LinearRegressionModel¶
-
class
pyspark.ml.regression.
LinearRegressionModel
(java_model: Optional[JavaObject] = None)¶ Model fitted by
LinearRegression
.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 elasticNetParam or its default value.
Gets the value of epsilon 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.
getLoss
()Gets the value of loss or its default value.
Gets the value of maxBlockSizeInMB or its default value.
Gets the value of maxIter 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.
Gets the value of standardization or its default value.
getTol
()Gets the value of tol 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
.setPredictionCol
(value)Sets the value of
predictionCol
.transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an GeneralMLWriter 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.
The value by which \(\|y - X'w\|\) is scaled down when loss is “huber”, otherwise 1.0.
Gets summary (residuals, MSE, r-squared ) of model on training set.
Methods Documentation
-
clear
(param: pyspark.ml.param.Param) → None¶ Clears a param from the param map if it has been explicitly set.
-
copy
(extra: Optional[ParamMap] = None) → JP¶ 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: pyspark.sql.dataframe.DataFrame) → pyspark.ml.regression.LinearRegressionSummary¶ Evaluates the model on a test dataset.
- Parameters
- dataset
pyspark.sql.DataFrame
Test dataset to evaluate model on, where dataset is an instance of
pyspark.sql.DataFrame
- dataset
-
explainParam
(param: Union[str, pyspark.ml.param.Param]) → str¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
-
explainParams
() → str¶ Returns the documentation of all params with their optionally default values and user-supplied values.
-
extractParamMap
(extra: Optional[ParamMap] = None) → ParamMap¶ 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
() → int¶ Gets the value of aggregationDepth or its default value.
-
getElasticNetParam
() → float¶ Gets the value of elasticNetParam or its default value.
-
getEpsilon
() → float¶ Gets the value of epsilon or its default value.
-
getFeaturesCol
() → str¶ Gets the value of featuresCol or its default value.
-
getFitIntercept
() → bool¶ Gets the value of fitIntercept or its default value.
-
getLabelCol
() → str¶ Gets the value of labelCol or its default value.
-
getLoss
() → str¶ Gets the value of loss or its default value.
-
getMaxBlockSizeInMB
() → float¶ Gets the value of maxBlockSizeInMB or its default value.
-
getMaxIter
() → int¶ Gets the value of maxIter or its default value.
-
getOrDefault
(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶ 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: str) → pyspark.ml.param.Param¶ Gets a param by its name.
-
getPredictionCol
() → str¶ Gets the value of predictionCol or its default value.
-
getRegParam
() → float¶ Gets the value of regParam or its default value.
-
getSolver
() → str¶ Gets the value of solver or its default value.
-
getStandardization
() → bool¶ Gets the value of standardization or its default value.
-
getTol
() → float¶ Gets the value of tol or its default value.
-
getWeightCol
() → str¶ Gets the value of weightCol or its default value.
-
hasDefault
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param has a default value.
-
hasParam
(paramName: str) → bool¶ Tests whether this instance contains a param with a given (string) name.
-
isDefined
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user or has a default value.
-
isSet
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user.
-
classmethod
load
(path: str) → RL¶ Reads an ML instance from the input path, a shortcut of read().load(path).
-
predict
(value: T) → float¶ Predict label for the given features.
-
classmethod
read
() → pyspark.ml.util.JavaMLReader[RL]¶ Returns an MLReader instance for this class.
-
save
(path: str) → None¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
-
set
(param: pyspark.ml.param.Param, value: Any) → None¶ Sets a parameter in the embedded param map.
-
setFeaturesCol
(value: str) → P¶ Sets the value of
featuresCol
.
-
setPredictionCol
(value: str) → P¶ Sets the value of
predictionCol
.
-
transform
(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶ Transforms the input dataset with optional parameters.
- 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
() → pyspark.ml.util.GeneralJavaMLWriter¶ Returns an GeneralMLWriter instance for this ML instance.
Attributes Documentation
-
aggregationDepth
= Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')¶
-
coefficients
¶ Model coefficients.
-
elasticNetParam
= Param(parent='undefined', name='elasticNetParam', doc='the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.')¶
-
epsilon
: pyspark.ml.param.Param[float] = Param(parent='undefined', name='epsilon', doc='The shape parameter to control the amount of robustness. Must be > 1.0. Only valid when loss is huber')¶
-
featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
-
fitIntercept
= Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')¶
-
hasSummary
¶ Indicates whether a training summary exists for this model instance.
-
intercept
¶ Model intercept.
-
labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
-
loss
: pyspark.ml.param.Param[str] = Param(parent='undefined', name='loss', doc='The loss function to be optimized. Supported options: squaredError, huber.')¶
-
maxBlockSizeInMB
= Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')¶
-
maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
-
numFeatures
¶ Returns the number of features the model was trained on. If unknown, returns -1
-
params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
-
predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
-
regParam
= Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')¶
-
scale
¶ The value by which \(\|y - X'w\|\) is scaled down when loss is “huber”, otherwise 1.0.
-
solver
: pyspark.ml.param.Param[str] = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: auto, normal, l-bfgs.')¶
-
standardization
= Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')¶
-
summary
¶ Gets summary (residuals, MSE, r-squared ) of model on training set. An exception is thrown if trainingSummary is None.
-
tol
= Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶
-
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.')¶
-