Source code for h2o.estimators.glm

#!/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 h2o.utils.metaclass import deprecated_params, deprecated_property
import h2o
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 H2OGeneralizedLinearEstimator(H2OEstimator): """ Generalized Linear Modeling Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution. A subclass of :class:`ModelBase` is returned. The specific subclass depends on the machine learning task at hand (if it's binomial classification, then an H2OBinomialModel is returned, if it's regression then a H2ORegressionModel is returned). The default print-out of the models is shown, but further GLM-specific information can be queried out of the object. Upon completion of the GLM, the resulting object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices. """ algo = "glm" supervised_learning = True _options_ = {'model_extensions': ['h2o.model.extensions.ScoringHistoryGLM', 'h2o.model.extensions.StandardCoef', 'h2o.model.extensions.VariableImportance', 'h2o.model.extensions.Fairness', 'h2o.model.extensions.Contributions']} @deprecated_params({'Lambda': 'lambda_'}) def __init__(self, model_id=None, # type: Optional[Union[None, str, H2OEstimator]] training_frame=None, # type: Optional[Union[None, str, H2OFrame]] validation_frame=None, # type: Optional[Union[None, str, H2OFrame]] nfolds=0, # type: int checkpoint=None, # type: Optional[Union[None, str, H2OEstimator]] export_checkpoints_dir=None, # type: Optional[str] seed=-1, # type: int keep_cross_validation_models=True, # type: bool keep_cross_validation_predictions=False, # type: bool keep_cross_validation_fold_assignment=False, # type: bool fold_assignment="auto", # type: Literal["auto", "random", "modulo", "stratified"] fold_column=None, # type: Optional[str] response_column=None, # type: Optional[str] ignored_columns=None, # type: Optional[List[str]] random_columns=None, # type: Optional[List[int]] ignore_const_cols=True, # type: bool score_each_iteration=False, # type: bool score_iteration_interval=-1, # type: int offset_column=None, # type: Optional[str] weights_column=None, # type: Optional[str] family="auto", # type: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"] rand_family=None, # type: Optional[List[Literal["[gaussian]"]]] tweedie_variance_power=0.0, # type: float tweedie_link_power=1.0, # type: float theta=1e-10, # type: float solver="auto", # type: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] alpha=None, # type: Optional[List[float]] lambda_=None, # type: Optional[List[float]] lambda_search=False, # type: bool early_stopping=True, # type: bool nlambdas=-1, # type: int standardize=True, # type: bool missing_values_handling="mean_imputation", # type: Literal["mean_imputation", "skip", "plug_values"] plug_values=None, # type: Optional[Union[None, str, H2OFrame]] compute_p_values=False, # type: bool dispersion_parameter_method="pearson", # type: Literal["deviance", "pearson", "ml"] init_dispersion_parameter=1.0, # type: float remove_collinear_columns=False, # type: bool intercept=True, # type: bool non_negative=False, # type: bool max_iterations=-1, # type: int objective_epsilon=-1.0, # type: float beta_epsilon=0.0001, # type: float gradient_epsilon=-1.0, # type: float link="family_default", # type: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] rand_link=None, # type: Optional[List[Literal["[identity]", "[family_default]"]]] startval=None, # type: Optional[List[float]] calc_like=False, # type: bool HGLM=False, # type: bool prior=-1.0, # type: float cold_start=False, # type: bool lambda_min_ratio=-1.0, # type: float beta_constraints=None, # type: Optional[Union[None, str, H2OFrame]] max_active_predictors=-1, # type: int interactions=None, # type: Optional[List[str]] interaction_pairs=None, # type: Optional[List[tuple]] obj_reg=-1.0, # type: float 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"] 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_confusion_matrix_size=20, # type: int max_runtime_secs=0.0, # type: float custom_metric_func=None, # type: Optional[str] generate_scoring_history=False, # type: bool auc_type="auto", # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"] dispersion_epsilon=0.0001, # type: float tweedie_epsilon=8e-17, # type: float max_iterations_dispersion=3000, # type: int build_null_model=False, # type: bool fix_dispersion_parameter=False, # type: bool generate_variable_inflation_factors=False, # type: bool fix_tweedie_variance_power=True, # type: bool dispersion_learning_rate=0.5, # type: float influence=None, # type: Optional[Literal["dfbetas"]] gainslift_bins=-1, # 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 validation_frame: Id of the validation data frame. Defaults to ``None``. :type validation_frame: Union[None, str, H2OFrame], optional :param nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to ``0``. :type nfolds: int :param checkpoint: Model checkpoint to resume training with. Defaults to ``None``. :type checkpoint: Union[None, str, H2OEstimator], optional :param export_checkpoints_dir: Automatically export generated models to this directory. Defaults to ``None``. :type export_checkpoints_dir: str, optional :param seed: Seed for pseudo random number generator (if applicable) Defaults to ``-1``. :type seed: int :param keep_cross_validation_models: Whether to keep the cross-validation models. Defaults to ``True``. :type keep_cross_validation_models: bool :param keep_cross_validation_predictions: Whether to keep the predictions of the cross-validation models. Defaults to ``False``. :type keep_cross_validation_predictions: bool :param keep_cross_validation_fold_assignment: Whether to keep the cross-validation fold assignment. Defaults to ``False``. :type keep_cross_validation_fold_assignment: bool :param fold_assignment: Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Defaults to ``"auto"``. :type fold_assignment: Literal["auto", "random", "modulo", "stratified"] :param fold_column: Column with cross-validation fold index assignment per observation. Defaults to ``None``. :type fold_column: str, optional :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 random_columns: random columns indices for HGLM. Defaults to ``None``. :type random_columns: List[int], 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 score_iteration_interval: Perform scoring for every score_iteration_interval iterations Defaults to ``-1``. :type score_iteration_interval: int :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", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"] :param rand_family: Random Component