def test_gmm_deterministic(self):
from pyspark.mllib.clustering import GaussianMixture
x = range(0, 100, 10)
y = range(0, 100, 10)
data = self.sc.parallelize([[a, b] for a, b in zip(x, y)])
clusters1 = GaussianMixture.train(data, 5, convergenceTol=0.001, maxIterations=100, seed=63)
clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001, maxIterations=100, seed=63)
for c1, c2 in zip(clusters1.weights, clusters2.weights):
self.assertEquals(round(c1, 7), round(c2, 7))
def test_gmm_with_initial_model(self):
from pyspark.mllib.clustering import GaussianMixture
data = self.sc.parallelize([
(-10, -5), (-9, -4), (10, 5), (9, 4)
])
gmm1 = GaussianMixture.train(data, 2, convergenceTol=0.001,
maxIterations=10, seed=63)
gmm2 = GaussianMixture.train(data, 2, convergenceTol=0.001,
maxIterations=10, seed=63, initialModel=gmm1)
self.assertAlmostEqual((gmm1.weights - gmm2.weights).sum(), 0.0)
def gmm_spark(sc, X=None, clusters=3):
if X is None:
X = users_as_parallelizable_sparse_data(users)
X = sc.parallelize(X)
gmm = GaussianMixture.train(X, k=clusters)
for i in range(2):
print ("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu, "sigma = ", gmm.gaussians[i].sigma.toArray())
def test_gmm(self):
from pyspark.mllib.clustering import GaussianMixture
data = self.sc.parallelize([[1, 2], [8, 9], [-4, -3], [-6, -7]])
clusters = GaussianMixture.train(data, 2, convergenceTol=0.001, maxIterations=100, seed=56)
labels = clusters.predict(data).collect()
self.assertEquals(labels[0], labels[1])
self.assertEquals(labels[2], labels[3])
default=1e-3,
type=float,
help='convergence threshold')
parser.add_argument('--maxIterations',
default=100,
type=int,
help='Number of iterations')
parser.add_argument('--seed',
default=random.getrandbits(19),
type=long,
help='Random seed')
args = parser.parse_args()
conf = SparkConf().setAppName("GMM")
sc = SparkContext(conf=conf)
lines = sc.textFile(args.inputFile)
data = lines.map(parseVector)
model = GaussianMixture.train(data, args.k, args.convergenceTol,
args.maxIterations, args.seed)
for i in range(args.k):
print(("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu,
"sigma = ", model.gaussians[i].sigma.toArray()))
print("\n")
print((
"The membership value of each vector to all mixture components (first 100): ",
model.predictSoft(data).take(100)))
print("\n")
print(("Cluster labels (first 100): ", model.predict(data).take(100)))
sc.stop()
:param convergenceTol: Convergence threshold. Default to 1e-3
:param maxIterations: Number of EM iterations to perform. Default to 100
:param seed: Random seed
"""
parser = argparse.ArgumentParser()
parser.add_argument('inputFile', help='Input File')
parser.add_argument('k', type=int, help='Number of clusters')
parser.add_argument('--convergenceTol', default=1e-3, type=float, help='convergence threshold')
parser.add_argument('--maxIterations', default=100, type=int, help='Number of iterations')
parser.add_argument('--seed', default=random.getrandbits(19),
type=long, help='Random seed')
args = parser.parse_args()
conf = SparkConf().setAppName("GMM")
sc = SparkContext(conf=conf)
lines = sc.textFile(args.inputFile)
data = lines.map(parseVector)
model = GaussianMixture.train(data, args.k, args.convergenceTol,
args.maxIterations, args.seed)
for i in range(args.k):
print(("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu,
"sigma = ", model.gaussians[i].sigma.toArray()))
print("\n")
print(("The membership value of each vector to all mixture components (first 100): ",
model.predictSoft(data).take(100)))
print("\n")
print(("Cluster labels (first 100): ", model.predict(data).take(100)))
sc.stop()
