LogisticRegression#

class pyspark.ml.connect.classification.LogisticRegression(*, featuresCol='features', labelCol='label', predictionCol='prediction', probabilityCol='probability', maxIter=100, tol=1e-06, numTrainWorkers=1, batchSize=32, learningRate=0.001, momentum=0.9, seed=0)[source]#

Logistic regression estimator.

New in version 3.5.0.

Examples

>>> from pyspark.ml.connect.classification import LogisticRegression, LogisticRegressionModel
>>> lor = LogisticRegression(maxIter=20, learningRate=0.01)
>>> dataset = spark.createDataFrame([
...     ([1.0, 2.0], 1),
...     ([2.0, -1.0], 1),
...     ([-3.0, -2.0], 0),
...     ([-1.0, -2.0], 0),
... ], schema=['features', 'label'])
>>> lor_model = lor.fit(dataset)
>>> transformed_dataset = lor_model.transform(dataset)
>>> transformed_dataset.show()
+------------+-----+----------+--------------------+
|    features|label|prediction|         probability|
+------------+-----+----------+--------------------+
|  [1.0, 2.0]|    1|         1|[0.02423273026943...|
| [2.0, -1.0]|    1|         1|[0.09334788471460...|
|[-3.0, -2.0]|    0|         0|[0.99808156490325...|
|[-1.0, -2.0]|    0|         0|[0.96210002899169...|
+------------+-----+----------+--------------------+
>>> lor_model.saveToLocal("/tmp/lor_model")
>>> LogisticRegressionModel.loadFromLocal("/tmp/lor_model")
LogisticRegression_...

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.
`getBatchSize`()	Gets the value of batchSize 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.
`getLearningRate`()	Gets the value of learningRate or its default value.
`getMaxIter`()	Gets the value of maxIter or its default value.
`getMomentum`()	Gets the value of momentum or its default value.
`getNumTrainWorkers`()	Gets the value of numTrainWorkers 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.
`getSeed`()	Gets the value of seed or its default value.
`getTol`()	Gets the value of tol or its default value.
`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)	Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.
`loadFromLocal`(path)	Load Estimator / Transformer / Model / Evaluator from provided local path.
`save`(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.
`saveToLocal`(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided local path.
`set`(param, value)	Sets a parameter in the embedded param map.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.

Attributes

`batchSize`
`featuresCol`
`fitIntercept`
`labelCol`
`learningRate`
`maxIter`
`momentum`
`numTrainWorkers`
`params`	Returns all params ordered by name.
`predictionCol`
`probabilityCol`
`seed`
`tol`
`weightCol`

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. The default implementation creates a shallow copy using copy.copy(), and then copies the embedded and extra parameters over and returns the copy. Subclasses should override this method if the default approach is not sufficient.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

Params: Copy of this instance

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

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 3.5.0.

Parameters

datasetpyspark.sql.DataFrame or py:class:pandas.DataFrame: input dataset, it can be either pandas dataframe or spark dataframe.
paramsa dict of param values, optional: an optional param map that overrides embedded params.

Returns

Transformer: fitted model

getBatchSize()#: Gets the value of batchSize 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.

getLearningRate()#: Gets the value of learningRate or its default value.

getMaxIter()#: Gets the value of maxIter or its default value.

getMomentum()#: Gets the value of momentum or its default value.

getNumTrainWorkers()#: Gets the value of numTrainWorkers or its default value.

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.

getPredictionCol()#: Gets the value of predictionCol or its default value.

getProbabilityCol()#: Gets the value of probabilityCol or its default value.

getSeed()#: Gets the value of seed or its default value.

getTol()#: Gets the value of tol or its default value.

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.

classmethod load(path)#: Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.

New in version 3.5.0.

classmethod loadFromLocal(path)#: Load Estimator / Transformer / Model / Evaluator from provided local path.

New in version 3.5.0.

save(path, *, overwrite=False)#: Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.

New in version 3.5.0.

saveToLocal(path, *, overwrite=False)#: Save Estimator / Transformer / Model / Evaluator to provided local path.

New in version 3.5.0.

set(param, value)#: Sets a parameter in the embedded param map.

setFeaturesCol(value)#: Sets the value of featuresCol.

New in version 3.5.0.

setLabelCol(value)#: Sets the value of labelCol.

New in version 3.5.0.

setPredictionCol(value)#: Sets the value of predictionCol.

New in version 3.5.0.

Attributes Documentation

batchSize = Param(parent='undefined', name='batchSize', doc='number of training batch size')#

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.')#

learningRate = Param(parent='undefined', name='learningRate', doc='learning rate for training')#

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')#

momentum = Param(parent='undefined', name='momentum', doc='momentum for training optimizer')#

numTrainWorkers = Param(parent='undefined', name='numTrainWorkers', doc='number of training workers')#

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.')#

seed = Param(parent='undefined', name='seed', doc='random seed.')#

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.')#

uid#: A unique id for the object.