LogisticRegression

class pyspark.ml.classification.LogisticRegression(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, regParam: float = 0.0, elasticNetParam: float = 0.0, tol: float = 1e-06, fitIntercept: bool = True, threshold: float = 0.5, thresholds: Optional[List[float]] = None, probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction', standardization: bool = True, weightCol: Optional[str] = None, aggregationDepth: int = 2, family: str = 'auto', lowerBoundsOnCoefficients: Optional[pyspark.ml.linalg.Matrix] = None, upperBoundsOnCoefficients: Optional[pyspark.ml.linalg.Matrix] = None, lowerBoundsOnIntercepts: Optional[pyspark.ml.linalg.Vector] = None, upperBoundsOnIntercepts: Optional[pyspark.ml.linalg.Vector] = None, maxBlockSizeInMB: float = 0.0)

Logistic regression. This class supports multinomial logistic (softmax) and binomial logistic regression.

Examples

>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> bdf = sc.parallelize([
...     Row(label=1.0, weight=1.0, features=Vectors.dense(0.0, 5.0)),
...     Row(label=0.0, weight=2.0, features=Vectors.dense(1.0, 2.0)),
...     Row(label=1.0, weight=3.0, features=Vectors.dense(2.0, 1.0)),
...     Row(label=0.0, weight=4.0, features=Vectors.dense(3.0, 3.0))]).toDF()
>>> blor = LogisticRegression(weightCol="weight")
>>> blor.getRegParam()
0.0
>>> blor.setRegParam(0.01)
LogisticRegression...
>>> blor.getRegParam()
0.01
>>> blor.setMaxIter(10)
LogisticRegression...
>>> blor.getMaxIter()
10
>>> blor.clear(blor.maxIter)
>>> blorModel = blor.fit(bdf)
>>> blorModel.setFeaturesCol("features")
LogisticRegressionModel...
>>> blorModel.setProbabilityCol("newProbability")
LogisticRegressionModel...
>>> blorModel.getProbabilityCol()
'newProbability'
>>> blorModel.getMaxBlockSizeInMB()
0.0
>>> blorModel.setThreshold(0.1)
LogisticRegressionModel...
>>> blorModel.getThreshold()
0.1
>>> blorModel.coefficients
DenseVector([-1.080..., -0.646...])
>>> blorModel.intercept
3.112...
>>> blorModel.evaluate(bdf).accuracy == blorModel.summary.accuracy
True
>>> data_path = "data/mllib/sample_multiclass_classification_data.txt"
>>> mdf = spark.read.format("libsvm").load(data_path)
>>> mlor = LogisticRegression(regParam=0.1, elasticNetParam=1.0, family="multinomial")
>>> mlorModel = mlor.fit(mdf)
>>> mlorModel.coefficientMatrix
SparseMatrix(3, 4, [0, 1, 2, 3], [3, 2, 1], [1.87..., -2.75..., -0.50...], 1)
>>> mlorModel.interceptVector
DenseVector([0.04..., -0.42..., 0.37...])
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 1.0))]).toDF()
>>> blorModel.predict(test0.head().features)
1.0
>>> blorModel.predictRaw(test0.head().features)
DenseVector([-3.54..., 3.54...])
>>> blorModel.predictProbability(test0.head().features)
DenseVector([0.028, 0.972])
>>> result = blorModel.transform(test0).head()
>>> result.prediction
1.0
>>> result.newProbability
DenseVector([0.02..., 0.97...])
>>> result.rawPrediction
DenseVector([-3.54..., 3.54...])
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
>>> blorModel.transform(test1).head().prediction
1.0
>>> blor.setParams("vector")
Traceback (most recent call last):
    ...
TypeError: Method setParams forces keyword arguments.
>>> lr_path = temp_path + "/lr"
>>> blor.save(lr_path)
>>> lr2 = LogisticRegression.load(lr_path)
>>> lr2.getRegParam()
0.01
>>> model_path = temp_path + "/lr_model"
>>> blorModel.save(model_path)
>>> model2 = LogisticRegressionModel.load(model_path)
>>> blorModel.coefficients[0] == model2.coefficients[0]
True
>>> blorModel.intercept == model2.intercept
True
>>> model2
LogisticRegressionModel: uid=..., numClasses=2, numFeatures=2
>>> blorModel.transform(test0).take(1) == model2.transform(test0).take(1)
True