Family array. One for each random component. Only support gaussian for now. Defaults to ``None``. :type rand_family: List[Literal["[gaussian]"]], optional :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 ``1e-10``. :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 ``"auto"``. :type solver: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] :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 ``None``. :type lambda_: List[float], optional :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 early_stopping: Stop early when there is no more relative improvement on train or validation (if provided) Defaults to ``True``. :type early_stopping: bool :param nlambdas: Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Defaults to ``-1``. :type nlambdas: int :param standardize: Standardize numeric columns to have zero mean and unit variance Defaults to ``True``. :type standardize: bool :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 ``False``. :type compute_p_values: bool :param dispersion_parameter_method: Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only. Defaults to ``"pearson"``. :type dispersion_parameter_method: Literal["deviance", "pearson", "ml"] :param init_dispersion_parameter: Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If fix_dispersion_parameter is set, this value will be used in the calculation of p-values.Default to 1.0. Defaults to ``1.0``. :type init_dispersion_parameter: float :param remove_collinear_columns: In case of linearly dependent columns, remove some of the dependent columns Defaults to ``False``. :type remove_collinear_columns: bool :param intercept: Include constant term in the model Defaults to ``True``. :type intercept: 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 ``-1``. :type max_iterations: int :param objective_epsilon: Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Defaults to ``-1.0``. :type objective_epsilon: float :param beta_epsilon: Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to ``0.0001``. :type beta_epsilon: float :param gradient_epsilon: Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Defaults to ``-1.0``. :type gradient_epsilon: float :param link: Link function. Defaults to ``"family_default"``. :type link: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] :param rand_link: Link function array for random component in HGLM. Defaults to ``None``. :type rand_link: List[Literal["[identity]", "[family_default]"]], optional :param startval: double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. Defaults to ``None``. :type startval: List[float], optional :param calc_like: if true, will return likelihood function value. Defaults to ``False``. :type calc_like: bool :param HGLM: If set to true, will return HGLM model. Otherwise, normal GLM model will be returned Defaults to ``False``. :type HGLM: bool :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 ``-1.0``. :type prior: float :param cold_start: Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch. Defaults to ``False``. :type cold_start: bool :param lambda_min_ratio: Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. Defaults to ``-1.0``. :type lambda_min_ratio: float :param beta_constraints: Beta constraints Defaults to ``None``. :type beta_constraints: Union[None, str, H2OFrame], optional :param max_active_predictors: Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. Defaults to ``-1``. :type max_active_predictors: int :param interactions: A list of predictor column indices to interact. All pairwise combinations will be computed for the list. Defaults to ``None``. :type interactions: List[str], optional :param interaction_pairs: A list of pairwise (first order) column interactions. Defaults to ``None``. :type interaction_pairs: List[tuple], optional :param obj_reg: Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs Defaults to ``-1.0``. :type obj_reg: float :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 anomaly_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 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_confusion_matrix_size: [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs Defaults to ``20``. :type max_confusion_matrix_size: int :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 custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName` Defaults to ``None``. :type custom_metric_func: str, optional :param generate_scoring_history: If set to true, will generate scoring history for GLM. This may significantly slow down the algo. Defaults to ``False``. :type generate_scoring_history: bool :param auc_type: Set default multinomial AUC type. Defaults to ``"auto"``. :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"] :param dispersion_epsilon: If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will break out of the dispersion parameter estimation loop using maximum likelihood. Defaults to ``0.0001``. :type dispersion_epsilon: float :param tweedie_epsilon: In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper indices in the approximating of the infinite series summation. Defaults to ``8e-17``. :type tweedie_epsilon: float :param max_iterations_dispersion: Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood. Defaults to ``3000``. :type max_iterations_dispersion: int :param build_null_model: If set, will build a model with only the intercept. Default to false. Defaults to ``False``. :type build_null_model: bool :param fix_dispersion_parameter: Only used for Tweedie, Gamma and Negative Binomial GLM. If set, will use the dispsersion parameter in init_dispersion_parameter as the standard error and use it to calculate the p-values. Default to false. Defaults to ``False``. :type fix_dispersion_parameter: bool :param generate_variable_inflation_factors: if true, will generate variable inflation factors for numerical predictors. Default to false. Defaults to ``False``. :type generate_variable_inflation_factors: bool :param fix_tweedie_variance_power: If true, will fix tweedie variance power value to the value set in tweedie_variance_power. Defaults to ``True``. :type fix_tweedie_variance_power: bool :param dispersion_learning_rate: Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be > 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion + change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5. Defaults to ``0.5``. :type dispersion_learning_rate: float :param influence: If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Defaults to ``None``. :type influence: Literal["dfbetas"], optional :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to ``-1``. :type gainslift_bins: int """ super(H2OGeneralizedLinearEstimator, self).