def fit_predict(enet, fortran, mTrain, n, mvalid, validX, validY, validX2, validY2, a, b, c, d, e, sourceDev):
## Solve
print("Solving")
double_precision = 0 # float
order = 'c' if fortran else 'r'
enet.fit_ptr(mTrain, n, mvalid, double_precision, order, a, b, c, d, e, source_dev=sourceDev)
print("Done Solving")
# show something about Xvsalphalambda and Xvsalpha
print("Xvsalpha")
print(enet.X)
rmse = enet.error
print("rmse")
print(rmse)
print("lambdas")
lambdas = enet.lambdas
print(lambdas)
print("alphas")
alphas = enet.alphas
print(alphas)
print("tols")
tols = enet.tols
print(tols)
print("Predicting")
if validX is not None:
if 1 == 1:
validPredsvsalphapure = enet.predict_ptr(c, d)
else:
validPredsvsalphapure = enet.predict(validX, validY)
print("validPredsvsalphapure")
print(validPredsvsalphapure)
# upload new validation for new predict
_, _, e, f, _ = upload_data(enet, None, None, validX2, validY2, None, source_dev=sourceDev)
print("Predicting2")
if 1 == 1:
validPredsvsalphapure2 = enet.predict_ptr(e, f)
else:
validPredsvsalphapure2 = enet.predict(validX2, validY2)
print("validPredsvsalphapure2")
print(validPredsvsalphapure2)
print("Done Predicting")
def ElasticNet(X,
y,
nGPUs=0,
nlambda=100,
nfolds=5,
nalpha=5,
validFraction=0.2):
Solver = h2o4gpu.ElasticNetH2O
sourceDev = 0
intercept = True
lambda_min_ratio = 1e-9
nFolds = nfolds
nLambdas = nlambda
nAlphas = nalpha
# Setup Train/validation Set Split
morig = X.shape[0]
norig = X.shape[1]
print("Original m=%d n=%d" % (morig, norig))
fortran = X.flags.f_contiguous
print("fortran=%d" % (fortran))
# Do train/valid split
HO = int(validFraction * morig)
H = morig - HO
print("Size of Train rows=%d valid rows=%d" % (H, HO))
trainX = np.copy(X[0:H, :])
trainY = np.copy(y[0:H])
if validFraction > 0:
validX = np.copy(X[H:norig, :])
validY = np.copy(y[H:norig])
else:
validX = None
validY = None
trainW = np.copy(trainY) * 0.0 + 1.0 # constant unity weight
half = int(H / 2)
validX2 = np.copy(X[H - half:norig - half, :])
validY2 = np.copy(y[H - half:norig - half])
mTrain = trainX.shape[0]
if validX is not None:
mvalid = validX.shape[0]
else:
mvalid = 0
mvalid2 = validX2.shape[0]
print("mTrain=%d mvalid=%d mvalid2=%d" % (mTrain, mvalid, mvalid2))
if intercept == 1:
trainX = np.hstack(
[trainX, np.ones((trainX.shape[0], 1), dtype=trainX.dtype)])
if validX is not None:
validX = np.hstack(
[validX,
np.ones((validX.shape[0], 1), dtype=validX.dtype)])
validX2 = np.hstack(
[validX2,
np.ones((validX2.shape[0], 1), dtype=validX2.dtype)])
n = trainX.shape[1]
print("New n=%d" % (n))
## Constructor
print("Setting up solver")
enet = Solver(n_gpus=nGPUs,
fit_intercept=intercept,
lambda_min_ratio=lambda_min_ratio,
n_lambdas=nLambdas,
n_folds=nFolds,
n_alphas=nAlphas)
## First, get backend pointers
print("Uploading")
print(trainX.dtype)
print(trainY.dtype)
if validX is not None:
print(validX.dtype)
print(validY.dtype)
print(trainW.dtype)
a, b, c, d, e = prepare_and_upload_data(enet,
trainX,
trainY,
validX,
validY,
trainW,
source_dev=sourceDev)
## Solve
print("Solving")
double_precision = 0 # float
order = 'c' if fortran else 'r'
enet.fit_ptr(mTrain,
n,
mvalid,
double_precision,
order,
a,
b,
c,
d,
e,
source_dev=sourceDev)
print("Done Solving")
# show something about Xvsalphalambda and Xvsalpha
print("Xvsalpha")
print(enet.X)
rmse = enet.error
print("rmse")
print(rmse)
print("lambdas")
lambdas = enet.lambdas
print(lambdas)
print("alphas")
alphas = enet.alphas
print(alphas)
print("tols")
tols = enet.tols
print(tols)
print("Predicting")
if validX is not None:
if 1 == 1:
