def __init__(self,
n_components=2,
algorithm="arpack",
n_iter=5,
random_state=None,
tol=0.,
verbose=False,
backend='auto'):
self.algorithm = algorithm
self.n_components = n_components
self.n_iter = n_iter
self.random_state = random_state
self.tol = tol
import os
_backend = os.environ.get('H2O4GPU_BACKEND', None)
if _backend is not None:
backend = _backend
# Fall back to Sklearn
# Can remove if fully implement sklearn functionality
self.do_sklearn = False
if backend == 'auto':
params_string = ['algorithm', 'n_iter', 'random_state', 'tol']
params = [algorithm, n_iter, random_state, tol]
params_default = ['arpack', 5, None, 0.]
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 TruncatedSVD.")
self.do_sklearn = True
i = i + 1
elif backend == 'sklearn':
self.do_sklearn = True
elif backend == 'h2o4gpu':
self.do_sklearn = False
if self.do_sklearn:
self.backend = 'sklearn'
else:
self.backend = 'h2o4gpu'
from h2o4gpu.decomposition.truncated_svd import TruncatedSVDSklearn
self.model_sklearn = TruncatedSVDSklearn(
n_components=n_components,
algorithm=algorithm,
n_iter=n_iter,
random_state=random_state,
tol=tol)
self.model_h2o4gpu = TruncatedSVDH2O(n_components=n_components)
if self.do_sklearn:
self.model = self.model_sklearn
else:
self.model = self.model_h2o4gpu
def __init__(self,
n_components=2,
algorithm="power",
n_iter=100,
random_state=None,
tol=1E-5,
verbose=False,
backend='auto',
n_gpus=1,
gpu_id=0):
if isinstance(algorithm, list):
self.algorithm = algorithm[0]
else:
self.algorithm = algorithm
self.n_components = n_components
if isinstance(n_iter, list):
self.n_iter = n_iter[0]
else:
self.n_iter = n_iter
if random_state is not None:
self.random_state = random_state
else:
self.random_state = np.random.randint(0, 2**31 - 1)
if isinstance(tol, list):
self.tol = tol[0]
else:
self.tol = tol
self.verbose = 1 if verbose else 0
self.n_gpus = n_gpus
self.gpu_id = gpu_id
import os
_backend = os.environ.get('H2O4GPU_BACKEND', None)
if _backend is not None:
backend = _backend
# Fall back to Sklearn
# Can remove if fully implement sklearn functionality
self.do_sklearn = False
self.do_daal = False
sklearn_algorithm = "arpack" # Default scikit
sklearn_n_iter = 5
sklearn_tol = 1E-5
if n_gpus == 0:
# we don't have CPU back-end for SVD yet.
backend = 'sklearn'
else:
backend = 'h2o4gpu'
if backend in ['auto', 'sklearn']:
self.do_sklearn = True
self.backend = 'sklearn'
params_string = ['algorithm']
params = [self.algorithm]
params_gpu = [['cusolver', 'power']]
i = 0
for param in params:
if param not in params_gpu[i]:
self.do_sklearn = True
if verbose:
print("WARNING:"
" The parameter " + params_string[i] + "is " +
str(param) + " and not supported by GPU." +
"Will run Sklearn TruncatedSVD.")
self.do_sklearn = True
i = i + 1
if isinstance(algorithm, list):
sklearn_algorithm = algorithm[1]
if isinstance(n_iter, list):
sklearn_n_iter = n_iter[1]
if isinstance(tol, list):
sklearn_tol = tol[1]
elif backend == 'h2o4gpu':
self.backend = 'h2o4gpu'
elif backend == 'daal':
from h2o4gpu import DAAL_SUPPORTED
if DAAL_SUPPORTED:
from h2o4gpu.solvers.daal_solver.svd import SVD
self.do_daal = True
self.backend = 'daal'
self.model_daal = SVD(n_components=self.n_components,
verbose=self.verbose)
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'
from h2o4gpu.decomposition.truncated_svd import TruncatedSVDSklearn
self.model_sklearn = TruncatedSVDSklearn(
n_components=self.n_components,
algorithm=sklearn_algorithm,
n_iter=sklearn_n_iter,
random_state=self.random_state,
tol=sklearn_tol)
self.model_h2o4gpu = TruncatedSVDH2O(n_components=self.n_components,
algorithm=self.algorithm,
n_iter=self.n_iter,
random_state=self.random_state,
tol=self.tol,
verbose=self.verbose,
gpu_id=self.gpu_id)
# select final model type
if self.do_sklearn:
self.model = self.model_sklearn
elif self.do_daal:
self.model = self.model_daal
else:
self.model = self.model_h2o4gpu
def __init__(self,
n_components=2,
algorithm="power",
n_iter=100,
random_state=None,
tol=1E-5,
verbose=False,
backend='auto',
n_gpus=1,
gpu_id=0):
if isinstance(algorithm, list):
self.algorithm = algorithm[0]
else:
self.algorithm = algorithm
self.n_components = n_components
if isinstance(n_iter, list):
self.n_iter = n_iter[0]
else:
self.n_iter = n_iter
if random_state is not None:
self.random_state = random_state
else:
self.random_state = np.random.randint(0, 2 ** 32 - 1)
if isinstance(tol, list):
self.tol = tol[0]
else:
self.tol = tol
self.verbose = 1 if verbose else 0
self.n_gpus = n_gpus
self.gpu_id = gpu_id
import os
_backend = os.environ.get('H2O4GPU_BACKEND', None)
if _backend is not None:
backend = _backend
# Fall back to Sklearn
# Can remove if fully implement sklearn functionality
self.do_sklearn = False
if backend == 'auto':
params_string = ['algorithm']
params = [self.algorithm]
params_gpu = [['cusolver', 'power']]
i = 0
for param in params:
if param not in params_gpu[i]:
self.do_sklearn = True
if verbose:
print("WARNING:"
" The parameter " + params_string[i]
+ "is "
+ str(param)
+ " and not supported by GPU."
+ "Will run Sklearn TruncatedSVD.")
self.do_sklearn = True
i = i + 1
elif backend == 'sklearn':
self.do_sklearn = True
elif backend == 'h2o4gpu':
self.do_sklearn = False
if n_gpus == 0:
# we don't have CPU back-end for SVD yet.
self.do_sklearn = True
if n_gpus != 0:
# sklearn can't do GPUs
self.do_sklearn = False
sklearn_algorithm = "arpack" # Default scikit
sklearn_n_iter = 5
sklearn_tol = 1E-5
if self.do_sklearn:
self.backend = 'sklearn'
if isinstance(algorithm, list):
sklearn_algorithm = algorithm[1]
if isinstance(n_iter, list):
sklearn_n_iter = n_iter[1]
if isinstance(tol, list):
sklearn_tol = tol[1]
else:
self.backend = 'h2o4gpu'
from h2o4gpu.decomposition.truncated_svd import TruncatedSVDSklearn
self.model_sklearn = TruncatedSVDSklearn(
n_components=self.n_components,
algorithm=sklearn_algorithm,
n_iter=sklearn_n_iter,
random_state=self.random_state,
tol=sklearn_tol)
self.model_h2o4gpu = TruncatedSVDH2O(n_components=self.n_components,
algorithm=self.algorithm,
n_iter=self.n_iter,
random_state=self.random_state,
tol=self.tol,
verbose=self.verbose,
gpu_id=self.gpu_id)
# select final model type
if self.do_sklearn:
self.model = self.model_sklearn
else:
self.model = self.model_h2o4gpu