def test_predictOn_model(self):
"""Test that the model predicts correctly on toy data."""
stkm = StreamingKMeans()
stkm._model = StreamingKMeansModel(
clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]],
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]]
predict_data = [self.sc.parallelize(batch, 1) for batch in predict_data]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
result = []
def update(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
result.append(rdd_collect)
predict_val.foreachRDD(update)
self.ssc.start()
def condition():
self.assertEqual(result, [[0], [1], [2], [3]])
return True
self._eventually(condition, catch_assertions=True)
def test_trainOn_model(self):
"""Test the model on toy data with four clusters."""
stkm = StreamingKMeans()
initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]]
stkm.setInitialCenters(
centers=initCenters, weights=[1.0, 1.0, 1.0, 1.0])
# Create a toy dataset by setting a tiny offset for each point.
offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
batches = []
for offset in offsets:
batches.append([[offset[0] + center[0], offset[1] + center[1]]
for center in initCenters])
batches = [self.sc.parallelize(batch, 1) for batch in batches]
input_stream = self.ssc.queueStream(batches)
stkm.trainOn(input_stream)
self.ssc.start()
# Give enough time to train the model.
def condition():
finalModel = stkm.latestModel()
self.assertTrue(all(finalModel.centers == array(initCenters)))
self.assertEqual(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
return True
self._eventually(condition, catch_assertions=True)
def test_predictOn_model(self):
"""Test that the model predicts correctly on toy data."""
stkm = StreamingKMeans()
stkm._model = StreamingKMeansModel(clusterCenters=[[1.0, 1.0],
[-1.0, 1.0],
[-1.0, -1.0],
[1.0, -1.0]],
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]],
[[1.5, -1.5]]]
predict_data = [
self.sc.parallelize(batch, 1) for batch in predict_data
]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
result = []
def update(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
result.append(rdd_collect)
predict_val.foreachRDD(update)
self.ssc.start()
def condition():
self.assertEqual(result, [[0], [1], [2], [3]])
return True
eventually(condition, catch_assertions=True)
def main():
conf = SparkConf().setAppName("twitterclassifier")
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc, 10)
tweets = ssc.socketTextStream("localhost", PORT) \
.map(lambda x: json.loads(x)) \
.filter(lambda x: 'text' in x) \
.map(lambda x: x['text'].encode('utf-8'))
hasher = HashingTF(DIM)
features = tweets.map(lambda x:
(x, hasher.transform(featurize(x)))).cache()
# We create a model with random clusters and specify the number of clusters to find
# decay = 1: total memory; decay = 0: no memory
model = StreamingKMeans(k=N, decayFactor=0.1).setRandomCenters(DIM, 1.0, 0)
model.trainOn(features.map(lambda x: x[1]))
results = model.predictOnValues(features).cache()
# Need a closure over i here.
def print_group(i):
results.filter(lambda x: x[1] == i).map(lambda x: '%i: %s' %
(x[1], x[0])).pprint(3)
for i in xrange(N):
print_group(i)
ssc.start()
ssc.awaitTermination()
def test_predictOn_model(self):
"""Test that the model predicts correctly on toy data."""
stkm = StreamingKMeans()
stkm._model = StreamingKMeansModel(clusterCenters=[[1.0, 1.0],
[-1.0, 1.0],
[-1.0, -1.0],
[1.0, -1.0]],
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]],
[[1.5, -1.5]]]
predict_data = [sc.parallelize(batch, 1) for batch in predict_data]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
result = []
def update(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
result.append(rdd_collect)
predict_val.foreachRDD(update)
t = time()
self.ssc.start()
self._ssc_wait(t, 6.0, 0.01)
self.assertEquals(result, [[0], [1], [2], [3]])
def perform_training(sc: SparkContext, params_dict: dict):
batch_duration = 1 if 'batch_duration' not in params_dict else params_dict[
'batch_duration']
training_duration = 20 if 'training_duration' not in params_dict else params_dict[
'training_duration']
ssc = StreamingContext(sc, batch_duration)
topics = ['normal-ekg-stream']
kafka_params = {'metadata.broker.list': 'localhost:9092'}
kvs = KafkaUtils.createDirectStream(
ssc,
topics,
kafkaParams=kafka_params,
valueDecoder=lambda val: json.loads(val.decode('utf-8')))
windowed_signal = kvs.map(lambda msg: Vectors.dense(
[float(value) for value in msg[1]['signal_values']]))
# windowed_signal.foreachRDD(Plotter.plot_signal_window)
model = StreamingKMeans(k=20,
decayFactor=1.0).setRandomCenters(188, 1.0, 0)
model.trainOn(windowed_signal)
ssc.start()
ssc.awaitTerminationOrTimeout(training_duration)
ssc.stop(stopSparkContext=False, stopGraceFully=True)
return model.latestModel()
def test_accuracy_for_single_center(self):
"""Test that parameters obtained are correct for a single center."""
