#!/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)
#
import h2o
from h2o.base import Keyed
from h2o.frame import H2OFrame
from h2o.expr import ExprNode
from h2o.expr import ASTId
from h2o.estimators.estimator_base import H2OEstimator
from h2o.exceptions import H2OValueError
from h2o.frame import H2OFrame
from h2o.utils.typechecks import assert_is_type, Enum, numeric
[docs]class H2OANOVAGLMEstimator(H2OEstimator):
"""
ANOVA for Generalized Linear Model
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors
and their interactions to a model. Predictors or interactions with negligible contributions to the model will have
high p-values while those with more contributions will have low p-values.
"""
algo = "anovaglm"
supervised_learning = True
def __init__(self,
model_id=None, # type: Optional[Union[None, str, H2OEstimator]]
training_frame=None, # type: Optional[Union[None, str, H2OFrame]]
seed=-1, # type: int
response_column=None, # type: Optional[str]
ignored_columns=None, # type: Optional[List[str]]
ignore_const_cols=True, # type: bool
score_each_iteration=False, # type: bool
offset_column=None, # type: Optional[str]
weights_column=None, # type: Optional[str]
family="auto", # type: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"]
tweedie_variance_power=0.0, # type: float
tweedie_link_power=1.0, # type: float
theta=0.0, # type: float
solver="irlsm", # type: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]
missing_values_handling="mean_imputation", # type: Literal["mean_imputation", "skip", "plug_values"]
plug_values=None, # type: Optional[Union[None, str, H2OFrame]]
compute_p_values=True, # type: bool
standardize=True, # type: bool
non_negative=False, # type: bool
max_iterations=0, # type: int
link="family_default", # type: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
prior=0.0, # type: float
alpha=None, # type: Optional[List[float]]
lambda_=[0.0], # type: List[float]
lambda_search=False, # type: bool
stopping_rounds=0, # type: int
stopping_metric="auto", # type: Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
early_stopping=False, # type: bool
stopping_tolerance=0.001, # type: float
balance_classes=False, # type: bool
class_sampling_factors=None, # type: Optional[List[float]]
max_after_balance_size=5.0, # type: float
max_runtime_secs=0.0, # type: float
save_transformed_framekeys=False, # type: bool
highest_interaction_term=0, # type: int
nparallelism=4, # type: int
type=0, # type: int
):
"""
:param model_id: Destination id for this model; auto-generated if not specified.
Defaults to ``None``.
:type model_id: Union[None, str, H2OEstimator], optional
:param training_frame: Id of the training data frame.
Defaults to ``None``.
:type training_frame: Union[None, str, H2OFrame], optional
:param seed: Seed for pseudo random number generator (if applicable)
Defaults to ``-1``.
:type seed: int
:param response_column: Response variable column.
Defaults to ``None``.
:type response_column: str, optional
:param ignored_columns: Names of columns to ignore for training.
Defaults to ``None``.
:type ignored_columns: List[str], optional
:param ignore_const_cols: Ignore constant columns.
Defaults to ``True``.
:type ignore_const_cols: bool
:param score_each_iteration: Whether to score during each iteration of model training.
Defaults to ``False``.
:type score_each_iteration: bool
:param offset_column: Offset column. This will be added to the combination of columns before applying the link
function.
Defaults to ``None``.
:type offset_column: str, optional
:param weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent
to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating
that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do
not increase the size of the data frame. This is typically the number of times a row is repeated, but
non-integer values are supported as well. During training, rows with higher weights matter more, due to
the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at
that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Defaults to ``None``.
:type weights_column: str, optional
:param family: Family. Use binomial for classification with logistic regression, others are for regression
problems.
Defaults to ``"auto"``.
:type family: Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma",
"tweedie", "negativebinomial"]
:param tweedie_variance_power: Tweedie variance power
Defaults to ``0.0``.
:type tweedie_variance_power: float
:param tweedie_link_power: Tweedie link power
Defaults to ``1.0``.
:type tweedie_link_power: float
:param theta: Theta
Defaults to ``0.0``.
:type theta: float
:param solver: AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on
problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for
datasets with many columns.
Defaults to ``"irlsm"``.
:type solver: Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent",
"gradient_descent_lh", "gradient_descent_sqerr"]
:param missing_values_handling: Handling of missing values. Either MeanImputation, Skip or PlugValues.
Defaults to ``"mean_imputation"``.
:type missing_values_handling: Literal["mean_imputation", "skip", "plug_values"]
:param plug_values: Plug Values (a single row frame containing values that will be used to impute missing values
of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Defaults to ``None``.