df = pd.DataFrame(l, index = ['gp1_P', 'gp2_P', 'gp3_P', 'gp4_P', 'gp5_P', 'gp6_P'],
columns = ['gp1_R', 'gp2_R', 'gp3_R', 'gp4_R', 'gp5_R', 'gp6_R'])
df
# ### Interprétation (à finir)
Avec Kmeans, 2 groupes se distinguent : 4 et 6
Le groupe gp1_P regroupe 123 des individus et mélange nettement gp1_R / gp2_R / gp3_R
# ## Gaussian Mixture
# In[12]:
from pyspark.mllib.clustering import GaussianMixture
# Construction du model avc le mm dataTrain que Kmeans
gmm = GaussianMixture.train(dataTrain, 6)
# sortie des parameters du modele
for i in range(2):
print ("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
# ### Interprétation (à finir)
# # Mesures d'évaluation (en cours)
# In[30]:
from pyspark.mllib.evaluation import MultilabelMetrics
# $example off$
from pyspark import SparkContext
# $example on$
from pyspark.mllib.clustering import GaussianMixture, GaussianMixtureModel
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="GaussianMixtureExample") # SparkContext
# $example on$
# Load and parse the data
data = sc.textFile("data/mllib/gmm_data.txt")
parsedData = data.map(lambda line: array([float(x) for x in line.strip().split(' ')]))
# Build the model (cluster the data)
gmm = GaussianMixture.train(parsedData, 2)
# Save and load model
gmm.save(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
sameModel = GaussianMixtureModel\
.load(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
# output parameters of model
for i in range(2):
print("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
# $example off$
sc.stop()
# -*- coding:utf-8 -*- """" Program: GMM Description: 调用spark内置的GMM算法示例 Author: zhenglei - [email protected] Date: 2016-01-14 13:38:58 Last modified: 2016-01-14 13:50:11 Python release: 2.7 """ # 调用spark内部的kmeans算法实现完成机器学习实战中的第十章示例 from numpy import array from pyspark import SparkContext from pyspark.mllib.clustering import GaussianMixture if __name__ == '__main__': sc = SparkContext() datas = sc.textFile('testSet.txt') clusters_num = 4 parseData = datas.map(lambda x: array([float(y) for y in x.split('\t')])) model = GaussianMixture.train(parseData, clusters_num, maxIterations=10) clusters = [[] for i in range(clusters_num)] labels = model.predict(parseData).collect() nums = len(labels) for i in xrange(nums): clusters[labels[i]].append(parseData.collect()[i]) print clusters sc.stop()
# print data1.take(5)
# Without converting the features into dense vectors, transformation with zero mean will raise
# exception on sparse vector.
# data2 will be unit variance and zero mean.
data2 = label.zip(scaler1.transform(features.map(lambda x: Vectors.dense(x.toArray()))))
parsedData = data2.map (lambda x: x[1])
parsedData.cache()
modelList = [];
d = dict()
noClusters = 5
convergenceTol = 1e-3
maxIterations = 1000
seed = random.getrandbits(19)
# Build the model (cluster the data)
gmm = GaussianMixture.train(parsedData, noClusters, convergenceTol,
maxIterations, seed)
# output parameters of model
for i in range(noOfClusters):
print ("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
"""
for clusterSize in range(2, 21, 2):
# Build the model (cluster the data)
clusters = KMeans.train(parsedData, clusterSize, maxIterations=10,runs=10, initializationMode="random")
modelList.append(clusters)
# Evaluate clustering by computing Within Set Sum of Squared Errors
def error(point):
center = clusters.centers[clusters.predict(point)]
return sqrt(sum([x**2 for x in (point - center)]))
### Local default options
k = 2 # "k" (int) Set the number of Gaussians in the mixture model. Default: 2
convergenceTol = 0.001 # "convergenceTol" (double) Set the largest change in log-likelihood at which convergence is considered to have occurred.
