h2o-py/h2o/demos.py
def glm(interactive=True, echo=True, testing=False):
    """GLM model demo."""

    def demo_body(go):
        """
        Demo of H2O's Generalized Linear Estimator.

        This demo uploads a dataset to h2o, parses it, and shows a description.
        Then it divides the dataset into training and test sets, builds a GLM
        from the training set, and makes predictions for the test set.
        Finally, default performance metrics are displayed.
        """
        go()
        # Connect to H2O
        h2o.init()

        go()
        # Upload the prostate dataset that comes included in the h2o python package
        prostate = h2o.load_dataset("prostate")

        go()
        # Print a description of the prostate data
        prostate.describe()

        go()
        # Randomly split the dataset into ~70/30, training/test sets
        train, test = prostate.split_frame(ratios=[0.70])

        go()
        # Convert the response columns to factors (for binary classification problems)
        train["CAPSULE"] = train["CAPSULE"].asfactor()
        test["CAPSULE"] = test["CAPSULE"].asfactor()

        go()
        # Build a (classification) GLM
        from h2o.estimators import H2OGeneralizedLinearEstimator
        prostate_glm = H2OGeneralizedLinearEstimator(family="binomial", alpha=[0.5])
        prostate_glm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
                           y="CAPSULE", training_frame=train)

        go()
        # Show the model
        prostate_glm.show()

        go()
        # Predict on the test set and show the first ten predictions
        predictions = prostate_glm.predict(test)
        predictions.show()

        go()
        # Show default performance metrics
        performance = prostate_glm.model_performance(test)
        performance.show()

    # Execute:
    _run_demo(demo_body, interactive, echo, testing)
def glm(interactive=True, echo=True, testing=False):
    """GLM model demo."""

    def demo_body(go):
        """
        Demo of H2O's Generalized Linear Estimator.

        This demo uploads a dataset to h2o, parses it, and shows a description.
        Then it divides the dataset into training and test sets, builds a GLM
        from the training set, and makes predictions for the test set.
        Finally, default performance metrics are displayed.
        """
        go()
        # Connect to H2O
        h2o.init()

        go()
        # Upload the prostate dataset that comes included in the h2o python package
        prostate = h2o.load_dataset("prostate")

        go()
        # Print a description of the prostate data
        prostate.describe()

        go()
        # Randomly split the dataset into ~70/30, training/test sets
        train, test = prostate.split_frame(ratios=[0.70])

        go()
        # Convert the response columns to factors (for binary classification problems)
        train["CAPSULE"] = train["CAPSULE"].asfactor()
        test["CAPSULE"] = test["CAPSULE"].asfactor()

        go()
        # Build a (classification) GLM
        from h2o.estimators import H2OGeneralizedLinearEstimator
        prostate_glm = H2OGeneralizedLinearEstimator(family="binomial", alpha=[0.5])
        prostate_glm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
                           y="CAPSULE", training_frame=train)

        go()
        # Show the model
        prostate_glm.show()

        go()
        # Predict on the test set and show the first ten predictions
        predictions = prostate_glm.predict(test)
        predictions.show()

        go()
        # Show default performance metrics
        performance = prostate_glm.model_performance(test)
        performance.show()

    # Execute:
    _run_demo(demo_body, interactive, echo, testing)
GLM model demo.