#!/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 H2OIsotonicRegressionEstimator(H2OEstimator):
"""
Isotonic Regression
"""
algo = "isotonicregression"
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]]
response_column=None, # type: Optional[str]
ignored_columns=None, # type: Optional[List[str]]
weights_column=None, # type: Optional[str]
out_of_bounds="na", # type: Literal["na", "clip"]
custom_metric_func=None, # type: Optional[str]
nfolds=0, # type: int
keep_cross_validation_models=True, # type: bool
keep_cross_validation_predictions=False, # type: bool
keep_cross_validation_fold_assignment=False, # type: bool
fold_assignment="auto", # type: Literal["auto", "random", "modulo", "stratified"]
fold_column=None, # type: Optional[str]
):
"""
: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 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 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 out_of_bounds: Method of handling values of X predictor that are outside of the bounds seen in training.
Defaults to ``"na"``.
:type out_of_bounds: Literal["na", "clip"]
:param custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName`
Defaults to ``None``.
:type custom_metric_func: str, optional
:param nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2).
Defaults to ``0``.
:type nfolds: int
:param keep_cross_validation_models: Whether to keep the cross-validation models.
Defaults to ``True``.
:type keep_cross_validation_models: bool
:param keep_cross_validation_predictions: Whether to keep the predictions of the cross-validation models.
Defaults to ``False``.
:type keep_cross_validation_predictions: bool
:param keep_cross_validation_fold_assignment: Whether to keep the cross-validation fold assignment.
Defaults to ``False``.
:type keep_cross_validation_fold_assignment: bool
:param fold_assignment: Cross-validation fold assignment scheme, if fold_column is not specified. The
'Stratified' option will stratify the folds based on the response variable, for classification problems.
Defaults to ``"auto"``.
:type fold_assignment: Literal["auto", "random", "modulo", "stratified"]
:param fold_column: Column with cross-validation fold index assignment per observation.
Defaults to ``None``.
:type fold_column: str, optional
"""
super(H2OIsotonicRegressionEstimator, self).__init__()
self._parms = {}
self._id = self._parms['model_id'] = model_id
self.training_frame = training_frame
self.validation_frame = validation_frame
self.response_column = response_column
self.ignored_columns = ignored_columns
self.weights_column = weights_column
self.out_of_bounds = out_of_bounds
self.custom_metric_func = custom_metric_func
self.nfolds = nfolds
self.keep_cross_validation_models = keep_cross_validation_models
self.keep_cross_validation_predictions = keep_cross_validation_predictions
self.keep_cross_validation_fold_assignment = keep_cross_validation_fold_assignment
self.fold_assignment = fold_assignment
self.fold_column = fold_column
@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 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 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 out_of_bounds(self):
"""
Method of handling values of X predictor that are outside of the bounds seen in training.
Type: ``Literal["na", "clip"]``, defaults to ``"na"``.
"""
return self._parms.get("out_of_bounds")
@out_of_bounds.setter
def out_of_bounds(self, out_of_bounds):
assert_is_type(out_of_bounds, None, Enum("na", "clip"))
self._parms["out_of_bounds"] = out_of_bounds
@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 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 keep_cross_validation_models(self):
"""
Whether to keep the cross-validation models.
Type: ``bool``, defaults to ``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``, defaults to ``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``, defaults to ``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 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