LogisticRegressionModel¶

class pyspark.ml.classification.LogisticRegressionModel(java_model: Optional[JavaObject] = None)¶

Model fitted by 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.
`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.
`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.
`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).
`predict`(value)	Predict label for the given features.
`predictProbability`(value)	Predict the probability of each class given the features.
`predictRaw`(value)	Raw prediction for each possible label.
`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`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setThreshold`(value)	Sets the value of `threshold`.
`setThresholds`(value)	Sets the value of `thresholds`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`aggregationDepth`
`coefficientMatrix`	Model coefficients.
`coefficients`	Model coefficients of binomial logistic regression.
`elasticNetParam`
`family`
`featuresCol`
`fitIntercept`
`hasSummary`	Indicates whether a training summary exists for this model instance.
`intercept`	Model intercept of binomial logistic regression.
`interceptVector`	Model intercept.
`labelCol`
`lowerBoundsOnCoefficients`
`lowerBoundsOnIntercepts`
`maxBlockSizeInMB`
`maxIter`
`numClasses`	Number of classes (values which the label can take).
`numFeatures`	Returns the number of features the model was trained on.
`params`	Returns all params ordered by name.
`predictionCol`
`probabilityCol`
`rawPredictionCol`
`regParam`
`standardization`
`summary`	Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set.
`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

evaluate(dataset: pyspark.sql.dataframe.DataFrame) → pyspark.ml.classification.LogisticRegressionSummary ¶

Evaluates the model on a test dataset.

Parameters

datasetpyspark.sql.DataFrame: Test dataset to evaluate model on.

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.

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).

predict(value: T) → float¶: Predict label for the given features.

predictProbability(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector ¶: Predict the probability of each class given the features.

predictRaw(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector ¶: Raw prediction for each possible label.

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.

setProbabilityCol(value: str) → CM¶: Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P¶: Sets the value of rawPredictionCol.

setThreshold(value: float) → P¶: Sets the value of threshold. Clears value of thresholds if it has been set.

setThresholds(value: List[float]) → CM¶: Sets the value of thresholds.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

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).')¶

coefficientMatrix¶: Model coefficients.

coefficients¶: Model coefficients of binomial logistic regression. An exception is thrown in the case of multinomial logistic regression.

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: pyspark.ml.param.Param[str] = 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.')¶

hasSummary¶: Indicates whether a training summary exists for this model instance.

intercept¶: Model intercept of binomial logistic regression. An exception is thrown in the case of multinomial logistic regression.

interceptVector¶: Model intercept.

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

lowerBoundsOnCoefficients: pyspark.ml.param.Param[pyspark.ml.linalg.Matrix] = 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: pyspark.ml.param.Param[pyspark.ml.linalg.Vector] = 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).')¶

numClasses¶: Number of classes (values which the label can take).

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 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.')¶

summary¶: Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

threshold: pyspark.ml.param.Param[float] = 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: pyspark.ml.param.Param[pyspark.ml.linalg.Matrix] = 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: pyspark.ml.param.Param[pyspark.ml.linalg.Vector] = 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.')¶

LogisticRegression

LogisticRegressionSummary