SVMModel¶
-
class
pyspark.mllib.classification.
SVMModel
(weights: pyspark.mllib.linalg.Vector, intercept: float)¶ Model for Support Vector Machines (SVMs).
- Parameters
- weights
pyspark.mllib.linalg.Vector
Weights computed for every feature.
- interceptfloat
Intercept computed for this model.
- weights
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
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 computed for this model.
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
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.SVMModel¶ 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.
-
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.