__init__() self._parms = {} self._id = self._parms['model_id'] = model_id self.training_frame = training_frame self.validation_frame = validation_frame self.nfolds = nfolds self.checkpoint = checkpoint self.export_checkpoints_dir = export_checkpoints_dir self.seed = seed self.keep_cross_validation_models = keep_cross_validation_models self.keep_cross_validation_predictions = keep_cross_validation_predictions self.keep_cross_validation_fold_assignment = keep_cross_validation_fold_assignment self.fold_assignment = fold_assignment self.fold_column = fold_column self.response_column = response_column self.ignored_columns = ignored_columns self.random_columns = random_columns self.ignore_const_cols = ignore_const_cols self.score_each_iteration = score_each_iteration self.score_iteration_interval = score_iteration_interval self.offset_column = offset_column self.weights_column = weights_column self.family = family self.rand_family = rand_family self.tweedie_variance_power = tweedie_variance_power self.tweedie_link_power = tweedie_link_power self.theta = theta self.solver = solver self.alpha = alpha self.lambda_ = lambda_ self.lambda_search = lambda_search self.early_stopping = early_stopping self.nlambdas = nlambdas self.standardize = standardize self.missing_values_handling = missing_values_handling self.plug_values = plug_values self.compute_p_values = compute_p_values self.dispersion_parameter_method = dispersion_parameter_method self.init_dispersion_parameter = init_dispersion_parameter self.remove_collinear_columns = remove_collinear_columns self.intercept = intercept self.non_negative = non_negative self.max_iterations = max_iterations self.objective_epsilon = objective_epsilon self.beta_epsilon = beta_epsilon self.gradient_epsilon = gradient_epsilon self.link = link self.rand_link = rand_link self.startval = startval self.calc_like = calc_like self.HGLM = HGLM self.prior = prior self.cold_start = cold_start self.lambda_min_ratio = lambda_min_ratio self.beta_constraints = beta_constraints self.max_active_predictors = max_active_predictors self.interactions = interactions self.interaction_pairs = interaction_pairs self.obj_reg = obj_reg self.stopping_rounds = stopping_rounds self.stopping_metric = stopping_metric 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_confusion_matrix_size = max_confusion_matrix_size self.max_runtime_secs = max_runtime_secs self.custom_metric_func = custom_metric_func self.generate_scoring_history = generate_scoring_history self.auc_type = auc_type self.dispersion_epsilon = dispersion_epsilon self.tweedie_epsilon = tweedie_epsilon self.max_iterations_dispersion = max_iterations_dispersion self.build_null_model = build_null_model self.fix_dispersion_parameter = fix_dispersion_parameter self.generate_variable_inflation_factors = generate_variable_inflation_factors self.fix_tweedie_variance_power = fix_tweedie_variance_power self.dispersion_learning_rate = dispersion_learning_rate self.influence = influence self.gainslift_bins = gainslift_bins @property def training_frame(self): """ Id of the training data frame. Type: ``Union[None, str, H2OFrame]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(train=True) """ 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 validation_frame(self): """ Id of the validation data frame. Type: ``Union[None, str, H2OFrame]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=True) """ return self._parms.get("validation_frame") @validation_frame.setter def validation_frame(self, validation_frame): self._parms["validation_frame"] = H2OFrame._validate(validation_frame, 'validation_frame') @property def nfolds(self): """ Number of folds for K-fold cross-validation (0 to disable or >= 2). Type: ``int``, defaults to ``0``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> cars_glm = H2OGeneralizedLinearEstimator(nfolds=folds, ... seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_glm.auc(xval=True) """ return self._parms.get("nfolds") @nfolds.setter def nfolds(self, nfolds): assert_is_type(nfolds, None, int) self._parms["nfolds"] = nfolds @property def checkpoint(self): """ Model checkpoint to resume training with. Type: ``Union[None, str, H2OEstimator]``. """ return self._parms.get("checkpoint") @checkpoint.setter def checkpoint(self, checkpoint): assert_is_type(checkpoint, None, str, H2OEstimator) self._parms["checkpoint"] = checkpoint @property def export_checkpoints_dir(self): """ Automatically export generated models to this directory. Type: ``str``. :examples: >>> import tempfile >>> from os import listdir >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> checkpoints = tempfile.mkdtemp() >>> cars_glm = H2OGeneralizedLinearEstimator(export_checkpoints_dir=checkpoints, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() >>> len(listdir(checkpoints_dir)) """ return self._parms.get("export_checkpoints_dir") @export_checkpoints_dir.setter def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir @property def seed(self): """ Seed for pseudo random number generator (if applicable) Type: ``int``, defaults to ``-1``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> glm_w_seed = H2OGeneralizedLinearEstimator(family='binomial', ... seed=1234) >>> glm_w_seed.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(glm_w_seed_1.auc(valid=True)) """ return self._parms.get("seed") @seed.setter def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed @property def keep_cross_validation_models(self): """ Whether to keep the cross-validation models. Type: ``bool``, defaults to ``True``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm_cv_models = cars_glm.cross_validation_models() >>> print(cars_glm.cross_validation_models()) """ return self._parms.get("keep_cross_validation_models") @keep_cross_validation_models.setter def keep_cross_validation_models(self, keep_cross_validation_models): assert_is_type(keep_cross_validation_models, None, bool) self._parms["keep_cross_validation_models"] = keep_cross_validation_models @property def keep_cross_validation_predictions(self): """ Whether to keep the predictions of the cross-validation models. Type: ``bool``, defaults to ``False``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm.cross_validation_predictions() """ return self._parms.get("keep_cross_validation_predictions") @keep_cross_validation_predictions.setter def keep_cross_validation_predictions(self, keep_cross_validation_predictions): assert_is_type(keep_cross_validation_predictions, None, bool) self._parms["keep_cross_validation_predictions"] = keep_cross_validation_predictions @property def keep_cross_validation_fold_assignment(self): """ Whether to keep the cross-validation fold assignment. Type: ``bool``, defaults to ``False``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm.cross_validation_fold_assignment() """ return self._parms.get("keep_cross_validation_fold_assignment") @keep_cross_validation_fold_assignment.setter def keep_cross_validation_fold_assignment(self, keep_cross_validation_fold_assignment): assert_is_type(keep_cross_validation_fold_assignment, None, bool) self._parms["keep_cross_validation_fold_assignment"] = keep_cross_validation_fold_assignment @property def fold_assignment(self): """ Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Type: ``Literal["auto", "random", "modulo", "stratified"]``, defaults to ``"auto"``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> assignment_type = "Random" >>> cars_gml = H2OGeneralizedLinearEstimator(fold_assignment=assignment_type, ... nfolds=5, ... family='binomial', ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_glm.auc(train=True) """ return self._parms.get("fold_assignment") @fold_assignment.setter def fold_assignment(self, fold_assignment): assert_is_type(fold_assignment, None, Enum("auto", "random", "modulo", "stratified")) self._parms["fold_assignment"] = fold_assignment @property def fold_column(self): """ Column with cross-validation fold index assignment per observation. Type: ``str``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> print(cars['fold_numbers']) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_glm.auc(xval=True) """ return self._parms.get("fold_column") @fold_column.setter def fold_column(self, fold_column): assert_is_type(fold_column, None, str) self._parms["fold_column"] = fold_column @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 random_columns(self): """ random columns indices for HGLM. Type: ``List[int]``. """ return self._parms.get("random_columns") @random_columns.setter def random_columns(self, random_columns): assert_is_type(random_columns, None, [int]) self._parms["random_columns"] = random_columns @property def ignore_const_cols(self): """ Ignore constant columns. Type: ``bool``, defaults to ``True``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... ignore_const_cols=True, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=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``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(score_each_iteration=True, ... seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.scoring_history() """ 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 score_iteration_interval(self): """ Perform scoring for every score_iteration_interval iterations Type: ``int``, defaults to ``-1``. """ return self._parms.get("score_iteration_interval") @score_iteration_interval.setter def score_iteration_interval(self, score_iteration_interval): assert_is_type(score_iteration_interval, None, int) self._parms["score_iteration_interval"] = score_iteration_interval @property def offset_column(self): """ Offset column. This will be added to the combination of columns before applying the link function. Type: ``str``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston["offset"] = boston["medv"].log() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_glm = H2OGeneralizedLinearEstimator(offset_column="offset", ... seed=1234) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse(valid=True) """ 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``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid, ... weights_column="weight") >>> cars_glm.auc(valid=True) """ 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", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"]``, defaults to ``"auto"``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid = True) """ return self._parms.get("family") @family.setter def family(self, family): assert_is_type(family, None, Enum("auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial")) self._parms["family"] = family @property def rand_family(self): """ Random Component Family array. One for each random component. Only support gaussian for now. Type: ``List[Literal["[gaussian]"]]``. """ return self._parms.get("rand_family") @rand_family.setter def rand_family(self, rand_family): assert_is_type(rand_family, None, [Enum("[gaussian]")]) self._parms["rand_family"] = rand_family @property def tweedie_variance_power(self): """ Tweedie variance power Type: ``float``, defaults to ``0.0``. :examples: >>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv") >>> predictors = auto.names >>> predictors.remove('y') >>> response = "y" >>> train, valid = auto.split_frame(ratios=[.8]) >>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie', ... tweedie_variance_power=1) >>> auto_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(auto_glm.mse(valid=True)) """ 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``. :examples: >>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv") >>> predictors = auto.names >>> predictors.remove('y') >>> response = "y" >>> train, valid = auto.split_frame(ratios=[.8]) >>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie', ... tweedie_link_power=1) >>> auto_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(auto_glm.mse(valid=True)) """ 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 ``1e-10``. :examples: >>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/glm_test/Motor_insurance_sweden.txt") >>> predictors = ["Payment", "Insured", "Kilometres", "Zone", "Bonus", "Make"] >>> response = "Claims" >>> negativebinomial_fit = H2OGeneralizedLinearEstimator(family="negativebinomial", ... link="identity", ... theta=0.5) >>> negativebinomial_fit.train(x=predictors, ... y=response, ... training_frame=h2o_df) >>> negativebinomial_fit.scoring_history() """ 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 ``"auto"``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(solver='irlsm') >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True)) """ 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 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]``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(alpha=.25) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True)) """ 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]``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=.0001) >>> airlines_glm.train(x=predictors, ... y=response ... trainig_frame=train, ... validation_frame=valid) >>> print(airlines_glm.auc(valid=True)) """ 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``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True)) """ 