#!/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
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"
def __init__(self, **kwargs):
super(H2ONaiveBayesEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "nfolds", "seed", "fold_assignment", "fold_column", "keep_cross_validation_models",
"keep_cross_validation_predictions", "keep_cross_validation_fold_assignment", "training_frame",
"validation_frame", "response_column", "ignored_columns", "ignore_const_cols",
"score_each_iteration", "balance_classes", "class_sampling_factors", "max_after_balance_size",
"max_confusion_matrix_size", "max_hit_ratio_k", "laplace", "min_sdev", "eps_sdev", "min_prob",
"eps_prob", "compute_metrics", "max_runtime_secs"}
if "Lambda" in kwargs: kwargs["lambda_"] = kwargs.pop("Lambda")
for pname, pvalue in kwargs.items():
if pname == 'model_id':
self._id = pvalue
self._parms["model_id"] = pvalue
elif pname in names_list:
# Using setattr(...) will invoke type-checking of the arguments
setattr(self, pname, pvalue)
else:
raise H2OValueError("Unknown parameter %s = %r" % (pname, pvalue))
@property
def nfolds(self):
"""
Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type: ``int`` (default: ``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 (only used for cross-validation and fold_assignment="Random" or "AUTO")
Type: ``int`` (default: ``-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.
One of: ``"auto"``, ``"random"``, ``"modulo"``, ``"stratified"`` (default: ``"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 keep_cross_validation_models(self):
"""
Whether to keep the cross-validation models.
Type: ``bool`` (default: ``True``).
"""
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`` (default: ``False``).
"""
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`` (default: ``False``).
"""
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: ``H2OFrame``.
"""
return self._parms.get("training_frame")
@training_frame.setter
def training_frame(self, training_frame):
assert_is_type(training_frame, None, H2OFrame)
self._parms["training_frame"] = training_frame
@property
def validation_frame(self):
"""
Id of the validation data frame.
Type: ``H2OFrame``.
"""
return self._parms.get("validation_frame")
@validation_frame.setter
def validation_frame(self, validation_frame):
assert_is_type(validation_frame, None, H2OFrame)
self._parms["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`` (default: ``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`` (default: ``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 balance_classes(self):
"""
Balance training data class counts via over/under-sampling (for imbalanced data).
Type: ``bool`` (default: ``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`` (default: ``5``).
"""
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`` (default: ``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_hit_ratio_k(self):
"""
Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
Type: ``int`` (default: ``0``).
"""
return self._parms.get("max_hit_ratio_k")
@max_hit_ratio_k.setter
def max_hit_ratio_k(self, max_hit_ratio_k):
assert_is_type(max_hit_ratio_k, None, int)
self._parms["max_hit_ratio_k"] = max_hit_ratio_k
@property
def laplace(self):
"""
Laplace smoothing parameter
Type: ``float`` (default: ``0``).
"""
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`` (default: ``0.001``).
"""
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`` (default: ``0``).
"""
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`` (default: ``0.001``).
"""
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`` (default: ``0``).
"""
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`` (default: ``True``).
"""
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`` (default: ``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