:type plug_values: Union[None, str, H2OFrame], optional
:param compute_p_values: Request p-values computation, p-values work only with IRLSM solver and no
regularization
Defaults to ``True``.
:type compute_p_values: bool
:param standardize: Standardize numeric columns to have zero mean and unit variance
Defaults to ``True``.
:type standardize: bool
:param non_negative: Restrict coefficients (not intercept) to be non-negative
Defaults to ``False``.
:type non_negative: bool
:param max_iterations: Maximum number of iterations
Defaults to ``0``.
:type max_iterations: int
:param link: Link function.
Defaults to ``"family_default"``.
:type link: Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
:param prior: Prior probability for y==1. To be used only for logistic regression iff the data has been sampled
and the mean of response does not reflect reality.
Defaults to ``0.0``.
:type prior: float
:param alpha: Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for
alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between
specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5
otherwise.
Defaults to ``None``.
:type alpha: List[float], optional
:param lambda_: Regularization strength
Defaults to ``[0.0]``.
:type lambda_: List[float]
:param lambda_search: Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Defaults to ``False``.
:type lambda_search: bool
:param stopping_rounds: Early stopping based on convergence of stopping_metric. Stop if simple moving average of
length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Defaults to ``0``.
:type stopping_rounds: int
:param stopping_metric: Metric to use for early stopping (AUTO: logloss for classification, deviance for
regression and 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 early_stopping: Stop early when there is no more relative improvement on train or validation (if
provided).
Defaults to ``False``.
:type early_stopping: bool
:param stopping_tolerance: Relative tolerance for metric-based stopping criterion (stop if relative improvement
is not at least this much)
Defaults to ``0.001``.
:type stopping_tolerance: float
:param balance_classes: Balance training data class counts via over/under-sampling (for imbalanced data).
Defaults to ``False``.
:type balance_classes: bool
:param class_sampling_factors: Desired over/under-sampling ratios per class (in lexicographic order). If not
specified, sampling factors will be automatically computed to obtain class balance during training.
Requires balance_classes.
Defaults to ``None``.
:type class_sampling_factors: List[float], optional
:param max_after_balance_size: Maximum relative size of the training data after balancing class counts (can be
less than 1.0). Requires balance_classes.
Defaults to ``5.0``.
:type max_after_balance_size: float
:param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
Defaults to ``0.0``.
:type max_runtime_secs: float
:param save_transformed_framekeys: true to save the keys of transformed predictors and interaction column.
Defaults to ``False``.
:type save_transformed_framekeys: bool
:param highest_interaction_term: Limit the number of interaction terms, if 2 means interaction between 2 columns
only, 3 for three columns and so on... Default to 2.
Defaults to ``0``.
:type highest_interaction_term: int
:param nparallelism: Number of models to build in parallel. Default to 4. Adjust according to your system.
Defaults to ``4``.
:type nparallelism: int
:param type: Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3
Defaults to ``0``.
:type type: int
"""
super(H2OANOVAGLMEstimator, self).__init__()
self._parms = {}
self._id = self._parms['model_id'] = model_id
self.training_frame = training_frame
self.seed = seed
self.response_column = response_column
self.ignored_columns = ignored_columns
self.ignore_const_cols = ignore_const_cols
self.score_each_iteration = score_each_iteration
self.offset_column = offset_column
self.weights_column = weights_column
self.family = family
self.tweedie_variance_power = tweedie_variance_power
self.tweedie_link_power = tweedie_link_power
self.theta = theta
self.solver = solver
self.missing_values_handling = missing_values_handling
self.plug_values = plug_values
self.compute_p_values = compute_p_values
self.standardize = standardize
self.non_negative = non_negative
self.max_iterations = max_iterations
self.link = link
self.prior = prior
self.alpha = alpha
self.lambda_ = lambda_
self.lambda_search = lambda_search
self.stopping_rounds = stopping_rounds
self.stopping_metric = stopping_metric
self.early_stopping = early_stopping
self.stopping_tolerance = stopping_tolerance
self.balance_classes = balance_classes
self.class_sampling_factors = class_sampling_factors
self.max_after_balance_size = max_after_balance_size
self.max_runtime_secs = max_runtime_secs
self.save_transformed_framekeys = save_transformed_framekeys
self.highest_interaction_term = highest_interaction_term
self.nparallelism = nparallelism
self.type = type
self._parms["_rest_version"] = 3
@property
def training_frame(self):
"""
Id of the training data frame.
Type: ``Union[None, str, H2OFrame]``.