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 early_stopping(self): """ Stop early when there is no more relative improvement on train or validation (if provided) Type: ``bool``, defaults to ``True``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial', ... early_stopping=True) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=True) """ 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 nlambdas(self): """ Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Type: ``int``, defaults to ``-1``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True, ... nlambdas=50) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True)) """ return self._parms.get("nlambdas") @nlambdas.setter def nlambdas(self, nlambdas): assert_is_type(nlambdas, None, int) self._parms["nlambdas"] = nlambdas @property def standardize(self): """ Standardize numeric columns to have zero mean and unit variance Type: ``bool``, defaults to ``True``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(standardize=True) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ return self._parms.get("standardize") @standardize.setter def standardize(self, standardize): assert_is_type(standardize, None, bool) self._parms["standardize"] = standardize @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"``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston.insert_missing_values() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(missing_values_handling="skip") >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ 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]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars = cars.drop(0) >>> means = cars.mean() >>> means = H2OFrame._expr(ExprNode("mean", cars, True, 0)) >>> glm_means = H2OGeneralizedLinearEstimator(seed=42) >>> glm_means.train(training_frame=cars, y="cylinders") >>> glm_plugs1 = H2OGeneralizedLinearEstimator(seed=42, ... missing_values_handling="PlugValues", ... plug_values=means) >>> glm_plugs1.train(training_frame=cars, y="cylinders") >>> glm_means.coef() == glm_plugs1.coef() >>> not_means = 0.1 + (means * 0.5) >>> glm_plugs2 = H2OGeneralizedLinearEstimator(seed=42, ... missing_values_handling="PlugValues", ... plug_values=not_means) >>> glm_plugs2.train(training_frame=cars, y="cylinders") >>> glm_means.coef() != glm_plugs2.coef() """ 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 ``False``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=0, ... remove_collinear_columns=True, ... compute_p_values=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.mse() """ 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 dispersion_parameter_method(self): """ Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only. Type: ``Literal["deviance", "pearson", "ml"]``, defaults to ``"pearson"``. """ return self._parms.get("dispersion_parameter_method") @dispersion_parameter_method.setter def dispersion_parameter_method(self, dispersion_parameter_method): assert_is_type(dispersion_parameter_method, None, Enum("deviance", "pearson", "ml")) self._parms["dispersion_parameter_method"] = dispersion_parameter_method @property def init_dispersion_parameter(self): """ Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If fix_dispersion_parameter is set, this value will be used in the calculation of p-values.Default to 1.0. Type: ``float``, defaults to ``1.0``. """ return self._parms.get("init_dispersion_parameter") @init_dispersion_parameter.setter def init_dispersion_parameter(self, init_dispersion_parameter): assert_is_type(init_dispersion_parameter, None, numeric) self._parms["init_dispersion_parameter"] = init_dispersion_parameter @property def remove_collinear_columns(self): """ In case of linearly dependent columns, remove some of the dependent columns Type: ``bool``, defaults to ``False``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=0, ... remove_collinear_columns=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.auc() """ return self._parms.get("remove_collinear_columns") @remove_collinear_columns.setter def remove_collinear_columns(self, remove_collinear_columns): assert_is_type(remove_collinear_columns, None, bool) self._parms["remove_collinear_columns"] = remove_collinear_columns @property def intercept(self): """ Include constant term in the model Type: ``bool``, defaults to ``True``. :examples: >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> response = 'class' >>> train, valid = iris.split_frame(ratios=[.8]) >>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial', ... intercept=True) >>> iris_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> iris_glm.logloss(valid=True) """ return self._parms.get("intercept") @intercept.setter def intercept(self, intercept): assert_is_type(intercept, None, bool) self._parms["intercept"] = intercept @property def non_negative(self): """ Restrict coefficients (not intercept) to be non-negative Type: ``bool``, defaults to ``False``. :examples: >>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... non_negative=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.auc() """ 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 ``-1``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial', ... max_iterations=50) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ 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 objective_epsilon(self): """ Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Type: ``float``, defaults to ``-1.0``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(objective_epsilon=1e-3) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ return self._parms.get("objective_epsilon") @objective_epsilon.setter def objective_epsilon(self, objective_epsilon): assert_is_type(objective_epsilon, None, numeric) self._parms["objective_epsilon"] = objective_epsilon @property def beta_epsilon(self): """ Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Type: ``float``, defaults to ``0.0001``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(beta_epsilon=1e-3) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ return self._parms.get("beta_epsilon") @beta_epsilon.setter def beta_epsilon(self, beta_epsilon): assert_is_type(beta_epsilon, None, numeric) self._parms["beta_epsilon"] = beta_epsilon @property def gradient_epsilon(self): """ Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Type: ``float``, defaults to ``-1.0``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(gradient_epsilon=1e-3) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ return self._parms.get("gradient_epsilon") @gradient_epsilon.setter def