h2o-py/h2o/estimators/glm.py
def getGLMRegularizationPath(model):
        """
        Extract full regularization path explored during lambda search from glm model.

        :param model: source lambda search model
        """
        x = h2o.api("GET /3/GetGLMRegPath", data={"model": model._model_json["model_id"]["name"]})
        ns = x.pop("coefficient_names")
        res = {
            "lambdas": x["lambdas"],
            "explained_deviance_train": x["explained_deviance_train"],
            "explained_deviance_valid": x["explained_deviance_valid"],
            "coefficients": [dict(zip(ns, y)) for y in x["coefficients"]],
        }
        if "coefficients_std" in x:
            res["coefficients_std"] = [dict(zip(ns, y)) for y in x["coefficients_std"]]
        return res
def getGLMRegularizationPath(model):
        """
        Extract full regularization path explored during lambda search from glm model.

        :param model: source lambda search model
        """
        x = h2o.api("GET /3/GetGLMRegPath", data={"model": model._model_json["model_id"]["name"]})
        ns = x.pop("coefficient_names")
        res = {
            "lambdas": x["lambdas"],
            "explained_deviance_train": x["explained_deviance_train"],
            "explained_deviance_valid": x["explained_deviance_valid"],
            "coefficients": [dict(zip(ns, y)) for y in x["coefficients"]],
        }
        if "coefficients_std" in x:
            res["coefficients_std"] = [dict(zip(ns, y)) for y in x["coefficients_std"]]
        return res
Extract full regularization path explored during lambda search from glm model.

        :param model: source lambda search model