#!/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.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 H2ONaiveBayesEstimator(H2OEstimator):
"""
Naive Bayes
The naive Bayes classifier assumes independence between predictor variables
conditional on the response, and a Gaussian distribution of numeric predictors with
mean and standard deviation computed from the training dataset. When building a naive
Bayes classifier, every row in the training dataset that contains at least one NA will
be skipped completely. If the test dataset has missing values, then those predictors
are omitted in the probability calculation during prediction.
"""
algo = "naivebayes"
supervised_learning = True
def __init__(self,
model_id=None, # type: Optional[Union[None, str, H2OEstimator]]
nfolds=0, # type: int
seed=-1, # type: int
fold_assignment="auto", # type: Literal["auto", "random", "modulo", "stratified"]
fold_column=None, # type: Optional[str]
keep_cross_validation_models=True, # type: bool
keep_cross_validation_predictions=False, # type: bool
keep_cross_validation_fold_assignment=False, # type: bool
training_frame=None, # type: Optional[Union[None, str, H2OFrame]]
validation_frame=None, # type: Optional[Union[None, str, H2OFrame]]
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
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
laplace=0.0, # type: float
min_sdev=0.001, # type: float
eps_sdev=0.0, # type: float
min_prob=0.001, # type: float
eps_prob=0.0, # type: float
compute_metrics=True, # type: bool
max_runtime_secs=0.0, # type: float
export_checkpoints_dir=None, # type: Optional[str]
gainslift_bins=-1, # type: int
auc_type="auto", # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
):
"""
: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 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 (only used for cross-validation and
fold_assignment="Random" or "AUTO")
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 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 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 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 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 laplace: Laplace smoothing parameter
Defaults to ``0.0``.
:type laplace: float
:param min_sdev: Min. standard deviation to use for observations with not enough data
Defaults to ``0.001``.
:type min_sdev: float
:param eps_sdev: Cutoff below which standard deviation is replaced with min_sdev
Defaults to ``0.0``.
:type eps_sdev: float
:param min_prob: Min. probability to use for observations with not enough data
Defaults to ``0.001``.
:type min_prob: float
:param eps_prob: Cutoff below which probability is replaced with min_prob
Defaults to ``0.0``.
:type eps_prob: float
:param compute_metrics: Compute metrics on training data
Defaults to ``True``.
:type compute_metrics: bool
: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 export_checkpoints_dir: Automatically export generated models to this directory.
Defaults to ``None``.
:type export_checkpoints_dir: str, 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 auc_type: Set default multinomial AUC type.
Defaults to ``"auto"``.
:type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
"""
super(H2ONaiveBayesEstimator, self).__init__()
self._parms = {}
self._id = self._parms['model_id'] = model_id
self.nfolds = nfolds
self.seed = seed
self.fold_assignment = fold_assignment
self.fold_column = fold_column
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.training_frame = training_frame
self.validation_frame = validation_frame
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.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.laplace = laplace
self.min_sdev = min_sdev
self.eps_sdev = eps_sdev
self.min_prob = min_prob
self.eps_prob = eps_prob
self.compute_metrics = compute_metrics
self.max_runtime_secs = max_runtime_secs
self.export_checkpoints_dir = export_checkpoints_dir
self.gainslift_bins = gainslift_bins
self.auc_type = auc_type
@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"
>>> cars_nb = H2ONaiveBayesEstimator(nfolds=5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=cars)
>>> cars_nb.auc()
"""
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 (only used for cross-validation and fold_assignment="Random" or "AUTO")
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)
>>> nb_w_seed = H2ONaiveBayesEstimator(seed=1234)
>>> nb_w_seed.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> nb_wo_seed = H2ONaiveBayesEstimator()
>>> nb_wo_seed.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> nb_w_seed.auc()
>>> nb_wo_seed.auc()
"""
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"``.
:examples:
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_nb = H2ONaiveBayesEstimator(fold_assignment="Random",
... nfolds=5,
... seed=1234)
>>> response = "economy_20mpg"
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> cars_nb.train(x=predictors, y=response, training_frame=cars)
>>> cars_nb.auc()
"""
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)
>>> cars_nb = H2ONaiveBayesEstimator(seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=cars,
... fold_column="fold_numbers")
>>> cars_nb.auc()
"""
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 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_nb = H2ONaiveBayesEstimator(keep_cross_validation_models=True,
... nfolds=5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train)
>>> cars_nb.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_nb = H2ONaiveBayesEstimator(keep_cross_validation_predictions=True,
... nfolds=5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train)
>>> cars_nb.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_nb = H2ONaiveBayesEstimator(keep_cross_validation_fold_assignment=True,
... nfolds=5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train)
>>> cars_nb.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 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_nb = H2ONaiveBayesEstimator()
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.auc()
"""
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_nb = H2ONaiveBayesEstimator()
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.auc()
"""
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 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``.
: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_nb = H2ONaiveBayesEstimator(seed=1234,
... ignore_const_cols=True)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.auc()
"""
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_nb = H2ONaiveBayesEstimator(score_each_iteration=True,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.auc()
"""
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 balance_classes(self):
"""
Balance training data class counts via over/under-sampling (for imbalanced data).