centers, batches = self.streamingKMeansDataGenerator(batches=5,
numPoints=5,
k=1,
d=5,
r=0.1,
seed=0)
stkm = StreamingKMeans(1)
stkm.setInitialCenters([[0., 0., 0., 0., 0.]], [0.])
input_stream = self.ssc.queueStream(
[self.sc.parallelize(batch, 1) for batch in batches])
stkm.trainOn(input_stream)
self.ssc.start()
def condition():
self.assertEqual(stkm.latestModel().clusterWeights, [25.0])
return True
eventually(condition, catch_assertions=True)
realCenters = array_sum(array(centers), axis=0)
for i in range(5):
modelCenters = stkm.latestModel().centers[0][i]
self.assertAlmostEqual(centers[0][i], modelCenters, 1)
self.assertAlmostEqual(realCenters[i], modelCenters, 1)
def test_trainOn_predictOn(self):
"""Test that prediction happens on the updated model."""
stkm = StreamingKMeans(decayFactor=0.0, k=2)
stkm.setInitialCenters([[0.0], [1.0]], [1.0, 1.0])
# Since decay factor is set to zero, once the first batch
# is passed the clusterCenters are updated to [-0.5, 0.7]
# which causes 0.2 & 0.3 to be classified as 1, even though the
# classification based in the initial model would have been 0
# proving that the model is updated.
batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]]
batches = [sc.parallelize(batch) for batch in batches]
input_stream = self.ssc.queueStream(batches)
predict_results = []
def collect(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
predict_results.append(rdd_collect)
stkm.trainOn(input_stream)
predict_stream = stkm.predictOn(input_stream)
predict_stream.foreachRDD(collect)
t = time()
self.ssc.start()
self._ssc_wait(t, 6.0, 0.01)
self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
def test_trainOn_model(self):
"""Test the model on toy data with four clusters."""
stkm = StreamingKMeans()
initCenters = [[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]]
stkm.setInitialCenters(centers=initCenters,
weights=[1.0, 1.0, 1.0, 1.0])
# Create a toy dataset by setting a tiny offest for each point.
offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
batches = []
for offset in offsets:
batches.append([[offset[0] + center[0], offset[1] + center[1]]
for center in initCenters])
batches = [self.sc.parallelize(batch, 1) for batch in batches]
input_stream = self.ssc.queueStream(batches)
stkm.trainOn(input_stream)
t = time()
self.ssc.start()
# Give enough time to train the model.
self._ssc_wait(t, 6.0, 0.01)
finalModel = stkm.latestModel()
self.assertTrue(all(finalModel.centers == array(initCenters)))
self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
def test_model_params(self):
"""Test that the model params are set correctly"""
stkm = StreamingKMeans()
stkm.setK(5).setDecayFactor(0.0)
self.assertEqual(stkm._k, 5)
self.assertEqual(stkm._decayFactor, 0.0)
# Model not set yet.
self.assertIsNone(stkm.latestModel())
self.assertRaises(ValueError, stkm.trainOn, [0.0, 1.0])
stkm.setInitialCenters(
centers=[[0.0, 0.0], [1.0, 1.0]], weights=[1.0, 1.0])
self.assertEqual(
stkm.latestModel().centers, [[0.0, 0.0], [1.0, 1.0]])
self.assertEqual(stkm.latestModel().clusterWeights, [1.0, 1.0])
def test_trainOn_predictOn(self):
"""Test that prediction happens on the updated model."""
