GeneralizedLinearRegressionModel¶
-
class
pyspark.ml.regression.
GeneralizedLinearRegressionModel
(java_model=None)[source]¶ Model fitted by
GeneralizedLinearRegression
.New in version 2.0.0.
Methods
Attributes
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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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evaluate
(dataset)[source]¶ Evaluates the model on a test dataset.
- Parameters
dataset – Test dataset to evaluate model on, where dataset is an instance of
pyspark.sql.DataFrame
New in version 2.0.0.
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
extra – extra param values
- Returns
merged param map
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getAggregationDepth
()¶ Gets the value of aggregationDepth or its default value.
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getFamily
()¶ Gets the value of family or its default value.
New in version 2.0.0.
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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.
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getOffsetCol
()¶ Gets the value of offsetCol or its default value.
New in version 2.3.0.
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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.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getRegParam
()¶ Gets the value of regParam or its default value.
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getSolver
()¶ Gets the value of solver or its default value.
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getTol
()¶ Gets the value of tol or its default value.
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getVariancePower
()¶ Gets the value of variancePower or its default value.
New in version 2.2.0.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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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).
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predict
(value)¶ Predict label for the given features.
New in version 3.0.0.
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classmethod
read
()¶ Returns an MLReader instance for this class.
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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.
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setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
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setLinkPredictionCol
(value)[source]¶ Sets the value of
linkPredictionCol
.New in version 3.0.0.
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setPredictionCol
(value)¶ Sets the value of
predictionCol
.New in version 3.0.0.
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transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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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.
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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 (e.g. residuals, deviance, pValues) 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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