def h2odemo():
"""
Python API test: h2o.demo(funcname, interactive=True, echo=True, test=False)[source]
Copied from pyunit_glm_demo.py
"""
try:
h2o.demo(funcname="glm", interactive=False, echo=True, test=True)
except Exception as e:
assert False, "h2o.demo() command not is working."
def h2odemo():
"""
Python API test: h2o.demo(funcname, interactive=True, echo=True, test=False)[source]
Copied from pyunit_glm_demo.py
"""
ret = h2o.demo(funcname="glm", interactive=False, echo=True, test=True)
assert ret is None
def h2odemo():
"""
Python API test: h2o.demo(funcname, interactive=True, echo=True, test=False)[source]
Copied from pyunit_glm_demo.py
"""
ret = h2o.demo(funcname="glm", interactive=False, echo=True, test=True)
assert ret is None
def demo_glm():
h2o.demo(func="glm", interactive=False, test=True)
def demo_deeplearning():
h2o.demo(func="deeplearning", interactive=False, test=True)
def demo_gbm(ip,port):
h2o.demo(func="gbm", interactive=False, test=True)
def demo_gbm():
h2o.demo(funcname="gbm", interactive=False, test=True)
def demo_deeplearning(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# Execute gbm demo
h2o.demo(func="deeplearning", interactive=False, test=True)
def demo_gbm():
h2o.demo(funcname="gbm", interactive=False, test=True)
def demo_deeplearning(ip, port):
h2o.demo(func="deeplearning", interactive=False, test=True)
def demo_deeplearning(ip,port):
# Connect to a pre-existing cluster
h2o.init(ip,port)
# Execute gbm demo
h2o.demo(func="deeplearning", interactive=False, test=True)
def demo_glm():
h2o.demo(func="glm", interactive=False, test=True)
cluster_estimator
#h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor()
#model = h2o.glm(y = "CAPSULE",
# x = ["AGE", "RACE", "PSA", "GLEASON"],
# training_frame = h2o_df,
# family = "binomial")
#h2o.download_pojo(model)
#==============================================================================
# binary logistic
#==============================================================================
help(h2o.glm)
h2o.demo("glm")
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
h2o_df['FilteredFilename'] = h2o_df['FilteredFilename'].asfactor()
h2o_df.types
h2o_df.columns
train, test = h2o_df.split_frame(ratios=[0.70])
H2OGeneralizedLinearEstimator?
logistic_glm = H2OGeneralizedLinearEstimator(family="binomial")
logistic_glm.train(x=["size","numberofrecords"],
y='FilteredFilename',
def experiment02(self):
self.load_trainingset(False)
h2o.init()
h2o.demo("glm")
def demo_deeplearning():
h2o.demo(funcname="deeplearning", interactive=False, test=True)
