def test1():
X, y, W = test1_data()
print "X shape", X.shape, " Y shape:", y.shape
ntrain = int(y.shape[0] * 0.80)
g, l = 1e-10, 1e10
print "mssl"
mssl = pMSSL(max_epochs=100,
quiet=False,
gamma=g,
lambd=l,
walgo='admm',
w_epochs=50,
omega_epochs=100,
ytransform="log")
mssl.fit(X[:ntrain], y[:ntrain])
yhat = mssl.predict(X[ntrain:])
p = numpy.mean(
[pearsonr(y[ntrain:, i], yhat[:, i])[0] for i in range(yhat.shape[1])])
mse = numpy.mean((yhat - y[ntrain:])**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {
"gamma": g,
"lambda": l,
"mse": mse,
"omega_zeros": num_omega_zeros,
"w_zeros": num_w_zeros,
"pearson": p
}
#print mssl.W
print d
def test_joblib():
from sklearn.linear_model import Lasso
X, y, W = test_log_data()
print "X shape", X.shape, " Y shape:", y.shape
ntrain = int(y.shape[0]*0.80)
g, l = 1e-10, 1e10
X_train = X[:ntrain]
X_test = X[ntrain:]
ytrain = y[:ntrain]
ytest = y[ntrain:]
print "MSSL"
mssl = pMSSL(max_epochs=25, quiet=False, gamma=g, lambd=l,
walgo='multiprocessor', w_epochs=51, omega_epochs=100, num_proc=1,
ytransform=StandardScaler(), rho=1., xtransform=StandardScaler())
mssl.fit(X_train, ytrain)
yhat = mssl.predict(X_train)
p = numpy.mean([pearsonr(ytrain[:,i], yhat[:,i])[0] for i in range(yhat.shape[1])])
mse = numpy.mean((yhat - ytrain)**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {"gamma": g, "lambda": l, "mse": mse, "omega_zeros": num_omega_zeros, "w_zeros": num_w_zeros, "pearson": p}
pyplot.plot(yhat[:, 0])
pyplot.plot(ytrain[:, 0])
print "Trying to show plot"
pyplot.show()
print d
def asd_mssl():
model = pMSSL(max_epochs=5, quiet=False, lambd=1e10, gamma=1e-10, walgo='multiprocessor',
num_proc=48, w_epochs=50, omega_epochs=50)
f = "/home/vandal.t/repos/pydownscale/pydownscale/test_data/testdata.pkl"
lf = '/gss_gpfs_scratch/vandal.t/experiments/DownscaleData/newengland_MS_420_8835.pkl'
data = pickle.load(open(lf, 'r'))
asdm = ASDMultitask(data, model, season='JJA', ytransform=preprocessing.StandardScaler())
asdm.train()
out = asdm.predict(test_set=False)
out.to_netcdf("test_data/ne_test_set.nc")
def climate():
lambd = 1e2
gamma = 1e2
season = "DJF"
data = pickle.load(open("/scratch/vandal.t/experiments/DownscaleData/monthly_804_3150.pkl", "r"))
model = pMSSL(lambd=lambd, gamma=gamma, max_epochs=1000)
dmodel = DownscaleModel(data, model, season=season)
dmodel.train()
print dmodel.model.Omega[:5, :5]
print dmodel.model.W[:5, :5]
print "Omega to zero", numpy.sum(dmodel.model.Omega == 0)
print "W to zero", numpy.sum(dmodel.model.W == 0)
def test1_mpi():
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
X, y, W = test1_data()
ntrain = int(y.shape[0] * 0.80)
gspace = numpy.linspace(1e-4, 4, 10)
lspace = numpy.linspace(1e-4, 2, 10)
if rank == 0:
space = numpy.asarray([[g, l] for g in gspace for l in lspace])
split = numpy.array_split(space, size)
else:
split = None
split = comm.scatter(split, root=0)
results = []
for g, l in split:
try:
mssl = pMSSL(max_epochs=1000, quite=True, gamma=g, lambd=l)
mssl.fit(X[:ntrain], y[:ntrain])
yhat = mssl.predict(X[ntrain:])
mse = numpy.mean((yhat - y[ntrain:])**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {
"gamma": g,
"lambda": l,
"mse": mse,
"omega_zeros": num_omega_zeros,
"w_zeros": num_w_zeros
}
except Exception as err:
sys.stderr.write("Gamma: %f, Lambda: %f\n%s" % (g, l, err))
d = {"error": 1, "gamma": g, "lambda": l}
results.append(d)
sys.stdout.write(str(d))
newdata = comm.gather(results)
if rank == 0:
newdata = [item for l in newdata
for item in l] ## condense lists of lists
data = pandas.DataFrame(newdata)
data.to_csv("mssl-test1-mpi.csv", index=False)
def climate():