stkm = StreamingKMeans(decayFactor=0.0, k=2)
stkm.setInitialCenters([[0.0], [1.0]], [1.0, 1.0])
# Since decay factor is set to zero, once the first batch
# is passed the clusterCenters are updated to [-0.5, 0.7]
# which causes 0.2 & 0.3 to be classified as 1, even though the
# classification based in the initial model would have been 0
# proving that the model is updated.
batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]]
batches = [sc.parallelize(batch) for batch in batches]
input_stream = self.ssc.queueStream(batches)
predict_results = []
def collect(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
predict_results.append(rdd_collect)
stkm.trainOn(input_stream)
predict_stream = stkm.predictOn(input_stream)
predict_stream.foreachRDD(collect)
t = time()
self.ssc.start()
self._ssc_wait(t, 6.0, 0.01)
self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
def test_model_params(self):
"""Test that the model params are set correctly"""
stkm = StreamingKMeans()
stkm.setK(5).setDecayFactor(0.0)
self.assertEquals(stkm._k, 5)
self.assertEquals(stkm._decayFactor, 0.0)
# Model not set yet.
self.assertIsNone(stkm.latestModel())
self.assertRaises(ValueError, stkm.trainOn, [0.0, 1.0])
stkm.setInitialCenters(
centers=[[0.0, 0.0], [1.0, 1.0]], weights=[1.0, 1.0])
self.assertEquals(
stkm.latestModel().centers, [[0.0, 0.0], [1.0, 1.0]])
self.assertEquals(stkm.latestModel().clusterWeights, [1.0, 1.0])
def test_predictOn_model(self):
"""Test that the model predicts correctly on toy data."""
stkm = StreamingKMeans()
stkm._model = StreamingKMeansModel(
clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]],
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]]
predict_data = [sc.parallelize(batch, 1) for batch in predict_data]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
result = []
def update(rdd):
rdd_collect = rdd.collect()
if rdd_collect:
result.append(rdd_collect)
predict_val.foreachRDD(update)
t = time()
self.ssc.start()
self._ssc_wait(t, 6.0, 0.01)
self.assertEquals(result, [[0], [1], [2], [3]])
class StreamingUpdate(object):
"""
Streaming Update: DStream
"""
def __init__(self, init_clusters, decay_factor, time_unit, sc, ssc):
self.init_clusters=init_clusters
self.decay_factor=decay_factor
self.time_unit=time_unit
self.sc=sc
self.ssc=ssc
# implement
def streaming(self, mnk, clusters, init_clusters):
self.mnk=mnk
self.clusters=clusters
self.init_clusters=init_clusters
self.streaming_kmeans=StreamingKMeans(self.init_clusters, self.decay_factor, self.time_unit)
self.streaming_kmeans.setInitialCenters(self.mnk.cluster_centers_, np.ones([self.init_clusters]))
# update shape for centers in StreamingContext
"""
Từ Ä‘iển được cáºp nháºt khi có tin tức má»›i đến thì em cáºp nháºt lại kÃch thÆ°á»›c của các centroid
VD: Từ Ä‘iển ban đầu có kÃch thÆ°á»›c 10 từ
Em biểu diá»…n má»™t câu có 5 từ bằng sparse vector kÃch thÆ°á»›c 5x10
Từ Ä‘iển sau khi cáºp nháºt có 15 từ thì câu trên phải biểu diá»…n lại bằng sparse vector có kÃch thÆ°á»›c 5x15
Có cách biểu diá»…n khác mà không phải cáºp nháºt lại biểu diá»…n của câu không ạ
"""
def update_shape(self, docs, dictionary):
self.streaming_kmeans.setRandomCenters(matrix_tfidf(docs, dictionary).shape[1], 1.0, 0)
# save matrix update
def save_matrix_update(self, docs, dictionary):
np.savetxt('/home/ducvu/input_streaming.txt', matrix_tfidf(docs, dictionary))
# load dstream
def load_dstream(self):
self.dstream = self.sc.textFile("/home/ducvu/input_streaming.txt")\
.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
# make predict
def make_predict(self, docs, dictionary):
self.streaming_kmeans.trainOn(self.load_dstream())
self.pred_stream=[]
matrix=matrix_tfidf(docs)
for x in matrix:
self.pred_stream.append(self.streaming_kmeans.latest_model.predict(x))
self.pred_stream=np.array(self.pred_stream)
df = pd.DataFrame(matrix).groupby(self.pred_stream).mean()
for i, r in df.iterrows():
print('\nCluster {0}:'.format(i))
print(','.join([get_tfidf(dictionary).get_feature_names()[t] for t in np.argsort(r)[-15:]]))
def test_accuracy_for_single_center(self):
"""Test that parameters obtained are correct for a single center."""