maxIterations = 150 # "maxIterations" (int) Set the maximum number of iterations to run. Default: 100
seed = None # "seed" (long) Set the random seed
# Load and parse the data
data = sc.textFile("/var/mdp-cloud/gmm_data.txt")
parsedData = data.map(
lambda line: array([float(x) for x in line.strip().split(' ')]))
# filteredData = data.filter(lambda arr: int(arr[1]) != 0)
# Build and save the model (cluster the data)
gmm = GaussianMixture.train(parsedData,
k,
convergenceTol=0.001,
maxIterations=150,
seed=None)
# gmm.save(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
# gmm.save(sc, "GaussianMixtureModel_CV")
# The following line would load the model
# sameModel = GaussianMixtureModel.load(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
# output parameters of model
for i in range(k):
print("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
sc.stop()
" opp_score " \
"FROM team_avgs"
query = "SELECT " \
" team_id, " \
" team_name, " \
" AVG(t1_rush), " \
" AVG(t1_pass), " \
" AVG(t2_rush), " \
" AVG(t2_pass) " \
"FROM full_game_stats " \
"JOIN team ON 1=1 " \
" AND full_game_stats.t1_id = team.team_id " \
"GROUP BY team_id, team_name"
curs.execute(query)
sql_dat = curs.fetchall()
team_ids = [row[0] for row in sql_dat]
team_names = [row[1] for row in sql_dat]
features = [row[2:] for row in sql_dat]
data = sc.parallelize(features, 1)
model = GaussianMixture.train(data, k=10)
cluster_labels = model.predict(data).collect()
labels = zip(team_ids,team_names, cluster_labels)
df = spark.createDataFrame( labels,
["team_id", "team_name", "cluster_id"] )
df.createOrReplaceTempView("model")
for k in range(10):
spark.sql("SELECT * FROM model WHERE cluster_id = {}".format(k)).show()
maxIterations=100,
initialModel=KMeansModel(initial_centroids))
end = time()
elapsed_time = end - start
kmeans_output = [
"====================== KMeans ====================\n",
"Final centers: " + str(kmeans_model.clusterCenters),
"Total Cost: " + str(kmeans_model.computeCost(data)),
"Value of K: " + str(k),
"Elapsed time: %0.10f seconds." % elapsed_time
]
#path = "hdfs://masterNode:9000/user/spark/MODELOS-marcelo/KMEANS-2"
#kmeans_model.save(sc,path)
# Gauss KMeans
start = time()
gauss_model = GaussianMixture.train(data, k, maxIterations=20)
end = time()
elapsed_time = end - start
gauss_output = [
"====================== Gauss KMeans ====================\n"
]
for i in range(k):
v1 = ("weight = ", gauss_model.weights[i])
v2 = ("mu = ", gauss_model.gaussians[i].mu)
v3 = ("sigma = ", gauss_model.gaussians[i].sigma.toArray())
gauss_output.append((v1, v2, v3))
tiempo = "Tiempo: " + str(elapsed_time)
gauss_output.append(tiempo)
kmeans_info = sc.parallelize(kmeans_output)
gauss_info = sc.parallelize(gauss_output)
# -*- coding:utf-8 -*- """" Program: GMM Description: 调用spark内置的GMM算法示例 Author: zhenglei - [email protected] Date: 2016-01-14 13:38:58 Last modified: 2016-01-14 13:50:11 Python release: 2.7 """ # 调用spark内部的kmeans算法实现完成机器学习实战中的第十章示例 from numpy import array from pyspark import SparkContext from pyspark.mllib.clustering import GaussianMixture if __name__ == '__main__': sc = SparkContext() datas = sc.textFile('testSet.txt') clusters_num = 4 parseData = datas.map(lambda x: array([float(y) for y in x.split('\t')])) model = GaussianMixture.train(parseData, clusters_num, maxIterations=10) clusters = [[] for i in range(clusters_num)] labels = model.predict(parseData).collect() nums = len(labels) for i in xrange(nums): clusters[labels[i]].append(parseData.collect()[i]) print clusters sc.stop()
from pyspark.mllib.clustering import GaussianMixture
from pyspark import SparkContext
from scipy.stats import mvn
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import time
DIR = "/home/adrianj/Desktop/MachineLearning/Resources/"
FILE_PATH = DIR+"atemporalTest.txt"
NUM_GAUSSIANS = 500
sc = SparkContext(appName="GMM Trainer")
data = sc.textFile(FILE_PATH)
parsedData = data.map(lambda line: np.array([float(x) for x in line.strip().split(' ')]))
gmm = GaussianMixture.train(parsedData, NUM_GAUSSIANS, seed=10)
print("Dumping to "+DIR+"GMMA/...")
