if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
# sample 3x3 scipy csr matrix creation
data = np.array([1, 2, 3, 4, 5, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
indptr = np.array([0, 2, 3, 6])
mat = csr_matrix((data, indices, indptr), dtype=np.float64, shape=(3, 3))
# Creating Frovedis server side crs matrix from scipy data
# "mat" can be any sparse matrix or array-like python object
fmat = FrovedisCRSMatrix(mat)
# Viewing the created matrix (for debugging)
fmat.debug_print()
# sparse to dense conversion at server side
print("crs -> rowmajor")
fmat.to_frovedis_rowmajor_matrix().debug_print()
print("crs -> colmajor")
fmat.to_frovedis_colmajor_matrix().debug_print()
# Saving the created matrix
fmat.save("./out/crs_3x3")
# asCRS demo
desc = "Testing fit for FrovedisCRSMatrix : "
# initializing the frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument\n\
(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")')
quit()
FrovedisServer.initialize(argvs[1])
# sample numpy dense data (3x3)
mat = np.matrix([[0, 0, 0, 0],
[0, 1, 1, 1],
[1, 0, 1, 0],
[1, 1, 1, 0],
[1, 1, 1, 1]],
dtype=np.float64)
mat = FrovedisCRSMatrix(mat)
tsne = TSNE(n_components=2)
try:
tsne.fit(mat)
tsne.embedding_.release()
print(desc, "Passed")
except:
print(desc, "Failed")
FrovedisServer.shut_down()
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.mllib.cluster import KMeans
import sys
import numpy as np
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
train_mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/kmeans_data.txt")
test_mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/kmeans_tdata.txt")
# creating KMeans object
kmeans = KMeans(n_clusters=2, verbose=0)
# fitting the training matrix on KMEans object
kmeans.fit(train_mat)
kmeans.debug_print()
# saving the trained model
kmeans.save("./out/MyKMeansModel")
kmeans.release()
# loading the same trained model
kmeans.load("./out/MyKMeansModel", dtype=np.float64)
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.mllib.fm import FactorizationMachineRegressor
from frovedis.matrix.dvector import FrovedisDoubleDvector
#obj: passing negative barch size per node
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n')
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/classification.txt")
lbl = FrovedisDoubleDvector([3, 4, 3, 3, 4, 1, 5, 2, 5, 5])
# fitting input matrix and label on Factorization Machine Classifier object
fm_obj = FactorizationMachineRegressor(iteration=10,
init_stdev=0.1,
init_learn_rate=0.1,
optimizer="SGD",
dim=(True, True, 8),
reg=(0., 0., 0),
batch_size_pernode=-1,
verbose=0)
try:
model = fm_obj.fit(mat, lbl)
from frovedis.mllib.fm import FactorizationMachineClassifier
from frovedis.mllib.fm import FactorizationMachineRegressor
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
mat = np.random.randint(5, size=(6, 50))
mat = np.asmatrix(mat, dtype=np.float64)
cmat = FrovedisCRSMatrix(mat, dtype=np.float64)
lbl = np.array([1, 1, -1, 1, -1, 1], dtype=np.float64)
# fitting input matrix and label on Factorization Machine Classifier object
fmc = FactorizationMachineClassifier(iteration=100,
init_stdev=0.1,
init_learn_rate=0.01,
optimizer="SGD",
dim=(True, True, 8),
reg=(False, False, 0),
batch_size_pernode=5000,
verbose=0).fit(cmat, lbl)
# prediction on created model
print("predicting on factorization machine classifier model: ")
print(fmc.predict(cmat))
import sys
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.mllib.recommendation import ALS
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix().load("./input/libSVMFile.txt")
# fitting the input matrix on a ALS object
als = ALS().fit(mat, rank=4)
# ids (userId or prodId) start with 0
print(als.predict([(1, 1), (0, 1), (2, 3), (3, 1)]))
# saving the model
als.save("./out/MyMFModel")
# loading the same model
als.load("./out/MyMFModel")
# recommend 2 users for 2nd product
print(als.recommend_users(1, 2))
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
vectorizer = CountVectorizer()
mat = vectorizer.fit_transform(corpus)
fmat = FrovedisCRSMatrix(mat, dtype=np.int32, itype=np.int64)
#fmat.save('input_corpus_crs_001')
test_corpus = [
'This is the first second third document.', 'This This one third document.'
]
vectorizer = CountVectorizer()
mat_01 = vectorizer.fit_transform(test_corpus)
fmat_01 = FrovedisCRSMatrix(mat_01, dtype=np.int32, itype=np.int64)
#fmat_01.save('test_corpus_crs_001')
lda = LatentDirichletAllocation(n_components=4,
max_iter=5,
doc_topic_prior=0.25,
algorithm='original',
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.matrix.dvector import FrovedisDvector
from frovedis.mllib.svm import LinearSVC
import numpy as np
import sys
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")')
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/libSVMFile.txt")
lbl = FrovedisDvector([1,0,1,1,1,0,1,1],dtype=np.float64)
# fitting input matrix and label on linear svm object
svm = LinearSVC(solver='lbfgs',verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on lbfgs svm regression model: ")
print(svm.predict(mat))
# fitting input matrix and label on linear svm object
svm = LinearSVC(solver='sag',verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on sgd svm regression model: ")
print(svm.predict(mat))
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.mllib.naive_bayes import MultinomialNB
from frovedis.mllib.naive_bayes import BernoulliNB
#Objective: Run without error
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print 'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")'
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/multi.txt")
lbl = np.array([1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0])
nbm = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None,
verbose=0).fit(mat, lbl)
nbm.debug_print()
print("predicting on nbm multinomial classifier model: ")
mnb = nbm.predict(mat)
print(mnb)
print("Accuracy of model: ", nbm.score(mat, lbl))
ret1 = (mnb == lbl).all()
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/bern.txt")
mat.debug_print()
lbl = np.array([1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0])
nbm2 = BernoulliNB(alpha=1.0,
fit_prior=True,
from math import sqrt
from sklearn.metrics import mean_squared_error
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.matrix.dvector import FrovedisDoubleDvector
from frovedis.mllib.fm import FactorizationMachineRegressor
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n')
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/regression.txt")
lbl = FrovedisDoubleDvector([3,4,3,3,4,1,5,2,5,5])
# fitting input matrix and label on Factorization Machine Classifier object
fm_obj = FactorizationMachineRegressor(iteration = 100, init_stdev = 0.1,
init_learn_rate = 0.1, optimizer="SGD", dim = (True, True, 8),
reg = (0., 0., 0), batch_size_pernode = 1, verbose = 0)
# prediction on created model
model = fm_obj.fit(mat, lbl)
pr_res = model.predict(mat)
# saving the model
model.save("./out/FMCModel1")
import os
import numpy as np
from scipy.sparse import csr_matrix
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.crs import FrovedisCRSMatrix
# sample 3x3 scipy csr matrix creation
data = np.array([1, 2, 3, 4, 5, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
indptr = np.array([0, 2, 3, 6])
mat = csr_matrix((data, indices, indptr), dtype=np.float64, shape=(3, 3))
FrovedisServer.initialize("mpirun -np 2 {}".format(
os.environ['FROVEDIS_SERVER']))
print("-- matrix created from scipy csr matrix --")
fmat = FrovedisCRSMatrix(mat)
fmat.debug_print()
fmat.save("./result")
fmat.release()
print("\n-- matrix loaded from text file --")
fmat2 = FrovedisCRSMatrix().load_text("./result")
fmat2.debug_print()
fmat2.release()
FrovedisServer.shut_down()
