Source code for h2o.estimators.anovaglm

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py
# Copyright 2016 H2O.ai;  Apache License Version 2.0 (see LICENSE for details)
#
from __future__ import absolute_import, division, print_function, unicode_literals

import h2o
from h2o.base import Keyed
from h2o.frame import H2OFrame
from h2o.expr import ExprNode
from h2o.expr import ASTId
from h2o.estimators.estimator_base import H2OEstimator
from h2o.exceptions import H2OValueError
from h2o.frame import H2OFrame
from h2o.utils.typechecks import assert_is_type, Enum, numeric


[docs]class H2OANOVAGLMEstimator(H2OEstimator): """ ANOVA for Generalized Linear Model H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors and their interactions to a model. Predictors or interactions with negligible contributions to the model will have high p-values while those with more contributions will have low p-values. """ algo = "anovaglm" supervised_learning = True def __init__(self, model_id=None, # type: Optional[Union[None, str, H2OEstimator]] training_frame=None, # type: Optional[Union[None, str, H2OFrame]] seed=-1, # type: int response_column=None, # type: Optional[str] ignored_columns=None, # type: Optional[List[str]] ignore_const_cols=True, # type: bool score_each_iteration=False, # type: bool offset_column=None, # type: Optional[str] weights_column=None, # type: Optional[str] family="auto", # type: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"] tweedie_variance_power=0.0, # type: float tweedie_link_power=1.0, # type: float theta=0.0, # type: float solver="irlsm", # type: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] missing_values_handling="mean_imputation", # type: Literal["mean_imputation", "skip", "plug_values"] plug_values=None, # type: Optional[Union[None, str, H2OFrame]] compute_p_values=True, # type: bool standardize=True, # type: bool non_negative=False, # type: bool max_iterations=0, # type: int link="family_default", # type: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] prior=0.0, # type: float alpha=None, # type: Optional[List[float]] lambda_=[0.0], # type: List[float] lambda_search=False, # type: bool stopping_rounds=0, # type: int stopping_metric="auto", # type: Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"] early_stopping=False, # type: bool stopping_tolerance=0.001, # type: float balance_classes=False, # type: bool class_sampling_factors=None, # type: Optional[List[float]] max_after_balance_size=5.0, # type: float max_runtime_secs=0.0, # type: float save_transformed_framekeys=False, # type: bool highest_interaction_term=0, # type: int nparallelism=4, # type: int type=0, # type: int ): """ :param model_id: Destination id for this model; auto-generated if not specified. Defaults to ``None``. :type model_id: Union[None, str, H2OEstimator], optional :param training_frame: Id of the training data frame. Defaults to ``None``. :type training_frame: Union[None, str, H2OFrame], optional :param seed: Seed for pseudo random number generator (if applicable) Defaults to ``-1``. :type seed: int :param response_column: Response variable column. Defaults to ``None``. :type response_column: str, optional :param ignored_columns: Names of columns to ignore for training. Defaults to ``None``. :type ignored_columns: List[str], optional :param ignore_const_cols: Ignore constant columns. Defaults to ``True``. :type ignore_const_cols: bool :param score_each_iteration: Whether to score during each iteration of model training. Defaults to ``False``. :type score_each_iteration: bool :param offset_column: Offset column. This will be added to the combination of columns before applying the link function. Defaults to ``None``. :type offset_column: str, optional :param weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. Defaults to ``None``. :type weights_column: str, optional :param family: Family. Use binomial for classification with logistic regression, others are for regression problems. Defaults to ``"auto"``. :type family: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"] :param tweedie_variance_power: Tweedie variance power Defaults to ``0.0``. :type tweedie_variance_power: float :param tweedie_link_power: Tweedie link power Defaults to ``1.0``. :type tweedie_link_power: float :param theta: Theta Defaults to ``0.0``. :type theta: float :param solver: AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Defaults to ``"irlsm"``. :type solver: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] :param missing_values_handling: Handling of missing values. Either MeanImputation, Skip or PlugValues. Defaults to ``"mean_imputation"``. :type missing_values_handling: Literal["mean_imputation", "skip", "plug_values"] :param plug_values: Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) Defaults to ``None``. :type plug_values: Union[None, str, H2OFrame], optional :param compute_p_values: Request p-values computation, p-values work only with IRLSM solver and no regularization Defaults to ``True``. :type compute_p_values: bool :param standardize: Standardize numeric columns to have zero mean and unit variance Defaults to ``True``. :type standardize: bool :param non_negative: Restrict coefficients (not intercept) to be non-negative Defaults to ``False``. :type non_negative: bool :param max_iterations: Maximum number of iterations Defaults to ``0``. :type max_iterations: int :param link: Link function. Defaults to ``"family_default"``. :type link: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] :param