centers, batches = self.streamingKMeansDataGenerator(
batches=5, numPoints=5, k=1, d=5, r=0.1, seed=0)
stkm = StreamingKMeans(1)
stkm.setInitialCenters([[0., 0., 0., 0., 0.]], [0.])
input_stream = self.ssc.queueStream(
[self.sc.parallelize(batch, 1) for batch in batches])
stkm.trainOn(input_stream)
t = time()
self.ssc.start()
self._ssc_wait(t, 10.0, 0.01)
self.assertEquals(stkm.latestModel().clusterWeights, [25.0])
realCenters = array_sum(array(centers), axis=0)
for i in range(5):
modelCenters = stkm.latestModel().centers[0][i]
self.assertAlmostEqual(centers[0][i], modelCenters, 1)
self.assertAlmostEqual(realCenters[i], modelCenters, 1)
def detect(self, k, t):
# Encoding categorical features using one-hot.
df1 = self.cat2Num(self.rawDF, [0, 1]).cache()
df1.show(n=2, truncate=False)
# Clustering points using KMeans
features = df1.select("features").rdd.map(lambda row: row[0]).cache()
model = StreamingKMeans(k=7,
decayFactor=1.0).setRandomCenters(4, 1.0, 0)
# model = KMeans.train(features, k, maxIterations=40, runs=10, initializationMode="random", seed=20)
# Adding the prediction column to df1
modelBC = sc.broadcast(model)
predictUDF = udf(lambda x: modelBC.value.predict(x), StringType())
df2 = df1.withColumn("prediction", predictUDF(df1.features)).cache()
df2.show(n=3, truncate=False)
# Adding the score column to df2; The higher the score, the more likely it is an anomaly
df3 = self.addScore(df2).cache()
df3.show(n=3, truncate=False)
return df3.where(df3.score > t)
from pyspark.mllib.clustering import StreamingKMeans
if __name__ == "__main__":
sc = SparkContext(appName="StreamingErrorCount")
ssc = StreamingContext(sc, 2)
ssc.checkpoint("/tmp/spark")
def parseTrainingData(line):
cells = line.split(",")
return Vectors.dense([float(cells[0]), float(cells[1])])
trainingStream = ssc.textFileStream("./training")\
.map(parseTrainingData)
model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(2, 1.0, 0)
print "Intial centers: {0}".format(model.latestModel().centers)
model.trainOn(trainingStream)
ssc.start()
s = sched.scheduler(time.time, time.sleep)
def print_cluster_centers(sc, model):
print "Cluster centers: {0}".format(model.latestModel().centers)
s.enter(10, 1, print_cluster_centers, (sc, model))
s.enter(10, 1, print_cluster_centers, (s, model))
s.run()
# ptext[clus].set_text(str(clus)+ ':'+str(row[1][1]))
# ptext[clus].set_color(colors[clus])
# plt.pause(0.0001)
#
q = multiprocessing.Queue()
f = multiprocessing.Queue()
job_for_another_core2 = multiprocessing.Process(target=data_plotting,
args=(q, ))
job_for_another_core2.start()
sc = SparkContext('local[4]', 'Social Panic Analysis')
# Create a local StreamingContext with two working thread and batch interval of 1 second
ssc = StreamingContext(sc, 10)
dstream = ssc.socketTextStream("localhost", 9998)
trainingData = dstream.map(Vectors.parse)
trainingData.pprint()
testData = trainingData.map(lambda x: (x, x))
testData.pprint()
model = StreamingKMeans(k=clusterNum,
decayFactor=0.1).setRandomCenters(2, 1.0, 0)
model.trainOn(trainingData)
print(model.latestModel().clusterCenters)
clust = model.predictOnValues(testData)
clust.pprint()
#print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))))
clust.foreachRDD(lambda time, rdd: q.put(rdd.collect()))
ssc.start()
ssc.awaitTermination()
trainingData = sc.textFile("data/datatraining.txt")\
.map(lambda line: line.split(',')[2:-1]).map(lambda arr: Vectors.dense([float(x) for x in arr]))
centers = KMeans.train(trainingData, 2).centers
trainingQueue = [trainingData]
trainingStream = ssc.queueStream(trainingQueue)
# We create a model with random clusters and specify the number of clusters to find
model = StreamingKMeans(k=2, decayFactor=0.3)#.setRandomCenters(5, 1.0, 0)
