Python 如何从本地目录中读取,kmeans
当使用pyspark运行kmeans流时,我需要从本地目录读取的帮助。关于stackoverflow这个话题没有很好的答案 这是我的密码Python 如何从本地目录中读取,kmeans,python,stream,streaming,pyspark,k-means,Python,Stream,Streaming,Pyspark,K Means,当使用pyspark运行kmeans流时,我需要从本地目录读取的帮助。关于stackoverflow这个话题没有很好的答案 这是我的密码 if __name__ == "__main__": ssc = StreamingContext(sc, 1) training_data_raw, training_data_df = prepare_data(TRAINING_DATA_SET) trainingData = parse2(training_data_raw)
if __name__ == "__main__":
ssc = StreamingContext(sc, 1)
training_data_raw, training_data_df = prepare_data(TRAINING_DATA_SET)
trainingData = parse2(training_data_raw)
testing_data_raw, testing_data_df = prepare_data(TEST_DATA_SET)
testingData = testing_data_raw.map(parse1)
#print(testingData)
trainingQueue = [trainingData]
testingQueue = [testingData]
trainingStream = ssc.queueStream(trainingQueue)
testingStream = ssc.queueStream(testingQueue)
# We create a model with random clusters and specify the number of clusters to find
model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(3, 1.0, 0)
# Now register the streams for training and testing and start the job,
# printing the predicted cluster assignments on new data points as they arrive.
model.trainOn(trainingStream)
result = model.predictOnValues(testingStream.map(lambda lp: (lp.label, lp.features)))
result.pprint()
ssc.textFileStream('file:///Users/userrname/PycharmProjects/MLtest/training/data/')
ssc.start()
ssc.awaitTermination()
谢谢
from pyspark.mllib.linalg import Vectors
trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse)
用于测试示例
from pyspark.mllib.regression import LabeledPoint
def parse(lp):
label = float(lp[lp.find('(') + 1: lp.find(',')])
vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(','))
return LabeledPoint(label, vec)
testData = ssc.textFileStream("/testing/data/dir").map(parse)