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.
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])	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])	Fits a model to the input dataset with optional parameters.
fitMultiple(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
getAggregationDepth()	Gets the value of aggregationDepth or its default value.
getElasticNetParam()	Gets the value of elasticNetParam or its default value.
getFamily()	Gets the value of family or its default value.
getFeaturesCol()	Gets the value of featuresCol or its default value.
getFitIntercept()	Gets the value of fitIntercept or its default value.
getLabelCol()	Gets the value of labelCol or its default value.
getLowerBoundsOnCoefficients()	Gets the value of lowerBoundsOnCoefficients
getLowerBoundsOnIntercepts()	Gets the value of lowerBoundsOnIntercepts
getMaxBlockSizeInMB()	Gets the value of maxBlockSizeInMB or its default value.
getMaxIter()	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.
getPredictionCol()	Gets the value of predictionCol or its default value.
getProbabilityCol()	Gets the value of probabilityCol or its default value.
getRawPredictionCol()	Gets the value of rawPredictionCol or its default value.
getRegParam()	Gets the value of regParam or its default value.
getStandardization()	Gets the value of standardization or its default value.
getThreshold()	Get threshold for binary classification.
getThresholds()	If thresholds is set, return its value.
getTol()	Gets the value of tol or its default value.
getUpperBoundsOnCoefficients()	Gets the value of upperBoundsOnCoefficients
getUpperBoundsOnIntercepts()	Gets the value of upperBoundsOnIntercepts
getWeightCol()	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).
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.
setAggregationDepth(value)	Sets the value of aggregationDepth.
setElasticNetParam(value)	Sets the value of elasticNetParam.
setFamily(value)	Sets the value of family.
setFeaturesCol(value)	Sets the value of featuresCol.
setFitIntercept(value)	Sets the value of fitIntercept.
setLabelCol(value)	Sets the value of labelCol.
setLowerBoundsOnCoefficients(value)	Sets the value of lowerBoundsOnCoefficients
setLowerBoundsOnIntercepts(value)	Sets the value of lowerBoundsOnIntercepts
setMaxBlockSizeInMB(value)	Sets the value of maxBlockSizeInMB.
setMaxIter(value)	Sets the value of maxIter.
setParams(*[, featuresCol, labelCol, …])	setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol=”probability”, rawPredictionCol=”rawPrediction”, standardization=True, weightCol=None, aggregationDepth=2, family=”auto”, lowerBoundsOnCoefficients=None, upperBoundsOnCoefficients=None, lowerBoundsOnIntercepts=None, upperBoundsOnIntercepts=None, maxBlockSizeInMB=0.0): Sets params for logistic regression.
setPredictionCol(value)	Sets the value of predictionCol.
setProbabilityCol(value)	Sets the value of probabilityCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setRegParam(value)	Sets the value of regParam.
setStandardization(value)	Sets the value of standardization.
setThreshold(value)	Sets the value of threshold.
setThresholds(value)	Sets the value of thresholds.
setTol(value)	Sets the value of tol.
setUpperBoundsOnCoefficients(value)	Sets the value of upperBoundsOnCoefficients
setUpperBoundsOnIntercepts(value)	Sets the value of upperBoundsOnIntercepts
setWeightCol(value)	Sets the value of weightCol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

aggregationDepth
elasticNetParam
family
featuresCol
fitIntercept
labelCol
lowerBoundsOnCoefficients
lowerBoundsOnIntercepts
maxBlockSizeInMB
maxIter
params	Returns all params ordered by name.
predictionCol
probabilityCol
rawPredictionCol
regParam
standardization
threshold
thresholds
tol
upperBoundsOnCoefficients
upperBoundsOnIntercepts
weightCol

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

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

Fits a model to the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramsdict or list or tuple, optional

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.

Returns

Transformer or a list of Transformer

fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

Fits a model to the input dataset for each param map in paramMaps.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramMapscollections.abc.Sequence

A Sequence of param maps.

Returns

_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.

getAggregationDepth() → int

Gets the value of aggregationDepth or its default value.

getElasticNetParam() → float

Gets the value of elasticNetParam or its default value.

getFamily() → str

Gets the value of family 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.

getLowerBoundsOnCoefficients() → pyspark.ml.linalg.Matrix

Gets the value of lowerBoundsOnCoefficients

getLowerBoundsOnIntercepts() → pyspark.ml.linalg.Vector

Gets the value of lowerBoundsOnIntercepts

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.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getRegParam() → float

Gets the value of regParam or its default value.

getStandardization() → bool

Gets the value of standardization or its default value.

getThreshold() → float

Get threshold for binary classification.

If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold: \(\frac{1}{1 + \frac{thresholds(0)}{thresholds(1)}}\). Otherwise, returns threshold if set or its default value if unset.

getThresholds() → List[float]

If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an error.

getTol() → float

Gets the value of tol or its default value.

getUpperBoundsOnCoefficients() → pyspark.ml.linalg.Matrix

Gets the value of upperBoundsOnCoefficients

getUpperBoundsOnIntercepts() → pyspark.ml.linalg.Vector

Gets the value of upperBoundsOnIntercepts

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).

