h2o-py/h2o/estimators/glm.py
def makeGLMModel(model, coefs, threshold=.5):
        """
        Create a custom GLM model using the given coefficients.

        Needs to be passed source model trained on the dataset to extract the dataset information from.

        :param model: source model, used for extracting dataset information
        :param coefs: dictionary containing model coefficients
        :param threshold: (optional, only for binomial) decision threshold used for classification
        """
        model_json = h2o.api(
            "POST /3/MakeGLMModel",
            data={"model": model._model_json["model_id"]["name"],
                  "names": list(coefs.keys()),
                  "beta": list(coefs.values()),
                  "threshold": threshold}
        )
        m = H2OGeneralizedLinearEstimator()
        m._resolve_model(model_json["model_id"]["name"], model_json)
        return m
def makeGLMModel(model, coefs, threshold=.5):
        model_json = h2o.api(
            "POST /3/MakeGLMModel",
            data={"model": model._model_json["model_id"]["name"],
                  "names": list(coefs.keys()),
                  "beta": list(coefs.values()),
                  "threshold": threshold}
        )
        m = H2OGeneralizedLinearEstimator()
        m._resolve_model(model_json["model_id"]["name"], model_json)
        return m
def makeGLMModel ( model , coefs , threshold = .5 ) : model_json = h2o . api ( "POST /3/MakeGLMModel" , data = { "model" : model . _model_json [ "model_id" ] [ "name" ] , "names" : list ( coefs . keys ( ) ) , "beta" : list ( coefs . values ( ) ) , "threshold" : threshold } ) m = H2OGeneralizedLinearEstimator ( ) m . _resolve_model ( model_json [ "model_id" ] [ "name" ] , model_json ) return m
Create a custom GLM model using the given coefficients.

        Needs to be passed source model trained on the dataset to extract the dataset information from.

        :param model: source model, used for extracting dataset information
        :param coefs: dictionary containing model coefficients
        :param threshold: (optional, only for binomial) decision threshold used for classification
Create a custom GLM model using the given coefficients .