diff options
Diffstat (limited to 'docs/ml-linear-methods.md')
-rw-r--r-- | docs/ml-linear-methods.md | 22 |
1 files changed, 6 insertions, 16 deletions
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md index cdd9d4999f..4e94e2f9c7 100644 --- a/docs/ml-linear-methods.md +++ b/docs/ml-linear-methods.md @@ -59,10 +59,9 @@ $\alpha$ and `regParam` corresponds to $\lambda$. <div data-lang="scala" markdown="1"> {% highlight scala %} import org.apache.spark.ml.classification.LogisticRegression -import org.apache.spark.mllib.util.MLUtils // Load training data -val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() +val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") val lr = new LogisticRegression() .setMaxIter(10) @@ -81,8 +80,6 @@ println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}") {% highlight java %} import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.SparkConf; import org.apache.spark.SparkContext; import org.apache.spark.sql.DataFrame; @@ -98,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample { String path = "data/mllib/sample_libsvm_data.txt"; // Load training data - DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class); + DataFrame training = sqlContext.read.format("libsvm").load(path); LogisticRegression lr = new LogisticRegression() .setMaxIter(10) @@ -118,11 +115,9 @@ public class LogisticRegressionWithElasticNetExample { <div data-lang="python" markdown="1"> {% highlight python %} from pyspark.ml.classification import LogisticRegression -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.util import MLUtils # Load training data -training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() +training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) @@ -251,10 +246,9 @@ regression model and extracting model summary statistics. <div data-lang="scala" markdown="1"> {% highlight scala %} import org.apache.spark.ml.regression.LinearRegression -import org.apache.spark.mllib.util.MLUtils // Load training data -val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() +val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") val lr = new LinearRegression() .setMaxIter(10) @@ -283,8 +277,6 @@ import org.apache.spark.ml.regression.LinearRegression; import org.apache.spark.ml.regression.LinearRegressionModel; import org.apache.spark.ml.regression.LinearRegressionTrainingSummary; import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.SparkConf; import org.apache.spark.SparkContext; import org.apache.spark.sql.DataFrame; @@ -300,7 +292,7 @@ public class LinearRegressionWithElasticNetExample { String path = "data/mllib/sample_libsvm_data.txt"; // Load training data - DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class); + DataFrame training = sqlContext.read.format("libsvm").load(path); LinearRegression lr = new LinearRegression() .setMaxIter(10) @@ -329,11 +321,9 @@ public class LinearRegressionWithElasticNetExample { <!--- TODO: Add python model summaries once implemented --> {% highlight python %} from pyspark.ml.regression import LinearRegression -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.util import MLUtils # Load training data -training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() +training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) |