prior: Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to ``0.0``. :type prior: float :param alpha: Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. Defaults to ``None``. :type alpha: List[float], optional :param lambda_: Regularization strength Defaults to ``[0.0]``. :type lambda_: List[float] :param lambda_search: Use lambda search starting at lambda max, given lambda is then interpreted as lambda min Defaults to ``False``. :type lambda_search: bool :param stopping_rounds: Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to ``0``. :type stopping_rounds: int :param stopping_metric: Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Defaults to ``"auto"``. :type stopping_metric: Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"] :param early_stopping: Stop early when there is no more relative improvement on train or validation (if provided). Defaults to ``False``. :type early_stopping: bool :param stopping_tolerance: Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to ``0.001``. :type stopping_tolerance: float :param balance_classes: Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to ``False``. :type balance_classes: bool :param class_sampling_factors: Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. Defaults to ``None``. :type class_sampling_factors: List[float], optional :param max_after_balance_size: Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to ``5.0``. :type max_after_balance_size: float :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to ``0.0``. :type max_runtime_secs: float :param save_transformed_framekeys: true to save the keys of transformed predictors and interaction column. Defaults to ``False``. :type save_transformed_framekeys: bool :param highest_interaction_term: Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on... Default to 2. Defaults to ``0``. :type highest_interaction_term: int :param nparallelism: Number of models to build in parallel. Default to 4. Adjust according to your system. Defaults to ``4``. :type nparallelism: int :param type: Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3 Defaults to ``0``. :type type: int """ super(H2OANOVAGLMEstimator, self).__init__() self._parms = {} self._id = self._parms['model_id'] = model_id self.training_frame = training_frame self.seed = seed self.response_column = response_column self.ignored_columns = ignored_columns self.ignore_const_cols = ignore_const_cols self.score_each_iteration = score_each_iteration self.offset_column = offset_column self.weights_column = weights_column self.family = family self.tweedie_variance_power = tweedie_variance_power self.tweedie_link_power = tweedie_link_power self.theta = theta self.solver = solver self.missing_values_handling = missing_values_handling self.plug_values = plug_values self.compute_p_values = compute_p_values self.standardize = standardize self.non_negative = non_negative self.max_iterations = max_iterations self.link = link self.prior = prior self.alpha = alpha self.lambda_ = lambda_ self.lambda_search = lambda_search self.stopping_rounds = stopping_rounds self.stopping_metric = stopping_metric self.early_stopping = early_stopping self.stopping_tolerance = stopping_tolerance self.balance_classes = balance_classes self.class_sampling_factors = class_sampling_factors self.max_after_balance_size = max_after_balance_size self.max_runtime_secs = max_runtime_secs self.save_transformed_framekeys = save_transformed_framekeys self.highest_interaction_term = highest_interaction_term self.nparallelism = nparallelism self.type = type self._parms["_rest_version"] = 3 @property def training_frame(self): """ Id of the training data frame. Type: ``Union[None, str, H2OFrame]``. """ return self._parms.get("training_frame") @training_frame.setter def training_frame(self, training_frame): self._parms["training_frame"] = H2OFrame._validate(training_frame, 'training_frame') @property def seed(self): """ Seed for pseudo random number generator (if applicable) Type: ``int``, defaults to ``-1``. """ return self._parms.get("seed") @seed.setter def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed @property def response_column(self): """ Response variable column. Type: ``str``. """ return self._parms.get("response_column") @response_column.setter def response_column(self, response_column): assert_is_type(response_column, None, str) self._parms["response_column"] = response_column @property def ignored_columns(self): """ Names of columns to ignore for training. Type: ``List[str]``. """ return self._parms.get("ignored_columns") @ignored_columns.setter def ignored_columns(self, ignored_columns): assert_is_type(ignored_columns, None, [str]) self._parms["ignored_columns"] = ignored_columns @property def ignore_const_cols(self): """ Ignore constant columns. Type: ``bool``, defaults to ``True``. """ return self._parms.get("ignore_const_cols") @ignore_const_cols.setter def ignore_const_cols(self, ignore_const_cols): assert_is_type(ignore_const_cols, None, bool) self._parms["ignore_const_cols"] = ignore_const_cols @property def score_each_iteration(self): """ Whether to score during each iteration of model training. Type: ``bool``, defaults to ``False``. """ return self._parms.get("score_each_iteration") @score_each_iteration.setter def score_each_iteration(self, score_each_iteration): assert_is_type(score_each_iteration, None, bool) self._parms["score_each_iteration"] = score_each_iteration @property def offset_column(self): """ Offset column. This will be