"""
return self._parms.get("training_frame")
@training_frame.setter
def training_frame(self, training_frame):
self._parms["training_frame"] = H2OFrame._validate(training_frame, 'training_frame')
@property
def seed(self):
"""
Seed for pseudo random number generator (if applicable)
Type: ``int``, defaults to ``-1``.
"""
return self._parms.get("seed")
@seed.setter
def seed(self, seed):
assert_is_type(seed, None, int)
self._parms["seed"] = seed
@property
def response_column(self):
"""
Response variable column.
Type: ``str``.
"""
return self._parms.get("response_column")
@response_column.setter
def response_column(self, response_column):
assert_is_type(response_column, None, str)
self._parms["response_column"] = response_column
@property
def ignored_columns(self):
"""
Names of columns to ignore for training.
Type: ``List[str]``.
"""
return self._parms.get("ignored_columns")
@ignored_columns.setter
def ignored_columns(self, ignored_columns):
assert_is_type(ignored_columns, None, [str])
self._parms["ignored_columns"] = ignored_columns
@property
def ignore_const_cols(self):
"""
Ignore constant columns.
Type: ``bool``, defaults to ``True``.
"""
return self._parms.get("ignore_const_cols")
@ignore_const_cols.setter
def ignore_const_cols(self, ignore_const_cols):
assert_is_type(ignore_const_cols, None, bool)
self._parms["ignore_const_cols"] = ignore_const_cols
@property
def score_each_iteration(self):
"""
Whether to score during each iteration of model training.
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("score_each_iteration")
@score_each_iteration.setter
def score_each_iteration(self, score_each_iteration):
assert_is_type(score_each_iteration, None, bool)
self._parms["score_each_iteration"] = score_each_iteration
@property
def offset_column(self):
"""
Offset column. This will be added to the combination of columns before applying the link function.
Type: ``str``.
"""
return self._parms.get("offset_column")
@offset_column.setter
def offset_column(self, offset_column):
assert_is_type(offset_column, None, str)
self._parms["offset_column"] = offset_column
@property
def weights_column(self):
"""
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative
weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data
frame. This is typically the number of times a row is repeated, but non-integer values are supported as well.
During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set
weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an
accurate prediction, remove all rows with weight == 0.
Type: ``str``.
"""
return self._parms.get("weights_column")
@weights_column.setter
def weights_column(self, weights_column):
assert_is_type(weights_column, None, str)
self._parms["weights_column"] = weights_column
@property
def family(self):
"""
Family. Use binomial for classification with logistic regression, others are for regression problems.
Type: ``Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma",
"tweedie", "negativebinomial"]``, defaults to ``"auto"``.
"""
return self._parms.get("family")
@family.setter
def family(self, family):
assert_is_type(family, None, Enum("auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"))
self._parms["family"] = family
@property
def tweedie_variance_power(self):
"""
Tweedie variance power
Type: ``float``, defaults to ``0.0``.
"""
return self._parms.get("tweedie_variance_power")
@tweedie_variance_power.setter
def tweedie_variance_power(self, tweedie_variance_power):
assert_is_type(tweedie_variance_power, None, numeric)
self._parms["tweedie_variance_power"] = tweedie_variance_power
@property
def tweedie_link_power(self):
"""
Tweedie link power
Type: ``float``, defaults to ``1.0``.
"""
return self._parms.get("tweedie_link_power")
@tweedie_link_power.setter
def tweedie_link_power(self, tweedie_link_power):
assert_is_type(tweedie_link_power, None, numeric)
self._parms["tweedie_link_power"] = tweedie_link_power
@property
def theta(self):
"""
Theta
Type: ``float``, defaults to ``0.0``.
"""
return self._parms.get("theta")
@theta.setter
def theta(self, theta):
assert_is_type(theta, None, numeric)
self._parms["theta"] = theta
@property
def solver(self):
"""
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small
number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
Type: ``Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent",
"gradient_descent_lh", "gradient_descent_sqerr"]``, defaults to ``"irlsm"``.
"""
return self._parms.get("solver")
@solver.setter
def solver(self, solver):
assert_is_type(solver, None, Enum("auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"))
self._parms["solver"] = solver
@property
def missing_values_handling(self):
"""
Handling of missing values. Either MeanImputation, Skip or PlugValues.
Type: ``Literal["mean_imputation", "skip", "plug_values"]``, defaults to ``"mean_imputation"``.