model.setInitialCenters( centers, [1.0,1.0,1.0,1.0,1.0])
# 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)
def parse(lp):
#label = float(lp[lp.find('(') + 1: lp.find(')')])
#vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(','))
arr = lp.split(',')[2:-1]
label = lp.split(',')[0]
label = label[1:-1]
vec = Vectors.dense([float(x) for x in arr])
print(model.latestModel().centers)
return LabeledPoint(label, vec)
for center in file:
initialCenters.append(center.split())
logging.info(initialCenters)
initialWeights = []
for i in initialCenters:
initialWeights.append(1.0)
config = sc.broadcast(parameters)
numberClusters = config.value[0]
mongoIP = config.value[1]
mongoDataBase = config.value[2]
mongoCollection = config.value[3]
stkm = StreamingKMeans(k=numberClusters, decayFactor=1).setInitialCenters(
initialCenters, initialWeights)
#stkm = StreamingKMeans(k=int(numberClusters),decayFactor=1.0).setRandomCenters(2,1.0,100)
directKafkaStream = KafkaUtils.createDirectStream(
ssc, ['StreamingKMeansTFG'], {
"metadata.broker.list": "localhost:9092",
"auto_offset_reset": 'earliest'
})
parsed = directKafkaStream.map(lambda v: loads(v[1]))
parsed = parsed.map(
lambda line: Vectors.dense([float(x) for x in line.strip().split()]))
stkm.trainOn(parsed)
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)
trainingData = sc.textFile("spark-2.0.1-bin-hadoop2.7/data/mllib/kmeans_data.txt")\
.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
trainingQueue = [trainingData]
trainingStream = ssc.queueStream(trainingQueue)
testingStream = ssc.textFileStream('history').map(parse)
# 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.start()
ssc.awaitTermination()
#ssc.stop(stopSparkContext=True, stopGraceFully=True)
# $example off$
print("Final centers: " + str(model.latestModel().centers))
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.clustering import StreamingKMeans
if __name__ == "__main__":
sc = SparkContext(appName="sai twitter feed")
ssc = StreamingContext(sc, 10)
ssc.checkpoint("chkpfile")
def parserData(line):
cells = line.split(",")
return Vectors.dense([float(cells[0]), float(cells[1])])
trainingStream = ssc.textFileStream("/files").map(parserData)
model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(2, 1.0, 0)
print("Initial Centres" + str(model.latestModel().centers))
model.trainOn(trainingStream)
ssc.start()
s = sched.scheduler(time.time, time.sleep)
def print_cluster_centres(sc, model):
print(str(model.latestModel().centers))
s.enter(10, 1, print_cluster_centres, (s, model))
s.enter(10, 1, print_cluster_centres, (s, model))
s.run()
ssc.awaitTermination()
# to make this work
.filter(lambda post: 'created_at' in post)\
.map(lambda post: (get_coord2(post)[0],get_coord2(post)[1],post["text"]))\
.filter(lambda tpl: tpl[0] != 0)\
.filter(lambda tpl: tpl[2] != '')\
.map(lambda tpl: (tpl[0],tpl[1],tokenize(tpl[2])))\
.map(lambda tpl:(tpl[0],tpl[1],tpl[2],doc2vec(tpl[2])))
#dstream_tweets.pprint()
trainingData=dstream_tweets.map(lambda tpl: [tpl[0],tpl[1]]+tpl[3].tolist())
#trainingData.pprint()
testdata=dstream_tweets.map(lambda tpl: (([tpl[0],tpl[1]],tpl[2]),[tpl[0],tpl[1]]+tpl[3].tolist()))
#testdata.pprint()
#
model = StreamingKMeans(k=clusterNum, decayFactor=0.6).setRandomCenters(102, 1.0, 3)
model.trainOn(trainingData)
clust=model.predictOnValues(testdata)
#clust.pprint()
#words = lines.flatMap(lambda line: line.split(" "))
topic=clust.map(lambda x: (x[1],x[0][1]))
#topic.pprint()
topicAgg = topic.reduceByKey(lambda x,y: x+y)
#wordCollect.pprint()
topicAgg.map(lambda x: (x[0],freqcount(x[1]))).pprint()
clust.foreachRDD(lambda time, rdd: q.put(rdd.collect()))
