#!/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
linear_constraints=None, # type: Optional[Union[None, str, H2OFrame]]
init_optimal_glm=False, # type: bool
separate_linear_beta=False, # type: bool
constraint_eta0=0.1258925, # type: float
constraint_tau=10.0, # type: float
constraint_alpha=0.1, # type: float
constraint_beta=0.9, # type: float
constraint_c0=10.0, # type: float
):
"""
: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.
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.
Defaults to ``1.0``.
:type init_dispersion_parameter: float
:param remove_collinear_columns: In case of linearly dependent columns, remove 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. Value should >=1. A value of 0 is only set when only the
model coefficient names and model coefficient dimensions are needed.
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. If
standardize is true, the standardized coefficients should be used. Otherwise, use the regular
coefficients.
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
:param linear_constraints: Linear constraints: used to specify linear constraints involving more than one
coefficients in standard form. It is only supported for solver IRLSM. It contains four columns: names
(strings for coefficient names or constant), values, types ( strings of 'Equal' or 'LessThanEqual'),
constraint_numbers (0 for first linear constraint, 1 for second linear constraint, ...).
Defaults to ``None``.
:type linear_constraints: Union[None, str, H2OFrame], optional
:param init_optimal_glm: If true, will initialize coefficients with values derived from GLM runs without linear
constraints. Only available for linear constraints.
Defaults to ``False``.
:type init_optimal_glm: bool
:param separate_linear_beta: If true, will keep the beta constraints and linear constraints separate. After new
coefficients are found, first beta constraints will be applied followed by the application of linear
constraints. Note that the beta constraints in this case will not be part of the objective function. If
false, will combine the beta and linear constraints.
Defaults to ``False``.
:type separate_linear_beta: bool
:param constraint_eta0: For constrained GLM only. It affects the setting of eta_k+1=eta_0/power(ck+1, alpha).
Defaults to ``0.1258925``.
:type constraint_eta0: float
:param constraint_tau: For constrained GLM only. It affects the setting of c_k+1=tau*c_k.
Defaults to ``10.0``.
:type constraint_tau: float
:param constraint_alpha: For constrained GLM only. It affects the setting of eta_k = eta_0/pow(c_0, alpha).
Defaults to ``0.1``.
:type constraint_alpha: float
:param constraint_beta: For constrained GLM only. It affects the setting of eta_k+1 = eta_k/pow(c_k, beta).
Defaults to ``0.9``.
:type constraint_beta: float
:param constraint_c0: For constrained GLM only. It affects the initial setting of epsilon_k = 1/c_0.
Defaults to ``10.0``.
:type constraint_c0: float
"""
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
self.linear_constraints = linear_constraints
self.init_optimal_glm = init_optimal_glm
self.separate_linear_beta = separate_linear_beta
self.constraint_eta0 = constraint_eta0
self.constraint_tau = constraint_tau
self.constraint_alpha = constraint_alpha
self.constraint_beta = constraint_beta
self.constraint_c0 = constraint_c0
@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.
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.
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 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. Value should >=1. A value of 0 is only set when only the model coefficient names
and model coefficient dimensions are needed.
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. If standardize is
true, the standardized coefficients should be used. Otherwise, use the regular coefficients.
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
@property
def linear_constraints(self):
"""
Linear constraints: used to specify linear constraints involving more than one coefficients in standard form.
It is only supported for solver IRLSM. It contains four columns: names (strings for coefficient names or
constant), values, types ( strings of 'Equal' or 'LessThanEqual'), constraint_numbers (0 for first linear
constraint, 1 for second linear constraint, ...).
Type: ``Union[None, str, H2OFrame]``.
"""
return self._parms.get("linear_constraints")
@linear_constraints.setter
def linear_constraints(self, linear_constraints):
self._parms["linear_constraints"] = H2OFrame._validate(linear_constraints, 'linear_constraints')
@property
def init_optimal_glm(self):
"""
If true, will initialize coefficients with values derived from GLM runs without linear constraints. Only
available for linear constraints.
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("init_optimal_glm")
@init_optimal_glm.setter
def init_optimal_glm(self, init_optimal_glm):
assert_is_type(init_optimal_glm, None, bool)
self._parms["init_optimal_glm"] = init_optimal_glm
@property
def separate_linear_beta(self):
"""
If true, will keep the beta constraints and linear constraints separate. After new coefficients are found,
first beta constraints will be applied followed by the application of linear constraints. Note that the beta
constraints in this case will not be part of the objective function. If false, will combine the beta and linear
constraints.
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("separate_linear_beta")
@separate_linear_beta.setter
def separate_linear_beta(self, separate_linear_beta):
assert_is_type(separate_linear_beta, None, bool)
self._parms["separate_linear_beta"] = separate_linear_beta
@property
def constraint_eta0(self):
"""
For constrained GLM only. It affects the setting of eta_k+1=eta_0/power(ck+1, alpha).
Type: ``float``, defaults to ``0.1258925``.