"""
return self._parms.get("missing_values_handling")
@missing_values_handling.setter
def missing_values_handling(self, missing_values_handling):
assert_is_type(missing_values_handling, None, Enum("mean_imputation", "skip", "plug_values"))
self._parms["missing_values_handling"] = missing_values_handling
@property
def plug_values(self):
"""
Plug Values (a single row frame containing values that will be used to impute missing values of the
training/validation frame, use with conjunction missing_values_handling = PlugValues)
Type: ``Union[None, str, H2OFrame]``.
"""
return self._parms.get("plug_values")
@plug_values.setter
def plug_values(self, plug_values):
self._parms["plug_values"] = H2OFrame._validate(plug_values, 'plug_values')
@property
def compute_p_values(self):
"""
Request p-values computation, p-values work only with IRLSM solver and no regularization
Type: ``bool``, defaults to ``True``.
"""
return self._parms.get("compute_p_values")
@compute_p_values.setter
def compute_p_values(self, compute_p_values):
assert_is_type(compute_p_values, None, bool)
self._parms["compute_p_values"] = compute_p_values
@property
def standardize(self):
"""
Standardize numeric columns to have zero mean and unit variance
Type: ``bool``, defaults to ``True``.
"""
return self._parms.get("standardize")
@standardize.setter
def standardize(self, standardize):
assert_is_type(standardize, None, bool)
self._parms["standardize"] = standardize
@property
def non_negative(self):
"""
Restrict coefficients (not intercept) to be non-negative
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("non_negative")
@non_negative.setter
def non_negative(self, non_negative):
assert_is_type(non_negative, None, bool)
self._parms["non_negative"] = non_negative
@property
def max_iterations(self):
"""
Maximum number of iterations
Type: ``int``, defaults to ``0``.
"""
return self._parms.get("max_iterations")
@max_iterations.setter
def max_iterations(self, max_iterations):
assert_is_type(max_iterations, None, int)
self._parms["max_iterations"] = max_iterations
@property
def link(self):
"""
Link function.
Type: ``Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]``, defaults to
``"family_default"``.
:examples:
>>> import h2o
>>> h2o.init()
>>> from h2o.estimators import H2OANOVAGLMEstimator
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip")
>>> x = ['AGE', 'VOL', 'DCAPS']
>>> y = 'CAPSULE'
>>> anova_model = H2OANOVAGLMEstimator(family='binomial',
... lambda_=0,
... missing_values_handling="skip",
... link="family_default")
>>> anova_model.train(x=x, y=y, training_frame=train)
>>> anova_model.summary()
"""
return self._parms.get("link")
@link.setter
def link(self, link):
assert_is_type(link, None, Enum("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"))
self._parms["link"] = link
@property
def prior(self):
"""
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean
of response does not reflect reality.
Type: ``float``, defaults to ``0.0``.
"""
return self._parms.get("prior")
@prior.setter
def prior(self, prior):
assert_is_type(prior, None, numeric)
self._parms["prior"] = prior
@property
def alpha(self):
"""
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha
represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the
amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.
Type: ``List[float]``.
"""
return self._parms.get("alpha")
@alpha.setter
def alpha(self, alpha):
# For `alpha` and `lambda` the server reports type float[], while in practice simple floats are also ok
assert_is_type(alpha, None, numeric, [numeric])
self._parms["alpha"] = alpha
@property
def lambda_(self):
"""
Regularization strength
Type: ``List[float]``, defaults to ``[0.0]``.
"""
return self._parms.get("lambda")
@lambda_.setter
def lambda_(self, lambda_):
assert_is_type(lambda_, None, numeric, [numeric])
self._parms["lambda"] = lambda_
@property
def lambda_search(self):
"""
Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("lambda_search")
@lambda_search.setter
def lambda_search(self, lambda_search):
assert_is_type(lambda_search, None, bool)
self._parms["lambda_search"] = lambda_search
@property
def stopping_rounds(self):
"""
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type: ``int``, defaults to ``0``.
"""
return self._parms.get("stopping_rounds")
@stopping_rounds.setter
def stopping_rounds(self, stopping_rounds):
assert_is_type(stopping_rounds, None, int)
self._parms["stopping_rounds"] = stopping_rounds
@property
def stopping_metric(self):
"""
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and 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 early_stopping(self):
"""
Stop early when there is no more relative improvement on train or validation (if provided).
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("early_stopping")
@early_stopping.setter
def early_stopping(self, early_stopping):
assert_is_type(early_stopping, None, bool)
self._parms["early_stopping"] = early_stopping
@property
def stopping_tolerance(self):
"""
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type: ``float``, defaults to ``0.001``.
"""
return self._parms.get("stopping_tolerance")
@stopping_tolerance.setter
def stopping_tolerance(self, stopping_tolerance):
assert_is_type(stopping_tolerance, None, numeric)
self._parms["stopping_tolerance"] = stopping_tolerance
@property
def balance_classes(self):
"""
Balance training data class counts via over/under-sampling (for imbalanced data).