# Run!
ssc.start()
ssc.awaitTermination()
)
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.clustering import StreamingKMeans
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)
sc = SparkContext('local[4]', 'Social Panic Analysis')
# Create a local StreamingContext with two working thread and batch interval of 1 second
ssc = StreamingContext(sc, 10)
trainingData = ssc.textFileStream("./training/").map(Vectors.parse)
trainingData.pprint()
testData = ssc.textFileStream("./testing/").map(parse)
testData.pprint()
model = StreamingKMeans(k=5, decayFactor=1.0).setRandomCenters(3, 1.0, 0)
model.trainOn(trainingData)
model.predictOnValues(
testData.map(lambda lp: (lp.label, lp.features))).pprint()
#print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))))
ssc.start()
ssc.awaitTermination()
# clus=row[0]
# #ptext[clus].set_text(str(clus)+ ':'+str([x[0] for x in row[1][1]]))
# ptext[clus].set_text(str(clus)+ ':'+str(row[1][1]))
# ptext[clus].set_color(colors[clus])
# plt.pause(0.0001)
#
q = multiprocessing.Queue()
f = multiprocessing.Queue()
job_for_another_core2 = multiprocessing.Process(target=data_plotting,args=(q,))
job_for_another_core2.start()
sc = SparkContext('local[4]', 'Social Panic Analysis')
# Create a local StreamingContext with two working thread and batch interval of 1 second
ssc = StreamingContext(sc, 10)
dstream = ssc.socketTextStream("localhost", 9998)
trainingData = dstream.map(Vectors.parse)
trainingData.pprint()
testData=trainingData.map(lambda x: (x,x))
testData.pprint()
model = StreamingKMeans(k=clusterNum, decayFactor=0.1).setRandomCenters(2, 1.0, 0)
model.trainOn(trainingData)
print(model.latestModel().clusterCenters)
clust=model.predictOnValues(testData)
clust.pprint()
#print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))))
clust.foreachRDD(lambda time, rdd: q.put(rdd.collect()))
ssc.start()
ssc.awaitTermination()
os.makedirs("results")
except:
pass
output_file = open(RESULT_FILE, "w")
start = time.time()
#output_file.write("Measurement,Number_Partitions, Time\n")
#output_file.write("Spark Startup, %s, %.5f\n"%(NUMBER_PARTITIONS, time.time()-start))
#output_file.flush()
#######################################################################################
decayFactor = 1.0
timeUnit = "batches"
model = StreamingKMeans(k=10, decayFactor=decayFactor,
timeUnit=timeUnit).setRandomCenters(3, 1.0, 0)
#def printOffsetRanges(rdd):
# for o in offsetRanges:
# print "%s %s %s %s" % (o.topic, o.partition, o.fromOffset, o.untilOffset)
def count_records(rdd):
print str(type(rdd))
if rdd != None:
return rdd.collect()
return [0]
## OK
sc = SparkContext(appName="StreamingKMeansClustering")
ssc = StreamingContext(sc, 10)
ssc.checkpoint("file:///tmp/spark")
def parseTrainingData(line):
cells = line.split(",")
return Vectors.dense([float(cells[0]), float(cells[1])])
trainingStream = ssc.textFileStream("file:///Users/jananiravi/spark/spark-2.1.0-bin-without-hadoop/tweets/training")\
.map(parseTrainingData)
trainingStream.pprint();
model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(2, 1.0, 0)
print("Initial centers: " + str(model.latestModel().centers))
model.trainOn(trainingStream)
ssc.start()
s = sched.scheduler(time.time, time.sleep)
def print_cluster_centers(sc, model):
print("Cluster centers: " + str(model.latestModel().centers))
s.enter(10, 1, print_cluster_centers, (sc, model))
s.enter(10, 1, print_cluster_centers, (s, model))
s.run()
print('Applying PCA on training data...')