Type: ``bool``, defaults to ``False``.
:examples:
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_nb = H2ONaiveBayesEstimator(balance_classes=False,
... nfolds=3,
... seed=1234)
>>> iris_nb.train(x=list(range(4)),
... y=4,
... training_frame=iris)
>>> iris_nb.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:
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cov_nb = H2ONaiveBayesEstimator(class_sampling_factors=sample_factors,
... seed=1234)
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> cov_nb.train(x=predictors, y=response, training_frame=covtype)
>>> cov_nb.logloss()
"""
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:
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> max = .85
>>> cov_nb = H2ONaiveBayesEstimator(max_after_balance_size=max,
... seed=1234)
>>> cov_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.logloss()
"""
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 laplace(self):
"""
Laplace smoothing parameter
Type: ``float``, defaults to ``0.0``.
:examples:
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> prostate_nb = H2ONaiveBayesEstimator(laplace=1)
>>> prostate_nb.train(x=list(range(3,9)),
... y=response_col,
... training_frame=prostate)
>>> prostate_nb.mse()
"""
return self._parms.get("laplace")
@laplace.setter
def laplace(self, laplace):
assert_is_type(laplace, None, numeric)
self._parms["laplace"] = laplace
@property
def min_sdev(self):
"""
Min. standard deviation to use for observations with not enough data
Type: ``float``, defaults to ``0.001``.
:examples:
>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
... response_col = "economy_20mpg"
... else:
... response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1,
... eps_sdev=0.5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response_col,
... training_frame=cars)
>>> cars_nb.show()
"""
return self._parms.get("min_sdev")
@min_sdev.setter
def min_sdev(self, min_sdev):
assert_is_type(min_sdev, None, numeric)
self._parms["min_sdev"] = min_sdev
@property
def eps_sdev(self):
"""
Cutoff below which standard deviation is replaced with min_sdev
Type: ``float``, defaults to ``0.0``.
:examples:
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
... response_col = "economy_20mpg"
... else:
... response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1,
... eps_sdev=0.5,
... seed=1234)
>>> cars_nb.train(x=predictors, y=response_col, training_frame=cars)
>>> cars_nb.mse()
"""
return self._parms.get("eps_sdev")
@eps_sdev.setter
def eps_sdev(self, eps_sdev):
assert_is_type(eps_sdev, None, numeric)
self._parms["eps_sdev"] = eps_sdev
@property
def min_prob(self):
"""
Min. probability to use for observations with not enough data
Type: ``float``, defaults to ``0.001``.
:examples:
>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
... response_col = "economy_20mpg"
... else:
... response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1,
... eps_prob=0.5,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response_col,
... training_frame=cars)
>>> cars_nb.show()
"""
return self._parms.get("min_prob")
@min_prob.setter
def min_prob(self, min_prob):
assert_is_type(min_prob, None, numeric)
self._parms["min_prob"] = min_prob
@property
def eps_prob(self):
"""
Cutoff below which probability is replaced with min_prob
Type: ``float``, defaults to ``0.0``.
:examples:
>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
... response_col = "economy_20mpg"
... else:
... response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1,
... eps_prob=0.5,
... seed=1234)
>>> cars_nb.train(x=predictors, y=response_col, training_frame=cars)
>>> cars_nb.mse()
"""
return self._parms.get("eps_prob")
@eps_prob.setter
def eps_prob(self, eps_prob):
assert_is_type(eps_prob, None, numeric)
self._parms["eps_prob"] = eps_prob
@property
def compute_metrics(self):
"""
Compute metrics on training data
Type: ``bool``, defaults to ``True``.
:examples:
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> response_col = 'CAPSULE'
>>> prostate_nb = H2ONaiveBayesEstimator(laplace=0,
... compute_metrics=False)
>>> prostate_nb.train(x=list(range(3,9)),
... y=response_col,
... training_frame=prostate)
>>> prostate_nb.show()
"""
return self._parms.get("compute_metrics")
@compute_metrics.setter
def compute_metrics(self, compute_metrics):
assert_is_type(compute_metrics, None, bool)
self._parms["compute_metrics"] = compute_metrics
@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], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(max_runtime_secs=10,
... seed=1234)
>>> cars_nb.train(x=predictors,
... y=response,
... training_frame=train,
... validation_frame=valid)
>>> cars_nb.auc()
"""
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 export_checkpoints_dir(self):
"""
Automatically export generated models to this directory.
Type: ``str``.
:examples:
>>> import tempfile
>>> from os import listdir
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> response = "IsDepDelayed"
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_nb = H2ONaiveBayesEstimator(export_checkpoints_dir=checkpoints_dir)
>>> air_nb.train(x=predictors, y=response, training_frame=airlines)
>>> 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 gainslift_bins(self):
"""
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Type: ``int``, defaults to ``-1``.
:examples:
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> model = H2ONaiveBayesEstimator(gainslift_bins=20)
>>> model.train(x=["Origin", "Distance"],
... y="IsDepDelayed",
... training_frame=airlines)
>>> model.gains_lift()
"""
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 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