validPredsvsalphapure = enet.predict_ptr(c, d)
else:
validPredsvsalphapure = enet.predict(validX, validY)
print("validPredsvsalphapure")
print(validPredsvsalphapure)
# upload new validation for new predict
_, _, e, f, _ = upload_data(enet,
None,
None,
validX2,
validY2,
None,
source_dev=sourceDev)
print("Predicting2")
if 1 == 1:
validPredsvsalphapure2 = enet.predict_ptr(e, f)
else:
validPredsvsalphapure2 = enet.predict(validX2, validY2)
print("validPredsvsalphapure2")
print(validPredsvsalphapure2)
print("Done Predicting")
# show something about validPredsvsalphalambdapure, validPredsvsalphapure
return enet
def ElasticNet(X, y, nGPUs=0, nlambda=100, nfolds=5, nalpha=5, validFraction=0.2):
Solver = h2o4gpu.ElasticNetH2O
sourceDev = 0
intercept = True
lambda_min_ratio = 1e-9
nFolds = nfolds
nLambdas = nlambda
nAlphas = nalpha
# Setup Train/validation Set Split
morig = X.shape[0]
norig = X.shape[1]
print("Original m=%d n=%d" % (morig,norig))
fortran = X.flags.f_contiguous
print("fortran=%d" % (fortran))
# Do train/valid split
HO=int(validFraction*morig)
H=morig-HO
print("Size of Train rows=%d valid rows=%d" % (H,HO))
trainX = np.copy(X[0:H,:])
trainY = np.copy(y[0:H])
if validFraction>0:
validX = np.copy(X[H:norig,:])
validY = np.copy(y[H:norig])
else:
validX = None
validY = None
trainW = np.copy(trainY)*0.0 + 1.0 # constant unity weight
half = int(H/2)
validX2 = np.copy(X[H-half:norig-half,:])
validY2 = np.copy(y[H-half:norig-half])
mTrain = trainX.shape[0]
if validX is not None:
mvalid = validX.shape[0]
else:
mvalid = 0
mvalid2 = validX2.shape[0]
print("mTrain=%d mvalid=%d mvalid2=%d" % (mTrain,mvalid,mvalid2))
if intercept==1:
trainX = np.hstack([trainX, np.ones((trainX.shape[0],1),dtype=trainX.dtype)])
if validX is not None:
validX = np.hstack([validX, np.ones((validX.shape[0],1),dtype=validX.dtype)])
validX2 = np.hstack([validX2, np.ones((validX2.shape[0], 1), dtype=validX2.dtype)])
n = trainX.shape[1]
print("New n=%d" % (n))
## Constructor
print("Setting up solver")
enet = Solver(n_gpus = nGPUs, fit_intercept = intercept, lambda_min_ratio = lambda_min_ratio, n_lambdas = nLambdas, n_folds = nFolds, n_alphas = nAlphas)
## First, get backend pointers
print("Uploading")
print(trainX.dtype)
print(trainY.dtype)
if validX is not None:
print(validX.dtype)
print(validY.dtype)
print(trainW.dtype)
a,b,c,d,e = prepare_and_upload_data(enet, trainX, trainY, validX, validY, trainW, source_dev = sourceDev)
## Solve
print("Solving")
double_precision=0 # float
order = 'c' if fortran else 'r'
enet.fit_ptr(mTrain, n, mvalid, double_precision, order, a, b, c, d, e, source_dev = sourceDev)
print("Done Solving")
# show something about Xvsalphalambda and Xvsalpha
print("Xvsalpha")
print(enet.X)
rmse=enet.error
print("rmse")
print(rmse)
print("lambdas")
lambdas=enet.lambdas
print(lambdas)
print("alphas")
alphas=enet.alphas
print(alphas)
print("tols")
tols=enet.tols
print(tols)
print("Predicting")
if validX is not None:
if 1==1:
validPredsvsalphapure = enet.predict_ptr(c, d)
else:
validPredsvsalphapure = enet.predict(validX, validY)
print("validPredsvsalphapure")
print(validPredsvsalphapure)
# upload new validation for new predict
_,_,e,f,_ = upload_data(enet, None, None, validX2, validY2, None, source_dev = sourceDev)
print("Predicting2")
if 1==1:
validPredsvsalphapure2 = enet.predict_ptr(e, f)
else:
validPredsvsalphapure2 = enet.predict(validX2, validY2)
print("validPredsvsalphapure2")
print(validPredsvsalphapure2)
print("Done Predicting")
# show something about validPredsvsalphalambdapure, validPredsvsalphapure
return enet