gradient_epsilon(self, gradient_epsilon): assert_is_type(gradient_epsilon, None, numeric) self._parms["gradient_epsilon"] = gradient_epsilon @property def link(self): """ Link function. Type: ``Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]``, defaults to ``"family_default"``. :examples: >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> response = 'class' >>> train, valid = iris.split_frame(ratios=[.8]) >>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial', ... link='family_default') >>> iris_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> iris_glm.logloss() """ 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 rand_link(self): """ Link function array for random component in HGLM. Type: ``List[Literal["[identity]", "[family_default]"]]``. """ return self._parms.get("rand_link") @rand_link.setter def rand_link(self, rand_link): assert_is_type(rand_link, None, [Enum("[identity]", "[family_default]")]) self._parms["rand_link"] = rand_link @property def startval(self): """ double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. Type: ``List[float]``. """ return self._parms.get("startval") @startval.setter def startval(self, startval): assert_is_type(startval, None, [numeric]) self._parms["startval"] = startval @property def calc_like(self): """ if true, will return likelihood function value. Type: ``bool``, defaults to ``False``. """ return self._parms.get("calc_like") @calc_like.setter def calc_like(self, calc_like): assert_is_type(calc_like, None, bool) self._parms["calc_like"] = calc_like @property def HGLM(self): """ If set to true, will return HGLM model. Otherwise, normal GLM model will be returned Type: ``bool``, defaults to ``False``. """ return self._parms.get("HGLM") @HGLM.setter def HGLM(self, HGLM): assert_is_type(HGLM, None, bool) self._parms["HGLM"] = HGLM @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 ``-1.0``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm1 = H2OGeneralizedLinearEstimator(family='binomial', prior=0.5) >>> cars_glm1.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm1.mse() """ return self._parms.get("prior") @prior.setter def prior(self, prior): assert_is_type(prior, None, numeric) self._parms["prior"] = prior @property def cold_start(self): """ Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch. Type: ``bool``, defaults to ``False``. """ return self._parms.get("cold_start") @cold_start.setter def cold_start(self, cold_start): assert_is_type(cold_start, None, bool) self._parms["cold_start"] = cold_start @property def lambda_min_ratio(self): """ Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. Type: ``float``, defaults to ``-1.0``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_min_ratio=.0001) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ return self._parms.get("lambda_min_ratio") @lambda_min_ratio.setter def lambda_min_ratio(self, lambda_min_ratio): assert_is_type(lambda_min_ratio, None, numeric) self._parms["lambda_min_ratio"] = lambda_min_ratio @property def beta_constraints(self): """ Beta constraints Type: ``Union[None, str, H2OFrame]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> n = len(predictors) >>> constraints = h2o.H2OFrame({'names':predictors, ... 'lower_bounds': [-1000]*n, ... 'upper_bounds': [1000]*n, ... 'beta_given': [1]*n, ... 'rho': [0.2]*n}) >>> cars_glm = H2OGeneralizedLinearEstimator(standardize=True, ... beta_constraints=constraints) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ return self._parms.get("beta_constraints") @beta_constraints.setter def beta_constraints(self, beta_constraints): # beta_constraints can be specified as a H2OFrame or python dict assert_is_type(beta_constraints, None, dict, H2OFrame) if type(beta_constraints) is H2OFrame: self._parms["beta_constraints"]=beta_constraints if type(beta_constraints) is dict: colnames = beta_constraints.keys() col_names = [] upper_bounds = [] lower_bounds = [] for key in colnames: one_col_bounds = beta_constraints.get(key) col_names.append(key) upper_bounds.append(one_col_bounds.get('upper_bound')) lower_bounds.append(one_col_bounds.get('lower_bound')) constraints = h2o.H2OFrame(dict([("names",col_names), ("lower_bounds", lower_bounds), ("upper_bounds", upper_bounds)])) self._parms["beta_constraints"] = constraints[["names", "lower_bounds", "upper_bounds"]] @property def max_active_predictors(self): """ Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. Type: ``int``, defaults to ``-1``. :examples: >>> higgs= h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/testng/higgs_train_5k.csv") >>> predictors = higgs.names >>> predictors.remove('response') >>> response = "response" >>> train, valid = higgs.split_frame(ratios=[.8]) >>> higgs_glm = H2OGeneralizedLinearEstimator(family='binomial', ... max_active_predictors=200) >>> higgs_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> higgs_glm.auc() """ return self._parms.get("max_active_predictors") @max_active_predictors.setter def max_active_predictors(self, max_active_predictors): assert_is_type(max_active_predictors, None, int) self._parms["max_active_predictors"] = max_active_predictors @property def interactions(self): """ A list of predictor column indices to interact. All pairwise combinations will be computed for the list. Type: ``List[str]``. :examples: >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> interactions_list = ['crim', 'dis'] >>> boston_glm = H2OGeneralizedLinearEstimator(interactions=interactions_list) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse() """ return self._parms.get("interactions") @interactions.setter def interactions(self, interactions): assert_is_type(interactions, None, [str]) self._parms["interactions"] = interactions @property def interaction_pairs(self): """ A list of pairwise (first order) column interactions. Type: ``List[tuple]``. :examples: >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> XY = [df.names[i-1] for i in [1,2,3,4,6,8,9,13,17,18,19,31]] >>> interactions = [XY[i-1] for i in [5,7,9]] >>> m = H2OGeneralizedLinearEstimator(lambda_search=True, ... family="binomial", ... interactions=interactions) >>> m.train(x=XY[:len(XY)], y=XY[-1],training_frame=df) >>> m._model_json['output']['coefficients_table'] >>> coef_m = m._model_json['output']['coefficients_table'] >>> interaction_pairs = [("CRSDepTime", "UniqueCarrier"), ... ("CRSDepTime", "Origin"), ... ("UniqueCarrier", "Origin")] >>> mexp = H2OGeneralizedLinearEstimator(lambda_search=True, ... family="binomial", ... interaction_pairs=interaction_pairs) >>> mexp.train(x=XY[:len(XY)], y=XY[-1],training_frame=df) >>> mexp._model_json['output']['coefficients_table'] """ return self._parms.get("interaction_pairs") @interaction_pairs.setter def interaction_pairs(self, interaction_pairs): assert_is_type(interaction_pairs, None, [tuple]) self._parms["interaction_pairs"] = interaction_pairs @property def obj_reg(self): """ Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs Type: ``float``, defaults to ``-1.0``. :examples: >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv") >>> df["C11"] = df["C11"].asfactor() >>> ordinal_fit = H2OGeneralizedLinearEstimator(family="ordinal", ... alpha=1.0, ... lambda_=0.000000001, ... obj_reg=0.00001, ... max_iterations=1000, ... beta_epsilon=1e-8, ... objective_epsilon=1e-10) >>> ordinal_fit.train(x=list(range(0,10)), ... y="C11", ... training_frame=df) >>> ordinal_fit.mse() """ return self._parms.get("obj_reg") @obj_reg.setter def obj_reg(self, obj_reg): assert_is_type(obj_reg, None, numeric) self._parms["obj_reg"] = obj_reg @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 anomaly_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 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``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ 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]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... class_sampling_factors=sample_factors, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ 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``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> max = .85 >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... max_after_balance_size=max, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ 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_confusion_matrix_size(self): """ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs Type: ``int``, defaults to ``20``. """ return self._parms.get("max_confusion_matrix_size") @max_confusion_matrix_size.setter def max_confusion_matrix_size(self, max_confusion_matrix_size): assert_is_type(max_confusion_matrix_size, None, int) self._parms["max_confusion_matrix_size"] = max_confusion_matrix_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``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(max_runtime_secs=10, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() """ 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 custom_metric_func(self): """ Reference to custom evaluation function, format: `language:keyName=funcName` Type: ``str``. """ return self._parms.get("custom_metric_func") @custom_metric_func.setter def custom_metric_func(self, custom_metric_func): assert_is_type(custom_metric_func, None, str) self._parms["custom_metric_func"] = custom_metric_func @property def generate_scoring_history(self): """ If set to true, will generate scoring history for GLM. This may significantly slow down the algo. Type: ``bool``, defaults to ``False``. """ return self._parms.get("generate_scoring_history") @generate_scoring_history.setter def generate_scoring_history(self, generate_scoring_history): assert_is_type(generate_scoring_history, None, bool) self._parms["generate_scoring_history"] = generate_scoring_history @property def auc_type(self): """ Set default multinomial AUC type. Type: ``Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]``, defaults to ``"auto"``. """ return self._parms.get("auc_type") @auc_type.setter def auc_type(self, auc_type): assert_is_type(auc_type, None, Enum("auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo")) self._parms["auc_type"] = auc_type @property def dispersion_epsilon(self): """ If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will break out of the dispersion parameter estimation loop using maximum likelihood. Type: ``float``, defaults to ``0.0001``. """ return self._parms.get("dispersion_epsilon") @dispersion_epsilon.setter def dispersion_epsilon(self, dispersion_epsilon): assert_is_type(dispersion_epsilon, None, numeric) self._parms["dispersion_epsilon"] = dispersion_epsilon @property def tweedie_epsilon(self): """ In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper indices in the approximating of the infinite series summation. Type: ``float``, defaults to ``8e-17``. """ return self._parms.get("tweedie_epsilon") @tweedie_epsilon.setter def tweedie_epsilon(self, tweedie_epsilon): assert_is_type(tweedie_epsilon, None, numeric) self._parms["tweedie_epsilon"] = tweedie_epsilon @property def max_iterations_dispersion(self): """ Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood. Type: ``int``, defaults to ``3000``. """ return self._parms.get("max_iterations_dispersion") @max_iterations_dispersion.setter def max_iterations_dispersion(self, max_iterations_dispersion): assert_is_type(max_iterations_dispersion, None, int) self._parms["max_iterations_dispersion"] = max_iterations_dispersion @property def build_null_model(self): """ If set, will build a model with only the intercept. Default to false. Type: ``bool``, defaults to ``False``. """ return self._parms.get("build_null_model") @build_null_model.setter def build_null_model(self, build_null_model): assert_is_type(build_null_model, None, bool) self._parms["build_null_model"] = build_null_model @property def fix_dispersion_parameter(self): """ Only used for Tweedie, Gamma and Negative Binomial GLM. If set, will use the dispsersion parameter in init_dispersion_parameter as the standard error and use it to calculate the p-values. Default to false. Type: ``bool``, defaults to ``False``. """ return self._parms.get("fix_dispersion_parameter") @fix_dispersion_parameter.setter def fix_dispersion_parameter(self, fix_dispersion_parameter): assert_is_type(fix_dispersion_parameter, None, bool) self._parms["fix_dispersion_parameter"] = fix_dispersion_parameter @property def generate_variable_inflation_factors(self): """ if true, will generate variable inflation factors for numerical predictors. Default to false. Type: ``bool``, defaults to ``False``. :examples: >>> training_data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/glm_test/gamma_dispersion_factor_9_10kRows.csv") >>> predictors = ['abs.C1.','abs.C2.','abs.C3.','abs.C4.','abs.C5.'] >>> response = 'resp' >>> vif_glm = H2OGeneralizedLinearEstimator(family="gamma", ... lambda_=0, ... generate_variable_inflation_factors=True, ... fold_assignment="modulo", ... nfolds=3, ... keep_cross_validation_models=True) >>> vif_glm.train(x=predictors, y=response, training_frame=training_data) >>> vif_glm.get_variable_inflation_factors() """ return self._parms.get("generate_variable_inflation_factors") @generate_variable_inflation_factors.setter def