PCA_model = PCA(low_dim).fit(tfidf_training)
tfidf_training = PCA_model.transform(tfidf_training)
k = low_dim
# pcArray = model.transform(tfidf_training.first()).toArray()
#setting checkpoint
# ssc.checkpoint("/Users/davidenardone/Desktop/checkpoint")
# CREATING DStream FROM TRAINING'S RDD
trainingQueue = [tfidf_training]
trainingStream = ssc.queueStream(trainingQueue)
# CREATING A K-MEANS MODEL WITH RANDOM CLUSTERS SPECIFYING THE NUMBER OF CLUSTERS TO FIND
model = StreamingKMeans(k=2, decayFactor=1.0,
timeUnit='batches').setRandomCenters(k, 1.0, 0)
# print("K centers: " + str(model.latestModel().centers))
# TRAINING THE MODEL ON THE TRAINING TWEET'S DATA
print('Training K-means Model...')
model.trainOn(trainingStream)
print('done!')
# CREATE DIRECT KAFKA STREAM WITH BROKERS AND TOPICS
streamData = KafkaUtils.createDirectStream(
ssc, [kafka_topic], {"metadata.broker.list": kafka_brokers})
######### FROM NOW ON, EACH ACTION OR TRANSFORMATION IS DONE ON A SINGLE INCOMING BATCH OF TWEETS #########
# PRE-PROCESSING TWEETS DATA (TESTING)
sc = SparkContext(appName="StreamingKMeans")
ssc = StreamingContext(sc, 10)
ssc.checkpoint("/tmp/checkpoints/")
initialCenters = [[604328, 574379], [801908, 318382], [416383, 786204],
[822771, 732034], [850993, 157873], [338586, 563537],
[169274, 348574], [619259, 397671], [241071, 844424],
[321801, 165319], [139493, 557352], [508785, 174800],
[398934, 404142], [860858, 546059], [674365, 860464]]
initialWeights = [
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
]
stkm = StreamingKMeans(k=sys.argv[1], decayFactor=1.0).setInitialCenters(
[[500, 500], [600, 600]], [1.0, 1.0])
directKafkaStream = KafkaUtils.createDirectStream(
ssc, ['StreamingKMeansTFG'], {
"metadata.broker.list": "localhost:9092",
"auto_offset_reset": 'earliest'
})
parsed = directKafkaStream.map(lambda v: loads(v[1]))
parsed = parsed.map(
lambda line: Vectors.dense([float(x) for x in line.strip().split()]))
stkm.trainOn(parsed)
sys.path.append('/usr/local/Cellar/apache-spark/spark-1.5.2-bin-hadoop2.6/python/')
# Append the python/build to PYTHONPATH so that py4j could be found
sys.path.append('/usr/local/Cellar/apache-spark/spark-1.5.2-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip')
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.clustering import StreamingKMeans
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)
sc = SparkContext('local[4]', 'Social Panic Analysis')
# Create a local StreamingContext with two working thread and batch interval of 1 second
ssc = StreamingContext(sc, 10)
trainingData = ssc.textFileStream("./training/").map(Vectors.parse)
trainingData.pprint()
testData = ssc.textFileStream("./testing/").map(parse)
testData.pprint()
model = StreamingKMeans(k=5, decayFactor=1.0).setRandomCenters(3, 1.0, 0)
model.trainOn(trainingData)
model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))).pprint()
#print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features))))
ssc.start()
ssc.awaitTermination()
return LabeledPoint(label, vec)
trainingData = sc.textFile("/Users/tung/Documents/spark-2.4.3/data/mllib/kmeans_data.txt")\
.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
testingData = sc.textFile(
"/Users/tung/Documents/spark-2.4.3/data/mllib/streaming_kmeans_data_test.txt"
).map(parse)
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.start()
ssc.stop(stopSparkContext=True, stopGraceFully=True)
print("Final centers: " + str(model.latestModel().centers))
from pyspark.mllib.clustering import StreamingKMeans
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.linalg import Vectors
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
# Create a local StreamingContext with two working thread and batch interval of 1 second
sc = SparkContext("local[2]", "NetworkWordCount")
