Source code for h2o.estimators.model_selection

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

import h2o
from h2o.base import Keyed
from h2o.frame import H2OFrame
from h2o.expr import ExprNode
from h2o.expr import ASTId
from h2o.exceptions import H2OValueError
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 H2OModelSelectionEstimator(H2OEstimator): """ Model Selection H2O ModelSelection is used to build the best model with one predictor, two predictors, ... up to max_predictor_number specified in the algorithm parameters when mode=allsubsets. The best model is the one with the highest R2 value. When mode=maxr, the model returned is no longer guaranteed to have the best R2 value. """ algo = "modelselection" supervised_learning = True 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 seed=-1, # type: int 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]] ignore_const_cols=True, # type: bool score_each_iteration=False, # type: bool score_iteration_interval=0, # type: int offset_column=None, # type: Optional[str] weights_column=None, # type: Optional[str] family="auto", # type: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"] link="family_default", # type: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] tweedie_variance_power=0.0, # type: float tweedie_link_power=0.0, # type: float theta=0.0, # type: float solver="irlsm", # type: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] alpha=None, # type: Optional[List[float]] lambda_=[0.0], # type: List[float] lambda_search=False, # type: bool early_stopping=False, # type: bool nlambdas=0, # 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 remove_collinear_columns=False, # type: bool intercept=True, # type: bool non_negative=False, # type: bool max_iterations=0, # type: int objective_epsilon=-1.0, # type: float beta_epsilon=0.0001, # type: float gradient_epsilon=-1.0, # type: float startval=None, # type: Optional[List[float]] prior=0.0, # type: float cold_start=False, # type: bool lambda_min_ratio=0.0, # type: float beta_constraints=None, # type: Optional[Union[None, str, H2OFrame]] max_active_predictors=-1, # type: int 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] nparallelism=0, # type: int max_predictor_number=1, # type: int min_predictor_number=1, # type: int mode="maxr", # type: Literal["allsubsets", "maxr", "maxrsweep", "backward"] build_glm_model=True, # type: bool p_values_threshold=0.0, # 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 seed: Seed for pseudo random number generator (if applicable) Defaults to ``-1``. :type seed: int :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 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 ``0``. :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. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported Defaults to ``"auto"``. :type family: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"] :param link: Link function. Defaults to ``"family_default"``. :type link: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"] :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 ``0.0``. :type tweedie_link_power: float :param theta: Theta Defaults to ``0.0``. :type theta: float :param solver: AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Defaults to ``"irlsm"``. :type solver: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"] :param alpha: Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. Defaults to ``None``. :type alpha: List[float], optional :param lambda_: Regularization strength Defaults to ``[0.0]``. :type lambda_: List[float] :param lambda_search: Use lambda search starting at lambda max, given lambda is then interpreted as lambda min Defaults to ``False``. :type lambda_search: bool :param early_stopping: Stop early when there is no more relative improvement on train or validation (if provided) Defaults to ``False``. :type early_stopping: bool :param 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 ``0``. :type nlambdas: int :param standardize: Standardize numeric columns to have zero mean and unit variance Defaults to ``True``. :type standardize: bool :param missing_values_handling: Handling of missing values. Either MeanImputation, Skip or PlugValues. Defaults to ``"mean_imputation"``. :type missing_values_handling: Literal["mean_imputation", "skip", "plug_values"] :param plug_values: Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) Defaults to ``None``. :type plug_values: Union[None, str, H2OFrame], optional :param compute_p_values: Request p-values computation, p-values work only with IRLSM solver and no regularization Defaults to ``False``. :type compute_p_values: bool :param remove_collinear_columns: In case of linearly dependent columns, remove some of the dependent columns Defaults to ``False``. :type remove_collinear_columns: bool :param intercept: Include constant term in the model Defaults to ``True``. :type intercept: bool :param non_negative: Restrict coefficients (not intercept) to be non-negative Defaults to ``False``. :type non_negative: bool :param max_iterations: Maximum number of iterations Defaults to ``0``. :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 startval: double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. Defaults to ``None``. :type startval: List[float], optional :param prior: Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to ``0.0``. :type prior: float :param 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 ``0.0``. :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 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 nparallelism: number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability Defaults to ``0``. :type nparallelism: int :param max_predictor_number: Maximum number of predictors to be considered when building GLM models. Defaults to 1. Defaults to ``1``. :type max_predictor_number: int :param min_predictor_number: For mode = 'backward' only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1. Defaults to ``1``. :type min_predictor_number: int :param mode: Mode: Used to choose model selection algorithms to use. Options include 'allsubsets' for all subsets, 'maxr' that uses sequential replacement and GLM to build all models, slow but works with cross- validation, validation frames for more robust results, 'maxrsweep' that uses sequential replacement and sweeping action, much faster than 'maxr', 'backward' for backward selection. Defaults to ``"maxr"``. :type mode: Literal["allsubsets", "maxr", "maxrsweep", "backward"] :param build_glm_model: For maxrsweep mode only. If true, will return full blown GLM models with the desired predictorsubsets. If false, only the predictor subsets, predictor coefficients are returned. This is forspeeding up the model selection process. The users can choose to build the GLM models themselvesby using the predictor subsets themselves. Default to true. Defaults to ``True``. :type build_glm_model: bool :param p_values_threshold: For mode='backward' only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold Defaults to ``0.0``. :type p_values_threshold: float """ super(H2OModelSelectionEstimator, 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.seed = seed self.fold_assignment = fold_assignment self.fold_column = fold_column self.response_column = response_column self.ignored_columns = ignored_columns self.ignore_const_cols = ignore_const_cols self.score_each_iteration = score_each_iteration self.score_iteration_interval = score_iteration_interval self.offset_column = offset_column self.weights_column = weights_column self.family = family self.link = link 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.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.startval = startval 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.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.nparallelism = nparallelism self.max_predictor_number = max_predictor_number self.min_predictor_number = min_predictor_number self.mode = mode self.build_glm_model = build_glm_model self.p_values_threshold = p_values_threshold @property def training_frame(self): """ Id of the training data frame. Type: ``Union[None, str, H2OFrame]``. """ return self._parms.get("training_frame") @training_frame.setter def training_frame(self, training_frame): self._parms["training_frame"] = H2OFrame._validate(training_frame, 'training_frame') @property def validation_frame(self): """ Id of the validation data frame. Type: ``Union[None, str, H2OFrame]``. """ 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``. """ return self._parms.get("nfolds") @nfolds.setter def nfolds(self, nfolds): assert_is_type(nfolds, None, int) self._parms["nfolds"] = nfolds @property def seed(self): """ Seed for pseudo random number generator (if applicable) Type: ``int``, defaults to ``-1``. """ return self._parms.get("seed") @seed.setter def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed @property def 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"``. """ 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``. """ 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 ignore_const_cols(self): """ Ignore constant columns. Type: ``bool``, defaults to ``True``. """ return self._parms.get("ignore_const_cols") @ignore_const_cols.setter def ignore_const_cols(self, ignore_const_cols): assert_is_type(ignore_const_cols, None, bool) self._parms["ignore_const_cols"] = ignore_const_cols @property def score_each_iteration(self): """ Whether to score during each iteration of model training. Type: ``bool``, defaults to ``False``. """ return self._parms.get("score_each_iteration") @score_each_iteration.setter def score_each_iteration(self, score_each_iteration): assert_is_type(score_each_iteration, None, bool) self._parms["score_each_iteration"] = score_each_iteration @property def score_iteration_interval(self): """ Perform scoring for every score_iteration_interval iterations Type: ``int``, defaults to ``0``. """ 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``. """ return self._parms.get("offset_column") @offset_column.setter def offset_column(self, offset_column): assert_is_type(offset_column, None, str) self._parms["offset_column"] = offset_column @property def weights_column(self): """ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. Type: ``str``. """ return self._parms.get("weights_column") @weights_column.setter def weights_column(self, weights_column): assert_is_type(weights_column, None, str) self._parms["weights_column"] = weights_column @property def family(self): """ Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported Type: ``Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"]``, defaults to ``"auto"``. """ return self._parms.get("family") @family.setter def family(self, family): assert_is_type(family, None, Enum("auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial")) self._parms["family"] = family @property def link(self): """ Link function. Type: ``Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]``, defaults to ``"family_default"``. """ return self._parms.get("link") @link.setter def link(self, link): assert_is_type(link, None, Enum("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit")) self._parms["link"] = link @property def tweedie_variance_power(self): """ Tweedie variance power Type: ``float``, defaults to ``0.0``. """ return self._parms.get("tweedie_variance_power") @tweedie_variance_power.setter def tweedie_variance_power(self, tweedie_variance_power): assert_is_type(tweedie_variance_power, None, numeric) self._parms["tweedie_variance_power"] = tweedie_variance_power @property def tweedie_link_power(self): """ Tweedie link power Type: ``float``, defaults to ``0.0``. """ return self._parms.get("tweedie_link_power") @tweedie_link_power.setter def tweedie_link_power(self, tweedie_link_power): assert_is_type(tweedie_link_power, None, numeric) self._parms["tweedie_link_power"] = tweedie_link_power @property def theta(self): """ Theta Type: ``float``, defaults to ``0.0``. """ return self._parms.get("theta") @theta.setter def theta(self, theta): assert_is_type(theta, None, numeric) self._parms["theta"] = theta @property def solver(self): """ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Type: ``Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]``, defaults to ``"irlsm"``. """ return self._parms.get("solver") @solver.setter def solver(self, solver): assert_is_type(solver, None, Enum("auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr")) self._parms["solver"] = solver @property def alpha(self): """ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. Type: ``List[float]``. """ return self._parms.get("alpha") @alpha.setter def alpha(self, alpha): # For `alpha` and `lambda` the server reports type float[], while in practice simple floats are also ok assert_is_type(alpha, None, numeric, [numeric]) self._parms["alpha"] = alpha @property def lambda_(self): """ Regularization strength Type: ``List[float]``, defaults to ``[0.0]``. """ return self._parms.get("lambda") @lambda_.setter def lambda_(self, lambda_): assert_is_type(lambda_, None, numeric, [numeric]) self._parms["lambda"] = lambda_ @property def lambda_search(self): """ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min Type: ``bool``, defaults to ``False``. """ return self._parms.get("lambda_search") @lambda_search.setter def lambda_search(self, lambda_search): assert_is_type(lambda_search, None, bool) self._parms["lambda_search"] = lambda_search @property def early_stopping(self): """ Stop early when there is no more relative improvement on train or validation (if provided) Type: ``bool``, defaults to ``False``. """ return self._parms.get("early_stopping") @early_stopping.setter def early_stopping(self, early_stopping): assert_is_type(early_stopping, None, bool) self._parms["early_stopping"] = early_stopping @property def 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 ``0``. """ 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``. """ 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"``. """ return self._parms.get("missing_values_handling") @missing_values_handling.setter def missing_values_handling(self, missing_values_handling): assert_is_type(missing_values_handling, None, Enum("mean_imputation", "skip", "plug_values")) self._parms["missing_values_handling"] = missing_values_handling @property def plug_values(self): """ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) Type: ``Union[None, str, H2OFrame]``. """ return self._parms.get("plug_values") @plug_values.setter def plug_values(self, plug_values): self._parms["plug_values"] = H2OFrame._validate(plug_values, 'plug_values') @property def compute_p_values(self): """ Request p-values computation, p-values work only with IRLSM solver and no regularization Type: ``bool``, defaults to ``False``. """ 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 remove_collinear_columns(self): """ In case of linearly dependent columns, remove some of the dependent columns Type: ``bool``, defaults to ``False``. """ 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``. """ 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``. """ return self._parms.get("non_negative") @non_negative.setter def non_negative(self, non_negative): assert_is_type(non_negative, None, bool) self._parms["non_negative"] = non_negative @property def max_iterations(self): """ Maximum number of iterations Type: ``int``, defaults to ``0``. """ return self._parms.get("max_iterations") @max_iterations.setter def max_iterations(self, max_iterations): assert_is_type(max_iterations, None, int) self._parms["max_iterations"] = max_iterations @property def 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``. """ 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``. """ 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``. """ 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 startval(self): """ double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. Type: ``List[float]``. """ return self._parms.get("startval") @startval.setter def startval(self, startval): assert_is_type(startval, None, [numeric]) self._parms["startval"] = startval @property def prior(self): """ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Type: ``float``, defaults to ``0.0``. """ return