"""
return self._parms.get("constraint_eta0")
@constraint_eta0.setter
def constraint_eta0(self, constraint_eta0):
assert_is_type(constraint_eta0, None, numeric)
self._parms["constraint_eta0"] = constraint_eta0
@property
def constraint_tau(self):
"""
For constrained GLM only. It affects the setting of c_k+1=tau*c_k.
Type: ``float``, defaults to ``10.0``.
"""
return self._parms.get("constraint_tau")
@constraint_tau.setter
def constraint_tau(self, constraint_tau):
assert_is_type(constraint_tau, None, numeric)
self._parms["constraint_tau"] = constraint_tau
@property
def constraint_alpha(self):
"""
For constrained GLM only. It affects the setting of eta_k = eta_0/pow(c_0, alpha).
Type: ``float``, defaults to ``0.1``.
"""
return self._parms.get("constraint_alpha")
@constraint_alpha.setter
def constraint_alpha(self, constraint_alpha):
assert_is_type(constraint_alpha, None, numeric)
self._parms["constraint_alpha"] = constraint_alpha
@property
def constraint_beta(self):
"""
For constrained GLM only. It affects the setting of eta_k+1 = eta_k/pow(c_k, beta).
Type: ``float``, defaults to ``0.9``.
"""
return self._parms.get("constraint_beta")
@constraint_beta.setter
def constraint_beta(self, constraint_beta):
assert_is_type(constraint_beta, None, numeric)
self._parms["constraint_beta"] = constraint_beta
@property
def constraint_c0(self):
"""
For constrained GLM only. It affects the initial setting of epsilon_k = 1/c_0.
Type: ``float``, defaults to ``10.0``.
"""
return self._parms.get("constraint_c0")
@constraint_c0.setter
def constraint_c0(self, constraint_c0):
assert_is_type(constraint_c0, None, numeric)
self._parms["constraint_c0"] = constraint_c0
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
[docs] @staticmethod
def getConstraintsInfo(model):
"""
Given a constrained GLM model, the constraints descriptions, constraints values, constraints conditions and
whether the constraints are satisfied (true) or not (false) are returned.
:param model: GLM model with linear and beta (if applicable) constraints
:return: H2OTwoDimTable containing the above constraints information.
:example:
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/binomial_20_cols_10KRows.csv")
>>> response = "C21"
>>> predictors = list(range(0,20))
>>> loose_init_const = [] # this constraint is satisfied by default coefficient initialization
>>> # add loose constraints
>>> name = "C19"
>>> values = 0.5
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> name = "C20"
>>> values = -0.8
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> name = "constant"
>>> values = -1000
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> linear_constraints2 = h2o.H2OFrame(loose_init_const)
>>> linear_constraints2.set_names(["names", "values", "types", "constraint_numbers"])
>>> # GLM model with GLM coefficients with default initialization
>>> h2o_glm = H2OGeneralizedLinearEstimator(family="binomial", compute_p_values=True, remove_collinear_columns=True,
... lambda_=0.0, solver="irlsm", linear_constraints=linear_constraints2,
... init_optimal_glm = False, seed=12345)
>>> h2o_glm.train(x=predictors, y=response, training_frame=train)
>>> print(H2OGeneralizedLinearEstimator.getConstraintsInfo(h2o_glm))
"""
if model.actual_params["linear_constraints"] is not None:
return model._model_json["output"]["linear_constraints_table"]
else:
raise H2OValueError("getConstraintsInfo can only be called when there are linear constraints.")
[docs] @staticmethod
def allConstraintsPassed(model):
"""
Given a constrainted GLM model, this will return true if all beta (if exists) and linear constraints are
satified. It will return false even if one constraint is not satisfied. To see which ones failed, use
getConstraintsInfo function.
:param model: GLM model with linear and beta (if applicable) constraints
:return: boolean True or False
:example:
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/binomial_20_cols_10KRows.csv")
>>> response = "C21"
>>> predictors = list(range(0,20))
>>> loose_init_const = [] # this constraint is satisfied by default coefficient initialization
>>> # add loose constraints
>>> name = "C19"
>>> values = 0.5
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> name = "C20"
>>> values = -0.8
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> name = "constant"
>>> values = -1000
>>> types = "LessThanEqual"
>>> contraint_numbers = 0
>>> loose_init_const.append([name, values, types, contraint_numbers])
>>> linear_constraints2 = h2o.H2OFrame(loose_init_const)
>>> linear_constraints2.set_names(["names", "values", "types", "constraint_numbers"])
>>> # GLM model with GLM coefficients with default initialization
>>> h2o_glm = H2OGeneralizedLinearEstimator(family="binomial", compute_p_values=True, remove_collinear_columns=True,
... lambda_=0.0, solver="irlsm", linear_constraints=linear_constraints2,
... init_optimal_glm = False, seed=12345)
>>> h2o_glm.train(x=predictors, y=response, training_frame=train)
>>> print(H2OGeneralizedLinearEstimator.allConstraintsPassed(h2o_glm))
"""
if model.actual_params["linear_constraints"] is not None:
return model._model_json["output"]["all_constraints_satisfied"]
else:
raise H2OValueError("allConstraintsPassed can only be called when there are linear constraints.")