diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala | 20 | ||||
| -rw-r--r-- | python/pyspark/mllib/feature.py | 4 |
2 files changed, 13 insertions, 11 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index a80cca70f4..2ed6c6be1d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -126,13 +126,13 @@ private[python] class PythonMLLibAPI extends Serializable { k: Int, maxIterations: Int, minDivisibleClusterSize: Double, - seed: Long): BisectingKMeansModel = { - new BisectingKMeans() + seed: java.lang.Long): BisectingKMeansModel = { + val kmeans = new BisectingKMeans() .setK(k) .setMaxIterations(maxIterations) .setMinDivisibleClusterSize(minDivisibleClusterSize) - .setSeed(seed) - .run(data) + if (seed != null) kmeans.setSeed(seed) + kmeans.run(data) } /** @@ -678,7 +678,7 @@ private[python] class PythonMLLibAPI extends Serializable { learningRate: Double, numPartitions: Int, numIterations: Int, - seed: Long, + seed: java.lang.Long, minCount: Int, windowSize: Int): Word2VecModelWrapper = { val word2vec = new Word2Vec() @@ -686,9 +686,9 @@ private[python] class PythonMLLibAPI extends Serializable { .setLearningRate(learningRate) .setNumPartitions(numPartitions) .setNumIterations(numIterations) - .setSeed(seed) .setMinCount(minCount) .setWindowSize(windowSize) + if (seed != null) word2vec.setSeed(seed) try { val model = word2vec.fit(dataJRDD.rdd.persist(StorageLevel.MEMORY_AND_DISK_SER)) new Word2VecModelWrapper(model) @@ -751,7 +751,7 @@ private[python] class PythonMLLibAPI extends Serializable { impurityStr: String, maxDepth: Int, maxBins: Int, - seed: Int): RandomForestModel = { + seed: java.lang.Long): RandomForestModel = { val algo = Algo.fromString(algoStr) val impurity = Impurities.fromString(impurityStr) @@ -763,11 +763,13 @@ private[python] class PythonMLLibAPI extends Serializable { maxBins = maxBins, categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap) val cached = data.rdd.persist(StorageLevel.MEMORY_AND_DISK) + // Only done because methods below want an int, not an optional Long + val intSeed = getSeedOrDefault(seed).toInt try { if (algo == Algo.Classification) { - RandomForest.trainClassifier(cached, strategy, numTrees, featureSubsetStrategy, seed) + RandomForest.trainClassifier(cached, strategy, numTrees, featureSubsetStrategy, intSeed) } else { - RandomForest.trainRegressor(cached, strategy, numTrees, featureSubsetStrategy, seed) + RandomForest.trainRegressor(cached, strategy, numTrees, featureSubsetStrategy, intSeed) } } finally { cached.unpersist(blocking = false) diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 324ba9758e..b32d0c70ec 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -600,7 +600,7 @@ class Word2Vec(object): self.learningRate = 0.025 self.numPartitions = 1 self.numIterations = 1 - self.seed = random.randint(0, sys.maxsize) + self.seed = None self.minCount = 5 self.windowSize = 5 @@ -675,7 +675,7 @@ class Word2Vec(object): raise TypeError("data should be an RDD of list of string") jmodel = callMLlibFunc("trainWord2VecModel", data, int(self.vectorSize), float(self.learningRate), int(self.numPartitions), - int(self.numIterations), int(self.seed), + int(self.numIterations), self.seed, int(self.minCount), int(self.windowSize)) return Word2VecModel(jmodel) |