self._parms.get("prior") @prior.setter def prior(self, prior): assert_is_type(prior, None, numeric) self._parms["prior"] = prior @property def 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 ``0.0``. """ 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]``. """ return self._parms.get("beta_constraints") @beta_constraints.setter def beta_constraints(self, beta_constraints): self._parms["beta_constraints"] = H2OFrame._validate(beta_constraints, 'beta_constraints') @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``. """ 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 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``. """ 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``. """ return self._parms.get("balance_classes") @balance_classes.setter def balance_classes(self, balance_classes): assert_is_type(balance_classes, None, bool) self._parms["balance_classes"] = balance_classes @property def class_sampling_factors(self): """ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. Type: ``List[float]``. """ return self._parms.get("class_sampling_factors") @class_sampling_factors.setter def class_sampling_factors(self, class_sampling_factors): assert_is_type(class_sampling_factors, None, [float]) self._parms["class_sampling_factors"] = class_sampling_factors @property def max_after_balance_size(self): """ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Type: ``float``, defaults to ``5.0``. """ return self._parms.get("max_after_balance_size") @max_after_balance_size.setter def max_after_balance_size(self, max_after_balance_size): assert_is_type(max_after_balance_size, None, float) self._parms["max_after_balance_size"] = max_after_balance_size @property def max_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``. """ 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 nparallelism(self): """ number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability Type: ``int``, defaults to ``0``. """ return self._parms.get("nparallelism") @nparallelism.setter def nparallelism(self, nparallelism): assert_is_type(nparallelism, None, int) self._parms["nparallelism"] = nparallelism @property def max_predictor_number(self): """ Maximum number of predictors to be considered when building GLM models. Defaults to 1. Type: ``int``, defaults to ``1``. """ return self._parms.get("max_predictor_number") @max_predictor_number.setter def max_predictor_number(self, max_predictor_number): assert_is_type(max_predictor_number, None, int) self._parms["max_predictor_number"] = max_predictor_number @property def min_predictor_number(self): """ For mode = 'backward' only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1. Type: ``int``, defaults to ``1``. """ return self._parms.get("min_predictor_number") @min_predictor_number.setter def min_predictor_number(self, min_predictor_number): assert_is_type(min_predictor_number, None, int) self._parms["min_predictor_number"] = min_predictor_number @property def mode(self): """ Mode: Used to choose model selection algorithms to use. Options include 'allsubsets' for all subsets, 'maxr' that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation frames for more robust results, 'maxrsweep' that uses sequential replacement and sweeping action, much faster than 'maxr', 'backward' for backward selection. Type: ``Literal["allsubsets", "maxr", "maxrsweep", "backward"]``, defaults to ``"maxr"``. """ return self._parms.get("mode") @mode.setter def mode(self, mode): assert_is_type(mode, None, Enum("allsubsets", "maxr", "maxrsweep", "backward")) self._parms["mode"] = mode @property def build_glm_model(self): """ For maxrsweep mode only. If true, will return full blown GLM models with the desired predictorsubsets. If false, only the predictor subsets, predictor coefficients are returned. This is forspeeding up the model selection process. The users can choose to build the GLM models themselvesby using the predictor subsets themselves. Default to true. Type: ``bool``, defaults to ``True``. """ return self._parms.get("build_glm_model") @build_glm_model.setter def build_glm_model(self, build_glm_model): assert_is_type(build_glm_model, None, bool) self._parms["build_glm_model"] = build_glm_model @property def p_values_threshold(self): """ For mode='backward' only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold Type: ``float``, defaults to ``0.0``. """ return self._parms.get("p_values_threshold") @p_values_threshold.setter def p_values_threshold(self, p_values_threshold): assert_is_type(p_values_threshold, None, numeric) self._parms["p_values_threshold"] = p_values_threshold
[docs] def coef_norm(self, predictor_size=None): """ Get the normalized coefficients for all models built with different number of predictors. :param predictor_size: predictor subset size, will only return model coefficients of that subset size. :return: list of Python Dicts of coefficients for all models built with different predictor numbers """ model_ids = self._model_json["output"]["best_model_ids"] if not(self.actual_params["build_glm_model"]) and self.actual_params["mode"]=="maxrsweep": coef_names = self._model_json["output"]["coefficient_names"] coef_values = self._model_json["output"]["coefficient_values_normalized"] num_models = len(coef_names) if predictor_size==None: coefs = [None]*num_models for index in range(0, num_models): coef_name = coef_names[index] coef_val = coef_values[index] coefs[index] = dict(zip(coef_name, coef_val)) return