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.

setAggregationDepth(value: int) → pyspark.ml.classification.LogisticRegression

Sets the value of aggregationDepth.

setElasticNetParam(value: float) → pyspark.ml.classification.LogisticRegression

Sets the value of elasticNetParam.

setFamily(value: str) → pyspark.ml.classification.LogisticRegression

Sets the value of family.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

setFitIntercept(value: bool) → pyspark.ml.classification.LogisticRegression

Sets the value of fitIntercept.

setLabelCol(value: str) → P

Sets the value of labelCol.

setLowerBoundsOnCoefficients(value: pyspark.ml.linalg.Matrix) → pyspark.ml.classification.LogisticRegression

Sets the value of lowerBoundsOnCoefficients

setLowerBoundsOnIntercepts(value: pyspark.ml.linalg.Vector) → pyspark.ml.classification.LogisticRegression

Sets the value of lowerBoundsOnIntercepts

setMaxBlockSizeInMB(value: float) → pyspark.ml.classification.LogisticRegression

Sets the value of maxBlockSizeInMB.

setMaxIter(value: int) → pyspark.ml.classification.LogisticRegression

Sets the value of maxIter.

setParams(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, regParam: float = 0.0, elasticNetParam: float = 0.0, tol: float = 1e-06, fitIntercept: bool = True, threshold: float = 0.5, thresholds: Optional[List[float]] = None, probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction', standardization: bool = True, weightCol: Optional[str] = None, aggregationDepth: int = 2, family: str = 'auto', lowerBoundsOnCoefficients: Optional[pyspark.ml.linalg.Matrix] = None, upperBoundsOnCoefficients: Optional[pyspark.ml.linalg.Matrix] = None, lowerBoundsOnIntercepts: Optional[pyspark.ml.linalg.Vector] = None, upperBoundsOnIntercepts: Optional[pyspark.ml.linalg.Vector] = None, maxBlockSizeInMB: float = 0.0) → pyspark.ml.classification.LogisticRegression

setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol=”probability”, rawPredictionCol=”rawPrediction”, standardization=True, weightCol=None, aggregationDepth=2, family=”auto”, lowerBoundsOnCoefficients=None, upperBoundsOnCoefficients=None, lowerBoundsOnIntercepts=None, upperBoundsOnIntercepts=None, maxBlockSizeInMB=0.0): Sets params for logistic regression. If the threshold and thresholds Params are both set, they must be equivalent.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

setProbabilityCol(value: str) → P

Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

setRegParam(value: float) → pyspark.ml.classification.LogisticRegression

Sets the value of regParam.

setStandardization(value: bool) → pyspark.ml.classification.LogisticRegression

Sets the value of standardization.

setThreshold(value: float) → P

Sets the value of threshold. Clears value of thresholds if it has been set.

setThresholds(value: List[float]) → P

Sets the value of thresholds.

setTol(value: float) → pyspark.ml.classification.LogisticRegression

Sets the value of tol.

setUpperBoundsOnCoefficients(value: pyspark.ml.linalg.Matrix) → pyspark.ml.classification.LogisticRegression

Sets the value of upperBoundsOnCoefficients

setUpperBoundsOnIntercepts(value: pyspark.ml.linalg.Vector) → pyspark.ml.classification.LogisticRegression

Sets the value of upperBoundsOnIntercepts

setWeightCol(value: str) → pyspark.ml.classification.LogisticRegression

Sets the value of weightCol.

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

aggregationDepth = Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')

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.')

family = Param(parent='undefined', name='family', doc='The name of family which is a description of the label distribution to be used in the model. Supported options: auto, binomial, multinomial')

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

fitIntercept = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

lowerBoundsOnCoefficients = Param(parent='undefined', name='lowerBoundsOnCoefficients', doc='The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.')

lowerBoundsOnIntercepts = Param(parent='undefined', name='lowerBoundsOnIntercepts', doc='The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must beequal with 1 for binomial regression, or the number oflasses for multinomial regression.')

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).')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')

probabilityCol = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')

regParam = Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')

standardization = Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')

threshold = Param(parent='undefined', name='threshold', doc='Threshold in binary classification prediction, in range [0, 1]. If threshold and thresholds are both set, they must match.e.g. if threshold is p, then thresholds must be equal to [1-p, p].')

thresholds = Param(parent='undefined', name='thresholds', doc="Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.")

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')

upperBoundsOnCoefficients = Param(parent='undefined', name='upperBoundsOnCoefficients', doc='The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.')

upperBoundsOnIntercepts = Param(parent='undefined', name='upperBoundsOnIntercepts', doc='The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.')

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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