def init_for_pipeline(self):
"""
Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly.
All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.
:returns: H2OSVD object
:examples:
>>> from h2o.transforms.preprocessing import H2OScaler
>>> from h2o.estimators import H2ORandomForestEstimator
>>> from h2o.estimators import H2OSingularValueDecompositionEstimator
>>> from sklearn.pipeline import Pipeline
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> pipe = Pipeline([("standardize", H2OScaler()),
... ("svd", H2OSingularValueDecompositionEstimator(nv=3).init_for_pipeline()),
... ("rf", H2ORandomForestEstimator(seed=42,ntrees=50))])
>>> pipe.fit(arrests[1:], arrests[0])
"""
import inspect
from h2o.transforms.decomposition import H2OSVD
# check which parameters can be passed to H2OSVD init
var_names = list(
dict(inspect.getmembers(H2OSVD.__init__.__code__))['co_varnames'])
parameters = {k: v for k, v in self._parms.items() if k in var_names}
return H2OSVD(**parameters)
def scale_svd_rf_pipe():
from h2o.transforms.decomposition import H2OSVD
print("Importing USArrests.csv data...")
arrests = h2o.upload_file(
pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
# build transformation pipeline using sklearn's Pipeline and H2OSVD
pipe = Pipeline([("standardize", H2OScaler()), ("svd", H2OSVD()),
("rf", H2ORandomForestEstimator())])
params = {
"standardize__center": [True, False],
"standardize__scale": [True, False],
"svd__nv": [2, 3],
"rf__ntrees": randint(50, 60),
"rf__max_depth": randint(4, 8),
"rf__min_rows": randint(5, 10),
"svd__transform": ["none", "standardize"],
}
custom_cv = H2OKFold(arrests, n_folds=5, seed=42)
random_search = RandomizedSearchCV(pipe,
params,
n_iter=5,
scoring=make_scorer(h2o_r2_score),
cv=custom_cv,
random_state=42,
n_jobs=1)
random_search.fit(arrests[1:], arrests[0])
print(random_search.best_estimator_)
def svd_1_golden():
print("Importing USArrests.csv data...")
arrestsH2O = h2o.upload_file(
pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
print("Compare with SVD")
from h2o.transforms.decomposition import H2OSVD
fitH2O = H2OSVD(nv=4, transform="NONE", max_iterations=2000)
fitH2O.train(x=list(range(4)), training_frame=arrestsH2O)
print("Compare singular values (D)")
h2o_d = fitH2O._model_json['output']['d']
r_d = [
1419.06139509772, 194.825846110138, 45.6613376308754, 18.0695566224677
]
print("R Singular Values: {0}".format(r_d))
print("H2O Singular Values: {0}".format(h2o_d))
for r, h in zip(r_d, h2o_d):
assert abs(r - h) < 1e-6, "H2O got {0}, but R got {1}".format(h, r)
print("Compare right singular vectors (V)")
h2o_v = h2o.as_list(h2o.get_frame(
fitH2O._model_json['output']['v_key']['name']),
use_pandas=False)
h2o_v.pop(0)
r_v = [[-0.04239181, 0.01616262, -0.06588426, 0.99679535],
[-0.94395706, 0.32068580, 0.06655170, -0.04094568],
[-0.30842767, -0.93845891, 0.15496743, 0.01234261],
[-0.10963744, -0.12725666, -0.98347101, -0.06760284]]
print("R Right Singular Vectors: {0}".format(r_v))
print("H2O Right Singular Vectors: {0}".format(h2o_v))
for rl, hl in zip(r_v, h2o_v):
for r, h in zip(rl, hl):
assert abs(abs(r) - abs(float(h))
) < 1e-5, "H2O got {0}, but R got {1}".format(h, r)
print("Compare left singular vectors (U)")
h2o_u = h2o.as_list(h2o.get_frame(
fitH2O._model_json['output']['u_key']['name']),
use_pandas=False)
h2o_u.pop(0)
r_u = [[-0.1716251, 0.096325710, 0.06515480, 0.15369551],
[-0.1891166, 0.173452566, -0.42665785, -0.17801438],
[-0.2155930, 0.078998111, 0.02063740, -0.28070784],
[-0.1390244, 0.059889811, 0.01392269, 0.01610418],
[-0.2067788, -0.009812026, -0.17633244, -0.21867425],
[-0.1558794, -0.064555293, -0.28288280, -0.11797419]]
print("R Left Singular Vectors: {0}".format(r_u))
print("H2O Left Singular Vectors: {0}".format(h2o_u))
for rl, hl in zip(r_u, h2o_u):
for r, h in zip(rl, hl):
assert abs(abs(r) - abs(float(h))
) < 1e-5, "H2O got {0}, but R got {1}".format(h, r)
def scale_svd_rf_pipe():
from h2o.transforms.decomposition import H2OSVD
print("Importing USArrests.csv data...")
arrests = h2o.upload_file(pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
# build transformation pipeline using sklearn's Pipeline and H2O transforms
pipe = Pipeline([
("standardize", H2OScaler()),
("svd", H2OSVD(nv=3)),
("rf", H2ORandomForestEstimator(seed=42,ntrees=50))
])
pipe.fit(arrests[1:], arrests[0])
print(pipe)
def init_for_pipeline(self):
"""
Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly.
All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.
:returns: H2OSVD object
"""
import inspect
from h2o.transforms.decomposition import H2OSVD
# check which parameters can be passed to H2OSVD init
var_names = list(
dict(inspect.getmembers(H2OSVD.__init__.__code__))['co_varnames'])
parameters = {k: v for k, v in self._parms.items() if k in var_names}
return H2OSVD(**parameters)