SVMModel¶
-
class
pyspark.mllib.classification.
SVMModel
(weights, intercept)[source]¶ Model for Support Vector Machines (SVMs).
- Parameters
weights – Weights computed for every feature.
intercept – Intercept computed for this model.
>>> 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: ... pass
New in version 0.9.0.
Methods
Attributes
Methods Documentation
-
clearThreshold
()¶ Clears the threshold so that predict will output raw prediction scores. It is used for binary classification only.
New in version 1.4.0.
-
predict
(x)[source]¶ Predict values for a single data point or an RDD of points using the model trained.
New in version 0.9.0.
-
setThreshold
(value)¶ 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.