# LogisticRegressionModel¶

class pyspark.mllib.classification.LogisticRegressionModel(weights: pyspark.mllib.linalg.Vector, intercept: float, numFeatures: int, numClasses: int)

Classification model trained using Multinomial/Binary Logistic Regression.

Parameters
weightspyspark.mllib.linalg.Vector

Weights computed for every feature.

interceptfloat

Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.)

numFeaturesint

The dimension of the features.

numClassesint

The number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

Examples

>>> from pyspark.mllib.linalg import SparseVector
>>> data = [
...     LabeledPoint(0.0, [0.0, 1.0]),
...     LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
>>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
[1, 0]
>>> lrm.clearThreshold()
>>> lrm.predict([0.0, 1.0])
0.279...

>>> sparse_data = [
...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
...     LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
... ]
>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10)
>>> lrm.predict(numpy.array([0.0, 1.0]))
1
>>> lrm.predict(numpy.array([1.0, 0.0]))
0
>>> lrm.predict(SparseVector(2, {1: 1.0}))
1
>>> lrm.predict(SparseVector(2, {0: 1.0}))
0
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> lrm.save(sc, path)
>>> sameModel.predict(numpy.array([0.0, 1.0]))
1
>>> sameModel.predict(SparseVector(2, {0: 1.0}))
0
>>> from shutil import rmtree
>>> try:
...    rmtree(path)
... except BaseException:
...    pass
>>> multi_class_data = [
...     LabeledPoint(0.0, [0.0, 1.0, 0.0]),
...     LabeledPoint(1.0, [1.0, 0.0, 0.0]),
...     LabeledPoint(2.0, [0.0, 0.0, 1.0])
... ]
>>> data = sc.parallelize(multi_class_data)
>>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
>>> mcm.predict([0.0, 0.5, 0.0])
0
>>> mcm.predict([0.8, 0.0, 0.0])
1
>>> mcm.predict([0.0, 0.0, 0.3])
2


Methods

 Clears the threshold so that predict will output raw prediction scores. load(sc, path) Load a model from the given path. Predict values for a single data point or an RDD of points using the model trained. save(sc, path) Save this model to the given path. setThreshold(value) Sets the threshold that separates positive predictions from negative predictions.

Attributes

 intercept Intercept computed for this model. numClasses Number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. numFeatures Dimension of the features. threshold Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. weights Weights computed for every feature.

Methods Documentation

clearThreshold() → None

Clears the threshold so that predict will output raw prediction scores. It is used for binary classification only.

classmethod load(sc: pyspark.context.SparkContext, path: str)pyspark.mllib.classification.LogisticRegressionModel

Load a model from the given path.

predict(x: Union[VectorLike, pyspark.rdd.RDD[VectorLike]]) → Union[pyspark.rdd.RDD[Union[int, float]], int, float]

Predict values for a single data point or an RDD of points using the model trained.

save(sc: pyspark.context.SparkContext, path: str) → None

Save this model to the given path.

setThreshold(value: float) → None

Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as a positive, and negative otherwise. It is used for binary classification only.

Attributes Documentation

intercept

Intercept computed for this model.

numClasses

Number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.

numFeatures

Dimension of the features.

threshold

Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. It is used for binary classification only.

weights

Weights computed for every feature.