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])
#dense data
train_mat = np.loadtxt("./input/kmeans_data.txt")
#sparse data
#train_mat = csr_matrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(with_mean=True, with_std=True)
# fitting the training matrix on Standard Scaler object
ss.fit(train_mat)
#attributes
print("Mean: ")
print(ss.mean_)
print("Var: ")
print(ss.var_)
#transform
trans_mat = ss.transform(train_mat)
print("transformed data::")
print(trans_mat)
from scipy.sparse import csr_matrix
from scipy import sparse
desc = "Testing StandardScaler inverse_transform(), without fit. " #sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
sparseMatrix = sparse.csr_matrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(True, False, True, False, 0)
try:
trans_mat = ss.transform(train_mat)
inverse_tran_mat = ss.inverse_transform(trans_mat)
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.mllib.preprocessing import StandardScaler
desc = "Testing StandardScaler transform(), with fit. " #on dense data(numpy.array)
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
# fitting the training matrix on Standard Scaler object
ss.fit(train_mat)
try:
trans_mat = ss.transform(train_mat)
print(desc, "Passed")
except:
print(desc, "Failed")
FrovedisServer.shut_down()
from frovedis.exrpc.server import FrovedisServer
from frovedis.mllib.preprocessing import StandardScaler
desc = "Testing for accessing 'var_' attribute after calling fit() on Dense data(numpy.array) "
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
ss.fit(train_mat)
try:
ss.var_
print(desc, "Passed")
except:
print(desc, "Failed")
FrovedisServer.shut_down()
import sys
import numpy as np
from frovedis.exrpc.server import FrovedisServer
from frovedis.mllib.preprocessing import StandardScaler
desc = "Testing for accessing 'mean_' attribute without calling fit() on Dense data(numpy.array)"
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
try:
ss.mean_
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
desc = " Test for explicitly setting 'var_' attribute " #sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
sparseMatrix = sparse.csr_matrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(True, False, True, False, 0)
# fitting the training matrix on Standard Scaler object
ss.fit(sparseMatrix)
try:
ss.var_ = [32.75, 3.5]
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.exrpc.server import FrovedisServer
from frovedis.mllib.preprocessing import StandardScaler
from frovedis.matrix.dense import FrovedisColmajorMatrix
desc = "Testing StandardScaler transform(), without fit. " #sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
mat = FrovedisColmajorMatrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
try:
trans_mat = ss.transform(mat)
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.exrpc.server import FrovedisServer
from frovedis.mllib.preprocessing import StandardScaler
desc = "Test with_mean and with_std is None"
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
try:
ss = StandardScaler(True, None, None, False)
ss.fit(train_mat)
trans_mat = ss.transform(train_mat)
inverse_trans_mat = ss.inverse_transform(trans_mat)
print(ss.mean_)
print(ss.var_)
print(desc, "Passed")
except:
print(desc, "Failed")
FrovedisServer.shut_down()
from scipy.sparse import csr_matrix
from scipy import sparse
desc = "Testing StandardScaler transform(), with fit, with_mean = true " #sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
sparseMatrix = sparse.csr_matrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
try:
ss.fit(sparseMatrix)
trans_mat = ss.transform(train_mat)
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.mllib.preprocessing import StandardScaler
from scipy.sparse import csr_matrix
from scipy import sparse
desc = "Testing for accessing 'var_' attribute after calling fit(), with_mean = true " # Sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
sparseMatrix = sparse.csr_matrix(train_mat)
try:
ss = StandardScaler(True, True, True, False, 0)
ss.fit(sparseMatrix)
ss.var_
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from scipy import sparse
desc = " Test for explicitly setting 'mean' attribute " #sparse csr_matrix
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
sparseMatrix = sparse.csr_matrix(train_mat)
# creating Standard Scaler object
ss = StandardScaler(True, False, True, False, 0)
# fitting the training matrix on Standard Scaler object
ss.fit(sparseMatrix)
try:
ss.mean_ = [5.5, 2.0]
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.mllib.preprocessing import StandardScaler
desc = " Test for explicitly setting 'mean' attribute "
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
# fitting the training matrix on Standard Scaler object
ss.fit(train_mat)
try:
ss.mean_ = [5.5, 2.0]
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()
from frovedis.exrpc.server import FrovedisServer
from frovedis.mllib.preprocessing import StandardScaler
desc = " Testing for explicitly setting 'variance' attribute on Dense data(numpy.array) "
# 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])
#dense data
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating Standard Scaler object
ss = StandardScaler(True, True, True, False, 0)
# fitting the training matrix on Standard Scaler object
ss.fit(train_mat)
ss.transform(train_mat)
try:
ss.var_ = [32.75, 3.5]
print(desc, "Failed")
except:
print(desc, "Passed")
FrovedisServer.shut_down()