Esempio n. 1
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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."
Esempio n. 2
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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
Esempio n. 3
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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
Esempio n. 4
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def demo_glm():

    h2o.demo(func="glm", interactive=False, test=True)
def demo_deeplearning():

    h2o.demo(func="deeplearning", interactive=False, test=True)
Esempio n. 6
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def demo_gbm(ip,port):

    h2o.demo(func="gbm", interactive=False, test=True)
Esempio n. 7
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def demo_gbm():
    h2o.demo(funcname="gbm", interactive=False, test=True)
Esempio n. 8
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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)
Esempio n. 9
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def demo_gbm():
    h2o.demo(funcname="gbm", interactive=False, test=True)
Esempio n. 10
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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)
Esempio n. 12
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def demo_glm():

    h2o.demo(func="glm", interactive=False, test=True)
Esempio n. 13
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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',
Esempio n. 14
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    def experiment02(self):

        self.load_trainingset(False)

        h2o.init()
        h2o.demo("glm")
Esempio n. 15
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def demo_deeplearning():
    h2o.demo(funcname="deeplearning", interactive=False, test=True)