LogisticRegressionModel

class pyspark.ml.connect.classification.LogisticRegressionModel(torch_model=None, num_features=None, num_classes=None)

Model fitted by LogisticRegression.

New in version 3.5.0.

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.
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.
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.
setPredictionCol(value)	Sets the value of predictionCol.
transform(dataset[, params])	Transforms the input dataset.

Attributes

batchSize
featuresCol
fitIntercept
labelCol
learningRate
maxIter
momentum
numClasses
numFeatures	Returns the number of features the model was trained on.
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

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.

setPredictionCol(value)

Sets the value of predictionCol.

New in version 3.5.0.

transform(dataset, params=None)

Transforms the input dataset. The dataset can be either pandas dataframe or spark dataframe， if it is a spark DataFrame, the result of transformation is a new spark DataFrame that contains all existing columns and output columns with names, If it is a pandas DataFrame, the result of transformation is a shallow copy of the input pandas dataframe with output columns with names.

Note: Transformers does not allow output column having the same name with existing columns.

Parameters

datasetpyspark.sql.DataFrame or py:class:pandas.DataFrame

input dataset.

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame or py:class:pandas.DataFrame

transformed dataset, the type of output dataframe is consistent with input dataframe.

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')

numClasses

numFeatures

Returns the number of features the model was trained on. If unknown, returns -1

New in version 3.5.0.

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.