coefs else: if predictor_size > num_models: raise H2OValueError("predictor_size (predictor subset size) cannot exceed the total number of predictors used.") if predictor_size <= 0: raise H2OValueError("predictor_size (predictor subset size) must be between 0 and the total number of predictors used.") coef_name = coef_names[predictor_size-1] coef_val = coef_values[predictor_size-1] return dict(zip(coef_name, coef_val)) else: model_numbers = len(model_ids) mode = self.get_params()['mode'] if predictor_size==None: coefs = [None]*model_numbers for index in range(0, model_numbers): one_model = h2o.get_model(model_ids[index]['name']) tbl = one_model._model_json["output"]["coefficients_table"] if tbl is not None: coefs[index] = {name: coef for name, coef in zip(tbl["names"], tbl["standardized_coefficients"])} return coefs max_pred_numbers = len(self._model_json["output"]["best_predictors_subset"][model_numbers-1]) if predictor_size > max_pred_numbers: raise H2OValueError("predictor_size (predictor subset size) cannot exceed the total number of predictors used.") if predictor_size == 0: raise H2OValueError("predictor_size (predictor subset size) must be between 0 and the total number of predictors used.") if mode=='backward': offset = max_pred_numbers - predictor_size one_model = h2o.get_model(model_ids[model_numbers-1-offset]['name']) else: one_model = h2o.get_model(model_ids[predictor_size-1]['name']) tbl = one_model._model_json["output"]["coefficients_table"] if tbl is not None: return {name: coef for name, coef in zip(tbl["names"], tbl["standardized_coefficients"])}
[docs] def coef(self, predictor_size=None): """ Get the coefficients for all models built with different number of predictors. :param predictor_size: predictor subset size, will only return model coefficients of that subset size. :return: list of Python Dicts of coefficients for all models built with different predictor numbers """ if not self.actual_params["build_glm_model"] and self.actual_params["mode"]=="maxrsweep": coef_names = self._model_json["output"]["coefficient_names"] coef_values = self._model_json["output"]["coefficient_values"] num_models = len(coef_names) if predictor_size==None: coefs = [None]*num_models for index in range(0, num_models): coef_name = coef_names[index] coef_val = coef_values[index] coefs[index] = dict(zip(coef_name, coef_val)) return coefs else: if predictor_size > num_models: raise H2OValueError("predictor_size (predictor subset size) cannot exceed the total number of predictors used.") if predictor_size <= 0: raise H2OValueError("predictor_size (predictor subset size) must be between 0 and the total number of predictors used.") coef_name = coef_names[predictor_size-1] coef_val = coef_values[predictor_size-1] return dict(zip(coef_name, coef_val)) else: model_ids = self._model_json["output"]["best_model_ids"] if model_ids is None: return None else: model_numbers = len(model_ids) mode = self.get_params()['mode'] if predictor_size==None: coefs = [None]*model_numbers for index in range(0, model_numbers): one_model = h2o.get_model(model_ids[index]['name']) tbl = one_model._model_json["output"]["coefficients_table"] if tbl is not None: coefs[index] = dict(zip(tbl["names"], tbl["coefficients"])) return coefs max_pred_numbers = len(self._model_json["output"]["best_predictors_subset"][model_numbers-1]) if predictor_size > max_pred_numbers: raise H2OValueError("predictor_size (predictor subset size) cannot exceed the total number of predictors used.") if predictor_size == 0: raise H2OValueError("predictor_size (predictor subset size) must be between 0 and the total number of predictors used.") if mode=='backward': offset = max_pred_numbers - predictor_size one_model = h2o.get_model(model_ids[model_numbers-1-offset]['name']) else: one_model = h2o.get_model(model_ids[predictor_size-1]['name']) tbl = one_model._model_json["output"]["coefficients_table"] if tbl is not None: return dict(zip(tbl["names"], tbl["coefficients"]))
[docs] def result(self): """ Get result frame that contains information about the model building process like for modelselection and anovaglm. :return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm. """ return H2OFrame._expr(expr=ExprNode("result", ASTId(self.key)))._frame(fill_cache=True)
[docs] def get_best_R2_values(self): """ Get list of best R2 values of models with 1 predictor, 2 predictors, ..., max_predictor_number of predictors :return: a list of best r2 values """ return self._model_json["output"]["best_r2_values"]
[docs] def get_predictors_added_per_step(self): """ Get list of predictors added at each step of the model building process :return: a list of predictors added at each step """ if not(self.get_params()["mode"] == "backward"): return self._model_json["output"]["predictors_added_per_step"] else: print("backward mode does not have list predictor_added_per_step")
[docs] def get_predictors_removed_per_step(self): """ Get list of predictors removed at each step of the model building process :return: a list of predictors removed at each step """ return self._model_json["output"]["predictors_removed_per_step"]
[docs] def get_best_model_predictors(self): """ Get list of best models with 1 predictor, 2 predictors, ..., max_predictor_number of predictors that have the highest r2 values :return: a list of best predictors subset """ return self._model_json["output"]["best_predictors_subset"]