Type: ``bool``, defaults to ``False``.
"""
return self._parms.get("balance_classes")
@balance_classes.setter
def balance_classes(self, balance_classes):
assert_is_type(balance_classes, None, bool)
self._parms["balance_classes"] = balance_classes
@property
def class_sampling_factors(self):
"""
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will
be automatically computed to obtain class balance during training. Requires balance_classes.
Type: ``List[float]``.
"""
return self._parms.get("class_sampling_factors")
@class_sampling_factors.setter
def class_sampling_factors(self, class_sampling_factors):
assert_is_type(class_sampling_factors, None, [float])
self._parms["class_sampling_factors"] = class_sampling_factors
@property
def max_after_balance_size(self):
"""
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
balance_classes.
Type: ``float``, defaults to ``5.0``.
"""
return self._parms.get("max_after_balance_size")
@max_after_balance_size.setter
def max_after_balance_size(self, max_after_balance_size):
assert_is_type(max_after_balance_size, None, float)
self._parms["max_after_balance_size"] = max_after_balance_size
@property
def max_runtime_secs(self):
"""
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type: ``float``, defaults to ``0.0``.
"""
return self._parms.get("max_runtime_secs")
@max_runtime_secs.setter
def max_runtime_secs(self, max_runtime_secs):
assert_is_type(max_runtime_secs, None, numeric)
self._parms["max_runtime_secs"] = max_runtime_secs
@property
def save_transformed_framekeys(self):
"""
true to save the keys of transformed predictors and interaction column.
Type: ``bool``, defaults to ``False``.
:examples:
>>> import h2o
>>> h2o.init()
>>> from h2o.estimators import H2OANOVAGLMEstimator
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip")
>>> x = ['AGE', 'VOL', 'DCAPS']
>>> y = 'CAPSULE'
>>> anova_model = H2OANOVAGLMEstimator(family='binomial',
... lambda_=0,
... missing_values_handling="skip",
... save_transformed_framekeys=True)
>>> anova_model.train(x=x, y=y, training_frame=train)
>>> transformFrame = h2o.get_frame(anova_model._model_json['output']['transformed_columns_key']['name'])
>>> print(transformFrame)
"""
return self._parms.get("save_transformed_framekeys")
@save_transformed_framekeys.setter
def save_transformed_framekeys(self, save_transformed_framekeys):
assert_is_type(save_transformed_framekeys, None, bool)
self._parms["save_transformed_framekeys"] = save_transformed_framekeys
@property
def highest_interaction_term(self):
"""
Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so
on... Default to 2.
Type: ``int``, defaults to ``0``.
:examples:
>>> import h2o
>>> h2o.init()
>>> from h2o.estimators import H2OANOVAGLMEstimator
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip")
>>> x = ['AGE', 'VOL', 'DCAPS']
>>> y = 'CAPSULE'
>>> anova_model = H2OANOVAGLMEstimator(family='binomial',
... lambda_=0,
... missing_values_handling="skip",
... highest_interaction_term=2)
>>> anova_model.train(x=x, y=y, training_frame=train)
>>> anova_model.summary()
"""
return self._parms.get("highest_interaction_term")
@highest_interaction_term.setter
def highest_interaction_term(self, highest_interaction_term):
assert_is_type(highest_interaction_term, None, int)
self._parms["highest_interaction_term"] = highest_interaction_term
@property
def nparallelism(self):
"""
Number of models to build in parallel. Default to 4. Adjust according to your system.
Type: ``int``, defaults to ``4``.
"""
return self._parms.get("nparallelism")
@nparallelism.setter
def nparallelism(self, nparallelism):
assert_is_type(nparallelism, None, int)
self._parms["nparallelism"] = nparallelism
@property
def type(self):
"""
Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3
Type: ``int``, defaults to ``0``.
"""
return self._parms.get("type")
@type.setter
def type(self, type):
assert_is_type(type, None, int)
self._parms["type"] = type
@property
def Lambda(self):
"""DEPRECATED. Use ``self.lambda_`` instead"""
return self._parms["lambda"] if "lambda" in self._parms else None
@Lambda.setter
def Lambda(self, value):
self._parms["lambda"] = value
[docs] def result(self):
"""
Get result frame that contains information about the model building process like for modelselection and anovaglm.
:return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm.
"""
return H2OFrame._expr(expr=ExprNode("result", ASTId(self.key)))._frame(fill_cache=True)