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Diffstat (limited to 'docs/mllib-linear-methods.md')
-rw-r--r-- | docs/mllib-linear-methods.md | 21 |
1 files changed, 19 insertions, 2 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 44b7f67c57..d9fc63b37d 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -190,7 +190,7 @@ error. {% highlight scala %} import org.apache.spark.SparkContext -import org.apache.spark.mllib.classification.SVMWithSGD +import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.linalg.Vectors @@ -222,6 +222,9 @@ val metrics = new BinaryClassificationMetrics(scoreAndLabels) val auROC = metrics.areaUnderROC() println("Area under ROC = " + auROC) + +model.save("myModelPath") +val sameModel = SVMModel.load("myModelPath") {% endhighlight %} The `SVMWithSGD.train()` method by default performs L2 regularization with the @@ -304,6 +307,9 @@ public class SVMClassifier { double auROC = metrics.areaUnderROC(); System.out.println("Area under ROC = " + auROC); + + model.save("myModelPath"); + SVMModel sameModel = SVMModel.load("myModelPath"); } } {% endhighlight %} @@ -338,6 +344,8 @@ a dependency. The following example shows how to load a sample dataset, build Logistic Regression model, and make predictions with the resulting model to compute the training error. +Note that the Python API does not yet support model save/load but will in the future. + {% highlight python %} from pyspark.mllib.classification import LogisticRegressionWithSGD from pyspark.mllib.regression import LabeledPoint @@ -391,8 +399,9 @@ values. We compute the mean squared error at the end to evaluate [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit). {% highlight scala %} -import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.regression.LinearRegressionModel +import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.linalg.Vectors // Load and parse the data @@ -413,6 +422,9 @@ val valuesAndPreds = parsedData.map { point => } val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean() println("training Mean Squared Error = " + MSE) + +model.save("myModelPath") +val sameModel = LinearRegressionModel.load("myModelPath") {% endhighlight %} [`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) @@ -483,6 +495,9 @@ public class LinearRegression { } ).rdd()).mean(); System.out.println("training Mean Squared Error = " + MSE); + + model.save("myModelPath"); + LinearRegressionModel sameModel = LinearRegressionModel.load("myModelPath"); } } {% endhighlight %} @@ -494,6 +509,8 @@ The example then uses LinearRegressionWithSGD to build a simple linear model to values. We compute the mean squared error at the end to evaluate [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit). +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 numpy import array |