generate_variable_inflation_factors(self, generate_variable_inflation_factors): assert_is_type(generate_variable_inflation_factors, None, bool) self._parms["generate_variable_inflation_factors"] = generate_variable_inflation_factors @property def fix_tweedie_variance_power(self): """ If true, will fix tweedie variance power value to the value set in tweedie_variance_power. Type: ``bool``, defaults to ``True``. """ return self._parms.get("fix_tweedie_variance_power") @fix_tweedie_variance_power.setter def fix_tweedie_variance_power(self, fix_tweedie_variance_power): assert_is_type(fix_tweedie_variance_power, None, bool) self._parms["fix_tweedie_variance_power"] = fix_tweedie_variance_power @property def dispersion_learning_rate(self): """ Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be > 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion + change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5. Type: ``float``, defaults to ``0.5``. """ return self._parms.get("dispersion_learning_rate") @dispersion_learning_rate.setter def dispersion_learning_rate(self, dispersion_learning_rate): assert_is_type(dispersion_learning_rate, None, numeric) self._parms["dispersion_learning_rate"] = dispersion_learning_rate @property def influence(self): """ If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Type: ``Literal["dfbetas"]``. """ return self._parms.get("influence") @influence.setter def influence(self, influence): assert_is_type(influence, None, Enum("dfbetas")) self._parms["influence"] = influence @property def gainslift_bins(self): """ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Type: ``int``, defaults to ``-1``. """ return self._parms.get("gainslift_bins") @gainslift_bins.setter def gainslift_bins(self, gainslift_bins): assert_is_type(gainslift_bins, None, int) self._parms["gainslift_bins"] = gainslift_bins Lambda = deprecated_property('Lambda', lambda_)
[docs] def get_regression_influence_diagnostics(self): """ For GLM model, if influence is set to dfbetas, a frame containing the original predictors, response and DFBETA_ for each predictors that are used in building the model is returned. :return: H2OFrame containing predictors used in building the model, response and DFBETA_ for each predictor. :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_=0.0, ... standardize=False, ... influence="dfbetas") >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> ridFrame = m.get_regression_influence_diagnostics() >>> print("column names of regression influence diagnostics frame is {0}".format(ridFrame.names)) """ if self.actual_params["influence"]=="dfbetas": return h2o.get_frame(self._model_json["output"]["regression_influence_diagnostics"]['name']) else: raise H2OValueError("get_regression_influence_diagnostics can only be called if influence='dfbetas'.")
[docs] @staticmethod def getAlphaBest(model): """ Extract best alpha value found from glm model. :param model: source lambda search model :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> bestAlpha = H2OGeneralizedLinearEstimator.getAlphaBest(m) >>> print("Best alpha found is {0}".format(bestAlpha)) """ return model._model_json["output"]["alpha_best"]
[docs] @staticmethod def getLambdaBest(model): """ Extract best lambda value found from glm model. :param model: source lambda search model :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> bestLambda = H2OGeneralizedLinearEstimator.getLambdaBest(m) >>> print("Best lambda found is {0}".format(bestLambda)) """ return model._model_json["output"]["lambda_best"]
[docs] @staticmethod def getLambdaMax(model): """ Extract the maximum lambda value used during lambda search. :param model: source lambda search model :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> maxLambda = H2OGeneralizedLinearEstimator.getLambdaMax(m) >>> print("Maximum lambda found is {0}".format(maxLambda)) """ lambdaMax = model._model_json["output"]["lambda_max"] # will be -1 if lambda_search is disabled if lambdaMax >= 0: return lambdaMax else: raise H2OValueError("getLambdaMax(model) can only be called when lambda_search=True.")
[docs] @staticmethod def getLambdaMin(model): """ Extract the minimum lambda value calculated during lambda search from glm model. Note that due to early stop, this minimum lambda value may not be used in the actual lambda search. :param model: source lambda search model :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> minLambda = H2OGeneralizedLinearEstimator.getLambdaMin(m) >>> print("Minimum lambda found is {0}".format(minLambda)) """ lambdaMin = model._model_json["output"]["lambda_min"] # will be -1 if lambda_search is disabled if lambdaMin >= 0: return lambdaMin else: raise H2OValueError("getLambdaMin(model) can only be called when lambda_search=True.")
[docs] @staticmethod def getGLMRegularizationPath(model): """ Extract full regularization path explored during lambda search from glm model. :param model: source lambda search model :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m) >>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m, ... coefs=r['coefficients'][10]) >>> dev1 = r['explained_deviance_train'][10] >>> p = m2.model_performance(d) >>> dev2 = 1-p.residual_deviance()/p.null_deviance() >>> print(dev1, " =?= ", dev2) """ x = h2o.api("GET /3/GetGLMRegPath", data={"model": model._model_json["model_id"]["name"]}) ns = x.pop("coefficient_names") res = { "lambdas": x["lambdas"], "alphas": x["alphas"], "explained_deviance_train": x["explained_deviance_train"], "explained_deviance_valid": x["explained_deviance_valid"], "coefficients": [dict(zip(ns, y)) for y in x["coefficients"]], "z_values": None if (x["z_values"] is None) else [dict(zip(ns, z)) for z in x["z_values"]], "p_values": None if (x["p_values"] is None) else [dict(zip(ns, p)) for p in x["p_values"]], "std_errs": None if (x["std_errs"] is None) else [dict(zip(ns, s)) for s in x["std_errs"]], "names": ns } if "coefficients_std" in x and not(x["coefficients_std"] == None): res["coefficients_std"] = [dict(zip(ns, y)) for y in x["coefficients_std"]] return res
[docs] @staticmethod 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 :examples: >>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_search=True, ... solver='COORDINATE_DESCENT') >>> m.train(training_frame=d, ... x=[2,3,4,5,6,7,8], ... y=1) >>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m) >>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m, ... coefs=r['coefficients'][10]) >>> dev1 = r['explained_deviance_train'][10] >>> p = m2.model_performance(d) >>> dev2 = 1-p.residual_deviance()/p.null_deviance() >>> print(dev1, " =?= ", dev2) """ 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