#fig = plt.figure()
#ax = fig.gca(projection='3d')
# Record the model
gmm.save(sc, DIR+"GMMA/")
'''
for i in range(NUM_GAUSSIANS):
mu = gmm.gaussians[i].mu
sigma = (gmm.gaussians[i].sigma).toArray()
weight = gmm.weights[i]
#a, b = np.random.multivariate_normal(mu, sigma, 5000).T
#surf = ax.scatter(a, b, c, zdir='z')
#plt.plot(a, b, "x")
import numpy as np
def parse(data):
list = []
for i in range(len(data)):
value = float(data[i][1:-1])
list.append(value)
return (list)
parsedata = outdata.map(lambda line: line.encode('utf-8').split(",")).map(
lambda l: parse(l))
start_time = time.time()
gmm = GaussianMixture.train(parsedata, 80)
gmm.fit(parsedata)
print time.time() - start_time
#testing Gaussian mixture model for python
start_time = time.time()
#print sample1
gmix = mixture.GMM(n_components=90, covariance_type='full')
gmix.fit(parsedata)
#gmix.predict(parsedInSample1)
end_time = time.time()
gmpython = end_time - start_time
print gmpython
elements = repo.get(pk_aids)
for element in elements:
for col_index, col in enumerate(cols):
if element.get(col) is not None:
rows[index].get(pk_aids)[col_index] = element.get(col)
print(element.get(col))
for index, row in enumerate(rows):
for pk_aids in row:
if rows[index].get(pk_aids) is not None:
if index == 0:
data = rows[index].get(pk_aids)
else:
data = np.concatenate((data, rows[index].get(pk_aids)),
axis=0)
print(data)
#Parameters:
#data – RDD of data points
#k – Number of components
#convergenceTol – Threshold value to check the convergence criteria. Defaults to 1e-3
#maxIterations – Number of iterations. Default to 100
#seed – Random Seed
#initialModel – GaussianMixtureModel for initializing learning
model = GaussianMixture.train(data,
10,
convergenceTol=0.0001,
maxIterations=50)
labels = model.predict(data).collect()
print
today = dt.datetime.today()
spark_df = sc.parallelize(
spark.read.json("Data/yelp_academic_dataset_user.json").select(
"review_count", "average_stars", "yelping_since").rdd.map(lambda x: (x[
0], x[1], (today - par.parse(x[2])).days)).collect()[:1700])
scaler = MinMaxScaler(inputCol="_1",\
outputCol="scaled_1")
# Getting the input data
trial_df = spark_df.map(lambda x: pyspark.ml.linalg.Vectors.dense(x)).map(
lambda x: (x, )).toDF()
scalerModel = scaler.fit(trial_df)
vector_df = scalerModel.transform(trial_df).select("scaled_1").rdd.map(
lambda x: Vectors.dense(x))
# Initialize GMM
gmm = GaussianMixture.train(vector_df, k=4, maxIterations=20, seed=2018)
df = pandas.DataFrame({'features': [], 'cluster': []})
i = 0
for v in vector_df.collect():
df.loc[i] = [[float(v[0]), float(v[1]), float(v[2])], int(gmm.predict(v))]
i += 1
print df
df_with = spark.createDataFrame(
spark.createDataFrame(df).rdd.map(
lambda x: (x[0][0], x[0][1], x[0][2], int(x[1])))).toPandas()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(df_with['_1'],
row_num = info_df.filter(info_df.high == 'IT').count()
for index, repo in enumerate(repos):
for pk_aids in repo:
elements = repo.get(pk_aids)
for element in elements:
for col_index, col in enumerate(cols):
if element.get(col) is not None:
rows[index].get(pk_aids)[col_index]=element.get(col)
print(element.get(col))
for index, row in enumerate(rows):
for pk_aids in row:
if rows[index].get(pk_aids) is not None:
if index == 0:
data = rows[index].get(pk_aids)
else:
data = np.concatenate((data, rows[index].get(pk_aids)), axis=0)
print(data)
#Parameters:
#data – RDD of data points
#k – Number of components
