SVMModel¶

class pyspark.mllib.classification.SVMModel(weights: pyspark.mllib.linalg.Vector, intercept: float)[source]¶

Model for Support Vector Machines (SVMs).

New in version 0.9.0.

Parameters

weightspyspark.mllib.linalg.Vector: Weights computed for every feature.
interceptfloat: Intercept computed for this model.

Examples

>>> from pyspark.mllib.linalg import SparseVector
>>> data = [
...     LabeledPoint(0.0, [0.0]),
...     LabeledPoint(1.0, [1.0]),
...     LabeledPoint(1.0, [2.0]),
...     LabeledPoint(1.0, [3.0])
... ]
>>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10)
>>> svm.predict([1.0])
1
>>> svm.predict(sc.parallelize([[1.0]])).collect()
[1]
>>> svm.clearThreshold()
>>> svm.predict(numpy.array([1.0]))
1.44...

>>> sparse_data = [
...     LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
... ]
>>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10)
>>> svm.predict(SparseVector(2, {1: 1.0}))
1
>>> svm.predict(SparseVector(2, {0: -1.0}))
0
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> svm.save(sc, path)
>>> sameModel = SVMModel.load(sc, path)
>>> sameModel.predict(SparseVector(2, {1: 1.0}))
1
>>> sameModel.predict(SparseVector(2, {0: -1.0}))
0
>>> from shutil import rmtree
>>> try:
...    rmtree(path)
... except BaseException:
...    pass

Methods

`clearThreshold`()	Clears the threshold so that predict will output raw prediction scores.
`load`(sc, path)	Load a model from the given path.
`predict`(x)	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.
`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.

New in version 1.4.0.

classmethod load(sc: pyspark.context.SparkContext, path: str) → pyspark.mllib.classification.SVMModel [source]¶: Load a model from the given path.

New in version 1.4.0.

predict(x: Union[VectorLike, pyspark.rdd.RDD[VectorLike]]) → Union[pyspark.rdd.RDD[Union[int, float]], int, float][source]¶: Predict values for a single data point or an RDD of points using the model trained.

New in version 0.9.0.

save(sc: pyspark.context.SparkContext, path: str) → None[source]¶: Save this model to the given path.

New in version 1.4.0.

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.

New in version 1.4.0.

Attributes Documentation

intercept¶: Intercept computed for this model.

New in version 1.0.0.

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

New in version 1.4.0.

weights¶: Weights computed for every feature.

New in version 1.0.0.

LogisticRegressionWithLBFGS

SVMWithSGD