How to support Vector Machine in spark mllib Classification
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The metastatic data of gastric cancer showed that the metastasis of renal cell carcinoma (with metastasis 1, without metastasis 2) x1: age (age) x2: renal cell carcinoma vascular endothelial growth factor (VEGF), its positive expression from low to high a total of 3 grades x3: renal cell carcinoma tissue microvessel count (MVC) x4: renal cell carcinoma nuclear histological grade, from low to high grade x5: renal cell carcinoma stage. From low to high, four levels of y x 1 x 2 x 3 x 4 x 50 59 2 43.4 21 are run as follows: package spark.logisticRegressionimport org.apache.spark.mllib.classification. {SVMWithSGD} import org.apache.spark.mllib.evaluation.MulticlassMetricsimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.util.MLUtilsimport org.apache.spark. {SparkConf, SparkContext} / * MLLib classification, logical regression, is a classification Not regression * support vector machine analysis of gastric cancer metastasis * Created by eric on 16-7-17. * / object SVMTest {val conf = new SparkConf () / / create the environment variable .setMaster ("local") / / set the localization handler .setAppName ("LogisticRegression4") / / set the name val sc = new SparkContext (conf) Def main (args: Array [String]) {val data = MLUtils.loadLibSVMFile (sc) ". / src/main/spark/logisticRegression/wa.txt") / / when reading data files, be sure to pay attention to the text format val splits = data.randomSplit (Array (0.7,0.3)) Seed = 11L) / / A pair of data sets are split val parsedData = splits (0) / / split training data val parseTtest = splits (1) / / split test data val model = SVMWithSGD.train (parsedData 50) / / training model val predictionAndLabels = parseTtest.map {/ / calculate the test value case LabeledPoint (label Features) = > / / calculate test value val prediction = model.predict (features) / / calculate test value (prediction Label) / / Storage Test and Forecast} val metrics = new MulticlassMetrics (predictionAndLabels) / / create a validation class val precision = metrics.precision / / Compute Verification Value println ("Precision =" + precision) / / print verification value val patient = Vectors.dense (Array Calculate patient probability if (patient = = 1) println ("the patient's stomach cancer is likely to metastasize.") / / make a judgment of else println ("the patient's gastric cancer has no chance of metastasis.") / / make a judgment / / Precision = 0.55555555555556 / / the gastric cancer of the patient has no chance of metastasis. }} wa.txt0 1:59 2:2 3:43.4 4:2 5:10 1:36 2:1 3:57.2 4:1 5:10 1:61 2:2 3:190 4:2 5:11 1:58 2:3 3:128 4:4 5:31 1:55 2:3 3:80 4:3 5:40 1:61 2:1 3:94 4:4 5:20 1:38 2:1 3:76 4:1 5:10 1:42 2:1 3:240 4:3 5:20 1:50 2:1 3:74 4:1 5:10 1:58 2:2 3:68.6 4:2 5:20 1:68 2:3 3:132.8 4:4 5:21 1:25 2:2 3:94.6 4:4 5:30 1:52 2:1 3:56 4:1 5:10 1:31 2:1 3:47.8 4:2 5:11 1:36 2:3 3:31.6 4: 35: 10 1:42 2:1 3:66.2 4:2 5:11 1:14 2:3 3:138.6 4:3 5:30 1:32 2:1 3:114 4:2 5:30 1:35 2:1 3:40.2 4:2 5:11 1:70 2:3 3:177.2 4:4 5:31 1:65 2:2 3:51.6 4:4 5:40 1:45 2:2 3:124 4:2 5:41 1 68 2:3 3lav 127.2 4:3 5:30 1:31 2:2 3Rd 124.8 4:2 5:3 the result is shown in the picture
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