SVMModel

class pyspark.mllib.classification.SVMModel(weights: pyspark.mllib.linalg.Vector, intercept: float)

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

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]

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

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