#convergenceTol – Threshold value to check the convergence criteria. Defaults to 1e-3
#maxIterations – Number of iterations. Default to 100
#seed – Random Seed
#initialModel – GaussianMixtureModel for initializing learning
model = GaussianMixture.train(data, 10, convergenceTol=0.0001,maxIterations=50)
labels = model.predict(data).collect()
print
gmm.gaussians[i].mu, gmm.gaussians[i].sigma.toArray()).pdf(x)
# prob_x = gmm.predictSoft([x])
# rs = np.prod(prob_x)
return rs
if __name__ == "__main__":
sc = SparkContext(appName="GaussianMixtureExample") # SparkContext
# $example on$
# Load and parse the data
data = sc.textFile(sys.argv[1])
parsedData = data.map(lambda line: array(
([float(x) for x in line.strip().split(",")])[index]))
# Build the model (cluster the data)
gmm = GaussianMixture.train(parsedData, n_clusters)
# Save and load model
if (os.path.isdir('GMMResult')):
shutil.rmtree('GMMResult')
gmm.save(sc, "GMMResult")
sameModel = GaussianMixtureModel.load(sc, "GMMResult")
# output parameters of model
for i in range(n_clusters):
print("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
datfull = data.map(lambda line: array(
([float(x) for x in line.strip().split(",")])))
dat = datfull.take(datfull.count())
from numpy import array
from pyspark import SparkContext
import matplotlib.pyplot as plt
import numpy as np
#plt.figure()
sc=SparkContext()
data=sc.textFile("./coord.txt")
#test_plot=np.genfromtxt("./coord.txt",delimiter=',',dtype=float)
#plt.plot(test_plot[:,1],test_plot[:,0],'ro')
#plt.show()
parsedData=data.map(lambda line: array([float(x) for x in line.strip().split(',')]))
l=3
gmm = GaussianMixture.train(parsedData,l)
#x=np.zeros(90000)
#y=np.zeros(90000)
#for i in range(0,l):
#print "w= ",gmm.weights[i]
#print "sigma= ",gmm.gaussians[i].sigma.toArray()
#print "mu= ",gmm.gaussians[i].mu
#x1=gmm.weights[0]*np.random.multivariate_normal(gmm.gaussians[0].mu,gmm.gaussians[0].sigma.toArray(),90000)
#x2=gmm.weights[1]*np.random.multivariate_normal(gmm.gaussians[1].mu,gmm.gaussians[1].sigma.toArray(),90000)
file = open("./GMM.txt",'w')
for j in range(0,l):
file.write(str(gmm.weights[j])+'\n')
from pyspark import SparkContext
from pyspark.mllib.clustering import GaussianMixture, GaussianMixtureModel
if __name__ == "__main__":
sc = SparkContext(appName="GaussianMixtureExample") # SparkContext
### Local default options
k=2 # "k" (int) Set the number of Gaussians in the mixture model. Default: 2
convergenceTol=0.001 # "convergenceTol" (double) Set the largest change in log-likelihood at which convergence is considered to have occurred.
maxIterations=150 # "maxIterations" (int) Set the maximum number of iterations to run. Default: 100
seed=None # "seed" (long) Set the random seed
# Load and parse the data
data = sc.textFile("/var/mdp-cloud/gmm_data.txt")
parsedData = data.map(lambda line: array([float(x) for x in line.strip().split(' ')]))
# filteredData = data.filter(lambda arr: int(arr[1]) != 0)
# Build and save the model (cluster the data)
gmm = GaussianMixture.train(parsedData, k, convergenceTol=0.001, maxIterations=150, seed=None)
# gmm.save(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
# gmm.save(sc, "GaussianMixtureModel_CV")
# The following line would load the model
# sameModel = GaussianMixtureModel.load(sc, "target/org/apache/spark/PythonGaussianMixtureExample/GaussianMixtureModel")
# output parameters of model
for i in range(k):
print("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
sc.stop()
