def __init__(
self,
fit_intercept=True, #h2o4gpu
normalize=False,
copy_X=True,
n_jobs=1,
n_gpus=-1,
tol=1E-4,
glm_stop_early=True, # h2o4gpu
glm_stop_early_error_fraction=1.0, # h2o4gpu
verbose=False,
backend='auto',
**kwargs):
import os
_backend = os.environ.get('H2O4GPU_BACKEND', None)
if _backend is not None:
backend = _backend
self.do_daal = False
self.do_sklearn = False
if backend == 'auto':
# Fall back to Sklearn
# Can remove if fully implement sklearn functionality
self.do_sklearn = False
params_string = ['normalize']
params = [normalize]
params_default = [False]
i = 0
for param in params:
if param != params_default[i]:
self.do_sklearn = True
if verbose:
print("WARNING:"
" The sklearn parameter " + params_string[i] +
" has been changed from default to " +
str(param) +
". Will run Sklearn Linear Regression.")
self.do_sklearn = True
i = i + 1
elif backend == 'sklearn':
self.do_sklearn = True
self.backend = 'sklearn'
elif backend == 'h2o4gpu':
self.do_sklearn = False
self.backend = 'h2o4gpu'
elif backend == 'daal':
from h2o4gpu import DAAL_SUPPORTED
if DAAL_SUPPORTED:
from h2o4gpu.solvers.daal_solver.regression \
import LinearRegression as DLR
self.do_daal = True
self.backend = 'daal'
self.model_daal = DLR(fit_intercept=fit_intercept,
normalize=normalize,
**kwargs)
else:
import platform
print("WARNING:"
"DAAL is supported only for x86_64, "
"architecture detected {}. Sklearn model"
"used instead".format(platform.architecture()))
self.do_sklearn = True
self.backend = 'sklearn'
self.model_sklearn = sk.LinearRegressionSklearn(
fit_intercept=fit_intercept,
normalize=normalize,
copy_X=copy_X,
n_jobs=n_jobs)
# Equivalent Linear Regression parameters for h2o4gpu
n_threads = None
n_gpus = n_gpus
fit_intercept = fit_intercept
lambda_min_ratio = 0.0
n_lambdas = 1
n_folds = 1
n_alphas = 1
tol = tol
tol_seek_factor = 1E-1
lambda_stop_early = False
glm_stop_early = glm_stop_early
glm_stop_early_error_fraction = glm_stop_early_error_fraction
max_iter = 5000
verbose = verbose
family = 'elasticnet'
lambda_max = 0.0
alpha_max = 0.0
alpha_min = 0.0
alphas = None
lambdas = None
self.model_h2o4gpu = elastic_net.ElasticNetH2O(
n_threads=n_threads,
n_gpus=n_gpus,
fit_intercept=fit_intercept,
lambda_min_ratio=lambda_min_ratio,
n_lambdas=n_lambdas,
n_folds=n_folds,
n_alphas=n_alphas,
tol=tol,
lambda_stop_early=lambda_stop_early,
glm_stop_early=glm_stop_early,
glm_stop_early_error_fraction=glm_stop_early_error_fraction,
max_iter=max_iter,
verbose=verbose,
lambda_max=lambda_max,
alpha_max=alpha_max,
alpha_min=alpha_min,
alphas=alphas,
lambdas=lambdas,
tol_seek_factor=tol_seek_factor,
family=family,
order=None)
if self.do_sklearn:
if verbose:
print("Running sklearn Linear Regression")
self.model = self.model_sklearn
elif self.do_daal:
if verbose:
print("Running PyDAAL Linear Regression")
self.model = self.model_daal
else:
if verbose:
print("Running h2o4gpu Linear Regression")
self.model = self.model_h2o4gpu
self.verbose = verbose
def __init__(
self,
fit_intercept=True, #h2o4gpu
normalize=False,
copy_X=True,
n_jobs=1,
n_gpus=-1,
tol=1E-4,
glm_stop_early=True, # h2o4gpu
glm_stop_early_error_fraction=1.0, # h2o4gpu
verbose=False,
backend='auto'):
import os
_backend = os.environ.get('H2O4GPU_BACKEND', None)
if _backend is not None:
backend = _backend
assert_is_type(backend, str)
if backend == 'auto':
# Fall back to Sklearn
# Can remove if fully implement sklearn functionality
self.do_sklearn = False
params_string = ['normalize', 'copy_X', 'n_jobs']
params = [normalize, copy_X, n_jobs]
params_default = [False, True, 1]
i = 0
self.do_sklearn = False
for param in params:
if param != params_default[i]:
self.do_sklearn = True
print("WARNING: The sklearn parameter " +
params_string[i] +
" has been changed from default to " + str(param) +
". Will run Sklearn Linear Regression.")
self.do_sklearn = True
i = i + 1
elif backend == 'sklearn':
self.do_sklearn = True
elif backend == 'h2o4gpu':
self.do_sklearn = False
self.backend = backend
self.model_sklearn = sk.LinearRegressionSklearn(
fit_intercept=fit_intercept,
normalize=normalize,
copy_X=copy_X,
n_jobs=n_jobs)
# Equivalent Linear Regression parameters for h2o4gpu
n_threads = None
n_gpus = n_gpus
fit_intercept = fit_intercept
lambda_min_ratio = 0.0
n_lambdas = 1
n_folds = 1
n_alphas = 1
tol = tol
tol_seek_factor = 1E-1
lambda_stop_early = False
glm_stop_early = glm_stop_early
glm_stop_early_error_fraction = glm_stop_early_error_fraction
max_iter = 5000
verbose = verbose
family = 'elasticnet'
lambda_max = 0.0
alpha_max = 0.0
alpha_min = 0.0
alphas = None
lambdas = None
self.model_h2o4gpu = elastic_net.ElasticNetH2O(
n_threads=n_threads,
n_gpus=n_gpus,
fit_intercept=fit_intercept,
lambda_min_ratio=lambda_min_ratio,
n_lambdas=n_lambdas,
n_folds=n_folds,
n_alphas=n_alphas,
tol=tol,
lambda_stop_early=lambda_stop_early,
glm_stop_early=glm_stop_early,
glm_stop_early_error_fraction=glm_stop_early_error_fraction,
max_iter=max_iter,
verbose=verbose,
lambda_max=lambda_max,
alpha_max=alpha_max,
alpha_min=alpha_min,
alphas=alphas,
lambdas=lambdas,
tol_seek_factor=tol_seek_factor,
family=family,
order=None)
if self.do_sklearn:
print("Running sklearn Linear Regression")
self.model = self.model_sklearn
else:
print("Running h2o4gpu Linear Regression")
self.model = self.model_h2o4gpu