added to the combination of columns before applying the link function. Type: ``str``. """ return self._parms.get("offset_column") @offset_column.setter def offset_column(self, offset_column): assert_is_type(offset_column, None, str) self._parms["offset_column"] = offset_column @property def weights_column(self): """ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. Type: ``str``. """ return self._parms.get("weights_column") @weights_column.setter def weights_column(self, weights_column): assert_is_type(weights_column, None, str) self._parms["weights_column"] = weights_column @property def family(self): """ Family. Use binomial for classification with logistic regression, others are for regression problems. Type: ``Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"]``, defaults to ``"auto"``. """ return self._parms.get("family") @family.setter def family(self, family): assert_is_type(family, None, Enum("auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial")) self._parms["family"] = family @property def tweedie_variance_power(self): """ Tweedie variance power Type: ``float``, defaults to ``0.0``. """ return self._parms.get("tweedie_variance_power") @tweedie_variance_power.setter def tweedie_variance_power(self, tweedie_variance_power): assert_is_type(tweedie_variance_power, None, numeric) self._parms["tweedie_variance_power"] = tweedie_variance_power @property def tweedie_link_power(self): """ Tweedie link power Type: ``float``, defaults to ``1.0``. """ return self._parms.get("tweedie_link_power") @tweedie_link_power.setter def tweedie_link_power(self, tweedie_link_power): assert_is_type(tweedie_link_power, None, numeric) self._parms["tweedie_link_power"] = tweedie_link_power @property def theta(self): """ Theta Type: ``float``, defaults to ``0.0``. """ return self._parms.get("theta") @theta.setter def theta(self, theta): assert_is_type(theta, None, numeric) self._parms["theta"] = theta @property def solver(self): """ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Type: ``Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]``, defaults to ``"irlsm"``. """ return self._parms.get("solver") @solver.setter def solver(self, solver): assert_is_type(solver, None, Enum("auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr")) self._parms["solver"] = solver @property def missing_values_handling(self): """ Handling of missing values. Either MeanImputation, Skip or PlugValues. Type: ``Literal["mean_imputation", "skip", "plug_values"]``, defaults to ``"mean_imputation"``. """ return self._parms.get("missing_values_handling") @missing_values_handling.setter def missing_values_handling(self, missing_values_handling): assert_is_type(missing_values_handling, None, Enum("mean_imputation", "skip", "plug_values")) self._parms["missing_values_handling"] = missing_values_handling @property def plug_values(self): """ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) Type: ``Union[None, str, H2OFrame]``. """ return self._parms.get("plug_values") @plug_values.setter def plug_values(self, plug_values): self._parms["plug_values"] = H2OFrame._validate(plug_values, 'plug_values') @property def compute_p_values(self): """ Request p-values computation, p-values work only with IRLSM solver and no regularization Type: ``bool``, defaults to ``True``. """ return self._parms.get("compute_p_values") @compute_p_values.setter def compute_p_values(self, compute_p_values): assert_is_type(compute_p_values, None, bool) self._parms["compute_p_values"] = compute_p_values @property def standardize(self): """ Standardize numeric columns to have zero mean and unit variance Type: ``bool``, defaults to ``True``. """ return self._parms.get("standardize") @standardize.setter def standardize(self, standardize): assert_is_type(standardize, None, bool) self._parms["standardize"] = standardize @property def non_negative(self): """ Restrict coefficients (not intercept) to be non-negative Type: ``bool``, defaults to ``False``. """ return self._parms.get("non_negative") @non_negative.setter def non_negative(self, non_negative): assert_is_type(non_negative, None, bool) self._parms["non_negative"] = non_negative @property def max_iterations(self): """ Maximum number of iterations Type: ``int``, defaults to ``0``. """ return self._parms.get("max_iterations") @max_iterations.setter def max_iterations(self, max_iterations): assert_is_type(max_iterations, None, int) self._parms["max_iterations"] = max_iterations @property def link(self): """ Link function. Type: ``Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]``, defaults to ``"family_default"``. """ return self._parms.get("link") @link.setter def link(self, link): assert_is_type(link, None, Enum("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit")) self._parms["link"] = link @property def prior(self): """ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Type: ``float``, defaults to ``0.0``. """ return self._parms.get("prior") @prior.setter def prior(self, prior): assert_is_type(prior, None, numeric) self._parms["prior"] = prior @property def alpha(self): """ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. Type: ``List[float]``. """ return self._parms.get("alpha") @alpha.setter def alpha(self, alpha): # For `alpha` and `lambda` the server reports type float[], while in practice simple floats are also ok assert_is_type(alpha, None, numeric, [numeric]) self._parms["alpha"] = alpha @property def lambda_(self): """ Regularization strength Type: ``List[float]``, defaults to ``[0.0]``. """ return self._parms.get("lambda") @lambda_.setter def lambda_(self, lambda_): assert_is_type(lambda_, None, numeric, [numeric]) self._parms["lambda"] = lambda_ @property def lambda_search(self): """ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min Type: ``bool``, defaults to ``False``. """ return self._parms.get("lambda_search") @lambda_search.setter def lambda_search(self, lambda_search): assert_is_type(lambda_search, None, bool) self._parms["lambda_search"] = lambda_search @property def stopping_rounds(self): """ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Type: ``int``, defaults to ``0``. """ return self._parms.get("stopping_rounds") @stopping_rounds.setter def stopping_rounds(self, stopping_rounds): assert_is_type(stopping_rounds, None, int) self._parms["stopping_rounds"] = stopping_rounds @property def stopping_metric(self): """ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Type: ``Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]``, defaults to ``"auto"``. """ return self._parms.get("stopping_metric") @stopping_metric.setter def stopping_metric(self, stopping_metric): assert_is_type(stopping_metric, None, Enum("auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing")) self._parms["stopping_metric"] = stopping_metric @property def early_stopping(self): """ Stop early when there is no more relative improvement on train or validation (if provided). Type: ``bool``, defaults to ``False``. """ return self._parms.get("early_stopping") @early_stopping.setter def early_stopping(self, early_stopping): assert_is_type(early_stopping, None, bool) self._parms["early_stopping"] = early_stopping @property def stopping_tolerance(self): """ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Type: ``float``, defaults to ``0.001``. """ return self._parms.get("stopping_tolerance") @stopping_tolerance.setter def stopping_tolerance(self, stopping_tolerance): assert_is_type(stopping_tolerance, None, numeric) self._parms["stopping_tolerance"] = stopping_tolerance @property def balance_classes(self): """ Balance training data class counts via over/under-sampling (for imbalanced data). Type: ``bool``, defaults to ``False``. """ return self._parms.get("balance_classes") @balance_classes.setter def balance_classes(self, balance_classes): assert_is_type(balance_classes, None, bool) self._parms["balance_classes"] = balance_classes @property def class_sampling_factors(self): """ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. Type: ``List[float]``. """ return self._parms.get("class_sampling_factors") @class_sampling_factors.setter def class_sampling_factors(self, class_sampling_factors): assert_is_type(class_sampling_factors, None, [float]) self._parms["class_sampling_factors"] = class_sampling_factors @property def max_after_balance_size(self): """ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Type: ``float``, defaults to ``5.0``. """ return self._parms.get("max_after_balance_size") @max_after_balance_size.setter def max_after_balance_size(self, max_after_balance_size): assert_is_type(max_after_balance_size, None, float) self._parms["max_after_balance_size"] = max_after_balance_size @property def max_runtime_secs(self): """ Maximum allowed runtime in seconds for model training. Use 0 to disable. Type: ``float``, defaults to ``0.0``. """ return self._parms.get("max_runtime_secs") @max_runtime_secs.setter def max_runtime_secs(self, max_runtime_secs): assert_is_type(max_runtime_secs, None, numeric) self._parms["max_runtime_secs"] = max_runtime_secs @property def save_transformed_framekeys(self): """ true to save the keys of transformed predictors and interaction column. Type: ``bool``, defaults to ``False``. """ return self._parms.get("save_transformed_framekeys") @save_transformed_framekeys.setter def save_transformed_framekeys(self, save_transformed_framekeys): assert_is_type(save_transformed_framekeys, None, bool) self._parms["save_transformed_framekeys"] = save_transformed_framekeys @property def highest_interaction_term(self): """ Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on... Default to 2. Type: ``int``, defaults to ``0``. """ return self._parms.get("highest_interaction_term") @highest_interaction_term.setter def highest_interaction_term(self, highest_interaction_term): assert_is_type(highest_interaction_term, None, int) self._parms["highest_interaction_term"] = highest_interaction_term @property def nparallelism(self): """ Number of models to build in parallel. Default to 4. Adjust according to your system. Type: ``int``, defaults to ``4``. """ return self._parms.get("nparallelism") @nparallelism.setter def nparallelism(self, nparallelism): assert_is_type(nparallelism, None, int) self._parms["nparallelism"] = nparallelism @property def type(self): """ Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3 Type: ``int``, defaults to ``0``. """ return self._parms.get("type") @type.setter def type(self, type): assert_is_type(type, None, int) self._parms["type"] = type @property def Lambda(self): """DEPRECATED. Use ``self.lambda_`` instead""" return self._parms["lambda"] if "lambda" in self._parms else None @Lambda.setter def Lambda(self, value): self._parms["lambda"] = value
[docs] def result(self): """ Get result frame that contains information about the model building process like for modelselection and anovaglm. :return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm. """ return H2OFrame._expr(expr=ExprNode("result", ASTId(self.key)))._frame(fill_cache=True)