lambd = 1e2
gamma = 1e2
season = "DJF"
data = pickle.load(
open(
"/scratch/vandal.t/experiments/DownscaleData/monthly_804_3150.pkl",
"r"))
model = pMSSL(lambd=lambd, gamma=gamma, max_epochs=1000)
dmodel = DownscaleModel(data, model, season=season)
dmodel.train()
print dmodel.model.Omega[:5, :5]
print dmodel.model.W[:5, :5]
print "Omega to zero", numpy.sum(dmodel.model.Omega == 0)
print "W to zero", numpy.sum(dmodel.model.W == 0)
def test1():
X, y, W = test1_data()
print "X shape", X.shape, " Y shape:", y.shape
ntrain = int(y.shape[0]*0.80)
g, l = 1e-10, 1e10
print "mssl"
mssl = pMSSL(max_epochs=100, quiet=False, gamma=g, lambd=l,
walgo='admm', w_epochs=50, omega_epochs=100, ytransform="log")
mssl.fit(X[:ntrain], y[:ntrain])
yhat = mssl.predict(X[ntrain:])
p = numpy.mean([pearsonr(y[ntrain:,i], yhat[:, i])[0] for i in range(yhat.shape[1])])
mse = numpy.mean((yhat - y[ntrain:])**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {"gamma": g, "lambda": l, "mse": mse, "omega_zeros": num_omega_zeros, "w_zeros": num_w_zeros, "pearson": p}
#print mssl.W
print d
def test_joblib():
from sklearn.linear_model import Lasso
X, y, W = test_log_data()
print "X shape", X.shape, " Y shape:", y.shape
ntrain = int(y.shape[0] * 0.80)
g, l = 1e-10, 1e10
X_train = X[:ntrain]
X_test = X[ntrain:]
ytrain = y[:ntrain]
ytest = y[ntrain:]
print "MSSL"
mssl = pMSSL(max_epochs=25,
quiet=False,
gamma=g,
lambd=l,
walgo='multiprocessor',
w_epochs=51,
omega_epochs=100,
num_proc=1,
ytransform=StandardScaler(),
rho=1.,
xtransform=StandardScaler())
mssl.fit(X_train, ytrain)
yhat = mssl.predict(X_train)
p = numpy.mean(
[pearsonr(ytrain[:, i], yhat[:, i])[0] for i in range(yhat.shape[1])])
mse = numpy.mean((yhat - ytrain)**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {
"gamma": g,
"lambda": l,
"mse": mse,
"omega_zeros": num_omega_zeros,
"w_zeros": num_w_zeros,
"pearson": p
}
pyplot.plot(yhat[:, 0])
pyplot.plot(ytrain[:, 0])
print "Trying to show plot"
pyplot.show()
print d
def asd_mssl():
model = pMSSL(max_epochs=5,
quiet=False,
lambd=1e10,
gamma=1e-10,
walgo='multiprocessor',
num_proc=48,
w_epochs=50,
omega_epochs=50)
f = "/home/vandal.t/repos/pydownscale/pydownscale/test_data/testdata.pkl"
lf = '/gss_gpfs_scratch/vandal.t/experiments/DownscaleData/newengland_MS_420_8835.pkl'
data = pickle.load(open(lf, 'r'))
asdm = ASDMultitask(data,
model,
season='JJA',
ytransform=preprocessing.StandardScaler())
asdm.train()
out = asdm.predict(test_set=False)
out.to_netcdf("test_data/ne_test_set.nc")
def test1_mpi():
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
X, y, W = test1_data()
ntrain = int(y.shape[0]*0.80)
gspace = numpy.linspace(1e-4, 4, 10)
lspace = numpy.linspace(1e-4, 2, 10)
if rank == 0:
space = numpy.asarray([[g, l] for g in gspace for l in lspace])
split = numpy.array_split(space, size)
else:
split = None
split = comm.scatter(split, root=0)
results = []
for g, l in split:
try:
mssl = pMSSL(max_epochs=1000, quite=True, gamma=g, lambd=l)
mssl.fit(X[:ntrain], y[:ntrain])
yhat = mssl.predict(X[ntrain:])
mse = numpy.mean((yhat - y[ntrain:])**2)
num_omega_zeros = numpy.sum(mssl.Omega == 0)
num_w_zeros = numpy.sum(mssl.W == 0)
d = {"gamma": g, "lambda": l, "mse": mse,
"omega_zeros": num_omega_zeros, "w_zeros": num_w_zeros}
except Exception as err:
sys.stderr.write("Gamma: %f, Lambda: %f\n%s" % (g, l, err))
d = {"error": 1, "gamma": g, "lambda": l}
results.append(d)
sys.stdout.write(str(d))
newdata = comm.gather(results)
if rank == 0:
newdata = [item for l in newdata for item in l] ## condense lists of lists
data = pandas.DataFrame(newdata)
data.to_csv("mssl-test1-mpi.csv", index=False)