ssc = StreamingContext(sc, 1)
# continuous training
trainigData = ssc.textFileStream("/training/data/dir").map(Vectors.parse)
testData = ssc.textFileStream("/training/data/dir").map(Vectors.parse)
testData = ssc.textFileStream("/testing/data/dir").map(
lambda s: LabeledPoint.parse(s))
model = StreamingKMeans()\
.setK(3)\
.setDecayFactor(1.0)\
.setRandomCenters(dim=3, weight=0.0, seed=42)
model.trainOn(trainingData)
prediction = model.predictOnValues(testData)
print(prediction)
return LabeledPoint(label_, vec)
#testingData = sc.textFile("data/mllib/streaming_kmeans_data_test.txt").map(parse)
#testingData = sc.textFile("streaming_kmeans_data_test.txt").map(parse)
testingData = sc.parallelize(train_vec).map(parse_vec)
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)
model = StreamingKMeans(k=3, decayFactor=1.0).setRandomCenters(100, 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.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
#model.trainOn(trainingStream)
#print("TEST HERE")
#result = model.predictOnValues(testingStream.map(lambda lp: (lp.label, lp.features)))
model.trainOn(trainingStream)
print("TEST HERE")
result = model.predictOnValues(
testingStream.map(lambda lp: (lp.label, lp.features)))
#result.pprint(num = 20)
#if result.count() != 0:
sc = SparkContext(master="local[4]", appName="Streaming-KMeans", conf=conf)
ssc = StreamingContext(sc, 5)
# Kafka Stream
ks = KafkaUtils.createDirectStream(
ssc, ["test"], {"metadata.broker.list": "localhost:9092"})
trainingData = sc.textFile("data/datatraining.txt")\
.map(lambda line: line.split(',')[2:-1]).map(lambda arr: Vectors.dense([float(x) for x in arr]))
# Supplied to Streaming KMeans as the centers by StreamingKmeans are not giving good predictions
init_centers = KMeans.train(trainingData, 2).centers
model = StreamingKMeans(k=2, decayFactor=0.1)\
.setInitialCenters(init_centers, [1.0, 1.0, 1.0, 1.0, 1.0])
model.trainOn(ssc.queueStream([trainingData]))
def parse(lp):
arr = lp.split(',')[2:-1]
label = lp.split(',')[0]
vec = Vectors.dense([float(x) for x in arr])
return LabeledPoint(label, vec)
test_stream = ks.map(lambda x: x[1]).map(parse)
result = model.predictOnValues(
test_stream.map(lambda lp: (lp.label, lp.features)))
# Prints Prediction Prediction and Cluster Centers
.filter(lambda post: 'created_at' in post)\
.map(lambda post: (get_coord2(post)[0],get_coord2(post)[1],post["text"]))\
.filter(lambda tpl: tpl[0] != 0)\
.filter(lambda tpl: tpl[2] != '')\
.map(lambda tpl: (tpl[0],tpl[1],tokenize(tpl[2])))\
.map(lambda tpl:(tpl[0],tpl[1],tpl[2],doc2vec(tpl[2])))
#dstream_tweets.pprint()
trainingData = dstream_tweets.map(
lambda tpl: [tpl[0], tpl[1]] + tpl[3].tolist())
#trainingData.pprint()
testdata = dstream_tweets.map(lambda tpl: (
([tpl[0], tpl[1]], tpl[2]), [tpl[0], tpl[1]] + tpl[3].tolist()))
#testdata.pprint()
#
model = StreamingKMeans(k=clusterNum,
decayFactor=0.6).setRandomCenters(102, 1.0, 3)
model.trainOn(trainingData)
clust = model.predictOnValues(testdata)
#clust.pprint()
#words = lines.flatMap(lambda line: line.split(" "))
topic = clust.map(lambda x: (x[1], x[0][1]))
#topic.pprint()
topicAgg = topic.reduceByKey(lambda x, y: x + y)
#wordCollect.pprint()
topicAgg.map(lambda x: (x[0], freqcount(x[1]))).pprint()
clust.foreachRDD(lambda time, rdd: q.put(rdd.collect()))
# Run!
ssc.start()
ssc.awaitTermination()
def streaming(self, mnk, clusters, init_clusters):
self.mnk=mnk
self.clusters=clusters
self.init_clusters=init_clusters
self.streaming_kmeans=StreamingKMeans(self.init_clusters, self.decay_factor, self.time_unit)
self.streaming_kmeans.setInitialCenters(self.mnk.cluster_centers_, np.ones([self.init_clusters]))
