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Diffstat (limited to 'docs/mllib-linear-methods.md')
| -rw-r--r-- | docs/mllib-linear-methods.md | 12 |
1 files changed, 10 insertions, 2 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 2a2a7c1318..3927d65fbf 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -499,7 +499,7 @@ Note that the Python API does not yet support multiclass classification and mode will in the future. {% highlight python %} -from pyspark.mllib.classification import LogisticRegressionWithLBFGS +from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel from pyspark.mllib.regression import LabeledPoint from numpy import array @@ -518,6 +518,10 @@ model = LogisticRegressionWithLBFGS.train(parsedData) labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) print("Training Error = " + str(trainErr)) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = LogisticRegressionModel.load(sc, "myModelPath") {% endhighlight %} </div> </div> @@ -668,7 +672,7 @@ values. We compute the mean squared error at the end to evaluate Note that the Python API does not yet support model save/load but will in the future. {% highlight python %} -from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD +from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel from numpy import array # Load and parse the data @@ -686,6 +690,10 @@ model = LinearRegressionWithSGD.train(parsedData) valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count() print("Mean Squared Error = " + str(MSE)) + +# Save and load model +model.save(sc, "myModelPath") +sameModel = LinearRegressionModel.load(sc, "myModelPath") {% endhighlight %} </div> </div> |