linkPredictionCol
.setPredictionCol
(value)Sets the value of
predictionCol
.transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
Model coefficients.
Indicates whether a training summary exists for this model instance.
Model intercept.
Returns the number of features the model was trained on.
Returns all params ordered by name.
Gets summary (residuals, deviance, p-values) of model on training set.
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ 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.
- Parameters
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParams
Copy of this instance
-
evaluate
(dataset)[source]¶ Evaluates the model on a test dataset.
New in version 2.0.0.
- Parameters
- dataset
pyspark.sql.DataFrame
Test dataset to evaluate model on, where dataset is an instance of
pyspark.sql.DataFrame
- dataset
-
explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
-
explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
-
extractParamMap
(extra=None)¶ 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.
- Parameters
- extradict, optional
extra param values
- Returns
- dict
merged param map
-
getAggregationDepth
()¶ Gets the value of aggregationDepth or its default value.
-
getFamily
()¶ Gets the value of family or its default value.
New in version 2.0.0.
-
getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getFitIntercept
()¶ Gets the value of fitIntercept or its default value.
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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getLink
()¶ Gets the value of link or its default value.
New in version 2.0.0.
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getLinkPower
()¶ Gets the value of linkPower or its default value.
New in version 2.2.0.
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getLinkPredictionCol
()¶ Gets the value of linkPredictionCol or its default value.
New in version 2.0.0.
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getMaxIter
()¶ Gets the value of maxIter or its default value.
-
getOffsetCol
()¶ Gets the value of offsetCol or its default value.
New in version 2.3.0.
-
getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
-
getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
-
getRegParam
()¶ Gets the value of regParam or its default value.
-
getSolver
()¶ Gets the value of solver or its default value.
-
getTol
()¶ Gets the value of tol or its default value.
-
getVariancePower
()¶ Gets the value of variancePower or its default value.
New in version 2.2.0.
-
getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
-
hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
-
isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
-
isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
-
predict
(value)¶ Predict label for the given features.
New in version 3.0.0.
-
classmethod
read
()¶ Returns an MLReader instance for this class.
-
save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
-
setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
-
setLinkPredictionCol
(value)[source]¶ Sets the value of
linkPredictionCol
.New in version 3.0.0.
-
setPredictionCol
(value)¶ Sets the value of
predictionCol
.New in version 3.0.0.
-
transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
New in version 1.3.0.
- Parameters
- dataset
pyspark.sql.DataFrame
input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrame
transformed dataset
-
write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
-
aggregationDepth
= Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')¶
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coefficients
¶ Model coefficients.
New in version 2.0.0.
-
family
= Param(parent='undefined', name='family', doc='The name of family which is a description of the error distribution to be used in the model. Supported options: gaussian (default), binomial, poisson, gamma and tweedie.')¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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fitIntercept
= Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')¶
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hasSummary
¶ Indicates whether a training summary exists for this model instance.
New in version 2.1.0.
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intercept
¶ Model intercept.
New in version 2.0.0.
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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link
= Param(parent='undefined', name='link', doc='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.')¶
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linkPower
= Param(parent='undefined', name='linkPower', doc='The index in the power link function. Only applicable to the Tweedie family.')¶
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linkPredictionCol
= Param(parent='undefined', name='linkPredictionCol', doc='link prediction (linear predictor) column name')¶
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maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
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numFeatures
¶ Returns the number of features the model was trained on. If unknown, returns -1
New in version 2.1.0.
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offsetCol
= Param(parent='undefined', name='offsetCol', doc='The offset column name. If this is not set or empty, we treat all instance offsets as 0.0')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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regParam
= Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')¶
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solver
= Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: irls.')¶
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summary
¶ Gets summary (residuals, deviance, p-values) of model on training set. An exception is thrown if trainingSummary is None.
New in version 2.0.0.
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tol
= Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶
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variancePower
= Param(parent='undefined', name='variancePower', doc='The power in the variance function of the Tweedie distribution which characterizes the relationship between the variance and mean of the distribution. Only applicable for the Tweedie family. Supported values: 0 and [1, Inf).')¶
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weightCol
= Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶
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